A new state-of-charge estimation method for electric vehicle lithium-ion batteries based on multiple input parameter fitting model
| Autor: | Liu Xintian, Zheng Xinxin, Zeng Guojian, He Yao, Jiangfeng Zhang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Battery (electricity)
Engineering business.product_category Renewable Energy Sustainability and the Environment business.industry State of health 020209 energy Energy Engineering and Power Technology 02 engineering and technology Internal resistance 021001 nanoscience & nanotechnology Extended Kalman filter Fuel Technology State of charge Nuclear Energy and Engineering Control theory Electric vehicle 0202 electrical engineering electronic engineering information engineering Electronic engineering Curve fitting 0210 nano-technology business Voltage |
| Zdroj: | International Journal of Energy Research. 41:1265-1276 |
| ISSN: | 0363-907X |
| DOI: | 10.1002/er.3705 |
| Popis: | Summary The estimation of state-of-charge (SOC) is crucial to determine the remaining capacity of the Lithium-Ion battery, and thus plays an important role in many electric vehicle control and energy storage management problems. The accuracy of the estimated SOC depends mostly on the accuracy of the battery model, which is mainly affected by factors like temperature, State of Health (SOH), and chemical reactions. Also many characteristic parameters of the battery cell, such as the output voltage, the internal resistance and so on, have close relations with SOC. Battery models are often identified by a large amount of experiments under different SOCs and temperatures. To resolve this difficulty and also improve modeling accuracy, a multiple input parameter fitting model of the Lithium-Ion battery and the factors that would affect the accuracy of the battery model are derived from the Nernst equation in this paper. Statistics theory is applied to obtain a more accurate battery model while using less measurement data. The relevant parameters can be calculated by data fitting through measurement on factors like continuously changing temperatures. From the obtained battery model, Extended Kalman Filter algorithm is applied to estimate the SOC. Finally, simulation and experimental results are given to illustrate the advantage of the proposed SOC estimation method. It is found that the proposed SOC estimation method always satisfies the precision requirement in the relevant Standards under different environmental temperatures. Particularly, the SOC estimation accuracy can be improved by 14% under low temperatures below 0 °C compared with existing methods. Copyright © 2017 John Wiley & Sons, Ltd. |
| Databáze: | OpenAIRE |
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