MATLAB Implementation for Battery Data Analysis of Lithium-ion, Lead-acid, and Nickel-metal Hydride Batteries

This document presents experimental data analysis methodologies for multiple battery types, including lithium-ion batteries, lead-acid batteries, and nickel-metal hydride batteries. Battery performance data serves as a critical foundation for evaluating battery efficiency and operational characteristics, enabling informed selection decisions for specific applications. MATLAB implementations facilitate systematic data processing through specialized functions like batteryAnalyzer() that automatically extract key parameters from experimental datasets.

Core battery metrics typically encompass voltage profiles, current dynamics, capacity measurements, and discharge rates. Through MATLAB's signal processing toolbox, engineers can implement algorithms for smoothing voltage-current curves using smoothdata() functions and calculate capacity degradation trends with cumulative integration methods. These analytical approaches provide quantitative insights into battery behavior under varying operational conditions.

For lithium-ion batteries, MATLAB scripts can be developed to analyze energy density through trapz() integration of discharge curves, cycle life assessment via statistical pattern recognition, and safety performance evaluation using thermal data regression models. Similarly, lead-acid battery analysis may incorporate code for charge acceptance calculations through differential voltage analysis and efficiency mapping with polarization curve fitting techniques.

Context-aware battery selection requires MATLAB implementations that correlate application-specific requirements with battery characteristics. For mobile applications, algorithms can prioritize energy density optimization using fmincon() constrained optimization, while industrial applications may employ discharge rate analysis scripts with load profile simulations using simscape electrical libraries.

The integration of experimental data with MATLAB's computational capabilities enables comprehensive battery performance assessment. By implementing custom analysis functions and leveraging built-in toolboxes, engineers can develop data-driven battery selection frameworks that ensure optimal performance matching for diverse operational scenarios.