A novel method on estimating the degradation and state of charge of lithium-ion batteries used for electrical vehicles. (1st December 2017)

• Record Type: Journal Article
• Authors: Yang, Ruixin; Xiong, Rui; He, Hongwen; Mu, Hao; Wang, Chun
• Is Part Of: Applied energy. Volume 207(2017)
• Page Start: 336
• Page End: 345
• Publication Date: 2017-12-01
• Subjects: Electric vehicles -- Battery -- Capacity -- State of charge -- Degradation -- Recognition; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.05.183
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A three-dimensional response surface-based battery OCV model was constructed. A genetic algorithm is used to identify the model parameters. The accuracy and robustness of the proposed method are verified systematically. The proposed method shows high accuracy and robustness during the entire battery life. The proposed method can effectively improve the efficiency of on-line computation. Abstract: The accurate determination of the capacity degradation path and state of charge (SoC) is very important for the battery energy storage systems widely used in electric vehicles. This research can be summarized as follows. First, a three-dimensional response surface-based SoC-open circuit voltage (OCV) capacity method covering the entire lifetime of a battery has been constructed, which can be used to describe the battery capacity degradation characteristics and determine the corresponding SoC. Second, in order to capture the battery health state and energy state, a genetic algorithm (GA) is applied to identify the battery capacity and initial SoC based on a first-order RC model. Finally, to verify the proposed method, six experimental cases, including batteries with different aging states and with different data calculation durations, are considered. The results indicate that the maximum capacity and SoC estimation errors are less than 5.0% and 2.1%, respectively, for batteries with different aging states, which points to the high accuracy, stability and robustness of the proposed GA-based battery capacity and initial SoC estimator during the entire battery lifespan.