An adaptive firework algorithm optimization-based intelligent energy management strategy for plug-in hybrid electric vehicles

To enhance the energy management strategy (EMS) effect on improving the fuel economy of plug-in hybrid electric vehicle (PHEV), the method of optimizing the key parameters of EMS has become a common solution. However, there is still a certain gap between current fuel consumption and its theoretical optimum level of existing EMSs. The reasons might be that more control parameters of the EMS need to be optimized and the performance of the optimization algorithm should also be improved. Regard at this, this paper proposes an intelligent EMS for PHEVs using a novel adaptive firework algo-rithm (AFWA) for the efficient optimization of control parameters. The EMS includes a rule-based gear shift strategy, maintaining the driving shaft always rotating within a reasonable range by considering vehicle velocity, acceleration and current gear position, and Takagi-Sugeno fuzzy control-based torque distribution strategy, optimizing the engine operating points according to demand torque of powertrain and battery state of charge. Meanwhile, a modified AFWA is firstly proposed to efficiently optimize control parameters of these two strategies by more reasonably tune the search area of the core firework according to the number of iterations. Finally, the proposed EMS is verified and evaluated through simulation and HIL test platform. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes an intelligent EMS for PHEVs using a novel adaptive firework algorithm to optimize control parameters, aiming to improve fuel economy. The EMS includes rule-based gear shift strategy and torque distribution strategy, with a modified AFWA used to efficiently optimize control parameters of these strategies.