Development of Machine Learning Methods in Hybrid Energy Storage Systems in Electric Vehicles

Tzu-Chia Chen, Fouad Jameel Ibrahim Alazzawi, John William Grimaldo Guerrero, Paitoon Chetthamrongchai, Aleksei Dorofeev, Aras masood Ismael, Alim Al Ayub Ahmed (), Ravil Akhmadeev, Asslia Johar Latipah, Hussein Mohammed Esmail Abu Al-Rejal and Alireza Goli

Mathematical Problems in Engineering, 2022, vol. 2022, 1-8

Abstract: The hybrid energy storage systems are a practical tool to solve the issues in single energy storage systems in terms of specific power supply and high specific energy. These systems are especially applicable in electric and hybrid vehicles. Applying a dynamic and coherent strategy plays a key role in managing a hybrid energy storage system. The data obtained while driving and information collected from energy storage systems can be used to analyze the performance of the provided energy management method. Most existing energy management models follow predetermined rules that are unsuitable for vehicles moving in different modes and conditions. Therefore, it is so advantageous to provide an energy management system that can learn from the environment and the driving cycle and send the needed data to a control system for optimal management. In this research, the machine learning method and its application in increasing the efficiency of a hybrid energy storage management system are applied. In this regard, the energy management system is designed based on machine learning methods so that the system can learn to take the necessary actions in different situations directly and without the use of predicted select and run the predefined rules. The advantage of this method is accurate and effective control with high efficiency through direct interaction with the environment around the system. The numerical results show that the proposed machine learning method can achieve the least mean square error in all strategies.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:3693263

DOI: 10.1155/2022/3693263

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