Analysis, Modeling and Neural Network Traction Control of an Electric Vehicle without Differential Gears

Haddoun, A.; Benbouzid, Mohamed; Abdessemed, Rachid; Ghouili, Jamel; Srairi, K.

doi:10.1109/iemdc.2007.382780

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…From these information, the fixed-point format consisting of sign bit, word length and position of radix point can be derived, which in MATLAB is denoted as e.g. fixdt (1,16 automated Fixed-Point Advisor conversion process relies on the default data type definitions for classes of values, even if the hardware implementation option is set to FPGA/ASIC. So, globally fixed word lengths for all the input values, constant values and internal outputs, respectively, have to be set by the designer.…”

Section: Fixed-point Conversionmentioning

confidence: 99%

Technology and Engineering Applications of Simulink

Chakravarty¹

2012

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Dr S. C. Chakravarty did his Ph.D degree in space physics from the Physical Research Laboratory (PRL) , Ahmedabad, India and joined the Indian Space Research Organisation (ISRO) at Bangalore during 1973. He assumed various responsibilities to plan, organise and implement the coordinated atmospheric and space science research programmes sponsored by ISRO. As the Programme Director of Space Science research activities, Dr Chakravarty was involved in the planning and coordination of work related to India's first moon mission, Chandrayaan-1. He also coordinated large number of scientific research programmes involving scientific balloon and sounding rocket experiments, space science satellite missions including Astrosat, SROSS-C, and Megha-Tropiques. Dr Chakravarty also helped to enable Indian participation in internationally coordinated programmes like MAP, INDOEX, CAWSES etc. Presently his research activities include development of physics based and real time models on monsoon dynamics, the total electron content (TEC) of the ionosphere and aspects of mesospheric phenomena.

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Section: Fixed-point Conversionmentioning

confidence: 99%

Technology and Engineering Applications of Simulink

Chakravarty¹

2012

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“…Finally, some authors have proposed to use intelligent approaches, such as Neural Networks [10] or Fuzzy logic systems [11], demonstrating, though several manoeuvres, how torque vectoring controllers based on this last control technique improves vehicle's handling, stability and pathfollowing. In addition, the use of a fuzzy logic controller provides good results at low computational cost in comparison with previous approaches, being a good alternative to the implementation of torque distribution approaches.…”

Section: Introductionmentioning

confidence: 99%

Novel Fuzzy Torque Vectoring Controller for Electric Vehicles with per-wheel Motors

Delgado¹,

Jahnke²,

Rastelli³

2020

XXXVIII Jornadas De Automática: Gijón, 6, 7, Y 8 De Septiembre De 2017

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The continuous innovation on the electric vehicle area has led to the use of multi-motor powertrain topologies. These powertrains imply new degrees of freedom and better controllability, thus enabling elaborate control solutions for enhanced vehicle dynamics. This allows not only to achieve better vehicle stability, but also greater performance and responsiveness, leading to safer and more satisfying vehicle handling. This work presents a Fuzzy Torque Vectoring Controller for a rear-wheel drive car with independent motors per wheel. The developed design has been validated in a high fidelity vehicle dynamics simulation platform demonstrating its capability to enhance not only curve performance, but also stability.

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“…The vehicle is considered as a load is characterized by many torques which are mostly considered as resistive torques [4,5,16,17]. The different torques includes:…”

Section: The Vehicle Loadmentioning

confidence: 99%

Multi-input multi-output fuzzy logic controller for utility electric vehicle

Gasbaoui¹,

Chaker²,

Adellah³

2011

Archives of Electrical Engineering

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Currently commercialization of electric vehicle (EV) is based to minimize the time of starting and acceleration. To undergo this problem multi-input multi-output fuzzy logic controller (MIMO-FLC) affect on propelled traction system forming MMS process was proposed. This paper introduces a MIMO-FLC applied on speeds of electric vehicle, the electric drive consists of two directing wheels and two rear propulsion wheels equipped with two light weight induction motors. The EV is powered by two motors of 37 kilowatts each one, delivering a 476 Nm total torque. Its high torque (476Nm) is instantly available to ensure responsive acceleration performance in built-up areas. Acceleration and steering are ensured by an electronic differential system which maintains robust control for all cases of vehicle behavior on the road. It also allows controlling independently every driving wheel to turn at different speeds in any curve. Direct torque control based on space vector modulation (DTC-SVM) is proposed to achieve the tow rear driving wheel control. The MIMO-FLC control technique is simulated in MATLAB SIMULINK environment. The simulation results have proved that the MIMO-FLC method decreases the transient oscillations and assure efficiency comportment in all type of road constraints, straight, slope, descent and curved road compared to the single input single output fuzzy controller (SISO-FLC).

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Analysis, Modeling and Neural Network Traction Control of an Electric Vehicle without Differential Gears

Cited by 10 publications

References 14 publications

Technology and Engineering Applications of Simulink

Technology and Engineering Applications of Simulink

Novel Fuzzy Torque Vectoring Controller for Electric Vehicles with per-wheel Motors

Multi-input multi-output fuzzy logic controller for utility electric vehicle

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