Spatial–temporal Data-Driven Full Driving Cycle Prediction for Optimal Energy Management of Battery/supercapacitor Electric Vehicles

For multi-energy storage vehicles, the performance of online predictive energy management strategies largely relies on the length and effective utilization of predictive information. In this context, this paper proposes a novel velocity prediction method for the full driving cycle of electric vehicles based on the spatial–temporal commuting data, then the predicted velocity is applied to predictive energy management in electric vehicles with battery/supercapacitor hybrid energy storage system. Firstly, an one-year real-world commuting data set is collected on a Chinese arterial road with 10 intersections, 225 records are classified into 79 categories. Then, a real-time two-stage full driving cycle prediction method is proposed, where a medium-term prediction based on a long–short term memory (LSTM) network and a long-term prediction generated by a spatial–temporal interpolation method (STIM) are spliced for each category. The most probable category, i.e., the executed LSTM and STIM can be updated in real-time. Finally, a multi-horizon model predictive control method (MH-MPC) is established to leverage the predicted velocity for optimal power distribution. Compared with the conventional short-sighted MPC, the MH-MPC can reduce 4.2% battery degradation cost in a statistics form with real-time computation requirements satisfied.