Comparison of Eco-Driving Strategies for Different Traffic-Management Measures

Teichert, Olaf; Koch, Alexander; Ongel, Aybike

doi:10.1109/itsc45102.2020.9294517

Cited by 4 publications

(1 citation statement)

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“…First, in the case of using information outside the vehicle, studies have employed vehicle networks and communication technologies such as vehicle-to-vehicle (V2V) or connected and automated vehicles (CAVs) to extend the driving range. [3], [4], [5]. In this approach, the vehicle exploits information such as traffic lights, traffic congestion, or driving pattern information of the preceding vehicles.…”

Section: Introductionmentioning

confidence: 99%

Energy-Saving Algorithm Considering Cornering Resistance of a Four-Wheel Independent Drive Electric Vehicle With Vehicle-to-Vehicle (V2V) Information

Choi

Kim

et al. 2022

IEEE Access

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This study proposes an algorithm to save driving energy in an autonomous vehicle based on vehicle-to-vehicle technology. Saving the vehicular driving energy can be realized by reducing unnecessary deceleration and acceleration occurred in road congestion and by reducing the resistance caused by the internal factors of the vehicle. The algorithm proposed in this study defines cornering resistance, one of the internal resistance factors of a vehicle while driving, in terms of steering angle. Thereafter, the control inputs of a vehicle are adjusted to reduce the cornering resistance. In particular, because the target vehicle is to be a four-wheel independent drive vehicle, there are many control input values such as steering angle and yaw moment. To simultaneously obtain the desired driving performance and the minimized driving energy in such a vehicle environment, the control input values are optimally distributed by leveraging model predictive control (MPC). Moreover, a weighting factor for the MPC to yield appropriate control inputs is selected by considering the predefined cornering resistance. A simulation setup linked with CarSim-Simulink is established to verify the reduction of driving energy through the proposed algorithm. The simulation results evaluate the driving performance, driving safety, and energy-efficient driving in various driving scenarios.

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Section: Introductionmentioning

confidence: 99%