Energy saving analysis in electrified powertrain using look-ahead energy management scheme

Connectivity enabled energy management strategies rely on various look-ahead information sources to predict a future disturbance trajectory in order to optimize the operation of the vehicle. The look-ahead data and their combinations contribute to the accuracy of the predicted disturbance trajectory and hence, enhance the optimality of the energy management strategy. This paper presents a systematic methodology to evaluate the utility of look-ahead data provided by on-board sensors and connectivity. The look-ahead data is first used in a parametric model-based velocity predictor to generate accurate future velocity trajectory of the vehicle on a route. The predicted velocity is used to inform the model predictive control based energy management system. The utility of look-ahead data in improving the optimality of energy management system is evaluated in a simulation study with a class 6 series-hybrid electric powertrain. The 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 study shows energy saving benefits saturate as the levels of look-ahead data increase and a dependence of the route characteristics. •Utility of look-ahead data for vehicle energy management is quantified.•A methodology to use combinations of look-ahead data for energy management is proposed.•Diminishing horizon model predictive control with dynamic programming is described.•Results show diminishing benefits with increased look-ahead data.