An Application of LSTM-Based AI Technique for Prediction of Battery States in EV's

Abstract

As the automotive industry rapidly advances The expansion of the introduction of electric vehicles (EVs) is towards electric vehicles (EVs), accurately predicting in great demand for accurate forecasts of battery conditions, battery states is crucial for optimizing performance, safety, including load conditions (SOC) and health conditions (SO). and longevity. This project presents a novel approach Traditional methods for predicting these conditions often using Explainable Data-Driven Digital Twins to predict suffer from the complex and dynamic nature of battery battery states in electric vehicles. This methodology systems, leading to suboptimal performance of battery integrates a variety of advanced algorithms for machine management systems.. Inaccurate predictions can result in learning, including deep neuronal networks (DNN), reduced battery lifespan, unexpected failures, and inefficient long-term short-term memory (LSTM) networks, folding energy utilization, which in turn affects the overall reliability networks (CNN), support vector regression (SVR), support and user acceptance of EVs. The problem is further vectors (SVM), feedforward neural networks (FNN), compounded by the lack of interpretability in many machine laudial basic functionality (RBF), radial feature prophet learning models, making it difficult to understand the factors (RBF), and round-based functionality (SVM). Network), influencing battery states. The purpose of this project is to radial-based network (RBFSWARD neural network address these challenges by developing a comprehensive (FNN). Graduate school reinforcement (xgboost).The digital twin model using an explanatory data control approach primary objective of this study is to enhance the to accurately predict battery status and provide insight into the predictability of battery states by leveraging these diverse underlying factors affecting battery power. The main goal of algorithms to build a comprehensive digital twin model. this project is to develop an explanatory data-controlled digital Under various operating conditions, the model should twin model that predicts important batteriematas in electric provide accurate predictions of key battery parameters vehicles (EVs), particularly load condition (SOC) and health such as load status (SOC) and health status (SOH). By condition (SOH). The purpose of this project is to integrate a utilizing explainable AI techniques, the project also variety of advanced algorithms for machine learning, focuses on interpreting and understanding the underlying including deep neural networks (DNN), long distance memory factors influencing battery performance.Our approach (LSTM) networks, folding networks (CNN), and folding combines the strengths of different algorithms to improve networks (CNN). Support Vector Regression (SVR), and prediction accuracy and robustness. Preliminary results others, to build a robust and reliable prediction model. In indicate that the integrated model significantly addition to achieving high prediction accuracy, the project also outperforms traditional methods in terms of prediction seeks to incorporate explainable AI techniques to provide accuracy and reliability. This research contributes to the transparency and understanding of the model’s predictions. By development of more intelligent and adaptive battery achieving these objectives, the project aims to enhance battery management systems, which are essential for the future of management systems, ultimately contributing to the improved electric mobility. performance, safety, and longevity of batteries in EVs.