CN115402234A - Method for identifying running state of electric forklift in artificial awakening state - Google Patents

Method for identifying running state of electric forklift in artificial awakening state Download PDF

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CN115402234A
CN115402234A CN202211003375.4A CN202211003375A CN115402234A CN 115402234 A CN115402234 A CN 115402234A CN 202211003375 A CN202211003375 A CN 202211003375A CN 115402234 A CN115402234 A CN 115402234A
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庄伟超
刘永青
许奇
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Hangzhou Pengcheng New Energy Technology Co ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
    • B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
    • B60R16/023—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for transmission of signals between vehicle parts or subsystems
    • B60R16/0231—Circuits relating to the driving or the functioning of the vehicle
    • B60R16/0232—Circuits relating to the driving or the functioning of the vehicle for measuring vehicle parameters and indicating critical, abnormal or dangerous conditions

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  • Automation & Control Theory (AREA)
  • Mechanical Engineering (AREA)
  • Forklifts And Lifting Vehicles (AREA)

Abstract

The invention belongs to the field of electric forklifts, and particularly relates to a method for identifying an operation state of an electric forklift in an artificial awakening state, which comprises the following steps: step 1, a main control module collects data of a power supply and dynamically determines a current distinguishing threshold value of a dynamic and static working mode; and 2, judging the dynamic and static modes by the main control module. The invention can automatically analyze the characteristics of the equipment in different working modes, calculate the reasonable equipment working mode distinguishing threshold value, meet the working mode distinguishing requirements of equipment of different models or equipment of the same model in different use states and life cycles, have the characteristics of automatic learning analysis and have strong applicability.

Description

Method for identifying running state of electric forklift in artificial awakening state
Technical Field
The invention belongs to the field of electric forklifts, and particularly relates to a running state identification method of an electric forklift in an artificial awakening state.
Background
The discharge operation state of the electric forklift can be divided into dynamic state and static state. The dynamic finger electric forklift is in a working state, and at the moment, the forklift needs to load heavy objects and move, so that large current exists in a working loop; static state means that the electric forklift is in a static state or a power-off dormant state, and at the moment, the working current is very small or the current is zero. The judgment of the dynamic state and the static state of the forklift is used as follows: under dynamic and static mode, the running state and the operating parameter difference of fork truck are great, and in order to control fork truck's operation better, effective and accurate judgement fork truck's dynamic and static mode is indispensable. Meanwhile, the operation mode of the forklift is obtained, reference can be provided for design of control strategies of the forklift and the battery, different control logics are designed aiming at dynamic and static modes, and control requirements under different modes are better met.
For dynamic and static modes of operation of a forklift, a conventional way of distinguishing is to set a fixed current threshold. When the current passing through the working loop is detected to be larger than a preset current threshold value, the working loop is considered to be in a dynamic state; and when the current passing through the working circuit is detected to be smaller than a preset current threshold value, the working circuit is considered to be in a static state.
However, in practical applications, because the power of different types of electric forklifts and the static power consumption of vehicle-mounted equipment are different, it is difficult to distinguish the operating modes of different types of electric forklifts by using a fixed current threshold. Meanwhile, for the same type of vehicle, the current distinguishing threshold value of the dynamic and static working modes of the vehicle can be changed due to aging or faults of equipment and lines.
Based on the problems, the applicant provides an operation state identification method for the electric forklift in the artificial awakening state.
Disclosure of Invention
In order to make up for the defects of the prior art, the invention provides a technical scheme of a running state identification method of an electric forklift in an artificial awakening state.
A method for identifying the running state of an electric forklift in an artificial awakening state comprises the following steps:
step 1, a main control module collects data of a power supply and dynamically determines a current distinguishing threshold value of a dynamic and static working mode;
and 2, judging the dynamic and static modes by the main control module.
Further, the step 1 comprises:
step 1.1, a main control module collects current I and voltage V of a power supply;
step 1.2, the main control module distinguishes a threshold I according to a preset initial current 0 Will continuously collectCurrent data of
Figure BDA0003807132530000021
Two data sets are divided: dynamic current data set
Figure BDA0003807132530000022
And quiescent current data set
Figure BDA0003807132530000023
The expression is divided into:
Figure BDA0003807132530000024
Figure BDA0003807132530000025
step 1.3, when the data set
Figure BDA0003807132530000026
Length m and data set
Figure BDA0003807132530000027
After the length n of the two data sets exceeds a preset data length threshold value L, L data which are obtained most recently in the two data sets are intercepted, head data except the length L are abandoned, and the two data sets are obtained again:
Figure BDA0003807132530000028
Figure BDA0003807132530000029
step 1.4, calculating current data sets respectively
Figure BDA00038071325300000210
Mean value of (a) 1 And current data set
Figure BDA00038071325300000211
Mean value of (a) 2 And a current data set
Figure BDA00038071325300000212
Standard deviation of (a) 1 And current data set
Figure BDA00038071325300000213
Standard deviation of (a) 2 The expression is:
Figure BDA00038071325300000214
Figure BDA0003807132530000031
Figure BDA0003807132530000032
Figure BDA0003807132530000033
step 1.5: according to the calculated mean value mu 1 And mu 2 Updating dynamic and static current distinguishing threshold I 0 The expression is:
Figure BDA0003807132530000034
further, the step 1 further comprises:
with the use of the electric forklift, the main control module continuously acquires new current data, and the new current data are divided into a threshold I according to the dynamic and static states updated in a rolling manner 0 Is divided into dynamic current data sets
Figure BDA0003807132530000035
Or quiescent current data set
Figure BDA0003807132530000036
In, at the same time, current data set
Figure BDA0003807132530000037
And
Figure BDA0003807132530000038
always keeping the current data number as L, every time
Figure BDA0003807132530000039
Or
Figure BDA00038071325300000310
When a new data is obtained, the oldest current data is removed from the data set, so that the elements of the data set are updated in a rolling mode, and the mean value and the standard deviation of the updated data set are also calculated and updated.
Further, the step 2 comprises:
when Q current data [ I ] are continuously acquired 1 ，I 2 ，I 3 …I i ，…I Q ]And when the Q pieces of current data satisfy the following conditions:
μ 1 -σ 1 ≤I i ≤μ 1 +σ 1
judging that the electric forklift enters a dynamic mode;
when Q current data [ I ] are continuously acquired 1 ，I 2 ，I 3 …I i ，…I Q ]And Q current data satisfy:
μ 2 -σ 2 ≤I i ≤μ 2 +σ 2
it is determined that the electric forklift enters the static mode.
Compared with the prior art, the invention has the beneficial effects that:
the invention can automatically analyze the characteristics of the equipment in different working modes, actively collect related equipment parameters, calculate a reasonable equipment working mode distinguishing threshold value by a statistical analysis means, and meet the requirements of different types of equipment on intelligent distinguishing working modes. Meanwhile, the invention can update the data set in a rolling way, and can realize automatic and intelligent distinguishing of the working modes of the equipment when the equipment is in different use states and life cycles. The invention realizes automatic and accurate distinguishing of the working modes of the equipment, provides effective reference for the design of the control strategy of the equipment, and enables strategy developers to design different control strategies aiming at different working modes, thereby achieving the purpose of accurately controlling the equipment.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of step 1 of the present invention;
fig. 3 is a schematic circuit relationship diagram of the battery control system of the electric forklift.
Detailed Description
In the description of the present invention, it is to be understood that the terms "one end", "the other end", "outside", "upper", "inside", "horizontal", "coaxial", "central", "end", "length", "outer end", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, should not be construed as limiting the present invention.
The invention will be further explained with reference to the drawings.
As shown in fig. 1 to 3, a method for identifying an operation state of an electric forklift in an artificially awakened state includes the following steps:
step 1, a main control module collects data of a power supply and dynamically determines a current distinguishing threshold value of a dynamic and static working mode.
The step 1 comprises the following steps:
step 1.1, a main control module collects current I and voltage V of a power supply;
step 1.2, the main control module sets the initial current zone according to the presetSubthreshold value I 0 Continuously collecting current data
Figure BDA0003807132530000051
Two data sets are divided: dynamic current data set
Figure BDA0003807132530000052
And quiescent current data set
Figure BDA0003807132530000053
The expression is as follows:
Figure BDA0003807132530000054
Figure BDA0003807132530000055
step 1.3, when the data set
Figure BDA0003807132530000056
And
Figure BDA0003807132530000057
after the length m and the length n both exceed a preset data length threshold value L, intercepting the L data which are obtained recently in the two data sets, and abandoning the head data except the length L; so far, two data sets are obtained again, and the expression is as follows:
Figure BDA0003807132530000058
Figure BDA0003807132530000059
step 1.4, calculating current data sets respectively
Figure BDA00038071325300000510
And
Figure BDA00038071325300000511
mean value of (a) 1 And mu 2 And standard deviation σ 1 And σ 2 The expression is:
Figure BDA00038071325300000512
Figure BDA00038071325300000513
Figure BDA00038071325300000514
Figure BDA00038071325300000515
step 1.5, according to the calculated mean value mu 1 And mu 2 Updating the dynamic and static current distinguishing threshold I 0 The expression is:
Figure BDA00038071325300000516
with the use of the electric forklift, the main control module continuously collects new current data, and the new current data distinguishes the threshold I according to the dynamic and static states updated in a rolling way 0 Is divided into dynamic current data sets
Figure BDA0003807132530000061
Or quiescent current data set
Figure BDA0003807132530000062
In, at the same time, current data set
Figure BDA0003807132530000063
And
Figure BDA0003807132530000064
the number of current data is always kept at L, so that each time
Figure BDA0003807132530000065
Or
Figure BDA0003807132530000066
When a new data is obtained, it will remove the oldest current data from the data set, thus achieving a rolling update of the data set elements. And after the data set is updated, calculating and updating the mean value and the standard deviation of the data set.
And 2, judging the dynamic and static modes by the main control module.
The step 2 comprises the following steps:
when the equipment is in an automatic awakening state, directly considering that the equipment is in a static working mode;
when the equipment is in an artificial awakening state, dynamic and static distinguishing is carried out according to the following conditions:
when Q current data [ I ] are continuously acquired 1 ，I 2 ，I 3 …I i ，…I Q ]And Q pieces of current data satisfy the following conditions:
μ 1 -σ 1 ≤I i ≤μ 1 +σ 1
judging that the electric forklift enters a dynamic mode;
when Q current data [ I ] are continuously acquired 1 ，I 2 ，I 3 …I i ，…I Q ]And Q current data satisfy:
μ 2 -σ 2 ≤I i ≤μ 2 +σ 2
judging that the electric forklift enters a static mode;
and the rest conditions are kept as the last working mode judgment.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (4)

1. A running state identification method of an electric forklift in an artificial awakening state is characterized by comprising the following steps:
step 1, a main control module collects data of a power supply and dynamically determines a current distinguishing threshold value of a dynamic and static working mode;
and 2, judging the dynamic and static modes by the main control module.
2. The method for identifying the running state of the electric forklift in the artificial awakening state according to claim 1, wherein the step 1 comprises the following steps:
step 1.1, a main control module collects current I and voltage V of a power supply;
step 1.2, the main control module distinguishes a threshold I according to a preset initial current 0 Continuously collecting current data
Figure FDA0003807132520000011
Two data sets are divided: dynamic current data set
Figure FDA0003807132520000012
And quiescent current data set
Figure FDA0003807132520000013
The expression is divided into:
Figure FDA0003807132520000014
Figure FDA0003807132520000015
step 1.3, when the data set
Figure FDA0003807132520000016
Length m and data set
Figure FDA0003807132520000017
After the length n exceeds a preset data length threshold value L, intercepting the L data which are obtained recently in the two data sets, abandoning the head data except the length L, and obtaining the two data sets again:
Figure FDA0003807132520000018
Figure FDA0003807132520000019
step 1.4, calculating the current data sets respectively
Figure FDA00038071325200000110
Mean value of (a) 1 And current data set
Figure FDA00038071325200000111
Mean value of (a) 2 And a current data set
Figure FDA00038071325200000112
Standard deviation of (a) 1 And current data set
Figure FDA00038071325200000113
Standard deviation of (a) 2 The expression is:
Figure FDA00038071325200000114
Figure FDA0003807132520000021
Figure FDA0003807132520000022
Figure FDA0003807132520000023
step 1.5: according to the calculated mean value mu 1 And mu 2 Updating dynamic and static current distinguishing threshold I 0 The expression is:
Figure FDA0003807132520000024
3. the method for identifying the running state of the electric forklift in the artificial awakening state according to claim 2, wherein the step 1 further comprises the following steps:
with the use of the electric forklift, the main control module continuously collects new current data, and the new current data distinguishes the threshold I according to the dynamic and static states updated in a rolling way 0 Is divided into dynamic current data sets
Figure FDA0003807132520000025
Or quiescent current data set
Figure FDA0003807132520000026
In, at the same time, current data set
Figure FDA0003807132520000027
And
Figure FDA0003807132520000028
always keeping the current data number of L every time
Figure FDA0003807132520000029
Or
Figure FDA00038071325200000210
When a new data is obtained, the oldest current data is removed from the data set, so that the elements of the data set are updated in a rolling mode, and the mean value and the standard deviation of the updated data set are also calculated and updated.
4. The method for identifying the running state of the electric forklift in the artificial awakening state according to claim 1, wherein the step 2 comprises the following steps:
when Q current data [ I ] are continuously acquired 1 ,I 2 ,I 3 …I i ,…I Q ]And Q pieces of current data satisfy the following conditions:
μ 1 -σ 1 ≤I i ≤μ 1 +σ 1
judging that the electric forklift enters a dynamic mode;
when Q current data [ I ] are continuously acquired 1 ,I 2 ,I 3 …I i ,…I Q ]And Q current data satisfy:
μ 2 -σ 2 ≤I i ≤μ 2 +σ 2
it is determined that the electric forklift enters the static mode.
CN202211003375.4A 2022-08-19 2022-08-19 Method for identifying running state of electric forklift in artificial awakening state Pending CN115402234A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117148185A (en) * 2023-10-30 2023-12-01 四川赛科检测技术有限公司 Method, device and storage medium for testing quiescent current of battery system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117148185A (en) * 2023-10-30 2023-12-01 四川赛科检测技术有限公司 Method, device and storage medium for testing quiescent current of battery system
CN117148185B (en) * 2023-10-30 2024-02-09 四川赛科检测技术有限公司 Method, device and storage medium for testing quiescent current of battery system

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