Reducing range estimation uncertainty with a hybrid powertrain model and online parameter estimation

Range anxiety is a great problem for electric vehicle drivers due to limited infrastructure and battery capacity. One way to contradict this issue is more accurate range estimation, which can be achieved by reducing uncertainties in the energy consumption forecast on a selected route. In this work, we introduce a method for range estimation considering system uncertainties. To decrease aforementioned uncertainties, we present a recursive algorithm using multi-model estimation for including driving resistance parameters in the forecast. These parameters are transferred to a hybrid powertrain model which combines the standard longitudinal consumption model with a function approximation for the dynamic driving behavior on a specific route segment. This approximation relies on appropriate statistical driving features. With help of several real test drives we showed that the method reduces range estimation uncertainty significantly, it is less than half in one exemplary scenario. Implementing the findings of this work could lead to decreased range anxiety and increased acceptance of electric vehicles worldwide.