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There exists a growing need for standardized On-Board Diagnostics (OBD) for electric vehicles 1 to provide accurate health metrics and guarantees to both consumers and manufacturers. Previous work has shown the powerful LIB capacity-based State-of-Health (SoH) estimation capability of Electrochemical Impedance Spectroscopy (EIS) measurements and data-driven models 2 3. Since EIS measurements are dependent not only on SoH but also State-of-Charge (SoC) and temperature 4, it is important that the measurements are conducted after the cell reaches an equilibrium, and that these other variables are also tracked. Although EIS measurements are somewhat quicker than some traditional capacity-determination experimental methods, the time taken for such measurements is not insignificant. Therefore, building a pipeline to determin the EIS frequency measurements most important for SoH estimation is an important task in developing a suitable EIS-based OBD. By exploring a frequency range between 0.1 and 200 Hz, we study EIS measurements related to diffusion and charge transfer processes in LIB operation 5, with each process being partially distinguishable due to their varying timescales. In this work, using EIS data collected from 5Ah LIBs with an NMC-111 cathode and a graphite anode at various SoCs (0, 25, 50, 75 and 100%) and cell lifetime (0, 10, 20, 40 and 90 days) as input features, we develop sequential, data-driven health estimation models for LIBs. The 22 cells used for this analysis have been aged over a period of 90 days in two different ways: either through active cycling at different C-rates (0.2 and 1C) and temperatures (0, 25 and 40⁰C), or passive “calendar aging” where the cells are left without use at a specific temperature and SOC. Using feature attribution techniques (Shapley values, feature occlusion, etc.), we find the most influential of the frequency ranges in the EIS measurements that relate strongly with cell performance degradation. To develop a streamlined and efficient SoH estimation framework, we formulate an optimization problem to find the EIS experimental design in terms of frequency ranges that delivers maximum accuracy in SoH estimation at different points in the lifetime of the cell. These streamlined and optimized EIS experimental designs and SoH estimation models can be used directly in the development of rapid and efficient on-board diagnostic tools. References: 1 California Air Resources Board. (2023). Advanced Clean Cars II. https://ww2.arb.ca.gov/our-work/programs/advanced-clean-cars-program/advanced-clean-cars-ii 2 Zhang, Yunwei, et al. "Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning." Nature communications 11.1 (2020): 1706. 3 Jiang, Bo, et al. "A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries." Applied Energy 322 (2022): 119502. 4 Mc Carthy, Kieran, et al. "Use of impedance spectroscopy for the estimation of Li-ion battery state of charge, state of health and internal temperature." Journal of the Electrochemical Society 168.8 (2021): 080517. 5 Pastor-Fernández, Carlos, et al. "Identification and quantification of ageing mechanisms in Lithium-ion batteries using the EIS technique." 2016 IEEE Transportation Electrification Conference and Expo (ITEC). IEEE, 2016.
Thatipamula et al. (Fri,) studied this question.