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Cycle-Dependent Degradation of Lithium-Ion Battery State of Health Under Normal, High-Rate, and Thermal Stress Conditions: A Dynamic Simulation Study
Subject area: Science,Engineering and Technology · Area of research: Physics
Abstract
Battery state of health (SOH) degradation is a critical factor in the life and reliability of lithium-ion batteries in electric vehicles, portable electronic devices, and grid storage systems. Understanding how different loading conditions affect capacity degradation and internal resistance increase is essential for developing battery management systems and predicting battery life. In this study, we developed a dynamic degradation model based on OpenModelica and simulated the SOH changes over the number of cycles under three conditions: normal charge/discharge cycles, high-current loading, and thermal loading. This model accounted for capacity degradation and resistance increase using linear degradation-rate parameters based on empirically determined trends. We simulated 1000 charge/discharge cycles and tracked the changes in SOH, capacity, usable energy, and coulombic efficiency. The results showed that SOH remained highest under normal charge/discharge conditions, at 80.0% after 1000 cycles, exceeding the usual end-of-life criterion throughout the simulation. Under high load, capacity degradation progressed most rapidly, with SOH decreasing to 40.0% after 1000 cycles. Under thermal load, the increase in internal resistance was most pronounced (0.22 Ω after 1000 cycles, a 120% increase from baseline), with SOH falling below the 80% threshold after 500 cycles. After 1000 cycles, usable energy decreased by 20.0% under normal operation, 40.0% under thermal load, and 60.0% under high load. This study shows that OpenModelica provides an effective dynamic simulation framework for battery aging analysis and life prediction, and that high discharge currents and high temperatures, despite their different characteristics, pose significant quantitative risks to battery life.
Keywords
lithium-ion battery; state of health; capacity fade; internal resistance; battery degradation; OpenModelica; cycle life; end-of-life prediction.
How to cite this paper
@article{1722676,
author = {Ramalan Abubakar, Abdullahi Zakari},
title = {Cycle-Dependent Degradation of Lithium-Ion Battery State of Health Under Normal, High-Rate, and Thermal Stress Conditions: A Dynamic Simulation Study},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {2},
pages = {3241-3247},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722676.pdf},
abstract = {Battery state of health (SOH) degradation is a critical factor in the life and reliability of lithium-ion batteries in electric vehicles, portable electronic devices, and grid storage systems. Understanding how different loading conditions affect capacity degradation and internal resistance increase is essential for developing battery management systems and predicting battery life. In this study, we developed a dynamic degradation model based on OpenModelica and simulated the SOH changes over the number of cycles under three conditions: normal charge/discharge cycles, high-current loading, and thermal loading. This model accounted for capacity degradation and resistance increase using linear degradation-rate parameters based on empirically determined trends. We simulated 1000 charge/discharge cycles and tracked the changes in SOH, capacity, usable energy, and coulombic efficiency. The results showed that SOH remained highest under normal charge/discharge conditions, at 80.0% after 1000 cycles, exceeding the usual end-of-life criterion throughout the simulation. Under high load, capacity degradation progressed most rapidly, with SOH decreasing to 40.0% after 1000 cycles. Under thermal load, the increase in internal resistance was most pronounced (0.22 Ω after 1000 cycles, a 120% increase from baseline), with SOH falling below the 80% threshold after 500 cycles. After 1000 cycles, usable energy decreased by 20.0% under normal operation, 40.0% under thermal load, and 60.0% under high load. This study shows that OpenModelica provides an effective dynamic simulation framework for battery aging analysis and life prediction, and that high discharge currents and high temperatures, despite their different characteristics, pose significant quantitative risks to battery life.},
keywords = {lithium-ion battery; state of health; capacity fade; internal resistance; battery degradation; OpenModelica; cycle life; end-of-life prediction.},
month = {August},
}