Batteries, Vol. 8, Pages 31: A Regression-Based Way of Capacity Estimation of Lithium-Ion Batteries
Batteries doi: ten. 3390/batteries8040031
Authors: Seyed Saeed Madani Raziye Soghrati Carlos Ziebert
Electric vehicles (EVs) and cross types vehicles (HEVs) are being increasingly utilized for various reasons. The main reasons for the purpose of their implementation are that will they consume less or even do not consume fossil fuel (no carbon dioxide pollution) and don’t cause sound pollution. However , this technology has some challenges, including complex and troublesome precise state of health estimation, which is affected simply by different factors. According to the particular increase in electric plus hybrid vehicles& amp; rsquo; application, it is crucial to have a better plus reliable estimation of condition of charge (SOC) plus state of health (SOH) in different environmental conditions. This allows improving battery power management system operation with regard to optimal utilization of a battery pack in a variety of operating conditions. This article suggests an approach to estimate battery capacity based upon two parameters. First, the practical and straightforward technique is introduced to assess the battery& amp; rsquo; ring internal resistance, which can be directly related to the battery& amp; rsquo; h remaining useful life. Second, the different least square algorithm is explored. Finally, a promising, practical, basic, accurate, and reliable method is proposed to estimate battery capacity appropriately. The main mean square percentage mistake and the mean overall percentage error of the proposed methods were computed and were less compared to 0. 02%. It was determined the geometry method offers all the advantages associated with a recursive manner, including a fading memory, the close form of an answer, and being applicable within embedded systems.
