## 3 Results
### Statistical Results  
In order to ascertain the effects of engine type (between-subject factor, two levels: BEVs/ICEs), temperature class (within-subject factor, six levels: 1-6), ambient temperature class (between-subject factor, five levels: 1-5), and their interactions on the time share, a statistical analysis was conducted. The analysis was conducted using a mixed ANOVA, as mentioned in the Section 2.2 (see Table 3) [23]. The results demonstrated that the main effect of temperature class on the time share was statistically highly significant (F(5, 158,304) = 126.613, \(p\) < 0.001). This indicates that the temperature class exerted a significant influence on the time share. The effect size, as measured by the sum of squares, was the largest of all the factors analyzed, at over 73 million. The main effect of engine type and ambient temperature class and their interactions with other factors were not statistically significant, as the \(p\)-values were 1.0, which is well above the usual significance level of 0.05. The F-values of these effects were almost zero, indicating that there was negligible variance between the groups. The interaction between engine type and temperature class was also statistically significant (F(5, 158,304) = 4017.635, \(p\) < 0.001), indicating that the effect of temperature class on the position of the specific vehicles varied by engine type. The interaction between temperature class and ambient temperature class was also statistically significant (F(20, 158,304) = 1412.962, \(p\) < 0.001), indicating that the effect of temperature class on the time fraction varied by ambient temperature class. The three-way interaction between engine type, temperature class. and ambient temperature class was also statistically significant (F(20, 158,304) = 124.518, \(p\) < 0.001), indicating a complex interaction between these three factors. To summarize, temperature class has a strong influence on the time fraction, and this effect is modulated by engine type and ambient temperature class. As part of the statistical analysis, a mixed linear model (MLM) was used to analyze the influence of two fixed factors--vehicle engine type (BEV and ICE) and ambient temperature class (AT)--and their interactions on the proportion of time (time share) spent in a particular temperature class (TC) [23]. The results of the model showed that the main effects of temperature class (TC) on the proportion of time were statistically highly significant. The coefficients for TC2 to 6 ranged from 66.809 to \(-\)0.209, with TC2 having the strongest effect with a coefficient of 66.809 and a z-value of 257.494 (_p_ < 0.001). This indicates that TC2 had the greatest influence on the time share compared to the reference category (TC1). The interaction between engine type and TC also showed significant effects. In particular, for ICE vehicles in TC2, the time share was significantly lower (coefficient \(-\)13.379, z = -39.041, \(p\) < 0.001), whereas in TC3, the time share was significantly higher for ICE vehicles (coefficient 13.092, z = 38.202, \(p\) < 0.001). These significant interaction effects show that the influence of TC on the time share varied according to engine type. The three-way interaction between engine type, TC, and AT also showed significant effects. For example, the interaction between ICE vehicles, TC2, and AT2 was significantly negative (coefficient \(-\)4.128, z = -7.527, \(p\) < 0.001), indicating that the time share was lower for ICE vehicles in this particular combination of temperature conditions. In contrast, the interaction between ICE vehicles, TC3, and AT5 was significantly positive (coefficient 9.060, z = 10.090, \(p\) < 0.001), indicating a higher time share in this configuration. The variance component for the groups (vehicles) was estimated to be 0.198, indicating some variability in the proportion of time spent in temperature classes between vehicles. However, it should be noted that the model did not converge, which may affect the interpretation of the results.