A Novel Data-Driven Fast Capacity Estimation of Spent Electric Vehicle Lithium-ion Batteries

Caiping Zhang, Jiuchun Jiang, Weige Zhang, Yukun Wang, Suleiman M. Sharkh and Rui Xiong
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Caiping Zhang: National Active Distribution Network Center, Beijing Jiaotong University, Beijing 100044, China
Jiuchun Jiang: National Active Distribution Network Center, Beijing Jiaotong University, Beijing 100044, China
Weige Zhang: National Active Distribution Network Center, Beijing Jiaotong University, Beijing 100044, China
Yukun Wang: National Active Distribution Network Center, Beijing Jiaotong University, Beijing 100044, China
Suleiman M. Sharkh: School of Engineering and the Environment, University of Southampton, Highfield, Southampton SO17 1BJ, UK
Rui Xiong: National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

Energies, 2014, vol. 7, issue 12, 1-19

Abstract: Fast capacity estimation is a key enabling technique for second-life of lithium-ion batteries due to the hard work involved in determining the capacity of a large number of used electric vehicle (EV) batteries. This paper tries to make three contributions to the existing literature through a robust and advanced algorithm: (1) a three layer back propagation artificial neural network (BP ANN) model is developed to estimate the battery capacity. The model employs internal resistance expressing the battery’s kinetics as the model input, which can realize fast capacity estimation; (2) an estimation error model is established to investigate the relationship between the robustness coefficient and regression coefficient. It is revealed that commonly used ANN capacity estimation algorithm is flawed in providing robustness of parameter measurement uncertainties; (3) the law of large numbers is used as the basis for a proposed robust estimation approach, which optimally balances the relationship between estimation accuracy and disturbance rejection. An optimal range of the threshold for robustness coefficient is also discussed and proposed. Experimental results demonstrate the efficacy and the robustness of the BP ANN model together with the proposed identification approach, which can provide an important basis for large scale applications of second-life of batteries.

Keywords: lithium-ion batteries; second-life; fast capacity estimation; artificial neural networks; robustness (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: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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