Remaining Useful Life Prediction of Power Lithium-Ion Battery based on Artificial Neural Network Model

Abstract

Abstract. In order to improve the security and reliability of the power lithium batteries, this paper introduced forecast and health management technology of its core content-remaining useful life, established a power lithium battery remaining useful life prediction method, by collecting current, batteries, battery voltage, temperature, battery SOC and SOH etc data, artificial intelligence model based on neural network, training model parameters, the prediction power lithium battery remaining useful life, simulation results show the advances and reliability of this method.

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

“…Hence, the SOH estimation combined with the SOC prediction results greatly improved the accuracy of the estimation results. The authors of [100], directly adopted 10 groups of real vehicle road test data, and selected current, voltage, temperature, SOC and SOH parameters as the characteristic parameters of the neural network at the same time to predict the remaining service life, which improved the credibility of the real-time prediction ability under real vehicle operation. (1)(2)(3)(4)(5) and W 2 (1-3) are input and output weights.…”