Electric Vehicle Battery Health Monitoring System

Lithium-ion batteries are quite important these days. The goal of this project is to provide an effective process for using optimization techniques to determine the specifications of a model for lithium-ion batteries. The process of identifying the unknown parameters of a battery model using experimental data is known as parameter identification of lithium-ion batteries utilizing optimization methodologies. Lithium-ion battery models are very nonlinear and have a lot of factors, thus this is a difficult process. 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 using the Long Short-Term Memory (LSTM) or any other optimization technique to identify the parameters of a lithium-ion battery, it is imperative to have well-defined objectives to steer the process and facilitate the attainment of noteworthy outcomes. The LSTM optimization technique has several specific objectives for identifying lithiumion battery parameters. Determine the lithium-ion battery model's parameters so that it may be used to calculate the battery's voltage, current, state of charge (SOC), state of health (SOH), and remaining usable life (RUL). Determine the characteristics that characterize the power delivery capacity of the battery at varying discharge rates. These variables are utilized to determine the battery's performance and life.