Model-based EV range prediction for Electric Hybrid Vehicles

This paper describes a novel approach using model-based techniques to accurately predict EV range which can be applied to both BEV (Battery Electric Vehicle) and PHEV (Plug-in Hybrid Electric Vehicle) applications. The algorithm employs three models a physical model, energy model and State of Charge (SoC) model. The outputs of the models are averaged using a weighted average. This approach provides redundancy and more importantly availability of the function. Methods are employed to provide the driver with an accurate initialisation range value when the ignition is switched on. This utilises past driving history data and determines an output value based on the previous drive cycle. The work describes the flow sequence of the EV range function. Results for several drive cycles are analysed and show that accurate EV range prediction is achieved using the algorithm.