Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation

Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This study introduces a novel metabolic extreme learning machine (MELM) framework for accurate estimation of battery state-of-health (SOH) across different types of batteries with unknown usage levels. The framework utilizes a degradation state model based on extreme learning machine (ELM) to map the relationship between degradation features and dynamics. By incorporating a metabolic mechanism and grey model, the MELM framework effectively reduces errors and achieves reliable SOH estimation.