Open AccessEstimation of State-of-Charge and State-of-Health of Batteries by using Different Adaptive Techniques
Author(s) -
Rajakumar Sakile,
Umesh Kumar Sinha
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.c8975.019320
Subject(s) - state of charge , battery (electricity) , state (computer science) , computer science , kalman filter , state of health , lithium ion battery , voltage , control theory (sociology) , extended kalman filter , engineering , electrical engineering , algorithm , power (physics) , physics , artificial intelligence , control (management) , quantum mechanics
To know the performance and life cycle of the battery State-of-Charge (SOC) has to be calculated. SOC cannot calculate directly. Many chemical factors are involved in batteries, which causes non-linear elements in the battery. Therefore, the prediction of SOC is difficult.in this paper different adaptive techniques are used to find the SOC. Adaptive systems can automatically adjust the SOC for different type of batteries. 2Ah rating Lithium-ion battery is consider to estimate SOC and related parameters. Open circuit voltage method, current integral method and modified Kalman filter methods are discussed to obtain the internal parameters ( U ,R ,R,C oc int ) of the battery.