Electric vehicles (EVs) suffer from long charging time and inconvenient charging due to limited charging stations, which are the main causes of drivers’ range anxiety. Real-time and accurate driving range prediction can help drivers plan journeys, alleviate range anxiety, and promote EV development. However, predicting the EV driving range is challenging due to different weather, road conditions, driver habits, and limited available data. To address this issue, this article proposes a novel digital twin-based driving range prediction method. First, a one-year real-world EV dataset in Beijing is utilized. Detailed feature selection is conducted for the dataset, and six key features are extracted: battery SOC, consumed battery SOC, battery total voltage, battery maximum cell voltage, battery minimum cell voltage, and mileage already driven. Then, a random forest method is used to train the EV driving range prediction model using the features described earlier. Four prediction models with different adopted features are trained, respectively. Finally, the sliding window algorithm is proposed for the input of random forest to investigate its impact on prediction accuracy in the four prediction models, and different window sizes are evaluated. Results show that the sliding window algorithm can significantly improve the prediction model with only SOC as input, while it can deteriorate other models with more features. The most accurate model taking all six features as inputs provides 89.8% data that has an accuracy of over 80%, while data proportion of the prediction model without past energy consumption is only 31.8%.
Electric Vehicles (EVs) have transformed the automotive industry and are becoming a more reliable and consistent mode of public transportation. The development of a pollutionfree environment and improved ecological surroundings is being significantly assisted by battery-powered vehicles. Lithium-ion (Li-ion) batteries are the most widely used type of batteries in EVs because of their superior performance as compared to their counterparts. The core of EVs is their battery management systems (BMS), which can unarguably improve a battery's performance, operation, safety, and lifespan. Li-ion battery state estimation is one of the most important parts of the implementation of BMS, as it serves an important role in safe and reliable battery operation. Recently, researchers are working on the development of digital twin models to automate and optimize the BMS state estimation process by utilizing machine learning (ML) algorithms and cloud computing. The objective of this study is to review, characterize, and compare various ML-based approaches for the state estimation of different Li-ion battery states. Firstly, this study describes and characterizes several Li-ion battery state estimation approaches proposed in recent years. Secondly, the battery state estimation of electric vehicles is discussed. In addition, the challenges and prospects of Li-ion battery state estimation are put forward.
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