Estimation of State-of-Charge and State-of-Health of Batteries by using Different Adaptive Techniques
Rajakumar Sakile1, Umesh Kumar Sinha2
1Rajakumar Sakile*, Department of Electrical Engineering NIT Jamshedpur, Jharkhand, India.
2Dr. Umesh Kumar Sinha Department of Electrical Engineering NIT Jamshedpur, Jharkhand, India.
Manuscript received on December 16, 2019. | Revised Manuscript received on December 26, 2019. | Manuscript published on January 10, 2020. | PP: 2221-2225 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8975019320/2020©BEIESP | DOI: 10.35940/ijitee.C8975.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: IElectrical Vehicle (EV), Battery Electrical Vehicle (BEV), State of Charge (SOC), Depth of Discharge (DOD), Open Circuit Voltage (OCV), Partial Differential Equation (PDF)
Scope of the Article: Health Monitoring and Life Prediction of Structures