## 2 Methods and Models for Evaluating Battery SOH Literature Review
The algorithm developed can raise warnings of battery SOH deterioration, but it still needs improvements to increase system reliability. This application was tested in a controlled environment. For future research, [PERSON] et al. (2019) suggested applying the algorithm in real-world scenarios, along with field tests. In the article presented by [PERSON] et al. (2019), a model based on the Butler-Volmer equation was introduced, capable of accurately predicting the battery's voltage response across a wide range of discharge currents, SOC, and temperatures. The model successfully predicted the battery's voltage drop even in a dynamic environment with varying electrical loads and operating conditions, including scenarios with low battery SOC. [PERSON] (2019) proposed a method for estimating battery SOH during the startup phase, using only the SOC, battery temperature, and the voltage drop from the resting state to the first voltage dip, as well as between the first and second voltage dips. The result was a system capable of estimating the SOH without the need for a current sensor installed in the vehicle. In the work presented by [PERSON] et al. (2019), a design and implementation of an SOH and SOC estimator model were presented. The SOH estimation was based on the battery's internal resistance, while the SOC was determined using the Coulomb counting method during the battery's charge and discharge phases. For SOC estimation, the measurements considered the currents entering and exiting the battery, while the SOH was assessed during the initial charging stages of the battery. [PERSON] et al. (2019) provided an overview indicating that automotive lead-acid battery technologies still face numerous challenges and are expected to continue evolving over the next two decades. They also highlighted the importance of battery sensors (BMS) for real-time monitoring of battery current, voltage, and temperature, along with the implementation of adaptive algorithms and high parameterization work as key paths for advancement. [PERSON] et al. (2019) further suggested that determining the available battery capacity throughout its lifespan, which is directly related to the SOH, remains an unsolved issue. [PERSON] et al. (2019) presented a study with a focus on battery modeling. In this case, a distinction was made between the explanation of the thermal and electrical phenomena of the battery, as well as the evolution of major battery failures. This was followed by the application of a two-time-scale method in a model-based estimator. The algorithm in the broader time scale monitors the battery and adjusts the parameters of the model on the more immediate scale. It was concluded that the hybrid algorithm system was able to monitor the battery and continuously self-adjust over time. From a hardware perspective, and with regard to the study of battery modeling, [PERSON] et al. (2019) presented a battery SOC and SOH estimator based on a Neuro-Fuzzy approach and statistical models, implemented on a Field-Programmable Gate Array (FFGA) within a BMS. The model took into account the consumption time, electrical current drain, terminal voltage, temperature, and internal battery resistance. This study provided an alternative for BMS systems, highlighting advantages such as low non-recurring engineering costs, low power consumption, high processing speed, configurable logic, large storage capacity, and more flexible interfaces. [PERSON] et al. (2019) developed Kalman filter studies by using an extended double Kalman Filter (KF) to estimate the SOC and SOH in a case study focused on battery life cycle analysis. They used battery resistance and capacity estimates as parameters. The study concluded that this method is essential for improving model accuracy and enhancing the robustness of the filters, especially for aged batteries. Further, [PERSON] et al. [34] proposed a data-driven algorithm under multiple conditions to estimate the SOH using known load applications.