A Novel Electric Vehicle Battery Management System Using an Artificial Neural Network-Based Adaptive Droop Control Theory-Reference-Cited by-同舟云学术

A Novel Electric Vehicle Battery Management System Using an Artificial Neural Network-Based Adaptive Droop Control Theory

Published:2023-07-28 Issue: Volume:2023 Page:1-15
ISSN:1099-114X
Container-title:International Journal of Energy Research
language:en
Short-container-title:International Journal of Energy Research

Author:

Afzal Muhammad Zeshan¹,Aurangzeb Muhammad²,Iqbal Sheeraz³,Pushkarna Mukesh⁴,Rehman Anis Ur³,Kotb Hossam⁵^ORCID,AboRas Kareem M.⁵^ORCID,Alshammari Nahar F.⁶^ORCID,Bajaj Mohit⁷⁸⁹,Bereznychenko Viktoriia¹⁰^ORCID

Affiliation:

1. Department of Electrical Engineering, Southeast University, Nanjing 210096, China

2. Laboratory of Alternate Electrical Power System with Renewable Energy Sources, NCEPU, Beijing 102206, China

3. Department of Electrical Engineering, University of Azad Jammu and Kashmir, Muzaffarabad, 13100 AJK, Pakistan

4. Department of Electrical Engineering, GLA University, Mathura 281406, India

5. Department of Electrical Power and Machines, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt

6. Department of Electrical Engineering, Faculty of Engineering, Jouf University, Sakaka 72388, Saudi Arabia

7. Department of Electrical Engineering, Graphic Era Hill University, Dehradun 248002, India

8. Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun 248002, India

9. Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan

10. Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Science of Ukraine, Peremogy, 56, Kyiv 57 03680, Ukraine

Abstract

The novelty of this research lies in the development of a new battery management system (BMS) for electric vehicles, which utilizes an artificial neural network (ANN) and fuzzy logic-based adaptive droop control theory. This innovative approach offers several advantages over traditional BMS systems, such as decentralized control architecture, communication-free capability, and improved reliability. The proposed BMS control system incorporates an adaptive virtual admittance, which adjusts the value of the virtual admittance based on the current state of charge (SOC) of each battery cell. This allows the connected battery cells to share the load evenly during charging and discharging, which improves the overall performance and efficiency of the electric vehicle. The effectiveness of the proposed control structure was verified through simulation and experimental prototype testing with three linked battery cells. The small signal model testing demonstrated the stability of the control, while the experimental results confirmed the system’s ability to evenly distribute the load among battery cells during charging and discharging. We introduce a unique battery management system (BMS) for electric cars in this paper. Our suggested BMS was implemented and tested satisfactorily on a 100 kWh lithium-ion battery pack. When compared to typical BMS systems, the results show a surprising 15% increase in overall energy efficiency. Furthermore, the adaptive virtual admission function resulted in a 20% boost in battery life. These large gains in energy efficiency and battery longevity demonstrate our BMS’s efficacy and superiority over competing systems. Overall, the proposed BMS represents a significant innovation in the field of electric vehicle battery management. This combination of ANN and adaptive droop control theory based on fuzzy logic provides a highly efficient, reliable, and economical solution for EV battery cell management.

Publisher

Hindawi Limited

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Link

http://downloads.hindawi.com/journals/ijer/2023/2581729.pdf

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