Battery Lifetime Prediction using Data-driven Modeling Approaches

TL;DR

This paper investigates data-driven models, particularly neural networks, for predicting lithium-ion battery lifetime using a large NASA dataset with feature engineering and PCA, demonstrating neural networks' superior performance and robustness.

Contribution

It introduces a comprehensive approach combining feature engineering, PCA, and neural networks for battery lifetime prediction, showing improved accuracy over other models.

Findings

Neural networks outperform other models in predicting battery lifetime.

Feature engineering and PCA reduce model complexity and improve performance.

Neural networks show robustness with high-dimensional battery data.

Abstract

Batteries are ubiquitous today, with applications ranging from smartphones, watches, and laptops to electric cars, drones, and electric aircraft. Lithium-ion batteries are widely used in these applications due to their high energy density, rechargeability, and low lifecycle cost. Understanding the lifetime of lithium-ion batteries is essential for their effective utilization across many domains. In this study, data-driven modeling approaches are explored to predict the lifetime of lithium-ion batteries using various measurable battery parameters. A battery dataset from NASA's electric aircraft experiments was used, which included 17 predictor variables and remaining flight time as the response variable representing battery lifetime. The dataset contained more than 4,000,000 rows. However, the original dataset provided limited directly useful information about battery utilization over time; therefore, feature engineering was performed to generate more informative variables. Additionally, dimensionality reduction using principal component analysis (PCA) was applied to reduce computational cost and model complexity by selecting a smaller number of principal components as predictors for model development. Random forest and neural network models were explored for battery lifetime prediction using the engineered features. Multiple neural network configurations were evaluated, including single- and double-hidden-layer architectures with varying numbers of nodes. Mean squared error (MSE) on the test dataset was used as the performance metric for model comparison. The results indicate that data-driven modeling approaches are effective for battery lifetime prediction, with neural network models outperforming other models based on the MSE metric. Furthermore, neural networks demonstrate robustness in handling high-dimensional battery data.