The State of Health Estimation Framework for Lithium-Ion Batteries Based on Health Feature Extraction and Construction of Mixed Model

Abstract

Accurate estimation of the state of health (SOH) is one of the most dominant contents for prognostics and health management of a lithium battery system. However, it has not been well solved owing to its internal electrochemical reactions and complex nonlinear system. Here, an SOH estimation framework for lithium-ion batteries depending on health features (HF) extraction and the construction of the mixed model is proposed. The overall trend and local dynamic changes of capacity degradation was simultaneously considered in the mixed model. First, the double exponential empirical model describing the overall degradation trend of capacity was established from historical data and the error between its output and the SOH reference value was calculated. Then, with the constructed HF as the input and error as the output, the improved gaussian process regression (IGPR) model describing the local dynamic changes of capacity degradation was established. Finally, the output of the mixed model was obtained by feeding back the output of the IGPR model to the result of the empirical model. The proposed framework has been verified on two different data sets. With the result of 5% relative percentage error, the proposed framework shows high accuracy and robustness in a lithium battery system.

A Polak-Ribière-Polyak Conjugate Gradient Algorithm Optimized Broad Learning System for Lithium-ion Battery State of Health Estimation

“…This method needs to measure the ratio of voltage to current of a single cell, and record the change of AC frequency signal with small amplitude, which is difficult to be applied in practice. 15 2) The model-based methods start from the models established by investigating the internal physicochemical mechanism and the external dynamic characteristics of batteries, then realize the final estimation by combining with other techniques, such as the filter and observer. 16,17 The conventional utilized models can be divided as the electrochemical model (EM) and the equivalent circuit model (ECM).…”

A chaotic firefly - Particle filtering method of dynamic migration modeling for the state-of-charge and state-of-health co-estimation of a lithium-ion battery performance

“…The research on its estimation methods is mainly based on models and data-driven. In [40], the gaussian process regression model which can dynamically describe the degradation characteristics of the battery was proposed. However, it is a single state estimation method, which can only estimate SOH effectively.…”

Fusion estimation strategy based on dual adaptive Kalman filtering algorithm for the state of charge and state of health of hybrid electric vehicle Li‐ion batteries

“…This means is simple to implement, but it cannot realize real-time estimation of SOC, and the impact of noise is not considered, hence the fitting of mathematical relations is prone to large errors. 17 The ampere-hour integration method refers to the calculation of battery electronic quantity variation by integrating the current under the time domain, 18 and then the estimation is realized according to the definition of SOC. This method can realize real-time estimation of SOC.…”

“…The ampere‐hour integration method refers to the calculation of battery electronic quantity variation by integrating the current under the time domain, 18 and then the estimation is realized according to the definition of SOC. This method can realize real‐time estimation of SOC 19 .…”