# State of Health Estimation for Lithium-Ion Batteries Based on TCN-RVM
###### Abstract  
A State of Health (SOH) estimation of lithium-ion batteries is a core function of battery management systems, directly affecting the safe operation, lifetime prediction, and economic efficiency of batteries. However, existing methods still face challenges in balancing feature robustness and model generalization ability; for instance, some studies rely on features whose physical correlation with SOH lacks strict verification, or the models struggle to simultaneously capture the temporal dynamics of health factors and nonlinear mapping relationships. To address this, this paper proposes an SOH estimation method based on incremental capacity (IC) curves and a Temporal Convolutional Network--Relevance Vector Machine (TCN-RVM) model, with core innovations reflected in two aspects. Firstly, five health factors are extracted from IC curves, and the strong correlation between these features and SOH is verified using both Pearson and Spearman coefficients, ensuring the physical rationality and statistical significance of feature selection. Secondly, the TCN-RVM model is constructed to achieve complementary advantages. The dilated causal convolution of TCN is used to extract temporal local features of health factors, addressing the insufficient capture of long-range dependencies in traditional models; meanwhile, the Bayesian inference framework of RVM is integrated to enhance the nonlinear mapping capability and small-sample generalization, avoiding the overfitting tendency of complex models. Experimental validation is conducted using the lithium-ion battery dataset from the University of Maryland. The results show that the mean absolute error of the SOH estimation using the proposed method does not exceed 0.72%, which is significantly superior to comparative models such as CNN-GRU, KELM, and SVM, demonstrating higher accuracy and reliability compared with other models.  
lithium-ion battery; health indicators; TCN-RVM algorithm; SOH estimation 2025158213400 2025158213400 2025158213400 2025158213400 25158213400 25158213400 251 However, during long-term use, batteries inevitably undergo complex electrochemical aging processes, specifically manifested as capacity fade, increased resistance, and decreased thermal stability [4]. These aging phenomena directly threaten system safety and shorten the service life. It should be noted that SOH discussed in this paper specifically refers to the indicator of capacity fade, rather than other SOH measurement methods such as increased internal resistance.  
As a core indicator for quantifying the degree of aging, the State of Health (SOH) of batteries reflects the percentage of the current maximum capacity to the initial capacity. Its accurate estimation is the cornerstone for battery management systems (BMS) to realize intelligent charge-discharge control, thermal management optimization, and lifetime prediction. State monitoring is a basic function of BMS [5], specifically including the evaluation of State of Charge (SOC) [6], State of Health (SOH), State of Energy (SOE), and State of Power (SOP) [7]. SOH is defined as the ratio of the aged capacity to the new capacity of the battery. However, SOH is not a directly observable quantity; it is a highly nonlinear entity and largely dependent on factors such as the fluctuation of load curves, ambient temperature, depth of discharge, and self-discharge. Therefore, the accurate estimation of SOH is usually a cumbersome calculation [8]. Thus, the development of high-precision and high-robustness SOH estimation methods has become an important issue for promoting the sustainable development of battery technologies. Similar modeling techniques have been explored for predicting performance degradation in perovskite photovoltaic systems, which face comparable challenges in reliability and lifespan estimations [9].