Chunxia Shangguan’s research while affiliated with Shanghai Ocean University and other places

The key to improving the fuel economy of plug-in hybrid electric vehicles (PHEVs) lies in the energy management strategy (EMS). Existing EMS often neglects engine operating conditions, leading to frequent start–stop events, which affect fuel economy and engine lifespan. This paper proposes an Integrated Engine Start–Stop Dynamic Programming (IESS-DP) energy management strategy, aiming to optimize energy consumption. An enhanced rule-based strategy is designed for the engine’s operating conditions, significantly reducing fuel consumption during idling through engine start–stop control. Furthermore, the IESS-DP energy management strategy is designed. This strategy comprehensively considers engine start–stop control states and introduces weighting coefficients to balance fuel consumption and engine start–stop costs. Precise control of energy flow is achieved through a global optimization framework to improve fuel economy. Simulation results show that under the World Light Vehicle Test Cycle (WLTC), the IESS-DP EMS achieves a fuel consumption of 3.36 L/100 km. This represents a reduction of 6.15% compared to the traditional DP strategy and 5.35% compared to the deep reinforcement learning-based EMS combined with engine start–stop (DDRL/SS) strategy. Additionally, the number of engine start–stop events is reduced by 43% compared to the DP strategy and 16% compared to the DDRL/SS strategy.

Addressing the sensitivity of permanent magnet synchronous motors to external disturbances, a novel sliding mode control (NSMC) strategy is proposed to suppress sliding mode jitter and enhance speed regulation performance. First, an advanced nonsingular fast terminal sliding mode (ANFTSM) surface and a new adaptive power rate reaching law (NAPRRL) were developed. A new switching function replaces the conventional sign function to enhance the system's disturbance immunity and dynamic response speed. Then, the system's anti‐interference performance was further enhanced by introducing an improved novel sliding mode observer (INSMO) for feedback compensation of the aggregate disturbance. Finally, MATLAB/Simulink simulations and experimental validations demonstrate that the NSMC control strategy exhibits superior performance in both the start‐up response and load disturbance phases, with enhanced dither resistance, rapid dynamic response, and disturbance suppression capabilities.