Improving the state of charge estimation of reused lithium-ion batteries by abating hysteresis using machine learning technique
| Autor: | Qi Fan, Jie Hong, Zhicheng Xu, Jun Wang, Peter Lund |
|---|---|
| Přispěvatelé: | Southeast University, Nanjing, New Energy Technologies, Jiangsu New Energy Development Co. Ltd., Department of Applied Physics, Aalto-yliopisto, Aalto University |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Mean squared error
Computer science 020209 energy Energy Engineering and Power Technology chemistry.chemical_element Battery 02 engineering and technology Machine learning computer.software_genre 0202 electrical engineering electronic engineering information engineering Range (statistics) Electrical and Electronic Engineering Hysteresis phenomenon Equivalent circuit model Renewable Energy Sustainability and the Environment business.industry State of charge Kalman filter Sigma-point Kalman filter 021001 nanoscience & nanotechnology Battery pack Hysteresis chemistry Equivalent circuit Lithium Artificial intelligence 0210 nano-technology business computer |
| Popis: | The accuracy of the state of charge (SoC) estimation is of great importance to the operational safety of a battery pack, especially for secondary applications with retired batteries. Here, a novel approach combining Sigma-point Kalman filter and machine learning technique based on an equivalent circuit model is proposed to improve the state of charge estimation accuracy of a reused battery pack (LiFePO4) by abating the negative effect of the hysteresis phenomenon. Compared to traditional estimation methods, this approach can reduce the root mean square error by up to 8.3%. The maximum estimation error for three experimental tests is only 0.016 being within acceptable range and demonstrating the effectiveness of the proposed approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|