Date of Award
5-28-2025
Publication Type
Thesis
Degree Name
M.A.Sc.
Department
Electrical and Computer Engineering
Keywords
Electrochemical Impedance Spectroscopy; Li-ion Battery; Polynomial Ridge Regression
Supervisor
Narayan Kar
Supervisor
Lakshmi Varaha Iyer
Rights
info:eu-repo/semantics/embargoedAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Abstract
Lithium-ion battery performance and longevity are significantly influenced by temperature, charging-discharging protocols, and depth of discharge. Due to the complexity of inherent electrochemical processes in batteries, estimating state of health (SOH) and predicting remaining useful life (RUL) remain open challenges in safety-critical applications like electric vehicles (EVs). Most existing approaches rely on voltage-capacity data for battery health prognostics, whereas electrochemical impedance spectroscopy (EIS)-based prognostics have recently gained attention. In both cases, existing studies proposed different data-driven models for different cell chemistries, which lack generalizability. To address this, this study develops two data-driven methods that perform well across different cell chemistries and provide accurate battery health prognostics in two separate cases. For voltage-capacity-based health prognostics, empirical mode decomposition (EMD) is applied for noise reduction, and polynomial ridge regression (PRR) is used for feature extraction and RUL prediction. This methodology has the capability of starting prediction earlier than existing studies. Validation involves 24 batteries from real-world datasets, including NASA, CALCE, and HNEI. For EIS-based health prognostics, this paper introduces a fast and data-efficient approach for online battery health estimation using only initial and current-period EIS data. The proposed method can utilize EIS measurements regardless of the resting period, enabling real-time integration into battery management systems (BMS). By analyzing the relationship between the EIS Nyquist plot and equivalent circuit model parameters, key health indicators are identified across three different Li-ion chemistries based on electrochemical significance. These indicators are integrated into a polynomial ridge regression model and optimized using random search cross-validation to estimate SOH and predict RUL without requiring continuous historical EIS or capacity data. The method achieves good accuracy under various operating conditions. Additionally, by utilizing a reduced frequency range (0.5–1,000 Hz), the experimental time per cycle is reduced by 13–18 times compared to full-spectrum EIS measurements.
Recommended Citation
Rahman, Ahnaf Akif, "Lithium-Ion Battery State of Health Estimation and Remaining Useful Life Prediction: Practical Techniques for Varied Operating Conditions" (2025). Electronic Theses and Dissertations. 9730.
https://scholar.uwindsor.ca/etd/9730