An Energy-Aware Predictive Longitudinal Control Framework for ADAS-Equipped Battery Electric Vehicles: Coupling Speed Planning with Regenerative Braking for Range Extension

In a battery electric vehicle, the way an adaptive cruise controller brakes decides how much energy a trip loses, because braking beyond the regeneration power limit is dissipated in the friction brakes. We develop a longitudinal model predictive controller that plans speed for safe gap-keeping, comfort, and low battery energy together, using electronic-horizon and lead-vehicle preview. The battery state of charge is carried through the prediction horizon, the regeneration limit enters as a hard constraint tied to that state of charge and to temperature, and friction braking is priced in the cost so decelerations stay within the recoverable envelope. A responsibility-sensitive safe-distance constraint, enforced by a runtime monitor and backed by a hazard analysis with ISO 26262 safety goals, separates the energy layer from the safety-critical braking path, and the controller solves a single quadratic program online. In closed loop against a production-style baseline it cuts energy per kilometer by 2.7% on WLTC class 3 and 3.2% on US06 with the trip schedule matched to within 0.2%. On a cut-in with hard braking it cuts energy by 6.8% while recovering 95% of the braking energy and holding a positive safety margin. The benefit approaches 14% when the regeneration envelope is tight, at low temperature or high charge, and the single quadratic program matches the full nonlinear program at about a third of the solve time, near 10 ms.