A Comprehensive Review of Health Indicators of Li-ion Battery for Online State of Health Estimation

Liu, Wei; Xu, Yan

doi:10.1109/ei247390.2019.9062037

Cited by 11 publications

(2 citation statements)

References 49 publications

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“…Lithium-ion batteries (LiBs) are extensively used in various applications, including new energy vehicles and battery energy storage systems, due to their excellent energy efficiency, high power density, and prolonged self-discharge life [1]. The state of health (SOH) of LiBs is influenced by complex electrochemical reactions, resulting in internal irreversible changes [2]. Additionally, external factors such as ambient temperature, storage conditions, charge-discharge rate, and discharge depth can also impact the SOH [3,4].…”

Section: Introductionmentioning

confidence: 99%

Improved Deep Extreme Learning Machine for State of Health Estimation of Lithium-Ion Battery

Chen,

Meng,

Ming

et al. 2024

International Journal of Energy Research

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Prediction of state of health (SOH), a crucial aspect of battery management systems, necessitates accurate and reliable estimations for lithium-ion batteries. However, achieving high-precision SOH estimation using the deep extreme learning machine (DELM) in complex environments is challenging due to the instability caused by its random key parameters. To address this, we propose a novel approach that combines the improved bald eagle search (IBES) algorithm with DELM. By utilizing the IBES algorithm, we can extract highly relevant health indicators from the battery’s parameter curve during charging and discharging, as well as the incremental capacity curve. These indicators serve as inputs to the constructed estimation model, which predicts SOH under different working conditions. The proposed method has been validated using publicly available experimental data, with an overall error in SOH estimation of less than 1%. In comparison to the existing model, the proposed method achieves RMSE, MAE, and MAPE values lower than 0.35%, 0.26%, and 0.21%, respectively. These findings demonstrate that the proposed method excels in terms of both the speed and accuracy of SOH estimation, showcasing its enhanced robustness and reliability.

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Section: Introductionmentioning

confidence: 99%

Improved Deep Extreme Learning Machine for State of Health Estimation of Lithium-Ion Battery

Chen,

Meng,

Ming

et al. 2024

International Journal of Energy Research

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show abstract

“…To manage it, a battery management system (BMS) is required for state of health (SOH) estimation. SOH initially comes from SOC which cannot be measured directly so estimation is needed [9], [10], [11]. Accurate estimation will affect battery health and prevent battery from damage.…”

Section: Introductionmentioning

confidence: 99%

Direct Comparison using Coulomb Counting and Open Circuit Voltage Method for the State of Health Li-Po Battery

Amifia¹

2022

Journal of Robotics and Control

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Electric cars have undergone many developments in the current digital era. This is to avoid the use of increasingly scarce fuel. Recent studies on electric cars show that battery estimation is an interesting topic to be implemented directly. The battery estimation strategy is carried out by the Battery Management System (BMS). BMS is an indispensable part of electric vehicles or hybrid vehicles to ensure optimal and reliable operation of regulating, monitoring, and protecting batteries. A reliable BMS can extend battery life by setting voltage, temperature, and charging and discharging current limits. The main estimation strategy used by BMS is battery fault, SOH, and battery life. Battery State of Health (SOH) is part of the information provided by the BMS to avoid battery damage and failure. SOC is the proportion of battery capacity SOH is a measure of battery health. This study aims to develop a method for estimating SOH simultaneously using Coulomb Counting and Open Circuit Voltage (OCV) algorithms. The battery is modeled to obtain battery parameters and components of internal resistance, capacitance polarization and OCV voltage source. Several tests were implemented in this research by applying the constant current (CC)-charge CC-discharge test. The state-space system is then formed to apply the Coulomb Counting and OCV algorithms so that SOH can be estimated simultaneously. The OCV-SOC function is obtained in the form of a tenth order polynomial and the battery model parameters say that these parameters change with the health of the battery. The results of the model validation are able to accurately model the battery with an average relative error of 0.027%. Coulomb Counting resulted in an accurate SOH estimation with an error of 3.4%.

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State‐of‐health estimation of lithium‐ion batteries: A comprehensive literature review from cell to pack levels

Su,

Xu,

Dong

2024

Energy Conversion and Econom

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Lithium‐ion battery state‐of‐health (SOH) monitoring is essential for maintaining the safety and reliability of electric vehicles and efficiency of energy storage systems. When the SOH of lithium‐ion batteries reaches the end‐of‐life threshold, replacement and maintenance are required to avoid fire and explosion hazards. This paper provides a comprehensive literature review of lithium‐ion battery SOH estimation methods at the cell, module, and pack levels. Analysis and summary of the SOH definition based on the resistance, capacity, and energy indices are presented at each battery hierarchy level. A Comparison of SOH indices in terms of modelling complexity, required measurement time, and accuracy is provided. To the best of knowledge, a comprehensive classification of SOH estimation methods at different battery hierarchy levels is presented for the first time in this review. In addition, SOH estimation methods are further classified based on the applied methodologies, including direct measurement, model‐based methods, data‐driven methods, and hybrid model‐data methods. Advantages and disadvantages of SOH estimation methods are summarized and compared across different battery hierarchy levels. A detailed summary of typical SOH estimation methods is presented along with the battery topology, operating conditions, and performance. The challenges and research prospects of lithium‐ion battery SOH estimation are discussed from the cell to pack levels.

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A Comprehensive Review of Health Indicators of Li-ion Battery for Online State of Health Estimation

Cited by 11 publications

References 49 publications

Improved Deep Extreme Learning Machine for State of Health Estimation of Lithium-Ion Battery

Improved Deep Extreme Learning Machine for State of Health Estimation of Lithium-Ion Battery

Direct Comparison using Coulomb Counting and Open Circuit Voltage Method for the State of Health Li-Po Battery

State‐of‐health estimation of lithium‐ion batteries: A comprehensive literature review from cell to pack levels

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