CN113526413A - Forklift lifting device and power generation efficiency control method thereof - Google Patents
Forklift lifting device and power generation efficiency control method thereof Download PDFInfo
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- CN113526413A CN113526413A CN202110818970.2A CN202110818970A CN113526413A CN 113526413 A CN113526413 A CN 113526413A CN 202110818970 A CN202110818970 A CN 202110818970A CN 113526413 A CN113526413 A CN 113526413A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66F—HOISTING, LIFTING, HAULING OR PUSHING, NOT OTHERWISE PROVIDED FOR, e.g. DEVICES WHICH APPLY A LIFTING OR PUSHING FORCE DIRECTLY TO THE SURFACE OF A LOAD
- B66F9/00—Devices for lifting or lowering bulky or heavy goods for loading or unloading purposes
- B66F9/06—Devices for lifting or lowering bulky or heavy goods for loading or unloading purposes movable, with their loads, on wheels or the like, e.g. fork-lift trucks
- B66F9/075—Constructional features or details
- B66F9/07504—Accessories, e.g. for towing, charging, locking
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66F—HOISTING, LIFTING, HAULING OR PUSHING, NOT OTHERWISE PROVIDED FOR, e.g. DEVICES WHICH APPLY A LIFTING OR PUSHING FORCE DIRECTLY TO THE SURFACE OF A LOAD
- B66F9/00—Devices for lifting or lowering bulky or heavy goods for loading or unloading purposes
- B66F9/06—Devices for lifting or lowering bulky or heavy goods for loading or unloading purposes movable, with their loads, on wheels or the like, e.g. fork-lift trucks
- B66F9/075—Constructional features or details
- B66F9/20—Means for actuating or controlling masts, platforms, or forks
- B66F9/205—Arrangements for transmitting pneumatic, hydraulic or electric power to movable parts or devices
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/14—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries for charging batteries from dynamo-electric generators driven at varying speed, e.g. on vehicle
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Abstract
The invention provides a forklift lifting device and a power generation efficiency control method thereof, belonging to the technical field of electric forklifts and comprising the following steps: s1: collecting oil pressure values and current values and inputting the oil pressure values and the current values into an efficiency model of the power generation system; s2: performing iterative calculation of a particle swarm algorithm on the oil pressure value and the generator current value according to the power generation system efficiency model to obtain an optimal empirical current value; s3: performing online optimization near the optimal empirical current value by a disturbance observation method to obtain an optimal current value; s4: the optimal current value is input to the power module through the current controller, and the generator is controlled to achieve the maximum generating efficiency. According to the invention, the optimal current value can be rapidly and accurately obtained by establishing the efficiency model of the power generation system and carrying out online optimization according to the fitness function, and the optimal current value is input to the power module through the current controller, so that the rotating speed of the generator is controlled to maximize the power generation efficiency, the energy recovery is carried out to the greatest extent, and the energy recovery efficiency is greatly improved.
Description
Technical Field
The invention belongs to the technical field of electric forklifts, and particularly relates to a forklift lifting device and a power generation efficiency control method thereof.
Background
With the rapid development of social economy, modern industrial logistics systems become infrastructure for promoting social development and economic construction, and have important significance for national economy scale formation and modern industrial development. The types, frequency and scale of logistics in modern industrial logistics systems are increasing, so that the importance of loading, unloading and carrying work is more remarkable, and the electric forklift is widely applied to various places in the industrial transportation industry by virtue of high-efficiency carrying capacity and high operation flexibility. Meanwhile, the pressure conditions of energy crisis and energy conservation and emission reduction are severe, the energy conservation and environmental protection career greatly promotes the transformation and upgrading of the electric forklift, and higher standards and requirements are provided for the energy consumption of a hydraulic system of the electric forklift.
Traditional fork truck hydraulic system realizes the load through the overflow valve directly to the oil tank release pressure oil and descends, and such mode is great to fork truck load, and the operating characteristic that needs frequent lift causes energy loss huge. The traditional non-environment-friendly and energy-saving electric forklift generally has the problem of low energy utilization efficiency. Therefore, a novel energy-saving design needs to be carried out on the lifting system of the electric forklift, lifting potential energy of the electric forklift is reasonably recycled, energy-saving transformation of the electric forklift is achieved, and in addition, a power generation efficiency control method needs to be found, so that the energy recovery efficiency reaches the maximum.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a forklift lifting device capable of recovering energy of a forklift and obtaining maximum power generation efficiency and a power generation efficiency control method thereof.
In order to achieve the purpose, the invention adopts the technical scheme that:
a power generation efficiency control method of a forklift lifting device comprises the following steps:
s1: collecting an oil pressure value and a generator current value and inputting the values into a power generation system efficiency model;
s2: performing iterative calculation of a particle swarm algorithm on the oil pressure value and the generator current value according to the power generation system efficiency model to obtain an optimal empirical current value;
s3: performing online optimization near the optimal empirical current value by a disturbance observation method to obtain an optimal current value;
s4: the optimal current value is input to the power module through the current controller, and the generator is controlled to achieve the maximum generating efficiency.
In the above method for controlling the power generation efficiency of the forklift lifting device, step S2 specifically includes:
s21: constructing a particle swarm composed of an oil pressure value and a generator current value, and initializing the particle swarm;
s22: inputting a particle swarm into a power generation system efficiency model, and outputting a power generation efficiency value corresponding to each particle according to a preset function;
s23: judging whether the current value corresponding to the highest power generation efficiency value meets the convergence condition, if not, going to step S24, and if so, going to step S25;
s24: updating the current value and the current change rate of each particle in the particle swarm, and going to step S22;
s25: and outputting a current value corresponding to the highest power generation efficiency value, namely the optimal empirical current value.
In the above method for controlling the power generation efficiency of the forklift lifting device, step S3 specifically includes:
s31: inputting a target current value to the power module through a current controller, wherein the initial state input is an optimal empirical current value;
s32: collecting oil pressure value, flow value, voltage value and real-time current value, and calculating to obtain a power generation efficiency value eta of two adjacent times according to a preset efficiency formulak-1And ηk；
S33: judging whether the power generation efficiency value eta meets the convergence condition, if not, going to step S34, and if so, going to step S35;
s34: judging whether the value of the generating efficiency meets etak＞ηk-1If not, the current is changedThe trend is reversed from the previous one and goes to step S31; if yes, the current is changed and the change trend is the same as the previous direction, and the step S31 is executed;
s35: and outputting a current value when the power generation efficiency value meets the convergence condition, namely the optimal current value.
In the above method for controlling the power generation efficiency of the forklift lifting device, the specific method for establishing the power generation system efficiency model in step S1 is as follows:
a1: collecting actual data to obtain a data set about the oil pressure value, the generator current value and the power generation efficiency value;
a2: and establishing an efficiency model of the power generation system according to the data set, and obtaining a fitness function.
In the above method for controlling the power generation efficiency of the forklift lifting device, step a1 specifically includes:
a11: collecting actual data to obtain an oil pressure value, a generator current value and a power generation efficiency value;
a12: and controlling the single variable oil pressure value and the generator current value one by one, and obtaining a data set through a preset algorithm.
In the above method for controlling the power generation efficiency of the forklift lifting device, step a2 specifically includes:
a21: performing radial basis function neural network training by using the data set;
a22: and outputting a fitness function of the oil pressure value, the generator current value and the power generation efficiency value.
In the above method for controlling the power generation efficiency of the forklift lifting device, the preset function in step S22 is a fitness function, and specifically the method includes:
wherein 1 is<i<N, eta is power generation efficiency, P is oil pressure, I is generator current, Ci,1And Ci,2Are respectively the constituent elements of the central point vector of the radial basis function after training, sigmaiIs the radial basis function width, wiIs a weight vector.
Another object of the present invention is to provide a forklift lifting device, including:
the power source is used for providing power for the system and converting hydraulic energy and mechanical energy;
the control system is connected with the power source and is used for controlling the pressure, flow and direction of the hydraulic oil;
the execution system is connected with the control system and used for executing lifting action according to hydraulic energy;
and the energy storage system is connected with the power source and used for converting mechanical energy and electric energy and storing and releasing the electric energy, wherein the energy storage system comprises a power module, a generator and a current control module, the generator and the current control module are respectively connected with the power module, the current control module inputs the optimal current value obtained by the power generation efficiency control method of the forklift lifting device into the power module, and the power module controls the rotating speed of the generator so as to achieve the maximum power generation efficiency.
In the above forklift lifting device, the energy storage system includes:
and the storage battery is connected with the power supply module and is used for storing and releasing electric energy.
In the above forklift lifting device, the following are provided:
the power source includes a fuel tank and a motor, wherein the motor is a pump-motor multiplex.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, the optimal current value can be rapidly and accurately obtained by establishing the efficiency model of the power generation system and carrying out online optimization according to the fitness function, and the optimal current value is input to the power module through the current controller, so that the rotating speed of the generator is controlled to maximize the power generation efficiency, the energy recovery is carried out to the maximum extent, and the energy recovery efficiency is greatly improved;
2. the current optimization is carried out by combining the particle swarm algorithm and the disturbance observation method, and the two optimization methods are combined, so that the method has the advantages of high convergence speed and difficulty in falling into local optimization, can accurately reflect the characteristics of a system, and can obtain the optimal current value at the highest speed;
3. by arranging the energy storage system on the forklift, the forklift lifting device provided by the invention can convert potential energy generated when a heavy object descends into electric energy to be stored in the storage battery, and provides the forklift with the electric energy when the forklift works, so that the energy recycling is realized, the energy utilization rate is greatly improved, and the energy consumption is saved;
4. the pump and the motor are multiplexed, so that the two-way functions of the pump and the motor can be realized simultaneously only through one element, the use of the element is reduced, the installation space is saved, and the manufacturing cost is saved.
Drawings
FIG. 1 is a diagram of the steps in the present invention.
Fig. 2 is a control flow chart in the present invention.
Fig. 3 is a diagram illustrating a specific step S2 in the present invention.
Fig. 4 is a diagram illustrating a specific step S3 in the present invention.
Fig. 5 is a diagram of a power generation system efficiency model establishing step in the present invention.
Fig. 6 is a diagram illustrating a specific step a1 in the present invention.
Fig. 7 is a diagram illustrating a specific step a2 in the present invention.
Fig. 8 is a hydraulic schematic diagram of the present invention.
In the figure, 10, the power source; 11. an oil tank; 12. a motor; 20. a control system; 21. a first electromagnetic directional valve; 22. a second electromagnetic directional valve; 23. a third electromagnetic directional valve; 24. a fourth electromagnetic directional valve; 25. a first check valve; 26. a second one-way valve; 27. a third check valve; 28. a fourth check valve; 29. an overflow valve; 210. a throttle valve; 211. a filter; 30. an execution system; 31. a lifting frame; 40. an energy storage system; 41. a power supply module; 42. a generator; 43. a current control module; 44. and (4) a storage battery.
Detailed Description
The following are specific embodiments of the present invention and are further described with reference to the drawings, but the present invention is not limited to these embodiments.
It should be noted that all the directional indicators (such as up, down, left, right, front, and rear … …) in the embodiment of the present invention are only used to explain the relative position relationship between the components, the movement situation, etc. in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indicator is changed accordingly.
As shown in fig. 1 to 7, the present invention provides a method for controlling power generation efficiency of a forklift lifting apparatus, including:
s1: collecting an oil pressure value and a generator current value and inputting the values into a power generation system efficiency model;
s2: performing iterative calculation of a particle swarm algorithm on the oil pressure value and the generator current value according to the power generation system efficiency model to obtain an optimal empirical current value;
s3: performing online optimization near the optimal empirical current value by a disturbance observation method to obtain an optimal current value;
s4: the optimal current value is input to the power module through the current controller, and the generator is controlled to achieve the maximum generating efficiency.
According to the power generation efficiency control method of the forklift lifting device, the optimal current value can be quickly and accurately obtained by establishing the power generation system efficiency model and performing online optimization, the optimal current value is input to the power supply module through the current controller, the generator is controlled to maximize the power generation efficiency, the energy is recovered to the maximum extent, and the energy recovery efficiency is greatly improved.
In the present embodiment, the power generation system efficiency model is trained based on a radial basis neural network. The power generation efficiency is comprehensively influenced by the oil pressure P and the generator current I, and the oil pressure is from the weight of goods during power generation and cannot be adjusted; therefore, the oil pressure is used as an observed quantity, the current is used as an optimization variable, and the maximization of the power generation efficiency is realized by searching the optimal current under the observation pressure based on the established power generation system efficiency model.
Preferably, as shown in fig. 3, step S2 specifically includes:
s21: constructing a particle swarm composed of an oil pressure value and a generator current value, and initializing the particle swarm;
s22: inputting a particle swarm into a power generation system efficiency model, and outputting a power generation efficiency value corresponding to each particle according to a preset function;
s23: judging whether the current value corresponding to the highest power generation efficiency value meets the convergence condition, if not, going to step S24, and if so, going to step S25;
s24: updating the current value and the current change rate of each particle in the particle swarm, and going to step S22;
s25: and outputting a current value corresponding to the highest power generation efficiency value, namely the optimal empirical current value.
In this embodiment, the particle swarm algorithm based on the radial basis function has the advantages of fast convergence speed, high precision and being not easy to fall into local optimization, so that the preset function based on the radial basis function is taken as the fitness function of the particle swarm algorithm, and the optimization of the maximum power generation efficiency can be obtained.
In particle swarm optimization, a population of particles is initially defined, each particle having two dimensions, position and velocity. The position is determined by a fitness function, which is an evaluation equation that quantifies the particle performance. The velocity represents the direction and magnitude of the particle flight. The speed of the particles is determined by the speed of the last moment, the individual optimal position and the current optimal position of the whole particle swarm, and the speed of the particles is updated through a speed updating formula until a convergence condition is met, so that the optimal empirical current value can be obtained.
The velocity and position of the particle are updated using the following velocity update formula:
in the formula: v. ofiIs the velocity of the ith particle, xiFor the position of the ith particle, k represents the number of iterations, wiIs a weight vector, a1And a2Are respectively the acceleration coefficient, r1 And r2From 0 to 1Free factor, piThe historical optimal position of the ith particle, and g (k) the optimal position obtained in the kth iteration of the whole particle swarm.
Preferably, as shown in fig. 4, step S3 specifically includes:
s31: inputting a target current value to the power module through a current controller, wherein the initial state input is an optimal empirical current value;
s32: collecting oil pressure value, flow value, voltage value and real-time current value, and calculating to obtain a power generation efficiency value eta of two adjacent times according to a preset efficiency formulak-1And ηk；
S33: judging whether the power generation efficiency value eta meets the convergence condition, if not, going to step S34, and if so, going to step S35;
s34: judging whether the value of the generating efficiency meets etak＞ηk-1If not, the current is changed and the changing trend is opposite to the last time, and go to step S31; if yes, the current is changed and the change trend is the same as the previous direction, and the step S31 is executed;
s35: and outputting a current value when the power generation efficiency value meets the convergence condition, namely the optimal current value.
Further preferably, the preset efficiency formula is:
wherein eta is the power generation efficiency, U is the voltage value, I is the real-time current value, P is the oil pressure value, and Q is the flow value.
In the embodiment, after the optimal empirical current value is obtained through a particle swarm algorithm, the optimal empirical current value is input into the power module through the current controller, meanwhile, the oil pressure value, the flow value, the voltage value and the real-time current value are collected to calculate the real-time power generation efficiency, online optimization is carried out near the optimal empirical current value through a disturbance observation method, the current real-time value is obtained, the obtained current value can accurately reflect the characteristics of a specific system, and the optimal current value is obtained.
Due to the influences of temperature, hydraulic oil viscosity, pipeline and installation errors and the like, the preset function based on the radial basis function neural network can only approximately reflect the characteristics of a specific system but cannot accurately reflect the characteristics of the specific system. Therefore, a disturbance observation method is added for carrying out online optimization, and the disturbance observation method carries out optimization by applying disturbance quantity to the control quantity and observing the change direction of the output efficiency at the moment before and after disturbance. The optimization is carried out through the combination of the particle swarm optimization and the disturbance observation method, namely, the empirical value of the optimal current is obtained through the particle swarm optimization, then the online optimization is carried out near the empirical value through the disturbance observation method, the optimal current value is finally obtained, and the obtained data are more accurate and reliable.
Preferably, as shown in fig. 5, the specific establishing method of the power generation system efficiency model in step S1 is as follows:
a1: collecting actual data to obtain a data set about the oil pressure value, the generator current value and the power generation efficiency value;
a2: and establishing an efficiency model of the power generation system according to the data set, and obtaining a fitness function.
Preferably, as shown in fig. 6, step a1 specifically includes:
a11: collecting actual data to obtain an oil pressure value, a generator current value and a power generation efficiency value;
a12: and controlling the single variable oil pressure value and the generator current value one by one, and obtaining a data set through a preset algorithm.
In this embodiment, before establishing the efficiency model of the power generation system, actual data needs to be collected to obtain an actual oil pressure value, a generator current value and a power generation efficiency value, so as to study the influence mechanism of each input variable on the power generation efficiency, and then a data set is obtained by controlling the variables one by one through a double-layer traversal method.
As shown in fig. 7, step a2 specifically includes:
a21: performing radial basis function neural network training by using the data set;
a22: and outputting a fitness function of the oil pressure value, the generator current value and the power generation efficiency value.
Further preferably, the equations and parameters used for radial basis function neural network training are as follows
In the formula 1<i<N, x is the input vector, P is the oil pressure, I is the generator current, phii(x)The most common gaussian distribution is chosen here as a function of the radial symmetry along the center point. Mu.siIs a two-dimensional vector, sigma, representing the central point of the ith node radial basis functioniIs the radial basis function width, wiIs a weight vector.
Further preferably, the preset function in step S22 is a fitness function, and specifically includes:
wherein 1 is<i<N, eta is power generation efficiency, P is oil pressure, I is generator current, Ci,1And Ci,2Are respectively the constituent elements of the central point vector of the radial basis function after training, sigmaiIs the radial basis function width.
In this embodiment, a radial basis function is trained by using a data set, the oil pressure and the generator current are used as inputs, the power generation efficiency is used as an output, and a relationship function between the oil pressure and the generator current is obtained, that is, an influence mechanism of the oil pressure and the generator current on the power generation efficiency can be determined, and the obtained relationship function is a fitness function of the particle swarm optimization.
As shown in fig. 8, the present invention also provides a forklift lifting device, comprising:
a power source 10 for supplying power to the system and converting hydraulic energy and mechanical energy;
the control system 20 is connected with the power source 10 and used for controlling the pressure, flow and direction of hydraulic oil;
the execution system 30 is connected with the control system 20 and is used for executing lifting action according to hydraulic energy;
and an energy storage system 40 connected to the power source 10, for converting mechanical energy and electric energy, and storing and releasing the electric energy, wherein the energy storage system 40 includes a power module 41, and a generator 42 and a current control module 43 respectively connected to the power module 41, and the current control module 43 inputs an optimal current value obtained by a method for controlling the power generation efficiency of the forklift lifting apparatus to the power module 41, and controls the rotation speed of the generator 42 by the power module 41, so as to achieve the maximum power generation efficiency.
Further preferably, the energy storage system 40 comprises: and a storage battery 44 connected to the power module 41 for storing and discharging electric energy.
Further preferably, the power source 10 includes a fuel tank 11 and a motor 12.
In this embodiment, by providing the energy storage system 40, the potential energy generated when the heavy object descends can be converted into electric energy to be stored in the storage battery 44, and the electric energy is provided for the forklift during the operation of the forklift, so that the energy can be recycled. Specifically, when the heavy object descends, the weight of the heavy object generates pressure on oil, the oil pressure is converted into mechanical energy through the motor 12, the motor 12 is connected with the generator 42, the generator 42 converts the mechanical energy transmitted by the motor 12 into electric energy, and the electric energy is stored in the storage battery 44, meanwhile, the optimal current value is input into the power module 41 through the current control module 43, the rotating speed of the generator 42 is controlled through the power module 41, the maximum power generation efficiency is further achieved, the maximum utilization of heavy object potential energy is realized, the energy utilization rate is greatly improved, and the energy consumption is saved.
As shown in fig. 8, the control system 20 includes a first electromagnetic directional valve 21, a second electromagnetic directional valve 22, a third electromagnetic directional valve 23, a fourth electromagnetic directional valve 24, a first check valve 25, a second check valve 26, a third check valve 27, a fourth check valve 28, an overflow valve 29, a throttle valve 210, and a filter 211; wherein, the motor 12 is respectively connected with an oil outlet of the first check valve 25, an oil outlet of the second check valve 26 and an oil inlet of the fourth check valve 28, an oil outlet of the fourth check valve 28 is respectively connected with an oil inlet of the third check valve 27 and one end of the filter 211, two ends of the third check valve 27 are connected in parallel with the filter 211, the other end of the filter 211 is respectively connected with an oil inlet of the second check valve 26, an oil inlet of the overflow valve 29 and one end of the fourth electromagnetic directional valve 24, the other end of the fourth electromagnetic directional valve 24 is connected with one end of the third electromagnetic directional valve 23, the other end of the third electromagnetic directional valve 23 is connected with one end of the second electromagnetic directional valve 22, the other end of the second electromagnetic directional valve 22 is connected with the execution system 30, the oil inlet of the overflow valve 29 is also connected with one end of the first electromagnetic directional valve 21, the other end of the first electromagnetic directional valve 21 and the oil outlet of the overflow valve 29 are both connected with the oil tank 11, a throttle valve 210 is arranged between the oil outlet of the overflow valve 29 and the execution system 30.
Further preferably, the performing system 30 comprises a lifting frame 31.
It is further preferred that the pump be multiplexed with the motor 12 and the generator 42 be multiplexed with the motor.
In this embodiment, through setting up various hydrovalves, the pressure, flow and the direction of all-round control fluid for lifting frame 31 can rise steadily, accurately and descend, makes the heavy object remove more steadily, and the moving direction is more accurate, and simultaneously, still multiplexing pump and motor 12, motor and generator 42 are multiplexing, can realize dual function through single part, has reduced the use of component, has saved installation space, has practiced thrift manufacturing cost, makes entire system overall arrangement reasonable, high-efficient orderly.
It should be noted that the descriptions related to "first", "second", "a", etc. in the present invention are only used for descriptive purposes and are not to be construed as indicating or implying relative importance or implicit indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise. The terms "connected," "fixed," and the like are to be construed broadly, e.g., "fixed" may be a fixed connection, a removable connection, or an integral part; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In addition, the technical solutions in the embodiments of the present invention may be combined with each other, but it must be based on the realization of those skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination of technical solutions should not be considered to exist, and is not within the protection scope of the present invention.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (10)
1. A power generation efficiency control method of a forklift lifting device is characterized by comprising the following steps:
s1: collecting an oil pressure value and a generator current value and inputting the values into a power generation system efficiency model;
s2: performing iterative calculation of a particle swarm algorithm on the oil pressure value and the generator current value according to the power generation system efficiency model to obtain an optimal empirical current value;
s3: performing online optimization near the optimal empirical current value by a disturbance observation method to obtain an optimal current value;
s4: the optimal current value is input to the power module through the current controller, and the generator is controlled to achieve the maximum generating efficiency.
2. The method for controlling the power generation efficiency of a forklift lifting device according to claim 1, wherein step S2 specifically includes:
s21: constructing a particle swarm composed of an oil pressure value and a generator current value, and initializing the particle swarm;
s22: inputting a particle swarm into a power generation system efficiency model, and outputting a power generation efficiency value corresponding to each particle according to a preset function;
s23: judging whether the current value corresponding to the highest power generation efficiency value meets the convergence condition, if not, going to step S24, and if so, going to step S25;
s24: updating the current value and the current change rate of each particle in the particle swarm, and going to step S22;
s25: and outputting a current value corresponding to the highest power generation efficiency value, namely the optimal empirical current value.
3. The method for controlling the power generation efficiency of a forklift lifting device according to claim 2, wherein step S3 specifically includes:
s31: inputting a target current value to the power module through a current controller, wherein the initial state input is an optimal empirical current value;
s32: collecting oil pressure value, flow value, voltage value and real-time current value, and calculating to obtain a power generation efficiency value eta of two adjacent times according to a preset efficiency formulak-1And ηk；
S33: judging whether the power generation efficiency value eta meets the convergence condition, if not, going to step S34, and if so, going to step S35;
s34: judging whether the value of the generating efficiency meets etak＞ηk-1If not, the current is changed and the changing trend is opposite to the last time, and go to step S31; if yes, the current is changed and the change trend is the same as the previous direction, and the step S31 is executed;
s35: and outputting a current value when the power generation efficiency value meets the convergence condition, namely the optimal current value.
4. The method for controlling the power generation efficiency of a forklift lifting device according to claim 1, wherein the method for specifically establishing the power generation system efficiency model in the step S1 specifically includes the steps of:
a1: collecting actual data to obtain a data set about the oil pressure value, the generator current value and the power generation efficiency value;
a2: and establishing an efficiency model of the power generation system according to the data set, and obtaining a fitness function.
5. The method for controlling the power generation efficiency of a forklift lifting device according to claim 4, wherein the step A1 specifically includes:
a11: collecting actual data to obtain an oil pressure value, a generator current value and a power generation efficiency value;
a12: and controlling the single variable oil pressure value and the generator current value one by one, and obtaining a data set through a preset algorithm.
6. The method for controlling the power generation efficiency of a forklift lifting device according to claim 5, wherein the step A2 specifically includes:
a21: performing radial basis function neural network training by using the data set;
a22: and outputting a fitness function of the oil pressure value, the generator current value and the power generation efficiency value.
7. The method for controlling the power generation efficiency of the forklift lifting device according to claim 2, wherein the preset function in the step S22 is a fitness function, and specifically comprises:
wherein 1 is<i<N, eta is power generation efficiency, P is oil pressure, I is generator current, Ci,1And Ci,2Are respectively the constituent elements of the central point vector of the radial basis function after training, sigmaiFor radial basis function widthDegree, wiIs a weight vector.
8. A forklift lifting device, comprising:
the power source is used for providing power for the system and converting hydraulic energy and mechanical energy;
the control system is connected with the power source and is used for controlling the pressure, flow and direction of the hydraulic oil;
the execution system is connected with the control system and used for executing lifting action according to hydraulic energy;
an energy storage system connected with the power source for converting mechanical energy and electric energy and storing and releasing the electric energy, wherein the energy storage system comprises a power module, and a generator and a current control module respectively connected with the power module, the current control module inputs the optimal current value in the method for controlling the power generation efficiency of the forklift lifting device according to any one of claims 1-7 into the power module, and the power module controls the rotation speed of the generator to achieve the maximum power generation efficiency.
9. A forklift lifting device according to claim 8,
the energy storage system includes:
and the storage battery is connected with the power supply module and is used for storing and releasing electric energy.
10. A forklift lifting device according to claim 8,
the power source includes a fuel tank and a motor, wherein the motor is a pump-motor multiplex.
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