MATLAB Implementation for Battery Data Processing

Processing battery experimental data in MATLAB is a common task, particularly for performance analysis of different battery types such as lithium-ion, lead-acid, and nickel-metal hydride batteries. This data typically includes key parameters like charge-discharge curves, cycle life, and internal resistance variations. ### Data Structure Design Battery experimental data can be organized using structures or tables for efficient storage and management. Each battery type's data may contain fields for time series, voltage, current, and temperature. MATLAB's table data structure is particularly suitable for this task as it allows labeling and categorization of different columns, facilitating subsequent processing. Implementation typically involves creating structured arrays with consistent field names for different battery parameters. ### Data Import and Preprocessing Experimental data is commonly stored in CSV, Excel, or TXT formats. MATLAB's `readtable` function provides convenient import capabilities for these file types. Preprocessing steps may include outlier removal using statistical methods like the three-sigma rule, data interpolation (e.g., using `interp1` to handle inconsistent sampling rates), and normalization processing (using functions like `normalize` or `zscore`) to ensure data comparability. ### Data Visualization Key battery performance indicators are typically presented through visualization techniques: - Charge-discharge curves: Plotting voltage-capacity curves using `plot` function to compare performance differences among battery types - Cycle life analysis: Evaluating long-term battery stability through capacity decay curves using cumulative plotting methods - Temperature effects: Analyzing temperature impact on battery internal resistance or efficiency trends using surface plots or heatmaps MATLAB's plotting functions (such as `plot` and `scatter`) can clearly display this data, while `subplot` enables comparison of multiple battery test results within a single figure, allowing for efficient performance benchmarking. ### Advanced Analysis For more in-depth analysis, MATLAB's Statistics and Machine Learning Toolbox can be utilized: - Fitting battery degradation models using `fitlm` or `nlinfit` functions to predict remaining useful life (RUL) - Performance classification of different batteries using clustering analysis with `kmeans` or hierarchical clustering algorithms - Building battery state estimation models using neural networks through Deep Learning Toolbox functions like `fitnet` or `patternnet` These methods enable efficient processing of experimental data for lithium-ion, lead-acid, and nickel-metal hydride batteries, providing strong support for battery system research, development, and optimization. The implementation typically follows a modular approach where each analytical component can be developed as separate functions for maintainability and reusability.