# Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Health Features
However, the model has several disadvantages, including high model complexity (requiring the solution of high-dimensional partial differential equations), time-consuming calculations (with a single simulation taking several hours); difficulty in parameter identification (e.g., diffusion coefficients and reaction rate constants); and challenges in real-time online application. The principle of the equivalent circuit model is to use circuit elements such as resistors and capacitors to simulate the dynamic response of the battery [13]. Common models usually include the following: (1) Rint model: only contains ohmic internal resistance; (2) Thevenin model: series RC network simulates polarization effect; (3) Dual Polarization (DP) model: dual RC network distinguishes between fast and slow polarization processes. The advantages and disadvantages of this model are also obvious. The advantage is that the calculation is simple (high real-time performance) and suitable for BMS online estimation [14]. One disadvantage is that it cannot describe the internal aging mechanism of the battery and can only reflect the changes in macroscopic parameters. In order to solve the problem of inconsistency of electrochemical parameters caused by differences in battery manufacturing, [PERSON] et al. [15] characterized individual differences through initial cycle state data and constructed a physical information dual neural network (PIDNN) to achieve dual functions: dynamic estimation of electrochemical parameters (such as lithium ion diffusion coefficient, reaction rate constant, etc.) and synchronous simulation of lithium ion concentration distribution in solid electrodes and electrolytes. The electrochemical model based on physical principles is combined with the deep learning model to break through the limitations of traditional data-driven methods that lack physical interpretability. It provides a new paradigm for SOH estimation with few samples, high precision, and clear interpretability. [PERSON] et al. [16] designed the battery physical information neural network (BatterPINN) based on the mathematical model of solid electrolyte interface (SEI) film growth, and they embedded physical laws such as SEI film thickness evolution into the network structure. By explicitly modeling the SEI film growth process, the quantitative relationship between capacity decay and SEI film thickness is revealed, providing physical insights into the prediction results and overcoming the limitations of the \"black box model\". In addition, the improved composite multi-scale Hilbert cumulative residual entropy algorithm is used to automatically extract high-quality health features directly from battery voltage and current data, providing a new idea for accurate, interpretable, and easy-to-deploy battery SOH estimation. [PERSON] et al. [17] continuously monitored Thevenin ECM (equivalent circuit model) parameters through cycle aging experiments and quantified their evolution with SOH degradation. After integrating the SOH factor, the voltage prediction error of the Thevenin model on the aged battery was reduced by about 40%, and the average accuracy of the whole life cycle was more than 99%. The system revealed the quantitative influence of SOH on Thevenin ECM parameters and realized the efficient prediction of ECM parameters under the synergy of multiple factors. It provided a core algorithm for adaptive ECM parameter update for smart BMS. [PERSON] et al. [18] constructed an enhanced equivalent circuit model (ECMC) by adding capacitor elements to the classic ECM to improve the fitting ability of full-band EIS data. Optimization algorithms such as the nonlinear least squares method were used to identify ECMC model parameters and capture the evolution of parameters with battery aging. This provides a high-precision and highly adaptable SOH estimation scheme for BMS, particularly for actual working conditions under severe temperature fluctuations.