Distributed cooperative energy management system of connected hybrid electric vehicles with personalized non-stationary inference

This paper develops a distributed cooperative energy management system with two distributed control layers for speed-coupling plug-in hybrid electric vehicles. By introducing personalized non-stationary inference, this system can fuse driving behavior and vehicle state information to adaptively adjust power-split control parameters for the improvement of vehicle energy economy. In the on-board control layer, five sets of personalized control parameters are optimized offline by using chaos-enhanced accelerated particle swarm optimization. In the distributed control layer, interval type-2 fuzzy sets are applied to develop a real-time driving style recognition function. Driving behavior is detected remotely, via the vehicle to everything network, and downloaded to adaptively adjust power-split control parameters in the on-board vehicle controller. Hardware-in-the-loop testing is carried out based on the four laboratory driving cycles and four personal driving cycles. The proposed system has been demonstrated with strong robustness that saves energy by up to 5.25% over the equivalent consumption minimization strategy (ECMS), especially for gentle drivers. Even under harsh communication conditions (with signal loss 80+%), it still performs better than the ECMS (by 0.57%) and the series-parallel control strategy (by 2.66%).