Life Span Prediction of Lithium-Ion Battery Using Optimization Technique

The process to determine the unknown parameters of a battery model using experimental data is known as parameter identification of lithium-ion batteries utilizing optimization techniques. This challenge is solved by using optimization techniques to identify the set of parameters that minimizes the error between the experimental data and the model output. When a lithium-ion battery is being parameter identified utilizing the Grey Wolf Algorithm (GWO). Specific goals of a lithium-ion battery parameter identification using the Grey Wolf Optimization Method include figuring out the parameters of a model that can be used to estimate the battery's voltage, current, state of charge (SOC), state of temperature (SOT), state of health (SOH), and remaining useful life (RUL). Identify the features that define the battery's ability to produce electricity at different discharge rates. The performance and lifespan of the battery are determined by these factors.