Abstract

This paper presents a real-time energy management strategy to distribute the power demand between two independent motors properly. Based on the characteristics of the novel transmission system, an enumeration-based searching approach is used to hunt for the optimal working points for both motors to maximize the overall efficiency. Like many energy management strategies, approaches that focus on reducing energy consumption can result in frequent gearshifts. To improve drivability and make a balance between energy consumption and gearshifts, a cost function is designed. To verify the effectiveness of the proposed method, a mathematical model is built, and the simulation results demonstrate the achieved improvements. Document type: Article

Abstract

This article presents an energy management method for a parallel hybrid electric vehicle (HEV) based on approximate Pontryagin’s Minimum Principle (A-PMP). The A-PMP optimizes gearshift commands and torque distribution for overall energy efficiency. As a practical numerical solution in PMP, the proposed methodology utilizes a piecewise linear approximation of the engine fuel rate and state of charge (SOC) derivative by considering drivability and fuel economy simultaneously. Moreover, battery aging is explicitly studied by introducing a control-oriented model, which aims to investigate the effect of battery aging on the optimization performance in the development of the HEVs. An approximate energy management strategy with piecewise linear models is then formulated by the A-PMP, which targets a better performance for the Hamiltonian optimization. The gearshift map is extracted from the optimal results in the standard PMP to hinder frequent gearshift by considering both drivability and fuel economy. Utilizing an approximated Hamilton function, the torque distribution, gearshift command, and the battery aging degradation are jointly optimized under a unified framework. Simulations are performed for dynamic programming (DP), PMP, and A-PMP to validate the effectiveness of the proposed approach. The results indicate that the proposed methodology achieves a close fuel economy compared with the DP-based optimal solution. Moreover, it improves the computation efficiency by 50% and energy saving by 3.5%, compared with the PMP, while ensuring good drivability and fuel efficiency. Document type: Article

Abstract

Energy management strategies for an electric vehicle (EV) with multiple power sources have been widely described in literature. The investigated energy sources are batteries, ultracapacitors, fuel cells, flywheels and solar panels. The management strategy decides how to combine two or more sources in an optimal way. However, the behavior of these sources and also the behavior of the electric drives depend on their temperature. Moreover, the temperature can have extreme values in automotive applications and affect the energy management task. In this paper, to investigate the temperature effect on battery/ultracapacitor powered EV, temperature dependent models are presented for these storage components, as well as for the drive train components itself: power electronics and motor. The average motor iron loss and ultracapacitor loss tend to decrease with increasing temperature, while the average motor copper loss and power electronics loss tend to increase with increasing temperature. These two opposing trends cause the total loss of the drive train to have a rather small variation with temperature for the considered EV and in the considered temperature range. By consequence, the energy management strategy of the EV does not have to depend on the temperature in order to obtain maximal efficiency. (C) 2017 Elsevier Ltd. All rights reserved.