Online power management strategy for plug-in hybrid electric vehicles based on deep reinforcement learning and driving cycle reconstruction
Zhiyuan Fang, Zeyu Chen, Quanqing Yu, Bo Zhang, Ruixin Yang
Abstract
This paper proposes a novel power management strategy for plug-in hybrid electric vehicles based on deep reinforcement learning algorithm. Three parallel soft actor-critic (SAC) networks are trained for high speed, medium speed, and low-speed conditions respectively; the reward function is designed as minimizing the cost of energy cost and battery aging. During operation, the driving condition is recognized at each moment for the algorithm invoking based on the learning vector quantization (LVQ) neural network. On top of that, a driving cycle reconstruction algorithm is proposed. The historical speed segments that were recorded during the operation are reconstructed into the three categories of high speed, medium speed, and low speed, based on which the algorithms are online updated. The SAC-based control strategy is evaluated based on the standard driving cycles and Shenyang practical data. The results indicate the presented method can obtain the effect close to dynamic programming and can be further improved by up to 6.38% after the online update for uncertain driving conditions.