# State of Health Estimation for Lithium-Ion Batteries Based on TCN-RVM
### Literature Review
Reference [21] proposed a method for estimating the SOH of lithium-ion batteries using incremental capacity analysis (ICA), which combines grey relational analysis with the entropy weight method to evaluate battery SOH by calculating the grey relational degree between reference sequences and comparison sequences. In summary, the extraction of health factors for lithium-ion batteries plays an important role in SOH estimations. Similar modeling techniques have been used to predict performance degradation in perovskite photovoltaic systems, which face comparable challenges in reliability and lifespan estimations [9]. However, the effectiveness of data-driven models depends not only on the quality of battery aging data but also crucially on the selection of the model type and the setting of key parameters. Generally, data-driven models commonly include neural network-based models, support vector machine (SVM)-based models, and Gaussian process regression (GPR)-based models, among others. For instance, [PERSON] et al. [22] proposed a novel integrated prediction method for lithium-ion batteries based on Local Tangent Space Alignment (LTSA) feature extraction and an Adaptive Sliding Window Long Short-Term Memory Neural Network (ASW-LSTM NN). The authors of reference [23] conducted a comprehensive and independent review of various types and subcategories of machine learning techniques for State of Health (SOH) and Remaining Useful Life (RUL) estimation. [PERSON] et al. [24] employed the Lion Swarm Optimization algorithm to optimize the kernel features of an SVM model for estimating the SOH of NASA batteries. [PERSON] et al. [25] developed two machine learning data-driven models based on an improved Gaussian process regression. Furthermore, [PERSON] et al. [26] introduced an MIGA-GPR model based on incremental capacity analysis (ICA) for battery SOH estimation. Convolutional Neural Networks (CNNs), characterized by their unique architecture featuring local receptive fields and weight sharing, possess exceptional capabilities for local feature extraction and spatial information modeling [27; 28]. In the context of lithium-ion battery State of Health (SOH) estimations, battery charge/discharge curves exhibit complex temporal fluctuation characteristics, including local features such as voltage plateau variations and capacity regeneration segments. CNNs can adaptively capture critical segments within these charge/discharge curves (e.g., voltage inflection points during the constant-current charging phase) through multi-level convolutional kernels. By employing pooling operations to compress redundant information, CNNs effectively identify local nonlinear patterns inherent in the battery degradation process [29]. Furthermore, the weight sharing mechanism of CNNs significantly reduces the number of model parameters, making them more suitable than fully connected networks for processing high-dimensional temporal data. However, traditional CNN algorithms still exhibit significant limitations. Firstly, the stacking of deep network layers can easily lead to the vanishing gradient problem, impeding the propagation of degradation features through deep layers. Secondly, standard convolution operations exhibit a limited capability in modeling long-term temporal dependencies, potentially overlooking the global degradation trend of capacity fade during battery cycling cycles. To address the aforementioned limitations, current research proposes the following solutions. For instance, [PERSON] et al. [30] introduced Bayesian probability optimization to enhance CNN parameter robustness, thereby mitigating gradient instability issues. Reference [31] adopted a CNN-BiLSTM architecture to integrate local features with long-range dependencies; however, the recurrent structure suffers from inherent bottlenecks, including low training efficiency and poor real-time performance.