State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges

S, Vignesh; Che, Hang Seng; Selvaraj, Jeyraj; Tey, Kok Soon; Lee, Jia Woon; Shareef, Hussain; Errouissi, Rachid

doi:10.1016/j.apenergy.2024.123542

Applied Energy

2024

DOI: 10.1016/j.apenergy.2024.123542

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State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges

Vignesh S,

Hang Seng Che,

Jeyraj Selvaraj

et al.

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2024

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Cited by 4 publications

References 147 publications

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State of Charge Estimation of Lithium‐Ion Batteries with Adaptive Square Root Central Difference Kalman Filter

Du,

Yuan,

Zheng

et al. 2024

Advcd Theory and Sims

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Lithium‐ion batteries have been a major energy source in electric vehicles because of their strong adaptability in operating conditions. Accurate estimation of state of charge (SOC) for lithium‐ion batteries can efficiently improve the efficiency of battery energy utilization. However, SOC estimation is complicated in operating conditions with unknown model parameters. This study proposes an adaptive square root central difference Kalman filter (ASRCDKF) algorithm based on the equivalent circuit model to achieve high‐precision estimation of SOC. First of all, to avoid an open‐circuit voltage test, a linear Kalman filter is constructed to realize real‐time estimation of unknown parameters in the measurement equation. Then, to improve the stability of the algorithm, a square root method is used to ensure a positive semi‐definite of the error covariance matrix that is based on the adaptive central difference Kalman filter algorithm. Model parameters are considered as the state to be estimated, and the joint estimation of the model parameters and SOC is realized by an ASRCDKF algorithm. After that, the linear Kalman filter is coupled with the ASRCDKF to realize the accurate estimation of SOC in the case of both the state equation and the measurement equation including unknown parameters. Last, the ASRCDKF algorithm is compared with the adaptive central difference Kalman filter algorithm and the adaptive cubature Kalman filter algorithm under two sets of operating conditions. The results show that the SOC estimation of the ASRCDKF algorithm is more significantly accurate than other algorithms under different operating conditions.

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State of Charge Estimation of Lithium‐Ion Batteries with Adaptive Square Root Central Difference Kalman Filter

Du,

Yuan,

Zheng

et al. 2024

Advcd Theory and Sims

View full text Add to dashboard Cite

show abstract

SOH early prediction of lithium-ion batteries based on voltage interval selection and features fusion

Peng,

Zhu,

et al. 2024

Energy

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A Method for Estimating the SOH of Lithium-Ion Batteries Based on Graph Perceptual Neural Network

Chen,

Wang,

Guo

2024

Batteries

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The accurate estimation of battery state of health (SOH) is critical for ensuring the safety and reliability of devices. Considering the variation in health degradation across different types of lithium-ion battery materials, this paper proposes an SOH estimation method based on a graph perceptual neural network, designed to adapt to multiple battery materials. This method adapts to various battery materials by extracting crucial features from current, voltage, voltage–capacity, and temperature data, and it constructs a graph structure to encapsulate these features. This approach effectively captures the complex interactions and dependencies among different battery types. The novel technique of randomly removing features addresses feature redundancy. Initially, a mutual information graph structure is defined to illustrate the interdependencies among battery features. Moreover, a graph perceptual self-attention mechanism is implemented, integrating the adjacency matrix and edge features into the self-attention calculations. This enhancement aids the model’s understanding of battery behaviors, thereby improving the transparency and interpretability of predictions. The experimental results demonstrate that this method outperforms traditional models in both accuracy and generalizability across various battery types, particularly those with significant chemical and degradation discrepancies. The model achieves a minimum mean absolute error of 0.357, a root mean square error of 0.560, and a maximum error of 0.941.

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State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges

Cited by 4 publications

References 147 publications

State of Charge Estimation of Lithium‐Ion Batteries with Adaptive Square Root Central Difference Kalman Filter

State of Charge Estimation of Lithium‐Ion Batteries with Adaptive Square Root Central Difference Kalman Filter

SOH early prediction of lithium-ion batteries based on voltage interval selection and features fusion

A Method for Estimating the SOH of Lithium-Ion Batteries Based on Graph Perceptual Neural Network

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