A new technique for highly efficient sensor-less control of electric vehicles by using neural networks

Asaii, B.; Gosden, D; Sathiakumar, S.

doi:10.1109/pet.1996.565922

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…In future, they intend to improve their hybrid model by including 5 phase SRM. Grigorescu et al [6] Asaii and Sathiakumar [7] introduced a new controller based on Artificial Neural Network (ANN). They investigated on different neural networks and their efficiency in controlling electric vehicles.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence (AI) Framework for Multi-Modal Learning and Decision Making towards Autonomous and Electric Vehicles

Ramesh¹,

Praveen²

2021

E3S Web Conf.

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An electric vehicle with autonomous driving is a possibility provided technology innovations in multi-disciplinary approach. Electric vehicles leverage environmental conditions and are much desired in the contemporary world. Another great possibility is to strive for making the vehicle to drive itself (autonomous driving) provided instructions. When the two are combined, it leads to a different dimension of environmental safety and technology driven driving that has many pros and cons as well. It is still in its infancy and there is much research to be carried out. In this context, this paper is aimed at building an Artificial Intelligence (AI) framework that has dual goal of “monitoring and regulating power usage” and facilitating autonomous driving with technology-driven and real time knowledge required. A methodology is proposed with multiple deep learning methods. For instance, deep learning is used for localization of vehicle, path planning at high level and path planning for low level. Apart from this, there is reinforcement learning and transfer learning to speed up the process of gaining real time business intelligence. To facilitate real time knowledge discovery from given scenarios, both edge and cloud resources are appropriately exploited to benefit the vehicle as driving safety is given paramount importance. There is power management module where modular Recurrent Neural Network is used. Another module known as speed control is used to have real time control over the speed of the vehicle. The usage of AI framework makes the electronic and autonomous vehicles realize unprecedented possibilities in power management and safe autonomous driving. Key words: Artificial Intelligence Autonomous Driving Recurrent Neural Network Transfer Learning

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Section: Related Workmentioning

confidence: 99%

Artificial Intelligence (AI) Framework for Multi-Modal Learning and Decision Making towards Autonomous and Electric Vehicles

Ramesh¹,

Praveen²

2021

E3S Web Conf.

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“…Using the sliding mode, VSC has also been applied to motor drives [38]. By employing the emerging technologies of fuzzy and neural networks to realize the concept of intelligent controllers, FC and NNC offer promising applications to EV and HEV propulsion [39]- [43].…”

Section: B Ev or Hev Casesmentioning

confidence: 99%

A Fault-Tolerant Control Architecture for Induction Motor Drives in Automotive Applications

Diallo

Benbouzid

Makouf

2004

IEEE Trans. Veh. Technol.

162

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International audienceThis paper describes a fault-tolerant control system for a high-performance induction motor drive that propels an electrical vehicle (EV) or hybrid electric vehicle (HEV). In the proposed control scheme, the developed system takes into account the controller transition smoothness in the event of sensor failure. Moreover, due to the EV or HEV requirements for sensorless operations, a practical sensorless control scheme is developed and used within the proposed fault-tolerant control system. This requires the presence of an adaptive flux observer. The speed estimator is based on the approximation of the magnetic characteristic slope of the induction motor to the mutual inductance value. Simulation results, in terms of speed and torque responses, show the effectiveness of the proposed approach

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“…ANN controllers have been interested in research on the problems of conventional proportional-integral (PI) controllers in a complementary form. ANN control for induction motor drives has been studied for current control, efficiency optimization control, and speed control [20][21][22][23][24][25][26][27]. Also, a study on vector control of induction motor by ANN control and adaptive ANN control for the nonlinear system was conducted [28].…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks applied on induction motor drive for an electric vehicle propulsion system

2021

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This article presents an artificial neural network (ANN) for loss minimization of direct torque-controlled induction motor (IM)-driven electric vehicles. The IM drive can consume more power than it needs to be, especially when it is operating under conditions under full load. The proposed architecture and its control strategies use ANN to control the amplitude starting current and save more power. The performance of these controllers was verified by simulation using the MATLAB/SIMULINK package, and the results showed good and high performance in the time domain response and rapid rejection of the system disturbance compared to the conventional proportional-integral derivative (PID) controller. Thus, the core loss of IM greatly reduces, thus improving the efficiency of the driving system. Finally, the experimental results were validated which were highly consistent with the simulation results using the DSPACE MicroLabBox controller.

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A new technique for highly efficient sensor-less control of electric vehicles by using neural networks

Cited by 6 publications

References 10 publications

Artificial Intelligence (AI) Framework for Multi-Modal Learning and Decision Making towards Autonomous and Electric Vehicles

Artificial Intelligence (AI) Framework for Multi-Modal Learning and Decision Making towards Autonomous and Electric Vehicles

A Fault-Tolerant Control Architecture for Induction Motor Drives in Automotive Applications

Artificial neural networks applied on induction motor drive for an electric vehicle propulsion system

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