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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zheng Fangdan Jiang Jiuchun Zhang Weige Sun Bingxiang Zhang Caiping Wang Yukun |
| Copyright Year | 2014 |
| Description | Author affiliation: Nat. Active Distrib. Network Technol. Res. Center (NANTEC), Beijing Jiaotong Univ., Beijing, China (Zheng Fangdan; Jiang Jiuchun; Zhang Weige; Sun Bingxiang; Zhang Caiping; Wang Yukun) |
| Abstract | Capacities of li-ion batteries are difficult to estimate quickly and accurately when second use batteries are in large scale with dispersed parameters. This may result in costing too much time and money to reuse batteries. By analyzing the capacity and resistance characteristics, there is no functional relationship between them. In order to solve this nonlinear problem, a SVM model with 3 parameters (i.e. penalty coefficient, kernel function parameter and loss function parameter) is established. The inputs are five calculated values of resistance and the output is the capacity value of batteries. Data of 70 battery modules are adopted to train the model while data of other 30 battery modules are used to test the model. In this process, two parameters optimization methods using genetic algorithm have been proposed. By comparison, the coefficient of determination (COD) value of method 2 is higher than method 1 both in training model and testing model. The average error of method 2 between measured values and estimated values is 0.67% whereas that of method 1 is 1.35%. In method 2, 90% of the estimation errors are under 2.5%. The results provide valuable insights for large-scale retired li-ion batteries into second use. |
| Sponsorship | IEEE Ind. Electron. Soc. |
| Starting Page | 1628 |
| Ending Page | 1634 |
| File Size | 634215 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781479923991 |
| ISSN | 21635145 |
| DOI | 10.1109/ISIE.2014.6864859 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-06-01 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Batteries Resistance Support vector machines Kernel Genetic algorithms Estimation Correlation capacity estimation li-ion battery second use support vector machine (SVM) genetic algorithm (GA) |
| Content Type | Text |
| Resource Type | Article |
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