HEV power management control strategy for urban driving

• Published in: Applied energy Vol. 194; pp. 705 - 714
• Main Authors: Wei, Zhen, Xu, John, Halim, Dunant
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.05.2017
• Subjects: algorithms; batteries; Control strategy; dynamic programming; fuels; fuzzy logic; Hybrid electric vehicle; Power management; torque; Urban driving cycle; Hybrid electric vehicle; Urban driving cycle; Control strategy; Power management
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2016.10.023

•The conventional DP method is modified with the consideration of SOC balance.•Rules of torque slip behaviors have been found in DP results.•Rules of torque slip behaviors are utilized to design the fuzzy controller.•An urban driving pattern recognition method is proposed base on similarity degree.•The proposed strategy is combined with the driving pattern recognition method. The aim of this paper is to further improve the fuel efficiency and keep the balance of battery SOC simultaneously during urban driving. A dynamic programming (DP)-optimized HEV power management control strategy is proposed. The DP algorithm is applied to obtain the optimal engine/motor power distribution and utilized for the design of the fuzzy control strategy. The traditional DP algorithm is modified with the consideration of SOC balance for HEVs. In the analysis of DP simulation results, rules of torque slip behaviors have been found, which are directly utilized in the design of fuzzy control strategy. In order to improve the practicality of the control strategy to meet the diversities of city driving patterns, an urban driving pattern recognition method is presented. To evaluate the control performance, the proposed control strategy is also compared with the conventional rule-based strategy. The simulation results indicate that by adopting the proposed strategy the fuel efficiency of HEV is improved, and the SOC of the battery is kept in balance during different urban driving cycles.