The increasing stringent emissions regulation over the years have shifted the focus of automotive industry towards more efficient fuel economy solutions. One such solution is Hybrid electric architecture, which is able to improve the fuel economy and consequently cutting down emissions. A well known control strategy to solve optimization problem for energy management of Hybrid electric vehicles is ECMS (Equivalent Consumption Minimization Strategy). Finding the best control parameters (equivalence factors) of this strategy may become quite involved. This paper proposes a method for the selection of the optimal equivalence factors, for charging and discharging, by applying genetic algorithm in the case of a P0 mild hybrid electric vehicle. This method is a systematic and deterministic way to guarantee an optimal solution with respect to the trial and error method. The proposed ECMS is compared to a technique available in literature, known as the shooting method, which relies only on one equivalence factor for discharging. It is demonstrated that the performance in terms of pollutant emissions are comparable. However, ECMS with GA always guarantees an optimal solution even in the case of heavy accessory load, when shooting method is not valid anymore, as it does not guarantee a charge sustaining condition.

Herein, we have confirmed the importance of formulating product proposals and product-development processes equipped to cope effectively with uncertainty in the early design stage. The objective of this study was to derive the target performance and design priority order taking into account uncertainties in system design. Following the concept of set-based design, the approach adopted was to secure a set of solutions as design space that satisfy the target variables demands, dividing the design space into several clusters and evaluating each of the clusters, then gradually narrowing the cluster as the design progresses, and finally extracting the solution space that is desirable. Priority order of design was developed based on the strategy of increasing the degree of freedom of the subsequent process. The effectiveness of the proposed method was verified using the model of a plug-in hybrid vehicle. From the results, we confirmed the existence of a trade-off between design and target variables preference and development risk, that it is possible to determine the extent to which the solution space can be narrowed, that the shape of the solution space determines the design priorities, and we were able to derive a desirable design priority order according to the target performance.

In recent years technology development for the design of electric and hybrid-electric vehicle systems has reached a peak, due to ever increasing restrictions on fuel economy and reduced vehicle emissions. An international race among car manufacturers to bring production hybrid-electric vehicles to market has generated a great deal of interest in the scientific community. The design of these systems requires development of new simulation and optimization tools. In this paper, a description of a real-time numerical environment for Virtual Proving Grounds studies for hybrid-electric vehicles is presented. Within this environment, vehicle models are developed using a recursive multibody dynamics formulation that results in a set of Differential-Algebraic Equations (DAE), and vehicle subsystem models are created using Ordinary Differential Equations (ODE). Based on engineering knowledge of vehicle systems, two time scales are identified. The first time scale, referred to as slow time scale, contains generalized coordinates describing the mechanical vehicle system that includs the chassis, steering rack, and suspension assemblies. The second time scale, referred to as fast time scale, contains the hybrid-electric powertrain components and vehicle tires. Multirate techniques to integrate the combined set of DAE and ODE in two time scales are used to obtain computational gains that will allow solution of the system’s governing equations for state derivatives, and efficient numerical integration in real time.

Power management in all-electric powertrains has a significant potential to optimally handle the limited energy and power density of electric power sources. Situation-based power management strategies (SB-PMSs), defining optimized solutions related to specific vehicle situations, offer the ability to reduce computational requirements and enhance the solution optimality of simple rule-based algorithms. Moreover, the local optimality of SB-PMSs can be addressed by considering online optimization of the situated solutions for limited horizons. This paper presents a novel PMSs using model predictive control (MPC) to define optimal control strategies based on situated solutions for fuel cell hybrid vehicles. Vehicle states are defined in terms of multiple characteristic variables and power management decisions are optimized offline for each vehicle states. Prediction of vehicle states is conducted using statistical predictive model based on state transitions in a number of driving cycles. Preoptimized solutions related to predicted states are iterated online to achieve better optimality over the look-ahead horizon. Results analysis from online testing revealed the ability of SB-MPC to improve the optimality of situation-based solutions and hence reduce total energy cost in different driving cycles.

This paper presents a novel transmission mechanism for use in a parallel hybrid vehicle. A parallel hybrid vehicle typically employs two or more power sources to drive a vehicle. For such a vehicle to function properly, a non-conventional transmission mechanism and a microprocessor-based controller are needed to manage the power flow among the various power sources. The transmission mechanism described in this paper can provide a parallel hybrid with thirteen clutching conditions that can be grouped into five major modes of operation, namely, electric motor mode, power mode, CVT/charging mode, engine mode, and regenerative braking mode. The kinematics, statics, and power flow of each mode of operation are analyzed. A numerical example is used to illustrate the principle of operation. Furthermore, a clutching sequence control logic is developed.

The hybrid passenger car of the Technical University of Munich contains a complex i 2 -CVT-gearbox with one PIV-chain CVT-stage, six spur gears and internal clutches. This paper presents a method to access its noise emission and to identify important sources based on design parameters. A multibody system model of the gearbox and the entire drive train as set up on the testbed is built. Excitation mechanisms like impact and frictional forces between rocker pins of the chain and the discs of the CVT as well as toothing forces are modelled in detail by complex nonlinear stiffness functions. A method to calculate the A-rated equivalent continuous force level of the bearing forces, which is proportional to the sound is presented here. As it is based on a Fourier transform of the bearing force trajectories, the influence of excitation mechanisms on structural borne sound can be shown and confirmed by measurement.

We examine optimal-profit product design platforming problems for products sold across multiple markets. Firms have an incentive to platform to take advantage of cost reductions that are possible with increased production quantity, such as learning-by-doing. However, platforming may decrease sales compared to if the designs were customized for each market. The problem can be represented as a Nash equilibrium between multiple competing firms, each with a nonconvex mixed-integer nonlinear programing (MINLP) problem for maximizing their individual profits. We derive the Karush-Kuhn-Tucker (KKT) conditions for the problem and compare results from two algorithmic approaches: (1) an iterative MINLP approach that uses the BARON algorithm to solve each firm’s design and platforming problem and iterates until convergence to an equilibrium, and (2) an approach that solves the KKT conditions directly holding platforming decisions fixed, and compares profits for these platforming decisions to find an equilibrium. Results are presented for a case study of plug-in hybrid electric vehicles (PHEVs) where firms choose whether or not to platform the battery pack across the U.S. and China, and set the optimal battery capacity. We vary the learning rate and the difference in consumer willingness to pay for all-electric range between China and the U.S. Both algorithms agree on the same equilibrium solution in 98.4% of the cases. Results show that the optimum for each firm is to platform when learning rates are low, or the difference between optimal battery capacity in each market is relatively small.

This paper documents the vehicle modeling and fuel economy simulation efforts for the Georgia Tech EcoCAR Mobility Challenge team during Year 1 of the current Advanced Vehicle Technology Competition (AVTC). The goal of the first year of the competition was to propose two possible hybrid electric vehicle (HEV) architectures based on a 2019 Chevrolet Blazer platform that could be built and refined throughout Years 2–4 of the competition. A Simulink vehicle model was used to compare a variety of HEV architectures with several different combinations of engines, batteries, and electric machines. An adaptive Equivalent Consumption Minimization Strategy was used to split torque between the internal combustion engine and two electric machines so that architectures with two electric machines could be modeled. The model was validating by comparing results from simulating a conventional Chevrolet Blazer with the 2.5L naturally aspirated engine to fuel economy results obtained by the Environmental Protection Agency (EPA). In addition to developing a hybrid vehicle, the competition is focused on exploring Mobility as a Service (MaaS), which introduces some special considerations when choosing a hybrid vehicle architecture. The results of the Simulink model as well as requirements set by the MaaS market led the Georgia Tech EcoCAR team to pursue a dual electric machine P0P4 parallel through the road hybrid vehicle.

This paper presents a comparison between a Fuzzy Logic and an Equivalent Consumption Minimization Strategy for the energy management of a Hybrid Electric Vehicle in P2 configuration, i.e. with the secondary energy converter located downstream the clutch. The design of the two methods is conducted aiming to minimize the fuel consumption. Although the adopted strategies are not charge sustaining, an additional goal of the techniques is to obtain a net energy extracted from the battery over a driving cycle that is not far from zero. The presented simulation results are obtained in the case of two homologation driving cycles, namely NEDC and WLTP. The objective of the study is to demonstrate that a non-optimal rule-based method can achieve a performance that is equivalent to a model-based optimal analytical approach.

This paper investigates the design of a new transmission system without a friction synchronizer for high-performance hybrid vehicles. Manual and automated manual transmission systems traditionally use friction synchronizers to facilitate smooth transitions during a gearshift, ensuring speed matching and proper engagement of the gears. Active position sensing technology for dogteeth is being developed, along with the potential of speed matching using electric motors, eliminating the need for the friction synchronizer. However, in removing these friction synchronizer components, significant shock could be introduced to the transmission system with speed or position errors during a shift. This paper proposes a solution through a gear system that utilizes a face mesh design, torsional springs, and alternating teeth height. A prototype of this design was created and successfully tested as a proof of concept for a transmission system, which has the potential to improve hybrid, automated manual transmission design.

Fuel economy and emission standards for internal combustion engine (ICE) vehicles lead to emergence of hybrid powertrain mechanisms. Hybrid powertrains can enable significant fuel economy improvements without sacrificing vehicle performance or utility. This requires optimization of engine operation, regenerative braking, and use of a wide range of possible combinations of engine and battery usage. The multi-mode hybrid powertrain in this paper combines many options to meet a complex driving requirement while maintaining the desired fuel economy. In this paper, a systematic design methodology is used to design a full-size hybrid vehicle with multiple components. This involves the modeling, simulation and development of optimal energy management strategy. This vehicle (full size car) has dual battery, dual fuel V6 engine with cylinder deactivation and bi-directional power flow in and from dual motor/generator. The design includes multiple gearboxes to connect these pieces. The vehicle model allows many degrees of freedom including various modes of operation depending upon the combination of degree of driver involvement, vehicle power requirement and optimized fuel economy resulting in automatic switching between modes. This model is tested for different Environmental Protection Agency (EPA) driving cycles. By integrating all components of this hybrid electric vehicle (HEV) and the highly coordinated energy management control system that performs optimum blending of torque, speed, and power from multiple power sources, the benefit from this hybridization is maximized.

An important question regarding vehicles design optimization and environmental care are energy management strategy and efficiency determination. Automotive brands work with a wide range of technologies and electrified mobility is considered to be one of the solutions to the growing environmental question. The present paper develops a mathematical model to predict the light-duty electric vehicle overall consumed energy depending on architecture and configuration of vehicles with different degrees of electrification (e.g. electric vehicle and hybrid electric vehicle), on the type of electric motor (e.g. hybrid synchronous electric motor, permanent magnet motor or induction motor) and engine (e.g. gasoline (Otto or Atkinson) or Diesel), on technology of energy-storage system (e.g. lithium-ion or nickel-metal hydride battery) and on weight and geometry of the car being flexible drive cycles and for all types of wheel drive (four wheel, front and rear). The method is verified making a component-to-component revision through real automobiles that are available in the market to demonstrate the validity of the system.

Optimal power management in real-time is a core technology of hybrid electric vehicles (HEVs). The online application of optimized power split ratios based on driver demand is a promising approach allowing near optimal power handling decisions in real-time. However, the fulfillment of exact power delivery (driveability) is an open challenge of this approach due to interpolation of driver’s demand to the optimized discrete solutions. Finding balanced power management that handles unscheduled loads and sustains required power conversion efficiency may significantly improve the experimental application of this approach. This work proposes an observer-based control method integrated to the power management system that ensures better driveability with minimal effect on power handling optimality. Modeling of traction system for observer design is based on a simplified brushless DC motor model. Identification of system parameters is achieved by a newly introduced error minimization algorithm using NSGA-II to obtain the same dynamic response as the original system. Results analysis shows 7% reduction of power consumption and 98% improvement of driveability at minimal mitigation of power conversion efficiency.

An intuitive and comprehensive lithium-ion polymer battery cell model is developed in the Simulink environment. The developed model has capability of transient function in the Thevenin-based model, AC-equivalent function in the impedance-based model, and runtime prediction in the runtime-based model. Several model parameters are determined through five experimental discharging currents (6.67A, 10A, 20A, 30A, and 40A). The model is correlated and validated with other three continuous discharging currents (4A, 15A, and 50A) and two pulse discharges (20A and 30A). The validation indicates a less than 7% discrepancy between model simulation and experiment. The model is currently effective for lithium-metal-oxides polymer battery cell with capacity between 18Ah and 22Ah (20Ah +/− 10%). For other capacity battery cell, the parameters of series resistors need to be adjusted in the model. These parameters can be determined using three to five continuous constant current discharging tests. It is intended to make the developed cell model scalable and accurate in a wider ranges of battery specifications. Expending the developed cell model to a 12 voltage Starting-Lighting-Ignition (SLI) battery used in the start-stop or 48 voltage battery pack for mild hybrid electric vehicle is an example.

This article proposes a fuzzy based fuel-efficient propulsion selection logic for a hybrid electric vehicle (HEV) in ‘Highway Fuel Economy Test (HWFET)’ driving cycle. Optimal utilisation of combustion engine, in HEV, reduces the fossil fuel consumption. This can be realized through an electronic control unit, embedded with effective propulsion selection logic that governs the power split device in series-parallel HEV. A propulsion control logic, based on the road gradeability, velocity, torque demand and vehicle battery state of charge (SOC) is presented in this article. A comparison with conventional propulsion selection logic based system shows that the HEV modelled with proposed fuzzy based one, results in better speed tracking with steep road grades, as it provides better torque supply at desired speed points. The analysis indicates a reduction in consumption of both the fossil fuel as well as the electrical fuel (SOC).

Hybrid vehicles are increasingly common due to fuel efficiency regulations in place worldwide. High performance hybrids have typically been designed with a focus on improving performance, rather than the combination of both performance and efficiency. In order to improve efficiency of high performance cars, new hybrid architectures are necessary. When incorporating an electric motor, careful focus on operational modes allows for removal of certain elements, such as the reverse gear. Additionally, installing an electric motor directly coupled to the transmission without a clutch gives performance benefits, but requires detailed control of motor speed and novel methodology for shifting. In this paper, the design of an experimental setup for the electric drive in a high performance car hybrid transmission is presented. This architecture allows for characterization of synchronizer behavior during two different shifting methodologies. The first methodology is synchronizing a large rotational inertia with a small shaft speed difference (this differs from a gear shift in a traditional transmission with a large speed difference but small inertia). This situation is encountered when coupling an electric motor to the drivetrain, as the inertia of the electric motor is relatively large compared to a transmission layshaft, but the speed difference is small. The second is testing shifting of a synchronizer where dog tooth engagement happens immediately, with no friction cone to match the speed. This type of shifting is possible with precise electric motor speed control, sensing of the dog tooth position, and fast actuation. This methodology eliminates the need for a friction cone in the synchronizers, while maintaining fast gearshifts for performance driving. Our experimental setup for the electric drive in a hybrid transmission will be used to characterize synchronizer performance with these new shifting methodologies. The insights gained from this setup will aid in designing advanced hybrid architectures.

This paper presents a simulation-based analysis of a model of a mid-sized vehicle while exploring powertrains of interest. In addition to a baseline conventional vehicle (CV), the explored powertrain architectures include: hybrid electric vehicle (HEV), plugin hybrid electric vehicle (PHEV) and batterW2Wy-only electric vehicle (BEV). The modeling also considers several different all electric driving range (AER) of the PHEVs and BEVs. Fuel economy/energy-efficiency assessment is conducted by with open source software (FASTSim), and by analyzing a large set of real-world driving trips from California Household Travel Survey (CHTS-2013), which contains a record of more than 65 thousand trips with one second interval recording of the vehicle seed. Gas and/or electric energy usage from the analyzed trips are then used to generate greenhouse gas (GHG) statistical distributions (in units of gm-CO 2 /mile) for a modelled vehicle powertrain. Gas and/or electric energy usage are also utilized in the calculation of the running cost, and ultimately the net average cost (in units of $/mile) for the modelled powertrains. Pareto trade-off analysis (Cost vs GHG) is then conducted for four sub-population segments of CHTS vehicle samples in a baseline scenario as well as four future-looking scenarios where carbon intensity in electric power generation gets lower, gas gets more expensive and batteries get less expensive. While noting limitations of the conducted analysis, key findings suggest that: i) mix of PHEVs and BEVs with various AER that is properly matched to driver needs would be better than one single powertrain design for all drivers, and ii) electrified powertrains do not become cost-competitive in their own right (without incentives or subsidies) until some of the future battery technology goals are attained.

Energy crisis and environment pollution are closely correlated with the widespread use of internal combustion engine (ICE) vehicles. Considering multi-factors, developing hybrid electric vehicle (HEV) is an ideal solution to solve the problems since its environmental protection and practicability. Integrated Starter Generator (ISG), integrated by two motors between engine and mechanical transmission system, performs better in efficiency and economy. This paper attempts to design a parameter matching method of hybrid electric system according to its dynamic performance, driving condition and control strategy. An ISG model and the model of parameter matching HEV system are proposed in this paper.

The increasing interest into hybrid electric vehicles imposes the need to provide computational support for design choices during the development of these vehicles. This paper presents the use of a new methodology which allows for automatically generating topologically different hybrid powertrain concepts, and evaluating their performance. Since the energy management controller plays an important role in the performance assessment, a modular framework based on optimal control theory is used for assessing the closed-loop performance of different hybrid powertrain architectures. This paper illustrates how the concept generation methodology can be combined with optimal control in order to compare the closed-loop performance of a series and parallel hybrid powertrain.

Power management in multi-source hybrid electric vehicles (HEVs) is a nontrivial problem dealing with different forms of energy. Optimal-based approaches are not facile to apply in realtime due to their high computational requirements. Rule-based (RB) algorithms are suitable for realtime control; however, the solution provided is non-optimal. Development of applicable optimal-based solution in realtime control can ensure higher efficiency of HEVs. This paper presents a new method for realtime optimal control of multisource HEVs using adaptive dynamic programming (ADP). The developed concept is based on drive state recognition in terms of physics-based parameters. Vehicle operating conditions are offline optimized for each state using NSGA-II optimization tool. The optimized solution can be applied state-wise in realtime using adaptive RB method. To apply ADP, probabilistic drive state model is developed to provide a lookahead window and generate state transition network for the specified horizon. The algorithm is customized in terms of prediction stepsize/length to solve the shortest path problem in realtime. Experimental application is conducted using emulation test-rig to validate the results. Both simulation and experimental results show reduction of total cost function in terms of fuel consumption and on-board charge sustaining.