An Online Prediction of Capacity and Remaining Useful Life of Lithium-Ion Batteries Based on Simultaneous Input and State Estimation Algorithm

For lithium-ion batteries used in the electric vehicles, accurate prediction of capacity and remaining useful life online is extremely important. However, most of the research works focus on the prediction accuracy but neglect the complexity of the test environment, which makes many methods show poor robustness in application. To solve the problem, in this article, we first introduce the simultaneous input and state estimation algorithm into the online prediction of state-of-charge and capacity, and combine the Gauss-Hermite extended particle filter to predict the remaining useful life. By setting different gradients of state noises in experiments, the proposed algorithm demonstrates the best accuracy and robustness in comparison with other algorithms. Through the two-factor authentication in simulations, the maximum error of capacity estimation is 35 mAh. For the prediction of remaining useful life, the minimum relative error of the proposed method is 0.4%. Therefore, the high accuracy and strong robustness of the proposed algorithm are verified.

Automated summary

This article focuses on the online prediction of capacity and remaining useful life for lithium-ion batteries used in electric vehicles. An algorithm combining simultaneous input and state estimation with the Gauss-Hermite extended particle filter is proposed, showing high accuracy and robustness compared to other methods. The method demonstrates a maximum error of 35 mAh for capacity estimation and a minimum relative error of 0.4% for the prediction of remaining useful life, confirming its high accuracy and strong robustness.