Exploring the Effects of Cell-to-Cell Variability on Battery Aging through Stochastic Simulation Techniques

Abstract

This work introduces a comprehensive modeling framework designed to simulate the electrical, thermal, and aging behavior of battery cells connected in various parallel and series configurations. By utilizing Monte Carlo simulation techniques, the framework is used to investigate the inherent variability in cell attributes, including initial capacity, aging rate, and application profiles. Besides the estimation of expected battery life, this simulation environment enables the detailed investigation of failure distributions across different cell configurations and intensities of parameter variations. Results obtained from these simulations can be used, as an example, in the context of the automotive industry, where the insights of simulation in understanding the inherent variability of the aging process are particularly vital. As electric vehicles become more prevalent, understanding the performance and longevity of battery packs under various conditions is essential for effective design and management strategies, optimizing vehicle range, safety, and cost-effectiveness also on a fleet-level. Moreover, the ability to investigate failure distributions provides invaluable information for improving battery reliability and safety, key factors in the consumer acceptance of electric vehicles. Ultimately, the simulation environment provides a powerful tool for designing and optimizing efficient and durable battery technologies, with a focus on failure distribution analysis.