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Energies 2014, 7(12), 8076-8094; doi:10.3390/en7128076

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

1
National Active Distribution Network Center, Beijing Jiaotong University, Beijing 100044, China
2
School of Engineering and the Environment, University of Southampton, Highfield, Southampton SO17 1BJ, UK
3
National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Received: 10 October 2014 / Revised: 15 November 2014 / Accepted: 24 November 2014 / Published: 1 December 2014
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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. View Full-Text
Keywords: lithium-ion batteries; second-life; fast capacity estimation; artificial neural networks; robustness lithium-ion batteries; second-life; fast capacity estimation; artificial neural networks; robustness
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhang, C.; Jiang, J.; Zhang, W.; Wang, Y.; Sharkh, S.M.; Xiong, R. A Novel Data-Driven Fast Capacity Estimation of Spent Electric Vehicle Lithium-ion Batteries. Energies 2014, 7, 8076-8094.

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