Cyber-physical data fusion in surrogate-assisted strength pareto evolutionary algorithm for PHEV energy management optimization

This paper proposes a new form of algorithm environment for multi-objective optimization of energy management system in plug-in hybrid vehicles (PHEVs). The surrogate-assisted strength Pareto evolutionary algorithm (SSPEA) is developed to optimize the power-split control parameters guided by the data from the physical PHEV and its digital twins. By introducing a ''confidence factor'', the SSPEA uses the fused data of physically measured and virtually simulated vehicle performances (energy consumption and remaining battery state-of-charge) to converge the optimization process. Gaussian noisy models are adopted to emulate the real vehicle system on the hardware-in-the-loop platform for experimental evaluation. The testing results suggest that the proposed SSPEA requires less R&D costs than the model-free method that only uses the physical information, and more than 44.6% energy can be saved during the R&D process. Driven by the SSPEA, the optimized energy management system surpasses other non-DT-assisted systems by saving more than 4.8% energy.