# State of Health Prediction for Lithium-Ion Batteries Using Transformer-LSTM Fusion Model
## 1 Introduction
Such models usually simulate the current and voltage changes of batteries and internal chemical reactions to achieve accurate prediction of battery SOH [15; 16; 17; 18; 19; 20]. For example, [PERSON] et al. [15] used the particle swarm optimization (PSO) algorithm to identify the key parameters of the electrochemical model of lithium batteries containing LiCoO\({}_{2}\) cathode materials, thereby generating corresponding battery models for healthy batteries and degraded batteries. [PERSON] et al. [16] simplified the battery model into differential algebraic equations and used parameters such as potential, lithium-ion concentration, and lithium molar flux to calculate and solve them. [PERSON] et al. [17] identified parameters such as anode equilibrium potential, cathode equilibrium potential, and solution conductivity based on the voltage and current-cycle data of the battery, and further established an electrochemical model for lithium-ion batteries. These electrochemical models can describe the internal electrochemical reactions of the battery with high accuracy, thereby achieving accurate prediction of the battery SOH. However, these models also have certain limitations. They usually rely on prior knowledge of battery electrochemical principles and a large amount of empirical data. In addition, the model establishment process is computationally intensive, the equations are complex, and the solutions are complicated, making them difficult to apply in actual scenarios. With the continuous development of artificial intelligence technology, data-driven machine learning and deep-learning methods have become a hot research direction for lithium battery SOH prediction. These methods analyze various measurement data collected from the battery during operation and use machine learning and deep-learning models to extract features and make predictions. Unlike physical model-based methods, data-driven methods do not rely on prior knowledge or experience of battery electrochemical principles. Instead, they process the data obtained during the charging and discharging process to extract features related to SOH, thereby realizing the prediction of battery status [21, 22, 23, 24, 25, 26]. For example, [PERSON] et al. [24] extracted four health indicators during the charging and discharging process, established an LSTM model to map the relationship between health indicators and battery SOH, and used a particle swarm optimization algorithm to optimize the key hyperparameters of the neural network. By utilizing optimization algorithms to search for hyperparameters, the model can effectively achieve the optimal performance. However, using only four direct health indicators as input may limit the model's generalization capabilities and the applicability of its features. Additionally, this approach may not fully leverage the potential of the model architecture, potentially hindering its ability to process more complex or diverse data. [PERSON] et al. [25] used a convolutional neural network (CNN) to extract health indicators and SOH changes between two consecutive charging and discharging cycles, and combined it with a random forest algorithm to generate the final SOH estimate. Although CNN demonstrates robust feature extraction capabilities, the process of extracting indicators is relatively complex and cumbersome. Moreover, it is highly susceptible to noise interference, which can adversely affect the model's stability and robustness. Furthermore, the generalization performance of the model still requires validation, potentially limiting its effectiveness and reliability in practical applications. [PERSON] et al. [26] extracted seven health indicators from the reference discharge data of the battery and combined a deep neural network (DNN) and a Markov chain model to predict SOH. Constructing a nonlinear DNN model with multiple hidden layers effectively extracts local features from data. However, this model is susceptible to capturing random noise from the training set, which can lead to overfitting.