Performance and Life Analysis of Lithium-Ion Batteries Aided by Data-Driven Analysis

Abstract

The performance and lifespan of Li-ion batteries used in electric vehicles are influenced by operating and environmental conditions. An understanding of the mechanisms leading to performance degradation and capacity fading can aid in the design of better battery systems. In the present study, numerical models are developed to estimate the capacity fading, battery performance, and residual life. Furthermore, key associated parameters are identified as state of charge, charging protocols, and temperature. Later on, a deep machine learning (DML) model consisting of one input, four hidden, and one output layer is developed to estimate the residual life of a battery system. The five input parameters considered include voltage, current, temperature, number of cycles, and time, apart from residual life as the output parameter. The proposed DML model consists of five dense layers and three dropout layers with 2889 trainable parameters in total, with higher neuron counts in initial layers to process diverse inputs and fewer neurons in later layers to ensure compact feature representation as well as to make better and faster predictions. Results from the numerical and DML models are compared to the reported experimental results, where good agreement is observed. Thus, the developed model is tested on Lithium based Nickel Manganese Cobalt Oxide and Nickel Cobalt Aluminum Oxide batteries, for which parametric studies are performed to investigate the influence of the operating temperature, rate of charge/discharge, and pulse charging on the battery life. Therefore, the technologies proposed in this study can contribute to the development of intelligent battery management systems, enabling enhanced performance, and hence prolonged life of battery systems.