Research on Sensorless Technology of a Magnetic Suspension Flywheel Battery Based on a Genetic BP Neural Network

The research object of this paper is a new type of multi-functional, air-gap-type, vehicle-mounted magnetic suspension flywheel battery. It is a new energy storage technology with a long working life, high energy conversion efficiency, multiple charging and discharging times, low carbon and environmental protection. However, when the vehicle-mounted flywheel battery is operating, it will inevitably be disturbed by road conditions, resulting in loose sensors and feedback errors, thereby reducing the control accuracy and reliability of the system. To solve these problems, a sensorless control system came into being. It samples the current of the magnetic bearing coil through the hardware circuit and converts it into displacement for real-time control, eliminating the risk of sensor failure. However, the control accuracy of the traditional sensorless system is relatively low. Therefore, this paper adopts a BP (backpropagation) neural network PID controller based on genetic algorithm optimization on the basis of the sensorless control system. Through the joint simulation of the dynamic simulation software ADAMS/VIEW2018 and MATLAB2022b, the optimal PID control parameter database for complex road conditions is established. Through sensorless technology, the current of the flywheel battery is converted into the position error for extensive training so that the genetic BP neural network PID controller can accurately identify the current complex road conditions according to the position error, so as to provide the optimal PID control parameters corresponding to the road conditions to carry out accurate real-time stability control of the flywheel rotor. The experimental results show that the method can effectively reduce feedback errors, improve the control accuracy, and output optimal control parameters in real time under complex road conditions, which significantly improves the reliability and control performance of the vehicle flywheel battery system.

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Institutions

Publication Details

Journal
Actuators
Published
2025-04-02
DOI
https://doi.org/10.3390/act14040174
Citations
4
Primary Topic
Advanced Algorithms and Applications
Type
article
Field-Weighted Citation Impact
4.86

Funders

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article

Research on Sensorless Technology of a Magnetic Suspension Flywheel Battery Based on a Genetic BP Neural Network

Weiyu Zhang, Fei Guo
4 citations
Actuators
Advanced Algorithms and Applications
4.86
article

Research on Sensorless Technology of a Magnetic Suspension Flywheel Battery Based on a Genetic BP Neural Network

Weiyu Zhang, Fei Guo
article en
4 citations

Abstract

The research object of this paper is a new type of multi-functional, air-gap-type, vehicle-mounted magnetic suspension flywheel battery. It is a new energy storage technology with a long working life, high energy conversion efficiency, multiple charging and discharging times, low carbon and environmental protection. However, when the vehicle-mounted flywheel battery is operating, it will inevitably be disturbed by road conditions, resulting in loose sensors and feedback errors, thereby reducing the control accuracy and reliability of the system. To solve these problems, a sensorless control system came into being. It samples the current of the magnetic bearing coil through the hardware circuit and converts it into displacement for real-time control, eliminating the risk of sensor failure. However, the control accuracy of the traditional sensorless system is relatively low. Therefore, this paper adopts a BP (backpropagation) neural network PID controller based on genetic algorithm optimization on the basis of the sensorless control system. Through the joint simulation of the dynamic simulation software ADAMS/VIEW2018 and MATLAB2022b, the optimal PID control parameter database for complex road conditions is established. Through sensorless technology, the current of the flywheel battery is converted into the position error for extensive training so that the genetic BP neural network PID controller can accurately identify the current complex road conditions according to the position error, so as to provide the optimal PID control parameters corresponding to the road conditions to carry out accurate real-time stability control of the flywheel rotor. The experimental results show that the method can effectively reduce feedback errors, improve the control accuracy, and output optimal control parameters in real time under complex road conditions, which significantly improves the reliability and control performance of the vehicle flywheel battery system.

ActuatorsVol. 14(4)
Jiangsu University (CN)
National Natural Science Foundation of China, Priority Academic Program Development of Jiangsu Higher Education Institutions
Openalex Percentile: Top 5%
Advanced Algorithms and Applications
4.86
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