# Dynamic Energy Management Strategy of a Solar-and-Energy Storage-Integrated Smart Charging Station
## 2 Description
### Energy Storage System  
The ESS utilizes lithium-ion battery cells, selected for their high energy density and efficient energy conversion. The estimated lifespan of these cells is around 10-15 years, a figure that is based on manufacturer specifications and empirical data gathered under typical operational conditions. This lifespan projection incorporates various factors, includ \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **ESS Power** & **ESS Capacity** & **Solar Installation Capacity** \\ \hline Proportion of & & & \\ Configuration & 1.0 (kW) & 2.61 (kWh) & 1.5 (kW) \\ \hline Installation Capacity & 300 kW & 800 kWh & 450 kW \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Configuration of the solar-and-energy storage-integrated charging station. Figure 2: System architectural diagram. ing the number of charge-discharge cycles, depth of discharge, operational temperature, and the effectiveness of battery management practices. These batteries are notable for their high round-trip efficiency and rapid responsiveness, making them particularly suitable for critical functions such as smoothing out solar energy fluctuations, aligning with time-based electricity pricing to reduce peak demand, participating in electricity price arbitrage, avoiding penalties associated with operating charging stations beyond their contracted capacities, and ensuring a steady power supply to these stations. The equivalent model of lithium-ion battery employed in this study is based on the battery model proposed by [PERSON] [16]. The output voltage, discharge voltage, and charge voltage are, respectively, derived from Equations (1)-(3). \[V_{BAT}=E_{BAT}-R_{int}i \tag{1}\]  
\[E_{bat}^{dis}=E_{0}-K\frac{Q}{Q-i_{t}}(i_{t}+i^{*})+Ae^{(-B\;i)} \tag{2}\]  
\[E_{bat}^{ch}=E_{0}-K\frac{Q}{i_{t}-0.1Q}i^{*}-K\frac{Q}{Q-i_{t}}i_{t}-Ae^{(-B\; i_{t})} \tag{3}\]  
where \(V_{bat}\) is the output voltage of the battery, \(E_{bat}^{dis}\) is the discharge voltage of the battery, \(E_{bat}^{ch}\) is the charge voltage of the battery, \(E_{BAT}\) is the no-load voltage of the battery, \(R_{int}\) is the internal resistance of the battery, \(i\) is the battery current, \(E_{0}\) is the battery constant voltage, \(K\) is the polarization constant, \(Q\) is the battery capacity, \(i_{t}\) is the actual battery charge, \(i^{*}\) is the filtered current, \(A\) is the exponential zone amplitude, and \(B\) is the exponential zone time constant inverse. Regarding the treatment of losses and round-trip efficiency in the battery system, the current model does not explicitly quantify these aspects. This approach stems from the inherent complexities involved in accurately calculating both round-trip efficiency and system losses, which can vary significantly based on specific usage patterns, environmental conditions, and battery aging. Round-trip efficiency, in particular, is a crucial parameter that can impact overall system performance; however, its precise value is challenging to determine due to fluctuations in operational conditions and the efficiency of energy conversion processes. Similarly, the losses in the battery system, which may include factors such as thermal losses, energy conversion inefficiencies, and degradation over time, are also not directly accounted for in the model. These exclusions are a reflection of the difficulties in providing a one-size-fits-all quantification for these parameters, given their dependency on a wide range of variables. According to the system design, the energy storage device has a capacity of 300 kW/800 kWh. Taking into account the depth of discharge (DoD) of the lithium-ion battery, the \(SOC\) is set to be between 20% and 90%. The \(SOC\) calculation method is shown in Equation (4), and this capacity ensures the system can sustain the provision of charging services for a certain duration.