Modeling and simulation of high energy density lithium-ion battery for multiple fault detection

Chandrani Sadhukhan; Swarup Kumar Mitra; Suvanjan Bhattacharyya; Eydhah Almatrafi; Bahaa Saleh; Mrinal Kanti Naskar

doi:10.1038/s41598-022-13771-4

Modeling and simulation of high energy density lithium-ion battery for multiple fault detection

Sci Rep. 2022 Jun 13;12(1):9800. doi: 10.1038/s41598-022-13771-4.

Authors

Chandrani Sadhukhan¹, Swarup Kumar Mitra², Suvanjan Bhattacharyya³, Eydhah Almatrafi^{4

5

6}, Bahaa Saleh⁷, Mrinal Kanti Naskar⁸

Affiliations

¹ Electrical Engineering Department, MCKV Institute of Engineering, Liluah, Howrah, West Bengal, 712104, India.
² Electronic & Telecommunication Engineering Department, MCKV Institute of Engineering, Liluah, Howrah, West Bengal, 712104, India.
³ Department of Mechanical Engineering, Birla Institute of Technology & Science, Pilani, Pilani Campus, VidyaVihar, Rajasthan, 333 031, India. suvanjan.bhattacharyya@pilani.bits-pilani.ac.in.
⁴ Mechanical Engineering Department, College of Engineering Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia.
⁵ K. A. CARE Energy Research and Innovation Centre, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
⁶ Center of Excellence in Renewable Energy and Power System, King Abdulaziz University, Jeddah, Saudi Arabia.
⁷ Mechanical Engineering Department, College of Engineering, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia.
⁸ Electronic & Telecommunication Engineering Department, Jadavpur University, Jadavpur, West Bengal, 700032, India.

Abstract

Lithium-ion battery, a high energy density storage device has extensive applications in electrical and electronic gadgets, computers, hybrid electric vehicles, and electric vehicles. This paper presents multiple fault detection of lithium-ion battery using two non-linear Kalman filters. A discrete non-linear mathematical model of lithium ion battery has been developed and Unscented Kalman filter (UKF) is employed to estimate the model parameter. Occurrences of multiple faults such as over-charge, over-discharge and short circuit faults between inter cell power batteries, affects the parameter variation of system model. Parallel combinations of some UKF (bank of filters) compare the model parameter variation between the normal and faulty situation and generates residual signal indicating different fault. Simulation results of multiple numbers of statistical tests have been performed for residual based fault diagnosis and threshold calculation. The performance of UKF is then compared with Extended Kalman filter (EKF) with same battery model and fault scenario. The simulation result proves that UKF model responses better and quicker than that of EKF for fault diagnosis.