Analysis of the stability of synchronous motor &amp;#x2014; Generator unit used in propulsion systems of autonomous electric vehicles

Voncila, Ion; Voncila, Elena; Fetecau, Grigore

doi:10.1109/iseee.2010.5628526

Cited by 3 publications

(1 citation statement)

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“…The vehicle mechanical load is characterized by three resistive torques [7,[12][13][14][15]. The different torques includes:…”

Section: Vehicle As Mechanical Loadmentioning

confidence: 99%

Neuro-Fuzzy control for four Wheels electric vehicle safety

Nasri,

Gasbaoui

2017

J. Ren. Energies

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This paper present novel approach for traction control of four wheel electric propulsion system for each wheel. This algorithm is necessary to improve the vehicle safety, the independent Machine Control philosophy is employed using an intelligent controller for torque and speed estimation, the proposed system replace the classical PI controller in order to improve vehicle dynamic performances. When the classical proposed control can't ensure the electric vehicle stability in several road topology situation's. The electronic differential system using adaptive fuzzy logic controller permit to vehicle to achieve intelligent driving. To show the efficiency of vehicle system control both of classical and intelligent controller are tested on Matlab Simulink environment ,the results obtained present satisfactory and show clearly the best response of the intelligent control during driving trajectory. The obtained data prove clearly that the worst performances of driving using PI comparing with adaptive fuzzy logic controller with no overshoot and no speed error and less estimated current ,and optimized autonomy.

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“…The vehicle mechanical load is characterized by three resistive torques [7,[12][13][14][15]. The different torques includes:…”

Section: Vehicle As Mechanical Loadmentioning

confidence: 99%

Neuro-Fuzzy control for four Wheels electric vehicle safety

Nasri,

Gasbaoui

2017

J. Ren. Energies

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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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