Trip Based Stochastic Prediction of Battery State of Charge for Electric Vehicles

For electric vehicle (EV) operations, a major concern for the customers is whether the available charge in the on-board battery pack could sustain a specific trip or not. Within foreseeable future, the charging infrastructure will still remain relatively short. The time for battery charging is also significant longer compared to that for filling gasoline tank. It is thus of practical benefit for EV operation to predict the battery energy demand for a specific trip a prior. In this paper, we present a trip-specific scheme for estimating the battery SOC change based on the trip information from GIS and ITS traffic data. In particular, this study incorporates the stochastic nature of traffic data. For the EV battery pack, the probabilistic change of battery SOC can thus be estimated throughout the spatial domain by combining the stochastic driving cycle, vehicle propulsion dynamics and battery dynamics. In consequence, for the overall trip, a stochastic evaluation can be provided for the possible SOC change. An approximate constant-acceleration model is assumed, and the relevant model parameters are modeled as Gaussian processes, with the probabilistic model established with test drive data. The stochastic characteristics of the SOC change for a specific trip is then evaluated via the Monte Carlo method. The proposed scheme is validated with an EV model and a typical commute trip in the Greater Milwaukee area. Historical traffic data are used to generate the stochastic model of driving cycles for the individual trip segments. Simulation results have shown that for the example trip, the SOC change is 33.4% ± 6.4% with 95% confidence.