###### Abstract
Intelligent network technology, combined with a variety of advanced technologies, establishes connections between vehicles, drivers and road conditions, which is an effective means of solving EMS problems. Considering future traffic information, [PERSON] et al. [15] proposed a vehicle automatic cruise control system to reduce red light parking time and fuel consumption. [PERSON] [16] obtained the speed limit and road topography information as constraints for MPC speed prediction through intelligent network technology and calculated the safe speed as a reference. [PERSON] [17] planned the speed curve based on V2X and tested the communication between controllers and intelligent transportation systems (ITS) over cellular networks. For a hybrid powertrain, the battery pack is a critically important part. Lower battery temperature inhibits battery discharge capacity. High battery temperature affects the battery life and has an impact on the stable operation of HEAGV. [PERSON] et al. [18] added the battery temperature as an optimization term to the cost function, which can achieve a balance between economy and battery aging. [PERSON] et al. [19] illustrated the enhancement mechanism of the thermal conductivity of composite phase change materials from the perspective of microstructure evolution. [PERSON] et al. [20] proposed a novel battery heat dissipation model to model and optimize battery thermal management for series hybrid electric vehicles under different standard driving cycles. [PERSON] et al. [21] reviewed the battery thermal management methods and compared the advantages and disadvantages of each method. [PERSON] et al. [22] proposed a new definition of the operating point for discharging Li-ion batteries based on degradation, autonomy, and heat generation to maximize battery life. [PERSON] and [PERSON] [23] predicted the thermal performance and thermal runaway risk of the battery pack during electric vertical takeoff and landing operation through multi-physics field system simulations. Battery temperature is also very important for HEAGVs. Current EMSs that consider the thermal dynamics of the battery in HEAGVs are less researched. Therefore, there is still room for improvement in EMSs. The SOC reference trajectory in hybrid electric vehicles is important for the MPC of EMSs and has been investigated by several researchers. [PERSON] et al. [24] aimed to divide the driving cycle into multiple segments and determine the reference SOC trajectory for each segment by a simple analytical formula. [PERSON] et al. [25] used Q-learning to quickly plan the optimal SOC reference. [PERSON] et al. [26] used DDPG to plan SOC reference trajectory adaptively and quickly. However, the driving cycles of HEAGV include air and ground phases, and the SOC reference trajectory is different from that of hybrid electric vehicles. When HEAGV is operating on the ground, the engine-generator set charges the battery, and the SOC curve tends to rise in preparation for the flight mode. When HEAGV is flying in the air, it consumes much electrical energy. The SOC changes drastically and shows a downward trend. Under the mode transition, the SOC reference trajectories in the two modes are different and segmented for planning. Therefore, it is necessary to plan the SOC reference trajectory of HEAGV. To bridge these gaps, this paper proposes an MPC-based EMS considering battery thermal dynamics. This paper has the following contributions. First, speed information is obtained through intelligent network technology to achieve the prediction of demand power, and then the SOC reference trajectory is planned in segments. Second, a PMP-MPC EMS framework is proposed. Third, the thermal dynamics of the battery are considered within the PMP-MPC framework. Finally, the effectiveness and superiority of the proposed HEAGV control strategy are verified by comparing them with different methods. The rest of this paper is as follows. In Section 2, the series hybrid powertrain model is established, including the battery thermal model.