Abstract
With the rise in an energy crisis, electric vehicles have become a necessity. An integral part of the electric/hybrid vehicle is batteries. Out of many types, Li-ion batteries are providing features like high power as well as energy density. The features make Li-ion is an excellent choice for multiple applications from electronic appliances to electric vehicles. Li-ion batteries have their limitations while using in electric vehicles, and battery parameter monitoring like temperature, voltage, current, State of Charge (SoC), etc. is very much essential. The monitoring is dependent on actual physical measurements, which are subject to error contributing factors such as measurement noise, errors etc. With the estimation of SOC and State of Health (SoH) of the battery model, the lifetime of the battery will be calculated out, and along these lines sparing significant cost. In this paper, a study on SoH estimation and Li-ion battery SoC is estimated using a Kalman Filter (KF) algorithm estimation and results are presented to validate the Li-ion operating performance
Highlights
Nowadays, Li-ion batteries are widely used in various types of applications which have a comparatively small size range of device use into large size type application
Li-ion batteries have their limitations while using in electric vehicles, and battery parameter monitoring like temperature, voltage, current, State of Charge (SoC), etc. is very much essential
Well known Kalman Filter algorithm technique is useful to estimate the SoC of Li-ion battery
Summary
Li-ion batteries are widely used in various types of applications which have a comparatively small size range of device use into large size type application. SoC Estimation and Monitoring of Li-ion Cell using Kalman-Filter Algorithm Li-ion (LiCoO2) cell type battery [8] is taken for analysis The battery parameters such as SoC, current, voltage and temperature is monitored. During actual testing of battery, with fully charged and discharging, SoC estimation saves time by predicting battery performance with modelling. Kalman Filter algorithm is the process used for estimating the State of Charge of the battery. For SoC estimation using Kalman Filter, state space model discussed in the mathematical section was used. Kalman filtering is an algorithm which uses state measurements over time and having noise and various other inaccuracies and uncertainties and gives an estimate of unknown state values from available data. In this paper, the estimation of the SoC has been presented
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