## 5 Conclusions  
To increase the driving range and market share of CAEFVs as well as to solve the problem of their power and time wastage at signal intersections owing to the frequent speed fluctuations, an optimal driving model of CAEFVs at signal intersections with different passing strategies is proposed. Based on the WCSSI, a joint multi-objective optimisation model that considers the passing efficiency, vehicle energy consumption, driving comfort, and charging efficiency was developed. Based on the actual needs of CAEFVs, the passing modes were divided into the CPS, BPS, and TPS with various weighting coefficients. The obtained results revealed that a single vehicle could pass faster through a signalised intersection in the TPS mode, while more charging power could be obtained with the CPS. The effectiveness of the proposed model was verified by estimating the power consumption in different passing modes using the initial speed, WCL length, and charging efficiency as the key parameters. It was found that the CPS significantly increased the charging power at the expense of a small amount of passing time, while the BPS increased both the passing efficiency and charging power, and the model exhibited high robustness in different initial states. Moreover, the WCL length and charging efficiency produced a significant impact on the SOC and power replenishment of CAEFVs. For the fleet of CAEFVs, the adaptability of the optimal driving model was verified by evaluating and analysing the trajectories, speeds, and SOCs of the vehicles under the influence of different policies. The obtained results indicated that the model utilising a proper passing strategy could identify the optimal vehicle driving path through multi-objective speed planning. Moreover, speed-guidance recommendations for fleets that could help pass through signalised intersections without stopping were provided to satisfy the differentiated demand while taking the WCL into account. For the mixed traffic, the TPS significantly outperformed the unguided model in terms of the power consumption and average travel time. The BPS and CPS outperformed the unguided model in terms of the charging benefits, while the CPS negatively influenced the average travel time. It is noteworthy that a lower MPR generates trajectory fluctuations strongly affecting the vehicle efficiency and that traffic benefits are closely related to the type of the leading vehicle. However, owing to the complexity of the existing traffic systems, it is necessary to extend the list of the key factors affecting the model validity and calibrate the model parameters more accurately. In addition, the influence of background vehicles on lane changes must be considered to satisfy the real-world requirements established by the current developments in autonomous driving and other technologies. Future research should focus on (1) studying the game effects between conflicting control strategies in mixed traffic where the three strategies coexist with human driving and (2) improving and optimising the proposed control method. Conceptualization, W.W., S.F., Z.W., X.Y. and K.M. ; methodology, W.W., S.F., Z.W., X.Y. and K.M. ; software, W.W., S.F. and Z.W. ; validation, W.W., X.Y. and K.M. ; investigation, W.W., S.F., Z.W., X.Y. and K.M. ; resources, S.F., Z.W., X.Y. and K.M. ; writing--original draft preparation, W.W., S.F. and Z.W. ; writing--review and editing, W.W., X.Y. and K.M. ; supervision, W.W., S.F., Z.W., X.Y. and K.M. All authors have read and agreed to the published version of the manuscript. Figure 7: Power consumption (consumption, recovery, and charging) values and average travel times obtained in different driving modes. **Funding:** This research was funded by Open Project of Shandong Key Laboratory of Smart Transportation (Preparation), grant number 2021 SDKLT5003; and National Natural Science Foundation of China, grant number 52202489. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.