# Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Health Features
Compared with the above methods, the data-driven method analyzes the characteristics of battery aging cycle data in a simple way and establishes a relationship model between battery characteristics and SOH [19]. The principle of the data-driven method is that the data-driven method abandons physical modeling and directly mines the statistical association between battery aging characteristics and SOH from historical data. The nonlinear mapping relationship between input (health features) and output (SOH) is constructed through machine learning algorithms [20; 21]. The core steps of this data-driven model are health feature (HF) extraction, typical algorithm selection, and model training and verification. Among them, common traditional machine learning includes support vector regression (SVR): mapping high-dimensional space through kernel functions (such as RBF) to construct regression hyperplanes; random forests: integrating multiple decision trees to reduce the risk of overfitting. Deep learning includes long short-term memory networks (LSTM): capturing the temporal dependence of capacity decay; convolutional neural networks (CNNs): extracting local aging features from charge and discharge curve images [22; 23; 24; 25]. The above models have the advantages of not requiring prior physical knowledge, adapting to complex nonlinear relationships, and being able to integrate multi-source data (such as temperature, current, and voltage series) [26]. However, they rely on a large amount of labeled data (complete aging cycle data is required); the model has poor interpretability (the \"black box\" feature limits engineering trust); and the generalization ability for unseen working conditions (such as extreme temperature and fast charging strategy) is insufficient [27]. [PERSON] et al. [28] proposed a CNN-LSTM-Skip hybrid model. The CNN extracts local time series features, LSTM captures long-term dependencies, and skip connections achieve cross-layer feature complementarity, enhancing the model's ability to characterize battery aging patterns. The method was verified on a public dataset, covering different battery types, charging and discharging conditions, and aging trajectories. It provides an efficient, robust, and scalable SOH estimation solution for battery health management. [PERSON] et al. [29] used CNN-LSTM-Attention tri-modal fusion to extract the local spatiotemporal features of the battery capacity/voltage curve (such as charging and discharging platform fluctuations); the LSTM layer captured the long-term sequence dependencies of capacity decay, and the attention mechanism dynamically focused on key degradation stages (such as capacity inflection points and mutation intervals). Through attention weight allocation, the model's sensitivity to capacity regeneration phenomena was enhanced, and noise interference was suppressed. This approach improves the ability to characterize nonlinear degradation trajectories, providing a highly robust and generalizable prediction scheme for achieving more accurate SOH prediction and estimation of batteries. Combining the advantages and disadvantages of the above models, this paper focuses on analyzing and studying the key challenges in health feature construction and algorithms in response to the insufficient research on existing lithium-ion battery aging prediction trajectories. This paper innovatively adds the four-vector (FVIM) optimization algorithm to CNN-LSTM-Attention and integrates multiple health factors to predict and estimate battery SOH. First, HFs with high correlation with SOH are extracted from the battery charging cycle, then the Pearson and Spearman correlation coefficients are used to evaluate the correlation between health factors and SOH. Next, a data-driven model is constructed, and the model parameters are optimized using the four-vector optimization algorithm. Subsequently some key network layers are fine-tuned to adapt to the dataset used.