Real-time energy management strategy for a plug-in hybrid electric bus considering the battery degradation. (15th September 2022)

• Record Type: Journal Article
• Authors: Wang, Zhiguo; Wei, Hongqian; Xiao, Gongwei; Zhang, Youtong
• Is Part Of: Energy conversion and management. Volume 268(2022)
• Publication Date: 2022-09-15
• Subjects: Energy management strategy -- Electric vehicles -- Global optimization -- Battery health; AMT automated-mechanical-transmission -- BMS battery management system -- CD/CS charge-depleting/charge-sustaining -- CNG compressed natural gas -- DP dynamic programming -- ECMS equivalent consumption minimization strategy -- ECU electric control unit -- EKF adaptive extended Kalman filter -- EMS 3nergy management strategy -- EV electric vehicle -- EV-CS pure electric-charge-sustaining -- GA genetic algorithm -- HEV hybrid electric vehicle -- MAP manifold absolute pressure -- MPC well as model prediction control -- MSE mean square error -- PHEV plug-in hybrid electric vehicle -- PI proportion-integration -- PMP Pontryagin's minimum principle -- PMSM permanent magnet synchronous machine -- PSO particle swarm optimization -- RC resistance-capacitor -- SOC state of the charge -- SOH state of the health -- SVM space vector machine -- TUBS typical urban bus cycles -- VCU vehicle control unit; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2022.116053
• Languages: English
• ISSNs: 0196-8904

Abstract

Graphical abstract: Highlights: An integrated EMS is proposed by incorporating the offline global parameter extraction and the online optimization execution. The battery degradation is considered in the EMS, which is of significance to the energy economy. The intelligent SVM algorithm is established to predict the battery health state, which is further integrated into the SOC estimation. The in-laboratory experiment is implemented to validate the practicability of the proposed EMS. Abstract: The energy conservation remains the key issue for the hybrid electric vehicles (HEVs) today. However, most existing energy management strategies (EMS) only focus on the fuel consumption or battery preservation, and little considers the battery health. Besides, the global energy optimality and real-time execution are two trade-off counterparts for the EMS application. To this end, this paper proposed a real time EMS of the HEVs considering the battery health. Explicitly, the battery health status and SOC values are predicted with the space vector machine algorithm and adaptive Kalman filter algorithm, respectively. Then, the offline energy optimization is realized with the Pontryagin's minimum principle; thereby, the offline energy conversion coefficient can be further extracted into the simple rules. On this basis, the online equivalent consumption minimization strategy (ECMS) is adopted to output the optimal control variables including the motor torque, engine torque, clutch states and gear information. Besides, to apply the proposed EMS in the practical vehicles, the energy conversion coefficients are further modified according to the estimated battery state of the charge (SOC). The simulation and experimental tests have validated the superiority of the proposed EMS in terms of energy economy and maneuverability. Explicitly, the proposed EMS considering the battery degradation has effectively prevented the SOC trajectory into charge-sustaining stage earlier and meanwhile the fuel utilization is improved by 4.1 % than that without considering the battery degradation. Besides, compared with the existing ECMS method, the proposed EMS can reduce the fuel consumption by about 8–14 % and meanwhile enhance the handling adaptiveness by about 15–20 %. In general, the proposed EMS is of significance to the vehicular energy conservation and the real-time executability for the HEVs.