Dedicated Adaptive Particle Swarm Optimization Algorithm for Digital Twin Based Control Optimization of the Plug-In Hybrid Vehicle

The digital twin (DT) is a promising technology that will provide a cyber-physical platform to enable rapid product development boosted by artificial intelligence and big data. This article focuses on DT-based optimization of the energy management strategy (EMS) for a plug-in hybrid vehicle (PHEV). A dedicated adaptive particle swarm optimization (DAPSO) algorithm is developed to enhance the optimality and trustworthiness of the DT-based EMS optimization in a variety of driving conditions. By using the chassis dynamometer data, the DAPSO is developed by incorporating the widely used particle swarm optimization (PSO) algorithm with an adaptive swarm control strategy that coordinates the exploration and exploitation of the individual swarming particles following the global information extracted from the swarm. With the cross-validation testing under three worldwide driving conditions, this study determines the unified settings of the DAPSO algorithm for the PHEV application. Experimental evaluations are conducted by monitoring the PHEV performance using different EMSs with control thresholds optimized by the DAPSO and conventional PSO algorithms (baseline). The results show that by introducing DAPSO for DT-based EMS optimization, up to 24% improvement in cost function value [weighted sum of the fuel consumption and state-of-charge (SoC) sustaining error] and more than 49.1% reduction in computing time can be achieved compared to the baseline methods.