State of health prediction for lithium‐ion batteries with a novel online sequential extreme learning machine method

Abstract

Summary State of health (SOH) prediction is always a research hotspot in the field of lithium‐ion batteries (LIBs). Machine learning (ML) methods have received widespread attention for their high prediction accuracy. However, the existing studies only focus on extracting features from simple constant current charge‐discharge curves or using features that require pre‐processing, while the actual discharge current is random and can affect battery aging. Besides, the online sequential extreme learning machine (OSELM) currently used in ML lacks a more efficient online learning and update mechanism in terms of prediction. Therefore, this paper firstly extracts effective features from the random discharge data and conducts a mechanism analysis to verify its rationality. Then, we propose a drift detection based on the Bernstein inequality (BI‐DD) algorithm and use it to guide the OSELM to save learning time. The experimental results show the OSELM based on the BI‐DD can perform good learning for SOH prediction in a shorter time. The learning time can be reduced by up to 88.87% and the mean absolute error (MAE) does not exceed 1%, which is a promising SOH prediction method. Highlights Extract aging features from random discharge data and the rationality of the extracted features is analyzed according to the mechanism. A drift detection algorithm based on Bernstein inequality (BI‐DD) is proposed. An OSELM based on concept drift detection is proposed and for SOH online learning and prediction.

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

“…proposes a drift detection method based on the Bernstein inequality algorithm to guide the OSELM and save the learning time. The experimental results show that the improved OSELM can reduce the learning time by up to 88.87%, and the mean absolute error (MAE) can be limited within 1% ( Tian and Qin, 2021 ).…”

A Novel Autoregressive Rainflow—Integrated Moving Average Modeling Method for the Accurate State of Health Prediction of Lithium-Ion Batteries

“…Therefore, the significance test of the model is the white noise test of the residual sequence. The original hypothesis, alternative hypothesis, and Ljung-Box (LB) statistics are shown in Equation (10).…”

“…The estimation methods of battery SOH include empirical-based methods [1][2][3][4], modelbased methods [5,6], and data-driven methods [7][8][9][10]. Empirical-based methods include the cycle number method, ampere-hour method, weighted ampere-hour method, and event-oriented aging accumulation method.…”

Improved sparrow search algorithm optimization deep extreme learning machine for lithium-ion battery state-of-health prediction

“…Tian et al. ( Tian and Qin, 2021 ) extracted the discharge time, the voltage upward appreciation after discharge, and the variance of discharge voltage curve from random discharge data to predict the SOH of LIBs. Venugopal et al.…”