A novel dual-motor coupled drive system and its multi-mode drive characteristics

In this paper, a new type of dual-motor coupling-propulsion electric vehicle (DMCP-EV) is taken as the research object, and a driving mode control strategy based on PSO algorithm system efficiency optimization is formulated, which improves the economy of the whole vehicle while meeting the power requirements.

1 Dual-motor coupling power system model

1.1 Introduction to the dual-motor power system configuration

The new coupling drive system is shown in Figure 1. In this coupling drive configuration, the motor M1 is connected to the sun gear S, and the motor M2 is connected to the connector T. The brake L1 is coaxial with the sun gear S. When L1 is closed, the sun gear is fixed and the motor M1 stops running. The brake L2 is connected to the ring gear R. When L2 is closed, the ring gear R is fixed. The reduction gear G1 is connected to the ring gear R. When the connector T is at the right end, the three are connected and run with the operation of the motor M2; when the connector T is in the middle, the motor M2 is turned off; when the connector T is at the left end, the torques of the two motors are coupled at the sun gear C.

Figure 1 New dual-motor coupling system configuration

When the car is running normally, the vehicle controller receives and processes the signals from the sensor, sends instructions to the motor controller and other actuators, and controls the opening and closing of connector T and brakes L1 and L2 to make the power system work in different working modes: motor M1 single drive mode (defined as SM1), motor M2 single drive mode (defined as SM2), dual motor torque coupling mode (defined as TC), dual motor speed coupling mode (defined as SC) and regenerative braking mode. Since this paper focuses on the driving performance of the drive system, the control of regenerative braking is not considered here. The specific power component parameters of a pure electric vehicle studied in this paper are shown in Table 1.

Table 1 Parameters of EV power components

1.2 Dual-motor powertrain modeling

1.2.1 Working mode analysis

When brake L1 is disconnected, L2 is closed, and connector T is in the middle position, motor M1 is working, M2 is off, and the system is in motor M1 single drive mode. Then the SM1 system dynamics model is:

Wherein: n1 is the speed of motor M1; T1 is the torque of motor M1; r is the wheel radius; k is the characteristic parameter of the planetary carrier; i0 is the transmission ratio of the main reducer; Ft is the driving force; v is the vehicle speed.

When brake L1 is closed, L2 is released and connector T is at the right end, motor M1 stops and M2 runs, and power is output through the reduction gear and planetary carrier. At this time, motor M2 is in single operation mode, and the system dynamics model of SM2 mode is:

In the formula: ig is the transmission ratio of the reduction gear set; n2 is the speed of motor M2; T2 is the torque of motor M2.

When brake L1 is disconnected, L2 is closed, and connector T is at the left end, the torques of the two motors are coupled at the sun gear and transmitted to the wheels through the planetary carrier to drive the car. The system is in dual motor torque coupling mode TC, and the system dynamics model at this time is

When the brakes L1 and L2 are disconnected and the connector T is at the right end, the speeds of the two motors are coupled at the planetary carrier, and the power is transmitted to the wheels through the planetary carrier to drive the car. The system is in the dual-motor speed coupling mode, and the system dynamics model can be expressed as

1.2.2 System efficiency modeling

The mathematical model of system efficiency under different modes is:

Where: ηSM1, ηSM2, ηTC, ηSC are the system efficiencies in SM1, SM2, TC, and SC modes respectively; ηinv is the inverter efficiency.

The constraints are

Wherein: n1max and n2max are the maximum speeds of motors M1 and M2 respectively; SOC min is the minimum state of charge of the battery pack; SOC max is the maximum state of charge of the battery pack; P battmax is the maximum discharge power corresponding to the current SOC.

2. Driving system mode division and control strategy

2.1 Division of working range of different modes

Based on the above analysis, it can be seen that DMCP-EV has 4 driving modes. Under the constraints of power demand, the vehicle controller obtains the working range of each mode based on the real-time driving speed, acceleration signal, working characteristics of the drive motor and the working principle of each mode. The division process of the working range of each mode is shown in Figure 2, which can be briefly described as follows: first, the speed and acceleration signals are collected by the on-board sensor, and then the motor torque and speed required by each mode under the working condition are calculated according to the dynamic model and speed information of each mode, so that the effective working range of each mode can be obtained, as shown in Figure 3.

Figure 2. Process of dividing working range of different modes

2.2 System efficiency optimization in coupling mode based on PSO algorithm

Figure 3 Effective working range in different modes

According to the division of the working range of each mode above, there may be multiple working modes that meet the current speed, acceleration and driver needs. In order to improve economy, PSO is used to optimize the system efficiency of each mode, and the working mode with the best efficiency is selected according to the current driving conditions. Based on this, this paper formulates a control strategy for the dual-motor coupling drive system based on PSO system efficiency optimization, and its framework is shown in Figure 4. The control strategy is to select the working mode with the best efficiency according to the current working conditions. The specific steps are as follows:

(1) Determine the working mode that meets the current working conditions; if there is only one suitable mode, select that mode; if there are multiple driving modes, enter the system efficiency optimization control mode;

(2) Calculate the system efficiency of each working mode that meets the current working conditions. The specific calculation process is detailed in Section 1.2.2;

(3) Select the operating mode with the highest system efficiency as the current operating mode to improve the economy of the vehicle.

Figure 4 Mode division and control process based on particle swarm algorithm

2.2.1 System efficiency optimization in TC mode

In TC mode, the speed of the two motors is proportional to the vehicle speed, and the torque of the two motors is coupled and can be adjusted within the constraint range. The particle swarm optimization algorithm is used to optimize the torque distribution of the two motors to obtain the optimal efficiency of the system. The initial parameters of the PSO algorithm and the system efficiency process are shown in Figure 5. The TC mode system efficiency optimization process is: given the speed and acceleration, the target torque of motors M1 and M2 is obtained by the particle swarm optimization algorithm to optimize the system efficiency ηTC. The optimization model is as follows.

Objective function (fitness function):

Figure 5 Flowchart of particle swarm optimization system efficiency

Constraints:

Select the torque T1 of the M1 motor as the control variable, then the position of the corresponding particle is

Where: i is the particle number; j is the number of iterations.

The system efficiency optimization result is shown in Figure 6. It can be seen from the figure that the system efficiency converges to the optimal value around the 40th generation. The optimal torque distribution in TC mode is shown in Figure 7.

Figure 6 PSO algorithm optimization iteration diagram

2.2.2 Mode control based on system efficiency optimization

Figure 7 Optimal torque distribution in TC mode

In SC mode, the torque of the two motors is proportional to the driving torque, and the speed of the two motors is coupled and can be adjusted within the constraint range. Similar to TC mode, the key to obtaining the optimal system efficiency in SC mode is to reasonably distribute the speed of the two motors. Particle swarm algorithm optimization is also used, and the specific solution process is similar to that of TC mode. Figure 8 shows the system efficiency in different modes after optimization when SOC = 0.9. The best driving mode suitable for the current working condition can be obtained by comparing the system efficiency of the four driving modes under the working condition, that is, the working boundaries of the four working modes can be obtained, as shown in Figure 9 (a). In order to reduce the amount of calculation and improve the working efficiency of the vehicle controller, the working boundaries of each working mode are divided in advance, as shown in Figure 9 (b). The results are stored in the controller in a table, and the current optimal working mode is obtained by looking up the table.

Figure 8 Optimal system efficiency in different modes when SOC is 0.9

3 Simulation and experimental verification

3.1 Simulation verification before and after PSO optimization

The whole vehicle model equipped with the new dual-motor coupled drive system is built in the Matlab/Simulink environment. The effectiveness of the proposed mode division and selection strategy based on PSO system efficiency optimization is verified by comparing the results before and after optimization. In order to reflect the fairness of the comparison results, the mode division method of the system without PSO efficiency optimization is the same as that of this paper. The mode selection is also based on the principle of instantaneous optimality. The mode selection strategy calculates the required power of the four working modes under the current working condition in real time, and selects the mode with the smallest required power as the working mode under the current working condition. Both are simulated under the urban road cycle UDDS, and the results are shown in Figure 10 (a). It can be seen from the figure that during the entire working condition, the actual vehicle speed follows the target vehicle speed well. When driving, the system automatically matches the working mode suitable for the current working condition based on the proposed control strategy, thereby ensuring low energy consumption while meeting the vehicle power performance requirements. Figures 10 (b) and 10 (c) show the working mode switching of the system before and after optimization with the UDDS working condition. "1~4" represent SM1, SM2, TC, and SC working modes respectively.