## 2 Related Work  
In the burgeoning field of EV battery health prediction, a multitude of research endeavors have delved into the efficacy of diverse deep learning (DL) methodologies [13; 14]. [PERSON] et al. [15] systematically review DL methods for lithium battery management, focusing on predicting the remaining useful life (RUL), SoH, and thermal management. DL techniques like RNN, LSTM, CNN, GRU, DBN, DNN, and GAN are evaluated, highlighting their performance and limitations. The study emphasizes DL's superiority over traditional methods while acknowledging challenges like computational complexity and data acquisition, suggesting future research directions. [PERSON] et al. [16] introduced a deep-learning-based method for forecasting the SoH and RUL of lithium-ion batteries (LIBs), addressing diverse operating conditions and electrochemical properties. Their approach utilizes transfer learning to predict across battery types, minimizing data collection efforts and accurately forecasting SoH, even for in-use batteries. This pioneering study showcases transfer learning's efficacy in RUL prediction, promising practical battery management system advancements. The study [17] introduces a hybrid attention and deep learning approach for predicting the SoH of lithium-ion batteries. This method integrates temperature difference curves and health features from charging data, incorporating CNN, GRU RNN, and attention mechanisms. Comparative analysis against eleven methods shows superior prediction performance, with estimation errors consistently below 1.3%. The approach is robust to battery variations, ensuring accurate SoH estimation across diverse cells, thus enhancing electric vehicle reliability. In a related contribution, [PERSON] et al. [18] propose a data-driven framework for lithium-ion battery SoH estimation and forecasting. Their approach combines a DNN model, knowledge transfer, and ARIMA, achieving an impressive approximately 96% accuracy in SoH estimation using only 25% of the training dataset, marking a notable 10% improvement over prior methodologies. [PERSON] et al. [19] present a framework for in-operando using fast-rate voltage charge/discharge responses of Li-ion battery SoH during fast cycling, aiming to mitigate aging variability. Their approach integrates a pseudo-2D model, deep learning, and physical interpretation to diagnose SoH. By simulating electrochemical impacts and utilizing synthetic data for training, they achieve accurate real-time SoH identification during high-ratecharge/discharge cycles. Validation against standard analyses underscores the benefits of physics-based model synthesis for deep learning training. [PERSON] et al. (2020) propose a method for the accurate prediction of lithium-ion battery RUL, addressing complex degradation mechanisms and operational noise. Their approach combines complete ensemble empirical mode decomposition with adaptive noise and a Transformer-based network to separately handle short-term fluctuations and long-term residuals. Validated on public datasets, their method surpasses advanced approaches in prediction accuracy and training time, demonstrating its effectiveness and superiority. [PERSON] et al. (2021) introduced a physics-informed ensemble deep learning (PIEDL) framework to enhance SoC estimation in lithium-ion batteries. They integrate physical information extracted from a simplified electrochemical model (SEM) into the DL model, forming the physics-informed deep learning (PIDL) component. The ensemble deep learning (EDL) component combines DL models enhanced by various techniques, demonstrating superior SoC estimation compared to individual models in rigorous evaluations. In another contribution, the paper (2022) presents a comprehensive review of recent advancements in deep learning for lithium-ion battery SoC estimation. It outlines challenges, introduces a general deep learning framework, and reviews prevalent deep neural networks such as fully connected, recurrent, and convolutional networks for SoC estimation.