Velocity and energy trajectory prediction of electrified powertrain for look ahead control

Bharatkumar Hegde, Qadeer Ahmed and Giorgio Rizzoni

Applied Energy, 2020, vol. 279, issue C, No S0306261920313684

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.

Keywords: Velocity prediction; Look ahead control; Optimal energy management; Electrified powertrain (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.apenergy.2020.115903

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