A data-driven approach for estimating state-of-health of lithium-ion batteries considering internal resistance

Lin, Mingqiang; Yan, Chenhao; Wang, Wei; Dong, Guangzhong; Meng, Jinhao; Wu, J.

doi:10.1016/j.energy.2023.127675

Cited by 42 publications

(9 citation statements)

References 21 publications

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“…Data-driven methods do not consider the electrochemical reaction mechanism within the battery but instead use machine learning techniques to create a nonlinear mapping connection between the charging characteristics of LIB and its charging state [56]. Data mining techniques are used to analyze and extract lithium battery characteristics, train and establish models utilizing the patterns exhibited by large amounts of test data, and then use online monitoring data parameters to input the models in order to predict the SOC of the lithium battery efficiently.…”

Section: Data-driven Approachmentioning

confidence: 99%

Advances in the Study of Techniques to Determine the Lithium-Ion Battery’s State of Charge

Liu,

Gao,

Marma

et al. 2024

Energies

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This study explores the challenges and advances in the estimation of the state of charge (SOC) of lithium-ion batteries (LIBs), which are crucial to optimizing their performance and lifespan. This review focuses on four main techniques of SOC estimation: experimental measurement, modeling approach, data-driven approach, and joint estimation approach, highlighting the limitations and potential inaccuracies of each method. This study suggests a combined approach, incorporating correction parameters and closed-loop feedback, to improve measurement accuracy. It introduces a multi-physics model that considers temperature, charging rate, and aging effects and proposes the integration of models and algorithms for optimal estimation of SOC. This research emphasizes the importance of considering temperature and aging factors in data-driven approaches. It suggests that the fusion of different methods could lead to more accurate SOC predictions, an important area for future research.

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Section: Data-driven Approachmentioning

confidence: 99%

Advances in the Study of Techniques to Determine the Lithium-Ion Battery’s State of Charge

Liu,

Gao,

Marma

et al. 2024

Energies

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“…The data-driven method only needs to extract features using physical quantities measured during battery charging and discharging, and then uses these features to train a model to establish a mapping model between battery data features and the SOC. Reference [7] proposed a low-dimensional classification model based on machine learning and an equivalent circuit model, which can estimate the SOC with an accuracy of more than 93%. Reference [8] used 18 machine learning algorithms to predict the SOC and applied different filters to improve the estimator.…”

Section: Introductionmentioning

confidence: 99%

Joint Estimation of State of Charge and State of Health of Lithium-Ion Batteries Based on Stacking Machine Learning Algorithm

Dong,

Chen,

Zhang

et al. 2024

WEVJ

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Conducting online estimation studies of the SOH of lithium-ion batteries is indispensable for extending the cycle life of energy storage batteries. Data-driven methods are efficient, accurate, and do not depend on accurate battery models, which is an important direction for battery state estimation research. However, the relationships between variables in lithium-ion battery datasets are mostly nonlinear, and a single data-driven algorithm is susceptible to a weak generalization ability affected by the dataset itself. Meanwhile, most of the related studies on battery health estimation are offline estimation, and the inability for online estimation is also a problem to be solved. In this study, an integrated learning method based on a stacking algorithm is proposed. In this study, the end voltage and discharge temperature were selected as the characteristics based on the sample data of NASA batteries, and the B0005 battery was used as the training set. After training on the dataset and parameter optimization using a Bayesian algorithm, the trained model was used to predict the SOH of B0007 and B0018 models. After comparative analysis, it was found that the prediction results obtained based on the proposed model not only have high accuracy and a short running time, but also have a strong generalization ability, which has a great potential to achieve online estimation.

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“…The data-driven models have high accuracy and efficiency, but its generalizability depends on the extracted features and have poor stability 14 , 33 . For instance, due to the high usage variability, existing methods 30 , 34 , 35 need to extract specific features for different datasets or different working conditions, leading to the fact that models are dataset-specific, resulting in a waste of computing resources. The promising prospect of physics-informed neural network (PINN) 36 , 37 lies in amalgamating the strengths of physics-based and data-driven approaches, potentially addressing the aforementioned challenges.…”

Section: Introductionmentioning

confidence: 99%

Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis

Wang,

Zhai,

Zhao

et al. 2024

Nat Commun

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Accurate state-of-health (SOH) estimation is critical for reliable and safe operation of lithium-ion batteries. However, reliable and stable battery SOH estimation remains challenging due to diverse battery types and operating conditions. In this paper, we propose a physics-informed neural network (PINN) for accurate and stable estimation of battery SOH. Specifically, we model the attributes that affect the battery degradation from the perspective of empirical degradation and state space equations, and utilize neural networks to capture battery degradation dynamics. A general feature extraction method is designed to extract statistical features from a short period of data before the battery is fully charged, enabling our method applicable to different battery types and charge/discharge protocols. Additionally, we generate a comprehensive dataset consisting of 55 lithium-nickel-cobalt-manganese-oxide (NCM) batteries. Combined with three other datasets from different manufacturers, we use a total of 387 batteries with 310,705 samples to validate our method. The mean absolute percentage error (MAPE) is 0.87%. Our proposed PINN has demonstrated remarkable performance in regular experiments, small sample experiments, and transfer experiments when compared to alternative neural networks. This study highlights the promise of physics-informed machine learning for battery degradation modeling and SOH estimation.

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A data-driven approach for estimating state-of-health of lithium-ion batteries considering internal resistance

Cited by 42 publications

References 21 publications

Advances in the Study of Techniques to Determine the Lithium-Ion Battery’s State of Charge

Advances in the Study of Techniques to Determine the Lithium-Ion Battery’s State of Charge

Joint Estimation of State of Charge and State of Health of Lithium-Ion Batteries Based on Stacking Machine Learning Algorithm

Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis

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