A vehicle-cloud collaboration strategy for remaining driving range estimation based on online traffic route information and future operation condition prediction

Due to the complexity of real driving and operating conditions of Battery Electric Vehicles, it is challenging to accurately estimate the remaining driving range of the vehicle. Relying only on traditional energy consumption prediction based on the historical data shows obvious low-fidelity and hysteresis, especially when the traffic route is unknown. The accuracy of future travel energy consumption prediction fails to be guaranteed once the switching of operating conditions is involved. For this reason, a map named “Driving Route Planning” Application Programming Interface server is built on the cloud, receiving online traffic route information, and the Hidden Markov Model is applied for prediction optimization of future operating conditions. The remaining driving range of Battery Electric Vehicles is finally estimated according to the future energy consumption and the remaining dischargeable energy. The results show that the prediction of operating condition combined with traffic route information and Hidden Markov Model reflects the switching of future operating conditions more accurately and quickly. The relative error of the remaining driving range estimation proposed keeps within 5% under the real operating verification. • A novel operating condition prediction is applied to driving range estimation. • Driving route information and Hidden Markov Model are employed. • Proposed method has higher range estimation accuracy than conventional method.