Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition. (1st May 2020)

• Record Type: Journal Article
• Authors: Hu, Jie; Liu, Di; Du, Changqing; Yan, Fuwu; Lv, Chen
• Is Part Of: Energy. Volume 198(2020)
• Publication Date: 2020-05-01
• Subjects: Hybrid energy storage system -- Driving patterns recognition -- Transient power -- Adaptive wavelet transform -- Fuzzy logic control; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.117298
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: To achieve optimal power distribution of hybrid energy storage system composed of batteries and supercapacitors in electric vehicles, an adaptive wavelet transform-fuzzy logic control energy management strategy based on driving pattern recognition (DPR) is proposed in view of the fact that driving cycle greatly affects the performance of EMS. The DPR uses cluster analysis to classify driving cycles into different patterns according to the features extracted from the historical driving data sampling window and utilizes pattern recognition to identify real-time driving patterns. After recognition results are obtained, an adaptive wavelet transform is employed to allocate the high frequency components of power demand to supercapacitor which contains transient power and rapid variations, while the low frequency components are distributed to battery accordingly. The use of fuzzy logic control is to maintain the SOC of supercapacitor within desired level. The simulation results indicate that the proposed control strategy can effectively decrease the maximum charge/discharge current of battery by 58.2%, and improve the battery lifetime by 6.16% and the vehicle endurance range by 11.06% compared with conventional control strategies. Further demonstrate the advantage of hybrid energy storage system and the presented energy management strategy. Highlights: Three typical driving patterns have been classified with cluster analysis. A driving pattern recognition has been developed with Euclidean distance. Wavelet transform with different levels was used for different driving patterns. Adaptive energy management strategy for HESS has been proposed and verified. The system efficiency and lifespan are obviously improved with supercapacitor adopted.