Design and Validation of Energy-optimal Adaptive Cruise Control (EACC)
2022-01-0503
03/29/2022
- Event
- Content
- The energy consumption of electric vehicles (EVs) is not only influenced by the efficiency of the powertrain components (battery, electric machines, inverters, etc.), but also to a large extend by the driving profile. Therefore, the new function of energy-optimal adaptive cruise control (EACC) is proposed as a new feature in this paper. This function exploits a large potential for energy consumption reduction by optimizing the EVs’ speed trajectory using model predictive control (MPC). EACC optimizes the speed profile of EVs by considering various connected driving information (speed limits, road gradients, predicted future behavior of the vehicle ahead, etc.). To validate the performance of EACC, a prototype system was implemented in an EV. The EV controlled by EACC was driven as the ego-vehicle on a test track. In the test cycles, which represent realistic urban driving scenarios, EACC reduces the energy consumption of the ego-vehicle by up to 13% in comparison to an ego-vehicle controlled by standard mass-production ACC. The results of the vehicle tests not only demonstrate significant energy savings, but also show that EACC can reduce the risk of rear-end collisions by reacting earlier to a severe deceleration of the vehicle in front. Furthermore, the passengers feel more comfortable when the vehicle speed is smoothened by EACC. Finally, to investigate the real-time implementation capability of the optimization algorithms, this work also examines the computation time of the MPC-based EACC function at different control horizon lengths. Keywords: energy-optimal adaptive cruise control, predictive driving, model predictive control, vehicle validation on the test track.
- Citation
- Jia, Y., Abdelkarim, A., Klingbeil, X., and Görges, D., "Design and Validation of Energy-optimal Adaptive Cruise Control (EACC)," SAE Technical Paper 2022-01-0503, 2022, .