电池(电)
电池组
健康状况
商业化
荷电状态
汽车工程
能源管理
计算机科学
储能
管理制度
工程类
系统工程
可靠性工程
能量(信号处理)
功率(物理)
运营管理
法学
物理
统计
量子力学
数学
政治学
作者
Maryam Ghalkhani,Saeid Habibi
出处
期刊:Energies
[MDPI AG]
日期:2022-12-24
卷期号:16 (1): 185-185
被引量:58
摘要
With the large-scale commercialization and growing market share of electric vehicles (EVs), many studies have been dedicated to battery systems design and development. Their focus has been on higher energy efficiency, improved thermal performance and optimized multi-material battery enclosure designs. The integration of simulation-based design optimization of the battery pack and Battery Management System (BMS) is evolving and has expanded to include novelties such as artificial intelligence/machine learning (AI/ML) to improve efficiencies in design, manufacturing, and operations for their application in electric vehicles and energy storage systems. Specific to BMS, these advanced concepts enable a more accurate prediction of battery performance such as its State of Health (SOH), State of Charge (SOC), and State of Power (SOP). This study presents a comprehensive review of the latest developments and technologies in battery design, thermal management, and the application of AI in Battery Management Systems (BMS) for Electric Vehicles (EV).
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