# State of Health Estimation for Lithium-Ion Batteries Based on TCN-RVM
### Literature Review  
Currently, the commonly used methods for estimating the SOH of lithium-ion batteries mainly include model-based methods [10] and data-driven methods [11]. Model-based estimation methods for lithium-ion batteries are mainly divided into electrochemical models [12] and equivalent circuit models [13] according to different mechanisms. Electrochemical models simulate battery behavior by describing internal chemical reactions and mass transfer processes, involving complex partial differential equations and requiring accurate parameters such as the diffusion coefficients of positive and negative electrode materials and reaction rate parameters [14]. Although they offer high estimation accuracy and strong physical interpretability, they involve complex partial differential equations with a large number of parameters. Their solution relies on numerical algorithms such as the finite element method, resulting in high computational complexity for a single simulation. This requires support from high-performance computing equipment, making it difficult to meet real-time requirements. Moreover, in practice, they are highly affected by parameters, are not suitable for control design, and their engineering applications are limited. Equivalent circuit models estimate SOH through changes in internal resistance and capacity parameters, which can be further divided into Rint, RC, Thevenin, etc. Reference [15] proposed a method based on a second-order RC model, which combines incremental capacity analysis (ICA) and differential voltage analysis (DVA) with an improved bidirectional gated recurrent unit (BiGRU) to estimate the State of Health (SOH) of batteries. Reference [16] realized the joint estimation of the SOC and SOH of lithium-ion batteries by optimizing the Thevenin model. Equivalent circuit models are suitable for real-time online estimation scenarios and have low hardware resource requirements (e.g., they can be easily deployed on embedded systems). However, due to the simplified physical assumptions, their estimation accuracy in complex aging scenarios is easily affected, and they have problems such as difficult parameter identification and poor adaptability to dynamic working conditions [17]. In contrast, data-driven methods do not rely on complex physical mechanism modeling. Instead, they directly establish the mapping relationship between input features and SOH through data mining, which can reduce the computational cost to a lower level. Meanwhile, they can balance estimation accuracy and real-time performance to a certain extent, thus providing an effective alternative approach to address the engineering bottlenecks of high-complexity physical models. In contrast, data-driven SOH estimation models predict SOH by mining statistical patterns in historical data, without the necessary reliance on the internal degradation mechanisms of batteries, thus avoiding complex mechanism modeling. They have gained favor among researchers in recent years [17; 18]. A data-driven SOH estimation model can be regarded as a black box, which includes inputs of potential features affecting battery aging and outputs of SOH estimations [19]. Therefore, data-driven models require a large battery dataset. Only through processing and analyzing the data in the dataset and extracting the required health factors can an accurate estimation of the SOH of lithium-ion batteries be achieved. Reference [20] proposed a fast online State of Health estimation method for lithium-ion batteries based on incremental capacity curves processed by a Gaussian filter. This method reduces noise in the IC curve through the Gaussian filter, making the identification of battery aging characteristics more accurate. Using this technology, partial charging voltage curves can be applied to SOH estimations.