基于电动汽车工况识别预测的锂离子电池SOE估计

doi:10.19595/j.cnki.1000-6753.tces.161325

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Abstract State-of-energy (SOE) is an important index of the internal state of electric vehicle traction batteries that determines the range of electric vehicles directly and which is influenced by the driving condition significantly. In order to estimate SOE based on the driving condition, the SOE estimation algorithm, driving condition identification algorithm, driving condition prediction algorithm were studied in this paper. A battery state of residual energy (SOR) estimation algorithm based on battery model was established. A driving condition identification algorithm based on the informational entropy theory was built. A driving condition prediction algorithm was proposed with Markov chain theory. The battery predicted working condition schedule was achieved by modeling the electric vehicle system. In the end, the SOE estimation algorithm based on the identification and prediction of driving condition was achieved. Validation results show that the proposed SOE estimation algorithm was efficient.

Key words： Lithium-ion battery SOE estimation identification algorithm prediction algorithm electric vehicle model

Received: 24 August 2016 Published: 16 January 2018

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	Liu Weilong
	Wang Lifang
	Wang Liye

Cite this article:

Liu Weilong,Wang Lifang,Wang Liye. Estimation of State-of-Energy for Electric Vehicles Based on the Identification and Prediction of Driving Condition[J]. Transactions of China Electrotechnical Society, 2018, 33(1): 17-25.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.161325 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I1/17

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