Full-Life Lithium-Ion Battery Time-Series Dataset for Medical Device Applications: Simulated Charge-Discharge Cycles with Degradation and Thermal Dynamics

This dataset provides a comprehensive, physics-inspired simulation of lithium-ion battery behavior specifically designed for medical and clinical device contexts. It contains high-resolution time-series data for current (A), voltage (V), temperature (°C), state of charge (SOC), and state of health (SOH) across multiple charge-discharge cycles, capturing both intra-cycle dynamics and long-term degradation. Each cycle is simulated using Coulomb counting to track SOC, a Thevenin-equivalent voltage model scaled by SOH to account for aging, and a thermal coupling model for battery temperature, with stochastic Gaussian noise added to replicate real sensor variability. SOH is incrementally reduced per cycle to simulate gradual capacity fade until reaching a defined end-of-life threshold. The dataset combines multiple cycles into a full-life record, including over hundreds to thousands of cycles depending on operating parameters, and is stored in CSV format with clearly labeled columns: time, current, voltage, temperature, SOC, SOH, and cycle number. Accompanying analyses demonstrate the realism and reliability of the data, including time-domain plots of individual cycles, statistical summaries confirming operational ranges, frequency-domain inspection to validate low-frequency cyclic behavior, and autocorrelation to demonstrate periodicity across cycles. This dataset is suitable for machine learning, degradation modeling, feature extraction, and other battery health management research, providing a reproducible and fully synthetic resource that closely reflects the behavior of implantable and portable medical lithium-ion batteries while enabling exploration of long-term performance and reliability without requiring costly physical testing.