One of the main factors limiting the efficiency of spark-ignited (SI) engines is the occurrence of engine knock. In high temperature and high pressure in-cylinder conditions, the fuel–air mixture auto-ignites creating pressure shock waves in the cylinder. Knock can significantly damage the engine and hinder its performance; as such, conservative knock control strategies are generally implemented which avoid such operating conditions at the cost of lower thermal efficiencies. Significant improvements in the performance of conventional knock controllers are possible if the properties of the knock process are better characterized and exploited in knock controller designs. One of the methods undertaken to better characterize knocking instances is to employ a probabilistic approach, in which the likelihood of knock is derived from the statistical distribution of knock intensity (KI). In this paper, it is shown that KI values at a fixed operating point for single fuel and dual fuel engines are accurately described using a mixed lognormal distribution. The fitting accuracy is compared against those for a randomly generated mixed-lognormally distributed dataset, and shown to exceed a 95% accuracy threshold for almost all of the operating points tested. Additionally, this paper discusses a stochastic knock control approach that leverages the mixed lognormal distribution to adjust spark timing based on KI measurements. This more informed knock control strategy would allow for improvements in engine performance and fuel efficiency by minimizing knock occurrences.

Adaptive cruise control of autonomous vehicles can be posed as a multi-objective optimization problem where several conflicting criteria, e.g., fuel economy, tracking capability, ride comfort, and safety, need to be satisfied simultaneously. In order to reconcile these conflicting criteria, this paper presents a novel multi-objective predictive cruise control (MOPCC) approach in the feasible perturbation-based real-time iterative optimization framework. The longitudinal dynamics of vehicles are described as nonlinear car-tracking models. The new cost function for MOPCC is defined as the distance of the criteria vector to the vector of separately minimized criteria (i.e., a utopia point of the criteria). The weight-free MOPCC is then obtained by solving a constrained nonlinear optimal control problem in receding horizon fashion. Due to the difficulty in solving the optimization problem, the integrated perturbation analysis and sequential quadratic programming (InPA-SQP) is employed to compute the cruise controller. The merit of the proposed MOPCC is that it can systematically handle different cruise scenarios regardless of the weights of the predictive cruise control (PCC) criteria. Several driving cases are used to demonstrate the effectiveness and benefits of the proposed approach via comparing to weighted PCC approaches.

In this paper, the nonlinear model predictive control (NMPC) for the energy management of a power-split hybrid electric vehicle (HEV) has been studied to improve battery aging while maintaining the fuel economy at a reasonable level. A first principle battery model is built with simulation capacity of the battery aging features. The built battery model is integrated with an HEV model from autonomie software to investigate the vehicle and battery performance under control strategies. The NMPC has simplified battery models to predict the state of charge (SOC) change, the fuel consumption of the engine, and the battery aging index over the predicted horizon. The purpose of the NMPC is to find an optimized control sequence over the prediction horizon, which minimizes the designed cost function. The proposed control strategy is compared with that of an NMPC, which does not consider the battery aging. It is found that, with the optimized weighting factor selection, the NMPC with the consideration of battery aging has better battery aging performance and similar fuel economy performance comparing with the NMPC without the consideration of battery aging.

Hybrid tracked vehicles are common in construction, agriculture, and military applications. Most use a series hybrid powertrain with large motors and operate at a relatively low efficiency. Although some researchers have proposed power-split powertrains, most of these would require an additional mechanism to achieve skid steering. To solve this problem and enhance drivability, a single-mode power-split hybrid powertrain for tracked vehicles with two outputs connected to the left and right tracks is proposed. The powertrain with three planetary gears (PGs) would then be able to control the torque on the two tracks independently and achieve skid steering. This powertrain has three degrees-of-freedom (DOF), allowing for control of the output torques and the engine speed independently from the vehicle running speed. All design candidates with three PGs are exhaustively searched by analyzing the dynamic characteristics and control to obtain the optimal design. Efficient topology design selection with parameter sizing and component sizing is accomplished using the enhanced progressive iteration approach to achieve better fuel economy using downsized components.

The efficiency of the spark ignition (SI) engine degrades while working at part loads. It can be optimally dealt with a slightly different thermodynamic cycle termed as an Atkinson cycle. It can be implemented in the conventional SI engines by incorporating advanced mechanisms as variable valve timing (VVT) and variable compression ratio (VCR). In this research, a control framework for the Atkinson cycle engine with flexible intake valve load control strategy is designed and developed. The control framework based on the extended mean value engine model (EMVEM) of the Atkinson cycle engine is evaluated in the view of fuel economy at the medium and higher load operating conditions for the standard new European driving cycle (NEDC), federal urban driving schedule (FUDS), and federal highway driving schedule (FHDS) cycles. In this context, the authors have already proposed a control-oriented EMVEM model of the Atkinson cycle engine with variable intake valve actuation. To demonstrate the potential benefits of the VCR Atkinson cycle VVT engine, for the various driving cycles, in the presence of auxiliary loads and uncertain road loads, its EMVEM model is simulated by using a controller having similar specifications as that of the conventional gasoline engine. The simulation results point toward the significant reduction in engine part load losses and improvement in the thermal efficiency. Consequently, considerable enhancement in the fuel economy of the VCR Atkinson cycle VVT engine is achieved over conventional Otto cycle engine during the NEDC, FUDS, and FHDS cycles.

Planetary gear (PG) power-split hybrid powertrains have been used in producing hybrid and plug-in hybrid vehicles from the Toyota, General Motor, and Ford for years. Some of the most recent designs use clutches to enable multiple operating modes to improve launching performance and/or fuel economy. Adding clutches and multiple operating modes, however, also increases production cost and design complexity. To enable an exhaustive but fast search for optimal designs among a large number of hardware configurations, clutch locations, and mode selections, an automated modeling and screening process is developed in this paper. Combining this process with the power-weighted efficiency analysis for rapid sizing method (PEARS), an optimal and computationally efficient energy management strategy, the extremely large design space of configuration, component sizing, and control becomes feasible to search through. This methodology to identify optimal designs has yet to be reported in the literature. A case study to evaluate the proposed methodology uses the configuration adopted in the Toyota Hybrid Synergy (THS-II) system used in the Prius model year 2010 and the Hybrid Camry. Two designs are investigated to compare with the simulated Prius design: one uses all possible operating modes; and the other uses a suboptimal design that limits the number of clutches to three.

The low-temperature operations of diesel engines and aftertreatment systems have attracted increasing attention over the past decade due to the stringent diesel emission regulations and excessive tailpipe emissions at low temperatures. The removal of NO x emissions using selective catalytic reduction (SCR) systems during low-temperature operations remains a significant challenge. One of the popular techniques for alleviating this issue is to employ active thermal management via in-cylinder postinjection to promote aftertreatment system temperatures. Meanwhile, numerous studies have focused on ammonia coverage ratio controls with the aim to maintain high NO x conversion efficiency and low tailpipe ammonia slip. However, most of the active thermal management and SCR controls in the existing literatures were separately and conservatively designed, which can lead to higher cost of SCR operation than needed including diesel fuel consumption through active thermal management and urea solution consumption. The main purpose of this study is to design and coordinate active thermal management and SCR control using nonlinear model predictive control (NMPC) approach to minimize the total cost of SCR operation while obtaining high NO x conversion efficiency and low tailpipe ammonia slip. Simulation results demonstrate that, compared to the baseline control which consists of separated active thermal management and SCR control, the coordinated control is capable of reducing the total cost of SCR operation by 25.6% while maintaining the tailpipe NO x emissions and ammonia slip at comparable levels. Such an innovative coordinated control design concept shows its promise in achieving low tailpipe emissions during low-temperature operations in a cost-effective fashion.

The applications of diesel engines in ground vehicles have attracted much attention over the past decade for the reasons of outstanding fuel economy, power capability, and reliability. With the increasing demand of less greenhouse gas emissions, the current diesel engine fuel efficiency remains unsatisfactory partially due to the conflict between the engine fuel efficiency and engine-out NOx emissions. While advanced aftertreatment systems, such as selective catalytic reduction (SCR) systems or lean NOx trap, have been integrated to diesel engines for reducing the tailpipe NOx emissions, the integrated controls for coordinating diesel engine and SCR system to achieve high engine efficiency and low tailpipe emissions are still limited. The purpose of this study is to develop such an integrated diesel engine and SCR system control method using nonlinear model predictive control (NMPC) approach with both start of injection (SOI) timing and urea solution injection rate as the control inputs. Control-oriented engine models were developed to quantify the influences of SOI timing on engine efficiency and engine-out NOx emissions. Simulation results under US06 driving cycle demonstrate that, given the same catalyst size in total, the proposed controllers are capable of reducing total engine fuel consumption over the driving cycle by 9.36% and 9.50%, respectively, for lumped SCR system and two-cell SCR system, while maintaining high NOx conversion efficiencies and low tailpipe ammonia slip.

Highly diluted, low temperature homogeneous charge compression ignition (HCCI) combustion leads to ultralow levels of engine-out NO x emissions. A standard drive cycle, however, would require switches between HCCI and spark-ignited (SI) combustion modes. In this paper we quantify the efficiency benefits of such a multimode combustion engine, when emission constraints are to be met with a three-way catalytic converter (TWC). The TWC needs unoccupied oxygen storage sites in order to achieve acceptable performance. The lean exhaust gas during HCCI operation, however, fills the oxygen storage and leads to a drop in NO x conversion efficiency. If levels of tailpipe NO x become unacceptable, a mode switch to a fuel rich combustion mode is necessary in order to deplete the oxygen storage and restore TWC efficiency. The resulting lean-rich cycling leads to a penalty in fuel economy. Another form of penalty originates from the lower combustion efficiency during a combustion mode switch itself. In order to evaluate the impact on fuel economy of those penalties, a finite state model for combustion mode switches is combined with a longitudinal vehicle model and a phenomenological TWC model, focused on oxygen storage. The aftertreatment model is calibrated using combustion mode switch experiments from lean HCCI to rich spark-assisted HCCI (SA-HCCI) and back. Fuel and emission maps acquired in steady-state experiments are used. Different depletion strategies are compared in terms of their influence on drive cycle fuel economy and NO x emissions. It is shown that even an aggressive lean-rich cycling strategy will marginally satisfy the cumulated tailpipe NO x emission standards under warmed-up conditions. More notably, the cycling leads to substantial fuel penalties that negate most of HCCI's efficiency benefits.

Hybrid vehicle fuel economy and drive quality are coupled through the “energy management” controller that regulates power flow among the various energy sources and sinks. This paper studies energy management controllers designed using shortest path stochastic dynamic programming (SP-SDP), a stochastic optimal control design method which can respect constraints on drivetrain activity while minimizing fuel consumption for an assumed distribution of driver power demand. The performance of SP-SDP controllers is evaluated through simulation on large numbers of real-world drive cycles and compared to a baseline industrial controller provided by a major auto manufacturer. On real-world driving data, the SP-SDP-based controllers yield 10% better fuel economy than the baseline industrial controller, for the same engine and gear activity. The SP-SDP controllers are further evaluated for robustness to the drive cycle statistics used in their design. Simplified drivability metrics introduced in previous work are validated on large real-world data sets.

This paper presents a direct mathematical approach for determining the state of charge (SOC)-dependent equivalent cost factor in hybrid-electric vehicle (HEV) supervisory control problems using globally optimal dynamic programming (DP). It therefore provides a rational basis for designing equivalent cost minimization strategies (ECMS) which achieve near optimal fuel economy (FE). The suggested approach makes use of the Pareto optimality criterion that exists in both ECMS and DP, and as such predicts the optimal equivalence factor for a drive cycle using DP marginal cost. The equivalence factor is then further modified with corrections based on battery SOC, with the aim of making the equivalence factor robust to drive cycle variations. Adaptive logic is also implemented to ensure battery charge sustaining operation at the desired SOC. Simulations performed on parallel and power-split HEV architectures demonstrate the cross-platform applicability of the DP-informed ECMS approach. Fuel economy data resulting from the simulations demonstrate that the robust controller consistently achieves FE within 1% of the global optimum prescribed by DP. Additionally, even when the equivalence factor deviates substantially from the optimal value for a drive cycle, the robust controller can still produce FE within 1–2% of the global optimum. This compares favorably with a traditional ECMS controller based on a constant equivalence factor, which can produce FE 20–30% less than the global optimum under the same conditions. As such, the controller approach detailed should result in ECMS supervisory controllers that can achieve near optimal FE performance, even if component parameters vary from assumed values (e.g., due to manufacturing variation, environmental effects or aging), or actual driving conditions deviate largely from standard drive cycles.

Past research on hybrid electric vehicles (HEVs) focused primarily on improving their fuel economy. Emission reduction is another important performance attribute that needs to be addressed. When emissions are considered for hybrid vehicles with a gasoline engine, horizon-based optimization methodologies should be used because the light-off of the three-way catalytic converter heavily depends on the warming-up of catalyst temperature. In this paper, we propose a systematic design method for a cold-start supervisory control algorithm based on the dynamic programming (DP) methodology. First, a system-level parallel HEV model is developed to efficiently predict tailpipe emissions as well as fuel economy. The optimal control problem for minimization of cold-start emissions and fuel consumption is then solved via DP. Since DP solution cannot be directly implemented as a real-time controller, more useful control strategies are extracted from DP solutions over the entire state space via the comprehensive extraction method. The extracted DP results indicate that the engine on/off, gear-shift, and power-split strategies must be properly adjusted to achieve fast catalyst warm-up and low cold-start tailpipe emissions. Based on DP results, we proposed a rule-based control algorithm that is easy to implement and adjust while achieving near-optimal fuel economy and emission performance.

Advanced internal combustion engine technologies have afforded an increase in the number of controllable variables and the ability to optimize engine operation. Values for these variables are determined during engine calibration by means of a tabular static correlation between the controllable variables and the corresponding steady-state engine operating points to achieve desirable engine performance, for example, in fuel economy, pollutant emissions, and engine acceleration. In engine use, table values are interpolated to match actual operating points. State-of-the-art calibration methods cannot guarantee continuously the optimal engine operation for the entire operating domain, especially in transient cases encountered in the driving styles of different drivers. This article presents brief theory and algorithmic implementation that make the engine an autonomous intelligent system capable of learning the required values of controllable variables in real time while operating a vehicle. The engine controller progressively perceives the driver’s driving style and eventually learns to operate in a manner that optimizes specified performance criteria. A gasoline engine model, which learns to optimize fuel economy with respect to spark ignition timing, demonstrates the approach.

Hybrid electric vehicles provide promising alternatives to conventional engine-powered vehicles with better fuel economy and less emission. Realization of these benefits depends, in part, on proper control of the vehicle. This paper examines a variable structure control that switches between two operating points. The resulting sliding optimal control provides a better energy management strategy than that obtained conventionally from Pontryagin’s minimum principle. Since one of the operating points is at zero engine power, this sliding optimal control is also referred to as engine start-stop strategy. Contrary to the general understanding that the engine should only stop at low speeds or during decelerations, it is shown that engine start-stop also improves fuel economy during highway cruising. The main contribution of this paper is to theoretically prove that this “duty-cycle” operation mode is indeed optimal using Pontryagin’s minimum principle.

This paper proposes a control solution for a vehicular driveline with an internal combustion engine, a continuously variable transmission and an additional flywheel unit. This unit plays a part only in transient situations. It compensates for the engine inertia, enabling optimal fuel economy in stationary situations without losing driveability during transients. For control design, a simple, nonlinear model is developed and used for feedback linearization. The proposed controller is evaluated by simulations, using an advanced simulation model. The compensation of the engine inertia by the additional flywheel is demonstrated by vehicle experiments.

The competition to deliver fuel efficient and environmentally friendly vehicles is driving the automotive industry to consider even more complex powertrain systems. Adequate performance of these new highly interactive systems can no longer be obtained through traditional approaches, which are intensive in hardware use and final control software calibration. This paper explores the use of Dynamic Programming to make model-based design decisions for a lean burn, direct injection spark ignition engine, in combination with a three way catalyst and an additional three-way catalyst, often referred to as a lean NO X trap. The primary contribution is the development of a very rapid method to evaluate the tradeoffs in fuel economy and emissions for this novel powertrain system, as a function of design parameters and controller structure, over a standard emission test cycle.

Hydraulic regeneration systems have been considered by the automotive industry for implementation in hybrid vehicles for a number of years. The combination of an internal combustion engine and an energy storage device has great potential for improving vehicle performance and fuel economy as well as reducing brake wear. This study describes an analytical model of a hydraulic regeneration system consisting of an accumulator, an oil reservoir, a variable-displacement pump/motor, connecting lines and a flywheel which is used to simulate vehicle inertia. An integration algorithm is used to simultaneously solve the governing equations and predict the system performance. Variables including accumulator pressure and temperature, pump/motor torque and efficiencies, pressure losses, and flywheel speed as functions of time are predicted. Power losses and round-trip efficiencies can be readily determined once the system performance variables have been calculated.

Continuously variable automatic transmissions can bring improved fuel economy benefits under good speed ratio changes for automobile propulsion systems in which engines can produce their power under optimum conditions. These systems require computer control for the calculation of optimum speed ratio. The paper presents design features of the electrohydraulic interface between micro-computer and cone-roller toroidal traction drive CVT, dynamic characteristics of cone roller motion, and test results of the practical computer control of CVT.

A nonlinear dynamic simulation of a turbocharged diesel engine is presented. The model is designed to be used as an engine simulator to aid development of advanced microelectronic control systems of varying degrees of complexity and performance. The objective is to establish the potential benefit of quite different control system concepts in advance of hardware being constructed and tested on an engine. The detail of the model is governed by the desire to accurately predict fuel economy of new engine designs currently on the drawing board, without empirical input, and respond correctly to changing ambient conditions, design alterations etc. Thus the model treats cylinders and manifolds as thermodynamic control volumes, solving energy and mass conservation equations with subroutines for combustion, heat transfer, turbocharger, dynamic aspects etc. In-cylinder calculations are performed in small engine crank-angle steps so that the correct ignition crank angle is predicted as well as the subsequent fuel burning rate. This enables parameters such as cylinder pressure and diffusion burning factor (which correlates with exhaust smoke) to be predicted. The conflict between accuracy and computer run time and cost is addressed, and it is shown how the run time of a previous model (see SAE 770123) has been reduced by an order of magnitude. The accuracy of the model is illustrated by comparing measured and predicted performance over the complete engine speed and load range under steady conditions and engine response to “full throttle” acceleration and full-load application. The model is then used to show the influence of design parameters such as injection timing and turbocharger characteristics as well as external influence such as fuel cetane number and ambient conditions on steady speed and dynamic performance.

This paper is a review of current research on applications of control systems and theory to achieve energy conservation in automotive vehicles. The development of internal combustion engine control systems that modulate fuel flow, air flow, ignition timing and duration, and exhaust gas recirculation is discussed. The relative advantages of physical and empirical models for engine performance are reviewed. Control strategies presented include optimized open-loop schedule type systems, closed-loop feedback systems, and adaptive controllers. The development of power train and hybrid vehicle control systems is presented, including controllers for both conventional transmissions and those employing flywheel energy storage.