Remaining discharge energy estimation of lithium-ion batteries based on average working condition prediction and multi-parameter updating

The remaining discharge energy (RDE) estimation of lithium-ion batteries heavily depends on the battery's future working conditions. However, the traditional time series-based method for predicting future working conditions is too burdensome to be applied online. In this study, an RDE estimation method based on average working condition prediction and multi-parameter updating is proposed. First, the ohmic resistance of batteries is identified online, the temperature-aging factor is introduced against battery aging and temperature changes, and the OCV-SOC is estimated by curve scaling. Then, the future working conditions of the battery are predicted based on the average working condition prediction with less calculation. Finally, the RDE is estimated under complex working conditions. The experimental results show that the RDE estimation error of the battery is less than 3% during battery aging and temperature changes under complex working conditions. Moreover, in addition, the computational burden of the proposed method is only 1% of that of traditional methods, making it very suitable for online applications.

Automated summary

In this study, a method for estimating the remaining discharge energy (RDE) of lithium-ion batteries based on average working condition prediction and multi-parameter updating is proposed. Online identification of battery's ohmic resistance, introduction of temperature-aging factor, and estimation of OCV-SOC by curve scaling are performed. Future working conditions are predicted based on average working condition prediction with less calculation, and the RDE is then estimated under complex working conditions. Experimental results show that the RDE estimation error of the battery is less than 3% during battery aging and temperature changes under complex working conditions. Moreover, the proposed method has only 1% of the computational burden of traditional methods, making it suitable for online applications.