A data-driven energy management method for parallel PHEVs based on action dependent heuristic dynamic programming (ADHDP) model. (15th February 2023) - Kong, Yan; Xu, Nan; Liu, Qiao; Sui, Yan; Yue, Fenglai
Abstract: Due to the strong self-learning ability and adaptability of neural network, adaptive dynamic programming (ADP) method is regarded as an effective method to improve the vehicle economy in the case of uncertain driving conditions. With the look-ahead information, a data-driven energy management method is proposed based on action dependent heuristic dynamic programming (ADHDP) algorithm. Firstly, based on statistical analysis of DP behavior, a reference SOC trajectory is generated to limit the electricity consumption, which is adjusted with the dynamically updated driving information. Then, a data-driven energy management control method is developed for HEV architectures, including information acquisition module, shift scheduling module, energy distribution module. The gear shift command is designed by enumeration and the power distribution is performed by multiple ADHDP models, the core of which is to determine the utility function, action network (ANN), critic network (CNN) and training process. To ensure practical driveability, the restrictions on gear shifting and engine starting-stopping are taken as additional conditions in ADHDP optimizing process. Finally, two case studies under different driving scenarios are given. Simulation results demonstrate that the proposed method gains a good performance in both optimality approximation and adaptivity to uncertain driving conditions. Highlights: ADHDP-based data-driven energy management method is proposed for HEV architectures. Drivability constraints are considered in optimizing process. Adaptivity and near-optimality of ADHDP strategy are verified.