Lifespan prediction of lithium-ion batteries based on various extracted features and gradient boosting regression tree model

Yang, Fangfang; Wang, Dong; Xu, Fan; Huang, Zhelin; Tsui, Kwok‐Leung

doi:10.1016/j.jpowsour.2020.228654

Cited by 131 publications

(36 citation statements)

References 33 publications

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“…Instead of considering the electrochemical reaction and the failure mechanism inside batteries, the model-less methods, which require no explicit battery models and regard the battery system as a black box, and then infer battery SOH or lifespan directly from extracted features. In literature, many statistical, computational, and artificial intelligence algorithms and models, such as Artificial Neural Network (ANN) [33]- [38], SVM [39], [40], RVM [41], [42], GPR [43], Gradient Boosted Regression (GBR) [44], [45], have been adopted for battery state estimation in various applications. However, data-driven techniques are usually unstable as they may show different performances with different datasets [46].…”

Section: B Model-less Methodsmentioning

confidence: 99%

Data-Driven Methods for Battery SOH Estimation: Survey and a Critical Analysis

Oji

Zhou

et al. 2021

IEEE Access

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State-of-health (SOH) estimation is a critical factor in ensuring the efficiency, reliability, and safety of lithium-ion batteries (LIBs) in electric vehicles (EVs). However, due to the complexity of electrochemical processes in batteries and the dynamics of working conditions, it is challenging to estimate SOH accurately, especially in real-world EV application scenarios. Thus, various data-driven methods with robust and adaptive features for SOH estimation have been widely proposed in the current literature. However, there is a lack of a comprehensive investigation and performance comparison of those methods, which makes them hard to be adopted in practice. Hence, in this paper, we have studied current major data-driven methods with real-world EV battery data to evaluate the performance. Besides, we summarize each method's advantages and limitations with the consideration of the critical features required to achieve accurate SOH estimation in real-world applications. Hopefully, this paper provides a practical insight into the related fields.INDEX TERMS Lithium-ion battery, state of health, data-driven methods, state estimation.

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Section: B Model-less Methodsmentioning

confidence: 99%

Data-Driven Methods for Battery SOH Estimation: Survey and a Critical Analysis

Oji

Zhou

et al. 2021

IEEE Access

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“…Figure 6D shows the trend between the prediction effect of GBR models and the descriptor number. GBR is an enhancement to the Boosting algorithm [59] . Boosting is a type of integrated machine-learning algorithm that transforms the poor learner into the strong learner.…”

Section: Extreme Feature Engineeringmentioning

confidence: 99%

Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage

Qin¹,

Lin²

2022

Energy Mater

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“…The gradient boosting regression model is a very powerful tool in modeling and prediction, and has been applied in many different fields [ 24 , 25 , 26 , 27 , 28 ]. However, this model has not been used in pollution predictions widely.…”

Section: Literature Review Of Ai Researchmentioning

confidence: 99%

Features Importance Analysis of Diesel Vehicles’ NOx and CO2 Emission Predictions in Real Road Driving Based on Gradient Boosting Regression Model

Wen

Jhang

2021

IJERPH

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In order to have an accurate and fast prediction of the artificial intelligence (AI) model, the choice of input features is at least as important as the choice of model. The effect of input features selection on the emission models of light diesel vehicles driven on real roads was investigated in this paper. The gradient boosting regression (GBR) model was used to train and to predict the emissions of nitrogen oxide (NOx), carbon dioxide (CO2), and the fuel consumption of real driving diesel vehicles in urban scenarios, the suburbs, and on highways. A portable emissions measurement system (PEMS) system was used to collect data of vehicles as well as environmental conditions. The vehicle was run on two routes. The model was trained with the first route data and was used to predict the emissions of the second route. There were ten features related to the NOx model and nine features associated with the CO2 model. The importance of each feature was sorted, and a different number of features were used as input to train the models. The best NOx model had the coefficient of determination (R2) values of 0.99, 0.99, and 0.99 in each driving pattern (urban, suburbs, and highways). Predictions of the second route had the R2 values of 0.88, 0.89, and 0.96 respectively. The best CO2 model had the R2 values of 0.98, 0.99, and 0.99 in each driving pattern, respectively. Predictions of the second route had the R2 values are 0.79, 0.82, and 0.83, respectively. The most important features for the NOx model are mass air flow rate (g/s), exhaust flow rate (m3/min), and CO2 (ppm), while the important features for the CO2 model are exhaust flow rate (m3/min) and mass air flow rate (g/s). It is noted that the regression models based on the top three features may give predictions very close to the measured data.

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Lifespan prediction of lithium-ion batteries based on various extracted features and gradient boosting regression tree model

Cited by 131 publications

References 33 publications

Data-Driven Methods for Battery SOH Estimation: Survey and a Critical Analysis

Data-Driven Methods for Battery SOH Estimation: Survey and a Critical Analysis

Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage

Features Importance Analysis of Diesel Vehicles’ NOx and CO2 Emission Predictions in Real Road Driving Based on Gradient Boosting Regression Model

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