CN113625567A - Excavator rotation energy recovery control method and system, excavator and storage medium - Google Patents
Excavator rotation energy recovery control method and system, excavator and storage medium Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
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- E—FIXED CONSTRUCTIONS
- E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
- E02F—DREDGING; SOIL-SHIFTING
- E02F9/00—Component parts of dredgers or soil-shifting machines, not restricted to one of the kinds covered by groups E02F3/00 - E02F7/00
- E02F9/20—Drives; Control devices
- E02F9/22—Hydraulic or pneumatic drives
- E02F9/2203—Arrangements for controlling the attitude of actuators, e.g. speed, floating function
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- E—FIXED CONSTRUCTIONS
- E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
- E02F—DREDGING; SOIL-SHIFTING
- E02F9/00—Component parts of dredgers or soil-shifting machines, not restricted to one of the kinds covered by groups E02F3/00 - E02F7/00
- E02F9/20—Drives; Control devices
- E02F9/22—Hydraulic or pneumatic drives
- E02F9/2217—Hydraulic or pneumatic drives with energy recovery arrangements, e.g. using accumulators, flywheels
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Abstract
The application discloses an excavator rotation energy recovery control method, which comprises the following steps: establishing an engine equivalent fuel consumption function of the excavator under a circulation working condition; the control variable of the engine equivalent fuel consumption function is motor torque or generator torque, and the state variable of the engine equivalent fuel consumption function is a super capacitor SOC value of a system energy storage element; dividing the cycle working condition into a plurality of stage working conditions, and solving an engine equivalent fuel consumption function corresponding to each stage working condition by adopting an iterative dynamic programming algorithm to obtain a global rotation energy control strategy; and executing the rotation energy recovery operation according to the global rotation energy control strategy. The high-efficiency recovery of the rotation energy of the excavator can be realized, and the fuel economy of the excavator in the operation cycle process is improved. The application also discloses an excavator rotation energy recovery control system, an excavator and a storage medium, and the excavator rotation energy recovery control system has the beneficial effects.
Description
Technical Field
The application relates to the technical field of engineering machinery control, in particular to a method and a system for controlling the rotation energy recovery of an excavator, the excavator and a storage medium.
Background
Under the large background of global environmental pollution and energy shortage, the energy-saving and emission-reducing technology of large-scale mechanical equipment becomes a research hotspot in the field of engineering mechanical equipment. When the hydraulic excavator is braked in a rotating mode, the inertia energy of the rotating platform is converted into hydraulic energy to be consumed at an overflow valve port of the rotating motor, the basic reason for system overflow is that the flow required by the rotating motor is not matched with the output flow of a main pump, the rotating motor cannot completely absorb the output flow of the pump, and the energy utilization rate in the rotating process is low, so that a large amount of recoverable energy exists in the excavating operation process of the excavator in frequent rotation, and the energy is converted into heat energy in an original hydraulic system and is not fully utilized.
Therefore, how to achieve efficient recovery of the slewing energy of the excavator is a technical problem to be solved by those skilled in the art at present.
Disclosure of Invention
The application aims to provide an excavator rotation energy recovery control method and system, an excavator and a storage medium, and the excavator rotation energy can be efficiently recovered.
In order to solve the technical problem, the present application provides a method for controlling recovery of rotational energy of an excavator, including:
establishing an engine equivalent fuel consumption function of the excavator under a circulation working condition; the control variable of the engine equivalent fuel consumption function is motor torque or generator torque, and the state variable of the engine equivalent fuel consumption function is a super capacitor SOC value of a system energy storage element;
dividing the cycle working condition into a plurality of stage working conditions, and solving an engine equivalent fuel consumption function corresponding to each stage working condition by adopting an iterative dynamic programming algorithm to obtain a global rotation energy control strategy; the global rotation energy control strategy comprises a control variable optimal value and a state variable optimal value corresponding to each stage working condition;
and executing the rotation energy recovery operation according to the global rotation energy control strategy.
Optionally, the step of solving the engine equivalent fuel consumption function corresponding to the working condition of each stage by using an iterative dynamic programming algorithm includes:
determining a constraint condition set according to the working parameters of the excavator; wherein the constraint condition set comprises any one or combination of any several of a capacitance constraint condition, a motor speed constraint condition, an engine torque constraint condition and a motor torque constraint condition; the capacitance constraint condition is determined according to the value range of the SOC value of the super capacitor; the motor rotating speed constraint condition is determined according to the rotating speed value range of a motor or a generator of the excavator; the engine torque constraint condition is determined according to a torque value range of an engine of the excavator; the motor torque constraint condition is determined according to a torque value range of a motor or a generator of the excavator;
and under the constraint of the constraint condition set, solving an engine equivalent fuel consumption function corresponding to the working condition of each stage by adopting the iterative dynamic programming algorithm.
Optionally, establishing an engine equivalent fuel consumption function of the excavator under a cycle condition, including:
generating a penalty function according to the square of the change quantity of the SOC value of the super capacitor; the change quantity of the SOC value of the super capacitor is the difference between the SOC value of the super capacitor and the initial value of the SOC of the super capacitor under the working condition of the current stage;
generating a cost function according to the control variable, the state variable and the working time;
and taking the sum of the penalty function and the cost function as the engine equivalent fuel consumption function.
Optionally, an iterative dynamic programming algorithm is adopted to solve an engine equivalent fuel consumption function corresponding to the working condition of each stage, so as to obtain a global rotation energy control strategy, including:
calculating equivalent fuel consumption functions of the engine corresponding to all the stage working conditions according to the sequence from back to front to obtain reverse calculation data; the reverse calculation data comprises a control variable optimal value and a state variable optimal value which are obtained through reverse calculation;
and calculating the equivalent fuel consumption functions of the engines corresponding to all the stage working conditions by adopting the iterative dynamic programming algorithm according to the sequence from front to back according to the initial value of the super capacitor SOC and the reverse calculation data to obtain a global rotary energy control strategy.
Optionally, calculating the equivalent fuel consumption functions of the engine corresponding to all the stage working conditions according to a sequence from back to front to obtain reverse calculation data, including:
calculating the SOC variation of the super capacitor between the adjacent stage working conditions, and obtaining a state variable set according to the minimum value of the SOC of the super capacitor and the SOC variation of the super capacitor; the state variable set comprises a super capacitor SOC value corresponding to each stage working condition;
calculating a state variable transfer equation at equal intervals within a constraint range of motor torque or generator torque, and solving an engine equivalent fuel consumption function through an interpolation method according to the state variable transfer equation to obtain an optimal motor torque value;
and setting the optimal motor torque value as a control variable optimal value obtained by reverse calculation, and setting the super capacitor SOC value in the state variable set as a state variable optimal value obtained by reverse calculation to obtain reverse calculation data.
Optionally, the engine equivalent fuel consumption function includes an engine equivalent fuel consumption function when the super capacitor is discharged and an engine equivalent fuel consumption function when the super capacitor is charged.
Optionally, in the process of solving the engine equivalent fuel consumption function corresponding to the working condition of each stage by using the iterative dynamic programming algorithm, the method further includes:
and when the iteration times are increased by one, reducing the distribution range of the state variable and the control variable according to a preset proportion.
The present application further provides an excavator gyration energy recovery control system, the system includes:
the fuel consumption function establishing module is used for establishing an engine equivalent fuel consumption function of the excavator under a circulation working condition; the control variable of the engine equivalent fuel consumption function is motor torque or generator torque, and the state variable of the engine equivalent fuel consumption function is a super capacitor SOC value of a system energy storage element;
the control strategy generation module is used for dividing the cycle working condition into a plurality of stage working conditions, and solving an engine equivalent fuel consumption function corresponding to each stage working condition by adopting an iterative dynamic programming algorithm to obtain a global rotary energy control strategy; the global rotation energy control strategy comprises a control variable optimal value and a state variable optimal value corresponding to each stage working condition;
and the energy recovery module is used for executing the rotation energy recovery operation according to the global rotation energy control strategy.
The application also provides a storage medium, wherein a computer program is stored on the storage medium, and the computer program realizes the steps executed by the excavator swing energy recovery control method when executed.
The application also provides an excavator, which comprises a memory and a processor, wherein a computer program is stored in the memory, and the processor realizes the steps executed by the excavator swing energy recovery control method when calling the computer program in the memory.
The application provides an excavator rotation energy recovery control method, which comprises the following steps: establishing an engine equivalent fuel consumption function of the excavator under a circulation working condition; the control variable of the engine equivalent fuel consumption function is motor torque or generator torque, and the state variable of the engine equivalent fuel consumption function is a super capacitor SOC value of a system energy storage element; dividing the cycle working condition into a plurality of stage working conditions, and solving an engine equivalent fuel consumption function corresponding to each stage working condition by adopting an iterative dynamic programming algorithm to obtain a global rotation energy control strategy; the global rotation energy control strategy comprises a control variable optimal value and a state variable optimal value corresponding to each stage working condition; and executing the rotation energy recovery operation according to the global rotation energy control strategy.
After the engine equivalent fuel consumption function of the excavator under the cyclic working condition is established, the engine equivalent fuel consumption function corresponding to each stage of the cyclic working condition is solved by using an iterative dynamic programming algorithm, and a global rotation energy control strategy is obtained. And determining the optimal value of the control variable and the optimal value of the state variable corresponding to the working condition of each stage according to the global rotation energy control strategy. According to the scheme, the energy distribution global optimal control problem of the system is solved based on the iterative dynamic programming algorithm, the fuel economy of the excavator in the operation cycle process is improved, and the efficient recovery of the rotation energy of the excavator can be realized. This application still provides an excavator gyration energy recuperation control system, an excavator and a storage medium simultaneously, has above-mentioned beneficial effect, no longer gives unnecessary detail here.
Drawings
In order to more clearly illustrate the embodiments of the present application, the drawings needed for the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
Fig. 1 is a flowchart of a method for controlling the recovery of swing energy of an excavator according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a swing energy recovery system of a hydraulic excavator according to an embodiment of the present disclosure;
FIG. 3 is a control flow chart of a dynamic programming algorithm of a swing braking energy recovery system according to an embodiment of the present application;
FIG. 4 is a diagram of an energy recovery system model based on a dynamic programming algorithm according to an embodiment of the present disclosure;
FIG. 5 is a graph illustrating the operation of an energy recovery system of a hydraulic excavator according to an embodiment of the present disclosure;
fig. 6 is a fuel consumption graph of a swing energy recovery system of a hydraulic excavator according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a flowchart of a swing energy recovery control method of an excavator according to an embodiment of the present disclosure.
The specific steps may include:
s101: establishing an engine equivalent fuel consumption function of the excavator under a circulation working condition;
wherein, this embodiment can be applied to the treater chip of excavator, and the cycle condition of excavator indicates: the excavator needs to perform a large amount of rotation operation under the circulation working condition, and in order to improve the fuel economy of the excavator in the working circulation process, the rotation energy of the excavator is recovered.
The step can establish an engine equivalent fuel consumption function of the excavator under the circulation working condition in the following mode: generating a penalty function according to the square of the change quantity of the SOC value of the super capacitor; the change quantity of the SOC value of the super capacitor is the difference between the SOC value of the super capacitor and the initial value of the SOC of the super capacitor under the working condition of the current stage; generating a cost function according to the control variable, the state variable and the working time; and taking the sum of the penalty function and the cost function as the engine equivalent fuel consumption function.
Specifically, the control variable Of the engine equivalent fuel consumption function is a motor torque or a generator torque, and the State variable Of the engine equivalent fuel consumption function is a super capacitor SOC (State Of Charge) value Of a system energy storage element.
S102: dividing the cycle working condition into a plurality of stage working conditions, and solving an engine equivalent fuel consumption function corresponding to each stage working condition by adopting an iterative dynamic programming algorithm to obtain a global rotation energy control strategy;
in the overall optimization control strategy of the excavator rotation energy recovery system, the aim is to minimize the equivalent fuel consumption of the engine under the circulation working condition. The method comprises the steps of setting a system control variable as a motor/generator torque, setting a state variable as a super capacitor SOC, setting each section of step length as unit time, dividing typical working conditions into N sections to obtain N stage working conditions, converting a system optimal control problem into a plurality of stage target optimization, and solving a hydraulic excavator energy recovery system fuel economy global optimal control strategy.
The engine fuel consumption calculation modes of the super capacitor of the system energy storage element in the excavator in the charging state and the discharging state are different, so that the engine equivalent fuel consumption function in the embodiment comprises the engine equivalent fuel consumption function when the super capacitor is discharged and the engine equivalent fuel consumption function when the super capacitor is charged. When the global rotation energy control strategy is determined, whether the super capacitor is in a charging state or a discharging state under each stage of working condition can be judged, and then an engine equivalent fuel consumption function under the corresponding charging and discharging state is solved.
S103: and executing the rotation energy recovery operation according to the global rotation energy control strategy.
The global rotation energy control strategy comprises a control variable optimal value and a state variable optimal value corresponding to each stage of working condition, and under a circulating working condition, the embodiment can select corresponding optimal motor torque or optimal generator torque and optimal super capacitor SOC value according to the current stage working condition of the excavator.
After the engine equivalent fuel consumption function of the excavator under the cyclic working condition is established, the engine equivalent fuel consumption function corresponding to each stage of the cyclic working condition is solved by using an iterative dynamic programming algorithm, and a global rotation energy control strategy is obtained. And determining the optimal value of the control variable and the optimal value of the state variable corresponding to the working condition of each stage according to the global rotation energy control strategy. According to the scheme, the energy distribution global optimal control problem of the system is solved based on the iterative dynamic programming algorithm, the fuel economy of the excavator in the operation cycle process is improved, and the efficient recovery of the rotation energy of the excavator can be realized.
As a further description of the corresponding embodiment of fig. 1, a corresponding constraint condition set may be set for the engine equivalent fuel consumption function, so that the iterative dynamic programming algorithm is adopted to solve the engine equivalent fuel consumption function corresponding to each of the phase operating conditions under the constraint condition set.
Specifically, the constraint condition set may be determined according to the working parameters of the excavator in the embodiment; wherein the constraint condition set comprises any one or combination of any several of a capacitance constraint condition, a motor speed constraint condition, an engine torque constraint condition and a motor torque constraint condition; the capacitance constraint condition is determined according to the value range of the SOC value of the super capacitor; the motor rotating speed constraint condition is determined according to the rotating speed value range of a motor or a generator of the excavator; the engine torque constraint condition is determined according to a torque value range of an engine of the excavator; and the motor torque constraint condition is determined according to the torque value range of the motor or the generator of the excavator.
At present, the research direction of energy recovery of a hydraulic excavator mainly focuses on the recovery of the potential energy generated by descending a movable arm, the research on the recovery of the rotary braking energy of a platform is few, two typical research schemes are summarized, and the two research schemes have respective defects and limitations: one is that a variable frequency motor replaces a hydraulic motor to directly drive a platform, rotary braking energy is converted into electric energy through the motor to be stored in an electric energy storage element, and the system integrates rotary driving and energy recovery functions together, so that the reliability of the system is reduced although the energy transmission efficiency is improved. The other scheme is a slewing braking energy recovery scheme based on a hydraulic accumulator, but when the accumulator releases pressure oil to a main pump outlet to assist in driving a load, if the pressure of the accumulator is lower than the load pressure, the pressure oil cannot be released, and the recovered energy cannot be reused while the energy recovery of the next stage is influenced; and the time that the energy accumulator releases pressure oil is short, so that the speed of the actuating mechanism is uncontrollable, if the actuating mechanism works normally by adjusting the opening area of the speed regulating valve, the system has certain throttling loss, and the energy recovery and reutilization efficiency is not high.
Due to the fact that the units in the excavator energy recovery system are mutually coupled and have the characteristics of nonlinearity and discontinuous complexity, the hydraulic excavator energy recovery system cannot solve to obtain a more accurate system differential equation, and the optimal energy management strategy of the energy recovery system is difficult to solve. The energy recovery system of the hydraulic excavator is complex to control, and the optimal control of the energy output can be realized through an energy management strategy based on a dynamic programming optimization algorithm. For a hydraulic excavator with determined model tonnage and system structure, the referential fuel economy and operation performance limit range exists under typical operation working conditions, and the optimal control problem of energy distribution of the rotary energy recovery system is solved through an iterative dynamic programming global optimization algorithm, so that the optimal fuel economy of the hydraulic excavator in the typical operation cycle process is realized. The slewing energy recovery control scheme based on the iterative dynamic programming optimization provided by the embodiment is as follows:
(1) global optimal energy management control problem
Under typical working conditions, an optimal control strategy is designed for an energy recovery system of the hydraulic excavator to realize the distribution of energy flow of a power system, so that the optimal fuel economy index of the whole excavator is reached. Therefore, the control targets for the energy management policy of the energy recovery system are determined as: on the basis of keeping the reasonable working range of the super capacitor SOC and meeting the power requirement of the system, the hydraulic excavator obtains the optimal fuel economy.
During the operation of the hydraulic excavator, the control variables (the rotating speed and the torque of an engine and a motor) and the state variables (the SOC of the super capacitor bank) of the energy recovery system need to be in a normal working range. The optimal control of the excavator rotation energy recovery system, that is, the optimal control strategy for solving the minimum objective function value under the constraint condition based on the system state variables and the control variables, can be described by formula 1:
in the above formula, J is an optimization problem objective function; t is tfWorking time is taken; x (t) and
is a state variable; x is the number of0Is the initial value of the state variable; u (t) is a control variable; l is a cost function; theta is a penalty function;
constraint for control vectors and state vectors; u. ofl、uuRespectively a maximum value and a minimum value of the control variable; x is the number oflAnd xuMaximum and minimum values of the state variable, respectively, r representing a continuous count.
The optimal energy management strategy of the hydraulic excavator rotation energy recovery system is that the fuel consumption J of the excavator under typical operation working conditions is the minimum. When the system is in the set typical working condition period [0, tf]From the initial state x (0) to the final state x (t)f) And (5) transferring to optimize a system performance objective function.
(2) Iterative dynamic programming energy management control strategy optimization design
Selecting the motor torque as a control variable at a k stage, wherein the SOC of the super capacitor is a state variable:
xksoc (k) (equation 2)
uk＝Tm(k) (formula 3)
The following constraint conditions are formulated in the dynamic programming solving process:
the transfer equation for the state variables is:
SOC(k+1)＝g[SOC(k),Tm(k)](formula 5)
In the formula: SOC (k) is the state of charge of the super capacitor at the moment k; SOCminAnd SOCmaxRespectively representing the minimum value and the maximum value of the super capacitor SOC; n ism(k)、Tm(k) The rotating speed and the torque of the motor at the moment k are obtained; t ise(k) Is the engine torque at time k, in units of N · m; t ism_minAnd Tm_maxThe unit is N.m, which is the upper and lower boundaries of the motor torque; t ise_maxIs the maximum torque N.m of the engine; n ism_maxThe maximum value of the motor rotating speed is expressed in rpm; g is the state transfer function.
The optimal working range of the SOC of the super capacitor is generally 50% -90%, the service life of the super capacitor is prolonged when the super capacitor works in the optimal working range, and the charge-discharge efficiency, the power and other performances are good. In the forward global optimization process, the SOC of the super capacitor in the working process of the hydraulic excavator is kept in state balance. Therefore, a penalty function is added to the target function, the square of the difference between the SOC value of the super capacitor in unit time and the initial value is used as the penalty function, and the penalty function is as follows:
Lα＝λ(SOC(k)-SOCobj)2(formula 6)
In the above formula: SOCobjThe target value of the super capacitor SOC is obtained; and lambda is a penalty coefficient.
The engine fuel consumption calculation modes of the super capacitor of the system energy storage element in the excavator in the charging state and the discharging state are different, so that the engine equivalent fuel consumption function in the embodiment comprises the engine equivalent fuel consumption function when the super capacitor is discharged and the engine equivalent fuel consumption function when the super capacitor is charged. Defining a stage index function as the equivalent fuel consumption of the energy recovery system within time delta t, establishing an objective function under two discharging and charging modes, and adding a super capacitor SOC penalty function to ensure that the equivalent fuel consumption (namely the equivalent fuel consumption function of the engine) of the discharging working condition and the charging working condition is as follows:
discharge regime (P)mNot less than 0) equivalent fuel consumption is:
charging regime (P)mLess than or equal to 0), the equivalent fuel consumption is as follows:
in the above formula: j. the design is a squarefAnd JcEquivalent fuel consumption (unit is g) when the energy recovery system is charged and discharged; pmMotor power (in kW); peInstalling power (unit is kW) for an engine;
the equivalent fuel consumption is given in g/(kWh); b iseThe fuel consumption rate of the engine is given as g/(kWh); etabdThe discharge efficiency of the super capacitor is obtained; etamTo the motor efficiency.
The discrete dynamic programming algorithm is mainly divided into forward calculation and reverse calculation, and the engine equivalent fuel consumption function corresponding to the working condition of each stage can be solved in the following mode: calculating equivalent fuel consumption functions of the engine corresponding to all the stage working conditions according to the sequence from back to front to obtain reverse calculation data; the reverse calculation data comprises a control variable optimal value and a state variable optimal value which are obtained through reverse calculation; and calculating the equivalent fuel consumption functions of the engine corresponding to all the stage working conditions by adopting the iterative dynamic programming algorithm according to the SOC initial value of the super capacitor and the reverse calculation data in a sequence from front to back. Specifically, the process of determining the reverse calculation data includes: calculating the SOC variation of the super capacitor between the adjacent stage working conditions, and obtaining a state variable set according to the minimum value of the SOC of the super capacitor and the SOC variation of the super capacitor; the state variable set comprises a super capacitor SOC value corresponding to each stage working condition; calculating a state variable transfer equation at equal intervals within a constraint range of motor torque or generator torque, and solving an engine equivalent fuel consumption function through an interpolation method according to the state variable transfer equation to obtain an optimal motor torque value; and setting the optimal motor torque value as a control variable optimal value obtained by reverse calculation, and setting the super capacitor SOC value in the state variable set as a state variable optimal value obtained by reverse calculation to obtain reverse calculation data.
And each state value deduced to the first stage point from the Nth stage point reverse solution is reverse calculation, at the moment, the whole period is divided into N-1 levels, and the super capacitor SOC is discretized into a plurality of state value points at different times. And calculating and formulating a working area of the super capacitor SOC, and dispersing the SOC between the upper limit and the lower limit of the reachable range of each stage. Taking Δ SOC as a discrete step, the super capacitor SOC state set at the time k is as follows:
xk＝[SOCmin(k),SOCmin(k)+ΔSOC,…,SOCmax(k)](formula 9)
Points are taken at equal intervals in the constraint range of the motor torque, and the transfer equation SOC of the state variable is calculatedk+1And SOCk+1Performance index function JkAnd J' skAnd solving by an interpolation method, wherein the optimal motor torque value is the minimum value of the function. After the reverse calculation is finished, the vector formed by the value points of the super capacitor SOC corresponding to the optimal motor torque value and the performance index function is taken so as to prepare for the forward calculation.
In the first stage to the Nth stage in the whole working cycle, the optimal solution of interpolation solution in each stage is forward calculation. Under the conditions of the initial value of the super capacitor SOC and the reverse calculation data, the optimal motor torque is calculated in the first stage, the optimal values of the system state and the motor torque in the second stage, the third stage and the fourth stage … … are solved through a state transition equation, and the calculation is continuously carried out to the Nth stage. At the end of the forward calculation, the system global optimal solution is the optimal torque value of the motor at each instant.
In order to solve the problems of large calculation amount and low precision of the traditional dynamic programming method, an iterative dynamic programming optimization method is provided in this chapter, in order to compensate system errors caused by discretization, multi-step iteration is adopted, a search interval is gradually reduced to reduce the calculation amount of each iteration, and a control strategy of a global optimal solution is realized through multiple iterative search calculations. The recursion equation of the iterative dynamic programming method is as follows:
wherein h is the number of iteration steps, f is the state transfer function,
is an optimal objective function.
When the iteration state variable is a non-grid point each time, the optimal control variable of the iteration dynamic planning method is a grid point closest to the iteration state variable, namely:
in the formula, xgState vectors corresponding to the grid points; u. of*An optimal result is calculated for the iteration.
After each iteration, the optimal solution route is realized by repeatedly calculating forward from the initial state of the system. The state variable and control variable grids in the next iterative computation are formed by obtaining the optimal state and control variable through the previous solution, namely
In the formula, Z is the number of control variable points; and Y is the number of state variable points.
Furthermore, in the process of solving the engine equivalent fuel consumption function by adopting an iterative dynamic programming algorithm, when the iteration times are detected to be added with one, the distribution ranges of the state variables and the control variables are reduced according to a preset proportion, so that the search efficiency of the global rotation energy control strategy is improved.
The distribution range of the state variable and the control variable is reduced after each iterative calculation, namely:
in the formula, psi and Z are distribution ranges of control variables and state variables respectively; gamma is a contraction factor.
And on the state grid point of the system, the optimal control variable is the control variable with the minimum objective function value, the control variable value and the objective function value are stored, and the optimization solution of the iterative dynamic programming is completed by repeating the calculation. On the basis of iterative optimization calculation, control and state variable grids are reconstructed, the distribution range of control variables and state variables is synchronously reduced, iterative dynamic planning is completed through multiple times of calculation, the optimization result is optimized, and the optimized control method is the optimal control method for minimizing the objective function value of the excavator energy recovery system under typical operation conditions.
The control flow of the energy recovery system provided by the embodiment is as follows:
referring to fig. 2, fig. 2 is a schematic diagram of a swing energy recovery system of a hydraulic excavator according to an embodiment of the present disclosure, where fig. 2 shows an engine E1; 2 is a variable pump group; 3. 11, 14, 15 and 19 are one-way valves; 4. 10, 18, 20 and 26 are oil tanks; motor M1 is denoted 5; 6 is a rectifier/inverter Inv 2; 7 is a super capacitor SC; 8 is a motor controller MC; 9 is a generator M1; 12 is a recovery motor; 13 is a rotary braking energy recovery valve; 16 is a rotary motor; 17 is a rotary main valve pair; 21. 22, 23, 24 and 25 are pressure sensors Pg1, Pg2, Pg3, Pg4 and Pg 5.
The control flow of the dynamic programming algorithm of the rotary braking energy recovery system is shown in fig. 3, and the process comprises the following steps: initializing; calculating an optimal value F of an objective function, a decision quantity U (namely a control variable) and a state quantity x (namely the control variable) in the final stage; generating a K-stage state quantity and decision quantity network; calculating the distance from each node to the final pointOptimal value F of oil consumptionk(xk) Decision quantity UkState quantity xk(ii) a Subtracting 1 from the number K of the stages, outputting an optimal value F if the K is equal to 0, and re-entering the step of generating the K-stage state quantity and decision quantity network if the K is not equal to 0; the number of initialization stages i is 1; calculating an optimal value F, a decision quantity U (namely a control variable) and a state quantity x (namely a control variable) of an objective function in the first stage; generating a K-stage state quantity and decision quantity network; calculating the distance from each node to the final point, including the optimal value F of the fuel consumptionk(xk) Decision quantity UkState quantity xk(ii) a And judging whether the i is the final stage number, if so, drawing a state change graph and a shortest path graph, and if not, adding 1 to the stage number i and entering the step of generating a K-stage state quantity and decision quantity network. Optimal path { u } on right side of block diagram1,u2,……unSolving the SOC working curve of the super capacitor, and calculating the state, decision quantity and optimal value of each stage of the energy recovery system in a forward direction; the left side is reverse optimization, the state quantity and the decision quantity of the system are calculated step by step in the optimization process, and finally the optimal value f1(x1) of the output objective function is solved. The system control flow block diagram describes the control process of a dynamic programming optimization algorithm, and the system state optimization in each of a forward global optimization stage and a reverse global optimization stage is calculated through all control variables in an objective function state equation. In the calculation process, the control value that caused the exceeding of the state field is discarded, and the calculation is continued by using the next control value. And for the received control value, the oil consumption data of the engine of the hydraulic excavator in the optimal state is finally obtained through continuous accumulation calculation. After comparing all the control value sets in the state, the fuel consumption optimization value of the engine is recorded and stored. An energy recovery system model based on a dynamic programming algorithm is shown in fig. 4. The model comprises a controller, a super capacitor, an engine, a driving motor, a recovery motor, a hydraulic pump, a rotary motor and a rotary platform, wherein the engine generates fuel consumption.
FIG. 5 is a graph showing the main operation state of the energy recovery system of the hydraulic excavator during operation, and FIG. 5(a) is a power distribution curve P of the engine, the hydraulic pump and the motor M1engine、PpumpAnd PAMIn the figure, the motor M1 is used as an auxiliary power source and is coordinated with the engine to drive the load, so that the effect of 'peak clipping and valley filling' on the external load, namely the power output of the hydraulic pump is realized, the working condition of the stable engine is reduced from 30kW to 18kW, and the better fuel economy is obtained. The power output curves of the conventional system and the energy-recovery energy-saving system can be shown in fig. 5(b), and in the full-load rotation and no-load return secondary rotation movement stage, compared with the conventional hydraulic system, the power output of the energy-recovery system is obviously reduced, and the average power reduction amplitude reaches 9.3 kW. Fig. 5(c) and 5(d) show the charge and discharge current and the change of the state of charge SOC of the super capacitor of the energy storage element of the system, and the basic logic of the system energy management in one working cycle can be visually seen, the super capacitor outputs energy to drive the motor M1 when the loads such as boom lifting, slewing acceleration and the like are large, which is a main energy auxiliary stage, and the super capacitor absorbs the recovered electric energy when the boarding platform is braked by slewing, which is a main energy recovery stage. From a complete period, the SOC of the super capacitor is kept within the design range of 50% -90%, and the balance of the system energy is well realized.
Under a typical excavation work cycle, a fuel consumption curve of a rotary energy recovery system of the hydraulic excavator is shown in fig. 6, and compared with a traditional hydraulic system, the dynamic planning optimization algorithm performs optimal power distribution in the working process of the energy recovery system, so that the energy consumption is obviously reduced, and the energy-saving efficiency is improved by 21.3%.
As a maximum inertia load mechanism of the hydraulic excavator, the upper vehicle platform rotation system plays an important role in the typical operation cycle of the excavator, and has the characteristics of large inertia, low dynamic damping ratio, frequent starting and braking and the like. In order to recover the braking energy of the hydraulic excavator in the rotation motion stage, a rotation energy recovery system control method based on iterative dynamic programming optimization is provided to solve the energy distribution global optimal control problem of the system and realize the optimal fuel economy of the excavator in the operation cycle process. Through the implementation of patent scheme, not only realize high-efficient recycle the platform of getting on the bus gyration kinetic energy, guaranteed not to influence excavator complete machine operating performance moreover. The energy output of the engine is reduced, the fuel consumption is reduced, the exhaust emission is reduced, and the working environment is improved.
The embodiment of the present application further provides an excavator gyration energy recovery control system, and the system may include:
the fuel consumption function establishing module is used for establishing an engine equivalent fuel consumption function of the excavator under a circulation working condition; the control variable of the engine equivalent fuel consumption function is motor torque or generator torque, and the state variable of the engine equivalent fuel consumption function is a super capacitor SOC value of a system energy storage element;
the control strategy generation module is used for dividing the cycle working condition into a plurality of stage working conditions, and solving an engine equivalent fuel consumption function corresponding to each stage working condition by adopting an iterative dynamic programming algorithm to obtain a global rotary energy control strategy; the global rotation energy control strategy comprises a control variable optimal value and a state variable optimal value corresponding to each stage working condition;
and the energy recovery module is used for executing the rotation energy recovery operation according to the global rotation energy control strategy.
After the engine equivalent fuel consumption function of the excavator under the cyclic working condition is established, the engine equivalent fuel consumption function corresponding to each stage of the cyclic working condition is solved by using an iterative dynamic programming algorithm, and a global rotation energy control strategy is obtained. And determining the optimal value of the control variable and the optimal value of the state variable corresponding to the working condition of each stage according to the global rotation energy control strategy. According to the scheme, the energy distribution global optimal control problem of the system is solved based on the iterative dynamic programming algorithm, the fuel economy of the excavator in the operation cycle process is improved, and the efficient recovery of the rotation energy of the excavator can be realized.
Further, the control strategy generation module is used for determining a constraint condition set according to the working parameters of the excavator; wherein the constraint condition set comprises any one or combination of any several of a capacitance constraint condition, a motor speed constraint condition, an engine torque constraint condition and a motor torque constraint condition; the capacitance constraint condition is determined according to the value range of the SOC value of the super capacitor; the motor rotating speed constraint condition is determined according to the rotating speed value range of a motor or a generator of the excavator; the engine torque constraint condition is determined according to a torque value range of an engine of the excavator; the motor torque constraint condition is determined according to a torque value range of a motor or a generator of the excavator; and under the constraint of the constraint condition set, solving an engine equivalent fuel consumption function corresponding to the working condition of each stage by adopting the iterative dynamic programming algorithm to obtain a global rotary energy control strategy.
Further, the fuel consumption function establishing module is used for generating a penalty function according to the square of the change quantity of the super capacitor SOC value; the change quantity of the SOC value of the super capacitor is the difference between the SOC value of the super capacitor and the initial value of the SOC of the super capacitor under the working condition of the current stage; the system is also used for generating a cost function according to the control variable, the state variable and the working time; and for using the sum of the penalty function and the cost function as the engine equivalent fuel consumption function.
Further, the control strategy generation module comprises:
the reverse calculation unit is used for calculating the equivalent fuel consumption functions of the engine corresponding to all the stage working conditions from back to front to obtain reverse calculation data; the reverse calculation data comprises a control variable optimal value and a state variable optimal value which are obtained through reverse calculation;
and the forward calculation unit is used for calculating the equivalent fuel consumption functions of the engine corresponding to all the stage working conditions by adopting the iterative dynamic programming algorithm according to the sequence from front to back according to the initial value of the super capacitor SOC and the reverse calculation data to obtain a global rotary energy control strategy.
Further, the reverse calculation unit is used for calculating the SOC variation of the super capacitor between the adjacent stage working conditions, and a state variable set is obtained according to the minimum value of the SOC of the super capacitor and the SOC variation of the super capacitor; the state variable set comprises a super capacitor SOC value corresponding to each stage working condition; the method is also used for calculating a state variable transfer equation at equal intervals within the constraint range of the motor torque or the generator torque, and solving the engine equivalent fuel consumption function through an interpolation method according to the state variable transfer equation to obtain an optimal motor torque value; and the system is also used for setting the optimal motor torque value as a control variable optimal value obtained by reverse calculation, and setting the super capacitor SOC value in the state variable set as a state variable optimal value obtained by reverse calculation to obtain reverse calculation data.
Further, the engine equivalent fuel consumption function comprises an engine equivalent fuel consumption function when the super capacitor is discharged and an engine equivalent fuel consumption function when the super capacitor is charged.
Further, the method also comprises the following steps:
and the range reduction module is used for reducing the distribution ranges of the state variables and the control variables according to a preset proportion when detecting that the iteration times are increased by one in the process of solving the engine equivalent fuel consumption function corresponding to each stage working condition by adopting an iterative dynamic programming algorithm.
Since the embodiment of the system part corresponds to the embodiment of the method part, the embodiment of the system part is described with reference to the embodiment of the method part, and is not repeated here.
The present application also provides a storage medium having a computer program stored thereon, which when executed, may implement the steps provided by the above-described embodiments. The storage medium may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The present application further provides an excavator, which may include a memory and a processor, where the memory stores a computer program, and when the processor calls the computer program in the memory, the steps provided in the foregoing embodiments may be implemented. Of course, the excavator may also include various network interfaces, power supplies and other components.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. An excavator swing energy recovery control method is characterized by comprising the following steps:
establishing an engine equivalent fuel consumption function of the excavator under a circulation working condition; the control variable of the engine equivalent fuel consumption function is motor torque or generator torque, and the state variable of the engine equivalent fuel consumption function is a super capacitor SOC value of a system energy storage element;
dividing the cycle working condition into a plurality of stage working conditions, and solving an engine equivalent fuel consumption function corresponding to each stage working condition by adopting an iterative dynamic programming algorithm to obtain a global rotation energy control strategy; the global rotation energy control strategy comprises a control variable optimal value and a state variable optimal value corresponding to each stage working condition;
and executing the rotation energy recovery operation according to the global rotation energy control strategy.
2. The excavator rotation energy recovery control method according to claim 1, wherein the step of solving the engine equivalent fuel consumption function corresponding to the working condition of each stage by adopting an iterative dynamic programming algorithm comprises the following steps:
determining a constraint condition set according to the working parameters of the excavator; wherein the constraint condition set comprises any one or combination of any several of a capacitance constraint condition, a motor speed constraint condition, an engine torque constraint condition and a motor torque constraint condition; the capacitance constraint condition is determined according to the value range of the SOC value of the super capacitor; the motor rotating speed constraint condition is determined according to the rotating speed value range of a motor or a generator of the excavator; the engine torque constraint condition is determined according to a torque value range of an engine of the excavator; the motor torque constraint condition is determined according to a torque value range of a motor or a generator of the excavator;
and under the constraint of the constraint condition set, solving an engine equivalent fuel consumption function corresponding to the working condition of each stage by adopting the iterative dynamic programming algorithm.
3. The excavator slewing energy recovery control method of claim 1, wherein the establishing of the engine equivalent fuel consumption function of the excavator under the cyclic condition comprises:
generating a penalty function according to the square of the change quantity of the SOC value of the super capacitor; the change quantity of the SOC value of the super capacitor is the difference between the SOC value of the super capacitor and the initial value of the SOC of the super capacitor under the working condition of the current stage;
generating a cost function according to the control variable, the state variable and the working time;
and taking the sum of the penalty function and the cost function as the engine equivalent fuel consumption function.
4. The excavator rotation energy recovery control method according to claim 1, wherein the step of solving the engine equivalent fuel consumption function corresponding to each stage of working condition by using an iterative dynamic programming algorithm to obtain a global rotation energy control strategy comprises the following steps:
calculating equivalent fuel consumption functions of the engine corresponding to all the stage working conditions according to the sequence from back to front to obtain reverse calculation data; the reverse calculation data comprises a control variable optimal value and a state variable optimal value which are obtained through reverse calculation;
and calculating the equivalent fuel consumption functions of the engines corresponding to all the stage working conditions by adopting the iterative dynamic programming algorithm according to the sequence from front to back according to the initial value of the super capacitor SOC and the reverse calculation data to obtain a global rotary energy control strategy.
5. The excavator slewing energy recovery control method according to claim 4, wherein the step of calculating the engine equivalent fuel consumption functions corresponding to all the stage working conditions in the sequence from back to front to obtain reverse calculation data comprises the steps of:
calculating the SOC variation of the super capacitor between the adjacent stage working conditions, and obtaining a state variable set according to the minimum value of the SOC of the super capacitor and the SOC variation of the super capacitor; the state variable set comprises a super capacitor SOC value corresponding to each stage working condition;
calculating a state variable transfer equation at equal intervals within a constraint range of motor torque or generator torque, and solving an engine equivalent fuel consumption function through an interpolation method according to the state variable transfer equation to obtain an optimal motor torque value;
and setting the optimal motor torque value as a control variable optimal value obtained by reverse calculation, and setting the super capacitor SOC value in the state variable set as a state variable optimal value obtained by reverse calculation to obtain reverse calculation data.
6. The excavator slewing energy recovery control method as claimed in claim 1, wherein the engine equivalent fuel consumption function includes an engine equivalent fuel consumption function when the super capacitor is discharged and an engine equivalent fuel consumption function when the super capacitor is charged.
7. The excavator slewing energy recovery control method according to any one of claims 1 to 6, wherein in the process of solving the engine equivalent fuel consumption function corresponding to each stage of working condition by using an iterative dynamic programming algorithm, the method further comprises the following steps:
and when the iteration times are increased by one, reducing the distribution range of the state variable and the control variable according to a preset proportion.
8. An excavator swing energy recovery control system, comprising:
the fuel consumption function establishing module is used for establishing an engine equivalent fuel consumption function of the excavator under a circulation working condition; the control variable of the engine equivalent fuel consumption function is motor torque or generator torque, and the state variable of the engine equivalent fuel consumption function is a super capacitor SOC value of a system energy storage element;
the control strategy generation module is used for dividing the cycle working condition into a plurality of stage working conditions, and solving an engine equivalent fuel consumption function corresponding to each stage working condition by adopting an iterative dynamic programming algorithm to obtain a global rotary energy control strategy; the global rotation energy control strategy comprises a control variable optimal value and a state variable optimal value corresponding to each stage working condition;
and the energy recovery module is used for executing the rotation energy recovery operation according to the global rotation energy control strategy.
9. An excavator comprising a memory in which a computer program is stored and a processor which when called upon by the computer program in the memory carries out the steps of an excavator slewing energy recovery control method as claimed in any one of claims 1 to 7.
10. A storage medium having stored thereon computer-executable instructions that, when loaded and executed by a processor, carry out the steps of the excavator swing energy recovery control method of any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024011773A1 (en) * | 2022-07-13 | 2024-01-18 | 三一重机有限公司 | Braking energy consumption control method and apparatus, work machine, and electronic device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CH706518A1 (en) * | 2012-05-15 | 2013-11-15 | Liebherr Machines Bulle Sa | Control unit for drive system of work machine e.g. cable-operated excavator, sets operating point of engine to determine services and efficiency characteristic fields of components based on fuel consumption map of engine |
| CN109447357A (en) * | 2018-11-02 | 2019-03-08 | 浙江大学 | A kind of cargo loads the fuel consumption optimization method of haulage truck scheduling system |
| CN110543100A (en) * | 2019-09-27 | 2019-12-06 | 广东海洋大学 | large truss track optimization method based on minimum energy consumption principle |
| CN113183777A (en) * | 2021-06-10 | 2021-07-30 | 三一重机有限公司 | Hybrid power system energy management method and device and electronic equipment |
-
2021
- 2021-08-10 CN CN202110914802.3A patent/CN113625567A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CH706518A1 (en) * | 2012-05-15 | 2013-11-15 | Liebherr Machines Bulle Sa | Control unit for drive system of work machine e.g. cable-operated excavator, sets operating point of engine to determine services and efficiency characteristic fields of components based on fuel consumption map of engine |
| CN109447357A (en) * | 2018-11-02 | 2019-03-08 | 浙江大学 | A kind of cargo loads the fuel consumption optimization method of haulage truck scheduling system |
| CN110543100A (en) * | 2019-09-27 | 2019-12-06 | 广东海洋大学 | large truss track optimization method based on minimum energy consumption principle |
| CN113183777A (en) * | 2021-06-10 | 2021-07-30 | 三一重机有限公司 | Hybrid power system energy management method and device and electronic equipment |
Non-Patent Citations (7)
| Title |
|---|
| 张炳力;张平平;赵韩;田芳;徐小东;吴迪;: "基于离散动态规划的PHEV燃油经济性全局最优控制", 汽车工程, no. 11 * |
| 王磊;张勇;殷承良;: "混联式混合动力客车整车控制系统的开发", 汽车工程, no. 12 * |
| 王辉;孙梅迪;何哲文;黄守道;荣飞;袁小芳;: "混合动力挖掘机能量管理系统控制策略研究", 湖南大学学报(自然科学版), no. 02 * |
| 秦兆博: "混合动力履带车辆机电复合传动系统优化设计方法", 中国博士学位论文全文数据库 工程科技Ⅱ辑, pages 92 * |
| 郭浩;王辉;吴轩;陈建文;: "混合动力挖掘机电回转系统储能容量的优化配置", 中国机械工程, no. 12 * |
| 郭翠玲;: "PHEV转矩的自适应神经模糊推理系统分配策略", 机械设计与制造, no. 06, pages 164 - 169 * |
| 龚进;陈保卫;龚俊;张大庆;胡鹏;李曙;高继松;: "基于动态规划的挖掘机混合动力系统优化与试验", 机械设计与研究, no. 01, pages 137 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024011773A1 (en) * | 2022-07-13 | 2024-01-18 | 三一重机有限公司 | Braking energy consumption control method and apparatus, work machine, and electronic device |
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