一种混合动力电动汽车电池荷电状态

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Abstract In order to predict the battery’s state of charge (SOC) in the hybrid electric vehicles (HEV),the Bayesian extreme learning machine (BELM) is utilized.The basic principles of the extreme learning machine and the Bayesian linear regression are introduced in detail.To improve the abilities of fitting and generalization of the ELM,the Bayesian linear regression is used to optimize the weights of the output layer.The working voltage,the current,and the surface temperature of the battery are chosen to predict the real-time value of SOC under the driving cycle.At the same time,the energy feedback process is taken into account when the HEV is under regenerative braking model.Both the simulation results under ADVISOR and the experimental results indicate that the proposed prediction model has higher predicted accuracy and can achieve real-time and accurate SOC prediction.

Key words： Bayesian extreme learning machine hybrid electric vehicles state of charge

Received: 17 June 2015 Published: 07 July 2016

PACS:

TM912

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	Articles by authors
	Wang Qi
	Sun Yukun
	Ni Fuyin
	Luo Yinsheng

Cite this article:

Wang Qi,Sun Yukun,Ni Fuyin等. A New Method of Battery State of Charge Prediction in the Hybrid Electric Vehicle[J]. Transactions of China Electrotechnical Society, 2016, 31(9): 189-196.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2016/V31/I9/189

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