## 5 Analyses and Discussion
### Discussion of the Impacts on the Degree of Efficiency of PV-Powered Vehicles  
At present, PV installations integrated on top of electric vehicles still represent a new area for PV energy systems [9]. While concepts are often discussed, in this paper, we present measurement data illustrating some of the major challenges that need to be solved in order to provide PV-powered vehicles with enough PV energy. The PV energy gained could reduce the need for energy from the power grid and, therefore, reduce the amount of load that an electric vehicle represents to the grid [3]. This paper shows environmental data obtained in Oulu, Finland. It is worth noting that, in other geographical areas, the environmental conditions may be different. For example, in urban areas, shade from the surrounding environment will occur, reducing the amount of energy obtained from the vehicle's PV installation. While PV-powered charging stations are subject to stationary conditions (i.e., parking conditions), PV-powered electric vehicles are also subject to dynamic conditions (i.e., driving conditions). The PV energy will be lower in driving conditions than in parking conditions. As seen in Figures 19 and 20, the solar radiation level (\(\lambda\)) in parking conditions is approximately 680 W/m\({}^{2}\); in Figure 19, the average level is about 597 W/m\({}^{2}\); and in Figure 20, it is about 648 W/m\({}^{2}\). Various parameters, such as the  
As a result, even though the average solar radiation level is still notable, due to limitations with the MPPT tracker, the degree of efficiency will be reduced [13; 15]. Commonly, MPPT tracking algorithms update parameters on a regular basis. For example, in voltage-based maximum power point tracking (VMPPT), the power converter is disconnected to measure the open-circuit voltage (\(V_{\alpha c}\)). The voltage in the MPP (\(V_{mpp}\)) is estimated with the help of a multiplication factor, and then the operating voltage (\(V_{\varphi}\)) is adjusted to match \(V_{mpp}\). Likewise, in the perturb-and-observe (P&O) algorithm, the output voltage is measured, and, depending on the change in power, the \(V_{\alpha c}\) is either increased or decreased [32]. In driving conditions, for frequently changing environmental conditions, as seen in Figures 19 and 20 between 150 and 180 s, when measuring \(P_{out}\) at 6000 samples per second, the degree of efficiency of the P&O algorithm (\(\eta_{mppt}\)) is about 99.67%. At 1000 samples per second, \(\eta_{mppt}\) = 98.61%, and at 100 samples per second, \(\eta_{mppt}\) = 98.23%. Moreover, lowering the sampling rates further results in a \(\eta_{mppt}\) as low as 79.64%. The PV cell is disconnected from the power converter to measure \(P_{out}\), hence lowering \(\eta_{mppt}\). This reduction has not been taking into consideration in simulations. However, in driving conditions, \(V_{mpp}\) changes quickly and rapidly. For example, if \(P_{out}\) would be obtained more frequently, then the PV cell is disconnected from the power converter for a too long period of time, resulting in a very low degree of efficiency. Obtaining \(P_{out}\) from a reference cell is difficult, as the solar radiation level can be different from the main PV cell or panel. The P&O algorithm can fail under rapidly changing environmental conditions [32]. As a possible solution, \(V_{\varphi}\) could be kept constant at a voltage level that favors higher solar radiation levels. Doing so would eliminate the requirement to update parameters frequently, and the power converter could be connected to the PV cell at all times. The overall system efficiency would be than at about 95% under rapidly changing environmental conditions. PV simulation models can help to estimate potential extensions of the ERE of electric vehicles in different environmental conditions. However, as each PV cell in the installation receives a different amount of solar radiation, the cells also need to be simulated individually [33].