State of Health Prediction of Lithium-Ion Batteries Based on the Discharge Voltage and Temperature

Yanru Yang,Jie Wen,Yuanhao Shi,Jianchao Zeng

doi:10.3390/electronics10121497

Yanru Yang, Jie Wen + Show 2 more

Open Access

https://doi.org/10.3390/electronics10121497

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Journal: Electronics	Publication Date: Jun 21, 2021
Citations: 20	License type: CC BY 4.0

Affiliation: North University of China

Abstract

Accurate state of health (SOH) prediction of lithium-ion batteries is essential for battery health management. In this paper, a novel method of predicting the SOH of lithium-ion batteries based on the voltage and temperature in the discharging process is proposed to achieve the accurate prediction. Both the equal voltage discharge time and the temperature change during the discharge process are regarded as health indicators (HIs), and then, the Pearson and Spearman relational analysis methods are applied to evaluate the relevance between HIs and SOH. On this basis, we modify the relevance vector machine (RVM) to a multiple kernel relevance vector machine (MKRVM) by combining Gaussian with sigmoid function to improve the accuracy of SOH prediction. The particle swarm optimization (PSO) is used to find the optimal weight and kernel function parameters of MKRVM. The aging data from NASA Ames Prognostics Center of Excellence are used to verify the effectiveness and accuracy of the proposed method in numerical simulations, whose results show that the MKRVM method has higher SOH prediction accuracy of lithium-ion batteries than the relevant methods.

Highlights

Lithium-ion batteries have been widely used in aviation [1], electronic technology, and other fields [2] due to the high energy density, long service life, and low self-discharge rate
We propose a lithium-ion battery state of health (SOH) prediction method that considers the partial data of voltage and temperature during the discharge process
To verify the effectiveness of the multiple kernel relevance vector machine (MKRVM) method for the SOH prediction of batte of the proposed method are close to the true value, which can capture the local fluctuations at different temperatures, batteries

Summary

Introduction

Lithium-ion batteries have been widely used in aviation [1], electronic technology, and other fields [2] due to the high energy density, long service life, and low self-discharge rate. Because capacity measurement is time-consuming and requires expensive instruments [20] and obtaining real-time capacity is a challenging technology, the data-driven indirect prediction method has been widely used in the SOH prediction of lithium-ion batteries. The indirect extraction of useful information from the voltage, current, and temperature changes of lithium-ion batteries can reflect capacity degradation to achieve SOH prediction. Dong et al [30] proposed SVR and particle filter (PF) for the SOH monitoring and RUL prediction, and the models for battery SOH monitoring based on SVR-PF are developed with novel capacity degradation parameters introduced to determine battery health in real time. We propose a lithium-ion battery SOH prediction method that considers the partial data of voltage and temperature during the discharge process.

The Definition of SOH

Experiment Data

Feature

Methodologies

SOH Prediction Based on the PSO-MKRVM

Evaluation Indicators

Numerical Experiments and Results Analysis

SOH Prediction at Same Starting Points for Different HIs

SOH Prediction at Same Starting Points for Different Models

SOH Prediction at Different Starting Points for the Same Model

Conclusions