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

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

Optimization of a power electronic structure for hybrid Fuel Cell/Ultracapacitors vehicle

International audienceThis paper deals with the optimization of a parallel hybrid Fuel Cell (FC)/ Ultracapacitors (UCs) power source for automotive applications. The aim of this hybridization and its control are to fulfil the load requirements as well as to comply with the component constraints (high efficiency, reduced weight and cost, etc.). First, a classical hybrid architecture using FC/UCs and a two-converter structure is presented. After that, power requirements for automotive applications are analyzed, and the load power demands for the FC/UCs sources are deduced. Secondly, the model-based design approach is used for the optimization, and a selection of the main components to be optimized is presented. Thus, the simulation model and the control strategy are detailed. This model has been validated experimentally. Finally, an optimization algorithm is designed using Parallel Computing and the Genetic Algorithm toolbox of Matlab/Simulink. The retained criterion is based on the reduction of the total volume of the system

Analyses of energy management strategies for a PEMFC/UC electric vehicle

In this paper, two energy management strategies considering the hydrogen consumption of hybrid power sources using a PEM Fuel Cell (FC) and Ultracapacitors (UC) are described and compared. First, the Hybrid Electric Vehicle (HEV) architecture and the associated models with their control strategies are described. The two energy management strategies are evaluated based on the Energetic Macroscopic Representation (EMR). The comparison focuses on the global efficiency of the power sources energy management. In particular, a proposed strategy is to manage the UC State-OfCharge while stabilizing the FC around its maximal efficiency point. Finally, some simulations on a Fuel Cell / Ultracapacitors HEV show the differences between the compared control strategies.Postprint (published version

Thermal Impact on Powertrain Efficiency Improvement for Two Wheels Electric Vehicle

The energy required by a two wheels electric vehicle (TWEV) to complete a trip is lower than common electric cars or internal combustion vehicles. However, there are considerable losses along the electric driving chain. Those losses added to a limited energy storage cause an impact over the TWEV autonomy. This appears to be the main factor, which limits the large-scale market penetration of TWEV. This paper aims to analyze the multiphysic behavior of the complete power-chain in order to study its effect on its energetic losses. Even when many dynamics model oriented to hardware design approach can represent the come multiphysic behavior of one or two elements of the power train, the approach proposed in this paper presents a balanced representation of all power chain able to be used in real-time optimization. This study will help to improve the capabilities of an onboard TWEV efficiency estimator system which uses a longitudinal force model. As a conclusion, the error of autonomy estimation is compared with thermal considerations and without them according to different operating points

Analyses of energy management strategies for a PEMFC/UC electric vehicle

In this paper, two energy management strategies considering the hydrogen consumption of hybrid power sources using a PEM Fuel Cell (FC) and Ultracapacitors (UC) are described and compared. First, the Hybrid Electric Vehicle (HEV) architecture and the associated models with their control strategies are described. The two energy management strategies are evaluated based on the Energetic Macroscopic Representation (EMR). The comparison focuses on the global efficiency of the power sources energy management. In particular, a proposed strategy is to manage the UC State-OfCharge while stabilizing the FC around its maximal efficiency point. Finally, some simulations on a Fuel Cell / Ultracapacitors HEV show the differences between the compared control strategies

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