CN110667564B - Intelligent management method for autonomous queue running energy of parallel hybrid electric vehicle - Google Patents
Intelligent management method for autonomous queue running energy of parallel hybrid electric vehicle Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/11—Controlling the power contribution of each of the prime movers to meet required power demand using model predictive control [MPC] strategies, i.e. control methods based on models predicting performance
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Abstract
The invention discloses an intelligent management method for autonomous queue running energy of a parallel hybrid electric vehicle, which specifically comprises the following steps: establishing a motorcade, and determining the model of a parallel hybrid electric vehicle in the motorcade and the motion system parameters of the whole vehicle; analyzing the working modes of the parallel hybrid electric vehicle to obtain all driving working modes of the parallel hybrid electric vehicle; according to the automobile dynamics theory, establishing a dynamics equation of a transmission system of the whole automobile, and obtaining system efficiency calculation formulas under different driving working modes; establishing an energy management strategy model under the condition of optimal system efficiency; setting the vehicle head spacing of the vehicle fleet and the running speed requirement, and establishing a longitudinal dynamic model of the parallel hybrid vehicle fleet; and carrying out simulation analysis based on the simulation platform. The method has the beneficial effects of effectively improving the driving safety and traffic efficiency of the motorcade and the fuel economy.
Description
Technical Field
The invention relates to the technical field of management of running energy consumption of automobile fleets, in particular to an intelligent management method for running energy of a parallel hybrid electric vehicle in an autonomous queue.
Background
The autonomous queue driving of the automobile is used as an important component of an advanced driving auxiliary system, the acceleration and deceleration of the automobile are controlled according to the preset safe distance, the safety and the traffic rate of the automobile in the driving process are effectively improved, and the hybrid electric automobile is the best solution for realizing energy conservation and emission reduction in the current technical level. With the rapid progress of the 'new and quadrate' of the automobile, the combination of a hybrid power technology and an intelligent auxiliary driving technology is also necessarily promoted, and the aims of low oil consumption, low emission, safety and intellectualization are finally fulfilled.
In the aspect of intelligent vehicle longitudinal control, projects around the world are mentioned, for example, the european SARTRE project, the japanese ENERGY ITS project, the netherlands GCDC project and the like all indicate that vehicle queue running can significantly reduce traffic jam, improve traffic efficiency and improve fuel economy, and the intelligent vehicle longitudinal control project is one of the leading directions in the field of intelligent vehicle longitudinal control.
However, in the prior art, in terms of the intelligent vehicle longitudinal control technology, there is a lot of development space for the hybrid vehicle in terms of energy saving, and some technologies are necessary to improve the queue driving efficiency.
Disclosure of Invention
Aiming at the problems, the invention provides an intelligent management method for the autonomous queue running energy of a parallel hybrid electric vehicle, which establishes an energy management strategy for a train and improves the fuel economy of the queue running vehicles.
In order to achieve the purpose, the invention adopts the following specific technical scheme:
an intelligent management method for autonomous queue running energy of a parallel hybrid electric vehicle is characterized by comprising the following steps:
s1: establishing a parallel hybrid electric vehicle fleet, determining the model of the parallel hybrid electric vehicle in the fleet, and acquiring the motion system parameters of the whole parallel hybrid electric vehicle;
s2: analyzing the working modes of the parallel hybrid electric vehicle to obtain all driving working modes of the parallel hybrid electric vehicle;
s3: according to the automobile dynamics theory, establishing a dynamics equation of a transmission system of the whole automobile, and obtaining system efficiency calculation formulas under different driving working modes; establishing an energy management strategy model under the condition of optimal system efficiency;
s4: setting the vehicle head spacing of the vehicle fleet and the running speed requirement, and establishing a longitudinal dynamic model of the parallel hybrid vehicle fleet;
s5: and building a longitudinal dynamic model of the parallel hybrid electric vehicle fleet based on a simulation platform, and performing simulation analysis on the parallel hybrid electric vehicle fleet by combining the energy management strategy model.
In order to reduce the oil consumption of the parallel hybrid electric vehicle running in a queue while keeping the vehicle distance and the relative vehicle speed, firstly, establishing a whole vehicle system efficiency calculation model aiming at different working modes of the parallel hybrid electric vehicle, and designing a hybrid electric vehicle energy management control strategy based on the principle of the optimal whole vehicle system efficiency; then, establishing a longitudinal dynamics model of a parallel hybrid electric vehicle fleet based on a fuzzy intelligent control algorithm; and finally, building a longitudinal dynamic model and a corresponding energy matching model of the parallel hybrid electric vehicle fleet based on a simulation platform, verifying the effectiveness of an energy matching strategy of the parallel hybrid electric vehicle under the principle of optimal system efficiency through simulation analysis, and achieving the purpose of improving the driving safety, the traffic efficiency and the fuel economy of the fleet.
Further, in step S1, the motion system parameters at least include: windward area, servicing quality, full load quality, rolling resistance coefficient, wind resistance coefficient, wheel radius, final reduction ratio, engine performance parameter, motor performance parameter, power battery parameter, and transmission speed ratio.
Still further, in step S2, the driving operation mode of the parallel hybrid vehicle includes: a pure electric drive mode, a light load charging mode, a motor power-assisted mode and an engine independent drive mode;
the working states of the automobile motor, the engine and the clutch under the four driving working modes are as follows:
working state detail table for all driving working modes of meter parallel connection type hybrid electric vehicle
Wherein, 1 represents that the power source and the executive component are in a working state or a combined state, and 0 represents that the power source and the executive component are in a non-working state or disconnected state.
Compared with the conventional automobile, the clutch of the parallel hybrid automobile plays a role in connecting and disconnecting the output torque of the engine. The engine of the traditional automobile is maintained in a low-speed low-load area and a high-speed large-load area, so that the efficiency is low, and the oil consumption and the emission are increased. The motor plays a role of 'peak clipping and valley filling', and in a low-speed and low-load region, (1) under a pure electric driving working condition, the motor drives the automobile to drive independently, so that a low-efficiency region is avoided, and (2) under a light-load charging working condition, the motor charges the storage battery, so that the load rate of the engine is improved; under the power-assisted working condition of the motor, the load rate of the engine is reduced in a high-speed high-load area, so that the engine works in the optimal economic working area of the engine, and the efficiency of the engine is improved. (3) The engine drives the working condition independently, and when the vehicle running load is in the high-efficiency working area of the engine, the engine completes the running task independently.
According to the above analysis, the driving operation modes of the parallel hybrid electric vehicle can be divided into: the system comprises a pure electric driving mode, a light-load charging mode, a motor power assisting mode and an engine independent driving mode. The switching and implementation of each mode of the hybrid power system are realized by controlling the working states of the engine, the motor and the clutch.
The further technical scheme is as follows: in step S3, the driveline dynamics equation is:
wherein, IrTo translate to the equivalent moment of inertia of the wheel;
Imis the moment of inertia of the motor;
Ieis the rotational inertia of the engine;
ωris the angular velocity of the wheel;
ωeis the angular velocity of the engine output shaft;
ωmis the angular velocity of the motor output shaft;
Treqtorque required for the vehicle to travel at a certain vehicle speed;
Teis the torque of the motor output shaft;
Tmis the torque of the engine output shaft;
igis the transmission speed ratio;
i0the speed ratio of the speed reducer is adopted;
ηTfor transmission system efficiency;
plus or minus represents two driving modes, when plus or minus represents the motor boosting working mode, and when minus represents the light load charging working mode.
The rolling resistance, air resistance, acceleration resistance and ramp resistance when the automobile runs need to be overcome, and the calculation formula of the resistance when the automobile runs need to be overcome is as follows:
wherein m is the full load mass of the automobile; f is the road friction coefficient; cDIs the air resistance coefficient; a is the windward area; u is the running speed; alpha is the gradient;
the further technical scheme is as follows: in step S3, among the system efficiency calculation formulas in different driving operation modes, the system efficiency calculation formula in the pure electric driving mode is:
wherein eta issysFor current vehicle system efficiency, PbatIs the discharge power, η, of the accumulator batterymTo the motor efficiency, ηdis-chargeDischarging efficiency for the battery; pinInputting power for the system; poutSystem output power; δ is a rotating mass conversion factor.
The system efficiency calculation formula under the engine single driving mode is as follows:
wherein eta iseTo engine efficiency;
the system efficiency calculation formula of the vehicle in the light load charging mode is as follows:
wherein eta ischargeEfficiency of charging the battery;
the system efficiency calculation formula of the vehicle under the motor power-assisted working mode is as follows:
the further technical scheme is as follows: in S4, the parallel hybrid electric vehicle fleet longitudinal dynamics model at least comprises a parallel hybrid electric vehicle fleet following model, a pilot vehicle driver model and a following vehicle driver model;
the parallel hybrid electric vehicle fleet following model at least comprises a pilot vehicle and N following vehicles, and is provided with a target vehicle speed and a vehicle running distance; wherein N is a positive integer greater than or equal to 1.
The pilot vehicle driver model is used for controlling the actual driving speed by outputting and combining the opening degree of a throttle valve and the opening degree of a brake pedal after inputting a vehicle speed difference value of the current driving speed and a target vehicle speed and the change rate of the vehicle speed difference value into a pilot vehicle fuzzy logic controller;
the following vehicle driver model judges the opening degrees of an accelerator and a brake pedal of the current following vehicle according to the speed difference value of the current following vehicle and the previous vehicle and the displacement difference value of the previous vehicle; the following vehicle fuzzy logic controller is used for keeping the vehicle distance between the current following vehicle and the previous vehicle, the vehicle speed difference and the distance difference are used as the input of the following vehicle fuzzy logic controller, and the opening degree of an accelerator pedal and the opening degree of a brake pedal of the current following vehicle are used as the output of the following vehicle fuzzy logic controller.
In a further technical scheme, in S5, the parallel hybrid vehicle fleet longitudinal dynamics model at least includes a driver model, a gear shift model, an energy matching model, an engine model, a motor model, a battery model, and a vehicle model.
The invention has the beneficial effects that: on the premise of ensuring the safety and the traffic rate of the motorcade in the running process, the invention calculates the system efficiency models of the parallel hybrid electric vehicle under different working modes and establishes the energy management strategy under the condition of optimal system efficiency; establishing a longitudinal dynamic model of the parallel hybrid electric vehicle fleet according to the vehicle head spacing of the fleet and the running speed requirement; and finally, a simulation platform builds a longitudinal dynamics model and an energy management strategy model of the parallel hybrid electric vehicle fleet, performs simulation analysis on the longitudinal dynamics model and the energy management strategy model, and combines simulation results to know that the running speed difference of each vehicle in the fleet is less than 5km/h and the maximum fluctuation rate of the vehicle head distance is less than 28% under the randomly given road cycle working condition, so that the road traffic efficiency and safety requirements are met. Because the power output of power sources such as an engine, a motor and the like is different due to different driving power requirements of vehicles at different positions in a motorcade, the actually generated oil consumption is different, compared with the motorcade formed by traditional automobiles, the hundred kilometer average oil consumption of the motorcade of the hybrid electric automobile is reduced by about 52%, compared with a control strategy only considering an engine efficiency optimal interval, and the hundred kilometer average oil consumption of the motorcade considering the energy strategy that the efficiency of a whole automobile system is optimal is reduced by about 7.7%.
Drawings
FIG. 1 is a block diagram of a single axle parallel hybrid vehicle;
FIG. 2 is a diagram of overall vehicle system efficiency in the pure electric operating mode;
FIG. 3 is a schematic diagram comparing the efficiency of the entire vehicle system in the conventional mode and the combined driving mode;
FIG. 4 is a schematic diagram of the boundary of the switching between light-load charging and engine-only driving modes when the SOC is insufficient;
FIG. 5 is a schematic diagram of the switching boundary between the motor assist mode and the engine-only driving mode and the switching boundary between the electric-only driving mode and the engine-only driving mode when the SOC is sufficient;
FIG. 6 is a schematic diagram of a follow-up model of a hybrid vehicle intelligent fleet;
FIG. 7 is a schematic view of a pilot vehicle driver model;
FIG. 8 is a schematic view of a driver model of the follower vehicle;
FIG. 9 is a schematic view of a forward simulation model of a pilot vehicle;
FIG. 10 is a schematic diagram of the speed variation of the vehicles traveling in the platoon;
FIG. 11 is a graph of distance traveled during a NEDC cycle;
FIG. 12 is a graph showing changes in headway;
FIG. 13 is a flow chart of the intelligent management method for the autonomous queuing running energy of the parallel hybrid electric vehicle.
Detailed Description
The following provides a more detailed description of the embodiments and the operation of the present invention with reference to the accompanying drawings.
As can be seen from fig. 13, the intelligent management method for the autonomous queued running energy of the parallel hybrid electric vehicle is specifically performed according to the following steps:
s1: establishing a parallel hybrid electric vehicle fleet, determining the model of the parallel hybrid electric vehicle in the fleet, and acquiring the motion system parameters of the whole parallel hybrid electric vehicle;
in this embodiment, the fleet comprises a lead vehicle and 2 follower vehicles.
In step S1, the motion system parameters at least include: windward area, servicing quality, full load quality, rolling resistance coefficient, wind resistance coefficient, wheel radius, final reduction ratio, engine performance parameter, motor performance parameter, power battery parameter, and transmission speed ratio.
The front-wheel-drive single-shaft parallel hybrid electric vehicle is characterized in that the vehicle in the fleet is based on the same type of front-wheel-drive single-shaft parallel hybrid electric vehicle of a certain domestic vehicle enterprise, the parameters of the whole vehicle and a power system are shown in a table II, and the structure is shown in fig. 1.
Table two whole vehicle and power system parameter
In the table, coefficient f in the rolling resistance coefficient calculation formula0，f1And f4Is obtained by testing with a rotary drum test bed, and has the value ranges of 0.0081-0.0098,0.012-0.025 and 0.0002-0.0004。uaIs the running speed.
S2: analyzing the working modes of the parallel hybrid electric vehicle to obtain all driving working modes of the parallel hybrid electric vehicle;
in step S2, the driving operation mode of the parallel hybrid vehicle includes: a pure electric drive mode, a light load charging mode, a motor power-assisted mode and an engine independent drive mode;
the working states of the automobile motor, the engine and the clutch under the four driving working modes are as follows:
working state detail table for all driving working modes of meter parallel connection type hybrid electric vehicle
Wherein, 1 represents that the power source and the executive component are in a working state or a combined state, and 0 represents that the power source and the executive component are in a non-working state or disconnected state.
S3: according to the automobile dynamics theory, establishing a dynamics equation of a transmission system of the whole automobile, and obtaining system efficiency calculation formulas under different driving working modes; establishing an energy management strategy model under the condition of optimal system efficiency;
the working mode switching condition is judged by taking the optimal efficiency of the whole automobile system in the automobile running process as a constraint condition.
In step S3, the driveline dynamics equation is:
wherein, IrTo translate to the equivalent moment of inertia of the wheel;
Imis the moment of inertia of the motor;
Ieis the rotational inertia of the engine;
ωris the angular velocity of the wheel;
ωeis the angular velocity of the engine output shaft;
ωmis the angular velocity of the motor output shaft;
Treqtorque required for the vehicle to travel at a certain vehicle speed;
Teis the torque of the motor output shaft;
Tmis the torque of the engine output shaft;
igis the transmission speed ratio;
i0the speed ratio of the speed reducer is adopted;
ηTfor transmission system efficiency;
plus or minus represents two driving modes, when plus or minus represents the motor boosting working mode, and when minus represents the light load charging working mode.
The rolling resistance, air resistance, acceleration resistance and ramp resistance when the automobile runs need to be overcome, and the calculation formula of the resistance when the automobile runs need to be overcome is as follows:
wherein m is the full load mass of the automobile; f is the road friction coefficient; cDIs the air resistance coefficient; a is the windward area; u is the running speed; alpha is the gradient.
Obtaining system efficiency calculation formulas (3) - (6) under different driving modes according to the formulas (1) and (2); the system efficiency calculation formula in the pure electric driving mode is as follows:
wherein eta issysFor current vehicle system efficiency, PbatIs the discharge power, η, of the accumulator batterymTo the motor efficiency, ηdis-chargeDischarging efficiency for the battery; pinInputting power for the system; poutSystem output power; deltaIs a rotating mass scaling factor.
When the engine runs under low speed and small load, the parallel hybrid electric vehicle is in a low-load working state with high oil consumption and high emission. At this time, if the SOC value of the battery is high, the clutch is disconnected, the engine stops operating, the vehicle is driven by the motor alone to run, and the power required for running the vehicle is provided alone.
The system efficiency calculation formula under the engine single driving mode is as follows:
wherein eta iseTo engine efficiency;
when the SOC value of the storage battery is sufficient and the required power for driving the whole vehicle is high, the engine drives the vehicle to drive alone. The vehicle runs at a medium and high speed, and the working state of the engine is maintained in a medium and high load rate area, and the efficiency of the engine is relatively high.
The system efficiency calculation formula of the vehicle in the light load charging mode is as follows:
wherein eta ischargeEfficiency of charging the battery;
when the SOC value of the storage battery is insufficient and the required power for driving the whole vehicle is low, the power output by the engine not only meets the driving requirement of the whole vehicle, but also converts mechanical energy into electric energy through the motor to be stored in the storage battery to charge the storage battery, and at the moment, the storage battery is in a light-load charging working mode.
The system efficiency calculation formula of the vehicle under the motor power-assisted working mode is as follows:
when the value of the SOC of the storage battery is sufficient, the power required by the running of the whole vehicle exceeds the power provided by the engine running in the optimal working area. At this time, the torque output by the engine and the torque output by the motor are coupled through the power coupling device, and the vehicle is driven to run together, and the motor boosting working mode is adopted at this time.
The motor is in a light-load charging mode and a motor boosting working mode, the difference is that the motor in the light-load charging mode is in a charging state, the motor in the motor boosting working mode is in a discharging state, the highest efficiency of the whole vehicle system is taken as a constraint condition through model analysis, and the optimization solution is carried out on the working state of the motor based on a given cycle working condition. The above model is also performed under constraints within the maximum ranges Of the engine, motor, battery output, rotational speed, torque, SOC (State-Of-Charge) value, and transmission ratio AMT range.
Judging by taking the optimal efficiency of the whole automobile system in the automobile running process as a constraint condition, establishing a whole automobile system efficiency model under different working modes based on an MATLAB simulation platform, wherein the simulation result is shown in figures 2-3; in FIG. 2, System efficiency is the System efficiency, Veh _ spd is the vehicle speed in km/h, Veh _ acc is the vehicle acceleration in m/s2(ii) a In the fig. 2, the efficiency of the whole vehicle in the pure electric working mode is far higher than that in the case that the engine participates in driving, but the working range is limited; comparing the two curved surfaces in fig. 3, when the automobile runs at low speed, low load and high speed, the efficiency of the whole automobile system under the engine independent driving mode is low, so that the engine load is actively increased (light load charging) at low speed and low load, and the engine load is reduced (motor power assistance) at high speed and high load, so that the efficiency of the whole automobile system is obviously increased.
The efficiency curved surface in fig. 3 is projected on a "vehicle speed-acceleration" plane, and a working area Of the power source satisfying a condition Of high efficiency is obtained in consideration Of a State Of battery SOC (State-Of-Charge State), as shown in fig. 4. In the figure, a represents a light-load charging area, B represents a motor boosting area, C1 and C2 represent engine individual driving areas, D represents an electric-only working area, and boundary lines Line1, Line2 and Line3 represent switching boundaries of light-load charging and engine individual driving modes, switching boundaries of the motor boosting mode and the engine individual driving mode and switching boundaries of the electric-only and engine individual driving modes, respectively. As can be known from the analysis of subsection 2.2, the efficiency of the whole vehicle system is higher than that of other working modes under the pure electric working mode, so that the fuel economy under the driving cycle can be improved by adopting the pure electric drive as far as possible, and Line3 is a boundary Line which gives full play to the power of the motor under the driving working condition.
The three boundary lines represent the working ranges of the engine and the motor under different driving conditions (different vehicle speeds and different accelerations), so that the energy control mode of the hybrid electric vehicle, namely the energy management strategy of the hybrid electric vehicle is determined.
S4: setting the vehicle head spacing of the vehicle fleet and the running speed requirement, and establishing a longitudinal dynamic model of the parallel hybrid vehicle fleet;
in this embodiment, in S4, the parallel hybrid vehicle fleet longitudinal dynamics model at least includes a parallel hybrid vehicle fleet following model, a pilot vehicle driver model, and a following vehicle driver model;
as can be seen from fig. 6, the parallel hybrid vehicle fleet following model at least includes a pilot vehicle and N following vehicles, and the target vehicle speed and the vehicle driving distance are set; as can be seen from fig. 6, in this embodiment, N is 2.
As can be seen from fig. 7, the driver model of the pilot vehicle adopts an intelligent fuzzy control method to determine the acceleration degree or the braking degree of the pilot vehicle according to the vehicle speed difference Δ u between the current vehicle speed and the target vehicle speed of the pilot vehicle and the change rate du/dt of the vehicle speed difference, and the driver fuzzy rule is shown in table 3.
Wherein the vehicle speed difference value Δ u Δ u is divided into 7 fuzzy subsets: NB, NM, NS, Z, PS, PM, PB, respectively represent negative big, negative middle, negative small, zero, positive small, positive middle, positive big.
The rate of change du/dt of the vehicle speed difference is divided into 6 fuzzy subsets, where NB, NM, NS represent the brake pedal and PS, PM, PB represent the accelerator pedal.
The pilot vehicle driver model established based on the Matlab/Simulink platform is shown in FIG. 8, wherein the input quantity in the graph is the vehicle speed difference value and the change rate of the difference value between the current vehicle speed and the target vehicle speed. Kd and kb are respectively control coefficients, and Pilot Veh Dynamic Model is a Pilot vehicle Dynamic Model.
TABLE III fuzzy rule table for driver
The pilot vehicle driver model is used for controlling the actual driving speed by outputting and combining the opening degree of a throttle valve and the opening degree of a brake pedal after inputting a vehicle speed difference value of the current driving speed and a target vehicle speed and the change rate of the vehicle speed difference value into a pilot vehicle fuzzy logic controller;
as can be seen from fig. 9, the follower driver model determines the opening degrees of the accelerator and brake pedals of the current follower according to the speed difference Δ u between the current follower and the previous vehicle and the displacement difference Δ d between the current follower and the previous vehicle; the following vehicle fuzzy logic controller is used for keeping the vehicle distance between the current following vehicle and the previous vehicle, the vehicle speed difference and the distance difference are used as the input of the following vehicle fuzzy logic controller, and the opening degree of an accelerator pedal and the opening degree of a brake pedal of the current following vehicle are used as the output of the following vehicle fuzzy logic controller.
The fuzzy control rule of the following vehicle fuzzy logic controller is as shown in the fourth table; in the table, padal is a Pedal signal, Δ d is a difference value between a head position and a following position of a front vehicle in the fleet, and N, S, M, B respectively represent 4 fuzzy subsets of Δ d, which respectively represent negative, positive, small, positive and large.
Fuzzy control rule table of four-following vehicle fuzzy logic controller
S5: and building a longitudinal dynamic model of the parallel hybrid electric vehicle fleet based on a simulation platform, and performing simulation analysis on the parallel hybrid electric vehicle fleet by combining the energy management strategy model.
In S5, the parallel hybrid electric vehicle fleet longitudinal dynamics model at least comprises a driver model, a gear shifting model, an energy matching model, an engine model, a motor model, a battery model and a whole vehicle model.
Simulation verification:
after a model is built by MATLAB/Simulink, at the time when t is 0, the initial distance between the vehicle head and the vehicle head of each vehicle in a fleet is 15 meters, the pilot vehicle starts at the target vehicle speed under the NEDC European cruising condition, the simulation duration of the condition is 1185s, the driving mileage is 10.9km, and the initial SOC is 0.7.
The change of the running speed and the running distance of each vehicle in the fleet with time is shown in FIGS. 10-12, wherein the solid lines show the change trend of the pilot vehicle speed (Pilot _ veh _ spd) and the running distance (Pilot _ veh _ Dis), the dotted lines show the change situation of the Follow-up vehicle speed No. 1 (Follow _ veh _1) and the running distance (Follow _ veh1_ Dis), and the dotted lines show the change situation of the Follow-up vehicle speed No. 2 (Follow _ veh _2) and the running distance (Follow _ veh2_ Dis). In order to facilitate observation of the distance relationship between vehicles in a fleet, fig. 12 is a variation curve of the distance (Δ distancce) between the lead vehicle and the 1 # following vehicle under the NEDC road cruising condition.
In FIG. 10, the ordinate Speed represents the running vehicle Speed in km/h, and the abscissa Time represents the Time in s. As can be seen from fig. 10, the maximum difference between the vehicle speeds of the vehicles in the fleet occurs in the time range of 910s-920s under the suburban conditions EUDC, the difference between the vehicle speeds of the leading vehicle and the following vehicle No. 1 is about 4km/h when t is 911s, and the difference between the vehicle speeds of the following vehicle No. 1 and the following vehicle No. 2 is about 5km/h when t is 914 s.
As can be seen from fig. 11, the inter-vehicle distances in the fleet are kept relatively consistent, wherein the maximum inter-vehicle distance is less than 19m, the minimum inter-vehicle distance is greater than 12m, and compared with the initial inter-vehicle distance, the positive inter-vehicle distance (inter-vehicle separation) fluctuation rate is less than 28%, and the negative inter-vehicle distance (inter-vehicle convergence) fluctuation rate is less than 20%.
Under the given cyclic working condition of the NEDC road, the fuel economy of a hybrid power fleet and a traditional automobile fleet are compared, and meanwhile, the fuel economy is obtained by respectively comparing the energy management strategy and the energy management strategy only considering the efficiency characteristic of the engine aiming at the hybrid power fleet. The results are shown in Table 5.
The oil consumption of hundred kilometers in the star is the comprehensive oil consumption converted into the delta SOC of the battery.
On the premise of ensuring the safety and the traffic rate of the motorcade in the driving process, the system efficiency models of the hybrid electric vehicle in different working modes are calculated, and an energy management strategy under the condition of optimal system efficiency is established; establishing an intelligent hybrid electric vehicle fleet longitudinal dynamic model according to the fleet head distance and the running speed requirement; and finally, building a fleet longitudinal dynamics model and an energy management strategy model based on a Matlab/Simulink/Stateflow simulation platform, and performing simulation analysis on the models to obtain a result that the running speed difference of each vehicle in the fleet is less than 5km/h and the fluctuation rate maximum value between the vehicle heads is less than 28% under the given cycle working condition of the NEDC road, so that the requirements on road traffic efficiency and safety are met. Because the power output of power sources such as an engine, a motor and the like is different due to different driving power requirements of vehicles at different positions in a motorcade, the actually generated oil consumption is different, compared with the motorcade formed by traditional vehicles, the hundred-kilometer average oil consumption of the motorcade of the hybrid electric vehicle is reduced by 52.13%, compared with a control strategy only considering an engine efficiency optimal interval, and the hundred-kilometer average oil consumption of the motorcade considering the energy strategy that the efficiency of a whole vehicle system is optimal is reduced by 7.79%.
It should be noted that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make variations, modifications, additions or substitutions within the spirit and scope of the present invention.
Claims (6)
1. An intelligent management method for autonomous queue running energy of a parallel hybrid electric vehicle is characterized by comprising the following steps:
s1: establishing a parallel hybrid electric vehicle fleet, determining the model of the parallel hybrid electric vehicle in the fleet, and acquiring the motion system parameters of the whole parallel hybrid electric vehicle;
s2: analyzing the working modes of the parallel hybrid electric vehicle to obtain all driving working modes of the parallel hybrid electric vehicle;
s3: according to the automobile dynamics theory, establishing a dynamics equation of a transmission system of the whole automobile, and obtaining system efficiency calculation formulas under different driving working modes; establishing an energy management strategy model under the condition of optimal system efficiency; in step S3, the driveline dynamics equation is:
wherein, IrTo translate to the equivalent moment of inertia of the wheel;
Imis the moment of inertia of the motor;
Ieis the rotational inertia of the engine;
ωris the angular velocity of the wheel;
ωeis the angular velocity of the engine output shaft;
ωmis the angular velocity of the motor output shaft;
Treqtorque required for the vehicle to travel at a certain vehicle speed;
Teis the torque of the motor output shaft;
Tmis the torque of the engine output shaft;
igis the transmission speed ratio;
i0the speed ratio of the speed reducer is adopted;
ηTfor transmission system efficiency;
plus or minus represents two driving modes, when plus or minus represents the motor power-assisted working mode, and when minus represents the light-load charging working mode;
the rolling resistance, air resistance, acceleration resistance and ramp resistance when the automobile runs need to be overcome, and the calculation formula of the resistance when the automobile runs need to be overcome is as follows:
wherein m is the full load mass of the automobile; f is the road friction coefficient; cDIs the air resistance coefficient; a is the windward area; u is the running speed; alpha is the gradient;
s4: setting the vehicle head spacing of the vehicle fleet and the running speed requirement, and establishing a longitudinal dynamic model of the parallel hybrid vehicle fleet;
s5: and building a longitudinal dynamic model of the parallel hybrid electric vehicle fleet based on a simulation platform, and performing simulation analysis on the parallel hybrid electric vehicle fleet by combining the energy management strategy model.
2. A parallel hybrid electric vehicle autonomous queue travel energy intelligent management method according to claim 1, characterized in that: in step S1, the motion system parameters at least include: windward area, servicing quality, full load quality, rolling resistance coefficient, wind resistance coefficient, wheel radius, final reduction ratio, engine performance parameter, motor performance parameter, power battery parameter, and transmission speed ratio.
3. A parallel hybrid electric vehicle autonomous queue travel energy intelligent management method according to claim 1, characterized in that: in step S2, the driving operation mode of the parallel hybrid vehicle includes: a pure electric drive mode, a light load charging mode, a motor power-assisted mode and an engine independent drive mode;
the working states of the automobile motor, the engine and the clutch under the four driving working modes are as follows:
operating state detail table for showing each driving operating mode of parallel hybrid electric vehicle
Wherein, 1 represents that the power source and the executive component are in a working state or a combined state, and 0 represents that the power source and the executive component are in a non-working state or disconnected state.
4. A parallel hybrid electric vehicle autonomous queue travel energy intelligent management method according to claim 3, characterized in that: in step S3, among the system efficiency calculation formulas in different driving operation modes, the system efficiency calculation formula in the pure electric driving mode is:
wherein eta issysFor current vehicle system efficiency, PbatIs the discharge power, η, of the accumulator batterymTo the motor efficiency, ηdis-chargeDischarging efficiency for the battery; pinInputting power for the system; poutSystem output power; delta is a rotating mass conversion coefficient;
the system efficiency calculation formula under the engine single driving mode is as follows:
wherein eta iseTo engine efficiency;
the system efficiency calculation formula of the vehicle in the light load charging mode is as follows:
wherein eta ischargeEfficiency of charging the battery;
the system efficiency calculation formula of the vehicle under the motor power-assisted working mode is as follows:
5. a parallel hybrid vehicle autonomous queue driving energy intelligent management method according to claim 1, characterized in that in S4, the parallel hybrid vehicle fleet longitudinal dynamics model at least comprises a parallel hybrid vehicle fleet following model, a pilot vehicle driver model, a following vehicle driver model;
the parallel hybrid electric vehicle fleet following model at least comprises a pilot vehicle and N following vehicles, and is provided with a target vehicle speed and a vehicle running distance;
the pilot vehicle driver model is used for controlling the actual driving speed by outputting and combining the opening degree of a throttle valve and the opening degree of a brake pedal after inputting a vehicle speed difference value of the current driving speed and a target vehicle speed and the change rate of the vehicle speed difference value into a pilot vehicle fuzzy logic controller;
the following vehicle driver model judges the opening degrees of an accelerator and a brake pedal of the current following vehicle according to the speed difference value of the current following vehicle and the previous vehicle and the displacement difference value of the previous vehicle; the following vehicle fuzzy logic controller is used for keeping the vehicle distance between the current following vehicle and the previous vehicle, the vehicle speed difference and the distance difference are used as the input of the following vehicle fuzzy logic controller, and the opening degree of an accelerator pedal and the opening degree of a brake pedal of the current following vehicle are used as the output of the following vehicle fuzzy logic controller.
6. A parallel hybrid vehicle autonomous queued driving energy intelligent management method according to claim 1 characterized in that in S5, the parallel hybrid vehicle fleet longitudinal dynamics model at least includes driver model, shift model, energy matching model, engine model, electric machine model, battery model, full vehicle model.
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