Method for predicting the remaining mileage of electric vehicles based on dimension expansion and model fusion

Abstract

Accurately predicting the remaining mileage of electric vehicles (EVs) can effectively alleviate user's mileage anxiety and develop refinement of energy management strategy. However, traditional prediction methods not only consume time and resources, but also accumulate errors and lack interpretability. In this paper, we proposed a model based on dimension expansion and model fusion strategy, which uses the extreme gradient boosting (XGBoost) algorithm to directly predict the remaining mileage of EVs. After pre‐processing the real running data of EVs, we constructed the field of remaining driving range and analyzed the relationship between features and remaining driving range, and then directly predicted the remaining driving mileage. Compared with other machine learning methods, XGBoost model has the highest accuracy. Then dimensional extended data set was obtained based on prior knowledge and symbol conversion, which improved the model performance. Finally, the model fusion strategy was adopted to further improve the generalization ability and stability of the model. The experimental results show that the Bootstrap aggregating (Bagging) fusion model has the highest predictive performance on the test set and outperformed other methods. The maximum RAE is not more than 3.5%, RMSE is less than 3km and MAE is about 2 km.

Long-sequence voltage series forecasting for internal short circuit early detection of lithium-ion batteries

“…Similar to voltage prediction, when the historical voltage and current sequences are known, the power sequences in the future can be predicted accurately, which is beneficial for peak and frequency regulation of smart grids 31 and the calculation of remaining mileage or energy calculation of electric vehicles. 32 , 33 …”

“…Hence, a model that can accurately forecast long-sequence voltage series is required for early detection of ISCs. In addition, because of peak and frequency regulation requirements for smart grids 31 and highly accurate calculation of remaining mileage 32 or energy 33 for electric vehicles, long sequence power series forecasting has become another challenge.…”

The Application of Graph Neural Network Based on Multi-Head Attention Optimization in Information Recommendation Systems

“…In Figure 1, model fusion first uses the fusion strategy to merge the preferences of all objects in the group to form group preferences and then generate GR results based on group preferences [17]. Recommendation fusion first generates independent recommendation results for all objects in a group and then utilizes the fusion strategy to fuse these results to generate GR results.…”