Battery Health Diagnostics Using Retrospective-cost Subsystem Identification: Sensitivity to Noise and Initialization Errors

Health management of Li-ion batteries requires knowledge of certain battery internal dynamics (e.g., lithium consumption and film growth at the solid-electrolyte interface) whose inputs and outputs are not directly measurable with noninvasive methods. Therefore, identification of those dynamics can be classified as an inaccessible subsystem identification problem. To address this problem, the retrospective-cost subsystem identification (RCSI) method is adopted in this paper. Specifically, a simulation-based study is presented that represents the battery using an electrochemistry-based battery charge/discharge model of Doyle, Fuller, and Newman augmented with a battery-health model by Ramadass. The solid electrolyte interface (SEI) film growth portion of the battery-health model is defined as the inaccessible subsystem to be identified using RCSI. First, it is verified that RCSI with a first-order subsystem structure can accurately estimate the film growth when noise or modeling errors are ignored. Parameter convergence issues are highlighted. Second, allowable input and output noise levels for desirable film growth tracking performance are determined by studying the relationship between voltage change and film growth in the truth model. The performance of RCSI with measurement noise is illustrated. The results show that RCSI can identify the film growth within 1.5% when the output measurement noise level is comparable ∗Address all correspondence to this author. to the change in output voltage between successive cycles due to film growth, or when the input measurement noise is comparable to the difference in current that results in a difference in voltage that is the same as the voltage change between successive cycles. Finally, the sensitivity of the performance of RSCI to initial condition errors in the battery charge/discharge model is investigated. The results show that when the initial conditions have an error of 1%, the identified results change by 7%. These results will help with selecting the appropriate sensors for the experiments with the hardware. INTRODUCTION Due to their higher energy density compared to their leadacid and nickel-metal-hydride counterparts, Lithium-ion (Li-ion) batteries have found a wide range of applications from hand-held electronic devices to electrified vehicles. Understanding and optimally managing their health is critical for improving their reliability, durability, and cost. Li-ion batteries have various degradation mechanisms depending on which combination of anode, cathode, electrolyte, and dopant chemistries are used. A common type of Li-ion battery is the one with a lithium-iron-phosphate (LiFePO4) cathode. The predominant degradation mechanism of this battery type has been identified as the Solid-Electrolyte Interface (SEI) film formation in the anode [1], provided that the battery operates within its voltage limits. This mechanism affects battery State of Health 1 Copyright © 2013 by ASME Proceedings of the ASME 2013 Dynamic Systems and Control Conference DSCC2013 October 21-23, 2013, Palo Alto, California, USA