Energy Management for Plug-In Hybrid Electric Vehicle Based on Adaptive Simplified-ECMS

• Published in: Sustainability Vol. 10; no. 6; p. 2060
• Main Authors: Zeng, Yuping, Cai, Yang, Kou, Guiyue, Gao, Wei, Qin, Datong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 17.06.2018
• Subjects: algorithms; Electric vehicles; energy; energy use and consumption; fuels; prototypes; Sustainability; torque; Trucks; vehicles (equipment)
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su10062060

When searching for the optimal solution, Equivalent Consumption Minimum Strategy (ECMS) has to calculate and compare the total equivalent fuel rate of huge candidates covered all over the control domain for each time instant. Therefore, this strategy still has a heavy computation burden problem; it is a challenge for ECMS to be implemented online for real-time control. To reduce ECMS’s calculation load, this paper proposes an adaptive Simplified-ECMS-based strategy for a parallel plug-in hybrid electric vehicle (PHEV). A convex piecewise function is applied to fit the total equivalent fuel rate with respect to the motor torque, which is the control variable. Then, the ECMS problem is simplified to calculate and compare only five candidates’ total equivalent fuel rate to determine the optimal torque distribution. Particle swarm optimization (PSO) algorithm is applied to optimize the equivalent factor, and the MAPs of this factor under different driving cycles, driving distances and initial SOC are obtained. Based on this, the adaptive Simplified-ECMS-based strategy is proposed. Simulations were performed, and the results show that the Simplified-ECMS-based strategy can obviously shorten the calculation time compared to ECMS-based strategy, and the adaptive Simplified-ECMS-based strategy can decrease fuel consumption of plug-in hybrid electric vehicle by 16.43% under the testing driving cycle, compared to CD-CS-based strategy. A road test on the prototype vehicle is conducted and the effectiveness of the Simplified-ECMS-based strategy is validated by the test data.