Abstract
Accurate estimation of the degree of battery aging is essential to ensure safe operation of electric vehicles. In this paper, using real-world vehicles and their operational data, a battery aging estimation method is proposed based on a dual-polarization equivalent circuit (DPEC) model and multiple data-driven models. The DPEC model and the forgetting factor recursive least-squares method are used to determine the battery system’s ohmic internal resistance, with outliers being filtered using boxplots. Furthermore, eight common data-driven models are used to describe the relationship between battery degradation and the factors influencing this degradation, and these models are analyzed and compared in terms of both estimation accuracy and computational requirements. The results show that the gradient descent tree regression, XGBoost regression, and light GBM regression models are more accurate than the other methods, with root mean square errors of less than 6.9 mΩ. The AdaBoost and random forest regression models are regarded as alternative groups because of their relative instability. The linear regression, support vector machine regression, and k-nearest neighbor regression models are not recommended because of poor accuracy or excessively high computational requirements. This work can serve as a reference for subsequent battery degradation studies based on real-time operational data.
Highlights
Using Python 3.7, ohmic internal resistance (OIR) estimation models based on linear regression (LR), k-nearest neighbor (KNN) regression, support vector machine regression (SVR), random forest regression (RFR), AdaBoost regression, gradient descent tree regression (GBDT), XGboost regression, and light GBM (LGBM) regression are developed in this study
A dualpolarization equivalent circuit (DPEC) model of the entire battery system was constructed and the forgetting factor recursive least squares (FFRLS) method was used to perform parameter identification to enable extraction of the OIR of the battery pack, which was used to characterize the degree of degradation of the battery
The LR, KNN, SVR, RFR, Adaboost, GBDT, XGboost, and LGBM models were trained and validated, and were tested with different datasets collected from another two purely electric passenger cars to verify the robustness of the algorithms
Summary
Studies that use machine learning algorithms for battery SOH estimation based on data sets collected from laboratory measurements have become increasingly common. A feasible way to overcome this problem is to develop a battery SOH estimation model based on data that are collected during actual operation of the vehicle.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
