Fuzzy logic design for tele-control electrical vehicle

International audienc

Design of a Tele-Control Electrical Vehicle System Using a Fuzzy Logic Control

This paper presents a fuzzy logic design of a tele-control electrical vehicle system. We showed that the application of fuzzy logic control allows the stability of tele-vehicle system in spite of communication delays between the operator and the vehicle. A robust bilateral controller design using fuzzy logic frameworks was proposed. This approach allows a convenient means to trade off robustness and stability for a pre-specified time-delay margin. Both the performance and robustness of the proposed method were demonstrated by simulation results for a constant time delay between the operator and the electrical vehicle system

Optimal Design of Energy Storage System Using Different Battery Technologies for FCEV Applications

International audienc

Eco-Driving Optimal Controller for Autonomy Tracking of Two-Wheel Electric Vehicles

The eco-driving profiles are algorithms able to use additional information in order to create recommendations or limitation over the driver capabilities. They increase the autonomy of the vehicle but currently their usage is not related to the autonomy required by the driver. For this reason, in this paper, the eco-driving challenge is translated into two-layer optimal controller designed for pure electric vehicles. This controller is oriented to ensure that the energy available is enough to complete a demanded trip, adding speed limits to control the energy consumption rate. The mechanical and electrical models required are exposed and analyzed. The cost function is optimized to correspond to the needs of each trip according to driver behavior, vehicle, and traject information. The optimal controller proposed in this paper is a nonlinear model predictive controller (NMPC) associated with a nonlinear unidimensional optimization. The combination of both algorithms allows increasing around 50% the autonomy with a limitation of the 30% of the speed and acceleration capabilities. Also, the algorithm is able to ensure a final autonomy with a 1.25% of error in the presence of sensor and actuator noise