Abstract
Accurate state-of-health (SOH) estimation for battery packs in electric vehicles (EVs) plays a pivotal role in preventing battery fault occurrence and extending their service life. In this paper, a novel internal ohmic resistance estimation method is proposed by combining electric circuit models and data-driven algorithms. Firstly, an improved recursive least squares (RLS) is used to estimate the internal ohmic resistance. Then, an automatic outlier identification method is presented to filter out the abnormal ohmic resistance estimated under different temperatures. Finally, the ohmic resistance estimation model is established based on the Extreme Gradient Boosting (XGBoost) regression algorithm and inputs of temperature and driving distance. The proposed model is examined based on test datasets. The root mean square errors (RMSEs) are less than 4 mΩ while the mean absolute percentage errors (MAPEs) are less than 6%. The results show that the proposed method is feasible and accurate, and can be implemented in real-world EVs.
Highlights
With the increasing urgency of environmental pollution and fossil oil shortage, the development of electric vehicles (EVs) has gained increased attention from governments and automotive industries around the world [1]
The ohmic resistance is used as a battery SOH indicator, which is estimated by combining the ECM and data-driven model
The ohmic resistance estimation model is based on the dynamic open circuit voltage (OCV)–recursive least squares (RLS) and XGBoost algorithm
Summary
With the increasing urgency of environmental pollution and fossil oil shortage, the development of electric vehicles (EVs) has gained increased attention from governments and automotive industries around the world [1]. Various methods have been put forward in the literature for battery SOH estimation, which can be generally grouped into three groups: physics-based, equivalent circuit model-based (ECM-based) and data-driven approaches. The ohmic resistance can be regarded as the bulk internal resistance, and proves to be more obtained than battery capacity in real-world operating EVs. this study selects the ohmic resistance as indictor for battery SOH estimation. The selected critical factor and the battery aging parameter are used as the input datasets of the XGBoost model to establish the ohmic resistance estimation model. The accuracy and robustness of the ohmic resistance estimation model are verified using real-world operating datasets of EVs collected from the National Big Data Platform for.
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