Global optimization techniques, such as dynamic programming, serve mainly to evaluate the potential fuel economy of a given powertrain configuration. Unless the future driving conditions can be predicted during real-time operation but the results obtained using this noncausal approach establish a benchmark for evaluating the optimality of realizable control strategies. Real-time controllers must be simple in order to be implementable with limited computation and memory resources. Moreover, manual tuning of control parameters should be avoided. This article has analyzed two approaches, namely, feedback controllers and ECMS. Both of these approaches can lead to system behavior that is close to optimal, with feedback controllers based on dynamic programming. Additional challenges stem from the need to apply optimal energy-management controllers to advanced HEV architectures, such as combined and plug-in HEVs, as well as to optimization problems that include performance indices in addition to fuel economy, such as pollutant emissions, driveability, and thermal comfort

Control of hybrid electric vehicles

In this paper, a model-based strategy for the real-time load control of parallel hybrid vehicles is presented. The aim is to develop a fuel-optimal control which is not relying on the a priori knowledge of the future driving conditions (global optimal control), but only upon the current system operation. The methodology developed is valid for those problem that are characterized by hard constraints on the state-battery state-of-charge (SOC) in this application-and by an arc cost-fuel consumption rate-which is not an explicit function of the state. A suboptimal control is found with a proper definition of a cost function to be minimized at each time instant. The "instantaneous" cost function includes the fuel energy and the electrical energy, the latter related to the state constraints. In order to weight the two forms of energy, a new definition of the equivalence factors has been derived. The strategy has been applied to the "Hyper" prototype of DaimlerChrysler, obtained from the hybridization of the Mercedes A-Class. Simulation results illustrate the potential of the proposed control in terms of fuel economy and in keeping the deviations of SOC at a low level.

Optimal control of parallel hybrid electric vehicles

A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management

With the advent of more stringent regulations related to emissions, fuel economy, and global warming, as well as energy resource constraints, electric, hybrid, and fuel-cell vehicles have attracted increasing attention from vehicle constructors, governments, and consumers. Research and development efforts have focused on developing advanced powertrains and efficient energy systems. This paper reviews the state of the art for electric, hybrid, and fuel-cell vehicles, with a focus on architectures and modeling for energy management. Although classic modeling approaches have often been used, new systemic approaches that allow better understanding of the interaction between the numerous subsystems have recently been introduced.

https://www.eee.hku.hk/doc/ccchan/Electric,%20Hybrid,%20and%20Fuel-Cell%20Vehicles-%20Architectures%20and%20Modeling.pdf

Electric, Hybrid, and Fuel-Cell Vehicles: Architectures and Modeling

Plug-in hybrid electric vehicles are a midterm solution to reduce the transportation sector's dependency on oil. However, if implemented in a large scale without control, peak load increases significantly and the grid may be overloaded. Two algorithms to address this problem are proposed and analyzed. Both are based on a forecast of future electricity prices and use dynamic programming to find the economically optimal solution for the vehicle owner. The first optimizes the charging time and energy flows. It reduces daily electricity cost substantially without increasing battery degradation. The latter also takes into account vehicle to grid support as a means of generating additional profits by participating in ancillary service markets. Constraints caused by vehicle utilization as well as technical limitations are taken into account. An analysis, based on data of the California independent system operator, indicates that smart charge timing reduces daily electricity costs for driving from $0.43 to $0.2. Provision of regulating power substantially improves plug-in hybrid electric vehicle economics and the daily profits amount to $1.71, including the cost of driving.

Optimal Charge Control of Plug-In Hybrid Electric Vehicles in Deregulated Electricity Markets

Hybridizing automotive drivetrains, or using more than one type of energy converter, is considered an important step toward very low pollutant emission and high fuel economy. The automotive industry and governments in the United States, Europe, and Japan have formed strategic initiatives with the aim of cooperating in the development of new vehicle technologies. Efforts to meet fuel economy and exhaust emission targets have initiated major advances in hybrid drivetrain system components, including: high-efficiency high-specific power electric motors and controllers; load-leveling devices such as ultracapacitors and fly-wheels; hydrogen and direct-methanol fuel cells; direct injection diesel and Otto cycle engines; and advanced batteries. The design of hybrid electric vehicles is an excellent example of the need for mechatronic system analysis and design methods. If one is to fully realize the potential of using these technologies, a complete vehicle system approach for component selection and optimization over typical driving situations is required. The control problems that arise in connection with hybrid power trains are significant and pose additional challenges to power-train control engineers. The principal aim of the paper is to propose a framework for the analysis, design, and control of optimum hybrid vehicles within the context of energy and power flow analysis. The approaches and results presented in the paper are one step toward the development of a complete toolbox for the analysis and design of hybrid vehicles.

Bernd Baumann

Papers

Unified modeling of hybrid electric vehicle drivetrains

Intelligent Control of Hybrid Vehicles Using Neural Networks and Fuzzy Logic

Intelligent Control of the Ohio State University Hybrid-Electric Vehicle