###### Abstract  
Hybrid electric air-ground vehicles (HEAGVs), which can run on the land and fly in the air, are considered a promising future transportation. The operation of HEAGVs, accompanied by high energy consumption, could lead to increasing battery temperature, which may affect the lifespan of the battery. To make the battery last longer and improve energy efficiency, an effective energy management strategy (EMS) is necessary for the operation of HEAGVs. In this regard, this paper proposes a predictive EMS based on model predictive control (MPC). Firstly, speed information is obtained by intelligent network technology to achieve a prediction of power demand, and then the state of charge (SOC) reference trajectory is planned. Secondly, a [PERSON]'s minimum principle-based model predictive control (PMP-MPC) framework is proposed, including battery thermal dynamics. Under the framework, fuel efficiency is improved by reducing the temperature of the battery. Finally, the proposed method is compared to PMP, dynamic programming (DP), and rule-based (RB) methods. The effect of different preview horizon sizes on fuel economy and battery temperature is analyzed. Verification results under two driving cycles indicate that compared with the rule-based method, the proposed method improves fuel economy by 5.14% and 5.2% and decreases the temperature by 5.9% and 4.9%, respectively. The results demonstrate that the proposed PMP-MPC method can effectively improve fuel economy and reduce temperature. h +
Footnote †: journal: Applied Sciences  
hybrid powertrain, each motor-propeller unit can be controlled individually. The rotor and engine are not mechanically connected but electrically connected, and the space structure is relatively simple. Series hybrid electric air-ground vehicles (HEAGVs) are available as an optional solution. Rational distribution of power between the engine-generator set and the battery can improve the fuel economy of HEAGV, which is the main task of the energy management strategy (EMS). EMSs are divided into rule-based (RB) strategies and optimization-based strategies. RB EMSs rely on personnel experience, which makes it difficult to ensure optimal control is achieved. There are two kinds of rule-based EMSs, based on the logical threshold EMSs [6] and fuzzy EMSs [7]. The EMS based on logic threshold has the advantage of simple control logic. There are two types of optimization-based control strategies: global optimization control and real-time optimization control [8]. To obtain globally optimal results, global optimization algorithms such as dynamic programming (DP) [9] and Pontryagin's minimum principle (PMP) [10] are used to solve the problem. DP can achieve the optimal global solution; however, sometimes a large amount of calculation leads to dimensional disaster. [PERSON] et al. [11] used the DP method to determine the best torque distribution scheme for the whole driving cycle in the form of segmented decision making. The PMP method is also a global optimization method and is often considered an alternative to DP. DP can be used as a benchmark for other algorithms. However, these algorithms require complete driving cycle information in advance and are computationally intensive. These methods cannot be applied online and in real time. The equivalent consumption minimization strategy (ECMS) is a real-time optimization algorithm [12]. However, the equivalent factor is susceptible to the driving cycle, and the optimal control results take work to obtain. The model predictive control (MPC) method can be regarded as a combination of global and real-time optimization methods. [PERSON] et al. [13] proposed an exponentially varying speed prediction method based on MPC for power allocation. Based on deep learning methods, [PERSON] et al. [14] developed a set success rate predictor consisting of input state classification and sub-predictors.