## 1 Introduction  
Different from passenger cars, heavy-duty mining trucks have to face problems such as poor driving conditions, huge load capacity, and high fuel consumption [1, 2]. The high-power hydraulic automatic transmission (AT) uses clutches (or brakes) to shift gears, which can ensure shifting without interruption of power and achieve good shifting smoothness; the torque converter (TC) enables greater torque transmission at low speeds; and the application of planetary row structure is reliable when shifting frequently. These three features, which other types of transmissions do not have, make AT widely used in the heavy truck field [3, 4, 5]. According to statistics, fuel consumption costs account for more than 30% of all maintenance costs during the entire working cycle of heavy-duty mining trucks [6, 7]. To achieve energy saving and emission reduction, while meeting the power requirements of vehicles, formulating the best economical shift strategy is a current research focus in the field of automatic transmission [8, 9]. Some researchers improve the traditional two-parameter (vehicle speed and throttle opening) shifting rules by adding more reference parameters, such as vehicle acceleration [10], mass [11, 12], road slope [11], and the driver intention [13, 14]. In order to improve fuel economy and electric bus dynamic performance, [PERSON] et al. [10] proposed the concept of equivalent slope and used a three-parameter shift schedule based on vehicle acceleration. Through real-time identification of road conditions, [PERSON] and [PERSON] [11] proposed a generalized load theory under different road grades and vehicle masses, and then deduced a three-parameter shift strategy based on generalized load. [PERSON] et al. [14] analyzed and summarized the characteristics of driver intentions under different driving environments, using the driver's intention as the third parameter to optimize the shift schedule. However, these shift strategies with increasing typesof parameters often do not have the ability to predict the future driving environment. They only achieve the best instantaneous best fuel economy, but not the global optimal shift strategy. Moreover, these traditional economic shift schedules often sacrifice other performance (such as frequent shifting). Because of this, some scholars used predictive control to optimize the shift strategies [15; 16]. [PERSON] et al. [17] comprehensively considered the power distribution strategy, engine start-stop, and gear control problems of hybrid electric vehicles, and combined the dynamic programming and the minimum principle to propose a predictive shift control strategy. To reduce the fuel consumption of commercial vehicles, [PERSON] and [PERSON] [18] optimized the gear and vehicle speed within the predicted area in front of the vehicle based on the geographic location information provided by the navigation system. As the deterministic dynamic programming algorithm is complex and difficult to apply in real time simulation, the neural network was adopted by [PERSON] et al. [19] to make up for this shortcomings. The optimal control of the predictive shift strategy currently studied is based on the road cycle conditions for passenger vehicle. Whether the transmission control unit (TCU) can meet requirements of calculation speed by the real-time predict control still need to be discussed. However, the mining vehicle only travels between the mining source and the mining plant, which means it has a single driving route, so its road parameters and vehicle parameters are relatively fixed and easier to collect than for a passenger vehicle. Therefore, with the development of smart mining technology, it is possible to use a global optimization control algorithm to get the best shift strategy based on vehicle speed, throttle opening, vehicle load, and road slope in the whole working cycle to replace the traditional two-parameter shift strategy [20; 21].