Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan
Zheng Chen,
Jiapeng Xiao,
Xing Shu,
Shiquan Shen,
Jiangwei Shen and
Yonggang Liu
Additional contact information
Zheng Chen: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Jiapeng Xiao: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Xing Shu: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Shiquan Shen: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Jiangwei Shen: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Yonggang Liu: State Key Laboratory of Mechanical Transmissions & School of Automotive Engineering, Chongqing University, Chongqing 400044, China
Energies, 2020, vol. 13, issue 6, 1-15
Abstract:
In this paper, a co-estimation scheme of the state of charge (SOC) and available capacity is proposed for lithium–ion batteries based on the adaptive model-based algorithm. A three-dimensional response surface (TDRS) in terms of the open circuit voltage, the SOC and the available capacity in the scope of whole lifespan, is constructed to describe the capacity attenuation, and the battery available capacity is identified by a genetic algorithm (GA), together with the parameters related to SOC. The square root cubature Kalman filter (SRCKF) is employed to estimate the SOC with the consideration of capacity degradation. The experimental results demonstrate the effectiveness and feasibility of the co-estimation scheme.
Keywords: state of charge; available capacity; adaptive model-based algorithm; square root cubature Kalman filter; joint estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:6:p:1410-:d:333775
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