Neural fuzzy control of driving wheels for electric vehicle

Houacine, Karima; Mellah, Rabah; Guermah, Said; Charif, Moussa

doi:10.1109/icosc.2013.6750830

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…The simulation parameters used in this article were chosen after an investigation in the literature. They come from the thesis work of [14] with prototyping but also of [10] and [15]. The vehicle model used is a fourwheel drive model of medium power, these simulation parameters correspond to this type of model to attest to the performance of EV.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…They arouse a lot of interest due to their efficiency and power. Thus, Karima et al in [15], neuro-fuzzy control by the indirect orientation of rotor flux is introduced. The proposed propulsion system is a two-wheel drive system whose speeds are independently controlled by an electronic differential and taking into account the given road profile.…”

Section: Introductionmentioning

confidence: 99%

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DTC with fuzzy logic for multi-machine systems: traction applications

Max

Maurice

Eke

et al. 2021

IJPEDS

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In this work, a direct torque control (DTC) method for multi-machine systems is applied to electric vehicles (EVs). Initially, the DTC control method associated with the model reference adaptive system (MRAS) is used for speed control, and management of the magnetic quantities is ensured by the variable master-slave control (VMSC). In order to increase the technical performance of the studied system, a DTC method has been associated with a fuzzy logic approach. These two control methods are applied to the traction chain of an electric vehicle to highlight its speed, precision, stability, and robustness metric during particular stress tests imposed on the wheel motor. The results obtained in MATLAB/Simulink software made feasible a comparison of two proposed methods based on their technical performances. It should be noted that the direct fuzzy logic torque control (DFTC) has better performance than the DTC associated with the MRAS system as a rise time reduction of 1.4%, an oscillation of torque, and flux amplitude of less than 9%, static steady-state error near zero. The DTFC control method responds favorably to electric vehicle traction chain systems by the nature of the comfort and safety provided.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DTC with fuzzy logic for multi-machine systems: traction applications

Max

Maurice

Eke

et al. 2021

IJPEDS

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“…However in an Autonomous Electric Vehicle (AEV) [2] - [6], a Driver Model Controller (DMC) is required for this task. Depending on the objectives, different control strategies may be used for DMC [7] - [8]. For an AEV, it is important that the limited battery energy on board is optimally used while delivering performance.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Control for energy saving in Autonomous Electric Vehicles

Al-Jazaeri

Samaranayake

Longo

et al. 2014

2014 IEEE International Electric Vehicle Conference (IEVC)

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Limited battery capacity and excessive battery dimensions have been two major limiting factors in the rapid advancement of electric vehicles. An alternative to increasing battery capacities is to use better: intelligent control techniques which save energy on-board while preserving the performance that will extend the range with the same or even smaller battery capacity and dimensions. In this paper, we present a Type-2 Fuzzy Logic Controller (Type-2 FLC) as the speed controller, acting as the Driver Model Controller (DMC) in Autonomous Electric Vehicles (AEV). The DMC is implemented using realtime control hardware and tested on a scaled down version of a back to back connected brushless DC motor setup where the actual vehicle dynamics are modelled with a Hardware-In-the-Loop (HIL) system. Using the minimization of the Integral Absolute Error (IAE) has been the control design criteria and the performance is compared against Type-1 Fuzzy Logic and Proportional Integral Derivative DMCs. Particle swarm optimization is used in the control design. Comparisons on energy consumption and maximum power demand have been carried out using HIL system for NEDC and ARTEMIS drive cycles. Experimental results show that Type-2 FLC saves energy by a substantial amount while simultaneously achieving the best IAE of the control strategies tested.

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