Lithium-ion (Li-ion) battery pack is vital for storage of energy produced from different sources and has been extensively used for various applications such as electric vehicles (EVs), watches, cookers, etc. For an efficient real-time monitoring and fault diagnosis of battery operated systems, it is important to have a quantified information on the state-of-health (SoH) of batteries. This paper conducts comprehensive literature studies on advancement, challenges, concerns, and futuristic aspects of models and methods for SoH estimation of batteries. Based on the studies, the methods and models for SoH estimation have been summarized systematically with their advantages and disadvantages in tabular format. The prime emphasis of this review was attributed toward the development of a hybridized method which computes SoH of batteries accurately in real-time and takes self-discharge into its account. At the end, the summary of research findings and the future directions of research such as nondestructive tests (NDT) for real-time estimation of battery SoH, finding residual SoH for the recycled batteries from battery packs, integration of mechanical aspects of battery with temperature, easy assembling–dissembling of battery packs, and hybridization of battery packs with photovoltaic and super capacitor are discussed.

## Introduction

In the recent past, the demand for oil, coal, and gas is increasing globally, mainly due to an increase in per capita energy consumption and demand for electricity in all the sectors [1]. Because of limited oil and coal reserve in the planet and stringent emission norms, researchers are forced to think for alternative methods for sustainable power generation and energy supply. In this aspect, energy storage can play a weighty role in the field of power generation and energy supply [2,3].

In the transportation sector, electric vehicles (EVs) can play a major role toward mitigating the environmental problems with sustainability [46]. An EV may be powered through the collector system, battery, solar panels, an electric generator, etc. There are mainly three types of electric vehicles such as hybrid electric vehicle, plug-in hybrid electric vehicle (PHEV), and battery electric vehicle (BEV) [79]. The main advantages of using EV are

• Electric vehicles emit lower amount of toxic gases, smoke, and other harmful particles during its operation; hence, EVs are considered as ecofriendly vehicles [9].

• EVs are more efficient than internal combustion engine drive trains, for example, the well-to-wheel efficiency of BEVs and incremental capacity (IC) engine vehicles are about 28% and 13%, respectively [10].

• Running cost of EV is less. The electricity to charge an EV is almost one-third of a petrol vehicle per kilometer [11].

• EVs are capable of capturing some energy (nearly 20–25%) that is normally lost in braking by regenerative braking [10].

• EVs have relatively negligible start up time and can achieve required high speed and acceleration very easily [10].

• In the case of safety, it has been seen that EVs tend to have lower center of gravity that makes them less likely to roll over [12].

In the recent past, the growth of sales of all EVs is increasing significantly [13]. Recently, the demand for BEV is increasing ever more. Figure 1 shows the growing demand for BEV over PHEV in recent years [14], and Fig. 2 shows the countries with most electric vehicles, where China is leading with approximately 579,000 vehicles in the year 2017 [15].

In battery powered EV, the battery packs are the heart of the vehicle because it provides the primary energy to run the vehicle efficiently. A host of literature and experiments are reported to investigate the issues like safety, reliability, and viability in any operating conditions of the electric vehicle [1618]. In the recent past, lithium-ion batteries are used as a major source of power supply in the battery electric vehicle [1921]. The use of different kinds of batteries such as lithium cobalt oxide, lithium iron phosphate, lithium manganese oxide, lithium nickel–cobalt–aluminum oxide, lithium nickel–manganese–cobalt oxide, etc. has been reported by the researchers on their research work [22,23]. Table 1 compares these popularly used lithium-ion batteries that are commonly accessible to the market [24].

Lithium-based batteries have the highest cell potential and the lowest reduction potential compared to other elements. Lithium molecule is the third lightest element and has one of the smallest ionic radii of any single charged ion. This allows Li-based batteries to have high gravimetric and volumetric capacity and power density [25]. The energy density of the lithium-ion battery is in the range 200–250 W h/kg. Its columbic efficiency is very high with nearly 100% [26], and it has no memory effect [25]. Due to its high energy and power density, the lithium-ion battery becomes the most preferable choice over lead acid, nickel cadmium battery, and popularly used for various equipments such as portable electronics, power tools, and EVs [2729]. The research on the lithium-ion battery is attributed to present trends to increase cycle life, safety (both abusive and normal condition) [30], and other performance characteristics. At the same time, researchers have utilized other types of electrochemical energy storage systems with higher energy density in EV. Some advantages of the lithium-sulfur battery over Li-ion are higher energy density, improved safety, a wider operating temperature range, and lower cost (because of the much availability of sulfur), which makes it a promising technology for EV application [31]. However, lithium-sulfur technology has not been widely commercialized yet as it has some limitations such as low columbic efficiency, poor cycle, self-discharge, and capacity fade due to cycling, high discharge current, uncontrolled dendrite formation, etc. [32]. As hybrid and electric vehicle technologies are continuing to the field of advancement, most of the car manufacturers have begun to use lithium-ion batteries as the electrical device of energy storage for existing and future vehicles. The use of the lithium-ion battery in an electric vehicle has been progressing at a high pace, and as such, it is important to provide current and timely updates of this emerging technology. While reviewing the papers, the following issues were discussed by many of the researchers as far as the use of battery in an electric vehicle is concerned: battery management system (BMS) [5], battery thermal management system of the battery pack [33], design and manufacturing of batteries, state of charge (SoC) estimation, SoH estimation, etc. [34].

Battery state-of-health (SoH) estimation is an extremely important issue for the performance and cost-effectiveness of EVs. In order to ensure efficient and safe operation, prevent the battery from over-charging and over-discharging, increase the lifespan of the lithium-ion battery system and forecast its end life, and it is necessary and important to estimate the battery's SoH [35]. Often, SoH of batteries is monitored for an efficient battery management system design [36]. But estimating the dynamic status parameters of a battery, such as SoH and related issues, will be useful in obtaining the best suitable method for battery health monitoring. Furthermore, by summarizing the methods, its advantages and disadvantages, there is a potential for developing a new method by hybridization of various methods and integration of nondestructive methods for battery condition monitoring.

In this paper, SoH and its estimation methods are discussed in Sec. 2. Section 3 summarizes the various approaches for SoH estimation adopted by researchers. The research drawbacks and their scopes of research have been discussed in Sec. 4. Finally, the conclusion of the paper is summarized in Sec. 5.

## State-of-Health Estimation Methods

The SoH is a “measurement” that reflects the general condition of a battery and its ability to deliver the specified performance compared with an unused or fresh battery. It is defined as the ratio between full charge capacity of a battery in the current state and the full charge capacity of a battery when it is initially bought
$SoH=CurrentactualcapacityNominalcapacity$

State of charge (SoC is a short-term prediction while SoH is a long-term prediction) can be determined by measuring the actual amount of charge in the battery. SoH is a subjective measure in which different researchers derive different definitions by using a variety of different measurable battery performance parameters (current, voltage, resistance, temperature, self-discharge rate, stress, strain, etc.). Though SoH is a function of such parameters (which all affect the capability of the battery), for simplicity, it is generally expressed in terms of capacity, considering other parameters constant or keeping them unchanged during the moment [37]. An accurate estimation of SoH is important to forecast batteries’ reliability, efficiency, and power delivering capacity and proper operation of the system [38,39].

It has been reported that capacity, internal resistance, power fade, and cycle life change with battery's age and hence these parameters are useful in predicting the behavior of the cell or battery [40]. Aging processes of a battery are irreversible changes in the characteristics of the electrolyte, anode, and cathode and the alteration in the structure of the components used in the battery. Battery aging can be divided into cycle aging and calendar one [41]. Cycle aging associates with the impact of battery utilization periods, and the calendar aging associates with the consequences of battery storage. Aging is considered for the estimation of SoH as it is highly related to change in capacity, internal resistance, and power fade [42]. Changes of these parameters help the researchers to find out which could be the best parameter for SoH estimation in accordance with the situations. For example, changes in the performance of battery's external behavior due to loss of rated capacity or due to an increase in temperature because of internal changes like corrosion.

Figure 3 shows the variation of cycle life with the cell operating temperature. From Fig. 3, it is evident that cycle life is maximum when the cell operating temperature is maintained between 10 and 50 °C. The cycle life gradually decreases if the cell operating temperature reduces below 10 °C and increases above 50 °C. With further increase of the cell operating temperature, the cycle life decreases sharply due to thermal runaway [5,16].

Formulation of battery modeling is necessary to relate the battery parameters such as charging and discharging voltage, cycle life, temperature, etc. Battery modeling (Fig. 4) is classified into emperical models, electrochemical models, and equivalent circuit models (ECMs). In the empirical model, model formulation is based on the data obtained from the experiments on the batteries where inside information of the battery activity is not known completely. In order to predict the unknown information of the battery, some methods like genetic programing, fuzzy logic, Kalman filtering (KF), neural networks (NNs), etc. are used to build the empirical model. In the electrochemical model, the models are based on the chemical processes that take place inside the battery. The electrochemical models are more accurate; however, these models are complex to analyze. In order to reduce the comlexity of the model, some reduction models such as single particle model are used [43,44]. In the fusion model, the models are based on combination of empirical and electrochemical models. Data are obtained from the finite element simulation (electrochemical models) of the battery phenomenon. The quantification of data is then obtained by building empirical models using methods like fuzzy logic, Kalman filtering, neural networks, etc.

Some researchers explain the battery helath monitoring models/methods in different ways [45]. For example, Berecibar et al. [46] divided the methods of SoH estimation into two parts: experimental technique and adaptive method. In experimental technique, previous data were taking into account, whereas in adaptive technique, some parameters were introduced which had been sensitive to degradation or aging of the battery.

However, within these derived models, uncertainties and realistic conditions based on bumpy road, crash, and outside impact are not taken into consideration while estimating SoH or SoC. Generally, models work accurately when used as offline. But, the models do not work accurately when used in real-time and online; in this way, it is very difficult to model the SoH or SoC for the entire battery pack when compared to a single battery or cell. Therefore, designing of the best model considering all the necessary parameters is very much essential.

Distinct focus is given to the SoH and SoC estimation, and the methods or models were classified based on the given input conditions or parameters (charge, current, capacity, self-discharge rate, temperature, depth of discharge (DoD), time interval between full charge cycles, etc.). There are number of methods or models which are used by different researchers in different ways. Some of them that were used mostly are listed below.

### Coulomb Counting Method.

The Coulomb counting method is associated with monitoring the input and the output current continuously. Since capacity is the integral of current with respect to time, by measuring the input and the output current, change in capacity or capacity degradation of a battery can be measured easily [47]. In this method, SoH is calculated by dividing measured capacity (after discharging the battery to 0% SoC value) to its rated capacity. It is an extensively used method by researchers for its simplicity [4850]. But, the accuracy of this method is not very high. Therefore, to improve its accuracy, for example, Ng et al. [50] proposed a smart coulomb counting method to estimate both SoC and SoH accurately. Similarly, an adaptive neurofuzzy inference system (ANFIS) was modeled in the paper [51]. It considered the cell's nonlinear characteristics to get the relationship between SoC and open circuit voltage (OCV) at different temperatures. During the estimation of SoC, at some random OCV and temperature, modeling of cell characteristics was done by ANFIS. The assessment was done on the cell level instead of the pack level for better precision.

### Internal Resistance and Impedance Measurement Method.

The relationship of battery internal resistance and the actual measured impedance with battery aging leads a way to battery SoH estimation [5255]. As the aging process occurs gradually, the impedance value of the battery under different frequencies changes. Electrochemical impedance spectroscopy (EIS) helps in this context by measuring the actual impedance of the battery pack [5661]. For example, Mu et al. [62] proposed a novel fractional order impedance model for the lithium-ion battery. EIS is a powerful technique which separates the electrochemical reactions and tracks the variations of the performance under a different SoH of a battery in a nondestructive manner. By combining electrochemical impedance spectroscopy and hybrid pulse power characteristic test (HPPT) data, a fractional order impedance model can be derived. Measuring the increase of battery internal resistance is also a direct tool for measuring the health of a battery [63]. Joule effect, HPPT, and other resistance measuring models are available for the measurement of internal resistance [46].

### Neural Network.

A neural network (NN) is a mathematical model whose parameters have no direct reflection of the physical or chemical structures of the original model. Feed forward and recurrent are the types of NN architecture design. They utilized a time series prediction system. Yang et al. [64] used maximum available capacity to indicate the battery's SoH based on a back propagation neural network. A direct parameter extraction method was employed to identify the parameters of the first-order ECM. Then, a three-layer back propagation neural network was proposed to estimate SoH, whose inputs were the parameters of the first-order ECM and output was the current value of SoH. From the experiments, it was found that when ohmic resistance increases SoH reduced and when SoC ranges between 20% and 90% ohmic resistance increases and SoC decreases. Artificial neural network (ANN) is known for its simplicity. It can handle nonlinear data, and it is not necessary to take all the details of the battery during modeling [6568].

### Support Vector Machine.

This method depends on the given environmental conditions and load conditions. It is a Kernel function-based method, which uses regression algorithm to convert the nonlinear model in lower dimension to the linear model in high dimension [69]. To avoid degeneracy phenomenon in model building and keep the diversity of the particle, this method was used [70]. Klass et al. [71] measured capacities and instantaneous resistance over temperature and SoC range, and then, allow it for online estimation of battery degradation. By using the support vector machine (SVM) method, not only SoH but also many other useful parameters like SoC, remaining useful life (RUL), etc. can be measured accurately [7073]. Nuhic et al. [74] had developed a SVM model to identify the SoH of battery for electric vehicles. Nuhic had divided the available data into two-third of the data being for training and one-third of the data being for testing and predicted SoH with less than 0.0007 mean square error in real driving conditions, considering temperature change, SoC, and C-rate.

### Kalman Filter.

Kalman filtering is a well-designed and time-proven method to filter the measurements of system input and output to produce an intelligent estimation of a dynamic system's state. In the KF method, both input and output data are experimentally measured which help in obtaining the minimum mean square error assessment of the true state [75]. In KF, linear optimal filtering happens. If the system is nonlinear, extended Kalman filter (EKF) is used. In this method, its nonlinearity is linearized by using a linear time varying system [7583]. Claude et al. [82] presented mathematical equations to study the BMS of the electric vehicle and developed a battery electrical model. Mastali et al. [75] implemented both the extended Kalman filter and the dual Kalman filter where they used both the prismatic and cylindrical cell. Zheng and Fang [80] used relevance vector regression (a nonlinear time series production model) to give a prediction of the remaining useful life of a battery. Gholizadeh and Salmasi [84] proposed an inclusive and unobservable model for the determination of SoH and SoC. They developed multiscale EKF and used the macroscale to estimate the system parameter and the microscale to estimate the system state. Reliability and accuracy of this method were very high; also, this method largely reduced the computational cost of the control system. Andre et al. [85] had compared both EKF and NN to estimate the SoH of a battery. The comparison between EKF and NN confirmed that EKF was simpler to apply, required less input values, and required no functions of the dependencies to the working environment. Actually, NN needs recognized correlations among the input variables and internal states.

Similarly, Xiong et al. [79] proposed the multiscale extended Kalman filter, in which computational efficiency was less but they found higher estimation of accuracy. Thus, many researches have been done on the Kalman filter and made a number of suitable models for SoH and SoC estimation [7583].

### Sliding Mode Observer.

In recent years, sliding mode observer (SMO) is becoming a popular method for its flexibility to adapt with system uncertainty and noise during the SoH estimation process. Lin et al. [86] proposed the estimation of lithium-ion battery SoC/SoH using SMO for the electric vehicle. A single particle model was proposed for modeling the lithium-ion battery ignoring the spatial distribution in homogeneity of local volumetric transfer current density and the Li+ concentration in solid phase electrode and electrolyte. SMO algorithm proposed the offline identification of the model parameters. The offline model parameters were identified by the urban dynamometer driving schedule (UDDS) test. This model showed good performance in estimating the terminal voltage and the model parameters. The SMO method is advantageous since it can elude chattering effects [87]. Kim et al. [88] projected a dual sliding mode observer model for estimating both SoH and SoC.

### Fault Diagnostic Methods.

The major faults such as over-discharge and over charge causing large model parameter variation are used to form a multiple nonlinear model for the detection of faults. Identification of such failure aids in the evaluation of health condition of the battery, as such failures are inversely proportional to a good health condition of a battery. The equivalent circuit methodology combined with impedance spectroscopy of lithium-ion batteries was used in the formation of the nonlinear model for fault detection of lithium-ion batteries in Ref. [89]. Estimation of the terminal voltage for generation of residual signal was done using Kalman filtering. Then, the probability of fault occurrence was predicted accurately from these residual signals using a multiple model adaptive estimation technique. Similarly, a reduced order electrochemical-thermal model of the lithium-ion cell was used and the electrochemical faults were modeled as parametric/multiplicative faults in the system [90]. Sliding mode methodology was used to design the observer and its convergence as well as design was verified via Lyapunov's direct method. The effect of modeling uncertainties may be considered to improve the fault diagnostic scheme. However, Marcicki et al. [91] proposed a larger faults diagnostic method which also helped in estimation of SoH in the lithium-ion battery. A modified nonlinear parity equation was used for fault detection on lithium-ion batteries in EVs. Input voltage to the battery was estimated by sliding mode observer, and output voltage estimation was done by the open loop model. Minimum fault magnitude was accessed by estimation error of the observers using real-world driving cycle data. Maximum allowable probability of the error was taken into consideration by selection of optimal threshold. Smaller faults are difficult to determine by the fault detection technique presented in this paper as these faults are smaller than the normal estimation error of the observer. These faults could be corrected by performing a constant diagnostic test.

### Other Methods.

Besides these discussed methods, fuzzy logic [9295], incremental capacity analysis (ICA) method [96,97], Gaussian process regression method, Bayesian network, particle filter method, Thevenin model, and many other methods [98111] are available which are used for estimation of battery health conditions. Many researchers have attempted to combine more than one method simultaneously for better result and accuracy of the estimations [80,83].

Fuzzy logic uses fuzzy logic theory (combination of true and false statement) for complex and nonlinear models. The measured data from the system is separated into crisp sets and fuzzy sets. The fuzzy sets contain data with uncertainty. The members of the fuzzy sets belong to a membership function, which determines the accuracy of SoH estimation [112]. Schweiger et al. [113] made an assembly of both fuzzy logic and EIS for estimation of SoH of the lithium-ion battery.

Weng et al. [114] proposed state-of-health monitoring of lithium-ion battery modules and packs by incremental capacity peak tracking. ICA is a popularly used technique for lithium-ion battery SoH estimation. Efficiency and effectiveness of this method were greatly appreciated by many researchers. For example, in an electric vehicle, the current capacity is directly related with the driving range of the electric vehicle; on the other hand, power output capability determines the dynamic property of the electric vehicle. Capacity (Q) and the resistance (R) of the battery determine the power output capability, so the main aim for the SoH monitoring is to estimate the Q and R online. This paper reported the extension of the IC peak tracking-based SoH monitoring framework from single cells to multicell battery modules. At the first model, simulation was done and then validated using experimental data. The experiments consisted of 30 LiFePO4 cells with each cell having different aging conditions, and these test cells were combined into battery modules for estimation. Results obtained from it showed that the total capacity loss could be linked with the IC curve peaks, and it was for both single cell and pack of multiple cells.

Thevenin model was developed based on the Thevenin theory [115]. In this model, various electrical techniques were applied to the batteries where the load is more dynamic; the internal resistance model is not sufficient for making accurate estimation of the behavior of batteries. For simulating the dynamic behavior of batteries, the Thevenin model is used. This model can also be used with both the Kalman filter and the sliding mode observer [116].

Wu and Jossen [117] described a novel SoH indicator based on cell entropy and battery surface temperature at a constant current charging process.

Measuring SoH with piezoelectric sensors is another method for determining SoH and SoC of the lithium-ion battery. Time-of-flight and signal amplitude of the guided wave are the main revealing parameters for this estimation [118].

Marcicki et al. [119] proposed a reduced order electrochemical model where a Pade approximation reduction process was used for reducing model partial differential equations (PDEs) to lower order ordinary differential equations. Moura et al. [43] used adaptive PDE techniques where Pade approximation was also done to identify the diffusion coefficient. Adaptive output fraction inversion technique was used to enable a linear state estimation design. Other adaptive techniques like a nonlinear geometric approach are grounded on nonlinear geometric models. Exponential stability of state and parameter assessment are the main plus points of this method [120], whereas electrochemical–thermal model-based nonlinear adaptive techniques are more beneficial in accuracy compared to equivalent circuit models [121].

Retrospective-cost subsystem identification (RCSI) is another method which was adopted by Zhou et al. [122] RCSI helps in identifying the film growth of the electrolyte. The relationship between voltage change and film growth was studied accurately in this method. Chaoui and Gualous [123] proposed a hybrid estimation technique for lithium-ion batteries. This technique makes use of state-space observer theory to decrease the complexity of the design and the stability analysis. The hybrid estimation technique consists of a state-space observer and an online parameter estimator with temperature compensation. SoC estimation was achieved with reduced order observer using OCV-SoC characterization. Similarly, Lyapunov-based method is a hybrid estimation method, where battery health and other internal parameters are measured with the help of battery terminal voltage and noisy currents [124].

In the smart grid scenario, energy storage systems played a very important role, which allowed decoupling production and usage times. Landi and Gross [125] developed measurement techniques for online battery SoH estimation in vehicle-to-grid applications. They allowed for a full exploitation of renewable energy sources that can be used to shape load curves and constitute the energy reserve in the battery electric vehicles. Moreover, if such vehicles were plugged into the power grid, they could act as the support system for electricity storage and could also support the vehicle-to-grid (V2G) system. That is why measurement techniques to estimate the SoH of batteries were needed. The authors in this paper stated techniques to determine SoH of a lithium-ion battery. There are many papers available on offline methods for lithium-ion batteries. Such offline methods were widely used for characterization of aging of super capacitors [126] and fuel cells. Yuan and Dung [126] had worked offline for the SoH estimation of high power lithium-ion batteries by a three-point impedance extraction method. As reported, this methodology was found to be computationally fast and efficient.

Thus, it can be seen that numerous research works have been carried out for the estimation of SoH of the lithium-ion battery. In Sec. 3, a qualitative and quantitative assessment of these methods is tried to put forward by summarizing them in a tabular format.

## Summarization of Past Studies

The findings of various researchers related to SoH estimation methods and quantitative analysis with pros and cons of the various methods are presented and compared in Table 2, Table 3 [112], and Table 4, respectively.

## Research Gaps and Future Research Directions

This work discusses the following new directions (Fig. 5) of research and extensions of the existing works on SoH of battery packs for electric vehicles.

### Nondestructive Methods for Battery Condition Monitoring.

Methods discussed in this paper have some shortcomings. By eliminating or modifying those shortcomings, the efficiency of the methods can be increased. EIS determination method is applicable for similar charging conditions only. Similarly, previous values of SoH/SoC are required for estimation of SoH in the Coulomb counting method. Fuzzy method is practically not suitable for the electric vehicle. The cost of this method is very high, and the battery parameters frequently change with battery lifetime. The existing methods such as those based on sensors (stress and frequency) may not be accurate enough because of sensors error and micro monitoring is required to make sure the laboratory conditions are uniform. In this context, the nondestructive test (NDT) methods such as those based on laser or ultrasound/infrared approaches can be used to investigate the temperature distribution, state of charge, state-of-health, etc. of the battery pack. For example, a light optical system can be used for determining the damage of separator; X-ray computed tomography for analysing microstructural properties, short circuits, or even to analyze thermal runway; multidirectional laser scanning for aging; etc. Multidirectional laser scanning investigates local reversible and irreversible thickness change of the cell and establishes corelation with capacity fade and impedance [140]. Furthermore, the use of signal features made up the elements of statistical analysis; pattern vectors can be inputted to pattern recognition paradigms (ANN, SVM, and K-nearest-neighbor) for decision making on battery damage characterization. Thus, the integration of NDT methods with supervised learning-based regression methods shall pave the way for the design of an efficient battery management system.

### Comprehensive Design of the Battery Pack Considering Uniformity, Equalization, and Reuse Criteria Simultaneously.

In this study, the uniformity criterion was considered for design of battery packs. However, this criterion is useful before the manufacturing stage of the battery pack. There is no guarantee that the designed battery packs can perform well during the operation. During the operation of electric vehicles, the battery packs tend to fail in the nonequilibrium stage. This problem is termed as an equalization of cells in a battery pack [141,142]. After the life span of battery packs, these packs are often lying idle because the recycling and reuse methods are either ignored or the buyers are not aware of. One possibility is to accumulate these battery packs and identify and cluster the cells having life to form a new battery pack. Otherwise, these unused batteries will lead to a serious disposal problem and have bad impact on the environment [143]. Future work for authors could be to work on the proposition of comprehensive design methodology combining the uniformity criterion, equalization criterion, and reuse criterion. This shall be useful for the development of robust battery packs design being able to function synchronically during the operation of vehicles.

### Integration of Mechanical Aspects With Temperature-Related Problems.

It is also known that the temperature is the main enemy of the battery. The temperature-related problems such as thermal runaway, rupture, explosion, etc. can be well integrated with the existing research on safety design of the battery/battery pack. For example, the sensors (stress)-based monitoring of the battery is performed at a given temperature. However, in very cold or hot weather conditions, the results may not be applicable. Also, the battery pack enclosure design incorporates only mechanical aspects such as deformation, strength, frequency, etc. which can be well integrated with another objective of uniform temperature distribution (with maximum temperature below the threshold abnormal temperature). This shall result in robust battery pack and its components (enclosure) design, which can withstand impact from both the mechanical and thermal unforeseen shocks/accidents.

### Redesign, Installation, Placement of Battery Pack, and Its Components.

An important research direction could be to redesign the battery packs and its components so that it can be easily placed in the vehicle to optimize its space, have minimum impact from crash, and can be easily dismantled, disassembled, and replaced for user friendly and efficient recycling [144,145]. Topology design optimization of the electric vehicle and its integrated components including battery packs could be studied and explored in detail. The other growing aspects are the integration of batteries with photovoltaic systems and super capacitors for improving the efficiency, range of vehicle, and storage in the context of excess energy production from the hybrid systems. For example, combinations of photovoltaic system, wind power, and lithium-ion battery storage into microgrid EV charging station can offer backup power during loss of grid connection and permits exporting power when generation go beyond demand within the microgrid [146].

### Minimization of Safety-Related Problems and Negative Impact on the Environment.

Major concern should be taken toward safety-related problems such as cathode breakdown, electrolyte breakdown, over current, over voltage, low current, low voltage, etc. so as to protect the cells from irrecoverable damage. To diminish such problems, integration and improvization of pressure vent controller, circuit interrupter, advance switching techniques, and a reliable thermal management module inside BMS will be helpful.

Also, materials (like cobalt, nickel, etc.) used in the lithium-ion battery are not environmental friendly [112]. Extensive research on it shows that it paves a way toward global warming and environmental toxicity.

### Process-Based Cost Modeling.

For common public, price always plays the main decider factor for buying an automobile. Earlier, the price of the lithium-ion batteries, the main component of the battery electric vehicle, was very high. But in the last few years, its price has been dropping gradually. From studies, it is known that its price has been decreasing almost 73% in the last 5–6 years and this percentage will be increasing in upcoming years [112]. In this regard, process-based cost modeling helps in predicting and calculating the cost and sales price of several battery chemistries [147]. This cost is predicted on the basis of battery-related processes. Combination of the learning and dynamic curve model with process-based modeling leads to more precise and efficient production planning and cost forecasting [148]. But for this, exact processes and compositions of the battery chemistries should be known accurately. This can be overcome by recently published patents and through literature survey which describe the processes in detail.

## Conclusions

This paper reviewed various approaches for SoH estimation of lithium-ion batteries, with a focus on their use in the electric vehicles. From this review, it is evident that among all the models, electrochemical and equivalent circuit models perform well but cannot be directly applied to the other batteries. On the other hand, statistical methods are easily adjustable to the different batteries. However, obtaining the effective and accurate method for the estimation of battery health in the real conditions is a challenging task. In order to investigate the real-time SoH, a few attempts were made by researchers. NDT methods can be considered as the solution for overcoming the difficulties in real-time estimation of SoH of the lithium-ion battery, since it has the capability of changing its parameters according to demand conditions. Furthermore, the commonly used methods which have been reviewed are not favorable for aged battery's SoH estimation and it is found that their chemistry is very difficult to understand; hence, there is a need to study the battery chemistry so that it can correlate with SoH of the lithium-ion battery. From the extensive literature review, it can be concluded that in order to design and develop a new and efficient methodology for estimation of SoH of the lithium-ion battery, major focus should be given in all these issues like accuracy, easy assembling–disassembling of battery packs, end life forecast, suitability for realistic situations, and its effective implementation on BMS simultaneously. Thus, exhaustive research on battery electrochemistry and appropriate research based on realistic situations with its parameters will give a new dimension toward great invention in battery research.

## References

References
1.
Gulagi
,
A.
,
Bogdanov
,
D.
, and
Breyer
,
C.
,
2018
, “
The Role of Storage Technologies in Energy Transition Pathways Towards Achieving a Fully Sustainable Energy System for India
,”
J. Energy Storage
,
17
, pp.
525
539
.
2.
Socolow
,
R.
, and
Pacala
,
S.
,
2004
, “
(Talk) Stabilization Wedges: Solving the Climate Problem for the Next Half-Century With Technologies Available Today
,”
Science
,
305
(
5686
), pp.
968
972
.
3.
Grün
,
T.
,
Stella
,
K.
, and
Wollersheim
,
O.
,
2017
, “
Impacts on Load Distribution and Ageing in Lithium-Ion Home Storage Systems
,”
Energy Procedia
,
135
, pp.
236
248
.
4.
Wang
,
Y.
,
Zhang
,
C.
, and
Chen
,
Z.
,
2016
, “
Model-Based State-of-Energy Estimation of Lithium-Ion Batteries in Electric Vehicles
,”
Energy Procedia
,
88
, pp.
998
1004
.
5.
Lu
,
L.
,
Han
,
X.
,
Li
,
J.
,
Hua
,
J.
, and
Ouyang
,
M.
,
2013
, “
A Review on the Key Issues for Lithium-Ion Battery Management in Electric Vehicles
,”
J. Power Sources
,
226
, pp.
272
288
.
6.
Campestrini
,
C.
,
Keil
,
P.
,
Schuster
,
S. F.
, and
Jossen
,
A.
,
2016
, “
Ageing of Lithium-Ion Battery Modules With Dissipative Balancing Compared With Single-Cell Ageing
,”
J. Energy Storage
,
6
, pp.
142
152
.
7.
Bayat
,
P.
,
Baghramian
,
A.
, and
Bayat
,
P.
,
2018
, “
Implementation of Hybrid Electric Vehicle Energy Management System for Two Input Power Sources
,”
J. Energy Storage
,
17
, pp.
423
440
.
8.
Bouchhima
,
N.
,
Gossen
,
M.
,
Schulte
,
S.
, and
Birke
,
K. P.
,
2018
, “
Lifetime of Self-Reconfigurable Batteries Compared With Conventional Batteries
,”
J. Energy Storage
,
15
, pp.
400
407
.
9.
Lane
,
B. W.
,
Dumortier
,
J.
,
Carley
,
S.
,
Siddiki
,
S.
,
Clark-Sutton
,
K.
, and
Graham
,
J. D.
,
2018
, “
All Plug-In Electric Vehicles Are Not the Same: Predictors of Preference for a Plug-In Hybrid Versus a Battery-Electric Vehicle
,”
Transp. Res. Part D Transp. Environ.
,
65
, pp.
1
13
.
10.
Moghbelli
,
H.
,
Niasar
,
A. H.
, and
Langari
,
R.
,
2006
, “
New Generation of Passenger Vehicles: FCV or HEV?
,”
Proceedings of the IEEE International Conference on Industrial Technology
, pp.
452
459
.
11.
Weldon
,
P.
,
Morrissey
,
P.
, and
O’Mahony
,
M.
,
2018
, “
Long-Term Cost of Ownership Comparative Analysis Between Electric Vehicles and Internal Combustion Engine Vehicles
,”
Sustain. Cities Soc.
,
39
, pp.
578
591
.
12.
Pomponi
,
C.
,
Scalzi
,
S.
,
Pasquale
,
L.
,
Verrelli
,
C. M.
, and
Marino
,
R.
,
2018
, “
Automatic Motor Speed Reference Generators for Cruise and Lateral Control of Electric Vehicles With In-Wheel Motors
,”
Control Eng. Pract.
,
79
, pp.
126
143
.
13.
Breetz
,
H. L.
, and
Salon
,
D.
,
2018
, “
Do Electric Vehicles Need Subsidies? Ownership Costs for Conventional, Hybrid, and Electric Vehicles in 14 U.S. Cities
,”
Energy Policy
,
120
, pp.
238
249
.
14.
McKerracher
,
C.
,
2018
, www.bnef.com. Accessed October 11, 2018.
15.
16.
Feng
,
X.
,
Ouyang
,
M.
,
Liu
,
X.
,
Lu
,
L.
,
Xia
,
Y.
, and
He
,
X.
,
2018
, “
Thermal Runaway Mechanism of Lithium Ion Battery for Electric Vehicles: A Review
,”
Energy Storage Mater.
,
10
, pp.
246
267
.
17.
Foss
,
C. E. L.
,
Svensson
,
A. M.
,
Gullbrekken
,
Ø.
,
Sunde
,
S.
, and
Vullum-Bruer
,
F.
,
2018
, “
Temperature Effects on Performance of Graphite Anodes in Carbonate Based Electrolytes for Lithium Ion Batteries
,”
J. Energy Storage
,
17
, pp.
395
402
.
18.
Vijayaraghavan
,
V.
,
Garg
,
A.
, and
Gao
,
L.
,
2018
, “
Fracture Mechanics Modelling of Lithium-Ion Batteries Under Pinch Torsion Test
,”
Meas. J. Int. Meas. Confed.
,
114
, pp.
382
389
.
19.
Di Domenico
,
D.
,
Pognant-Gros
,
P.
,
Petit
,
M.
, and
Creff
,
Y.
,
2015
, “
State of Health Estimation for NCA-C Lithium-Ion Cells
,”
IFAC-PapersOnLine
,
28
(
15
), pp.
376
382
.
20.
Cen
,
J.
,
Li
,
Z.
, and
Jiang
,
F.
,
2018
, “
Experimental Investigation on Using the Electric Vehicle Air Conditioning System for Lithium-Ion Battery Thermal Management
,”
Energy Sustain. Dev.
,
45
, pp.
88
95
.
21.
Bobba
,
S.
,
Mathieux
,
F.
,
Ardente
,
F.
,
Andrea Blengini
,
G.
,
Anna Cusenza
,
M.
,
Podias
,
A.
, and
Pfrang
,
A.
,
2018
, “
Life Cycle Assessment of Repurposed Electric Vehicles Batteries : An Adapted Method Based on Modelling of Energy Flows
,”
J. Energy Storage
,
19
, pp.
213
225
.
22.
Wegmann
,
R.
,
Döge
,
V.
, and
Sauer
,
D. U.
,
2018
, “
Assessing the Potential of an Electric Vehicle Hybrid Battery System Comprising Solid-State Lithium Metal Polymer High Energy and Lithium-Ion High Power Batteries
,”
J. Energy Storage
,
18
, pp.
175
184
.
23.
Mier
,
F. A.
,
Morales
,
R.
,
Coultas-McKenney
,
C. A.
,
Hargather
,
M. J.
, and
Ostanek
,
J.
,
2017
, “
Overcharge and Thermal Destructive Testing of Lithium Metal Oxide and Lithium Metal Phosphate Batteries Incorporating Optical Diagnostics
,”
J. Energy Storage
,
13
, pp.
378
386
.
25.
,
M.
,
2018
, “
Watching the Teen Brain Grow
,”
Science
,
359
(
6371
), pp.
13
14
.
26.
Linden
,
D.
, and
Reddy
,
T. B.
,
2004
,
Handbook of Batteries
,
McGraw-Hill
,
New York
.
27.
Zhang
,
H.
,
Miao
,
Q.
,
Zhang
,
X.
, and
Liu
,
Z.
,
2018
, “
An Improved Unscented Particle Filter Approach for Lithium-Ion Battery Remaining Useful Life Prediction
,”
Microelectron. Reliab.
,
81
(
24
), pp.
288
298
.
28.
Feng
,
X.
,
Li
,
J.
,
Ouyang
,
M.
,
Lu
,
L.
,
Li
,
J.
, and
He
,
X.
,
2013
, “
Using Probability Density Function to Evaluate the State of Health of Lithium-Ion Batteries
,”
J. Power Sources
,
232
, pp.
209
218
.
29.
Nitta
,
N.
,
Wu
,
F.
,
Lee
,
J. T.
, and
Yushin
,
G.
,
2015
, “
Li-Ion Battery Materials: Present and Future
,”
Mater. Today
,
18
(
5
), pp.
252
264
.
30.
Selman
,
J. R.
,
Al Hallaj
,
S.
,
Uchida
,
I.
, and
Hirano
,
Y.
,
2001
, “
Cooperative Research on Safety Fundamentals of Lithium Batteries
,”
J. Power Sources
,
97–98
, pp.
726
732
.
31.
Vikström
,
H.
,
Davidsson
,
S.
, and
Höök
,
M.
,
2013
, “
Lithium Availability and Future Production Outlooks
,”
Appl. Energy
,
110
, pp.
252
266
.
32.
Xu
,
X. L.
,
Wang
,
S. J.
,
Wang
,
H.
,
Xu
,
B.
,
Hu
,
C.
,
Jin
,
Y.
,
Liu
,
J. B.
, and
Yan
,
H.
,
2017
, “
The Suppression of Lithium Dendrite Growth in Lithium Sulfur Batteries: A Review
,”
J. Energy Storage
,
13
, pp.
387
400
.
33.
Al-Zareer
,
M.
,
Dincer
,
I.
, and
Rosen
,
M. A.
,
2018
, “
A Review of Novel Thermal Management Systems for Batteries
,”
Int. J. Energy Res.
,
42
, pp.
1
24
.
34.
Rajan
,
A.
,
Vijayaraghavan
,
V.
,
Ooi
,
M. P. L.
,
Garg
,
A.
, and
Kuang
,
Y. C.
,
2018
, “
A Simulation-Based Probabilistic Framework for Lithium-Ion Battery Modelling
,”
Meas. J. Int. Meas. Confed.
,
115
, pp.
87
94
.
35.
Garg
,
A.
,
Peng
,
X.
,
Le
,
M. L. P.
,
Pareek
,
K.
, and
Chin
,
C. M. M.
,
2018
, “
Design and Analysis of Capacity Models for Lithium-Ion Battery
,”
Meas. J. Int. Meas. Confed.
,
120
, pp.
114
120
.
36.
Patil
,
M. A.
,
,
P.
,
Hariharan
,
K. S.
,
Kolake
,
S. M.
,
Song
,
T.
,
Yeo
,
T.
, and
Doo
,
S.
,
2015
, “
A Novel Multistage Support Vector Machine Based Approach for Li Ion Battery Remaining Useful Life Estimation
,”
Appl. Energy
,
159
, pp.
285
297
.
37.
Weicker
,
Phillip
,
2014
,
A Systems Approach to Lithium Ion Battery Management
,
Artech House Publisher
,
Boston
.
38.
Qing
,
D.
,
Huang
,
J.
, and
Sun
,
W.
,
2014
, “
SOH Estimation of Lithium-Ion Batteries for Electric Vehicles
,”
31st ISARC
,
ISARC
, pp.
2
5
.
39.
Wei
,
J.
,
Dong
,
G.
, and
Chen
,
Z.
,
2018
, “
Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression
,”
IEEE Trans. Ind. Electron.
,
65
(
7
), pp.
5634
5643
.
40.
Ibolya
,
Anna
,
I.
,
Magyar
,
Attila
, and
Hangos
,
Katalim
,
M.
,
2017
, “
Model identification and parameter estimation of lithium ion batteries for diagnostic purposes
,”
2017 International Symposium on Power Electronics (Ee)
,
,
October 19-21, 2017
.
41.
Lin
,
C.
,
Tang
,
A.
, and
Wang
,
W.
,
2015
, “
A Review of SOH Estimation Methods in Lithium-Ion Batteries for Electric Vehicle Applications
,”
Energy Procedia
,
75
, pp.
1920
1925
.
42.
Zou
,
Y.
,
Hu
,
X.
,
Ma
,
H.
, and
Li
,
S. E.
,
2015
, “
Combined State of Charge and State of Health Estimation Over Lithium-Ion Battery Cell Cycle Lifespan for Electric Vehicles
,”
J. Power Sources
,
273
, pp.
793
803
.
43.
Moura
,
S. J.
,
Chaturvedi
,
N. A.
, and
Krstić
,
M.
,
2014
, “
Adaptive PDE Observer for Battery SOC/SOH Estimation Via an Electrochemical Model
,”
J. Dyn. Syst. Meas. Control
,
136
(
1
), pp.
011
015
.
44.
Li
,
J.
,
,
K.
,
Lotfi
,
N.
,
Landers
,
R. G.
, and
Park
,
J.
,
2018
, “
A Single Particle Model With Chemical/Mechanical Degradation Physics for Lithium Ion Battery State of Health (SOH) Estimation
,”
Appl. Energy
,
212
, pp.
1178
1190
.
45.
Chen
,
L.
,
,
Z.
,
Lin
,
W.
,
Li
,
J.
, and
Pan
,
H.
,
2018
, “
A New State-of-Health Estimation Method for Lithium-Ion Batteries Through the Intrinsic Relationship Between Ohmic Internal Resistance and Capacity
,”
Meas. J. Int. Meas. Confed.
,
116
, pp.
586
595
.
46.
Berecibar
,
M.
,
Gandiaga
,
I.
,
Villarreal
,
I.
,
Omar
,
N.
,
Van Mierlo
,
J.
, and
Van Den Bossche
,
P.
,
2016
, “
Critical Review of State of Health Estimation Methods of Li-Ion Batteries for Real Applications
,”
Renew. Sustain. Energy Rev.
,
56
, pp.
572
587
.
47.
Williard
,
N.
,
He
,
W.
,
Osterman
,
M.
, and
Pecht
,
M.
,
2011
, “
Improved SOH Estimation Through Coulomb Counting
,” pp.
1
12
.
48.
Alzieu
,
J.
,
Smimite
,
H.
, and
Glaize
,
C.
,
1997
, “
Improvement of Intelligent Battery Controller: State-of-Charge Indicator and Associated Functions
,”
J. Power Sources
,
67
, pp.
157
161
.
49.
Piller
,
S.
,
Perrin
,
M.
, and
Jossen
,
A.
,
2001
, “
Methods for State-of-Charge Determination and Their Applications
,”
J. Power Sources
,
96
(
1
), pp.
113
120
.
50.
Ng
,
K. S.
,
Moo
,
C. S.
,
Chen
,
Y. P.
, and
Hsieh
,
Y. C.
,
2009
, “
Enhanced Coulomb Counting Method for Estimating State-of-Charge and State-of-Health of Lithium-Ion Batteries
,”
Appl. Energy
,
86
(
9
), pp.
1506
1511
.
51.
,
M. A.
, and
Venkatesh
,
B.
,
2016
, “
Accuracy Improvement of SOC Estimation in Lithium-Ion Batteries
,”
J. Energy Storage
,
6
, pp.
95
97
.
52.
Huet
,
F.
,
1998
, “
A Review of Impedance Measurements for Determination of the State-of-Charge or State-of-Health of Secondary Batteries
,”
J. Power Sources
,
70
(
1
), pp.
59
69
.
53.
Blanke
,
H.
,
Bohlen
,
O.
,
Buller
,
S.
,
De Doncker
,
R. W.
,
Fricke
,
B.
,
Hammouche
,
A.
,
Linzen
,
D.
,
Thele
,
M.
, and
Uwe Sauer
,
D.
,
2005
, “
Impedance Measurements on Lead-Acid Batteries for State-of-Charge, State-of-Health and Cranking Capability Prognosis in Electric and Hybrid Electric Vehicles
,”
J. Power Sources
,
144
(
2
), pp.
418
425
.
54.
Galeotti
,
M.
,
Giammanco
,
C.
,
Cina
,
L.
,
Cordiner
,
S.
, and
Di Carlo
,
A.
,
2014
, “
Diagnostic Methods for the Evaluation of the State of Health (SOH) of NiMH Batteries Through Electrochemical Impedance Spectroscopy
,”
2014 IEEE 23rd International Symposium on Industrial Electronics
, pp.
1641
1646
.
55.
Büschel
,
P.
,
Tröltzsch
,
U.
, and
Kanoun
,
O.
,
2011
, “
Use of Stochastic Methods for Robust Parameter Extraction From Impedance Spectra
,”
Electrochim. Acta
,
56
(
23
), pp.
8069
8077
.
56.
Kozlowski
,
J. D.
,
2003
, “
Electrochemical Cell Prognostics Using Online Impedance Measurements and Model-Based Data Fusion Techniques
,”
IEEE Aerosp. Conf. Proc.
,
7
, pp.
3257
3270
.
57.
Meissner
,
E.
, and
Richter
,
G.
,
2001
, “
Vehicle Electric Power Systems Are Under Change! Implications for Design, Monitoring and Management of Automotive Batteries
,”
J. Power Sources
,
95
, pp.
13
23
.
58.
Meissner
,
E.
, and
Richter
,
G.
,
2005
, “
The Challenge to the Automotive Battery Industry: The Battery Has to Become an Increasingly Integrated Component Within the Vehicle Electric Power System
,”
J. Power Sources
,
144
(
2
), pp.
438
460
.
59.
Andre
,
D.
,
Meiler
,
M.
,
Steiner
,
K.
,
Wimmer
,
C.
,
Soczka-Guth
,
T.
, and
Sauer
,
D. U.
,
2011
, “
Characterization of High-Power Lithium-Ion Batteries by Electrochemical Impedance Spectroscopy. I. Experimental Investigation
,”
J. Power Sources
,
196
(
12
), pp.
5334
5341
.
60.
Andre
,
D.
,
Meiler
,
M.
,
Steiner
,
K.
,
Walz
,
H.
,
Soczka-Guth
,
T.
, and
Sauer
,
D. U.
,
2011
, “
Characterization of High-Power Lithium-Ion Batteries by Electrochemical Impedance Spectroscopy. II: Modelling
,”
J. Power Sources
,
196
(
12
), pp.
5349
5356
.
61.
Hatzell
,
K. B.
,
Sharma
,
A.
, and
Fathy
,
H. K.
,
2012
, “
A Survey of Long-Term Health Modeling, Estimation, and Control of Lithium-Ion Batteries: Challenges and Opportunities
,”
American Control Conference
, pp.
584
591
.
62.
Mu
,
H.
,
Xiong
,
R.
,
Zheng
,
H.
,
Chang
,
Y.
, and
Chen
,
Z.
,
2017
, “
A Novel Fractional Order Model Based State-of-Charge Estimation Method for Lithium-Ion Battery
,”
Appl. Energy
,
207
, pp.
384
393
.
63.
Zhu
,
J.
,
Zhang
,
X.
,
Luo
,
H.
, and
Sahraei
,
E.
,
2018
, “
Investigation of the Deformation Mechanisms of Lithium-Ion Battery Components Using In-Situ Micro Tests
,”
Appl. Energy
,
224
, pp.
251
266
.
64.
Yang
,
D.
,
Wang
,
Y.
,
Pan
,
R.
,
Chen
,
R.
, and
Chen
,
Z.
,
2017
, “
A Neural Network Based State-of-Health Estimation of Lithium-Ion Battery in Electric Vehicles
,”
Energy Procedia
,
105
, pp.
2059
2064
.
65.
Ragsdale
,
M.
,
Brunet
,
J.
, and
Fahimi
,
B.
,
2008
, “
A Novel Battery Identification Method Based on Pattern Recognition
,”
2008 IEEE Vehicle Power and Propulsion Conference
, pp.
1
6
.
66.
Watrin
,
N.
,
Blunier
,
B.
, and
Miraoui
,
A.
,
2012
, “
Review of Adaptive Systems for Lithium Batteries State-of-Charge and State-of-Health Estimation
,”
2012 IEEE Transportation Electrification Conference and Expo (ITEC 2012)
, p. 3.
67.
Zhang
,
J.
, and
Lee
,
J.
,
2011
, “
A Review on Prognostics and Health Monitoring of Li-Ion Battery
,”
J. Power Sources
,
196
(
15
), pp.
6007
6014
.
68.
Eddahech
,
A.
,
Briat
,
O.
,
Bertrand
,
N.
,
Delétage
,
J.-Y.
, and
Vinassa
,
J.-M.
,
2012
, “
Behavior and State-of-Health Monitoring of Li-Ion Batteries Using Impedance Spectroscopy and Recurrent Neural Networks
,”
Int. J. Electr. Power Energy Syst.
,
42
(
1
), pp.
487
494
.
69.
Hannan
,
M. A.
,
Lipu
,
M. S. H.
,
Hussain
,
A.
, and
Mohamed
,
A.
,
2017
, “
A Review of Lithium-Ion Battery State of Charge Estimation and Management System in Electric Vehicle Applications: Challenges and Recommendations
,”
Renew. Sustain. Energy Rev.
,
78
, pp.
834
854
.
70.
Dong
,
H.
,
Jin
,
X.
,
Lou
,
Y.
, and
Wang
,
C.
,
2014
, “
Lithium-Ion Battery State of Health Monitoring and Remaining Useful Life Prediction Based on Support Vector Regression-Particle Filter
,”
J. Power Sources
,
271
, pp.
114
123
.
71.
Klass
,
V.
,
Behm
,
M.
, and
Lindbergh
,
G.
,
2014
, “
A Support Vector Machine-Based State-of-Health Estimation Method for Lithium-Ion Batteries Under Electric Vehicle Operation
,”
J. Power Sources
,
270
, pp.
262
272
.
72.
Klass
,
V.
,
Behm
,
M.
, and
Lindbergh
,
G.
,
2015
, “
Capturing Lithium-Ion Battery Dynamics With Support Vector Machine-Based Battery Model
,”
J. Power Sources
,
298
, pp.
92
101
.
73.
Álvarez Antón
,
J. C.
,
García Nieto
,
P. J.
,
de Cos Juez
,
F. J.
,
Sánchez Lasheras
,
F.
,
González Vega
,
M.
, and
Roqueñí Gutiérrez
,
M. N.
,
2013
, “
Battery State-of-Charge Estimator Using the SVM Technique
,”
Appl. Math. Model.
,
37
(
9
), pp.
6244
6253
.
74.
Nuhic
,
A.
,
Terzimehic
,
T.
,
Soczka-Guth
,
T.
,
Buchholz
,
M.
, and
Dietmayer
,
K.
,
2013
, “
Health Diagnosis and Remaining Useful Life Prognostics of Lithium-Ion Batteries Using Data-Driven Methods
,”
J. Power Sources
,
239
, pp.
680
688
.
75.
Mastali
,
M.
,
Vazquez-Arenas
,
J.
,
Fraser
,
R.
,
Fowler
,
M.
,
Afshar
,
S.
, and
Stevens
,
M.
,
2013
, “
Battery State of the Charge Estimation Using Kalman Filtering
,”
J. Power Sources
,
239
, pp.
294
307
.
76.
Plett
,
G. L.
,
2005
, “
Dual and Joint EKF for Simultaneous SOC and SOH Estimation
,”
Proceedings of the 21st Electric Vehicle Symposium
, pp.
1
12
.
77.
Cui
,
X.
,
Jing
,
Z.
,
Luo
,
M.
,
Guo
,
Y.
, and
Qiao
,
H.
,
2018
, “
A New Method for State of Charge Estimation of Lithium-Ion Batteries Using Square Root Cubature Kalman Filter
,”
Energies
,
11
(
1
), p.
1
21
.
78.
Dai
,
H.
,
Wei
,
X.
,
Sun
,
Z.
,
Wang
,
J.
, and
Gu
,
W.
,
2012
, “
Online Cell SOC Estimation of Li-Ion Battery Packs Using a Dual Time-Scale Kalman Filtering for EV Applications
,”
Appl. Energy
,
95
, pp.
227
237
.
79.
Xiong
,
R.
,
Sun
,
F.
,
Chen
,
Z.
, and
He
,
H.
,
2014
, “
A Data-Driven Multi-Scale Extended Kalman Filtering Based Parameter and State Estimation Approach of Lithium-Ion Olymer Battery in Electric Vehicles
,”
Appl. Energy
,
113
, pp.
463
476
.
80.
Zheng
,
X.
, and
Fang
,
H.
,
2015
, “
An Integrated Unscented Kalman Filter and Relevance Vector Regression Approach for Lithium-Ion Battery Remaining Useful Life and Short-Term Capacity Prediction
,”
Reliab. Eng. Syst. Saf.
,
144
, pp.
74
82
.
81.
Bizeray
,
A. M.
,
Zhao
,
S.
,
Duncan
,
S. R.
, and
Howey
,
D. A.
,
2015
, “
Lithium-Ion Battery Thermal-Electrochemical Model-Based State Estimation Using Orthogonal Collocation and a Modified Extended Kalman Filter
,”
J. Power Sources
,
296
, pp.
400
412
.
82.
Claude
,
F.
,
Becherif
,
M.
, and
,
H. S.
,
2017
, “
Experimental Validation for Li-Ion Battery Modeling Using Extended Kalman Filters
,”
Int. J. Hydrogen Energy
,
42
(
40
), pp.
25509
25517
.
83.
Sepasi
,
S.
,
Roose
,
L. R.
, and
Matsuura
,
M. M.
,
2015
, “
Extended Kalman Filter With a Fuzzy Method for Accurate Battery Pack State of Charge Estimation
,”
Energies
,
8
(
6
), pp.
5217
5233
.
84.
,
M.
, and
Salmasi
,
F. R.
,
2014
, “
Estimation of State of Charge, Unknown Nonlinearities, and State of Health of a Lithium-Ion Battery Based on a Comprehensive Unobservable Model
,”
IEEE Trans. Ind. Electron.
,
61
(
3
), pp.
1335
1344
.
85.
Andre
,
D.
,
Nuhic
,
A.
,
Soczka-Guth
,
T.
, and
Sauer
,
D. U.
,
2013
, “
Comparative Study of a Structured Neural Network and an Extended Kalman Filter for State of Health Determination of Lithium-Ion Batteries in Hybrid Electric Vehicles
,”
Eng. Appl. Artif. Intell.
,
26
(
3
), pp.
951
961
.
86.
Lin
,
C.
,
Xing
,
J.
, and
Tang
,
A.
,
2017
, “
Lithium-Ion Battery State of Charge/State of Health Estimation Using SMO for EVs
,”
Energy Procedia
,
105
, pp.
4383
4388
.
87.
Du
,
J.
,
Liu
,
Z.
,
Wang
,
Y.
, and
Wen
,
C.
,
2016
, “
An Adaptive Sliding Mode Observer for Lithium-Ion Battery State of Charge and State of Health Estimation in Electric Vehicles
,”
Control Eng. Pract.
,
54
, pp.
81
90
.
88.
Kim
,
J.
,
Lee
,
S.
, and
Cho
,
B. H.
,
2012
, “
Complementary Cooperation Algorithm Based on DEKF Combined With Pattern Recognition for SOC/Capacity Estimation and SOH Prediction
,”
IEEE Trans. Power Electron.
,
27
, pp.
436
451
.
89.
Sidhu
,
A.
,
,
A.
, and
Anwar
,
S.
,
2015
, “
Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries
,”
Ind. Electron. IEEE Trans.
,
62
(
2
), pp.
1002
1011
.
90.
Dey
,
S.
,
2015
, “
A Diagnostic Scheme for Detection, Isolation and Estimation of Electrochemical Faults in Lithium Ion Cell
,” DSCC2015-9699, pp.
1
10
.
91.
Marcicki
,
J.
,
Onori
,
S.
, and
Rizzoni
,
G.
,
2010
, “
Nonlinear Fault Detection and Isolation for a Lithium-Ion Battery Management System
,”
Proceedings of the ASME 2010 Dynamic Systems and Control Conference (DSCC20)
, pp.
1
8
.
92.
Singh
,
P.
, and
Reisner
,
D.
,
2002
, “
Fuzzy Logic-Based State-of-Health Determination of Lead Acid Batteries
,”
IEEE 24th Annual International Telecommunications Energy Conference 2002 (INTELEC)
, pp.
583
590
.
93.
Data
,
P. P.
,
Examiner
,
P.
, and
Upton
,
C.
,
2006
, “
(12) United States Patent (10) Patent No.: (58) Field of Classification
,” Vol.
2
, No.
12
, pp.
8
11
.
94.
Zenati
,
A.
,
Desprez
,
P.
,
Razik
,
H.
, and
Rael
,
S.
,
2010
, “
Impedance Measurements Combined With the Fuzzy Logic Methodology to Assess the SOC and SOH of Lithium-Ion Cells
,”
2010 IEEE Vehicle Power and Propulsion Conference (VPPC 2010)
, pp.
111
113
.
95.
Zenati
,
A.
,
Desprez
,
P.
, and
Razik
,
H.
,
2011
, “
Logic Inference to Cite This Version: HAL Id: hal-00564335 Estimation of the SOC and the SOH of Li-ion Batteries, by Combining Impedance Measurements With the Fuzzy Logic Inference
,” IECON 2010, pp.
1767
1172
.
96.
Tang
,
X.
,
Zou
,
C.
,
Yao
,
K.
,
Chen
,
G.
,
Liu
,
B.
,
He
,
Z.
, and
Gao
,
F.
,
2018
, “
A Fast Estimation Algorithm for Lithium-Ion Battery State of Health
,”
J. Power Sources
,
396
, pp.
453
458
.
97.
Li
,
Y.
,
Abdel-Monema
,
M.
,
Gopalakrishnan
,
R.
,
Berecibar
,
M.
,
Nanini-Maury
,
E.
,
Omar
,
N.
,
van den Bossche
,
P.
, and
Van Mierlo
,
J.
,
2018
, “
Erratum to ‘A Quick On-Line State of Health Estimation Method for Li-Ion Battery With Incremental Capacity Curves Processed by Gaussian Filter’ [J. Power Sources 373 (2018) 40–53] (S0378775317314532) (10.1016/j.jpowsour.2017.10.092)
,”
J. Power Sources
,
393
, p.
230
.
98.
Kunlong
,
C.
,
Jiuchun
,
J.
,
Fangdan
,
Z.
,
Bingxiang
,
S.
, and
Yanru
,
Z.
,
2016
, “
SOH Estimation for Lithium-Ion Batteries: A Cointegration and Error Correction Approach
,”
2016 IEEE International Conference on Prognostics and Health Management (ICPHM 2016)
.
99.
Li
,
Y.
,
,
P.
,
Ray
,
A.
, and
Rahn
,
C. D.
,
2015
, “
Identification of the Battery State-of-Health Parameter From Input–Output Pairs of Time Series Data
,”
J. Power Sources
,
285
, pp.
235
246
.
100.
Ma
,
Z.
,
Jiang
,
J.
,
Shi
,
W.
,
Zhang
,
W.
, and
Mi
,
C. C.
,
2015
, “
Investigation of Path Dependence in Commercial Lithium-Ion Cells for Pure Electric Bus Applications: Aging Mechanism Identification
,”
J. Power Sources
,
274
, pp.
29
40
.
101.
Ning
,
B.
,
Xu
,
J.
,
Cao
,
B.
,
Wang
,
B.
, and
Xu
,
G.
,
2016
, “
A Sliding Mode Observer SOC Estimation Method Based on Parameter Adaptive Battery Model
,”
Energy Procedia
,
88
, pp.
619
626
.
102.
Sarikurt
,
T.
,
Ceylan
,
M.
, and
Balikc
,
A.
,
2017
, “
A Parametric Battery State of Health Estimation Method for Electric Vehicle Applications
,”
Turk. J. Electr. Eng. Comput. Sci.
,
25
, pp.
2860
2870
.
103.
Wang
,
L.
,
Zhao
,
X.
,
Liu
,
L.
, and
Pan
,
C.
,
2017
, “
State of Health Estimation of Battery Modules Via Differential Voltage Analysis With Local Data Symmetry Method
,”
Electrochim. Acta
,
256
, pp.
81
89
.
104.
Xiao
,
R.
,
Shen
,
J.
,
Li
,
X.
,
Yan
,
W.
,
Pan
,
E.
, and
Chen
,
Z.
,
2016
, “
Comparisons of Modeling and State of Charge Estimation for Lithium-Ion Battery Based on Fractional Order and Integral Order Methods
,”
Energies
,
9
(
3
), p.
184
.
105.
Yang
,
J.
,
Xia
,
B.
,
Huang
,
W.
,
Fu
,
Y.
, and
Mi
,
C.
,
2018
, “
Online State-of-Health Estimation for Lithium-Ion Batteries Using Constant-Voltage Charging Current Analysis
,”
Appl. Energy
,
212
, pp.
1589
1600
.
106.
Yu
,
J.
,
2018
, “
State of Health Prediction of Lithium-Ion Batteries: Multiscale Logic Regression and Gaussian Process Regression Ensemble
,”
Reliab. Eng. Syst. Saf.
,
174
, pp.
82
95
.
107.
Andre
,
D.
,
Appel
,
C.
,
Soczka-Guth
,
T.
, and
Sauer
,
D. U.
,
2013
, “
Advanced Mathematical Methods of SOC and SOH Estimation for Lithium-Ion Batteries
,”
J. Power Sources
,
224
, pp.
20
27
.
108.
Bi
,
J.
,
Zhang
,
T.
,
Yu
,
H.
, and
Kang
,
Y.
,
2016
, “
State-of-Health Estimation of Lithium-Ion Battery Packs in Electric Vehicles Based on Genetic Resampling Particle Filter
,”
Appl. Energy
,
182
, pp.
558
568
.
109.
Cuma
,
M. U.
, and
Koroglu
,
T.
,
2015
, “
A Comprehensive Review on Estimation Strategies Used in Hybrid and Battery Electric Vehicles
,”
Renew. Sustain. Energy Rev.
,
42
, pp.
517
531
.
110.
Fleischer
,
C.
,
Waag
,
W.
,
Heyn
,
H.-M.
, and
Sauer
,
D. U.
,
2014
, “
On-Line Adaptive Battery Impedance Parameter and State Estimation Considering Physical Principles in Reduced Order Equivalent Circuit Battery Models. Part 1. Requirements, Critical Review of Methods and Modeling
,”
J. Power Sources
,
260
, pp.
276
291
.
111.
Guo
,
Z.
,
Qiu
,
X.
,
Hou
,
G.
,
Liaw
,
B. Y.
, and
Zhang
,
C.
,
2014
, “
State of Health Estimation for Lithium Ion Batteries Based on Charging Curves
,”
J. Power Sources
,
249
, pp.
457
462
.
112.
Lipu
,
M. S. H.
,
Hannan
,
M. A.
,
Hussain
,
A.
,
Hoque
,
M. M.
,
Ker
,
P. J.
,
,
M. H. M.
, and
Ayob
,
A.
,
2018
, “
A Review of State of Health and Remaining Useful Life Estimation Methods for Lithium-Ion Battery in Electric Vehicles: Challenges and Recommendations
,”
J. Clean. Prod.
,
205
, pp.
115
133
.
113.
Schweiger
,
H. G.
,
Obeidi
,
O.
,
Komesker
,
O.
,
Raschke
,
A.
,
Schiemann
,
M.
,
Zehner
,
C.
,
Gehnen
,
M.
,
Keller
,
M.
, and
Birke
,
P.
,
2010
, “
Comparison of Several Methods for Determining the Internal Resistance of Lithium Ion Cells
,”
Sensors
,
10
(
6
), pp.
5604
5625
.
114.
Weng
,
C.
,
Feng
,
X.
,
Sun
,
J.
, and
Peng
,
H.
,
2016
, “
State-of-Health Monitoring of Lithium-Ion Battery Modules and Packs Via Incremental Capacity Peak Tracking
,”
Appl. Energy
,
180
, pp.
360
368
.
115.
Topan
,
P. A.
,
,
M. N.
,
Fathoni
,
G.
,
,
A. I.
, and
Wahyunggoro
,
O.
,
2017
, “
State of Charge (SOC) and State of Health (SOH) Estimation on Lithium Polymer Battery Via Kalman Filter
,”
Proceedings of the 2016 2nd International Conference on Science and Technology (ICST 2016)
, no. 2, pp.
93
96
.
116.
Hongwen
,
H.
,
Rui
,
X.
,
Xiaowei
,
Z.
,
Fengchun
,
S.
, and
JinXin
,
F.
,
2011
, “
State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model
,”
Veh. Technol. IEEE Trans.
,
60
(
4
), pp.
1461
1469
.
117.
Wu
,
Y.
, and
Jossen
,
A.
,
2018
, “
Entropy-Induced Temperature Variation as a New Indicator for State of Health Estimation of Lithium-Ion Cells
,”
Electrochim. Acta
,
276
, pp.
370
376
.
118.
,
P.
,
Kopsaftopoulos
,
F.
, and
Chang
,
F. K.
,
2018
, “
Estimating State of Charge and Health of Lithium-Ion Batteries With Guided Waves Using Built-In Piezoelectric Sensors/Actuators
,”
J. Power Sources
,
384
, pp.
342
354
.
119.
Marcicki
,
J.
,
Canova
,
M.
,
Conlisk
,
A. T.
, and
Rizzoni
,
G.
,
2013
, “
Design and Parametrization Analysis of a Reduced-Order Electrochemical Model of Graphite/LiFePO4 cells for SOC/SOH Estimation
,”
J. Power Sources
,
237
, pp.
310
324
.
120.
Wang
,
Y.
,
Fang
,
H.
,
Sahinoglu
,
Z.
,
,
T.
, and
Hara
,
S.
,
2013
, “
Nonlinear Adaptive Estimation of the State of Charge for Lithium-Ion Batteries
,”
IEEE 52nd Annual Conference on Decision Control
, Vol.
23
, No.
3
, pp.
4405
4410
.
121.
Dey
,
S.
,
Ayalew
,
B.
, and
Pisu
,
P.
,
2013
, “
Adaptive Observer Design for a Li-Ion Cell Based on Coupled Electrochemical-Thermal Model
,”
ASME 2014 Dynamic Systems and Control Conference
,
San Antonio
,
Oct. 22–24
.
122.
Zhou
,
X.
,
Ersal
,
T.
,
Stein
,
J. L.
, and
Berstein
,
D. S.
,
2013
, “
Battery Health Diagnostics using Retrospective-cost subsystem Identification: Sensitivity to Noise and Initialization Errors
,”
ASME 2013 Dynamic Systems and Control Conference
,
Palo Alto
,
Oct. 21–23
.
123.
Chaoui
,
H.
, and
Gualous
,
H.
,
2017
, “
Online Parameter and State Estimation of Lithium-Ion Batteries Under Temperature Effects
,”
Electr. Power Syst. Res.
,
145
, pp.
73
82
.
124.
Wei
,
J.
,
Dong
,
G.
, and
Chen
,
Z.
,
2018
, “
Lyapunov-Based State of Charge Diagnosis and Health Prognosis for Lithium-Ion Batteries
,”
J. Power Sources
,
397
, pp.
352
360
.
125.
Landi
,
M.
, and
Gross
,
G.
,
2014
, “
Measurement Techniques for Online Battery State of Health Estimation in Vehicle-to-Grid Applications
,”
IEEE Trans. Instrum. Meas.
,
63
(
5
), pp.
1224
1234
.
126.
Yuan
,
H. F.
, and
Dung
,
L. R.
,
2017
, “
Offline State-of-Health Estimation for High-Power Lithium-Ion Batteries Using Three-Point Impedance Extraction Method
,”
IEEE Trans. Veh. Technol.
,
66
(
3
), pp.
2019
2032
.
127.
Chiang
,
Y. H.
,
Sean
,
W. Y.
, and
Ke
,
J. C.
,
2011
, “
Online Estimation of Internal Resistance and Open-Circuit Voltage of Lithium-Ion Batteries in Electric Vehicles
,”
J. Power Sources
,
196
(
8
), pp.
3921
3932
.
128.
Weng
,
C.
,
Sun
,
J.
, and
Peng
,
H.
,
2014
, “
A Unified Open-Circuit-Voltage Model of Lithium-Ion Batteries for State-of-Charge Estimation and State-of-Health Monitoring
,”
J. Power Sources
,
258
, pp.
228
237
.
129.
Le
,
D.
, and
Tang
,
X.
,
2011
, “
Lithium-Ion Battery State of Health Estimation Using Ah-V Characterization
,”
Annual Conference of the Prognostics and Health Management Society
, pp.
1
7
.
130.
Waag
,
W.
,
Käbitz
,
S.
, and
Sauer
,
D. U.
,
2013
, “
Experimental Investigation of the Lithium-Ion Battery Impedance Characteristic at Various Conditions and Aging States and Its Influence on the Application
,”
Appl. Energy
,
102
, pp.
885
897
.
131.
Rufus
,
F.
,
Lee
,
S.
, and
Thakker
,
A.
,
2008
, “
Health Monitoring Algorithms for Space Application Batteries
,”
2008 International Conference on Prognostics and Health Management (PHM)
.
132.
Schwunk
,
S.
,
Armbruster
,
N.
,
Straub
,
S.
,
Kehl
,
J.
, and
Vetter
,
M.
,
2013
, “
Particle Filter for State of Charge and State of Health Estimation for Lithium-Iron Phosphate Batteries
,”
J. Power Sources
,
239
, pp.
705
710
.
133.
Weng
,
C.
,
Cui
,
Y.
,
Sun
,
J.
, and
Peng
,
H.
,
2013
, “
On-Board State of Health Monitoring of Lithium-Ion Batteries Using Incremental Capacity Analysis With Support Vector Regression
,”
J. Power Sources
,
235
, pp.
36
44
.
134.
Hu
,
X.
,
Li
,
S. E.
,
Jia
,
Z.
, and
Egardt
,
B.
,
2014
, “
Enhanced Sample Entropy-Based Health Management of Li-Ion Battery for Electrified Vehicles
,”
Energy
,
64
, pp.
953
960
.
135.
Liu
,
D.
,
Pang
,
J.
,
Zhou
,
J.
,
Peng
,
Y.
, and
Pecht
,
M.
,
2013
, “
Prognostics for State of Health Estimation of Lithium-Ion Batteries Based on Combination Gaussian Process Functional Regression
,”
Microelectron. Reliab.
,
53
(
6
), pp.
832
839
.
136.
Chen
,
Z.
,
Mi
,
C. C.
,
Fu
,
Y.
,
Xu
,
J.
, and
Gong
,
X.
,
2013
, “
Online Battery State of Health Estimation Based on Genetic Algorithm for Electric and Hybrid Vehicle Applications
,”
J. Power Sources
,
240
, pp.
184
192
.
137.
Cannarella
,
J.
, and
Arnold
,
C. B.
,
2014
, “
State of Health and Charge Measurements in Lithium-Ion Batteries Using Mechanical Stress
,”
J. Power Sources
,
269
, pp.
7
14
.
138.
Saha
,
B.
,
Goebel
,
K.
,
Poll
,
S.
, and
Christophersen
,
J.
,
2009
, “
Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework
,”
Instrum. Meas. IEEE Trans.
,
58
(
2
), pp.
291
296
.
139.
Zhang
,
X.
,
Wang
,
Y.
,
Liu
,
C.
, and
Chen
,
Z.
,
2018
, “
A Novel Approach of Battery Pack State of Health Estimation Using Artificial Intelligence Optimization Algorithm
,”
J. Power Sources
,
376
, pp.
191
199
.
140.
Sturm
,
J.
,
Spingler
,
F. B.
,
Rieger
,
B.
,
Rheinfeld
,
A.
, and
Jossen
,
A.
,
2017
, “
Non-Destructive Detection of Local Aging in Lithium-Ion Pouch Cells by Multi-Directional Laser Scanning
,”
J. Electrochem. Soc.
164
(
7
), pp.
A1342
A1351
.
141.
Hannan
,
M. A.
,
Hoque
,
M. M.
,
Peng
,
S. E.
, and
Uddin
,
M. N.
,
2017
, “
Lithium-Ion Battery Charge Equalization Algorithm for Electric Vehicle Applications
,”
IEEE Trans. Ind. Appl.
,
53
(
3
), pp.
2541
2549
.
142.
Moore
,
S.
, and
Schneider
,
P.
,
2001
, “
A Review of Cell Equalization Methods for Lithium Ion and Lithium Polymer Battery Systems
,”
SAE World Congress
, Doc. 2001-01-0959.
143.
Yun
,
L.
,
Linh
,
D.
,
Shui
,
L.
,
Peng
,
X.
,
Garg
,
A.
,
Le
,
M. L. P.
,
Asghari
,
S.
, and
Sandoval
,
J.
,
2018
, “
Metallurgical and Mechanical Methods for Recycling of Lithium-Ion Battery Pack for Electric Vehicles
,”
Resour. Conserv. Recycl.
,
136
, pp.
198
208
.
144.
Xue
,
N.
,
2014
,
"Design and Optimization of Lithium-Ion Batteries for Electric-Vehicle Applications", Dissertation, Universitiy of Michigan, Michigan
.
145.
Arora
,
S.
,
Shen
,
W.
, and
Kapoor
,
A.
,
2016
, “
Review of Mechanical Design and Strategic Placement Technique of a Robust Battery Pack for Electric Vehicles
,”
Renew. Sustain. Energy Rev.
,
60
, pp.
1319
1331
.
146.
Hamidi
,
A.
,
Weber
,
L.
, and
Nasiri
,
A.
,
2013
, “
EV Charging Station Integrating Renewable Energy and Second-Life Battery
,”
Proceedings of the 2013 International Conference on Renewable Energy Research and Application (ICRERA 2013)
,
October
, pp.
1217
1221
.
147.
Berckmans
,
G.
,
Messagie
,
M.
,
Smekens
,
J.
,
Omar
,
N.
,
Vanhaverbeke
,
L.
, and
Van Mierlo
,
J.
,
2017
, “
Cost Projection of State of the Art Lithium-Ion Batteries for Electric Vehicles up to 2030
,”
Energies
,
10
(
9
), p.
1314
.
148.
Lieberman
,
M.
,
1984
, “
The Learning Curve and Pricing in the Chemical Processing Industries
,”
RAND J. Econ.
,
15
, pp.
213
228
.