Mathematical Analysis and Non-Linear Optimization of EV Battery Health Using Fuzzy Logic and Neural Computing Techniques

Abstract

This article is related to a mathematical optimization model of the electric vehicle (EV) battery health management, which is based on fuzzy logics and neural network models. The study achieves a neural network to recognize charging patterns and preceding SOC in addition to the full charge temperature dependent upon the duration and ampere within the Multiple-Stage Constant Current (MCC). The target is SOC of 83%, which is made in 43 minutes upon maintaining the optimal temperature factor (of about 35 degrees). 85°C, representing a 3. At this point we observed 30% higher specific output capacity (SOC) versus standard constant current-constant voltage (CC-CV) method with a very small temperature increase of only 0. 41°C. The project includes several technical specifications, such as 96s2p where the complete battery runs through 192 cells, 40 kWh nominal capacity and 350 V nominal voltage. The flow of work will be database creation and management, the design of the simulation, machine learning model creation, testing and evaluation. The dataset is built from a sample, which includes features like the temperature within the room, the starting temperature of the battery and the time points (t1, t2, t3, t4); these are used for the training the machine learning algorithm. The simulation is followed by determination of the SOC and battery temperature, which are utilized as target features by the model. In Exploratory Data Analysis (EDA) one can highlight all the essential statistics while developing neural network model. Approach incorporated means of data analysis which include distribution, identification, and correlation, and prepared the way for outliers to be handled. The optimization outcomes per 28 degrees display the performance of the joint neural network and fuzzy logic method. The model gives SOC level above 80% with final temperature below 40°C, in which the errors of the voltage and temperature are lower than 2% and 1%, respectively. The fuzzy logic does that it has a parameter choosing method that makes the final battery temperature to go down and thus improves SOC as compared to other parameters method. This paper gives an insight into the use of hybrid optimization via combining three main variables: fuzzy logic, neural networks as well as various criteria of battery health management, the final result is optimization of charging parameters into EV's life.