Road Grade Prediction for Predictive Energy Management in Hybrid Electric Vehicles

The uncertainty caused by the varying of road grades plays a critical role in impacting the hybrid electric vehicles (HEV) energy management performance, and therefore the fuel economy. This paper presents an autoregressive integrated moving average (ARIMA) based method, aiming to forecast the near future road grade in real-time with acceptable accuracy for predictive energy management of (P)HEVs. Real world road grade data is collected and employed to formulate the ARIMA model, and model predictive control (MPC) is used for the powertrain control. The model is integrated into the predictive energy management strategy to investigate and evaluate the potential gain in fuel economy. Simulation results show that the ARIMA method is able to predict the future road grade with high accuracy, and the corresponding fuel consumption is reduced by at least 4.7%.