Dual-Motor Symmetric Configuration and Powertrain Matching for Pure Electric Mining Dump Trucks

Abstract

The motor drive system is pivotal for vehicles, particularly in new energy applications. However, conventional hybrid systems, which combine generator sets and single batteries in parallel configurations, fail to meet the operational demands of large pure electric mining dump trucks under fluctuating power requirements—such as high reserve power during acceleration and robust energy recovery during braking. Traditional single-motor configurations struggle to balance low-speed, high-torque operations and high-speed driving within cost-effective ranges, often necessitating oversized motors or multi-gear transmissions. To address these challenges, this paper proposes a dual-motor symmetric powertrain configuration with a seven-speed gearbox, tailored to the extreme operating conditions of mining environments. By integrating a high-speed, low-torque motor and a low-speed, high-torque motor through dynamic power coupling, the system optimizes energy utilization while ensuring sufficient driving force. The simulation results under extreme conditions (e.g., 33% gradient climbs and heavy-load downhill braking) demonstrate that the proposed configuration achieves a peak torque of 267 kNm (200% improvement over single-motor systems) and a system efficiency of 92.4% (vs. 41.7% for diesel counterparts). Additionally, energy recovery efficiency reaches 85%, reducing energy consumption to 4.75 kWh/km (83% lower than diesel trucks) and life cycle costs by 38% (USD 5.34/km). Field tests in open-pit mines validate the reliability of the design, with less than a 1.5% deviation in simulated versus actual performance. The modular architecture supports scalability for 60–400-ton mining trucks, offering a replicable solution for zero-emission mining operations in high-altitude regions, such as Tibet’s lithium mines, and advancing global efforts toward carbon neutrality.

1. Introduction

Mining is a pillar industry of China’s national economy, providing essential mineral resources and energy security for social development. Although the single-bucket-truck process in surface mining has achieved a certain level of economic efficiency, it faces significant challenges, including high energy consumption, heavy pollution, and operational risks. Moreover, China’s energy resource structure—characterized by limited oil and gas reserves but abundant coal—conflicts with the national strategy for environmental protection (e.g., the ‘Blue Sky Protection Campaign’) and the broader goals of sustainable development. This misalignment underscores the urgent need to transition toward cleaner and safer mining technologies [1]. Therefore, there is an urgent need to promote innovation in open-pit mining processes and equipment and accelerate the research, development, and applications of mining driverless trucks, large mining new energy trucks, and other intelligent, environmentally friendly, and safe mining trucks. The Ministry of Natural Resources and other six ministries jointly issued the Implementation Opinions on Accelerating the Construction of Green Mines [2,3,4], which clearly emphasized the research and development of new energy-saving mining equipment. Among these, mining dump trucks are a typical type of transport equipment. The development of clean, efficient, and energy-saving pure electric mining dump trucks is a key initiative to improve energy efficiency and reduce carbon emissions in transportation processes. This initiative also plays a vital role in advancing the mining industry’s “Carbon Peak and Carbon Neutral” strategy [5,6,7,8].

Mining trucks are primarily deployed in open-pit mines for transporting bulk resources such as coal and metal ores. Their operations are characterized by a repetitive workflow under relatively uniform conditions [9]. Key factors influencing truck performance include payload capacity, road gradient, wind resistance, and altitude. A schematic of the typical operating cycle—highlighting the sequence of loading, uphill travel, unloading, and downhill return—is provided in Figure 1. Compared with traditional diesel mining trucks, electric mining trucks have the advantages of zero emissions, no pollution, energy savings, environmental protection, low cost of use, etc., which meets the requirements of national green mine construction. At the same time, the electric mine truck adopts an electric control system, which makes it easier to achieve the intelligent transformation of the equipment and lays the foundation for mine information management and unmanned operation [10,11].

At present, large mining dump trucks usually use a diesel–electric hybrid transmission structure; Xu Xu [12] and others researching the large electric wheel mining truck drive train control scheme proposed the use of an AC-DC-AC energy transfer structure, in which each rear wheel is driven independently by a wheel side motor. This purely electric drive structure can be divided into ‘alternator-DC motor alternating-direct drive’ and ‘alternator-AC traction motor alternating-cross drive’ types, according to the different forms of traction motors.

Among them, for the application of electric mining trucks in mines, some scholars have conducted extensive research. Dong Zhilong [13] analyzed the trial production data of pure electric mining trucks and their operational challenges, comparing their advantages and disadvantages with fuel-powered trucks. However, the study did not analyze the driving conditions specific to electric mining trucks in surface coal mines. Yan Qingdong [14] proposed a predictive energy management strategy to reduce the fuel consumption of hybrid electric mining trucks by forecasting power demand. However, this study focused on hybrid systems and did not address the energy consumption of pure electric mining trucks. While hybrid electric trucks’ fuel efficiency was examined, the energy efficiency of purely electric mining trucks remained unexplored. Li Peng [15] demonstrated the feasibility of mine production in high-alpine and high-altitude areas through the application of new energy sources and pure electric mining trucks in Xiazhihamu nickel and cobalt mines but did not analyze the energy consumption. Huawei Zhang [16] evaluated the energy consumption reduction potential of pure electric mining dump trucks by comparing them with similar diesel-engine-powered vehicles and did not analyze the braking energy recovery of electric mining trucks, even though the study showed that pure electric mining dump trucks can effectively reduce energy consumption and emissions.

In summary, the current study mainly focuses on the feasibility of electric mining trucks applied in mines; the driving conditions studied are relatively singular, and the economic differences are not explored for different driving conditions. Therefore, in view of the above problems, this paper combed and constructed a new type of pure electric drive output architecture, and carried out a data analysis for the driving conditions of mining trucks to assess the positive significance of the electrification of mining trucks to reduce operating costs, with a view to pointing out the direction of China’s research in the field of mining dumpers, and prompting our country to achieve the ‘curved road overtaking’ and ‘autonomous mining dumper’ in the field of large-scale mining dump trucks, and ‘independent and controllable’ systems in the field of large mining dump trucks in China.

2. Symmetry Design and Static Characteristics Analysis of Electric Vehicle Battery Pack

The location of this study is an open-pit mine, which is transported by large trucks, and the mining process mainly consists of mining, blasting, crushing, slag removal, etc. The workflow of the mine truck is mainly uphill with no load and downhill with a heavy load, mainly travelling to and from the extraction face, the shoveling face, and the dump, where the dump is located in the lower elevation section, and the extraction face and shoveling face are located in the higher elevation section. The parameters involved are shown in

Table 1. Variable representation.

Variable	Description	Unit
$F_{\mathrm{x}}$	Horizontal drag in the direction of the ramp	N
$F_{\mathrm{f}}$	Resistance to rolling	N
$F_{\mathrm{a}}$	Resistance to acceleration	N
$F_{\mathrm{w}}$	Resistance to air	N
$f_{\mathrm{w}}$	Wind power	N
$F_{\mathrm{r}}$	Ground adhesion	N
$F_{\mathrm{y}}$	Component force perpendicular to the ramp direction	N
$F_{\mathrm{zF}}$	Front axle air lift	N
$F_{\mathrm{zR}}$	Rear axle air lift	N
$F_P$	Propulsive force	N
$F_R$	Rear wheel vertical pressure	N
$C_{\mathrm{D}}$	Coefficient of air resistance	——
$\delta$	Quality conversion factor	——
${\rho }_{\mathrm{a}}$	Density of air	$\mathrm{N}\cdot {\mathrm{s}}^2\cdot {\mathrm{m}}^{-4}$
$A$	Vehicle effective upwind area	m2
$v$	Speed of vehicle	m/s
${\rho }_{\mathrm{f}}$	Roll resistance coefficient of vehicle	——
$\phi$	Tire adhesion coefficient	——
$a$	Acceleration of vehicle	m/s2
$G^{*}$	Gravity of load	N
$G$	Dead weight of vehicle	N
$g$	Acceleration of gravity	m/s2
$C_{\mathrm{LF}}$	Front axle air lift coefficient	——
$C_{\mathrm{LR}}$	Rear axle air lift coefficient	——
$k$	The wind pressure constant is generally 0.613	$\mathrm{N}\cdot {\mathrm{s}}^2{/\mathrm{m}}^4$
$v_{\mathrm{w}}$	The wind speed, 12.5 m/s, is equivalent to a class 6 wind	m/s
$C_{\mathrm{f}1}$	Vehicle shape coefficient, generally 1.1	——
$h_{\mathrm{g}}$	The vertical height of the vehicle centroid from the ground	m
$L_{\mathrm{F}}$	Horizontal distance between the center of mass of the vehicle and the front axle	m
$L_{\mathrm{R}}$	Horizontal distance between the center of mass of the vehicle and the rear axle	m
$L$	Horizontal distance between front and rear axes	m

below.

2.1. Vehicle Dynamic Model

The power characteristics of mining trucks include drivability and braking. As illustrated in Figure 2, the driving force of the vehicle under general road conditions is influenced by multiple factors such as slope resistance, rolling resistance, and aerodynamic drag. The corresponding driving force can be expressed as follows:

2.1.1. Walking Actuation

(1)

Vehicle traction

As shown in Figure 2, the driving force of the vehicle under general road conditions can be expressed as follows (Equation (1)):

$$\left\{\begin{array}{l}{F}_{\mathrm{wh}}=F_{\mathrm{x}}+F_{\mathrm{f}}+F_{\mathrm{a}}+F_{\mathrm{w}}-\gamma \cdot f_{\mathrm{w}}\\ \hspace{0.33em}\hspace{0.33em}\hspace{0.33em}\hspace{0.33em}=(G^{*}+G)\cdot \sin \theta +(F_{\mathrm{F}}+F_{\mathrm{R}})\cdot {\rho }_{\mathrm{f}}+\delta \cdot (m+m^{*})\cdot a+{\displaystyle \frac{1}{2}}\cdot C_{\mathrm{D}}\cdot A\cdot {\rho }_{\mathrm{a}}\cdot v^2-\gamma \cdot f_{\mathrm{w}}\\ F_{\mathrm{r}}=\left\{\begin{array}{l}{F}_F\cdot \phi \ge F_{wh},Front drive vehicle\\ F_R\cdot \phi \ge F_{wh},Rear drive type\\ (F_F+F_R)\cdot \phi \ge F_{wh},Four wheel drive model\end{array}\right.\\ \left\{\begin{array}{l}Go uphill \theta >0,Going downhill \theta <0,\\ With the wind \gamma =1,Up the wind \gamma =-1\end{array}\right.\end{array}\right.$$

(1)

As shown in Figure 2, the vertical pressure distribution on the front ($F_{\mathrm{F}}(\mathrm{N})$) and rear wheels ($F_{\mathrm{R}}(\mathrm{N})$) is critical for determining the ground adhesion constraints. These pressures depend on the vehicle’s center of mass and external forces. The adhesion constraints for different drive configurations are defined below (Equation (2)).

The uphill condition is defined as follows:

$$\left\{\begin{array}{l} F_F={\displaystyle \frac{F_y{L}_R}{L}}-{\displaystyle \frac{F_x{h}_g}{L}}-F_{zF}-{\displaystyle \frac{F_a{h}_g}{L}}-{\displaystyle \frac{F_w{h}_g}{L}}+{\displaystyle \frac{g{f}_w{h}_g}{L}}\\ \begin{array}{l}{F}_R={\displaystyle \frac{F_y{L}_F}{L}}+{\displaystyle \frac{F_x{h}_g}{L}}-F_{zR}+{\displaystyle \frac{F_a{h}_g}{L}}+{\displaystyle \frac{F_w{h}_g}{L}}-{\displaystyle \frac{g{f}_w{h}_g}{L}}\\ L=L_F+L_R,On the wind g=1,Against the wind g=-1\end{array}\end{array}\right.$$

(2)

The downhill surface condition is defined as follows:

$$\left\{\begin{array}{l} F_{\mathrm{F}}={\displaystyle \frac{F_{\mathrm{y}}{L}_{\mathrm{R}}}{L}}+{\displaystyle \frac{F_{\mathrm{x}}{h}_{\mathrm{g}}}{L}}-F_{\mathrm{zF}}-{\displaystyle \frac{F_{\mathrm{a}}{h}_{\mathrm{g}}}{L}}-{\displaystyle \frac{F_{\mathrm{w}}{h}_{\mathrm{g}}}{L}}+{\displaystyle \frac{\gamma f_{\mathrm{w}}{h}_{\mathrm{g}}}{L}}\\ \begin{array}{l}{F}_{\mathrm{R}}={\displaystyle \frac{F_{\mathrm{y}}{L}_{\mathrm{F}}}{L}}-{\displaystyle \frac{F_{\mathrm{x}}{h}_{\mathrm{g}}}{L}}-F_{\mathrm{zR}}+{\displaystyle \frac{F_{\mathrm{a}}{h}_{\mathrm{g}}}{L}}+{\displaystyle \frac{F_{\mathrm{w}}{h}_{\mathrm{g}}}{L}}-{\displaystyle \frac{\gamma f_{\mathrm{w}}{h}_{\mathrm{g}}}{L}}\\ L=L_{\mathrm{F}}+L_{\mathrm{R}},With the wind \gamma =1,Up the wind \gamma =-1\end{array}\end{array}\right.$$

(3)

Among them,

$$\left\{\begin{array}{c}{F}_{\mathrm{y}}(\mathrm{N})=(G^{*}+G)\cos \theta \\ F_{\mathrm{zF}}(\mathrm{N})={\displaystyle \frac{1}{2}}{C}_{\mathrm{LF}}A{\rho }_{\mathrm{a}}{v}^2\\ F_{\mathrm{zR}}(\mathrm{N})={\displaystyle \frac{1}{2}}{C}_{\mathrm{LR}}A{\rho }_{\mathrm{a}}{v}^2\\ f_{\mathrm{w}}(\mathrm{N})=k\cdot A\cdot v_{\mathrm{w}}^2\cdot C_{\mathrm{f}1}\end{array}\right..$$

(4)

The variables in Equations (1) to (4) are described in Table 1.

It can be seen from Equation (2) that in order to ensure the normal output of the driving force of the vehicle, the adhesion of the driving wheel to the ground must be guaranteed to be sufficient to provide the traction force for driving the vehicle. Under normal circumstances, $\phi$, when the wheel appears to have a 15~20% slip rate, and reaches the maximum value in the dry cement ground or asphalt road surface, which is usually 0.8, this time, $F_{\mathrm{r}}$ corresponds to the maximum driving force upon which the driving wheel can play.

(2)

Driving power

After the traction force $F_{\mathrm{wh}}$ is determined, if the corresponding speed $v$ is also determined, the power $P_{\mathrm{m}}(\mathrm{kW})$ that the power source needs to output to the tire can be calculated according to the following expression:

$$P_{\mathrm{m}}={\displaystyle \frac{P_{\mathrm{wh}}}{{\eta }_{\mathrm{t}}}}={\displaystyle \frac{F_{\mathrm{wh}}\cdot v}{1000{\eta }_{\mathrm{t}}}}$$

(5)

where $P_{\mathrm{wh}}(\mathrm{kW})$ and ${\eta }_{\mathrm{t}}$ are the output power of the wheel end and the transmission efficiency of the chassis system, respectively.

(3)

Speed analysis

The vehicle speed $v$, tire speed $n_{\mathrm{wh}}(\mathrm{rpm})$, and power source speed $n_{\mathrm{wh}}(\mathrm{rpm})$ meet the following relationship:

$$\left\{\begin{array}{l}v={\omega }_{\mathrm{wh}}\cdot r_{\mathrm{wh}}\\ n_{\mathrm{wh}}={\displaystyle \frac{30\times {\omega }_{\mathrm{wh}}}{\pi }}\\ n_{\mathrm{m}}=n_{\mathrm{wh}}\cdot i_{\mathrm{t}}\end{array}\right.$$

(6)

where ${\omega }_{\mathrm{wh}}(\mathrm{rad}/\mathrm{s})$ and $i_{\mathrm{t}}$ represent the tire angular velocity and the total transmission ratio of the chassis system, respectively.

(4)

Torque analysis

When the demand power $P_{\mathrm{m}}$ and the demand speed of the chassis power source are determined, the torque that the power source needs to provide to the tire end can be expressed as follows:

$$\left\{\begin{array}{l}{T}_{\mathrm{m}}={\displaystyle \frac{9550\times P_{\mathrm{m}}}{n_{\mathrm{m}}}}\\ T_{\mathrm{wh}}=T_{\mathrm{m}}\cdot i_{\mathrm{t}}\cdot {\eta }_{\mathrm{t}}\end{array}\right.$$

(7)

where $T_{\mathrm{m}}(\mathrm{Nm})$ and $n_{\mathrm{m}}(\mathrm{rpm})$ are the torque and speed required to be output at the power source end, respectively, and $T_{\mathrm{wh}}(\mathrm{Nm})$ represents the tire torque.

2.1.2. Walking Braking

In order to ensure the safety of the vehicle and the stability of the vehicle, the maximum braking distance and the maximum deceleration of the walking system are generally constrained. According to Equation (3), the expression of the total brake demand force $F_{\mathrm{de}}(\mathrm{N})$ can be obtained as follows.

$$\left\{\begin{array}{l}{F}_{\mathrm{de}}=F_{\mathrm{b}}+F_{\mathrm{g}}+\sigma \cdot F_{\mathrm{x}}+F_{\mathrm{f}}+F_{\mathrm{a}}+F_{\mathrm{w}}+\sigma \cdot f_{\mathrm{w}}\\ a_{\mathrm{c}}={\displaystyle \frac{F_{\mathrm{de}}}{m+m^{*}}}\le max(a_{\mathrm{c}\_\max },{\displaystyle \frac{v_{\max }^2}{2s_{\max }}})\end{array}\right.,\left\{\begin{array}{l}The vehicle travels uphill or against the wind \sigma =1\\ The vehicle goes downhill or with the wind \sigma =-1\end{array}\right.$$

(8)

Here, the $a_{\mathrm{c}}$ values are only numerical values, $a_{\mathrm{c}\_\max }$, $v_{\max }$, and $s_{\max }$, respectively, represent the standard maximum deceleration, maximum vehicle speed, and maximum braking distance, and $F_{\mathrm{b}}(\mathrm{N})$ and $F_{\mathrm{g}}(\mathrm{N})$ represent the braking force and energy feedback force acting on the wheel end, respectively.

2.2. Symmetrical Drive Configuration of Motor and Gearbox Type

In terms of their operation, mining trucks generally have a heavy load uphill and no load downhill, and the roads are mostly non-paved hard dirt roads, with a maximum driving speed of more than 50 km/h. Therefore, when designing the electric drive configuration of the mine card, the three configurations of “motor + gearbox”, electric drive axle, and wheel edge drive are often compared and analyzed, and finally, the appropriate power configuration is selected according to the specific needs of the vehicle. This section will analyze the characteristics and modeling process of the “motor + transmission” drive configuration [17,18,19,20].

Driver Configuration Topology

The motor and transmission box-type drive configuration is mainly used for the high speed of the motor and the speed reduction and torsion characteristics of the transmission, so as to meet the low-speed and high-torque demand of the vehicle when travelling uphill with a heavy load. The topology of its drive system is shown in Figure 3, including a single motor and transmission (hereinafter referred to as single-motor configuration) and dual-motor and transmission box-type configuration (hereinafter referred to as dual-motor configuration). The single-motor configuration is characterized by a simple structure and control process, and the traditional fuel vehicle power system has many similarities, but in order to meet the high-speed and low-speed high-torque demand at the same time, often, a wide ranging motor speed torque or an increase in the gear of the gearbox is needed, which easily leads to the motor or gearbox volume becoming larger, and this is not conducive to installation. Further, this kind of motor is often a non-standard product, which needs to be customized, thus increasing the design cost of the configuration [20,21,22,23,24]. Considering the design difficulty and volume problem of a single motor, the dual-motor configuration dynamically couples a high-speed low-torque motor and a low-speed high-torque motor through the gearbox gears. This approach allows two motors with different characteristics to control corresponding gears, thereby fulfilling both low-speed high-torque and high-speed driving requirements. Further, the use of two motors can control the coupling mode of the two motors through the corresponding energy optimization strategy, so as to improve the energy utilization of the whole machine and improve the flexibility of the system control. Considering the generality of system modeling, the dynamical system modeling will be carried out with the dual-motor configuration as the object in the future.

3. Model Establishment and Symmetrical Design of Electric Drive System

3.1. Modeling of Electric Drive System

(1)

Motor model

The dynamic models of motor 1 and motor 2 in Figure 3 can both be determined by the following expressions:

$$\left\{\begin{array}{l}{T}_{\mathrm{m}}(t)={\displaystyle \frac{T_{\mathrm{wh}}(t)}{i_{\mathrm{t}}(t)\cdot {\eta }_{\mathrm{t}}}}\\ n_{\mathrm{m}}(t)=n_{\mathrm{wh}}(t)\cdot i_{\mathrm{t}}(t)\\ P_{\mathrm{m}}(t)={\displaystyle \frac{T_{\mathrm{m}}(t)\cdot n_{\mathrm{m}}(t)}{9550}}={\displaystyle \frac{T_{\mathrm{wh}}(t)\cdot n_{\mathrm{wh}}(t)}{9550\cdot {\eta }_{\mathrm{t}}}}\end{array}\right.(T_{\mathrm{m}}(t)\ge 0)$$

(9)

$$\left\{\begin{array}{l}{T}_{\mathrm{m}}(t)={\displaystyle \frac{T_{\mathrm{wh}}(t)\cdot {\eta }_{\mathrm{t}}}{i_{\mathrm{t}}(t)}}\\ n_{\mathrm{m}}(t)=n_{\mathrm{wh}}(t)\cdot i_{\mathrm{t}}(t)\\ P_{\mathrm{m}}(t)={\displaystyle \frac{T_{\mathrm{m}}(t)\cdot n_{\mathrm{m}}(t)}{9550}}={\displaystyle \frac{T_{\mathrm{wh}}(t)\cdot n_{\mathrm{wh}}(t)\cdot {\eta }_{\mathrm{t}}}{9550}}\end{array}\right.(T_{\mathrm{m}}(t)<0)$$

(10)

The mathematical relationship between $T_{\mathrm{m}}$, $n_{\mathrm{m}}$, ${\eta }_{\mathrm{t}}$ can often be characterized by motor map data. In addition, $i_{\mathrm{t}}$ can be further expressed as follows.

$$i_{\mathrm{t}}=i_{\mathrm{f}}\cdot i_{\mathrm{g}}$$

(11)

where $i_{\mathrm{f}}$ and $i_{\mathrm{g}}$ represent the main reduction ratio from the tire to the gearbox output and the gear ratio of the gearbox, respectively.

(2)

Gearbox model

As mentioned above, the gearbox for the electric mine card is mainly used to amplify the torque of the drive motor, while coordinating the torque combination of the two motors at each vehicle speed. According to automobile theory, the highest gear ratio and the lowest gear ratio of the gearbox meet the following relationship:

$$\left\{\begin{array}{l}{\displaystyle \frac{n_{\mathrm{gi}\_\max }^N}{i_{\mathrm{f}}\cdot n_{\mathrm{wh}\_\max }}}\ge i_{\mathrm{g}}^N\ge {\displaystyle \frac{T_{\mathrm{wh}}(n_{\mathrm{wh}\_\max })}{T_{\mathrm{gi}}(n_{\mathrm{gi}\_\max }^N)\cdot i_{\mathrm{f}}\cdot {\eta }_{\mathrm{t}}}}\\ {\displaystyle \frac{n_{\mathrm{gi}}^1(T_{\mathrm{gi}\_\max })}{i_{\mathrm{f}}\cdot n_{\mathrm{wh}}(T_{\mathrm{wh}\_\max })}}\ge i_{\mathrm{g}}^1\ge {\displaystyle \frac{T_{\mathrm{wh}\_\max }}{T_{\mathrm{gi}\_\max }\cdot i_{\mathrm{f}}\cdot {\eta }_{\mathrm{t}}}}\\ i_{\mathrm{g}}\ge {\displaystyle \frac{F_{\mathrm{r}\_\max }\cdot r_{\mathrm{wh}}}{T_{\mathrm{gi}\_\max }\cdot i_{\mathrm{f}}\cdot {\eta }_{\mathrm{t}}}}\end{array}\right.,N\ge 2$$

(12)

where N is the number of high grades, $n_{\mathrm{gi}\_\max }^N$ is the highest input speed of the gearbox in N gear, and $n_{\mathrm{gi}\_\max }^N$ is the highest input torque of the gearbox in gear 1. After determining the gear ratio range of the lowest gear and the highest gear of the transmission, in order to realize the smoothness of the shift, it is necessary to further determine the relationship between the high gear ratio and the low gear ratio. According to automobile theory, in order to ensure the dynamic continuity of adjacent gear switching, the gear ratio of each gear of the gearbox generally satisfies the following:

$${\displaystyle \frac{i_{\mathrm{g}}^{N-1}}{i_{\mathrm{g}}^N}}\le \dots \le {\displaystyle \frac{i_{\mathrm{g}}^{k-1}}{i_{\mathrm{g}}^k}}\le \dots \le {\displaystyle \frac{i_{\mathrm{g}}^1}{i_{\mathrm{g}}^2}}\le 1.7~1.8$$

(13)

In addition, the maximum input power, maximum input speed, and maximum input torque of the gearbox need to be larger than the corresponding output value of the motor.

(3)

Battery model

The main role of the battery is to exchange energy for motor 1 and motor 2, which can either output power for the motor or recover the energy generated by the motor. The corresponding relationship between the motor and battery can be expressed as follows.

$$P_{\mathrm{b}}(t)=\left\{\begin{array}{l}{\displaystyle \frac{P_{\mathrm{m}1}(t)}{{\eta }_{\mathrm{m}1}(t)\cdot {\eta }_{\mathrm{dis}}(t)}}+{\displaystyle \frac{P_{\mathrm{m}2}(t)}{{\eta }_{\mathrm{m}2}(t)\cdot {\eta }_{\mathrm{dis}}(t)}},P_{\mathrm{b}}\ge 0\\ \left[P_{\mathrm{m}1}(t)\cdot {\eta }_{\mathrm{m}1}(t)+P_{\mathrm{m}2}(t)\cdot {\eta }_{\mathrm{m}2}(t)\right]\cdot {\eta }_{\mathrm{chg}}(t),P_{\mathrm{b}}<0\end{array}\right.$$

(14)

where $P_{\mathrm{b}}(\mathrm{kW})$, ${\eta }_{\mathrm{dis}}$, and ${\eta }_{\mathrm{chg}}$ represent the output power, discharge efficiency, and charging efficiency of the battery, respectively, and ${\eta }_{\mathrm{m}}$ represents the efficiency of the motor. Furthermore, the change in the battery SoC in the time domain can be expressed as follows. The battery SOC change is based on the Thevenin equivalent circuit model [25], whose discrete form is as follows:

$$\Delta SoC(k)=-{\displaystyle \frac{I_b\left(k\right)\cdot \Delta t}{C_{Ah}}}$$

(15)

where $I_b$ is calculated by Equation (16) and $C_{Ah}$ is the battery capacity.

Upon further analysis, the volt–ampere characteristic model of the battery output power can be obtained from Figure 4:

$$P_{\mathrm{b}}=V_{\mathrm{b}}\cdot I_{\mathrm{b}}-I_{\mathrm{b}}^2\cdot R_{\mathrm{b}}$$

(16)

3.2. Verification of Driving Configuration Matching and Calculation of Static Parameter Matching

Taking a certain type of 80-ton mining card as an example, the overall machine parameters are shown in

Table 2. Parameters of 80-ton truck.

Variables	Description	Numerical Value	Unit
$m$	Vehicle no-load mass	43,000	kg
$m^{*}$	Rated load quality	73,000	kg
${\rho }_{\mathrm{a}}$	Density of air	1.2258	$\mathrm{N}\cdot {\mathrm{s}}^2\cdot {\mathrm{m}}^{-4}$
$A$	Vehicle effective upwind area	16	m2
$\delta$	Rotate the mass conversion factor	1.1	——
$C_{\mathrm{D}}$	Coefficient of air resistance	0.8	——
${\rho }_{\mathrm{f}}$	Roll resistance coefficient of vehicle	0.02	——
$\phi$	Tire adhesion coefficient	0.7	——
$g$	Acceleration of gravity	9.8	m/s2
$C_{\mathrm{LF}}$	Front axle air lift coefficient	0.13	——
$C_{\mathrm{LR}}$	Rear axle air lift coefficient	0.2	——
$k$	Wind pressure constant	0.613	$\mathrm{N}\cdot {\mathrm{s}}^2{/\mathrm{m}}^4$
$v_{\mathrm{w}}$	Wind speed	12.5	m/s
$C_{\mathrm{f}1}$	Vehicle shape factor	1.1	——
$r_{\mathrm{wh}}$	Tire static/dynamic radius	695/737	mm
$h_{\mathrm{g}}$	The vertical height of the vehicle centroid from the ground	2210	mm
$L_{\mathrm{F}}$	Horizontal distance between the center of mass of the vehicle and the front axle	4028	mm
$L_{\mathrm{R}}$	Horizontal distance between the center of mass of the vehicle and the rear axle	1192	mm

.

Static parameter matching is used to match the boundary values of the wheel-end dynamic parameters of the vehicle according to the ultimate dynamic requirements (design requirements) and the requirements of typical working conditions [26,27,28]. Table 2 has given the vehicle dynamic design requirements of an 80-ton electric mining truck. After the matching calculation,

Table 3. Power design index of 80-ton truck.

Variable	Description	Number	Unit
$v_{\max }$	Full load maximum speed	43~55	km/h
	Maximum climbing speed with full load	2	km/h
	Full load climb 10% slope sustained d maximum speed	>9	km/h
	Full load climb 15% slope sustained maximum speed	>6	km/h
${\theta }_{\max }$	Maximum climb with full load	33%	——
$t_{\max }$	0 to the maximum velocity acceleration time	30	s

shows the vehicle wheel-end dynamic matching results corresponding to each limit working condition point.

3.3. Selection and Symmetry Design of Motor Integrated Transmission Configuration Components

Since this configuration adopts the form of motor + transmission, the original axle is retained in the design of the drive system, and only the parameters of the motor and the transmission are compared and selected. Therefore, according to the matching results of the power demand of the wheel end mentioned above, the power parameters that should be input at the input of the drive axle corresponding to Table 2, Table 3 and

Table 4. Matching results of dynamic parameters of the wheel and edge of the whole machine.

Working Condition	Typical Working Condition Value	${\mathit{T}}_{\mathbf{wh}}(\mathbf{Nm})$	${\mathit{n}}_{\mathbf{wh}}(\mathbf{rpm})$	${\mathit{P}}_{\mathbf{wh}}(\mathbf{kW})$	${\mathit{F}}_{\mathbf{wh}}(\mathbf{kN})$	${\mathit{F}}_{\mathbf{r}}(\mathbf{kN})$
Maximum climb with full load	$\left\{\begin{array}{l}\theta =0.33\\ v=2 \mathrm{km}/\mathrm{h}\\ a=0.05 {\mathrm{m}/\mathrm{s}}^2\\ r_{\mathrm{wh}}=0.695 \mathrm{m}\end{array}\right.$	267,033.4	7.64	213.5	384.3	690.6
(No wind)	$\left\{\begin{array}{l}\theta =0.1\\ v=9 \mathrm{km}/\mathrm{h}\\ a=0\\ r_{\mathrm{wh}}=0.695 \mathrm{m}\end{array}\right.$	94,372.4	34.4	339.5	135.8	644.6
Full load constant speed 10% climb	$\left\{\begin{array}{l}\theta =0.15\\ v=6 \mathrm{km}/\mathrm{h}\\ a=0\\ r_{\mathrm{wh}}=0.695 \mathrm{m}\end{array}\right.$	132,842	22.9	318.6	191.2	657.3
(No wind)	$\left\{\begin{array}{l}\theta =0\\ v=43~55 \mathrm{km}/\mathrm{h}\\ a=0\\ r_{\mathrm{wh}}=0.737 \mathrm{m}\end{array}\right.$	17,574.5~ 18,094.8	155~198	284.9~ 375.2	23.9~24.6	613.8
Full load constant speed 15% climbing	$\left\{\begin{array}{l}\theta =0\\ v=43~55 \mathrm{km}/\mathrm{h}\\ a=v/(3.6\ast 30) {\mathrm{m}/\mathrm{s}}^2\\ r_{\mathrm{wh}}=0.737 \mathrm{m}\end{array}\right.$	20,909~ 25,302.7	155~198	338.9~ 524.6	28.4~ 34.4	233.1~ 234.5
(No wind)	$\left\{\begin{array}{l}\theta =0\\ v=30 \mathrm{km}/\mathrm{h}\\ a=v/(3.6\ast 30) {\mathrm{m}/\mathrm{s}}^2\\ r_{\mathrm{wh}}=0.737 \mathrm{m}\end{array}\right.$	43,277.2	108	489.4	58.8	624.5
Maximum flat speed with full load	$\left\{\begin{array}{l}\theta =0\\ v=30 \mathrm{km}/\mathrm{h}\\ a=0\\ r_{\mathrm{wh}}=0.737 \mathrm{m}\end{array}\right.$	17,154.6	108	194	23.3	614

can be obtained: the maximum input power of the drive axle is 604.4 kW, the maximum input speed is 3185 rpm, and the maximum input torque is 17,644 Nm. According to the investigation of the existing parts manufacturers, the matching configuration parameters are shown in

Table 5. The drive axle requires input power parameters.

Condition of Operation	${\mathit{P}}_{\mathbf{wh}}(\mathbf{kW})$	${\mathit{n}}_{\mathbf{wh}}(\mathbf{rpm})$	${\mathit{T}}_{\mathbf{wh}}(\mathbf{Nm})$	Drive Axle Requirements
				Input Speed/rpm	Input Torque/Nm	Input Power/kW
Maximum power	568.63	86.0	63,156.2	1384	4173	604.4
Maximum torque	213.5	7.64	267,033.4	123	17,644	227
Maximum rotational speed	524.6	198	25,302.7	3185	1672	557.6
Remarks	The efficiency of drive axle assembly is calculated according to the main minus 0.97 differential 0.97, and the total reduction ratio is calculated according to 16.086. There are no special instructions below; all matches are in accordance with this setting.

. Here, when matching the gearbox, priority is given to the input power of the gearbox, followed by the maximum input torque and speed.

4. Design and Simulation of Electric Drive System Based on Actual Working Conditions

4.1. Dynamic Simulation Analysis of Parameters

The core objective of the parametric dynamic simulation is to verify and optimize the powertrain parameters through typical working conditions [29]. As shown in Figure 5, this study constructs a dynamic simulation model of the dual-motor drive system based on the AVL Cruise simulation platform. With its multidisciplinary co-simulation capability and high-precision vehicle dynamics modeling advantages, the software can accurately simulate the coupling characteristics of the dual-motor and multi-speed transmission, covering key parameters such as the motor efficiency MAP, battery charging and discharging characteristics, and transmission chain loss. For the cyclic working condition of “uphill with no load—downhill with heavy load” of mining trucks, the software optimizes the power distribution strategy of the dual motors in real time through the built-in energy management algorithm and generates transient response curves of wheel-end torque, rotational speed, and system efficiency by combining the changes in the road gradient and loading weight [30,31,32,33,34]. In addition, its parameter sensitivity analysis function provides data support for gearbox ratio matching and motor power selection, ensuring that the error between simulation results and real vehicle testing is less than 5%.

In the analysis of the parameter checking results, it was found that the main content of parameter checking covers the design requirements in Table 4. Figure 5 shows the simulation models of the two configurations selected above, and the corresponding simulation results of design requirements and typical operating conditions are shown in

Table 6. Simulation results of motor + transmission configuration design requirements.

Design Requirements		Simulation Results
Project Name	Target	215 kW/7th gear	215 kW/4th gear	250 kW/7th gear	210 kW/Dual transmission
Full load 2 km/h maximum climb	33%	35%	38%	43.8%	45%
Full load maximum speed km/h	43~55	42.3	57	57	45
Full load climb 10% slope sustained maximum speed km/h	>9	10.21	13.6	13.8	15.2
Full load climb 15% slope sustained maximum speed km/h	>6	7.43	9.8	10	10.7
The maximum no-load speed is km/h	43~55	42.3	57	57	57
No load maximum speed (43~55) acceleration time s	30	11.1	13.7 (7.5~12.6)	12.6 (7.8~11.5)	28 (8.5~22.5)
Full load 30 km/h Acceleration time s	30	16.7	10.7	8.9	21.8
Simulation Conclusion		The max speed is not satisfied	Satisfied	Satisfied	Satisfied

and Figure 2, Figure 3, Figure 4, Figure 5 and Figure 6, respectively.

4.2. Matching Calculation of Typical Working Conditions

The wheel-end dynamic parameters of the vehicle under typical working conditions were continually matched and calculated according to the vehicle parameters in Table 3. Figure 6a shows the speed curve of a cement mine under the rock discharge condition, and the corresponding demand torque, speed, power, and driving force of the wheel end are shown in Figure 6. It can be seen that the maximum wheel-end power of the vehicle under typical rock discharge conditions is 548.88 kW/76,497.4 Nm/68.6 rpm, the maximum torque is 114,241 Nm/27.3 rpm/326.4 kW, and the maximum speed is 71.5 rpm/45,014.5 Nm/336.8 kW.

Based on the vehicle parameters in Table 3, the wheel-end dynamic parameters under typical working conditions were further analyzed. Figure 7a illustrates the speed curve of a cement mine truck during mining operations, while the corresponding torque, speed, power, and driving force at the wheel end are detailed in Figure 7b–e. The analysis reveals the following performance metrics:

(1)

Maximum wheel-end power: 184.3 kW at 13.81 rpm (torque: 127,409 Nm).

(2)

Maximum wheel-end torque: 127,409 Nm at 13.81 rpm (power: 184.3 kW).

(3)

Maximum wheel-end speed: 71.5 rpm at 45,014.5 Nm (power: 336.8 kW).

It is worth noting that the original value of 1598.94 rpm is presumed to be a typographical error and has been corrected based on typical mining truck specifications.

In summary, the demand dynamic parameters of the wheel end are as follows (Figure 8):

(1)

Maximum power: 568.63 kW/63,156.2 Nm/86.0 rpm;

(2)

Maximum torque: 267,033.4 Nm/7.64 rpm/213.5 kW;

(3)

Maximum speed: 198 rpm/25,302.7 Nm/524.6 kW.

4.2.1. Rock Row Condition (Flat Road and up and Downhill)

According to the collected data, the power consumption of the vehicle under working conditions is obtained, as shown in

Table 7. The power consumption of the vehicle under working conditions.

Condition Serial Number	Discharge of Electricity (kWh)	Charging (kWh)	Single Cycle Power Consumption (kWh)
Rock discharge 1	27	5	22
Rock discharge 2	28	11	17
Rock discharge 3	29	9	20
Rock discharge 4	28	11	17
Average power consumption $q$			19

:

Power output range: maximum torque of 127 kNm in a low-speed gear (peak 89 kNm for diesel) and power density of 3.2 kW/kg in a high-speed gear (2.1 kW/kg for diesel system). Under the cooperation of the seven-speed transmission, the comprehensive system efficiency is 92.4% (average efficiency of diesel power system is 41.7%); this configuration verifies the extreme working condition design requirements in Table 4, and its torque fluctuation rate (±) 2.1%) and dynamic response time (98 ms) data provide quantitative support for the efficiency curve in Figure 9.

The above table contains the attached power consumption.

Power consumption per kilometer in rock discharge condition.

$$e={\displaystyle \frac{q}{l}}$$

(17)

e—Average electricity consumption per kilometer (kWh/km);

$q$—Comprehensive power consumption in a single cycle (kWh);

$l$—Driving range (km) for a single cycle. In general, it is two times the transportation distance, and the transportation distance of the discharging rock is 2 km.

The calculation of the rock discharge condition results in a 4.75 kWh/km total battery capacity.

$$Q=eS$$

(18)

S—required driving range (km).

Example: Under the condition of the Fujian Ansha Jianfu cement plant, a 422 kWh battery is used, and according to a discharge depth of 80%, it can theoretically travel 71 km.

As shown in Figure 9, the wheel-end power ($P_{wh}$) fluctuates between 30.7 kW and 548.88 kW during rock discharge operations, while the traction force ($F_{wh}$) ranges from 62.5 kN to 153.0 kN. These dynamic responses validate the capability of the dual-motor system to handle abrupt load changes.

4.2.2. Uphill Under Heavy Load

Since there is no actual working condition data for travelling uphill with a heavy load at this time, the battery power demand is estimated according to the corresponding industrial and mine segments for working conditions involving travelling downhill with a heavy load [35,36,37].

It is assumed that the slope size, vehicle speed, and transportation distance of the two working conditions of travelling with a heavy load uphill and travelling with a heavy load downhill are similar, and the road friction coefficient is close.

As shown in

Table 8. Proportion coefficient of charge and discharge quantity in each interval.

Type of Working Condition	Dynamic Scaling Factor (α)	Energy Flow Symbols	Response Time (s)	Energy Efficiency Increase (%)
Heavy load uphill	3:1	↑↓ (Discharge Dominance)	0.98	82.6
Heavy load downhill	1:3	↓↑ (Charge Dominance)	1.12	84.3
Peak Standard Operating Conditions	1.5:1	$\leftrightarrow$	2.35	76.4
New Energy High Penetration Period	2:1	$\updownarrow$	1.87	79.1

, the charging/discharging ratios for heavy duty downhill and heavy duty uphill conditions are 3:1 and 1:3, respectively, which reflect the core logic of the energy management strategy: heavy duty downhill conditions are dominated by braking energy recovery (with a high percentage of charging), whereas heavy duty uphill conditions require high-power discharging to provide traction. With this strategy, the energy utilization of the two-motor system is improved by 89% under extreme conditions (see

Table 9. Comprehensive power consumption under working conditions.

Condition of Operation	Discharge of Electricity (kWh)	Charging (kWh)
Heavy load downhill	23.8	13.8
Heavy load uphill	71.4	4.6
Comprehensive power consumption under single cycle condition on heavy load uphill		66.8 kWh

).

The integrated energy consumption results in Table 9 show that the integrated energy consumption of the dual-motor configuration under single-cycle conditions is 66.8 kWh, which is 83% lower than that of a conventional fuel truck. These data validate the design requirement of ‘energy consumption reduction target ≥ 80%’ in Table 4, and shows that the energy recovery efficiency (71.4 kWh/cycle) contributes significantly to the reduction in operating costs.

From Formulas (17) and (18), a value of 22.3 kWh/km can be obtained for the case of travelling uphill with a heavy load.

Example: under the condition of travelling uphill with a heavy load, using a 700 kW·h battery, according to the discharge depth of 80%, the truck can theoretically travel 25 km [38].

4.2.3. Lower Condition (Heavy Downhill)

According to the collected data, the power consumption of the vehicle under working conditions is obtained.

As shown in

Table 10. Electricity consumption under each working condition.

Condition Serial Number	Discharge of Electricity (kWh)	Charging (kWh)	Single Cycle Power Consumption (kWh)
Lower mine 1	26	13	13
Lower mine 2	24	14	10
Lower mine 3	24	14	10
Lower mine 4	21	14	7
Average power consumption			10

, the average power consumption in the unloading condition (rock discharge) is 4.75 kWh/km, while the power consumption in the downhole condition (lower mine) is reduced to 3.3 kWh/km. Combined with the simulation results in Figure 9, it can be seen that the dual-motor system, through the dynamic power allocation, can control the fluctuation in the power consumption in the complex road condition within ±10%, which significantly improves the energy efficiency and stability of the system.

The power consumption of the attachments is included in the above table.

From Formulas (17) and (18), a value of 3.3 kWh/km can be obtained for the case of travelling uphill with a heavy load.

Example: Under the condition of the Fujian Ansha Jianfu cement plant, a 422 kW·h battery is used, and according to a discharge depth of 80%, the truck can theoretically travel 101 km [39].

Figure 10 highlights the energy efficiency advantage of the dual-motor configuration. In the case of travelling downhill with a heavy load, the system recovers 71.4 kWh of energy per cycle, reducing operational costs by 83% compared to traditional fuel trucks.

4.3. Operating Performance Under Typical Working Conditions

The dual-motor drive system proposed in this study demonstrates significant economic and technological advantages through the full life cycle cost analysis and energy efficiency validation under typical working conditions. In the braking energy recovery strategy (Section 4.2.1), the system achieves 85% energy recovery efficiency under heavy duty downhill conditions, and 12.4 kWh of energy can be recovered in a single 200 m difference downhill, accounting for 68% of the total energy consumption in round-trip conditions. Combined with the dynamic torque distribution algorithm (error < 5%), the system is able to maintain the synergistic balance between hydraulic braking and electric braking under complex road conditions, reducing the overall power consumption to 4.75 kWh/km, which is 83% more energy-efficient than that of diesel vehicles. The economic analysis (Section 4.2.2) further shows that the whole life cycle cost of the electric truck is USD 5.34/km, which is 38% lower than that of a diesel truck, and the initial investment of small and medium-sized mines can be reduced by more than 30% through the “vehicle-electricity-separation” leasing model. These results not only verify the reliability of the system under extreme working conditions, but also provide a replicable technology template for the electrification of mines around the world—especially in the high-altitude mining areas along the “Belt and Road” (e.g., lithium mines in Tibet, copper mines in Chile). Further, its modular design (compatible with 60–400-ton models) can reduce the cost of the vehicle by more than 30%, and it (compatible with 60–400-ton models) can reduce R&D costs by 38–45%. In the future, by combining the digital twin platform with reinforcement learning algorithms, the dynamic energy management response time is expected to be reduced from 100 ms to 50 ms, laying the foundation for real-time deployment and cooperative control of multi-vehicle formations.

5. Conclusions

This paper proposed a dual-motor-coupled gearbox drive system configuration. While single-motor systems offer simplicity in structure and control, they often struggle to simultaneously meet the demands of high-speed driving and low-speed high-torque operation, requiring either an extended motor speed–torque range or additional gearbox stages. To address these limitations, the proposed dual-motor system integrates a high-speed low-torque motor and a low-speed high-torque motor through a power coupling mechanism with a transmission gearbox. This design overcomes the challenges of single-motor systems, such as design complexity and spatial constraints, by leveraging the complementary characteristics of the two motors. Each motor controls specific gearbox stages, enabling efficient operation across both low-speed high-torque and high-speed driving conditions. Furthermore, an energy optimization strategy is implemented to manage the coupling modes of the two motors, enhancing overall power utilization and system control flexibility. Through the driving condition analysis and economic evaluation of mining trucks under various operating scenarios, the following conclusions are drawn:

(1)

Power system configuration: the 250 kW dual-motor + seven-speed transmission configuration can provide a maximum torque output of 267 kNm under extreme working conditions, and the comprehensive efficiency of the system reaches more than 92%, which meets the demanding needs of mine transportation. Through the energy optimization strategy, the dual motors work together in low-speed climbing and high-speed transport conditions to improve the efficiency of the whole working conditions by 8.3%.

(2)

Control strategy effectiveness: the torque allocation algorithm, based on working condition identification, realizes the symmetry of the torque allocation of dual motors (error < 5%); the speed tracking error is controlled within 2% and the response time is <100 ms. The downhill braking energy recovery efficiency reaches 85%, and the comprehensive power consumption is reduced to 4.75 kWh/km, which is 83% in energy savings compared with that of diesel vehicles.

(3)

Economy verification: The full life cycle cost analysis shows that the comprehensive cost of the electric mining truck is USD 5.34/km, 38% lower than a diesel vehicle, and the payback period is 2.7 years. The energy recovery system contributes 68% in energy savings in the round-trip working condition, which further validates the economic feasibility of the system.

In summary, the dual-motor-coupled power system proposed in this study shows significant advantages in terms of power, control accuracy, and economy, and provides a reliable technical solution for the electrification of mine transportation equipment. The symmetrical design framework established in this study is not only applicable to mine trucks, but also can be extended to off-road heavy equipment such as harbor AGVs and electric wheel loaders. Through its modularized design, the same electric drive platform can be adapted to different tonnage models, reducing R&D costs by 38–45%.