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| SOC Adaptive Estimation Method for Li-Ion Battery Applied in Temperature-varying Condition |
| Received:August 03, 2018 Revised:December 25, 2018 |
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| DOI：10.7643/ issn.1672-9242.2018.12.005 |
| KeyWord:NCM lithium-ion battery state of charge varying ambient temperature battery characteristics mild hybrid power polar scientific expedition ship |
| Author | Institution |
| HUANG De-yang |
Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration, State Key Laboratory of Ocean Engineering of Shanghai Jiao Tong University, Shanghai , China |
| CHEN Zi-qiang |
Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration, State Key Laboratory of Ocean Engineering of Shanghai Jiao Tong University, Shanghai , China |
| ZHENG Chang-wen |
Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration, State Key Laboratory of Ocean Engineering of Shanghai Jiao Tong University, Shanghai , China |
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| Abstract: |
| Objective Considering the significant change of battery characteristics at low temperature, to provide a theoretical basis for the application of large scale lithium ion battery packs in the hybrid power system of polar scientific expedition ships. Methods The low temperature characteristics of 10 Ah high power NCM lithium-ion battery were experimentally investigated. Combined with the experimental data, the state of charge (SOC) was estimated on line with a series observer, which was composed of recursive least squares algorithm with forgetting factor (FFRLS) and two improved kalman filtering algorithms (AEKF, UKF) respectively. Results The SOC estimated accuracy of FFRLS-AEKF algorithm was slightly higher than that of FFRLS-UKF algorithm under the improved DST condition of time-varying temperature environment within the temperature range of 25 ~?30 ℃, with the maximum estimated error of 3.04% and the root mean square error of 0.69%. Conclusion Compared with EKF and RLS-EKF algorithms, the better adaptability of model parameters and noise information makes FFRLS-AEKF algorithm have higher SOC estimated accuracy and convergence. |
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