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Researchers are focused toward the development of electric vehicles and hybrid electric vehicles for the preservation of the ecosystem. Energy storage, conversely, became essential with these changes in vehicular technology. This opens new research fronts and introduces complex problems that are to be investigated regarding the primary storage element. Battery modeling offers improved efficiency, safety, and reliability. Lithium-ion batteries are a popular source of energy in many applications, especially for electric vehicles because of their high performance and high energy density. However, there exist some inherent issues like complex electrochemical reactions that make accurate monitoring a challenging task. This article performs a comparative study of the existing battery state of health (SOH) monitoring and capacity estimation techniques using the NASA dataset for lithium-ion batteries. Charging cycle datasets for voltage, current, and temperature are used for feature identification and extraction. After the modeling of extracted features, the SOH and the capacity of four batteries are estimated using three data-driven techniques. Including convolutional neural network, feed-forward neural network, and long short-term memory (LSTM), and their results are compared. The comparative analysis highlights an outstanding performance and high accuracy of the LSTM-based machine learning technique because of the inherited long-term memory of the LSTM. The study, therefore, recommends the use of LSTM to researchers for battery health monitoring and capacity estimation with the highest possible accuracy. [ABSTRACT FROM AUTHOR]

[Display omitted] • The kinetics of lithium ions during de/lithiation was studied by analyzing the diffusion coefficient. • The overpotential through GITT was measured at different temperatures and current rates. • The charge transfer resistance was evaluated using EIS at different temperatures and voltages. • The kinetics of lithium diffusion is most feasible at an optimum condition of the current rate and operational temperature. • The fluctuation in the diffusion coefficient values is associated with the structural changes in NCA. Cathode degradation during the charge and discharge process is inevitable due to phase transformations during the de-/lithiation process. Various factors such as temperature, cut-off voltage, and current rates tend to cause irreversibility in the phase transformations during cycling, resulting in the quick aging of lithium-ion batteries for electric vehicle applications. This study aims to evaluate the trends of the lithium-ion diffusion coefficient as a function of the lithium-ion concentration and potential in the lithium nickel cobalt aluminum oxide (NCA) cathode of the VTC5 lithium-ion cell through measurements by the galvanostatic intermittent titration technique (GITT). The lithium-ion cell has shown the lowest diffusion coefficient value of 1.08E−17 m2 s−1 at 0.2C rate at 25 °C temperature and the highest diffusion coefficient of 7.2829E−16 m2 s−1 at the lowest current rate of 0.05C rate and 45 °C temperature during discharging. The trend of diffusion coefficient values for lithium de-/intercalation with changing temperature and current rates is investigated and correlated with the phase transformations in the structure of the cathode and is indicative of a mixed-phase reaction mechanism. The electrochemical impedance spectroscopy data reinforces the dependence on lithium-ion diffusion on temperature, and voltage values and is supported by comparative analysis. [ABSTRACT FROM AUTHOR]