Explainability-driven model improvement for SOH estimation of lithium-ion battery

Wang, Fujin; Zhao, Zhibin; Zhai, Zhi; Shang, Zuogang; Yan, Ruqiang; Chen, Xuefeng

doi:10.1016/j.ress.2022.109046

Cited by 55 publications

(14 citation statements)

References 44 publications

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“…Reference [55] utilized an improved bidirectional gated recurrent unit method with an attention mechanism (BiGRU-AM) for SOH prediction. The comparative analysis reveals that the estimation method employed by the IBES-DELM model outperforms others when applied to the CALCE dataset, with lower RMSE, MAE, and MAPE values in comparison to Reference [54,55]. For instance, the IBES-DELM model reduces the RMSE, MAE, and MAPE values of the CS2-37 battery by 1.74%, 1.24%, and 2.0%, respectively, in comparison to Reference [55].…”

Section: Model Performance Analysismentioning

confidence: 92%

“…Table 4 illustrates the comparison results of various methods using the CALCE dataset. In Reference [54], a layer-wise relevance propagation-driven (LRP-driven) CNN model was proposed for SOH estimation. Reference [55] utilized an improved bidirectional gated recurrent unit method with an attention mechanism (BiGRU-AM) for SOH prediction.…”

Section: Model Performance Analysismentioning

confidence: 99%

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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: Model Performance Analysismentioning

confidence: 92%

Section: Model Performance Analysismentioning

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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“…A direct consequence is that it is almost impossible to understand why a deep learning model predicts a certain outcome and whether this prediction is reasonable and complies with physics or domain knowledge. Although efforts have been made to achieve varying degrees of interpretability mostly through post-processing 80 , deep learning models are still harder to interpret than simpler traditional ML models, some of which are inherently interpretable 77 .…”

Section: Standard ML Pipelinementioning

confidence: 99%

Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

Thelen,

Huan,

Paulson

et al. 2024

npj Mater. Sustain.

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Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.

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“…Model-based methods are commonly employed to develop battery life degradation models that are rooted in electrochemical mechanisms, enabling more accurate representation of battery's electrochemical characteristics. Nevertheless, the utilization of these methods is often restricted due to the demand for specialized expertise and battery design parameters, impeding their broader applicability [8].…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Mode Decomposition and Time Series

Wang,

Ye,

et al. 2023

Sustainability

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Accurately predicting the remaining useful life (RUL) of lithium-ion batteries holds significant importance for their health management. Due to the capacity regeneration phenomenon and random interference during the operation of lithium-ion batteries, a single model may exhibit poor prediction accuracy and generalization performance under a single scale signal. This paper proposes a method for predicting the RUL of lithium-ion batteries. The method is based on the improved sparrow search algorithm (ISSA), which optimizes the variational mode decomposition (VMD) and long- and short-term time-series network (LSTNet). First, this study utilized the ISSA-optimized VMD method to decompose the capacity degradation sequence of lithium-ion batteries, acquiring global degradation trend components and local capacity recovery components, then the ISSA–LSTNet–Attention model and ISSA–LSTNet–Skip model were employed to predict the trend component and capacity recovery component, respectively. Finally, the prediction results of these different models were integrated to accurately estimate the RUL of lithium-ion batteries. The proposed model was tested on two public lithium-ion battery datasets; the results indicate a root mean square error (RMSE) under 2%, a mean absolute error (MAE) under 1.5%, and an absolute correlation coefficient (R2) and Nash–Sutcliffe efficiency index (NSE) both above 92.9%, implying high prediction accuracy and superior performance compared to other models. Moreover, the model significantly reduces the complexity of the series.

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Explainability-driven model improvement for SOH estimation of lithium-ion battery

Cited by 55 publications

References 44 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

Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Mode Decomposition and Time Series

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