(23c) Control and Diagnosis of Battery State of Charge

Authors: 
Suresh, R., Columbia University
Rengaswamy, R., Indian Institute of Technology Madras
Control and Diagnosis of Battery State of Charge

Resmi Suresh M P¹ and *Raghunathan Rengaswamy1

1Department of Chemical Engineering, IIT Madras, Chennai, 600036, India

Corresponding author: raghur@iitm.ac.in

In the present world, life without gadgets is unimaginable and almost all of these gadgets use batteries. On the other hand, there are also reports of batteries catching fire or exploding and the reasons for these can be attributed to poor health of the battery due to either internal short circuit or excessive heat (especially due to overcharging) [1,2]. An efficient miniature online diagnostic tool, an accurate model and a controller could help avoid these mishaps by ensuring safe operation of batteries [3]. Battery health can be monitored using a diagnostic tool and this information could be used to update the model of the battery. A controller based on this updated model could be used to control the charging current and prevent overcharging. A framework for online diagnosis and control of batteries as shown in Figure 1 is presented in this work.

Figure 1: Framework for online diagnosis and control

A diagnostic tool based on impedance information derived using chirp voltage and current data is described in this work [4]. As a first step of battery diagnosis, the chirp technique is applied on a battery model instead of a real battery to understand the mapping between the impedance profiles obtained and battery state of health. A simple model incorporating all the possible failure modes and side reactions which can be solved in real time is necessary for this. The present work uses a model developed based on reaction engineering perspective to characterize parasitic reactions and the electrochemical reactions [5].

An optimization framework to reduce the losses due to side reactions is also proposed in this work. This aims at optimizing the charging current such that reduction in maximum attainable capacity is minimized and charging is achieved within the maximum time allowed. This optimized current is used as a set point for a model predictive controller that can control the charging current at desired levels.

References:

[1] Triggs R. Why phones explode sometimes, and what you can do to protect yourself 2016. http://www.androidauthority.com/what-makes-smartphones-explode-714380/.

[2] Hamill J. Samsung to recall all Galaxy Note 7s after smartphones exploded while charging 2016. https://www.thesun.co.uk/news/1715098/samsung-halts-sale-of-galaxy-s7-af....

[3] Suresh R, Kumar Tanneru H, Rengaswamy R. Modeling of rechargeable batteries. Curr Opin Chem Eng 2016;13:63–74. doi:10.1016/j.coche.2016.08.005.

[4] Bullecks B. Rapid fault detection and mitigation strategies in the low temperature polymer electrolyte membrane fuel cell. Texas Tech University, 2013.

[5] Suresh R, Rengaswamy R. Modeling Failure Modes in Li-Ion Battery. 2015 AIChE Annu. Meet., 2015.