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This study investigates using driver prediction to anticipate energy usage over a 160-meter look-ahead distance for a series, plug-in, hybrid-electric vehicle to improve conventional thermostatic powertrain control. Driver prediction algorithms utilize a hidden Markov model to predict route and a regression tree to predict speed over the route. Anticipated energy consumption is calculated by integrating force vectors over the look-ahead distance using the predicted incline slope and vehicle speed. Thermostatic powertrain control is improved by supplementing energy produced by the series generator with regenerative braking during events where anticipated energy consumption is negative, typically associated with declines or decelerations.

Regenerative braking is an important factor in improving hybrid electric vehicle efficiency. This paper proposes a new regenerative braking strategy that activates preemptively during a distracted driving scenario, before service brakes are utilized. The strategy uses onboard advanced driver assistance systems, such as forward facing radar, to detect when an object is approaching fast enough to enable regenerative braking in response. The proposed strategy is simulated on a full-vehicle model of a series plug-in hybrid electric vehicle. A driver model is developed to mimic the behavior of a distracted driver through delayed response time to the changing speed of a lead vehicle. Multiple trials are simulated using different combinations of existing regenerative braking strategies along with the proposed strategy. Results show that a preventative regenerative braking control strategy can recuperate significant amounts of energy while also improving vehicle safety.