Application of Particle Filter Algorithm in Battery Life Prediction

Core Concept of Particle Filter in Battery Life Prediction

The capacity degradation process of batteries is inherently a noisy nonlinear deterioration, which traditional physics-based models struggle to handle due to uncertainty. Particle filter achieves more robust predictions through the following mechanisms:

Degradation Modeling Transforms battery capacity degradation into a state-space model containing unobservable hidden states (e.g., internal chemical activity) and observable variables (e.g., voltage drop during charge-discharge cycles). Code implementation typically involves defining state transition functions and measurement equations.

Particle Dynamic Evolution Initializes hundreds of virtual particles, each carrying different state hypotheses (e.g., varying degradation rates). When new cycle data arrives, particle states are randomly perturbed according to system noise models using functions like numpy.random.normal() for Gaussian noise injection.

Weight Iterative Update Calculates each particle's compatibility with actual observation data through measurement equations, assigning higher weights to better-matched particles. The resampling phase eliminates low-weight particles while preserving high-weight hypotheses using algorithms like systematic resampling or multinomial resampling.

Remaining Useful Life Calculation When the particle cloud converges to a critical threshold (e.g., 80% of rated capacity), statistical analysis of different particles' cycle counts to reach this threshold generates probability-distributed life prediction curves. This involves histogram generation and confidence interval calculation using scipy.stats functions.

This method particularly suits handling: Performance variations among individual batteries Stochastic fluctuations during charge-discharge cycles Sudden capacity plunge phenomena

Compared to black-box neural network predictions, particle filtering provides interpretable uncertainty quantification, which is crucial for maintenance decisions in critical energy storage systems. The algorithm's modular structure allows customization of state transition models and noise distributions based on specific battery chemistry.