CN110667564A - Intelligent energy management method for autonomous platooning of parallel hybrid electric vehicles - Google Patents

Abstract

The invention discloses a method for intelligently managing the driving energy of an autonomous platoon of parallel hybrid electric vehicles, which is specifically carried out according to the following steps: establishing a fleet, determining the model of the parallel hybrid electric vehicle in the fleet and the motion system parameters of the whole vehicle; The working mode of the electric vehicle is analyzed, and all the driving working modes of the parallel hybrid electric vehicle are obtained; according to the vehicle dynamics theory, the dynamic equation of the transmission system of the whole vehicle is established, and the system efficiency calculation formula under different driving working modes is obtained; The energy management strategy model is established under the optimal conditions of system efficiency; the distance between the heads of the fleet and the speed requirements are set, and the longitudinal dynamics model of the parallel hybrid electric vehicle fleet is established; based on the simulation platform, the simulation analysis is carried out. The beneficial effects can effectively improve the driving safety, traffic efficiency and fuel economy of the fleet.

Description

Intelligent energy management method for autonomous platooning of parallel hybrid electric vehicles

technical field

The invention relates to the technical field of vehicle fleet driving energy consumption management, in particular to a parallel hybrid electric vehicle autonomous platoon driving energy intelligent management method.

Background technique

As an important part of the advanced driving assistance system, the autonomous platoon driving of the car controls the acceleration and deceleration of the car according to the preset safety distance, which effectively improves the safety and traffic rate during the driving process of the car, and the hybrid electric vehicle is the current technology. The best solution to achieve "energy saving and emission reduction" at the low level. With the rapid progress of the "new four modernizations" of automobiles, it will also promote the combination of hybrid technology and intelligent assisted driving technology, and ultimately achieve the goals of low fuel consumption, low emission, safety and intelligence.

In terms of longitudinal control of intelligent vehicles, projects all over the world have mentioned, for example, the SARTRE project in Europe, the ENERGY ITS project in Japan, and the GCDC project in the Netherlands, etc., all show that vehicle platooning can significantly reduce traffic congestion, improve traffic efficiency and improve Fuel economy has become one of the frontier directions in the field of longitudinal control of intelligent vehicles.

However, in the prior art, with regard to the longitudinal control technology of the intelligent vehicle, there is still a lot of room for development in terms of energy saving for hybrid electric vehicles, and it is necessary to propose some technologies to improve the efficiency of platooning.

SUMMARY OF THE INVENTION

In view of the above problems, the present invention provides an intelligent energy management method for autonomous platooning of parallel hybrid electric vehicles, establishes an energy management strategy for platooning vehicles, and improves the fuel economy of platooning vehicles.

In order to achieve the above object, the concrete technical scheme adopted in the present invention is as follows:

An intelligent management method for autonomous platoon driving energy of a parallel hybrid electric vehicle, which is characterized in that it is specifically carried out according to the following steps:

S1: Establish a parallel hybrid vehicle fleet, determine the model of the parallel hybrid vehicle in the fleet, and obtain the motion system parameters of the entire parallel hybrid vehicle;

S2: analyze the working mode of the parallel hybrid electric vehicle, and obtain all the driving working modes of the parallel hybrid electric vehicle;

S3: According to the vehicle dynamics theory, establish the dynamic equation of the whole vehicle's transmission system, and obtain the system efficiency calculation formula under different driving working modes; and establish the energy management strategy model under the optimal condition of the system efficiency;

S4: Set the fleet head spacing and driving speed requirements, and establish a parallel hybrid vehicle fleet longitudinal dynamics model;

S5: Based on the simulation platform, the longitudinal dynamics model of the parallel hybrid electric vehicle fleet is built, and the parallel hybrid electric vehicle fleet is simulated and analyzed in combination with the energy management strategy model.

In order to reduce the fuel consumption of the parallel hybrid electric vehicles while maintaining the distance and relative speed of the platoon, firstly, according to the different working modes of the parallel hybrid electric vehicle, a system efficiency calculation model of the whole vehicle is established, and based on the efficiency of the whole vehicle system Based on the optimal principle, the energy management control strategy of hybrid electric vehicle is designed; then based on the fuzzy intelligent control algorithm, the longitudinal dynamics model of the parallel hybrid electric vehicle fleet is established; finally, the longitudinal dynamics model of the parallel hybrid electric vehicle fleet is established based on the simulation platform and the corresponding The energy matching model, through simulation analysis, verifies the effectiveness of the parallel hybrid vehicle energy matching strategy under the principle of optimal system efficiency, and achieves the purpose of improving fleet driving safety, traffic efficiency and fuel economy.

Further, in step S1, the motion system parameters at least include: windward area, curb mass, full load mass, rolling resistance coefficient, wind resistance coefficient, wheel radius, final reduction ratio, engine performance parameters, motor performance parameters, power battery parameters , Transmission ratio.

Still further, in step S2, the driving working modes of the parallel hybrid electric vehicle include: pure electric driving mode, light-load charging mode, motor-assisting mode, and engine-only driving mode;

The working states of the automobile motor, engine and clutch under the four driving working modes are as follows:

Table 1 List of working states of each driving mode of the parallel hybrid electric vehicle

Among them, 1 indicates that the power source and the actuator are in a working state or combined state, and 0 indicates that they are in a non-working state or disconnected.

Compared with traditional vehicles, the clutch of parallel hybrid vehicles plays the role of connecting and disconnecting the output torque of the engine. When the engine of a traditional car is maintained in the low-speed low-load region and the high-speed high-load region, its efficiency is low, and fuel consumption and emissions increase. The motor plays the role of "shaving peaks and filling valleys". In the low-speed and low-load area, (1) in the pure electric driving condition, the motor drives the car alone to avoid low-efficiency areas; (2) in the light-load charging condition, the engine is The battery is charged to increase the engine load rate; in the motor boost condition, in the high-speed and high-load area, the engine load rate is reduced, so that it works in the best economical working area of the engine, and the engine efficiency is improved. (3) The engine is driven alone. When the vehicle driving load is in the high-efficiency working area of the engine, the engine alone completes the driving task.

According to the above analysis, the driving mode of the parallel hybrid vehicle can be divided into: pure electric driving mode, light-load charging mode, motor-assisted mode and engine-only driving mode. The switching and implementation of each mode of the hybrid system is realized by controlling the working states of the engine, the motor and the clutch.

A further technical solution is: in step S3, the dynamic equation of the transmission system is:

Among them, I _r is the equivalent moment of inertia converted to the wheel;

I _m is the moment of inertia of the motor;

I _e is the moment of inertia of the engine;

ω _r is the angular velocity of the wheel;

ω _e is the angular velocity of the engine output shaft;

ω _m is the angular velocity of the motor output shaft;

T _req is the torque required for the vehicle to travel at a certain speed;

T _e is the torque of the motor output shaft;

T _m is the torque of the engine output shaft;

i _g is the transmission speed ratio;

i ₀ is the speed ratio of the reducer;

η _T is the transmission system efficiency;

± represents two drive modes, when “+” is taken, it represents the motor assist working mode, and when “-” is taken, it represents the light-load charging working mode.

When the car is running, it needs to overcome the rolling resistance, air resistance, acceleration resistance and ramp resistance. The calculation formula of the resistance when the car is running is:

Among them, m is the full load mass of the car; f is the road friction coefficient; C _D is the air resistance coefficient; A is the windward area; u is the driving speed; α is the slope;

A further technical solution is: in step S3, in the system efficiency calculation formula under different driving working modes, the system efficiency calculation formula under the pure electric driving mode is:

Among them, _ηsys is the current vehicle system efficiency, _Pbat is the discharge power of the battery pack, _ηm is the motor efficiency, _{ηdis -charge} is the battery discharge efficiency; Pin is the system input power; _Pout is the system output power; δ is Rotating mass conversion factor.

The calculation formula of the system efficiency in the engine independent driving mode is:

where η _e is the engine efficiency;

The calculation formula of the system efficiency of the vehicle in the light-load charging mode is:

Among them, η _charge is the battery charging efficiency;

The calculation formula of the system efficiency of the vehicle in the motor-assist working mode is:

A still further technical solution is: in S4, the parallel hybrid vehicle fleet longitudinal dynamics model includes at least a parallel hybrid vehicle fleet following model, a pilot vehicle driver model, and a follower vehicle driver model;

The parallel hybrid electric vehicle fleet following model includes at least one leading vehicle and N following vehicles, and is set with target vehicle speed and vehicle travel distance; wherein, N is a positive integer greater than or equal to 1.

The pilot model of the pilot vehicle is input to the pilot vehicle fuzzy logic controller according to the speed difference between the current driving speed and the target vehicle speed, and the rate of change of the vehicle speed difference, and the output is combined with the throttle opening and the brake pedal opening. The actual driving speed is controlled;

The driver model of the following car is to judge the accelerator and brake pedal openings of the current following car according to the speed difference between the current following car and the preceding vehicle and the displacement difference with the preceding vehicle; The vehicle maintains the vehicle distance through the following vehicle fuzzy logic controller, takes the vehicle speed difference and distance difference as the input of the following vehicle fuzzy logic controller, and the current following vehicle accelerator pedal opening and the current following vehicle brake pedal opening are used as the following vehicle. The output of the fuzzy logic controller.

A further technical solution is that, in S5, the parallel hybrid vehicle fleet longitudinal dynamics model includes at least a driver model, a gear shift model, an energy matching model, an engine model, a motor model, a battery model, and a vehicle model.

Beneficial effects of the invention: On the premise of ensuring the safety and traffic rate of the fleet driving process, the invention calculates the system efficiency models of the parallel hybrid electric vehicle under different working modes, and establishes the energy management under the optimal conditions of the system efficiency. strategy; according to the requirements of the distance between the heads of the fleet and the driving speed, the longitudinal dynamics model of the parallel hybrid vehicle fleet is established; at the end of the simulation platform, the longitudinal dynamics model of the parallel hybrid vehicle fleet and the energy management strategy model are built, and the Simulation analysis, combined with the simulation results, it can be seen that under randomly given road cycle conditions, the speed difference of each vehicle in the fleet is less than 5km/h, and the maximum value of the fluctuation rate of the head-to-head distance is less than 28%, which is in line with road traffic efficiency and safety. sexual requirements. Due to the different driving requirements of vehicles in different positions in the fleet, the power output of power sources such as engines and motors is different, so the actual fuel consumption is different. The average fuel consumption is reduced by about 52%. Compared with the control strategy that only considers the optimal range of engine efficiency, the average fuel consumption per 100 kilometers of the fleet considering the energy strategy with the optimal system efficiency of the whole vehicle is reduced by about 7.7%.

Description of drawings

Figure 1 is a structural diagram of a single-axle parallel hybrid electric vehicle;

Figure 2 shows the system efficiency of the whole vehicle in pure electric working mode;

Figure 3 is a schematic diagram of the comparison of the efficiency of the entire vehicle system under the traditional mode and the combined driving mode;

Fig. 4 is a schematic diagram of the switching boundary between light-load charging and engine-only driving mode when the SOC is insufficient;

FIG. 5 is a schematic diagram of the switching boundary between the motor assist mode and the engine independent driving mode, and the switching boundary between the pure electric and the engine independent driving mode when the SOC is sufficient;

Figure 6 is a schematic diagram of a hybrid vehicle intelligent fleet following model;

Figure 7 is a schematic diagram of the pilot model of the pilot car;

8 is a schematic diagram of a driver model of a following vehicle;

FIG. 9 is a schematic diagram of the forward simulation model of the pilot vehicle;

Figure 10 is a schematic diagram of the speed change of the platooned vehicles;

FIG. 11 is a graph showing the change of driving distance under NEDC cycle conditions;

Fig. 12 is the change curve diagram of the head distance;

FIG. 13 is a flowchart of an intelligent management method for autonomous platoon driving energy of a parallel hybrid electric vehicle.

Detailed ways

The specific embodiments and working principles of the present invention will be further described in detail below with reference to the accompanying drawings.

With reference to Figure 13, it can be seen that a method for intelligent management of autonomous platoon driving energy of a parallel hybrid electric vehicle is characterized in that it is specifically carried out according to the following steps:

S1: Establish a parallel hybrid vehicle fleet, determine the model of the parallel hybrid vehicle in the fleet, and obtain the motion system parameters of the entire parallel hybrid vehicle;

In this embodiment, the team includes a leading car and two following cars.

In step S1, the motion system parameters at least include: windward area, curb mass, full load mass, rolling resistance coefficient, wind resistance coefficient, wheel radius, final reduction ratio, engine performance parameters, motor performance parameters, power battery parameters, transmission speed Compare.

The vehicles in the fleet are based on the same front-drive single-axle parallel hybrid vehicle from a domestic car company. The parameters of the entire vehicle and power system are shown in Table 2, and its structure is shown in Figure 1.

Table 2 Vehicle and power system parameters

In the table, the coefficients f ₀ , f ₁ and f ₄ in the calculation formula of the rolling resistance coefficient are obtained according to the test of the rotating drum test bench, and their value ranges are 0.0081-0.0098, 0.012-0.025 and 0.0002-0.0004 respectively. u _a is the driving speed.

S2: analyze the working mode of the parallel hybrid electric vehicle, and obtain all the driving working modes of the parallel hybrid electric vehicle;

In step S2, the driving working modes of the parallel hybrid electric vehicle include: pure electric driving mode, light-load charging mode, motor-assisting mode and engine-only driving mode;

The working states of the automobile motor, engine and clutch under the four driving working modes are as follows:

Table 1 List of working states of each driving mode of the parallel hybrid electric vehicle

Among them, 1 indicates that the power source and the actuator are in a working state or combined state, and 0 indicates that they are in a non-working state or disconnected.

S3: According to the vehicle dynamics theory, establish the dynamic equation of the whole vehicle's transmission system, and obtain the system efficiency calculation formula under different driving working modes; and establish the energy management strategy model under the optimal condition of the system efficiency;

Among them, the working mode switching condition is determined with the optimal system efficiency of the whole vehicle as the constraint condition during the driving process of the vehicle.

In step S3, the dynamic equation of the transmission system is:

Among them, I _r is the equivalent moment of inertia converted to the wheel;

I _m is the moment of inertia of the motor;

I _e is the moment of inertia of the engine;

ω _r is the angular velocity of the wheel;

ω _e is the angular velocity of the engine output shaft;

ω _m is the angular velocity of the motor output shaft;

T _req is the torque required for the vehicle to travel at a certain speed;

T _e is the torque of the motor output shaft;

T _m is the torque of the engine output shaft;

i _g is the transmission speed ratio;

i ₀ is the speed ratio of the reducer;

η _T is the transmission system efficiency;

± represents two drive modes, when “+” is taken, it represents the motor assist working mode, and when “-” is taken, it represents the light-load charging working mode.

When the car is running, it needs to overcome the rolling resistance, air resistance, acceleration resistance and ramp resistance. The calculation formula of the resistance when the car is running is:

Among them, m is the full load mass of the car; f is the road friction coefficient; C _D is the air resistance coefficient; A is the windward area; u is the driving speed; α is the slope.

According to formula (1) and formula (2), the system efficiency calculation formulas (3)-(6) under different driving modes are obtained; wherein, the system efficiency calculation formula under the pure electric driving mode is:

Among them, _ηsys is the current vehicle system efficiency, _Pbat is the discharge power of the battery pack, _ηm is the motor efficiency, _{ηdis -charge} is the battery discharge efficiency; Pin is the system input power; _Pout is the system output power; δ is Rotating mass conversion factor.

Because the engine is in a low-load working state of high fuel consumption and high emission when the parallel hybrid vehicle is running at low speed and light load. At this time, if the SOC value of the battery is high, the clutch is disconnected, the engine stops running, and the motor drives the vehicle alone to provide the power required for the vehicle to travel.

The calculation formula of the system efficiency in the engine independent driving mode is:

where η _e is the engine efficiency;

When the SOC value of the battery is sufficient and the required power of the whole vehicle is high, the engine drives the vehicle alone. The vehicle runs at a medium and high speed, and the engine working state is maintained in the medium and high load rate region. At this time, the engine efficiency is relatively high.

The calculation formula of the system efficiency of the vehicle in the light-load charging mode is:

Among them, η _charge is the battery charging efficiency;

When the SOC value of the battery is insufficient and the required power for the whole vehicle is low, the power output by the engine not only meets the driving demand of the whole vehicle, but also the additional power is converted into electrical energy through the motor and stored in the battery to charge the battery. load charging mode.

The calculation formula of the system efficiency of the vehicle in the motor-assist working mode is:

When the value of the battery SOC is sufficient, the power required by the entire vehicle has exceeded the power provided by the engine running in the optimal working area. At this time, the torque output by the engine and the electric motor is coupled through the power coupling device to jointly drive the vehicle to travel. At this time, it is the electric motor assist working mode.

In the light-load charging mode and the motor-assist working mode, the motor participates in the work. The difference is that the motor is in the charging state in the light-load charging mode, and the motor is in the discharging state in the motor-assist working mode. Under a given cycle condition, the working state of the motor is optimized. In addition, the above model is carried out under the constraints of the maximum output power, rotational speed, torque, SOC (State-Of-Charge) value of the engine, motor and battery, and the maximum range of the transmission speed ratio AMT.

Based on the MATLAB simulation platform, the system efficiency model of the whole vehicle under different working modes is established. The simulation results are shown in Figure 2-Figure 3; in Figure 2, Systemefficiency is the system efficiency, Veh_spd is the speed of the vehicle, the unit is km/h, and Veh_acc is the acceleration of the vehicle, the unit is m/s ² ; the efficiency of the whole vehicle in the pure electric working mode in Figure 2 is much higher than that with the engine involved in driving, but the working range is limited. ; Comparing the two curved surfaces in Figure 3, when the car is running at low speed and low load and high speed and high load, the efficiency of the whole vehicle system in the engine alone drive mode is low, so at low speed and low load, by actively increasing the engine load (light On-load charging), at high speed and high load, by reducing the engine load (motor boost), the efficiency of the entire vehicle system is significantly improved.

Projecting the efficiency surface in Figure 3 on the "vehicle speed-acceleration" plane, and considering the state of the battery SOC (State-Of-Charge), the working area of the power source that satisfies the condition of high efficiency is obtained, as shown in Figure 4 shown. In the figure, A represents the light-load charging area, B represents the motor boosting area, C1 and C2 represent the engine independent driving area, D represents the pure electric working area, and the boundary lines Line1, Line2, and Line3 represent the light-load charging and engine independent driving mode respectively. The switching boundary, the switching boundary between the motor-assisted mode and the engine-only driving mode, and the switching boundary between the pure electric and the engine-only driving mode. From the analysis in Section 2.2, it can be seen that the efficiency of the entire vehicle system in the pure electric working mode is higher than that of other working modes. Therefore, the use of pure electric drive as much as possible can improve the fuel economy under the driving cycle. boundary line.

The three boundary lines represent the working range of the engine and motor under different driving conditions (different vehicle speeds and different accelerations), so the energy control method of the hybrid vehicle, that is, its energy management strategy, is determined.

S4: Set the fleet head spacing and driving speed requirements, and establish a parallel hybrid vehicle fleet longitudinal dynamics model;

In this embodiment, in S4, the longitudinal dynamics model of the parallel hybrid vehicle fleet includes at least a parallel hybrid vehicle fleet following model, a pilot vehicle driver model, and a follower vehicle driver model;

It can be seen with reference to Fig. 6 that the parallel hybrid vehicle fleet following model includes at least one leading car and N following cars, and is provided with target vehicle speed and vehicle travel distance; it can be seen from Fig. 6 that in this embodiment , N=2.

Combining with Figure 7, it can be seen that the driver model of the pilot car uses the intelligent fuzzy control method to judge the acceleration of the pilot car according to the speed difference Δu between the current speed of the pilot car and the target speed, and the rate of change of the speed difference du/dt. degree or braking size, and its driver fuzzy rules are shown in Table 3.

The vehicle speed difference ΔuΔu is divided into 7 fuzzy subsets: NB, NM, NS, Z, PS, PM, PB, representing negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, respectively.

The rate of change du/dt of the vehicle speed difference is divided into 6 fuzzy subsets, where NB, NM, and NS represent the brake pedal, and PS, PM, and PB represent the accelerator pedal.

The pilot model based on the Matlab/Simulink platform is shown in Figure 8. The input in the figure is the speed difference between the current speed and the target speed and the rate of change of the difference. Kd and kb are the control coefficients respectively, and Pilot VehDynamic Model is the dynamic model of the pilot vehicle.

Table 3 Driver Fuzzy Rules Table

The pilot model of the pilot vehicle is input to the pilot vehicle fuzzy logic controller according to the speed difference between the current driving speed and the target vehicle speed, and the rate of change of the vehicle speed difference, and the output is combined with the throttle opening and the brake pedal opening. The actual driving speed is controlled;

It can be seen in conjunction with FIG. 9 that the driver model of the following car is to judge the accelerator and brake pedal of the current following car according to the speed difference Δu between the current following car and the preceding vehicle, and the displacement difference Δd between the current following car and the preceding vehicle. Opening; the current following car and the preceding vehicle maintain the distance between the following cars through the fuzzy logic controller of the following car, and use the speed difference and distance difference as the input of the fuzzy logic controller of the following car, the current following car accelerator pedal opening, the current following car braking The pedal opening is used as the output of the following vehicle fuzzy logic controller.

The fuzzy control rules of the following car fuzzy logic controller are shown in Table 4. In the table, Pedal is the pedal signal, Δd is the difference between the front position and the following position of the vehicle in the fleet, and N, S, M, and B represent the four Δd values respectively. Fuzzy subsets, representing negative, positive small, positive middle, and positive large, respectively.

Table 4 Fuzzy control rule table of the following car fuzzy logic controller

S5: Based on the simulation platform, the longitudinal dynamics model of the parallel hybrid electric vehicle fleet is built, and the parallel hybrid electric vehicle fleet is simulated and analyzed in combination with the energy management strategy model.

In S5, the longitudinal dynamics model of the parallel hybrid vehicle fleet includes at least a driver model, a gear shift model, an energy matching model, an engine model, a motor model, a battery model, and a vehicle model.

Simulation:

After the model is built in MATLAB/Simulink, at time t=0, the initial distance between the front and the front of each vehicle in the fleet is 15 meters, the pilot car starts at the target speed of the NEDC European cruise condition, and the simulation duration of the condition is 1185s , the driving distance is 10.9km, and the initial SOC=0.7.

Figure 10-12 shows the time-dependent changes in the speed and distance of each vehicle in the fleet. The solid line in the figure is the change trend of the speed of the leading vehicle (Piolt_veh_spd) and the distance traveled (Piolt_veh_Dis), and the dotted line is the speed of the No. 1 follower vehicle. (Follow_veh_1) and travel distance (Follow_veh1_Dis) change, the dotted line is the change of No. 2 vehicle speed (Follow_veh_2) and travel distance (Follow_veh2_Dis). In order to facilitate the observation of the distance relationship between vehicles in the fleet, Figure 12 shows the change curve of the distance (ΔDistacnce) between the leading vehicle and the No. 1 following vehicle under the NEDC road cruising condition.

In Figure 10, the ordinate Speed represents the driving speed, in km/h, and the abscissa Time represents the time, in s. It can be seen from Figure 10 that the maximum speed difference of vehicles in the fleet occurs in the time range of 910s-920s in EUDC in suburban conditions. At t=911s, the speed difference between the leading car and the following car No. t=914s, the speed difference between the following car No. 1 and the following car No. 2 is about 5km/h.

It can be seen from Figure 11 that the distance between vehicles in the fleet is relatively consistent. Among them, the maximum distance is less than 19m, and the minimum distance is greater than 12m. Compared with the initial distance, the fluctuation rate of the positive distance (the separation of vehicles and vehicles) is less than 28%, and the negative distance is less than 28%. The volatility of the distance between cars (cars and cars gathering together) is less than 20%.

Under the given NEDC road cycle conditions, the fuel economy of the hybrid vehicle fleet and the traditional vehicle fleet are compared, and for the hybrid vehicle fleet, the energy management strategy in this paper and the energy management strategy that only considers the engine efficiency characteristics are compared respectively. Gained fuel economy. The results are shown in Table 5.

*The fuel consumption per 100 kilometers is the comprehensive fuel consumption after converting the battery ΔSOC.

On the premise of ensuring the safety and traffic rate of the driving process of the fleet, the present invention calculates the system efficiency model of the hybrid vehicle under different working modes, and establishes an energy management strategy under the optimal condition of the system efficiency; Based on the vehicle speed requirements, the longitudinal dynamics model of the intelligent hybrid vehicle fleet was established; finally, based on the Matlab/Simulink/Stateflow simulation platform, the fleet longitudinal dynamics model and the energy management strategy model were built, and the simulation analysis was carried out. Under the given NEDC road cycle conditions, the speed difference of each vehicle in the fleet is less than 5km/h, and the maximum value of the fluctuation rate of the head-to-head distance is less than 28%, which meets the requirements of road traffic efficiency and safety. Due to the different driving requirements of vehicles in different positions in the fleet, the power output of power sources such as engines and motors is different, so the actual fuel consumption is different. The average fuel consumption is reduced by 52.13%. Compared with the control strategy that only considers the optimal range of engine efficiency, the average fuel consumption per 100 kilometers of the fleet considering the energy strategy with the optimal system efficiency of the whole vehicle is reduced by 7.79%.

It should be noted that the above descriptions are not intended to limit the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by those of ordinary skill in the art within the scope of the present invention, It should also belong to the protection scope of the present invention.

Claims (7)

1. a parallel hybrid electric vehicle autonomous platoon traveling energy intelligent management method is characterized in that carrying out specifically according to the following steps: S1: Establish a parallel hybrid vehicle fleet, determine the model of the parallel hybrid vehicle in the fleet, and obtain the motion system parameters of the entire parallel hybrid vehicle; S2: analyze the working mode of the parallel hybrid electric vehicle, and obtain all the driving working modes of the parallel hybrid electric vehicle; S3: According to the vehicle dynamics theory, establish the dynamic equation of the whole vehicle's transmission system, and obtain the system efficiency calculation formula under different driving working modes; and establish the energy management strategy model under the optimal condition of the system efficiency; S4: Set the fleet head spacing and driving speed requirements, and establish a parallel hybrid vehicle fleet longitudinal dynamics model; S5: Based on the simulation platform, the longitudinal dynamics model of the parallel hybrid electric vehicle fleet is built, and the parallel hybrid electric vehicle fleet is simulated and analyzed in combination with the energy management strategy model. 2. The method for intelligent management of autonomous platoon driving energy of parallel hybrid electric vehicles according to claim 1, wherein in step S1, the parameters of the motion system at least include: windward area, curb mass, full load mass, rolling resistance coefficient, wind resistance coefficient, wheel radius, main reduction ratio, engine performance parameters, motor performance parameters, power battery parameters, transmission speed ratio. 3. The method for intelligent management of autonomous platoon driving energy of a parallel hybrid electric vehicle according to claim 1, wherein in step S2, the driving working modes of the parallel hybrid electric vehicle include: pure electric drive mode, light Load charging mode, motor assist mode and engine independent drive mode; The working states of the automobile motor, engine and clutch under the four driving working modes are as follows: Table 1 List of working states of each driving mode of the parallel hybrid electric vehicle

Among them, 1 indicates that the power source and the actuator are in a working state or combined state, and 0 indicates that they are in a non-working state or disconnected. 4. The intelligent management method for autonomous platoon traveling energy of parallel hybrid electric vehicles according to claim 1, characterized in that: in step S3, the dynamic equation of the transmission system is: Among them, I _r is the equivalent moment of inertia converted to the wheel; I _m is the moment of inertia of the motor; I _e is the moment of inertia of the engine; ω _r is the angular velocity of the wheel; ω _e is the angular velocity of the engine output shaft; ω _m is the angular velocity of the motor output shaft; T _req is the torque required for the vehicle to travel at a certain speed; T _e is the torque of the motor output shaft; T _m is the torque of the engine output shaft; i _g is the transmission speed ratio; i ₀ is the speed ratio of the reducer; η _T is the transmission system efficiency; ±represents two drive modes, when "+" is taken, it represents the motor assist working mode, and when "-" is taken, it represents the light-load charging working mode; When the car is running, it needs to overcome the rolling resistance, air resistance, acceleration resistance and ramp resistance. The calculation formula of the resistance when the car is running is:

Among them, m is the full load mass of the car; f is the road friction coefficient; C _D is the air resistance coefficient; A is the windward area; u is the driving speed; α is the slope. 5. The method for intelligent management of autonomous platoon driving energy of parallel hybrid electric vehicles according to claim 4, wherein in step S3, in the system efficiency calculation formula under different driving working modes, in the pure electric driving mode The formula for calculating the system efficiency is: Among them, _ηsys is the current vehicle system efficiency, _Pbat is the discharge power of the battery pack, _ηm is the motor efficiency, _{ηdis -charge} is the battery discharge efficiency; Pin is the system input power; _Pout is the system output power; δ is Rotation mass conversion factor; The calculation formula of the system efficiency in the engine independent driving mode is:

where η _e is the engine efficiency; The calculation formula of the system efficiency of the vehicle in the light-load charging mode is:

Among them, η _charge is the battery charging efficiency; The calculation formula of the system efficiency of the vehicle in the motor-assist working mode is:

6. The method for intelligent management of driving energy in an autonomous platoon of parallel hybrid electric vehicles according to claim 1, characterized in that in S4, the parallel hybrid electric vehicle fleet longitudinal dynamics model at least includes parallel hybrid electric vehicle fleet following cars Model, pilot car driver model, follower car driver model; The parallel hybrid vehicle fleet following model includes at least one leading vehicle and N following vehicles, and is provided with a target vehicle speed and a vehicle travel distance; The pilot model of the pilot vehicle is input to the pilot vehicle fuzzy logic controller according to the speed difference between the current driving speed and the target vehicle speed, and the rate of change of the vehicle speed difference, and the output is combined with the throttle opening and the brake pedal opening. The actual driving speed is controlled; The driver model of the following car is to judge the accelerator and brake pedal openings of the current following car according to the speed difference between the current following car and the preceding vehicle and the displacement difference with the preceding vehicle; The vehicle maintains the vehicle distance through the following vehicle fuzzy logic controller, takes the vehicle speed difference and distance difference as the input of the following vehicle fuzzy logic controller, and the current following vehicle accelerator pedal opening and the current following vehicle brake pedal opening are used as the following vehicle. The output of the fuzzy logic controller. 7. The method for intelligent management of driving energy in an autonomous platoon of a parallel hybrid electric vehicle according to claim 1, wherein in S5, the longitudinal dynamics model of the parallel hybrid electric vehicle fleet at least comprises a driver model, a gear shift model, a Energy matching model, engine model, motor model, battery model, vehicle model.

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