<scp>State‐of‐health</scp>estimation for the lithium‐ion battery based on gradient boosting decision tree with autonomous selection of excellent features

Zhang, Zhiqi; Li, Li; Li, Xi; Hu, Yuping; Huang, Kai; Xue, Bingya; Wang, Yuqi; Yu, Yajuan

doi:10.1002/er.7292

Cited by 29 publications

(5 citation statements)

References 23 publications

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“…23,24 Benefiting from the rapid development of the machine learning (ML) technology, scholars can readily achieve SOH estimation by employing the support vector machine (SVM), the Gaussian process regression (GPS), the decision tree (DT), the neural network (NN), etc. [25][26][27][28] Among them, owing to the strong learning ability and scalability, various NN-based methods obtain a slew of attentions. 29,30 After feature selection and extraction, the NN-based methods can realize efficient estimation by inferring the relationship between the input and output.…”

Section: The Soh Estimation Methodsmentioning

confidence: 99%

A Polak-Ribière-Polyak Conjugate Gradient Algorithm Optimized Broad Learning System for Lithium-ion Battery State of Health Estimation

Gu¹,

Wang²,

Li³

2022

J. Electrochem. Soc.

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Accurate state of health (SOH) estimation plays a significant role in battery management systems (BMS). This paper investigates a Polak-Ribière-Polyak conjugate gradient (PRPCG) algorithm optimized broad learning system (BLS) for lithium-ion battery (LIB) SOH estimation. First, effective health indicators (HIs) are extracted from the voltage curve in the constant current charge process. Second, a hybrid four layers BLS structure with mapped feature nodes and enhancement nodes connecting to the output is established to build both the linear and nonlinear relationships between the HIs and SOH, in which only the output weights require to be trained. Again, the PRPCG algorithm is adopted for searching optimal output weights without matrix inverse calculation during the training process. Furthermore, certain Gaussian noises are added to enhance the training data for solving the locally low accuracy problem. Finally, under the Oxford battery degradation data set, experiments validate the investigated algorithm has high accuracy in SOH estimation with the mean absolute error (MAE) below 1%. The enhanced data can efficiently improve the model generalization ability.

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Section: The Soh Estimation Methodsmentioning

confidence: 99%

A Polak-Ribière-Polyak Conjugate Gradient Algorithm Optimized Broad Learning System for Lithium-ion Battery State of Health Estimation

Gu¹,

Wang²,

Li³

2022

J. Electrochem. Soc.

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“…It can classify and predict data, and it possesses advantages such as high accuracy, low computational cost, and high interpretability [17]. Based on the NASA LIB charge-discharge experimental data, Zhang et al [18] proposed a model based on the gradient-boosting decision trees model framework to achieve evaluation of LIB SOH; Salinas et al [19] predicted the SOH of an aging battery based on an enhanced decision trees model and analyzed its influencing factors; Wang et al [20] used DTs to analyze the adaptability of battery to working condition, temperature and degradation, and found that the dynamic characteristics of battery changed significantly during aging.…”

Section: Introductionmentioning

confidence: 99%

Lithium-Ion Battery State-of-Health Prediction for New-Energy Electric Vehicles Based on Random Forest Improved Model

Liang,

Wang,

Zhan

et al. 2023

Applied Sciences

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The lithium-ion battery (LIB) has become the primary power source for new-energy electric vehicles, and accurately predicting the state-of-health (SOH) of LIBs is of crucial significance for ensuring the stable operation of electric vehicles and the sustainable development of green transportation. We collected multiple sets of charge–discharge cycle experimental data for LiFePO4 LIB and employed several traditional machine learning models to predict the SOH of LIBs. It was found that the RF model yielded relatively superior predictive results, confirming the feasibility of applying the RF model to SOH prediction for the electric vehicle LIB. Building upon this foundation, further research was conducted on the RF improved model for LIB SOH prediction. The PSO algorithm was employed to adaptively optimize five major parameters of the RF model: max_depth, n_estimators, max_features, min_samples_split, and min_samples_leaf. This adaptation addresses the issue of prediction errors that stem from human experience to optimize parameters in the RF model. The results indicate that the RF improved model proposed in this paper can further improve the prediction accuracy of LIB SOH. Its model evaluation index also outperform others, demonstrating the effectiveness of this approach in the management of LIB SOH for new-energy electric vehicles. This contributes significantly to urban environmental protection and the development of green transportation.

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“…1 The high-precision battery-state estimation method is needed to develop a more efficient BMS. 2 Battery states include the state of charge (SOC) 3,4 state of energy (SOE), 5 state of health (SOH), 6 and state of power (SOP). 7 These battery states must be estimated accurately to prevent safety concerns, improve performance, and increase battery life.…”

Section: Introductionmentioning

confidence: 99%

Model‐based state of X estimation of lithium‐ion battery for electric vehicle applications

Shrivastava

Soon

Idris

et al. 2022

Intl J of Energy Research

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Summary In developing an efficient battery management system (BMS), an accurate and computationally efficient battery states estimation algorithm is always required. In this work, the highly accurate and computationally efficient model‐based state of X (SOX) estimation method is proposed to concurrently estimate the different battery states such as state of charge (SOC), state of energy (SOE), state of power (SOP), and state of health (SOH). First, the SOC and SOE estimation is performed using a new joint SOC and SOE estimation method, developed using a multi‐time scale dual extended Kalman filter (DEKF). Then, the SOP estimation using T‐method and 2RC battery model is performed to evaluate the non‐instantaneous peak power during charge/discharge. Finally, the battery current capacity estimation is performed using a simple coulomb counting method (CCM)‐based capacity estimation with a sliding window. The performance of the proposed SOX estimation method is compared and analyzed. The experimental results show that the estimated SOC and SOE error is less than 1% under considered dynamic load profile at three different temperatures. After the final convergence, the estimated capacity maximum value absolute error is ±0.08 Ah. In addition, the low value of evaluated mean execution time (MET) justifies the high computational efficiency of the proposed method.

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State‐of‐healthestimation for the lithium‐ion battery based on gradient boosting decision tree with autonomous selection of excellent features

Cited by 29 publications

References 23 publications

A Polak-Ribière-Polyak Conjugate Gradient Algorithm Optimized Broad Learning System for Lithium-ion Battery State of Health Estimation

A Polak-Ribière-Polyak Conjugate Gradient Algorithm Optimized Broad Learning System for Lithium-ion Battery State of Health Estimation

Lithium-Ion Battery State-of-Health Prediction for New-Energy Electric Vehicles Based on Random Forest Improved Model

Model‐based state of X estimation of lithium‐ion battery for electric vehicle applications

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