Electric vehicle charging system pressure control based on fuzzy neural network PID control

Li, Zhaopeng; Pang, Jie; Xu, Chuyuan; Zhang, Chengcai

doi:10.1109/tocs53301.2021.9688659

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Due to the possession of non-linearity, and the ability of online learning (transferring the real-time experience to AI models), the AI-aided PI/PR online control methods have been widely used in different areas, such as dc-dc converter [32], [3], [33], [34], spacecraft [35], exoskeleton systems [36], and electric vehicle charging system [37]. However, there are generally two groups of approaches utilized to do that, design and control, as discussed below.…”

Section: Ai Applications In Linear Controllers Of Power Convertersmentioning

confidence: 99%

Artificial Intelligence Techniques for Enhancing the Performance of Controllers in Power Converter-Based Systems—An Overview

Gao,

Wang,

Dragicevic

et al. 2023

IEEE Open J. Ind. Applicat.

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The integration of artificial intelligence (AI) techniques in power converter-based systems has the potential to revolutionize the way these systems are optimized and controlled. With the rapid advancements in AI and machine learning technologies, this paper presents the analysis and evaluation of these powerful tools as well as in computational capabilities of microprocessors that control the converter. This paper provides an overview of AI-based controllers, with a focus on online/offline supervised, unsupervised, and reinforcement-trained controllers. These controllers can be used to create surrogates for inner control loops, complete power converter controllers, and external supervisory or energy management control. The benefits of using AI-based controllers are discussed. AI-based controllers reduce the need for complex mathematical modeling and enable near-optimal real-time operation via computational efficiency. This can lead to increased efficiency, reliability, and scalability of power converter-based systems. By using physics-informed methods, a deeper understanding of the underlying physical processes in power converters can be achieved and the control performance can be made more robust. Finally, by using datadriven methods, the vast amounts of data generated by power converter-based systems can be leveraged to analyze the behavior of the surrounding system and thereby forming the basis for adaptive control. This paper discusses several other potential disruptive impacts that AI could have on a wide variety of power converter-based systems.

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Section: Ai Applications In Linear Controllers Of Power Convertersmentioning

confidence: 99%

Artificial Intelligence Techniques for Enhancing the Performance of Controllers in Power Converter-Based Systems—An Overview

Gao,

Wang,

Dragicevic

et al. 2023

IEEE Open J. Ind. Applicat.

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“…In [29], a neural network was implemented in the energy management system in electric vehicles using ultra capacitors. Fuzzy neural network PID control was developed in [30] in the pressure control of the EVs. In [31], Wang et al proposed a method using back propagation neural networks in estimation of the state of health of the battery in electric vehicles.…”

Section: Introductionmentioning

confidence: 99%

A Smart ANN-Based Converter for Efficient Bidirectional Power Flow in Hybrid Electric Vehicles

et al. 2022

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Electric vehicles (EV) are promising alternate fuel technologies to curtail vehicular emissions. A modeling framework in a hybrid electric vehicle system with a joint analysis of EV in powering and regenerative braking mode is introduced. Bidirectional DC–DC converters (BDC) are important for widespread voltage matching and effective for recovery of feedback energy. BDC connects the first voltage source (FVS) and second voltage source (SVS), and a DC-bus voltage at various levels is implemented. The main objectives of this work are coordinated control of the DC energy sources of various voltage levels, independent power flow between both the energy sources, and regulation of current flow from the DC-bus to the voltage sources. Optimization of the feedback control in the converter circuit of HEV is designed using an artificial neural network (ANN). Applicability of the EV in bidirectional power flow management is demonstrated. Furthermore, the dual-source low-voltage buck/boost mode enables independent power flow management between the two sources—FVS and SVS. In both modes of operation of the converter, drive performance with an ANN is compared with a conventional proportional–integral control. Simulations executed in MATLAB/Simulink demonstrate low steady-state error, peak overshoot, and settling time with the ANN controller.

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A neural network PID controller with dynamic structure adjustment for DC-DC switching converters

Liu

Wei

Chen

et al. 2022

2022 7th International Conference on Integrated Circuits and Microsystems (ICICM)

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Electric vehicle charging system pressure control based on fuzzy neural network PID control

Cited by 3 publications

References 6 publications

Artificial Intelligence Techniques for Enhancing the Performance of Controllers in Power Converter-Based Systems—An Overview

Artificial Intelligence Techniques for Enhancing the Performance of Controllers in Power Converter-Based Systems—An Overview

A Smart ANN-Based Converter for Efficient Bidirectional Power Flow in Hybrid Electric Vehicles

A neural network PID controller with dynamic structure adjustment for DC-DC switching converters

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