A fast estimation algorithm for lithium-ion battery state of health

By Xiaopeng Tang, Changfu Zou, K. Yao, Guohua Chen, Boyang Liu, Zhenwei He, Furong Gao. Journal of Power Sources, 2018

301 citations recorded by the source corpus.

Abstract This paper proposes a novel and computationally efficient estimation algorithm for lithium-ion battery state of health (SoH) under the hood of incremental capacity analysis. Concepts of regional capacity and regional voltage are introduced to develop an SoH model against experimental cycling data from four types of batteries. In the obtained models, SoH is a simple linear function of the regional capacity, and the R-square of linear fitting is up to 0.948 for all the considered batteries with properly selected regional voltage. The proposed method without using characteristic parameters directly from incremental capacity curves is insensitive to noise and filtering algorithms, and is effective for common current rates, where rates of up to 1C have been demonstrated. Then, a model-based SoH estimator is designed and shown to be capable of closely matching battery's aging data from NASA, with the error less than 2.5%. Furthermore, such a small scale of error is achieved in the absent of state of charge and impedance which are often used for SOH estimation in available methods.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/68ba1d9483afeca291dffeea408a1ae7c12da052

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Citations in the corpus

A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery (2021, methodology): “Detailed steps of whole prediction procedure are illustrated as follows: Step 1: Preprocessing data and models: for data preprocessing before any training process, a simple but efficient normalization method [32] is utilized to convert the raw capacity data Cb to a normalized scale C ′ b by computin”
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, background): “peak location [53], height [54] and integrated peak area [57].”
State-of-Health Estimation Based on Differential Temperature for Lithium Ion Batteries (2020, methodology): “The Kalman filter listed in Table I is implemented to further smooth the obtained DT curves [18].”
Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, methodology): “Features, such as IC peak value (ICP) and IC valley value (ICV) [27], IC peak location (ICPL) and IC valley location (ICVL) [37], and IC peak area (ICPA) [43] are widely used as HIs.”
SOH Estimation and SOC Recalibration of Lithium-Ion Battery with Incremental Capacity Analysis & Cubic Smoothing Spline (2020, background): “Tang et al.18 used regional capacity, which is equivalent to the area of peak with self-specified voltage range in IC curve, to estimate the SOH while the regional capacity and SOH show linear relationship.”