Individualized Optimal Shift Schedule for Single-Shaft Parallel Plug-in Hybrid Electric Vehicles

To optimize the individualized comprehensive performance of plug-in hybrid electric vehicles (PHEVs), an optimization method for the shift schedule of single-shaft parallel PHEVs considering both dynamic and economic performance while reflecting the driving intention was proposed. Firstly, the switching logic among different operating modes was determined according to the demand torque, the engine characteristic curves, and the state of charge (SOC) of the power battery, and torque distribution strategies under different operating modes were formulated. Subsequently, a fuzzy inference method was used to establish a quantitative model for driving intention, which could calculate the driver’s expectations for dynamic and economic performance based on the driver’s operation and vehicle status. Then, by taking the driver’s expectations for dynamic and economic performance as the weights of corresponding sub-objective functions, a linear weighting method was used to construct a comprehensive performance evaluation function, thereby optimizing the shift schedules under different driving intentions. Finally, a simulation model was developed using MATLAB/Simulink, and simulations under the WLTC test cycle were conducted with initial SOC values of 0.5 and 0.9, respectively, using the optimal dynamic, optimal economic, and individualized optimal shift schedules. Simulation results show that under both SOC initial conditions, while reflecting the driving intention, the equivalent fuel consumption (EFC) of the individualized optimal shift schedules is reduced significantly compared to that of the optimal dynamic shift schedule, with reductions of 10.1% at an SOC of 0.5 and 11.8% at an SOC of 0.9. Meanwhile, the EFC of the individualized optimal shift schedules is increased compared to that of the optimal economic shift schedule, with increases of 5.3% at an SOC of 0.5 and 1.7% at an SOC of 0.9.