CN112859878A - Automatic calibration method for control parameters of hybrid unmanned vehicle - Google Patents
Automatic calibration method for control parameters of hybrid unmanned vehicle Download PDFInfo
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Abstract
The invention relates to a control parameter automatic calibration method of a hybrid unmanned vehicle, which mainly adopts a simulated annealing algorithm to optimize control parameters, and selects an engine torque working upper limit correction coefficient T _ max, an engine torque working lower limit correction coefficient T _ min, a pure electric driving power upper limit P _ max and a braking energy recovery current I as the control parameters; and (4) performing iterative execution optimization, and performing test testing on the result until the test result meets the requirement. The invention is suitable for the control of the hybrid unmanned vehicle; and the finally obtained optimized calibration result can improve the fuel economy of the whole vehicle, the balance of an engine and a battery system and ensure the dynamic property of the vehicle.
Description
Technical Field
The invention relates to an unmanned vehicle automatic calibration method based on a hybrid power system.
Background
Before the unmanned vehicle can run on an open public road, the vehicle and other components are tested according to a traditional method, and special test calibration work needs to be carried out on control parameters of the whole vehicle controller.
The existing method for calibrating control parameters of the whole unmanned vehicle controller comprises the following steps:
and 1, manually calibrating. The method for calibrating the control parameter of the air conditioner has the advantages that an engineer corrects the parameter according to the test result and experience, tests are carried out again, and the process is circulated until a proper control parameter is found, a large amount of time and manpower are consumed by the method for calibrating the control parameter, the engineer selects the control parameter value by using the manual experience, the control parameter of the optimal solution is easily missed, and the calibration result is poor.
And 2, automatically calibrating. The control parameters are input into a vehicle control unit to be tested, the vehicle control unit controls the unmanned vehicle to operate, various feedback data are recorded in the operation process, whether the selection of the control parameters meets the requirements or not is analyzed according to the feedback data, and the appropriate control parameters are finally locked through multi-round tests.
For example, chinese patent publication No. CN 111123807 a discloses a method and a system for testing an entire unmanned vehicle, in which a virtual scene server obtains coordinate data of a simulated vehicle in a virtual scene, and then obtains corresponding simulated sensor data according to the coordinate data; the unmanned controller obtains a control instruction of the unmanned vehicle according to the simulation sensor data and sends the control instruction to the whole unmanned vehicle controller; the unmanned vehicle moves to the next position according to the control instruction. The invention can facilitate the matching and calibration work of the unmanned vehicle controller and the whole vehicle, and can shorten the road test time of the unmanned vehicle.
For another example, chinese patent publication No. CN112009266A discloses a power parameter calibration method, which controls a vehicle to run in a deceleration state according to each target power parameter value in a target power parameter value set; acquiring a speed value and an acceleration value of the vehicle under each target power parameter value; and establishing a corresponding relation among the target power parameter value, the acceleration value and the speed value. And analyzing whether the power parameters are proper or not according to the corresponding relation.
To complete the calibration, the control parameters need to be selected or optimized to determine the final control parameters. Different selection results or optimization results can be generated according to different control targets; but generally the object of the target is also a fuel vehicle.
Disclosure of Invention
The application aims to provide an unmanned vehicle automatic calibration method based on a hybrid power system, which is used for solving the calibration problem of the hybrid power vehicle and meeting the requirements of the power performance and the economical efficiency of the hybrid power vehicle.
In order to achieve the aim, the invention provides an automatic calibration method for control parameters of a hybrid unmanned vehicle, which comprises the following steps:
step 1, selecting an engine torque working upper limit correction coefficient T _ max, an engine torque working lower limit correction coefficient T _ min, a pure electric driving power upper limit P _ max and a braking energy recovery current I as control parameters; setting an initial temperature T0 of a simulated annealing algorithm and an attenuation factor K;
step 5, combining the current energy consumption and obtaining a solution E1 corresponding to the current particle state according to a simulated annealing algorithm; predicting the current particle state to obtain a new solution E2;
step 7, iteratively executing the steps 4-6 until the full search is completed at the current temperature T, and obtaining the optimal solution at the current temperature T;
9, if the energy consumption in the test result meets the requirements, stopping optimization; otherwise, updating the temperature of the simulated annealing algorithm according to the decay factor K, and executing the steps 2-8 at the new temperature until the calibration is completed.
Further, if Δ E is less than 0, the corresponding state is saved, and if Δ E is greater than or equal to 0, the corresponding state is accepted or discarded with a random probability:
further, the fuel consumption per hundred kilometers is calculated according to the energy consumption.
Further, the annealing function used for updating the temperature of the simulated annealing algorithm is as follows:
T(n+1)＝K×T(n)
in the formula: t (n +1) is the post-update algorithm temperature, and T (n) is the pre-update algorithm temperature.
Further, whether to perform sufficient search is judged by detecting whether to perform search for L times, where L is the number of iterations for each temperature.
The invention has the beneficial effects that: the method adopts a simulated annealing algorithm to optimize the control parameters, forms the particle state by combining the selection (T _ max, T _ min, P _ max and I) of the control parameters, and is suitable for controlling the hybrid unmanned vehicle; and the finally obtained optimized calibration result can improve the fuel economy of the whole vehicle, the balance of an engine and a battery system and ensure the dynamic property of the vehicle.
Drawings
FIG. 1 is a schematic diagram of an unmanned vehicle and an automatic optimization and calibration system of the present invention;
FIG. 2 is a flow chart of the automatic optimization and calibration system of the present invention;
FIG. 3 is a schematic structural view of an optimized calibration annular test site for an unmanned vehicle according to the present invention;
FIG. 4 is a schematic diagram of a structure of an unmanned vehicle optimized calibration linear test site according to the present invention;
FIG. 5 is a schematic view of the drive-by-wire chassis structure of the unmanned vehicle of the present invention; wherein, 1 is a right front wheel, 2 is a right front brake, 3 is a right front suspension, 4 is a left front suspension, 5 is a left front brake, 6 is a left front tire, 7 is a chassis frame, 8 is a right rear tire, 9 is a right rear brake, 10 is a first rotating speed torque sensor, 11 is a second rotating speed torque sensor, 12 is a left rear brake, 13 is a left rear wheel, and 14 is a differential mechanism;
FIG. 6 is a schematic diagram of a hybrid unmanned vehicle power system and an optimized calibration in the present embodiment;
fig. 7 is a flowchart illustrating the execution of the parameter optimization calibration of the hybrid unmanned vehicle according to the embodiment.
Detailed Description
The following detailed description is made with reference to the accompanying drawings.
The system implementing the automatic calibration method is shown in fig. 1, and comprises three major parts, namely, a drive-by-wire chassis of the unmanned automobile, an unmanned system and an automatic optimization and calibration system.
An automatic optimization and calibration system; the method is used for loading various parameters to the vehicle control unit, optimizing the parameters according to the feedback of the drive-by-wire chassis, and finally realizing calibration.
An unmanned system; mainly refers to an automatic driving software and hardware platform for realizing automatic driving control. In this embodiment, the unmanned system is divided into an unmanned controller and a vehicle controller (as another embodiment, only the vehicle controller may be adopted). The unmanned controller is used for running automatic driving software, and the vehicle control unit is used for realizing interaction with the drive-by-wire chassis.
A drive-by-wire chassis; the power part of the unmanned automobile is used for simulating a real unmanned automobile, and running and testing the unmanned automobile on a test site.
These several parts cover both the hardware and software platforms embodying the method of the invention, each of which is described in general terms below.
1. Automatic optimization and calibration system: the system comprises a data acquisition and optimization algorithm and a parameter calibration module.
1.1 data acquisition module.
The data acquisition module is used for classifying and storing test data acquired from the drive-by-wire chassis of the unmanned vehicle and the vehicle control unit, the test data comprises a test result and control parameters, all the data can be randomly called by the parameter optimization module, and the data acquisition module can also process the data according to task requirements to obtain data directly available by the optimization algorithm module. The data acquisition module can select different storage modes and store the test results of all times.
1.2 optimizing algorithm module. The optimization algorithm module is used for replacing manual work to continuously adjust the control parameters so as to enable the control parameters to quickly approach the optimal control parameters. Initial values and value ranges of control parameters to be optimized, parameter optimization conditions such as oil consumption indexes and the like need to be set manually. The initial value K0 of the control parameter can be determined by the calibration engineer according to the calibration experience in the initial value to be set for the first set of test control parameters, which has the advantage of providing a rough optimization area for the parameter optimization module, and improves the efficiency of parameter optimization calibration compared with the random generation of the first set of parameters by the parameter optimization module. The optimization algorithm module transmits the first group of control parameters to the parameter calibration module, calibrates the control parameters, and returns the tested energy consumption value from the data acquisition module after test waiting.
Firstly, judging whether the energy consumption value of the test result is lower than the minimum energy consumption Fmin or not, if not, searching a group of new control parameter values Kn by the optimization algorithm according to the control parameters and the energy consumption value of the current database; and if so, updating the current optimal control parameter Kopt and the minimum energy consumption value Fmin. Judging whether the current energy consumption meets the optimal energy consumption Fopt, and if so, ending the automatic optimization and calibration program; if not, the optimization algorithm then finds a new set of control parameter values Kn to optimize a better set of control parameter values, and the execution flow chart is shown in fig. 2. The parameter optimization module adopts a simulated annealing algorithm, and the specific method is described in detail below.
1.3 parameter calibration module. The parameter calibration module firstly reads a storage file in the vehicle controller, and stores storage addresses, storage structures, data types and conversion formulas of all parameters in the vehicle controller in the file. When the new set of calibration parameters is transmitted to the parameter calibration module, the parameter calibration module sends calibration protocol information to calibrate the vehicle controller on the communication line according to the storage address, the storage structure and the like of the calibration parameters, so that the parameter calibration work of the vehicle controller is completed.
2. Unmanned system: the unmanned controller comprises a target working condition loader and an unmanned module.
2.1 target Condition loader
The target working condition loader is used for obtaining a target vehicle speed according to preset running working condition information and transmitting the target vehicle speed to the unmanned system in real time. The target working condition loader can convert the input discrete vehicle speed and time data into continuous data, and sends target vehicle speed data corresponding to the running time to the controller of the unmanned system according to the running step length of the system.
2.2 unmanned Module
The unmanned driving module analyzes the opening degree of an accelerator pedal and the opening degree of a brake pedal by utilizing PID control according to the target working condition led in by the target working condition loader, the unmanned automobile can drive according to the target working condition, and then the analyzed opening degree signal of the accelerator pedal and the analyzed opening degree signal of the brake pedal are transmitted to the whole automobile controller to control the running of the real automobile. In the actual control process, the PID control is influenced by the external environment, and the PID control continuously adjusts the threshold value, so that the closed-loop control system can stably, quickly and accurately respond to the command. If the actual speed is smaller than the target working condition, the speed difference is fed back to PID control when the vehicle is currently in acceleration or uniform speed running, the analysis of the opening degree of an accelerator pedal is increased in a proportion link P, and the analysis of the opening degree of a brake pedal is further reduced in a proportion link when the vehicle is currently in deceleration running. If the actual speed is larger than the target working condition, the speed difference is fed back to a PID control input part when the vehicle is currently accelerated or driven at a constant speed, the opening of an accelerator pedal is reduced in a proportion link P, and the analysis of the opening of a brake pedal is increased in the proportion link P when the vehicle is currently decelerated, so that the feedback adjustment is continuously carried out. Meanwhile, in the PID fuzzy control, an integral link I continuously eliminates static error, the non-difference of the system is improved, and a differential link D effectively corrects signals in early stage, so that the action speed of the system is accelerated, the adjusting time is shortened, and the analysis of the PID fuzzy controller on the opening degree of an accelerator pedal is improved. The unmanned module utilizes a computer system to replace a driver, and ensures that the unmanned system can automatically drive in the process of optimizing the calibration test.
2.3 vehicle control unit
The vehicle control unit receives a decision result from the unmanned controller, mainly comprises an accelerator pedal value and a brake pedal value, calculates the working states of all parts of the drive-by-wire chassis of the unmanned vehicle through a control strategy, and sends braking, accelerating, gear shifting and steering instructions to the drive-by-wire chassis to control the unmanned vehicle to run. Meanwhile, the vehicle control unit can acquire real-time running state parameters of the drive-by-wire chassis, and actual vehicle speed information is acquired through the first rotating speed sensor and the second rotating speed sensor.
3. Drive-by-wire chassis of unmanned automobile
The drive-by-wire chassis corresponds to an unmanned automobile, and includes a right front wheel 1, a right front brake 2, a right front suspension 3, a left front suspension 4, a left front brake 5, a left front tire 6, a chassis frame 7, a right rear tire 8, a right rear brake 9, a first rotational speed torque sensor 10, a second rotational speed torque sensor 11, a left rear brake 12, a left rear wheel 13, and a differential 14, as shown in fig. 5. The steering system 15 controls the swing of the steering cross bar, thereby controlling the front right wheel 1 and the front left wheel 6 of the unmanned vehicle to steer. The brake system respectively sends braking energy to the right front brake 2, the left front brake 5, the right rear brake 9 and the left rear brake 12 so as to brake. All control systems and power components on the drive-by-wire chassis are arranged on a drive-by-wire chassis frame 7. The first rotational speed torque sensor 10 is between the differential 14 and the tires 8 and the second rotational speed torque sensor 11 is between the differential 14 and the tires 13.
In addition, in order to perform the test, a test site is required, and the test site can be a ring-shaped test site as shown in fig. 3 or a straight-line-shaped test site as shown in fig. 4.
And (4) forming an annular test field. The road surface test system is composed of a test road section A, a test road section B, a preparation road section A and a preparation road section B. After the engineer selects parameters to calibrate at the beginning stage, the vehicle runs from the starting point of the test road section A according to the target vehicle speed and finally reaches the end point of the test road section A, so that the real vehicle calibration test of the unmanned vehicle is completed. And then the unmanned automobile starts to run for a prepared road section B, the automatic optimization calibration system finishes test result data processing and analysis during the prepared road section B, a group of new parameters are selected by using an optimization algorithm and calibrated to the whole automobile controller. When the unmanned vehicle reaches the starting point of the test road section B, if the data processing analysis, parameter optimization and calibration work are completed, the unmanned vehicle performs a test on the test road section B according to the test mode of the test road section A, so that the calibration test of the unmanned vehicle is completed again; and if the data processing analysis, the parameter optimization and the calibration work are not finished, waiting for the completion of the work at the starting point of the test road section B. And after the test of the test road section B is finished, the unmanned automobile carries out data processing analysis, parameter optimization and calibration work on the preparation road section A, and when the unmanned automobile returns to the starting point of the test road section A, a circle of test of the annular test field is finished. And in the preparation road section B and the preparation road section D, the unmanned automobile runs at a low and constant speed, and the turning running is finished by controlling a steering system of the drive-by-wire chassis. The annular test field is shown in fig. 3, and the annular test field can be matched with an unmanned automobile to perform automatic optimization calibration test on control parameters of the whole automobile controller.
And (5) a linear test field. As shown in fig. 4, consists of a test section a. After the engineer selects parameters to calibrate in the starting stage, the vehicle runs from any end of the straight line test road section A as a starting point according to the target vehicle speed, and finally reaches the end point of the test road section A, so that the real vehicle calibration test of the unmanned vehicle is completed. The unmanned automobile runs at a low constant speed, turns around by controlling a steering system of the drive-by-wire chassis, takes the terminal point of A as the starting point of the next test to perform a new test, finishes the data processing and analysis of test results by an automatic optimization calibration system before restarting the next test, selects a group of new parameters by an optimization algorithm, and calibrates the parameters to the whole automobile controller. And then testing according to the new group of control parameters, and performing optimization calibration test of the control parameters by matching with the unmanned automobile in a circulating manner.
The hardware and software platforms for implementing the present invention are introduced above, and the core contents of the automatic calibration are explained in detail below.
The drive-by-wire chassis of the present embodiment is directed to a hybrid unmanned vehicle, and the structure of the power system is shown in fig. 6, and is composed of an engine, a motor 1, a motor 2, a transmission and a power battery, wherein signal transmission in fig. 6 is the same as that in fig. 1.
And selecting an engine torque working upper limit correction coefficient T _ max, an engine torque working lower limit correction coefficient T _ min, a pure electric driving power upper limit P _ max and a braking energy recovery current I as control parameters of an automatic optimization calibration test.
The optimization flow is shown in fig. 7, and the initial temperature T0 of the simulated annealing algorithm, the attenuation factor K, and the iteration number L of each value T are set.
1. And (3) randomly dereferencing the 4 parameters according to the dereferencing ranges of the 4 parameters by the simulated annealing algorithm at the initial temperature T, wherein the selected parameters in the simulated annealing algorithm correspond to the initial particle state X.
2. The simulated annealing algorithm needs to test the group of control parameters, and the parameter values of the group of control parameters are calibrated into the whole vehicle controller through the parameter calibration module to replace the original parameters.
3. After parameter calibration is completed, the unmanned controller loads a target working condition, carries out torque value analysis on the target working condition through a control strategy, sends a torque instruction to an engine and a motor of the linear control ground plate, and sends a braking instruction to the brake to carry out working condition testing. After the test is finished, the data acquisition module acquires a test result in real time, namely the energy consumption F, and calculates the hundred kilometers of the comprehensive energy consumption F under the target working conditionfuleAnd storing the control parameter values with the group of control parameter values.
4. The hundred kilometers of the group of comprehensive energy consumption FfuleAnd transmitting to a simulated annealing algorithm, and generating a new solution E2 by the simulated annealing algorithm according to the current temperature T, the particle state X of the particles and the solution E1 corresponding to the particle state.
5. Calculating to generate a new solution E2 evaluation function increment delta E by a simulated annealing algorithm, and if the delta E is less than 0, receiving the state by the system; otherwise, the state is accepted or discarded with a random probability, the probability accepted in state 2 being
6. Then, it is determined whether the new solution E2 has been searched for L times at the current temperature T, otherwise, it returns to step 4. If yes, the next step is continued.
7. Until the optimal solution of the current temperature T iterative optimization is found, calibrating the vehicle controller according to the control parameter value corresponding to the optimal solution, and testing the unmanned vehicle in a test field according to the target working condition.
8. Judging the hundred kilometers of the current test result and the comprehensive energy consumption FfuleAnd if the requirement of optimization precision is met, stopping optimization, otherwise, updating the temperature T of the simulated annealing algorithm according to the decay factor K value, returning to the step 4, and starting a new round of optimization iterative process at the new temperature T. The annealing function is as follows:
T(n+1)＝K×T(n)
in the formula: t (n +1) is the post-update algorithm temperature, and T (n) is the pre-update algorithm temperature.
9. And after all iterative tests are finished, the automatic optimization calibration system marks a group of control parameters with the lowest comprehensive energy consumption in the test results so as to finish the optimization calibration work of the engine torque working upper limit correction coefficient T _ max, the engine torque working lower limit correction coefficient T _ min, the pure electric driving power upper limit P _ max and the mode switching threshold value M.
The four control parameters are related to the control of the unmanned vehicle:
the method comprises the steps that an optimal working area of an engine is determined by optimizing an engine torque working upper limit correction coefficient T _ max and an engine torque working lower limit correction coefficient T _ min, when the hybrid unmanned vehicle enters an engine direct-drive mode, a required torque value of a target working condition is calculated, the engine works at a required torque value point (the engine works in the optimal area) according to the current rotating speed of the engine, and therefore greater torque is released in a low fuel consumption area; when the hybrid power mode is entered, the motor 1 is in a power generation mode to apply reactive torque, the working point of the engine is regulated and controlled, the engine works in the upper limit of the optimal region, namely, the low fuel consumption region is ensured to work, the motor 1 provides larger reactive torque through power generation, the engine is also ensured to provide larger torque to drive the automobile to run, and the rest driving torque is provided by the motor 2.
The upper limit value of the pure electric driving power P _ max is limited by optimizing the upper limit value P _ max of the pure electric driving power, and the high-power discharge of a power battery of an automobile in a pure electric mode is avoided, so that the service life attenuation of the power battery is delayed, and meanwhile, the upper limit value of the pure electric driving power cannot be too small, and the power performance index of the automobile in a starting stage is guaranteed.
The magnitude of the braking current is adjusted by optimizing the value of the braking energy recovery current I. In the process of braking the automobile, the requirement of braking safety is considered at the same time, the torque distribution between the motor energy recovery braking and the mechanical braking is adjusted, the magnitude of the braking current is continuously adjusted by optimizing the value of the braking energy recovery current I, so that the energy recovery braking torque is controlled, and the rest automobile braking torque is provided by the mechanical braking force.
The engine torque working upper limit correction coefficient T _ max, the engine torque working lower limit correction coefficient T _ min, the pure electric driving power upper limit P _ max and the braking energy recovery current I are selected as control parameters of an automatic optimization calibration test, and the reason is that: the hybrid power system adopts an optimal working curve control strategy of the engine, and the basic control idea is to control the working efficiency of the engine to be at the point of minimum fuel consumption rate, so that the fuel economy of the whole vehicle is effectively improved, and therefore, the upper limit and the lower limit of the optimal working area of the engine torque have great influence on the fuel economy of the whole vehicle. The power battery is used as another energy source on the hybrid electric vehicle, so that the situation that large discharging current and charging current appear in the process of the power battery is avoided, the amount of recharging energy of the power battery is determined by the braking energy recycling current, and if the braking energy recycling current is set to be high, the internal resistance consumption is increased, the power battery is damaged, and the driving range of the vehicle is influenced; if set low, energy may be wasted.
Claims (5)
1. A control parameter automatic calibration method of a hybrid unmanned vehicle is characterized by comprising the following steps:
step 1, selecting an engine torque working upper limit correction coefficient T _ max, an engine torque working lower limit correction coefficient T _ min, a pure electric driving power upper limit P _ max and a braking energy recovery current I as control parameters; setting an initial temperature T0 of a simulated annealing algorithm and an attenuation factor K;
step 2, under the current temperature T, taking values of the four parameters in a value range to obtain current control parameters corresponding to the current particle state X of the simulated annealing algorithm;
step 3, calibrating the current control parameters into the whole vehicle controller;
step 4, loading a target working condition and testing; after the test is finished, recording the current control parameters and the corresponding current energy consumption;
step 5, combining the current energy consumption and obtaining a solution E1 corresponding to the current particle state according to a simulated annealing algorithm; predicting the current particle state to obtain a new solution E2;
step 6, determining whether to save the corresponding state according to the increment delta E of the two continuous solutions;
step 7, iteratively executing the steps 4-6 until the full search is completed at the current temperature T, and obtaining the optimal solution at the current temperature T;
step 8, calibrating the control parameters corresponding to the optimal solution at the current temperature T to the whole vehicle controller, and loading the target working condition for testing;
9, if the energy consumption in the test result meets the requirements, stopping optimization; otherwise, updating the temperature of the simulated annealing algorithm according to the decay factor K, and executing the steps 2-8 at the new temperature until the calibration is completed.
2. The method of claim 1, wherein if Δ E < 0, the corresponding state is saved, and if Δ E ≧ 0, the corresponding state is accepted or discarded with a random probability:
3. the method of claim 1, wherein the hundred kilometers fuel consumption is calculated from the energy consumption.
4. The method of claim 1, wherein the annealing function used to update the temperature of the simulated annealing algorithm is as follows:
T(n+1)＝K×T(n)；
in the formula: t (n +1) is the post-update algorithm temperature, and T (n) is the pre-update algorithm temperature.
5. The method of claim 1, wherein the determination of whether to perform a sufficient search is made by detecting whether to perform L searches, L being the number of iterations for each temperature.
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