Adaptive ECMS considering SOC transient response characteristics for hybrid electric vehicle energy management strategy

Abstract

To improve the energy management performance of hybrid electric vehicles under dynamic driving conditions, this paper proposes a particle swarm optimization (PSO)-based adaptive equivalent consumption minimization strategy (PSO-AECMS) with state of charge (SOC) rate-of-change feedforward compensation. Conventional adaptive equivalent consumption minimization strategies (AECMSs) rely mainly on SOC deviation feedback, which may lead to a lag in the adjustment of the equivalent factor when power demand changes rapidly. To address this problem, an SOC rate-of-change feedforward term is introduced, and PSO is employed to optimize the parameters of the composite adaptive law offline. During online operation, the control parameters remain fixed, while the equivalent factor is dynamically updated according to the real-time SOC deviation and SOC rate of change. To improve the scientific rigor of the evaluation, dynamic programming (DP) is introduced as an offline globally optimal benchmark, and the Worldwide Harmonized Light Vehicles Test Cycle (WLTC), which is not involved in parameter calibration, is adopted for cross-validation. Simulation and hardware-in-the-loop (HIL) results show that the proposed strategy can reduce SOC fluctuations and improve fuel economy. Under the Urban Dynamometer Driving Schedule (UDDS) and the New European Driving Cycle (NEDC), the gaps in equivalent fuel consumption (EFC) between PSO-AECMS and DP are 2.88% and 6.15%, respectively. Compared with equivalent consumption minimization strategy (ECMS), the EFC of PSO-AECMS is reduced by 14.07% and 27.79%, respectively; compared with AECMS, it is reduced by 6.70% and 6.06%, respectively. The WLTC validation and the ablation comparison with Benchmark PSO-AECMS further demonstrate that the SOC rate-of-change feedforward term contributes to improving SOC fluctuation suppression under unknown dynamic driving conditions.