荷电状态
电池(电)
健康状况
电池组
汽车工程
锂(药物)
计算机科学
锂离子电池
练习场
电动汽车
电气工程
可靠性工程
功率(物理)
工程类
物理
医学
内分泌学
量子力学
出处
期刊:International Journal for Research in Applied Science and Engineering Technology
[International Journal for Research in Applied Science and Engineering Technology (IJRASET)]
日期:2021-08-31
卷期号:9 (8): 2750-2760
标识
DOI:10.22214/ijraset.2021.37859
摘要
Abstract: Lithium-ion battery packs constitute an important part of Electric vehicles. The usage of Lithium-ion based chemistries as the source of energy has various advantages like high efficiency, high energy density, high specific energy, longevity among others. However, the management of lithium-ion battery packs require a Battery Management System (BMS). The BMS deals with functions like safety, prevention of abusive usage of battery pack, overcharging & over-discharging protection, cell balancing and others. One of the prominent features of the BMS is the estimation of State of charge (SOC). SOC is like a fuel gauge in automobile, it indicates how much more the battery can be used before charging it again. SOC is also required for other functions of BMS like State of Health (SOH) tracking, Range calculation, power & energy availability calculations. However, there is no means of measuring it directly (at least not on-board a vehicle) or estimating it easily. Various techniques should be used to estimate SOC indirectly. This paper starts from classical techniques that have existed since long time and reviews some of the modern & developing methods for SOC estimation. It contains a brief review about most of these SOC estimation methods, thus highlighting the methodology, advantages & disadvantages of each of these techniques. A brief review of other developing SOC estimation techniques is also provided. Keywords: State of Charge, SOC, Lithium-ion battery packs, Electric vehicles, Kalman Filter.
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