A prediction model based on artificial neural network for surface temperature simulation of nickel-metal hydride battery during charging
- 作者：Kaizheng Fang ; Daobin Mu ; mudb@bit.edu.cn ; Shi Chen ; Borong Wu ; Feng Wu
- 关键词：Prediction model ; Back-propagation network ; Battery surface temperature ; Charging rate ; Ambient temperature
- 刊名：Journal of Power Sources
- 出版年：2012
- 期刊代码：58_03787753
- 类别：ce
- 出版时间：15 June, 2012
- 卷：208
- 期：Complete
- 页码：378-382
- 文件大小：479 K
摘要
In this study, a prediction model based on artificial neural network is constructed for surface temperature simulation of nickel-metal hydride battery. The model is developed from a back-propagation network which is trained by Levenberg-Marquardt algorithm. Under each ambient temperature of 10 掳C, 20 掳C, 30 掳C and 40 掳C, an 8 Ah cylindrical Ni-MH battery is charged in the rate of 1 C, 3 C and 5 C to its SOC of 110%in order to provide data for the model training. Linear regression method is adopted to check the quality of the model training, as well as mean square error and absolute error. It is shown that the constructed model is of excellent training quality for the guarantee of prediction accuracy. The surface temperature of battery during charging is predicted under various ambient temperatures of 50 掳C, 60 掳C, 70 掳C by the model. The results are validated in good agreement with experimental data. The value of battery surface temperature is calculated to exceed 90 掳C under the ambient temperature of 60 掳C if it is overcharged in 5 C, which might cause battery safety issues.