CN-122008962-A - Whole-vehicle temperature estimation method, system, equipment and medium for electric loader

CN122008962ACN 122008962 ACN122008962 ACN 122008962ACN-122008962-A

Abstract

The invention discloses a whole vehicle temperature estimation method, system, equipment and medium of an electric loader, which comprises the steps of arranging a low temperature control switch S1 and a high temperature control switch S2 at temperature measuring points of a battery, a traveling motor, a hydraulic motor and a high pressure box of the electric loader, respectively establishing a basic temperature MAP graph taking a working condition continuous operation time T1 and a power duty ratio N1 as input for the battery, the traveling motor, the hydraulic motor and the high pressure box, taking the maximum value of each basic temperature MAP graph as the whole vehicle basic temperature, introducing atmospheric pressure, vehicle speed, environmental temperature and a part depreciation coefficient as correction factors, establishing a correction mapping table, correcting the whole vehicle basic temperature in real time to obtain an estimated temperature T2, constructing a temperature output arbitration logic, fusing the estimated temperature T2 with a temperature truth signal T4 of the temperature control switch, and outputting a final temperature T3. The invention has the advantages of low cost, high reliability, high precision, self-adaptability and the like.

Inventors

GAO XIAOFENG
DONG WENWEN
ZHANG NING
YANG XINGUANG
LI ZHONGYU
LIU WENJIE

Assignees

徐工集团工程机械股份有限公司科技分公司

Dates

Publication Date: 20260512
Application Date: 20260320

Claims (12)

1. The whole vehicle temperature estimation method of the electric loader is characterized by comprising the following steps of: a. A low temperature control switch S1 and a high temperature control switch S2 are respectively arranged at key temperature measuring points of a battery, a traveling motor, a hydraulic motor and a high voltage box of the electric loader and are used for outputting a temperature true value signal; b. Respectively establishing basic temperature MAP graphs of a battery, a walking motor, a hydraulic motor and a high-voltage box, and taking the maximum value of each basic temperature MAP graph as the basic temperature of the whole vehicle; c. Introducing atmospheric pressure, vehicle speed, ambient temperature and part depreciation coefficient as correction factors, establishing a correction mapping table, and correcting the whole vehicle base temperature in real time to obtain an estimated temperature T2; d. constructing a temperature output arbitration logic, fusing an estimated temperature T2 with a temperature truth value signal T4 of the temperature control switch, and outputting a final temperature T3; e. Calculating a difference value delta T=T2-T4 between the estimated temperature T2 and the temperature truth value signal T4, driving a self-learning model based on the difference value delta T, dynamically correcting a basic temperature MAP and a correction mapping table, and enabling the accuracy of the estimated temperature T2 to be stable; f. and performing fault diagnosis on the temperature control switch and the self-learning model, if the verification is not passed, disabling the self-learning model, and using a historical optimal base temperature MAP graph and triggering an alarm.
2. The method for estimating the temperature of the whole electric loader according to claim 1, wherein in the step b, a basic temperature MAP is respectively established for the battery, the traveling motor, the hydraulic motor and the high-voltage box, wherein the basic temperature MAP is input by a working condition continuous operation time T1 and a power duty ratio N1.
3. The method for estimating the temperature of a whole electric loader according to claim 1, wherein in the step c, the calculation formula of the estimated temperature T2 is: T2=Max(MAP1, MAP2, MAP3, MAP4)×A1×A2×A3×A4; Wherein MAP1 is a basic temperature MAP graph output value corresponding to a walking motor, MAP2 is a basic temperature MAP graph output value corresponding to a hydraulic motor, MAP3 is a basic temperature MAP graph output value corresponding to a high-voltage box, MAP4 is a basic temperature MAP graph output value corresponding to a battery, A1 is an atmospheric pressure MAP correction value, A2 is a vehicle speed MAP correction value, A3 is an ambient temperature MAP correction value, and A4 is a component depreciation coefficient MAP correction value.
4. The method for estimating the temperature of a whole electric loader according to claim 1, wherein in the step d, the temperature output arbitration logic specifically comprises: when neither the low temperature control switch S1 nor the high temperature control switch S2 is triggered, outputting the estimated temperature T2 as a final temperature T3; When the low temperature control switch S1 is triggered and the high temperature control switch S2 is not triggered or the high temperature control switch S2 is triggered, a corresponding preset low temperature threshold value or a corresponding preset high temperature threshold value is used as a temperature truth value signal T4, a difference value delta T=T2-T4 is calculated, if the delta T is within a preset difference value threshold value range, the T4 is output as a final temperature T3, and if the delta T exceeds the preset difference value threshold value range, fault diagnosis is triggered and a self-learning model is activated.
5. The method for estimating the temperature of the whole electric loader according to claim 1, wherein in the step e, the triggering condition of the self-learning model includes: The starting signal of the electric loader is effective, the I delta T I is not less than a preset difference threshold value, the working condition continuous operation time T1 is not less than the minimum learning window time, and the power duty ratio N1 is in a preset effective interval.
6. The method for estimating the temperature of the whole electric loader according to claim 1, wherein in the step f, the fault diagnosis includes a temperature control switch fault diagnosis and a self-learning model fault diagnosis, specifically: Logic verification of the temperature control switch, namely, when the high temperature control switch S2 is closed, the low temperature control switch S1 is required to be closed, when the low temperature control switch S1 is closed, the closing time of the high temperature control switch S2 is required to be longer than the preset response time, and when the switch is frequently triggered in the preset time, the switch is judged to be faulty; and checking the self-learning model, namely judging whether the model output is valid or not through energy balance consistency check and numerical mutation check.
7. A whole electric loader temperature estimation system, characterized in that it is applied to the whole electric loader temperature estimation method as claimed in any one of claims 1-6, comprising: The temperature control switch module is used for respectively arranging a low temperature control switch S1 and a high temperature control switch S2 at key temperature measuring points of a battery, a traveling motor, a hydraulic motor and a high voltage box of the electric loader and outputting a temperature true value signal; the MAP calculation module is used for respectively establishing basic temperature MAP graphs of the battery, the walking motor, the hydraulic motor and the high-voltage box, and taking the maximum value of each basic temperature MAP graph as the basic temperature of the whole vehicle; The correction module is used for introducing atmospheric pressure, vehicle speed, ambient temperature and component depreciation coefficients as correction factors, establishing a correction mapping table, and correcting the whole vehicle base temperature in real time to obtain an estimated temperature T2; The arbitration module is used for constructing a temperature output arbitration logic, fusing the estimated temperature T2 with a temperature truth signal T4 of the temperature control switch and outputting a final temperature T3; the self-learning module is used for calculating a difference value delta T=T2-T4 between the estimated temperature T2 and the temperature truth value signal T4, driving the self-learning model based on the difference value delta T, dynamically correcting the basic temperature MAP and correcting the mapping table, and enabling the accuracy of the estimated temperature T2 to tend to be stable; The fault diagnosis module is used for carrying out fault diagnosis on the temperature control switch and the self-learning model, if the verification is not passed, the self-learning model is disabled, the historical optimal base temperature MAP is used, and an alarm is triggered.
8. The system for estimating the temperature of a complete vehicle of an electric loader according to claim 7, wherein the correction procedure of the self-learning module comprises: a. generating a dynamic learning gain based on the difference value delta T, the working condition continuous operation time T1, the power duty ratio N1 and the correction coefficients A1, A3 and A4; b. Preliminary correction is carried out on the base temperature MAP by utilizing dynamic learning gain, and a corrected MAP is obtained; c. if the error of the estimated temperature and the temperature true value of the corrected MAP is smaller than the preset threshold value and is continuously stable, the corrected MAP is judged to be effective, the corrected MAP is adopted, and otherwise, the historical optimal base temperature MAP is kept.
9. The system of claim 7, wherein the correction mapping table of the correction module is calibrated by bench test, simulation data and road test data, and supports dynamic updating of correction coefficients by the self-learning module.
10. The system of claim 7, wherein the output signal of the arbitration module is used to trigger the functions of fan heat dissipation control, temperature classification early warning and dynamic power allocation of the electric loader.
11. An electric loader whole vehicle temperature estimation device, characterized by comprising: A processor; a memory storing a computer program, which when executed by the processor, implements the method for estimating the temperature of an electric loader whole vehicle as claimed in any one of claims 1 to 6.
12. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program, which when executed by a processor, implements the electric loader whole vehicle temperature estimation method according to any one of claims 1-6.

Description

Whole-vehicle temperature estimation method, system, equipment and medium for electric loader Technical Field The invention relates to a method, a system, equipment and a medium for estimating the temperature of the whole electric loader, belonging to the technical field of vehicle thermal management and state estimation. Background The battery, the walking motor, the hydraulic motor, the high-voltage distribution box and other core components of the electric loader can generate a large amount of heat under the high-power working condition; the temperature state of these components directly determines the operation performance, safety level and reliability life of the whole machine. If the temperature is controlled improperly, overheat will cause a series of serious faults, i.e. the battery may have performance attenuation, thermal runaway or even fire explosion, the walking/hydraulic motor may have demagnetizing failure, and the insulating material may be aged rapidly, so that the linkage problems of contactor damage, circuit short circuit and the like are caused. Therefore, the conventional passive thermal management cannot meet the requirements, and it is needed to realize accurate monitoring, early prediction and active preventive management and control of the temperature of the key components so as to ensure safe and stable operation of the whole machine. The whole car heat management of the electric loader in the current industry mainly adopts the following three technical schemes, but all have technical defects that the whole car heat management is difficult to consider: 1. According to the scheme, the high-precision temperature sensors are densely arranged at each temperature measuring point, so that the temperature is directly and real-time monitored, the principle is simple, and the data feedback is direct. The high-precision sensor has the remarkable limitations that on one hand, the high-precision sensor is high in cost, a plurality of measuring points are required to be configured for each key component, so that the hardware cost is greatly increased, meanwhile, a large number of sensors are required to be matched with a complex wiring system, the assembly complexity of the whole automobile is increased, the fault probability is improved due to the problems of line contact, abrasion and the like, on the other hand, the sensor is easily influenced by severe working conditions (vibration, dust, high temperature) of engineering machinery, the risks of damage and drift exist, once the sensor fails, the temperature monitoring is inaccurate, the misjudgment of thermal management is further caused, and the whole machine fault is possibly caused when the sensor is serious. 2. The simple model estimation method is characterized in that the scheme is based on a thermodynamic basic law or a simplified empirical formula, a lightweight thermal model is constructed to estimate the temperature of the component, and the method has the advantages of low hardware cost and simple model structure. The method can only adapt to steady-state and single working conditions, cannot cope with complex transient working conditions (such as abrupt load change and frequent working condition switching) in the actual operation of the electric loader, and because the model does not fully consider details such as thermal inertia of components, dynamic heat dissipation response and the like, errors can be accumulated rapidly in long-term operation, the accuracy of temperature estimation is difficult to ensure, and the requirement of accurate thermal management cannot be met. 3. The method establishes a fixed temperature MAP graph with a few core parameters (such as power and time) as input based on limited bench test, simulation data and road test data, and rapidly acquires a temperature estimated value through table lookup, thereby realizing simple and convenient implementation. The MAP is based on standard working conditions and brand new equipment calibration, influences of factors such as environmental variables (environmental temperature, atmospheric pressure/altitude), equipment aging effects, individual differences and the like are not considered, the adaptive range is extremely narrow, complex working conditions of the whole scene cannot be covered, meanwhile, the fixed MAP lacks dynamic correction capability, estimation errors can be gradually increased along with the increase of the service life of equipment, and the accuracy and the effectiveness of thermal management are difficult to ensure for a long time. In summary, the prior art schemes have obvious short plates, namely the direct sensor monitoring method has the problems of high cost and high complexity, and the simple model estimation method and the fixed MAP graph checking method have difficulty in combining precision and environmental adaptability. Disclosure of Invention Aiming at the problems existing in the prior art, the invention provides a method, a syst