Adversarial Swarm Learning for Robust Equivalent Consumption Minimization of Electrified Vehicles

This paper develops a robust energy management method for plug-in hybrid electric vehicles (PHEVs) by proposing an adversary swarm learning (ASL) system. The proposed ASL system includes an attacking swarm and a defending swarm working adversarial on an iteration base to obtain the most robust equivalent consumption minimization strategy (ECMS) for PHEV energy management. In the attacking rounds, the defender fixes the ECMS settings while the attacker trains a model to generate the worst-case driving profiles that aim to obtain the maximum equivalent fuel and electricity consumption. In the defending rounds, the defender optimizes the setting of ECMS in the driving cycle generated by the attacker. By introducing two new concepts, attacking rate and defending rate, the robust settings for ECMS were obtained using the ASL systems based on the minimum attacking rate (MAR) and the maximum defending rate (MDR), respectively. Using the conventional ECMS optimized under four standard driving cycles as the baseline, this paper demonstrated the advantage of the proposed methods on both the software-in-the-loop and hardware-in-the-loop platforms. The study suggests that the robust ECMS obtained by the proposed ASL systems outperforms the baseline in all selected driving cycles, and the ASL using MAR strategy is shown more effective by achieving an average cost-reducing rate of 10.58% compared to the baseline.