Abstract
Deceleration rates have considerable influence on the fuel economy of hybrid electric vehicles. Given the vehicle characteristics and actual/measured operating conditions, as well as upcoming route information, optimal velocity trajectories can be constructed that maximize energy recovery. To support the driver in tracking of the energy optimal velocity trajectory, automatic cruise control is an important driver aid. In practice, perfect tracking of the optimal velocity trajectory is often not possible. An Adaptive Cruise Control (ACC) system is employed to react to the actual traffic situation. The combination of optimal velocity trajectory construction and ACC is presented as Predictive Cruise Control (PCC).
Highlights
Hybrid Electric Vehicles (HEVs) save fuel by reusing kinetic and potential energy, that is recovered and stored during braking or driving down hill
The remainder of this paper is organized as follows; Section 2 presents a model of heavy-duty HEV longitudinal dynamics and drive train components; Section 3 discusses the construction of an optimal velocity trajectory; Section 4 details the Adaptive Cruise Control (ACC) system; Section 5 integrates the velocity trajectory construction and the ACC, in the Predictive Cruise Control (PCC) setup; Section 6 shows simulation results; in Section 7 and 8 we conclude and look forward
Route optimization has only practical relevance when the driver can be assisted in following the optimal trajectory, and the optimization can adapt to the current traffic situation
Summary
Hybrid Electric Vehicles (HEVs) save fuel by reusing kinetic and potential energy, that is recovered and stored during braking or driving down hill. Taking HEV characteristics and current vehicle operating conditions into account, velocity trajectories can be determined that maximize the energy recovery [9]. In [13] it is suggested to use dynamic programming to numerically solve the optimal velocity trajectory problem in hilly environment This approach is successfully adapted in [4] using Model Predictive Control (MPC) in combination with an automated CC. The contribution of this paper consists of; i) presenting analytical solutions for the velocity trajectory optimization problem in HEVs; ii) combining the determination of optimal velocity trajectories for HEV and an ACC system This enables automatic following of these trajectories as well as anticipation of disturbances by actual traffic. The remainder of this paper is organized as follows; Section 2 presents a model of heavy-duty HEV longitudinal dynamics and drive train components; Section 3 discusses the construction of an optimal velocity trajectory; Section 4 details the ACC system; Section 5 integrates the velocity trajectory construction and the ACC, in the PCC setup; Section 6 shows simulation results; in Section 7 and 8 we conclude and look forward
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