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Energies 2015, 8(4), 2889-2907; doi:10.3390/en8042889

Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries

Department of Mechanical Engineering, Ming Chi University of Technology, 84 Gungjuan Road, Taishan, New Taipei City 24301, Taiwan
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Author to whom correspondence should be addressed.
Academic Editor: Haolin Tang
Received: 31 December 2014 / Revised: 13 March 2015 / Accepted: 7 April 2015 / Published: 15 April 2015
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Abstract

Accurate estimation of lithium-ion battery life is essential to assure the reliable operation of the energy supply system. This study develops regression models for battery prognostics using statistical methods. The resultant regression models can not only monitor a battery’s degradation trend but also accurately predict its remaining useful life (RUL) at an early stage. Three sets of test data are employed in the training stage for regression models. Another set of data is then applied to the regression models for validation. The fully discharged voltage (Vdis) and internal resistance (R) are adopted as aging parameters in two different mathematical models, with polynomial and exponential functions. A particle swarm optimization (PSO) process is applied to search for optimal coefficients of the regression models. Simulations indicate that the regression models using Vdis and R as aging parameters can build a real state of health profile more accurately than those using cycle number, N. The Monte Carlo method is further employed to make the models adaptive. The subsequent results, however, show that this results in an insignificant improvement of the battery life prediction. A reasonable speculation is that the PSO process already yields the major model coefficients. View Full-Text
Keywords: battery cycle life; battery state of health; battery reliability; particle swarm optimization; battery remaining useful life estimation battery cycle life; battery state of health; battery reliability; particle swarm optimization; battery remaining useful life estimation
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

Tseng, K.-H.; Liang, J.-W.; Chang, W.; Huang, S.-C. Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries. Energies 2015, 8, 2889-2907.

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