Electrochemical Model Based Fault Diagnosis of Lithium Ion Battery
Md. Ashiqur Rahman, Sohel Anwar* and Afshin Izadian
Department of Mechanical Engineering, Mechatronics Research Laboratory, School of Engineering and Technology, IUPUI, A Purdue University School, USA
- *Corresponding Author:
- Sohel Anwar
Associate Professor and Graduate, Chair Director
Department of Mechanical Engineering
Mechatronics Research Laboratory School of Engineering and Technology, IUPUI USA
Tel: (317) 274- 7640
E-mail: [email protected]
Received Date: July 02, 2016; Accepted Date: August 03, 2016; Published Date: August 07, 2016
Citation: Rahman MA, Anwar S, Izadian A (2016) Electrochemical Model Based Fault Diagnosis of Lithium Ion Battery. Adv Automob Eng 5: 159. doi: 10.4172/2167- 7670.1000159
Copyright: © 2016 Rahman MA, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
A Multiple Model Adaptive Estimation (MMAE) based approach of fault diagnosis for Li-Ion battery is illustrated in this paper. Electrochemical modelling approach is integrated with MMAE for fault diagnosis. This real physics based model of Li-ion battery (with Li-Co-O2 cathode chemistry) with nominal model parameters is considered as the healthy battery model. Battery fault conditions such as aging, overcharge and over discharge causes significant variations of parameters from nominal values and can be considered as separate models. Output error injection based Partial Differential Algebraic Equation (PDAE) observers are used to generate the residual voltage signals. These residuals are then used in MMAE algorithm to detect the ongoing fault conditions of the battery. Simulation results show that the fault conditions can be detected and identified accurately which indicates the effectiveness of the proposed battery fault detection method.