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README.md
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README.md
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# LiXX Cell Pack Matcher
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Tool for finding the best configuration in a LiXX Battery Pack.
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Matches capacity in parallel cell groups from a serial pack.
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A web-based tool for finding the optimal cell configuration in lithium battery packs. It matches cells based on capacity and internal resistance to maximize pack performance and longevity.
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## Working
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- Matches cells bases on capacity for varius Pack configuration. Set parallel and serial cell count respectively.
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- Supports labels as identifier for cells.
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## Not Working
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- Clould be faster, 6S2P needs more than 10min to compute
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- Support internal cell resistance matching
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- Support bigger cell pool for a pack that is needed
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## Features
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- **Pack Configuration**: Support for any SxP configuration (e.g., 6S2P, 4S3P, 12S4P)
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- **Cell Matching**: Optimize by capacity (mAh) and internal resistance (mΩ)
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- **Multiple Algorithms**:
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- Genetic Algorithm (fast, recommended)
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- Simulated Annealing (good for escaping local minima)
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- Exhaustive Search (optimal for small configurations)
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- **Surplus Cell Support**: Use more cells than needed; the algorithm selects the best subset
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- **Live Progress**: Watch the optimization in real-time
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- **Visual Pack Layout**: Color-coded visualization of the matched pack
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- **Export Options**: JSON, CSV, and clipboard support
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- **Keyboard Accessible**: Full keyboard navigation support
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- **No Dependencies**: Pure HTML/CSS/JavaScript, no build step required
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## Scientific Background
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This tool implements cell matching algorithms based on research findings about lithium-ion battery pack assembly:
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> **Internal resistance matching for parallel-connected lithium-ion cells and impacts on battery pack cycle life**
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>
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> Shi et al., Journal of Power Sources (2013)
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> DOI: [10.1016/j.jpowsour.2013.11.064](https://doi.org/10.1016/j.jpowsour.2013.11.064)
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Key findings:
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- A 20% difference in internal resistance between parallel-connected cells can reduce cycle life by approximately 40%
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- Resistance mismatch causes uneven current distribution
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- Uneven current leads to higher operating temperatures and accelerated capacity fade
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## Usage
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### Quick Start
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1. Open `index.html` in a web browser
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2. Set your pack configuration (e.g., 6S2P)
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3. Enter cell data (label, capacity, and optionally internal resistance)
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4. Click "Load Example" to see sample data
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5. Adjust weights for capacity vs. IR matching
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6. Click "Start Matching"
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7. Review results and export if needed
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### Keyboard Shortcuts
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| Shortcut | Action |
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|----------|--------|
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| `Alt + A` | Add new cell |
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| `Alt + S` | Start matching |
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| `Alt + E` | Load example data |
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| `Esc` | Stop matching / Close dialog |
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| `?` | Show keyboard shortcuts |
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### Cell Data Format
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Each cell requires:
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- **Label**: Unique identifier (e.g., "B01", "Cell-A")
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- **Capacity**: Measured capacity in mAh
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- **Internal Resistance** (optional): Measured IR in mΩ
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### Algorithm Selection
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| Algorithm | Best For | Speed |
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|-----------|----------|-------|
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| Genetic Algorithm | Most cases, large pools | Fast |
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| Simulated Annealing | Avoiding local optima | Medium |
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| Exhaustive | Small configs (<8 cells) | Slow |
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### Matching Weights
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- **Capacity Weight**: Importance of matching parallel group capacities
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- **IR Weight**: Importance of matching internal resistance within parallel groups
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For current high-rate applications (e.g., power tools, EVs), increase IR weight.
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For capacity-focused applications, increase capacity weight.
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## Project Structure
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```
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lixx_cell_pack_matcher/
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├── index.html # Main application
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├── css/
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│ └── styles.css # Application styles
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├── js/
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│ ├── app.js # Main application logic
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│ └── matching-algorithms.js # Matching algorithms
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├── data/
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│ └── favicon.svg # Application icon
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├── README.md # This file
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└── LICENSE # MIT License
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```
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## Technical Details
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### Scoring Algorithm
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The match quality score is calculated as:
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```
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score = (capacityWeight × capacityCV) + (irWeight × avgWithinGroupIRCV)
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```
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Where:
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- `capacityCV`: Coefficient of variation of parallel group capacities
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- `avgWithinGroupIRCV`: Average coefficient of variation of IR within each parallel group
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- Lower score = better match
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### Coefficient of Variation
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```
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CV = (σ / μ) × 100%
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```
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Where σ is the standard deviation and μ is the mean.
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## License
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MIT License - see [LICENSE](LICENSE) for details.
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