###### Abstract
In this paper, the authors compared Li-ion batteries with other battery technologies such as lead-acid, Ni-Cd, and Ni-MH batteries in terms of chemistry, specific energy density, cycle life, cell voltage, self-discharge characteristics, safety, and toxicity. Based on this comparison, it is very clear that Li-ion batteries surpass the energy densities of the other samplesand are less toxic when compared to Ni-MH batteries. Moreover, Li-ion batteries are currently in demand and will certainly meet or exceed the 2022 US Department of Energy's target in terms of cost estimate thanks to engineering improvements and material advancements, as per the authors' review. Various low-cost, high-performance composite nanomaterials will replace the precious, high-cost, and toxic materials, for example in cathodic and anodic electrodes, binders, and separators within and throughout Li-ion batteries. The innovation of such composite nanomaterials will undoubtedly enhance the specific capacity, rate capability, and cycle life of Li-ion batteries while ensuring their overall safety. Additionally, this review captured the ideal electrochemical impedance spectra (EIS) of standard indium vanadium oxide anode electrodes with different voltages during charge/discharge cycles and the resistance-capacitance equivalent circuit. An advanced equivalent circuit model (ECM) was developed to determine the accurate state-of-charge (SOC) by [PERSON] and co-workers [5]. Some of the unique contributions of the abovementioned paper include: (i) it addresses the New European Driving Cycle (NEDC) and the Dynamic Stress Test (DST) and hence obtains a more suitable battery model for the entire SOC area, (ii) it identifies the subarea parametric classification method using the particle swarm optimization (PSO) algorithm to improve the global accuracy, and (iii) it extends the SOC estimator using a Kalman filter to improve the ECM's accuracy and robustness. As per the single particle model, the authors derived the SOC at the particle surface, SOC\({}_{\text{surf}}\) = SOC\({}_{\text{avg}}\) + \(\Delta\)SOC, where SOC\({}_{\text{avg}}\) is the average SOC, meaning the average concentration of Li\({}^{+}\) in the electrode particle, and \(\Delta\)SOC is the difference between SOC\({}_{\text{avg}}\) and SOC\({}_{\text{surf}}\). An extended Kalman filter (EKF) model proposed by the authors showed greater accuracy than the ECM, specifically in low SOC areas. This is because the EKF model considers the noise characteristics of the current and voltage sensors, and effectively overcomes the problem of random errors. An optimal sizing of the electrochemical (battery) energy storage system for large-scale wind farms, considering auxiliary services compensation options such as scheduling plans, is outlined by [PERSON] et al. [6]. This optimization model not only smoothens the fluctuations of wind output based on the wind peaking demand, but also compensates the wind output to make up the wind forecast error. The multiple steps of the abovementioned optimization model are given here. Firstly, the uncertainty regarding hourly fluctuations due to wind output are analyzed and then the associated auxiliary service cost of EES mitigation is quantified. Secondly, the equivalent life loss is introduced by considering the impact of the battery storage systems' irregular charge/discharge characteristics on the cycle life. It is shown by the authors that the cycle life exponentially decreases with the increase of the charge/discharge depth in terms of percentage. An optimum of 9000 cycles was demonstrated by this model for the charge/discharge depth of 20%. The results also showed that the EES system can be integrated with large-scale wind farms with investment costs of less than $ 360/kWh. Moreover, cycle life of more than 2800 times can be achieved by taking into consideration of the auxiliary service compensation. In their excellent research paper, [PERSON] et al.