Hybrid vehicle techniques have been widely studied recently because of their potential to significantly improve the fuel economy and drivability of future ground vehicles. Due to the dual-power-source nature of these vehicles, control strategies based on engineering intuition frequently fail to fully explore the potential of these advanced vehicles. In this paper, we present a procedure for the design of a near-optimal power management strategy. The design procedure starts by defining a cost function, such as minimizing a combination of fuel consumption and selected emission species over a driving cycle. Dynamic programming (DP) is then utilized to find the optimal control actions including the gear-shifting sequence and the power split between the engine and motor while subject to a battery SOC-sustaining constraint. Through analysis of the behavior of DP control actions, near-optimal rules are extracted, which, unlike DP control signals, are implementable. The performance of this power management control strategy is studied by using the hybrid vehicle model HE-VESIM developed at the Automotive Research Center of the University of Michigan. A tradeoff study between fuel economy and emissions was performed. It was found that significant emission reduction could be achieved at the expense of a small increase in fuel consumption.

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Power management strategy for a parallel hybrid electric truck

The issues of global warming and depletion of fossil fuels have paved opportunities to electric vehicle (EV). Moreover, the rapid development of power electronics technologies has even realized high energy-efficient vehicles. EV could be the alternative to decrease the global green house gases emission as the energy consumption in the world transportation is high. However, EV faces huge challenges in battery cost since one-third of the EV cost lies on battery. This paper reviews state-of-the-art of the energy sources, storage devices, power converters, low-level control energy management strategies and high supervisor control algorithms used in EV. The comparison on advantages and disadvantages of vehicle technology is highlighted. In addition, the standards and patterns of drive cycles for EV are also outlined. The advancement of power electronics and power processors has enabled sophisticated controls (low-level and high supervisory algorithms) to be implemented in EV to achieve optimum performance as well as the realization of fast-charging stations. The rapid growth of EV has led to the integration of alternative resources to the utility grid and hence smart grid control plays an important role in managing the demand. The awareness of environmental issue and fuel crisis has brought up the sales of EV worldwide.

A review of energy sources and energy management system in electric vehicles

Introduction to heat transfer

As hybrid electric vehicles (HEVs) are gaining more popularity in the market, the rule of the energy management system in the hybrid drivetrain is escalating. This paper classifies and extensively overviews the state-of-the-art control strategies for HEVs. The pros and cons of each approach are discussed. From different perspectives, real-time solutions are qualitatively compared. Finally, a couple of important issues that should be addressed in future development of control strategies are suggested. The benefits of this paper are the following: (1) laying down a foundation for future improvements, (2) establishing a basis for comparing available methods, and (3) helping devoted researchers choose the right track, while avoiding doing that which has already been done.

Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends

Hybrid electric vehicles (HEVs) improvements in fuel economy and emissions strongly depend on the energy management strategy. The parallel HEV control problem involves the determination of the time profiles of the power flows from the engine and the electric motor. This is also referred to as the power split between the conventional and the electric sources. The objective of HEV control is in fact to find out the sequence of optimal power splits at each instant of time that minimizes the fuel consumption over a given driving schedule. Big obstacles to the control design are the model complexity and the necessity of "a priori" knowledge of torque and velocity profiles. This paper presents three different energy management approaches for the control of a parallel hybrid electric sport-utility-vehicle that do not require a priori knowledge of the driving cycle. The considered approaches are: a rule-based control, an adaptive equivalent fuel consumption minimization strategy (A-ECMS), and the Hinfin control. Results, compared with the optimal solution given by the dynamic programming, show that the A-ECMS strategy is the best performing strategy

A Comparative Study Of Supervisory Control Strategies for Hybrid Electric Vehicles

The work in this paper presents techniques for design, development, and control of hybrid electric vehicles (HEV). Toward these ends, four issues are explored. First, the development of HEV is presented. This synopsis includes a novel definition of degree of hybridization for automotive vehicles. Second, a load-leveling vehicle operation strategy is developed. In order to accomplish the strategy, a fuzzy logic controller is proposed. Fuzzy logic control is chosen because of the need for a controller for a nonlinear, multidomain, and time-varying plant with multiple uncertainties. Third, a novel technique for system integration and component sizing is presented. Fourth, the system design and control strategy is both simulated and then implemented in an actual vehicle. The controller examined in this study increased the fuel economy of a conventional full-sized vehicle from 40 to 55.7 mi/h and increased the average efficiency over the Federal Urban Driving Schedule from 23% to 35.4%. The paper concludes with a discussion of the implications of intelligent control and mechatronic systems as they apply to automobiles.

Mechatronic design and control of hybrid electric vehicles

This paper proposes a unified power flow approach to the modeling of hybrid electric vehicles (HEV), resulting in a highly scalable and reconfigurable modeling tool. Furthermore, this simulation tool is used in conjunction with a fuzzy logic, ruled-based controller to optimize the energy efficiency through the control of the power flows of a parallel HEV configuration. Finally, this modeling and control approach is applied to the design and optimization of a hybrid electric sport-utility vehicle. The results show that this modeling approach provides the required modeling flexibility, and that the model and the control strategy based on this power flow approach can be optimized to yield significant fuel economy gains.

Modeling, performance analysis and control design of a hybrid sport-utility vehicle

Operation and Control Strategies for Hybrid Electric Automobiles


 Currently Hybrid Electric Vehicles (HEV) are being considered as an alternative to conventional automobiles in order to improve efficiency and reduce emissions. To demonstrate the potential of an advanced control strategy for HEV’s, a fuzzy logic control strategy has been developed and implemented in simulation in the National Renewable Energy Laboratory’s simulator Advisor (version 2.0.2). The Fuzzy Logic Controller (FLC) utilizes the electric motor in a parallel hybrid electric vehicle (HEV) to force the ICE (66KW Volkswagen TDI) to operate at or near its peak point of efficiency or at or near its best fuel economy. Results with advisor show that the vehicle with the Fuzzy Logic Controller can achieve (56) mpg in the city, while maintaining a state of charge of .68 for the battery pack, compared to (43) mpg for a conventional vehicle. This scheme has also brought to light various rules of thumb for the design and operation of HEV’s.

Bradley Charles Glenn

Papers

Mechatronic design and control of hybrid electric vehicles

Modeling, performance analysis and control design of a hybrid sport-utility vehicle

Operation and Control Strategies for Hybrid Electric Automobiles

Intelligent Control of Parallel Hybrid Electric Vehicles

Coordinated control of the turbo electrically assisted variable geometry turbocharged diesel engine with exhaust gas recirculation