Comprehensive Review of Machine Learning, Deep Learning, and Digital Twin Data-Driven Approaches in Battery Health Prediction of Electric Vehicles

Abstract

This paper presents a comprehensive survey of machine learning, deep learning, and digital twin technology methods for predicting and managing the battery state of health in electric vehicles. Battery state of health estimation is essential for optimizing the battery usage, performance, safety, and cost-effectiveness of electric vehicles. Estimating the state of health of a battery is a complex undertaking due to its dependency on multiple factors. These factors include battery characteristics such as type, chemistry, size, temperature, current, voltage, impedance, cycle number, and driving pattern. There are drawbacks to traditional methods, such as experimental and model-based approaches, in terms of accuracy, complexity, expense, and viability for real-time applications. By employing a variety of algorithms to discover the nonlinear and dynamic link between the battery parameters and the state of health, data-driven techniques like machine learning, deep learning, and data-driven digital twin technologies can get beyond these restrictions. Data-driven methods can also incorporate physics and domain knowledge to improve the explainability and interpretability of the results. This paper reviews the latest advancements and challenges of using data-driven techniques for battery state of health estimation and management in electric vehicles. The paper also discusses the future directions and opportunities for further research and development in this field. The survey scope spans publications from the year 2021 to 2023.

Keywords

• Machine learning models
• deep learning models
• data-driven methods
• lithium-ion batteries
• digital twin technology
• battery health prediction