Spatial-Temporal Data-Driven Speed Prediction for Energy Management of Battery/Supercapacitor Electric Vehicles

Accurate speed prediction plays a critical role in the predictive energy management of electric vehicles. This paper proposes a spatial-temporal data-driven speed prediction method for the predictive energy management of battery/supercapacitor electric vehicles. The proposed speed prediction method is performed using a long short-term memory network and validated on a real-world commuting data set in China. Different from existing prediction methods based only on speed and acceleration, we take spatial information as an additional input to improve speed prediction accuracy. The predicted future speed is then leveraged by a model predictive control-based energy management strategy to minimize the battery degradation cost. Quantitative comparisons illustrate that the proposed speed prediction method can reduce the root mean square error and mean absolute error by 10.01-19.15% compared with no spatial information prediction method. The more accurate prediction can further improve the optimality of the predictive energy management strategy, i.e., reduce the battery capacity loss and yield closer results to model predictive control with completely accurate prediction.