EP3093397B1 - Verfahren und vorrichtung zur steuerung eines radladers - Google Patents

Verfahren und vorrichtung zur steuerung eines radladers Download PDF

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Publication number
EP3093397B1
EP3093397B1 EP16169390.8A EP16169390A EP3093397B1 EP 3093397 B1 EP3093397 B1 EP 3093397B1 EP 16169390 A EP16169390 A EP 16169390A EP 3093397 B1 EP3093397 B1 EP 3093397B1
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EP
European Patent Office
Prior art keywords
load
state
wheel loader
signal
work
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
EP16169390.8A
Other languages
English (en)
French (fr)
Other versions
EP3093397A1 (de
Inventor
Woo-Seok Oh
Soo-Kyung HEO
Kwang-Seok Park
Yeon-Haeng HEO
Woo-Yong Jung
Choon Lee
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
HD Hyundai Infracore Co Ltd
Original Assignee
Doosan Infracore Co Ltd
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Application filed by Doosan Infracore Co Ltd filed Critical Doosan Infracore Co Ltd
Publication of EP3093397A1 publication Critical patent/EP3093397A1/de
Application granted granted Critical
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Classifications

    • E—FIXED CONSTRUCTIONS
    • E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
    • E02F—DREDGING; SOIL-SHIFTING
    • E02F3/00—Dredgers; Soil-shifting machines
    • E02F3/04—Dredgers; Soil-shifting machines mechanically-driven
    • E02F3/28—Dredgers; Soil-shifting machines mechanically-driven with digging tools mounted on a dipper- or bucket-arm, i.e. there is either one arm or a pair of arms, e.g. dippers, buckets
    • E02F3/36—Component parts
    • E02F3/42—Drives for dippers, buckets, dipper-arms or bucket-arms
    • 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
    • E02F9/2246—Control of prime movers, e.g. depending on the hydraulic load of work tools
    • E—FIXED CONSTRUCTIONS
    • E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
    • E02F—DREDGING; SOIL-SHIFTING
    • E02F3/00—Dredgers; Soil-shifting machines
    • E02F3/04—Dredgers; Soil-shifting machines mechanically-driven
    • E02F3/28—Dredgers; Soil-shifting machines mechanically-driven with digging tools mounted on a dipper- or bucket-arm, i.e. there is either one arm or a pair of arms, e.g. dippers, buckets
    • E02F3/36—Component parts
    • E02F3/42—Drives for dippers, buckets, dipper-arms or bucket-arms
    • E02F3/422—Drive systems for bucket-arms, front-end loaders, dumpers or the like
    • 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
    • E02F9/08—Superstructures; Supports for superstructures
    • E02F9/0841—Articulated frame, i.e. having at least one pivot point between two travelling gear units
    • 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
    • E02F9/2058—Electric or electro-mechanical or mechanical control devices of vehicle sub-units
    • E02F9/2062—Control of propulsion units
    • E02F9/2066—Control of propulsion units of the type combustion engines

Definitions

  • the invention relates to a method of controlling a wheel loader, an apparatus for controlling the wheel loader, and a system for controlling the wheel loader. More particularly, the invention relates to a method of determining a work state of a wheel loader to automatically control the wheel loader, and a control apparatus and a control system for performing the same.
  • an industrial vehicle such as a wheel loader is widely used to excavate sand, gravel, and the like and load it into a dump truck.
  • wheel loaders are for example known from WO2014/098391A1 , US 6 618 658 B1 or DE 10 2013 208320 .
  • work load which consumes a power output of an engine of the wheel loader
  • work load may changes according to the work states.
  • These work states may be detected and then the engine or a transmission of the wheel loader may be controlled automatically based on the detected results, thereby improving fuel efficiency and preventing deterioration of operating performance. Accordingly, a new technique capable of precisely detecting a current work state and a current work load state in real time and automatically control the wheel loader may be required.
  • the invention sets-out to solve the above-mentioned problems of the art and provides a method of controlling a wheel loader, which reduces fuel consumption and improves operating performance.
  • the invention also seeks to provide a control apparatus for performing the above method.
  • the invention still also seeks to provide a control system for performing the above method.
  • signals representing a state of work currently performed by the wheel loader are received from sensors installed in the wheel loader.
  • One or more signals are selected of the received signals, the one or more signals able to be used to determine whether or not to be within a respective one of a plurality of individual load areas, wherein the individual load areas are divided according to work load which consumes a power output of an engine during a series of work states performed by the wheel loader.
  • Output values representing as to whether or not to be within the respective one of the plurality of individual load areas are calculated by using the selected signal. The output values are analyzed to determine a current load state of the work currently performed by the wheel loader.
  • calculating the output values may include using machine learning to calculate the output values.
  • Machine leaning may include neural networks approach, statistical approach, structural approach, fuzzy logic approach, decision tree approach, template matching approach, etc.
  • calculating the output values may include using a table stored in a memory by a wheel loader manufacturer to calculate the output values.
  • calculating the output values may include performing prediction algorithms obtained through training on the selected signal to calculate the output values.
  • the prediction algorithm may include connection weights between an input layer, a hidden layer and an output layer.
  • calculating the output values may include calculating an output value representing as to whether or not to be within a light load area, calculating an output value representing as to whether or not to be within a medium load area, calculating an output value representing as to whether or not to be within a heavy load area and calculating an output value representing as to whether or not to be within an acceleration/inclined-ground load area.
  • At least one of a boom cylinder pressure signal, an FNR signal, a main pressure signal of a hydraulic pump, a vehicle speed signal, a boom position signal and a torque converter speed ratio signal may be used to determine whether or not to be within the light load area and the heavy load area of the wheel loader, at least one of the main pressure signal of the hydraulic pump, the vehicle speed signal, a boom position signal and the torque converter speed ratio signal may be used to determine whether or not to be within the medium load area of the wheel loader.
  • At least one of a torque converter speed ratio signal and an accelerator pedal position signal may be used to determine whether or not to be within the acceleration/inclined-ground load area of the wheel loader.
  • a forward travelling work state, a reverse travelling work state and a reverse travelling and boom down work state in a V-shape driving of the wheel loader may be determined as a light load state
  • an excavation work state may be determined as a medium load state
  • a forward travelling and boom raising work state may be determined as a heavy load state.
  • the method may further include outputting a control signal for controlling an engine or a transmission of the wheel loader according to the current load state of the wheel loader.
  • an apparatus for controlling a wheel loader includes a signal receiver configured to receive signals representing a state of work currently performed by the wheel loader, from sensors installed in the wheel loader, a signal selector configured to provide a plurality of individual load areas according to work load which consumes a power output of an engine during a series of work states performed by the wheel loader and configured to select one or more signals of the received signals, the one or more signals able to be used to determine whether or not to be within a respective one of the plurality of individual load areas, an individual load area determiner configured to calculate output values representing as to whether or not to be within the respective one of the plurality of individual load areas, by using the selected signal, and a load state determiner configured to analyze the output values to determine a current load state of the work currently performed by the wheel loader.
  • the individual load area determiner may include individual calculating circuits which calculate the output values using machine learning.
  • machine leaning may include neural networks approach, statistical approach, structural approach, fuzzy logic approach, decision tree approach, template matching approach, etc.
  • the individual load area determiner may calculate the output values using a table stored in a memory.
  • the individual load area determiner may perform prediction algorithms obtained through training on the selected signal to calculate the output values.
  • the prediction algorithm may include connection weights between an input layer, a hidden layer and an output layer.
  • a state of the work currently performed by the wheel loader may be determined to automatically control the wheel loader.
  • the individual load area determiner may include a light load determining circuit which calculates an output value representing as to whether or not to be within a light load area, a medium load determining circuit which calculates an output value representing as to whether or not to be within a medium load area, a heavy load determining circuit which calculates an output value representing as to whether or not to be within a heavy load area, and an acceleration/inclined-ground determining circuit which calculates an output value representing as to whether or not to be within an acceleration/inclined-ground load area.
  • the light load determining circuit and the heavy load determining circuit may use at least one of a boom cylinder pressure signal, an FNR signal, a main pressure signal of a hydraulic pump, a vehicle speed signal, a boom position signal and a torque converter speed ratio signal to determine whether or not to be within the light load area and the heavy load area of the wheel loader
  • the medium load determining circuit may use at least one of the main pressure signal of the hydraulic pump, the vehicle speed signal, a boom position signal and the torque converter speed ratio signal to determine whether or not to be within the medium load area of the wheel loader.
  • the acceleration/inclined-ground load determining circuit may use at least one of a torque converter speed ratio signal and an accelerator pedal position signal to determine whether or not to be within the acceleration/inclined-ground load area of the wheel loader.
  • the load state determiner may determine a forward travelling work state, a reverse travelling work state and a reverse travelling and boom down work state in a V-shape driving of the wheel loader as a light load state, may determine an excavation work state as a medium load state, and may determine a forward travelling and boom raising work state determined as a heavy load state.
  • the apparatus may further include a control signal generator configured to output a control signal for controlling an engine or a transmission of the wheel loader according to the current load state of the wheel loader.
  • control signal generator may output a signal for controlling torque of an engine according to a predetermined auto engine torque map.
  • the auto engine torque map may be set different from a manual engine torque map, the engine being controlled according to the manual engine torque map when an operator select an manual mode.
  • a system for controlling a wheel loader includes an engine, a work apparatus and a travel apparatus driven by the engine, sensors installed in the engine, the work apparatus and the travel apparatus to detect signals representing a state of work currently performed by the wheel loader, and a control apparatus configured to one or more signals of the received signals, the one or more signals able to be used to determine whether or not to be within a respective one of a plurality of individual load areas which are divided according to work load which consumes a power output of an engine during a series of work states performed by the wheel loader and configured to perform prediction algorithms obtained through training to determine whether or not to be within the respective one of the plurality of individual load areas and responsively determine a current load state of the work currently performed by the wheel loader.
  • control apparatus may select the one or more signals of the received signals, may calculate output values representing as to whether or not to be within the respective one of the plurality of individual load areas, by using the selected signal, and may analyze the output values to determine the current load state.
  • control apparatus may perform neural network algorithms on the selected signals to calculate the output values.
  • control apparatus may calculate an output value representing as to whether or not to be within a light load area, may calculate an output value representing as to whether or not to be within a medium load area, may calculate an output value representing as to whether or not to be within a heavy load area, and may calculate an output value representing as to whether or not to be within an acceleration/inclined-ground load area.
  • control apparatus may determine a forward travelling work state, a reverse travelling work state and a reverse travelling and boom down work state in a V-shape driving of the wheel loader as a light load state, may determine an excavation work state as a medium load state, and may determine a forward travelling and boom raising work state determined as a heavy load state.
  • control apparatus may output a control signal for controlling the engine or a transmission of the wheel loader according to the current load state of the wheel loader.
  • a control apparatus for a wheel loader may select signals capable of effectively representing an individual load state (light load area, medium load area, heavy load area, acceleration/inclined-ground load area) of signals received from sensors and determine a load state of a current work or a current work state by using prediction algorithms obtained through training such as neural network algorithms.
  • the time and burden spent on calculations in order to determine a load state of work currently performed by the wheel loader may be reduced and the accuracy of the determinations may be improved.
  • an engine and a transmission may be controlled based on the determined work load state to thereby improve operating performance and fuel efficiency.
  • FIGS. 1 to 11C represent non-limiting, example embodiments as described herein.
  • first, second, third, fourth etc. may be used herein to describe various elements, components, regions, layers and/or sections, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms are only used to distinguish one element, component, region, layer or section from another region, layer or section. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of the present inventive concept.
  • FIG. 1 is a side view illustrating a wheel loader in accordance with example embodiments.
  • FIG. 2 is a block diagram illustrating a system for controlling the wheel loader in FIG. 1 .
  • a wheel loader 10 may include a front body 12 and a rear body 14 connected to each other.
  • the front body 12 may include a work apparatus and a front wheel 160.
  • the rear body 14 may include a driver cabin 40, an engine bay 50 and a rear wheel 162.
  • the work apparatus may include a boom 20 and a bucket 30.
  • the boom 20 may be freely pivotally attached to the front body 12, and the bucket 30 may be freely pivotally attached to an end portion of the boom 20.
  • the boom 20 may be coupled to the front body 12 by a pair of boom cylinders 22, and the boom 20 may be pivoted upwardly and downwardly by expansion and contraction of the boom cylinders 22.
  • a tilt arm 34 may be freely rotatably supported on the boom 20, almost at its central portion.
  • One end portion of the tilt arm 34 may be coupled to the front body 12 by a pair of bucket cylinders 32 and another end portion of the tilt arm 34 may be coupled to the bucket 30 by a tilt rod, so that the bucket 30 may pivot (crowd and dump) as the bucket cylinder 32 expands and contracts.
  • the front body 12 and the rear body 14 may be rotatably connected to each other through a center pin 16 so that the front body 12 may swing side to side with respect to the rear body 14 by expansion and contraction of a steering cylinder (not illustrated).
  • a travel apparatus for propelling the wheel loader 10 may be mounted at the rear body 14.
  • An engine 100 may be provided in the engine bay 50 to supply an output power to the travel apparatus.
  • the travel apparatus may include a torque converter 120, a transmission 130, a propeller shaft 150, axles 152, 154, etc.
  • the output power of the engine 100 may be transmitted to the front wheel 160 and the rear wheel 162 through the torque converter 120, the transmission 130, the propeller shaft 150 and the axles 152 and 154, and thus the wheel loader 10 may travels.
  • the output power of the engine 100 may be transmitted to the transmission 130 through the torque converter 120.
  • An input shaft of the torque converter 120 may be connected to an output shaft of the engine 100, and an output shaft of the torque converter 120 may be connected to the transmission 130.
  • the torque converter 120 may be a fluid clutch device including an impeller, a turbine and a stator.
  • the transmission 130 may include hydraulic clutches that shift speed steps between first to fourth speeds, and rotation of the output shaft of the torque converter 120 may be shifted by the transmission 130. The shifted rotation may be transmitted to the front wheel 160 and the rear wheel 162 through the propeller shaft 150 and the axles 152 and 154 and thus the wheel loader may travel.
  • the torque converter 120 may have a function to increase an output torque with respect to an input torque, i.e., a function to make the torque ratio 1 or greater.
  • Nt/Ni the torque converter speed ratio
  • the torque ratio may be increased and thus the vehicle may be allowed to travel with a greater travel driving force (traction force).
  • the transmission 130 may include a forward hydraulic clutch for forward movement, a reverse hydraulic clutch for reverse movement, and first to fourth hydraulic clutches for the first to the fourth speeds.
  • the hydraulic clutches may be each engaged or released by pressure oil (clutch pressure) supplied via a transmission control unit (TCU) 140.
  • TCU transmission control unit
  • the hydraulic clutches may be engaged when the clutch pressure supplied to the hydraulic clutches is increased, while the hydraulic clutches may be released when the clutch pressure is decreased.
  • a speed step may be shifted by one step.
  • the speed step may be shifted by one step.
  • the transmission 130 may be operable in a manual transmission mode or in a plurality of auto transmission modes.
  • the transmission mode may be determined by a changed by manipulation of a mode shift lever (not illustrated).
  • the transmission 130 may include manual transmission mode, 1-4 auto transmission mode and 1-3 auto transmission mode.
  • a speed step may be selected by a transmission shift lever.
  • a speed step may be automatically changed between speed steps equal to or less than a speed step selected by the transmission shift lever.
  • a variable capacity hydraulic pump 200 for supplying a pressurized hydraulic fluid to the boom cylinder 22 and the bucket cylinder 32 may be mounted at the rear body 14.
  • the variable capacity hydraulic pump 200 may be driven using a portion of the power outputted from the engine 100.
  • the output power of the engine 100 may drive the hydraulic pump 200 for the work apparatus and a hydraulic pump (not illustrated) for the steering cylinder via a power take-off (PTO) such as a gear train 110.
  • PTO power take-off
  • a pump control device may be connected to the variable capacity hydraulic pump 200, and a discharge fluid from the variable capacity hydraulic pump 200 may be controlled by the pump control device.
  • a main control valve (MCV) including a boom control valve 210 and a bucket control valve 212 may be installed on a hydraulic circuit of the hydraulic pump 200.
  • the discharge fluid from the hydraulic pump 200 may be supplied to the boom cylinder 22 and the bucket cylinder 32 through the boom control valve 210 and the bucket control valve installed in a hydraulic line 202 respectively.
  • the main control valve (MCV) may supply the discharge fluid from the hydraulic pump 200 to the boom cylinder 22 and the bucket cylinder 32 according to a pilot pressure in proportion to an operation rate of an operating lever.
  • a maneuvering device may be provided within the driver cabin 40.
  • the maneuvering device may include an accelerator pedal 142, a brake pedal 144, an FNR travel lever, the operating levers for operating the cylinders such as the boom cylinder 22 and the bucket cylinder 32, etc.
  • the wheel loader 10 may include a traveling operating system for driving the travel apparatus via the PTO and a hydraulic operating system for driving the work apparatus such as the boom 20 and the bucket 30 using the output power of the engine 100.
  • a control apparatus 300 for the wheel loader 10 such as a portion of a vehicle control unit (VCU) or a separate control unit may be mounted in the rear body 14.
  • the control apparatus 300 may include an arithmetic processing unit having a CPU which executes a program, a storage device such as a memory, other peripheral circuit, and the like.
  • the control apparatus 300 may receive signals from various sensors (detectors) which are installed in the wheel loader 10.
  • the control apparatus 300 may be connected to an engine speed sensor 102 for detecting a rotational speed of the engine, an accelerator pedal detection sensor 143 for detecting an operation amount of the accelerator pedal 142, a brake pedal detection sensor 145 for detecting an operation amount of the brake pedal 144, and an FNR travel lever position sensor 146 for detecting a manipulation position of the FNR travel lever, for example, the speed steps, forward (F), neutral (N) and reverse (R).
  • control apparatus 300 may connected to a rotational speed sensor 122a for detecting the number of rotations Ni of the input shaft of the torque converter 120, a rotational speed sensor 122b for detecting the number of rotations Nt of the output shaft of the torque converter 120, and a vehicle speed sensor 132 for detecting a rotational speed of an output shaft of the transmission 130, i.e., a vehicle speed v.
  • control apparatus 300 may be connected to a pressure sensor 204 installed in the hydraulic line in front end of the main control valve (MCV) to detect a pressure of the discharge fluid from the hydraulic pump 200, and a boom cylinder pressure sensor 222 for detecting a cylinder head pressure at a head of the boom cylinder 22. Furthermore, the control apparatus 300 may be connected to a boom angle sensor 224 for detecting a rotational angle of the boom 20 and a bucket angle sensor 234 for detecting a rotational angle of the bucket 30.
  • MCV main control valve
  • the signals detected by the sensors may be inputted into the control apparatus 100, as indicated by arrows in FIG. 2 .
  • the control apparatus 300 may select one or more signals of the signals received from the sensors installed in the wheel loader 10, perform prediction algorithms obtained through training such as neural network algorithms to calculate output values representing whether or not to be within individual work load areas and analyze the output values to determine a load state of a current work or a current work state of the wheel loader 10. Further, the control apparatus 300 may output a control signal to an engine control unit (ECU), the transmission control unit (TCU) 140, and the pump control device (EPOS), etc, to selectively control the engine 100, the transmission 130, the hydraulic pump 200, etc., based on the determined work load state or work state.
  • ECU engine control unit
  • TCU transmission control unit
  • EPOS pump control device
  • FIG. 3 is a block diagram illustrating a control apparatus for a wheel loader in accordance with example embodiments.
  • FIG. 4 is a block diagram illustrating a signal selector, an individual load area determiner and a load state determiner of the control apparatus in FIG. 3 .
  • FIG. 5 is a view illustrating a neural network circuit in the individual load area determiner in FIG. 4 .
  • FIG. 6 is a view illustrating a signal transfer in each layer of the neural network in FIG. 5 .
  • a control apparatus for a wheel loader 300 may include a work load determiner 310, a control signal generator 320 and a storage portion 330.
  • the work load determiner 310 may determine a load state of work currently performed by the wheel loader 10 or a state of work currently performed by the wheel loader 10.
  • the control signal generator 320 may determine a control type, for example, an output torque control of an engine, an rpm control of an engine, a transmission control of a transmission, etc., based on the determined load state of the current work or the state of the current work.
  • the storage portion 330 may store data required for learning in a predictive model and calculation in a neural network algorithm which are performed in the work load determiner 310, a control map required for determination of a control signal which is performed in the control signal generator 320, etc.
  • the work load determiner 310 may include a signal receiver 312, a signal selector 314, an individual load area determiner 316 and a load state determiner 318.
  • the signal receiver 312 may receive the signals capable of representing a state of work from the sensors installed in the wheel loader 10.
  • the signal receiver 312 may receive a boom cylinder pressure signal from the boom cylinder pressure sensor 222, an FNR signal from the FNR travel lever position sensor 146, a main pressure signal from the pressure sensor 204 of the hydraulic pump 200, a vehicle speed signal from the vehicle speed sensor 132, a boom position signal from the boom angle sensor 224, a torque converter speed ratio (ratio of the number of rotations Ni of the input shaft and the number of rotations Nt of the output shaft) signal from the rotational speed sensors 122a and 122b, an accelerator pedal position signal from the accelerator pedal detection sensor 143, etc.
  • the signals received in the signal receiver 312 may not be limited thereto, and various signals able to be used in determining a load state of work of the wheel loader or a work state of the wheel loader may be received in the signal receiver.
  • the signal receiver 312 may receive a selection signal of an operator.
  • the operator may operate an operation lever or a button to select a manual mode or an auto mode.
  • the control apparatus for the wheel loader may operate to determine the state of a current work of the wheel loader and automatically control the wheel loader.
  • the signal receiver 312 may include a data post processing portion.
  • the data post processing portion may filter the inputted sensor signals to remove noise and normalize the signals.
  • the signal selector 314 may select one or more signals able to be used to determine whether or not a load state of work which is currently being performed by the wheel loader is within a respective one of a plurality of individual load areas, for example, a respective one of at least four individual load areas, and may output the selected signal(s) to corresponding individual determining circuits (NN_1, NN_2, NN_3, NN_4) of the individual load area determiner 316.
  • the signal selector 314 may select one or more signals able to be used to determine whether or not the load state of work currently performed by the wheel loader is within a respective one of at least first to fourth individual load areas which are divided according to work load which consumes the power output of the engine during a series of work states.
  • the individual load areas may include a light load area, a medium load area, a heavy load area and an acceleration/inclined-ground load area according to the work load which consumes the power output during a series of work states performed by the wheel loader.
  • At least one signal selected from the group consisting of the received signals may be an indicator effectively representing a specific load state, i.e., at least one of the light load area, the medium load area, the heavy load area and the acceleration/inclined-ground load area.
  • the boom cylinder pressure signal may be an indicator directly representing a load state of work which is currently performed by the wheel loader, because the boom cylinder pressure signal is determined depending on a weight of sand, gravel and the like loaded in the bucket 30, a height of the boom 20, etc.
  • the boom cylinder pressure signal may be used to determine a traveling work state and a multiple work state (traveling and boom raising work state) of a current work of the wheel loader.
  • the FNR signal may be an indicator distinguishing a shift between work states such as an initiation of a reverse traveling work state after an excavation work state or a swift between forward and reverse traveling work states during a traveling work state.
  • the FNR signal may be used to determine a traveling work state and a multiple work state (traveling and boom raising work state) of a current work of the wheel loader.
  • the main pressure signal of the hydraulic pump may be an indicator representing an excavation work state or an operation of the boom 20 and the bucket 30, because the main pressure is maintained at a constant initial pressure when the operator does not operate the boom/bucket operation levers.
  • the main pressure signal of the hydraulic pump may be used to determine a traveling work state, a multiple work state (traveling and boom raising work state) and an excavation work state of a current work of the wheel loader.
  • the vehicle speed signal may be an indicator representing a travel speed of the wheel loader.
  • the vehicle speed signal may be used to determine a traveling work state, a multiple work state (traveling and boom raising work state) and an excavation work state of a current work of the wheel loader.
  • the boom position signal may be an indicator distinguishing a work state between a traveling work state, an excavation work state and a dumping work state depending on the boom position difference threrebetween.
  • the boom position signal may be used to determine a traveling work state, a multiple work state (traveling and boom raising work state) and an excavation work state of a current work of the wheel loader.
  • the torque converter speed ratio may be an indicator representing the excavation work state and an inclined-ground travelling work state depending on a travel load of the wheel loader.
  • the torque converter speed ratio may be used to determine a traveling work state, a multiple work state (travelling and boom raising work state), an excavation work state and an acceleration (inclined-ground) travelling work state.
  • the accelerator pedal position signal may be an indicator representing an acceleration intention of the operator.
  • the accelerator pedal position signal may be used to determine an acceleration travelling work state.
  • the individual load area determiner 316 may include a plurality of the individual determining circuits.
  • the individual load area determiner 316 may include first to fourth individual determining circuits.
  • the first to fourth individual determining circuits may calculate output values which represent whether or not to be within the first to fourth individual load areas respectively, using the selected signals.
  • the first to fourth individual determining circuits may calculate the output signals respectively using machine learning.
  • Machine learning may be related to the ability to make data-driven predictions or decisions after training.
  • machine leaning may include neural networks approach, statistical approach, structural approach, fuzzy logic approach, decision tree approach, template matching approach, etc.
  • the neural networks approach may be a method that learns mapping between inputs and outputs and processes data based on connection weights between inputs and outputs.
  • the decision tree approach may be a method that generates a decision tree through learning and processes data based on the decision tree.
  • Support vector machine may be used in supervised learning methods, and may be a method that, in many hyperplanes that might classify some given data, chooses the hyperplane that has the largest distance to the nearest training-data point of any class and processes data.
  • the statistical approach may be classified into Supervised learning and Unsupervised learning.
  • the neural networks approach may be classified into supervised learning, unsupervised learning, and reinforcement learning.
  • the first to fourth individual determining circuits may perform prediction algorithms obtained through training to output scale values which represent the first to fourth individual load areas respectively.
  • the first individual determining circuit may include a light load neural network determiner NN_1 which performs neural network algorithms to calculate an output value representing as to whether or not the current work load belongs within a light load area.
  • the light load neural network determiner NN_1 may receive the boom cylinder pressure signal, the FNR signal, the main pressure signal of the hydraulic pump, the vehicle speed signal, the boom position signal and the torque converter speed ratio signal from the signal selector 314.
  • the light load neural network determiner NN_1 may perform neural network algorithms to calculate a first output value representing whether or not a load area of work currently performed by the wheel loader is within the light load area.
  • the first output value may be a probability value representing whether or not the current work load corresponds to the light load state.
  • the first output value may be quantified as a number between 0 and 1.
  • the second individual determining circuit may include a medium load neural network determiner NN_2 which performs neural network algorithms to calculate an output value representing as to whether or not the current work load belongs within a medium load area.
  • the medium load neural network determiner NN_2 may receive the main pressure signal of the hydraulic pump, the vehicle speed signal, the boom position signal and the torque converter speed ratio signal from the signal selector 314.
  • the medium load neural network determiner NN_2 may perform neural network algorithms to calculate a second output value representing whether or not a load area of work currently performed by the wheel loader is within the medium load area.
  • the second output value may be a probability value representing whether or not the current work load corresponds to the medium load state.
  • the third individual determining circuit may include a heavy load neural network determiner NN_3 which performs neural network algorithms to calculate an output value representing as to whether or not the current work load belongs within a heavy load area.
  • the heavy load neural network determiner NN_3 may receive the boom cylinder pressure signal, the FNR signal, the main pressure signal of the hydraulic pump, the vehicle speed signal, the boom position signal and the torque converter speed ratio signal from the signal selector 314.
  • the heavy load neural network determiner NN_3 may perform neural network algorithms to calculate a third output value representing whether or not a load area of work currently performed by the wheel loader is within the heavy load area.
  • the third output value may be a probability value representing whether or not the current work load corresponds to the heavy load state.
  • the fourth individual determining circuit may include an acceleration/inclined-ground load neural network determiner NN_4 which performs neural network algorithms to calculate an output value representing as to whether or not the current work load belongs within an acceleration/inclined-ground load area.
  • the acceleration/inclined-ground load neural network determiner NN_4 may receive the torque converter speed ratio and the accelerator pedal position signal from the signal selector 314.
  • the acceleration/inclined-ground load neural network determiner NN_4 may perform neural network algorithms to calculate a fourth output value representing whether or not a load area of work currently performed by the wheel loader is within the acceleration/inclined-ground load area.
  • the fourth output value may be a probability value representing whether or not the current work load corresponds to the acceleration/inclined-ground load state.
  • the light load neural network determiner NN_1, the medium load neural network determiner NN_2, the heavy load neural network determiner NN_3 and the acceleration/inclined-ground neural network determiner NN_4 may include neural network circuits that performs neural network algorithms and calculates an output value representing an individual load state, respectively.
  • the neural network circuit may include multilayer perceptrons having a multi-input layer, a hidden layer and an output layer. Neurons may be arranged in each layer, and the neurons in each layer may be connected by connection weights. Input data may be inputted to the neurons in the input layer and transferred to the output layer though the hidden layer.
  • Training the neural network algorithm may be a process of tuning the interconnection weights between each nodes in order to minimize an error between an expectation value and an output value of the neural network algorithms for a specific input (actual detected data).
  • backpropagation algorithm may be used for training the neural networks.
  • the neural network circuits of the individual neural network determiners (NN_1, NN_2, NN_3, NN_4) may vary the connection weights between the input layer, the hidden layer and the output layer using the collected data to provide neural network algorithms as prediction models.
  • the neural network circuit may perform the neural network algorithms obtained through training and calculate an output value which represents the individual load state.
  • the load state determiner 318 may analyze the output values from the first to fourth individual determining circuits to determine a load state of work currently performed by the wheel loader 10 or a state of work currently performed by the wheel loader 10.
  • the load state determiner 318 may perform post-processing such as weighted applications on the output values from the individual neural network determiners (NN_1, NN_2, NN_3, NN_4) and output a final result value.
  • the load state determiner 318 may analyze the output values to determine a current load state of work currently performed by the wheel loader 10. Accordingly, the load state determiner 318 may determine which one of the light load state, the medium load state, the heavy load state and the acceleration/inclined-ground load state is the load state of work currently performed by the wheel loader 10.
  • the load state determiner 318 may consider additional signals received from other sensors to determine a current state of work currently performed by the wheel loader 10. Accordingly, the load state determiner 318 may determine a current load state or a current work state of the wheel loader 10.
  • the control signal generator 320 may output a control signal based on the determined current load state or the determined current work state of the wheel loader 10.
  • the control signal may be used to selectively control the engine 100, the transmission 130, the hydraulic pump 200, etc.
  • the control signal generator 320 may output a control signal for controlling engine output torque, engine rpm, transmission speed step, transmission timing, etc.
  • control signal generator 320 may control the engine 100 and the transmission 130 based on the finally determined work load state or work state to thereby improve operating performance and fuel efficiency.
  • the storage portion 330 may include a first storage portion 332 connected to the work load determiner 310 and storing data required to determine a work load state, and a second storage portion 334 connected to the control signal generator 320 and storing data required to generate the control signal.
  • the first storage portion 332 may store data required for training and performing the neural network algorithms.
  • the second storage portion 334 may store engine torque map, engine rpm map, transmission swift control map, etc., required for determining the control signal.
  • control apparatus for a wheel loader may select signals capable of effectively representing the individual load state (light load area, medium load area, heavy load area, acceleration/inclined-ground load area) of signals received from sensors installed in the wheel loader 10 and determine a load state of a current work or a current work state by using prediction algorithms obtained through training such as neural network algorithms.
  • the time and burden spent on calculations in order to determine a load state of work currently performed by the wheel loader may be reduced and the accuracy of the determinations may be improved.
  • the engine and the transmission may be controlled based on the finally determined work load state to thereby improve operating performance and fuel efficiency.
  • FIG. 7 is a flow chart illustrating a method of controlling a wheel loader in accordance with example embodiments.
  • the control apparatus for a wheel loader 300 may receive signals capable of representing a work state from sensors installed in the wheel loader.
  • the signal receiver 312 of the work load determiner 310 may receive a boom cylinder pressure signal, an FNR signal, a main pressure signal of a hydraulic pump, a vehicle speed signal, a boom position signal, a torque converter speed ratio signal, an accelerator pedal position signal, etc.
  • one or more signals able to be used to determine whether or not to be within a respective one of a plurality of individual load areas, of the received signals may be selected (S110).
  • the signal selector 314 may select one or more signals able to be used to determine whether or not the current work state is within a respective one of at least first to fourth individual load areas and output the selected signal to corresponding individual determining circuits (NN_1, NN_2, NN_3, NN_4) of the individual load area determiner 316.
  • the first to fourth individual load areas may correspond to a light load area, a medium load area, a heavy load area and an acceleration/inclined-ground load area according to work load which consumes the power output during a series of work states performed by the wheel loader.
  • the received signals may be classified according to whether the signal effectively represents a specific load state, i.e., at least one of the light load area, the medium load area, the heavy load area and the acceleration/inclined-ground load area.
  • the boom cylinder pressure signal, the FNR signal, the main pressure signal of the hydraulic pump, the vehicle speed signal, the boom position signal and the torque converter speed ratio signal of the received signals may be used to determine whether or not to be within the light load area and the heavy load area of the wheel loader, and thus may be inputted into the light load neural network NN_1 and the heavy load neural network NN_3 of the individual load area determiner 316.
  • the main pressure signal of the hydraulic pump, the vehicle speed signal, the boom position signal and the torque converter speed ratio signal may be used to determine whether or not to be within the medium load area, and thus may be inputted into the medium load neural network determiner NN_2 of the individual load area determiner 316.
  • the torque converter speed ratio signal and the accelerator pedal position signal may be used to determine whether or not to be within the acceleration/inclined-ground load area, and thus may be inputted into the acceleration/inclined-ground neural network determiner NN_4 of the individual load area determiner 316.
  • neural network algorithms obtained through training may be performed on the selected signals to determine whether or not to be within the respective one of the plurality of the individual load areas (S120).
  • the light load neural network determiner NN_1, the medium load neural network determines NN_2, the heavy load neural network determiner NN_3 and the acceleration/inclined-ground neural network determiner NN_4 of the individual load area determiner 316 may perform neural network algorithms on the selective signals to calculate output values representing as to whether or not the current work load belongs within the light load area, the medium load area, the heavy load area and the acceleration/inclined-ground load area respectively.
  • the output values may be analyzed to determine a load state of work currently performed by the wheel loader (S130).
  • the load state determiner 318 may analyze the output values to determine which one of the light load state, the medium load state, the heavy load state and the acceleration/inclined-ground load state is the load state of work currently performed by the wheel loader 10.
  • the load state determiner 318 may consider additional signals received from other sensors to determine a current state of work currently performed by the wheel loader 10.
  • an engine, a transmission, a hydraulic pump, etc. of the wheel loader may be selectively controlled in consideration of the current load sate or the current work state of the wheel loader.
  • FIG. 8 is a view illustrating V-shape driving of a wheel loader in accordance with example embodiments.
  • FIG. 9 is graphs illustrating output values representing whether or not to be within a respective one of individual load areas in each work state in the V-shape driving of FIG. 8 .
  • FIG. 10 is a graph illustrating a final load state obtained from the output values of FIG. 9 .
  • FIGS. 9 and 10 include a graph of a boom cylinder pressure versus time in the V-shape driving.
  • a wheel loader 10 may perform V-shape driving which is one of driving methods to load a subject material such as sand (S) into a dump truck (T).
  • V-shape driving the wheel loader 10 may perform sequentially a series of work states, i.e., a forward travelling work state (a), an excavation work state (b), a reverse travelling work state (c), a forward travelling and boom raising work state (d), a dumping work state (e), and a reverse travelling and boom down work state (f).
  • a light load neural network determiner NN_1 may calculate an output value representing as to whether or not to be within a light load area with respect to a series of the work states (a ⁇ f).
  • a medium load neural network determiner NN_2 may calculate an output value representing as to whether or not to be within a medium load area with respect to a series of the work states (a ⁇ f).
  • a heavy load neural network determiner NN_3 may calculate an output value representing as to whether or not to be within a heavy load area with respect to a series of the work states (a ⁇ f).
  • An acceleration/inclined-ground load neural network determiner NN_4 may calculate an output value representing as to whether or not to be within a heavy load area with respect to a series of the work states (a ⁇ f).
  • the output values may be synthetically analyzed to determine a load state of work currently being performed by the wheel loader 10.
  • a load state determiner 318 may determine which one of the light load state, the medium load state, the heavy load state and the acceleration/inclined-ground load state is the load state of a series of the work states (a ⁇ f) currently performed by the wheel loader 10.
  • the forward travelling work state (a), the reverse travelling work state (c) and the reverse travelling and boom down work state (f) may be determined as the light load state
  • the excavation work state (b) may be determined as the medium load state
  • the forward travelling and boom raising work state (d) may be determined as the heavy load state.
  • an inclined-ground travelling work state and an acceleration travelling work state of the work states performed by the wheel loader may be determined as the acceleration/inclined-ground load state.
  • the wheel loader may operate in a selected mode of a plurality of power modes.
  • the power modes may include economy mode (E-mode), standard mode (S-mode) and power mode (P-mode).
  • E-mode economy mode
  • S-mode standard mode
  • P-mode power mode
  • Each power mode may have a predetermined engine torque map.
  • E-mode economy mode
  • S-mode standard mode
  • P-mode power mode
  • Each power mode may have a predetermined engine torque map.
  • E-mode economy mode
  • S-mode standard mode
  • P-mode power mode
  • Each power mode may have a predetermined engine torque map.
  • E-mode economy mode
  • S-mode standard mode
  • P-mode power mode
  • Each power mode may have a predetermined engine torque map.
  • E-mode economy mode
  • S-mode standard mode
  • P-mode power mode
  • Each power mode may have a predetermined engine torque map.
  • the control apparatus may output a control signal for controlling an engine or a transmission of the wheel loader
  • the manual engine torque maps for power modes in the manual mode may be different from the auto engine torque map for power modes in the auto mode.

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  • Engineering & Computer Science (AREA)
  • Mining & Mineral Resources (AREA)
  • Civil Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Structural Engineering (AREA)
  • Mechanical Engineering (AREA)
  • Operation Control Of Excavators (AREA)

Claims (15)

  1. Verfahren zum Steuern eines Radladers (10), umfassend:
    Empfangen von Signalen, die einen momentan durch den Radlader ausgeführten Arbeitszustand darstellen, von in dem Radlader installierten Sensoren;
    Auswählen eines oder mehrerer Signale der empfangenen Signale, wobei das eine oder die mehreren Signale verwendet werden können, um zu bestimmen, ob sich der Radlader innerhalb eines jeweiligen von mehreren individuellen Lastbereichen befindet oder nicht, wobei die individuellen Lastbereiche gemäß der Arbeitslast, die eine Leistungsabgabe eines Motors (100) während einer Reihe von durch den Radlader ausgeführten Arbeitszuständen verbraucht, in einen Leichtlastbereich, einen Mittellastbereich, einen Schwerlastbereich und einen Beschleunigungs-/Schräglage-Lastbereich unterteilt werden;
    Berechnen von Ausgabewerten, die angeben, ob sich der Radlager innerhalb des jeweiligen der mehreren individuellen Lastbereiche befindet oder nicht, unter Verwendung des ausgewählten Signals; und
    Analysieren der Ausgabewerte, um einen momentanen Lastzustand der momentan durch den Radlader ausgeführten Arbeit als einen von einem Leichtlastzustand, einem Mittellastzustand, einem Schwerlastzustand und einem Beschleunigungs-/Schräglagen-Lastzustand zu bestimmen,
    wobei das Steuern der Motorleistungsabgabe umfasst:
    Berechnen der Ausgabewerte, umfassend: Berechnen eines ersten Ausgabewertes, der angibt, ob sich der Radlader innerhalb des Leichtlastbereichs befindet oder nicht, Berechnen eines zweiten Ausgabewertes, der angibt, ob sich der Radlader innerhalb des Mittellastbereichs befindet oder nicht, Berechnen eines dritten Ausgabewertes, der angibt, ob sich der Radlader innerhalb des Schwerlastbereichs befindet oder nicht, und Berechnen eines vierten Ausgabewertes, der angibt, ob sich der Radlader innerhalb des Beschleunigungs-/Schräglagen-Lastbereichs befindet oder nicht.
  2. Verfahren nach Anspruch 1, wobei das Berechnen der Ausgabewerte das Verwenden von maschinellem Lernen zum Berechnen der Ausgabewerte umfasst.
  3. Verfahren nach Anspruch 2, wobei das maschinelle Lernen eines von Folgendem umfasst, das aus der Gruppe ausgewählt ist, bestehend aus einem Neuronennetzansatz, einem statistischen Ansatz, einem strukturellen Ansatz, einem Fuzzy-Logik-Ansatz, einem Entscheidungsbaum-Ansatz und einem Vorlagenabgleichsansatz.
  4. Verfahren nach Anspruch 1, wobei das Berechnen der Ausgabewerte das Verwenden einer Tabelle umfasst, die durch einen Radladerhersteller in einem Speicher zum Berechnen der Ausgabewerte gespeichert wurde.
  5. Verfahren nach Anspruch 1, wobei das Berechnen der Ausgabewerte das Ausführen von Vorhersagealgorithmen umfasst, die durch Training an dem ausgewählten Signal erhalten wurden, um die Ausgabewerte zu berechnen.
  6. Verfahren nach Anspruch 5, wobei der Vorhersagealgorithmus Verbindungsgewichte zwischen einer Eingabeschicht, einer verborgenen Schicht und einer Ausgabeschicht umfasst.
  7. Verfahren nach Anspruch 1, wobei mindestens eines von einem Auslegerzylinderdrucksignal, einem FNR-Signal, einem Hauptdrucksignal einer Hydraulikpumpe, einem Fahrzeuggeschwindigkeitssignal, einem Auslegerpositionssignal und einem Drehmomentwandler-Drehzahlverhältnis-Signal verwendet wird, um zu bestimmen, ob sich der Radlader innerhalb seines Leichtlastbereichs und seines Schwerlastbereichs befindet oder nicht, wobei mindestens eines von dem Hauptdrucksignal der Hydraulikpumpe, dem Fahrzeuggeschwindigkeitssignal, einem Auslegerpositionssignal und dem Drehmomentwandler-Drehzahlverhältnis-Signal verwendet wird, um zu bestimmen, ob sich der Radlader innerhalb seines Mittellastbereichs befindet oder nicht.
  8. Verfahren nach Anspruch 1, wobei mindestens eines von einem Drehmomentwandler-Drehzahlverhältnis-Signal und einem Gaspedalpositionssignal verwendet wird, um zu bestimmen, ob sich der Radlader in seinem Beschleunigungs-/Schräglage-Lastbereich befindet oder nicht.
  9. Verfahren nach Anspruch 1, wobei ein Vorwärtsfahrt-Arbeitszustand, ein Rückwärtsfahrt-Arbeitszustand und ein Rückwärtsfahrt- und Auslegerabsenkungs-Arbeitszustand bei einer V-förmigen Fahrt des Radladers als der Leichtlastzustand bestimmt werden, ein Aushubarbeitszustand als ein Mittellastzustand bestimmt wird und der Vorwärtsfahrt- und Auslegeranhebe-Arbeitszustand als der Schwerlastzustand bestimmt wird.
  10. Verfahren nach Anspruch 1, des Weiteren umfassend das Ausgeben eines Steuersignals zum Steuern eines Motors oder eines Getriebes des Radladers gemäß dem momentanen Lastzustand des Radladers.
  11. Vorrichtung zum Steuern eines Radladers (10), umfassend:
    einen Signalempfänger (312), der dafür eingerichtet ist, von in dem Radlader installierten Sensoren Signale zu empfangen, die einen momentan durch den Radlader ausgeführten Arbeitszustand darstellen;
    einen Signalselektor (314), der dafür eingerichtet ist, mehrere individuelle Lastbereiche gemäß der Arbeitslast, die eine Leistungsabgabe eines Motors (100) während einer Reihe von durch den Radlader ausgeführten Arbeitszuständen verbraucht, bereitzustellen, und dafür eingerichtet ist, ein oder mehrere Signale der empfangenen Signale auszuwählen,
    wobei das eine oder die mehreren Signale verwendet werden können, um zu bestimmen, ob sich der Radlader innerhalb eines jeweiligen von mehreren individuellen Lastbereichen befindet oder nicht, wobei die individuellen Lastbereiche in einen Leichtlastbereich, einen Mittellastbereich, einen Schwerlastbereich und einen Beschleunigungs-/Schräglage-Lastbereich unterteilt werden;
    einen individuellen Lastbereichsbestimmer, der dafür eingerichtet ist, unter Verwendung des ausgewählten Signals Ausgabewerte zu berechnen, die anzeigen, ob sich der Radlader innerhalb des jeweiligen der mehreren individuellen Lastbereiche befindet oder nicht; und
    einen Lastzustandsbestimmer (318), der dafür eingerichtet ist, die Ausgabewerte zu analysieren, um einen momentanen Lastzustand der momentan durch den Radlader ausgeführten Arbeit als einen von einem Leichtlastzustand, einem Mittellastzustand, einem Schwerlastzustand und einem Beschleunigungs-/Schräglage-Lastzustand zu bestimmen,
    wobei der individuelle Lastbereichsbestimmer umfasst: eine Leichtlastbestimmungsschaltung, die einen ersten Ausgabewert berechnet, der angibt, ob sich der Radlader innerhalb des Leichtlastbereichs befindet oder nicht, eine Mittellastbestimmungsschaltung, die einen zweiten Ausgabewert berechnet, der angibt, ob sich der Radlader innerhalb des Mittellastbereichs befindet oder nicht, eine Schwerlastbestimmungsschaltung, die einen dritten Ausgabewert berechnet, der angibt, ob sich der Radlader innerhalb des Schwerlastbereichs befindet oder nicht, und
    eine Beschleunigungs-/Schräglagebestimmungsschaltung, die einen vierten Ausgabewert berechnet, der angibt, ob sich der Radlader innerhalb des Beschleunigungs-/Schräglage-Lastbereichs befindet oder nicht.
  12. Vorrichtung nach Anspruch 11, wobei der individuelle Lastbereichsbestimmer individuelle Berechnungsschaltungen umfasst, die die Ausgabewerte unter Verwendung von maschinellem Lernen berechnen, und das maschinelle Lernen eines von Folgendem umfasst, das aus der Gruppe ausgewählt ist, bestehend aus einem Neuronennetzansatz, einem statistischen Ansatz, einem strukturellen Ansatz, einem Fuzzy-Logik-Ansatz, einem Entscheidungsbaum-Ansatz und einem Vorlagenabgleichsansatz.
  13. Vorrichtung nach Anspruch 11, wobei der individuelle Lastbereichsbestimmer Vorhersagealgorithmen ausführt, die durch Training an dem ausgewählten Signal erhalten wurden, um die Ausgabewerte zu berechnen.
  14. Vorrichtung nach Anspruch 11, wobei die Leichtlastbestimmungsschaltung und die Schwerlastbestimmungsschaltung mindestens eines von einem Auslegerzylinderdrucksignal, einem FNR-Signal, einem Hauptdrucksignal einer Hydraulikpumpe, einem Fahrzeuggeschwindigkeitssignal, einem Auslegerpositionssignal und einem Drehmomentwandler-Drehzahlverhältnis-Signal verwenden, um zu bestimmen, ob sich der Radlader innerhalb seines Leichtlastbereichs und seines Schwerlastbereichs befindet oder nicht, und die Mittellastbestimmungsschaltung mindestens eines von dem Hauptdrucksignal der Hydraulikpumpe, dem Fahrzeuggeschwindigkeitssignal, einem Auslegerpositionssignal und dem Drehmomentwandler-Drehzahlverhältnis-Signal verwendet, um zu bestimmen, ob sich der Radlader im Mittellastbereich befindet oder nicht.
  15. Vorrichtung nach Anspruch 11, wobei der Lastzustandsbestimmer einen Vorwärtsfahrt-Arbeitszustand, einen Rückwärtsfahrt-Arbeitszustand und einen Rückwärtsfahrt- und Auslegerabsenkungs-Arbeitszustand bei einer V-förmigen Fahrt des Radladers als den Leichtlastzustand bestimmt, einen Aushubarbeitszustand als einen Mittellastzustand bestimmt und den Vorwärtsfahrt- und Auslegeranhebe-Arbeitszustand als den Schwerlastzustand bestimmt.
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US7832511B2 (en) * 2006-10-20 2010-11-16 Ford Global Technologies Hybrid electric vehicle control system and method of use
US9242633B2 (en) * 2007-05-10 2016-01-26 Volvo Construction Equipment Ab Method and a control system for controlling a work machine
KR20120072521A (ko) * 2010-12-24 2012-07-04 두산인프라코어 주식회사 휠로더의 변속기 자동 제어 방법
KR101950041B1 (ko) * 2012-12-18 2019-04-22 두산인프라코어 주식회사 산업용 차량의 엔진 제어 방법 및 장치
DE102013208320A1 (de) * 2013-05-07 2014-11-13 Deere & Company Verfahren zur Bestimmung eines Kontrollparameters eines Leistungs- oder Drehmomentverteilungsreglers für einen Hybridantrieb einer Arbeitsmaschine
CN204023637U (zh) * 2014-06-12 2014-12-17 天津市申都工程机械制造有限公司 混合动力挖掘机节能系统

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* Cited by examiner, † Cited by third party
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US12096716B2 (en) 2017-06-19 2024-09-24 Deere & Company Combine harvester control interface for operator and/or remote user

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KR20160133323A (ko) 2016-11-22
EP3093397A1 (de) 2016-11-16
CN106149775A (zh) 2016-11-23
CN106149775B (zh) 2018-11-02
KR102483801B1 (ko) 2023-01-02

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