Date of Award
9-12-2024
Publication Type
Thesis
Degree Name
M.A.Sc.
Department
Electrical and Computer Engineering
Keywords
Battery Aging;HPPC Test;Resistance Estimation;SOH Estimation
Supervisor
Balakumar Balasingam
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
A deterioration in performance of lithium ion batteries is observed with age, which is quantified as state of health (SOH) of the battery. This thesis considers the problem of hybrid pulse power characterization (HPPC), which is a standardized test to quantify the power capabilities of a battery. HPPC is performed at various stages of the battery life to obtain the state of power (SOP) which is used to calculate the SOH of the battery. We particularly focus on the estimation of internal resistance using the HPPC test which is further used to calculate SOP. The existing approach to estimate the internal resistance in the HPPC test neglects the open circuit voltage (OCV) drop which results in an overestimated resistance. A demonstration of the effect of the OCV drop in the internal resistance estimation and a novel method to accurately estimate the internal resistance by accounting for the OCV drop caused by the HPPC pulse is described in this thesis. The proposed approach is based on a novel observation model that allows to estimate of the effect of OCV without requiring any additional information such as the state of charge (SOC), parameters of the OCV-SOC curve, and the battery capacity. Furthermore, we explore a battery aging study in which nine batteries with a unique set of stressors are aged for 200 cycles. This thesis describes precise details of tests performed at regular intervals during the battery aging and quantifies the deterioration in the performance of the battery. The capacity fade and power fade, which are the indicators of SOH of the battery are calculated and analyzed from the collected battery aging data.
Recommended Citation
Desai, Smeet, "LITHIUM-ION BATTERY AGING STUDY THROUGH NOVEL APPROACHES FOR STATE OF HEALTH ESTIMATION" (2024). Electronic Theses and Dissertations. 9533.
https://scholar.uwindsor.ca/etd/9533