Energy Consumption Modeling in Presence of Uncertainty

This paper studies the behavior of an energy consumption model for electric vehicles in presence of input uncertainty, that is under heterogeneous driving behaviors and traffic conditions, or at varied performances of technology and operating scenarios. In fact, these sources of uncertainty are often neglected in customary applications, i.e. the model is evaluated only around nominal conditions, thus inhibiting the model's ability to capture the intrinsic variability of energy consumptions observed in real driving. Therefore, a general framework to perform global and regionalized sensitivity analysis on energy consumption models is here formulated and applied to identify which model inputs contribute the most to the variance of simulated energy consumption and recovery, and which input values lead to average or extreme model outputs. Results proved that driving behaviors and traffic flow dynamics have the greatest impact on simulated consumption and show strong interaction effects with the parameters of the regenerative braking strategy with respect to the recovered energy. While the vehicle weight and the auxiliary systems have an increasing impact at lower speeds, the technology performances and the vehicle characteristics were proved to be non-influential at all. Ultimately, the model was validated against its ability to reproduce the variability of consumptions observed in field experiments. Results show a tendency to over-estimating average consumption and underestimating average recovery, i.e. the model predictions are conservative. In addition, evidence shows that there are un-modeled details of the regenerative braking strategy to be further investigated via future experimental analyses.