Abstract
Energy management strategies for hybrid and electric vehicles require accurate prediction of future velocity and energy consumption trajectories. This paper presents a parametric, model-based methodology to utilize look-ahead data provided by sensors and connectivity to predict the future velocity trajectories of the vehicle. First, the utility of look-ahead data in improving the prediction accuracy of velocity is systematically quantified and the predicted velocity profile is used for the optimal energy management of range-extender type of electrified powertrain. A simulation study is conducted on two routes, calibrated with real world traffic and road data, to demonstrate the utility of the proposed methodology. The results of the simulation study indicated a trend of diminishing benefits with increased levels of look-ahead data with a strong dependence on nature of the route.
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