Open AccessDevelopment of an online adaptive energy management strategy for the novel hierarchical coupled electric powertrain
Author(s) -
Chen Xianbao,
Shu Hongyu,
Song Yitong
Publication year - 2021
Publication title -
energy science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 29
ISSN - 2050-0505
DOI - 10.1002/ese3.931
Subject(s) - powertrain , energy management , computer science , mode (computer interface) , adaptability , energy (signal processing) , artificial neural network , power (physics) , energy management system , principal component analysis , mathematical optimization , artificial intelligence , ecology , statistics , physics , mathematics , quantum mechanics , biology , torque , thermodynamics , operating system
This paper develops an online adaptive energy management strategy (EMS) for the promising hierarchical coupled electric powertrain (HCEP) to exert its energy‐saving potential while considering the adaptability to driving conditions and the suppression of mode switching frequency. First, the complex energy management issue of the HCEP is simplified by introducing a simple power allocation method. And, the simplified energy management issue is solved by the Dynamic Programming to obtain the offline optimal working mode sequences of the HCEP. Second, the online working mode decision rules of the HCEP are established according to the obtained working mode sequences. And, the auxiliary rules in the decision rules are further optimized for different types of driving conditions. Then, the principal component analysis and generalized regression neural network are used to construct the driving condition recognizer (DCR) with high prediction accuracy. And, based on the constructed DCR, working mode decision rules, and introduced power allocation method, an online adaptive EMS is developed for the HCEP. Finally, the rationality of the introduced power allocation method and the effectiveness of the developed online adaptive EMS are verified.