Lithium Battery Monitoring and Mathematically Modelling its Equation
Vurity Anudeep1, Harshith Ch.2, Ram Kumar Paidi3, Ch. Radha Charan4
1Vurity Anudeep, Electrical and Electronics Engineering, JNTUH College of Engineering Jagtial, Hyderabad, India.
2Harshith Ch., Electrical and Electronics Engineering, JNTUH College of Engineering Jagtial, Hyderabad, India.
3Ram Kumar Paidi, Electrical and Electronics Engineering, JNTUH College of Engineering Jagtial, Karimnagar, India.
4Ch. Radha Charan, Electrical and Electronics Engineering, JNTUH College of Engineering Jagtial, Karimnagar, India.
Manuscript received on 08 March 2019 | Revised Manuscript received on 16 March 2019 | Manuscript published on 30 July 2019 | PP: 699-702 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1707078219/19©BEIESP | DOI: 10.35940/ijrte.B1707.078219
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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: Ever since people have managed to start shifting to electric vehicles, the lack of a cost-efficient battery monitoring system has been a real lack of concern for this mode of transportation. Not everyone wants to start a car after 6 months only to realize later that the battery is dead. Hence knowing the status of the battery on the back of our hand is crucial. This system will provide a steady supply of data about the status of vulnerable lithium-ion car battery which may include parameters such as the state of discharge (SOD) and state of temperature (SOT) to smartphones thereby eliminating this nuisance and also have inbuilt protection to help extend the battery life and avoid unwanted repairs which increases the maintenance costs. Using machine learning concepts like linear regressions and plotting’s. A mathematical equation was developed for the lithium battery and this equation will provide an easy calculation for above discussed parameters and reduce the circuit complexity.
Keywords: Crucial, Data, Curve Fitting, Linear Regression, Lithium-ion, Maintenance, SOD, SOT.
Scope of the Article: Regression and Prediction