# Machine Learning-Based Lithium Battery State of Health Prediction Research
## 1 Introduction
In contrast, data-driven SOH estimation methods do not require precise modeling of the complex electrochemical mechanisms inside the battery, nor do they necessitate identification of the battery's internal parameters [12]. Instead, these methods directly capture health indicators (HIs) related to aging from large amounts of historical data and utilize machine learning algorithms to establish a mapping relationship between HIs and SOH, thereby achieving SOH prediction. As a result, data-driven methods exhibit high transferability, robustness, and generalization capabilities. SVR and artificial neural networks (ANNs) are currently two widely used data-driven techniques. ANNs are composed of a large number of interconnected neurons, following specific rules [13]. Through repeated training on experimental data, the model gradually optimizes the weight distributions and thresholds between neurons, thereby constructing accurate data models to predict system states. [PERSON] et al. [14] used a three-layer BP neural network for modeling to estimate battery SOH. However, traditional BP neural networks are prone to local optima, leading to inaccurate SOH predictions. SVR, as a supervised learning algorithm, effectively addresses the issues of local optima and low convergence efficiency inherent in traditional neural networks. [PERSON] et al. [15] reduced the dimensionality of battery current, voltage, and temperature data to obtain two His, operating temperature and energy efficiency, and combined these with the SVR algorithm to achieve high-accuracy SOH predictions. Similarly, [PERSON] et al. [16] applied deep neural networks (DNNs) for SOH estimation, demonstrating that DNNs have stronger high-level feature extraction capabilities and higher prediction accuracy compared to BP neural networks. CNN, as feedforward neural networks, can efficiently extract local features of battery performance data with high computational efficiency. [PERSON] et al. [17] implemented online capacity estimation of lithium batteries using a deep CNN model. The model automatically learned features from charging data, achieving root mean square errors below 2.0% under varying conditions. [PERSON] et al. [18] argued that, compared to feedforward neural networks, LSTM networks are capable of capturing time-dependent information in feature sequences, providing higher accuracy. [PERSON] et al. [19] compared the SOH estimation performance of three data-driven algorithms: nonlinear autoregressive neural networks, CNN, and LSTM. Their experimental results showed that LSTM outperformed the other two algorithms in estimation accuracy under different operating conditions. However, both SVR and neural network methods often face challenges such as difficulty in determining hyperparameters and slow convergence, which directly affect prediction performance. To address these challenges, various intelligent optimization algorithms have been developed to enhance these models. For example, [PERSON] et al. [20] used an SVR model based on an improved ant lion optimization algorithm to estimate battery SOH, achieving a MAPE of 0.53%, significantly improving prediction accuracy. [PERSON] et al. [21] addressed uncertainties in charge and discharge characteristics during practical applications by using a one-dimensional CNN model. They used data from multiple random time intervals of charging curves as input and optimized part of the model parameters with an improved PSO algorithm, thereby improving capacity prediction accuracy. [PERSON] et al. [22] proposed an adaptive LSTM model for battery SOH estimation, which, combined with Bayesian optimization, required only 25% of aging data for training to achieve relatively accurate SOH estimation. Additionally, integrating various data-driven methods has further enhanced SOH prediction accuracy. For instance, [PERSON] et al.