Optimization of energy management strategies for multi-mode hybrid electric vehicles driven by travelling road condition data.

The fuel efficiency of plug-in hybrid electric vehicle is influenced by various factors, including working conditions, driving style, and environmental variables, with the design of their energy management strategy (EMS) serving as the core and critical technology. In order to adapt to traffic environment, it is of great significance to construct driving cycles that align with driving characteristics, providing data support for the optimization of the EMS. This paper carried out research on the EMS optimization for multi-mode hybrid electric vehicle (MMHEV). Firstly, the traffic speed was established interval by using data envelopment analysis (DEA) and the urban comprehensive driving cycles based on the proportion of driving time was constructed. Then, an EMS optimized based on road condition information (RC-EMS) was developed according to the operating curves and interval thresholds of motors and engine. The initial optimal parameters for offline optimization of the EMS were obtained using a bare-bones multi-objective particle swarm optimization algorithm, with the constructed comprehensive driving cycle serving as the input information for the optimization model. After that, a fuzzy adaptive parameter optimization module (ARC-EMS) was designed to update the key parameters of the RC-EMS in real time, so as to realize the optimal dynamic energy allocation adaptively according to the road conditions. Finally, the simulation and experimental results fully showed that the proposed ARC-EMS can balance the performance more effectively. As anticipated, the strategy effectively balances energy savings and battery health, and it can be utilized to develop an EMS that enhances the overall performance of the MMHEV.