Multi-objective real-time energy management for series-parallel hybrid electric vehicles considering battery life

The real-time control of energy management strategy (EMS) is becoming increasingly challenging as the complexity of the model and control strategy increases. To address this issue while ensuring the accuracy of the EMS, a multi-objective real-time EMS based on model predictive control (MPC) that considers battery life is proposed in this paper. Firstly, to balance the accuracy and efficiency of the prediction module, a kernel extreme learning machine based on the whale optimization algorithm is proposed as a short-term speed prediction model. Secondly, an adaptive state of charge (SOC) trajectory planning method is established to plan MPC reference trajectory. Next, to optimize fuel efficiency, electrical energy consumption, and battery aging in real-time, a multi-objective real-time MPC (MOR-MPC) algorithm is proposed. Finally, the effectiveness, real-time perfor-mance, and robustness of the proposed strategy are verified. Simulation results demonstrate that the total cost of the strategy is reduced by 6.15% compared to the equivalent consumption minimization strategy (ECMS), with 98.17% of dynamic programming (DP) performance achieved. Real-time performance is improved by 97.89% compared to DP-MPC. Hardware-in-the-loop (HIL) testing is also carried out to evaluate the proposed strategy.

Automated summary

This paper proposes a multi-objective real-time energy management system based on model predictive control (MPC) to tackle the increasing complexity of energy management strategy (EMS) control. A short-term speed prediction model based on whale optimization algorithm is developed to balance accuracy and efficiency. An adaptive SOC trajectory planning method is established to plan MPC reference trajectory. Furthermore, a multi-objective real-time MPC (MOR-MPC) algorithm is proposed to optimize fuel efficiency, electrical energy consumption, and battery aging in real-time. Simulation and hardware-in-the-loop (HIL) testing validate the effectiveness and real-time performance of the proposed strategy, achieving a cost reduction of 6.15% and improved real-time performance.