CN116279601A - Prediction method, device and product for endurance mileage of railway vehicle - Google Patents
Prediction method, device and product for endurance mileage of railway vehicle Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
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- B61C3/00—Electric locomotives or railcars
- B61C3/02—Electric locomotives or railcars with electric accumulators
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/12—Recording operating variables ; Monitoring of operating variables
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
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Abstract
The embodiment of the application provides a method, a device and a product for predicting the endurance mileage of a railway vehicle, wherein the railway vehicle comprises: traction engine and traction battery; the traction storage battery is used for supplying power to the traction engine so that the traction engine drives the railway vehicle to run; the method comprises the following steps: under the condition that the rail vehicle is detected to be in a starting state, obtaining the information of the residual electric quantity of the traction storage battery in real time; acquiring traction parameters of the traction engine in real time; acquiring operation interval parameters corresponding to the operation interval of the railway vehicle in real time; and obtaining the endurance mileage predicted value of the railway vehicle in real time according to the residual electric quantity information, the traction force parameter and the running interval parameter. According to the embodiment of the application, the characteristic that the railway vehicle runs on a fixed line is fully utilized, and the endurance mileage is accurately and in real time predicted based on the traction parameters and the running interval parameters of the electric railway vehicle obtained in real time.
Description
Technical Field
The embodiment of the application relates to the technical field of rail transit, in particular to a method for predicting the endurance mileage of a rail vehicle, a device for predicting the endurance mileage of the rail vehicle, a computer readable storage medium and electronic equipment.
Background
The locomotive using the storage battery as the power energy has the characteristics of environmental protection, low noise and the like, and has more positive effect on environmental protection compared with the diesel locomotive using the traditional energy. Current rail vehicles have introduced traction batteries to provide energy for traction motors so that the rail vehicle can run on the rail on battery power. However, due to the fact that the electric quantity density and the volume of the storage battery are limited, the cruising ability of the storage battery of the electric railway vehicle at the present stage is still insufficient, and when a worker of the electric railway vehicle which adopts the power energy of the traction storage battery operates a locomotive to perform on-line operation, whether the electric quantity reaches a destination smoothly enough or returns to the base safely can not be estimated accurately, so that anxiety of the worker is caused, and serious potential safety hazards exist for on-line operation of the worker.
Disclosure of Invention
The embodiment of the application provides a method for predicting the endurance mileage of a railway vehicle, a device for predicting the endurance mileage of the railway vehicle, a computer-readable storage medium and electronic equipment, and aims to accurately predict the endurance mileage in real time based on traction parameters and operation interval parameters of the electric railway vehicle obtained in real time.
In one aspect, an embodiment of the present application provides a method for predicting a range of a rail vehicle, where the rail vehicle includes: traction engine and traction battery; the traction storage battery is used for supplying power to the traction engine so that the traction engine drives the railway vehicle to run; the method comprises the following steps:
under the condition that the rail vehicle is detected to be in a starting state, obtaining the information of the residual electric quantity of the traction storage battery in real time;
acquiring traction parameters of the traction engine in real time;
acquiring operation interval parameters corresponding to the operation interval of the railway vehicle in real time;
and obtaining the endurance mileage predicted value of the railway vehicle in real time according to the residual electric quantity information, the traction force parameter and the running interval parameter.
Optionally, the step of acquiring, in real time, an operation interval parameter corresponding to an operation interval of the railway vehicle includes:
the method comprises the steps that under the condition that the fact that the railway vehicle is in a starting state is detected, current position information of the railway vehicle is obtained in real time;
acquiring destination position information of the railway vehicle in real time;
generating the running interval of the railway vehicle according to the current position information and the destination position information of the railway vehicle;
and acquiring an operation interval parameter corresponding to the operation interval of the railway vehicle in real time.
Optionally, the operation interval parameter includes at least one of: slope information of the operation interval and speed limit information of the operation interval.
Optionally, obtaining, in real time, a range prediction value of the rail vehicle according to the remaining power information, the traction parameter and the operation interval parameter includes:
inputting the residual electric quantity information, the traction force parameter and the running interval parameter which are acquired in real time into a prediction model, and acquiring a range predicted value of the railway vehicle in real time;
the prediction model is obtained by inputting preset quantity of sample residual electric quantity information, sample traction force parameters and sample operation interval parameters into an initial neural network model for training.
Optionally, the method further comprises:
after the rail vehicle runs a first preset mileage in an operation interval, obtaining the actual power consumption of the rail vehicle running in the first preset mileage;
obtaining the predicted electricity consumption of the railway vehicle in the first preset mileage according to the traction parameters of the traction engine and the running interval parameters of the railway vehicle in the first preset mileage;
and comparing the actual power consumption of the railway vehicle running in the first preset mileage with the predicted power consumption, and correcting the predicted value of the endurance mileage of the railway vehicle in the residual running interval.
Optionally, the method further comprises:
under the condition that the rail vehicle is detected to be in a starting state, acquiring the total weight of the rail vehicle;
obtaining a predicted speed curve of the rail vehicle running in the running interval according to the total weight of the rail vehicle, the traction force parameter and the running interval parameter;
and obtaining the predicted value of the endurance mileage of the railway vehicle in real time according to the residual electric quantity information, the traction force parameter, the running interval parameter and the predicted speed curve.
In still another aspect, an embodiment of the present application provides a prediction apparatus for a range of a rail vehicle, the rail vehicle including: traction engine and traction battery; the traction storage battery can supply power to the traction engine so that the traction engine drives the railway vehicle to run; the device comprises:
the electricity metering unit is used for obtaining the information of the residual electric quantity of the traction storage battery;
the traction parameter acquisition unit is used for acquiring traction parameters of the traction engine in real time under the condition that the railway vehicle is detected to be in a starting state;
an operation interval parameter obtaining unit, configured to obtain an operation interval parameter corresponding to an operation interval of the rail vehicle in real time;
and the prediction unit is used for obtaining the endurance mileage predicted value of the railway vehicle in real time according to the residual electric quantity information, the traction force parameter and the running interval parameter.
In yet another aspect, another embodiment of the present application provides a rail vehicle comprising:
a traction engine;
a traction battery for powering the traction engine so that the traction engine drives the rail vehicle to travel;
prediction means for implementing the steps in a method according to any of the embodiments described herein.
In yet another aspect, another embodiment of the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs steps in a method as in any of the embodiments described herein.
In yet another aspect, another embodiment of the present application provides an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, which when executed performs the steps in the method of any of the embodiments of the present application.
Compared with the prior art, the advantage of this application lies in:
the method has the advantages that the characteristics that the rail vehicle runs on a fixed line are utilized, the residual electric quantity information, traction force parameters and running interval parameters of the electric rail vehicle are obtained in real time, the continuous voyage mileage of the electric rail vehicle is accurately and in real time predicted by taking the line to be run subsequently of the locomotive as a reference, the situation that the rail vehicle is anchored on the running line due to insufficient electric quantity of the rail vehicle is avoided, anxiety of workers is reduced, and potential safety hazards of the workers for on-line operation by the electric rail vehicle are reduced.
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The drawings are for reference and illustration purposes only and are not intended to limit the scope of the present application. The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
FIG. 1 is a flow chart illustrating steps of a method for predicting range of a rail vehicle in one embodiment provided herein;
FIG. 2 is a flow chart of a method for predicting range of a rail vehicle according to an embodiment of the present disclosure;
fig. 3 is a block diagram of a prediction apparatus for a range of a rail vehicle according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In the technical field of rail transit, the introduction of traction power supply of a storage battery brings a cost reduction space, and is also beneficial to realizing environmental protection. However, the disadvantage of using a battery as a power source is also apparent, and the endurance is limited, so that once a rail vehicle (also called a locomotive) running on a track loses power, the rail vehicle cannot easily reach a destination or safely return to a base, and the dispatching and safety of the whole rail system are affected. In the prior art, the mileage estimation of the electric railway vehicle only consumes power at a low speed, and the accuracy is quite low, and the estimated endurance mileage is far from the actual mileage. The inventor considers that the running interval of the rail vehicle is usually in a fixed environment, the power conversion efficiency of the storage battery is stable, and proposes to fully utilize the running characteristic of the rail vehicle in a fixed line, and accurately predict the endurance mileage of the electric rail vehicle by taking the line to be run subsequently of the locomotive as a reference and combining various parameters by collecting the data of the running line of the locomotive, thereby having good real-time performance and high accuracy.
In view of this, the embodiments of the present application provide a method for predicting a range of a rail vehicle, a device for predicting a range of a rail vehicle, a computer-readable storage medium, and an electronic device, which acquire, in real time, remaining power information, traction parameters, and running interval parameters of a rail vehicle, and accurately predict a range of a rail vehicle in real time.
Embodiments of the present application are described below with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a flowchart illustrating steps of a method for predicting a range of a rail vehicle according to an embodiment of the present disclosure. As shown in fig. 1, an embodiment of the present application provides a method for predicting a range of a rail vehicle, which may be applied to a rail vehicle that uses a battery to traction power and travels on a paved track, where the rail vehicle includes: traction engine and traction battery. The traction storage battery is used for supplying power to the traction engine so that the traction engine drives the railway vehicle to run.
Specifically, the traction battery may include any one of the following: lead-acid batteries, nickel-hydrogen batteries, sodium-sulfur batteries, secondary lithium batteries, air batteries, and ternary lithium batteries.
The method comprises the following steps:
step S301, when it is detected that the railway vehicle is in a start State, obtains State Of Charge (SOC) information Of the traction battery in real time.
The remaining capacity information of the traction battery may be expressed as a percentage of the total capacity or as the amount of remaining charge.
The information of the residual electric quantity of the traction storage battery can be obtained by starting self-checking of a storage battery management system in the railway vehicle after the railway vehicle is started, and then can be obtained from the storage battery management system by a locomotive control system.
Step S302, traction parameters of the traction engine are obtained in real time.
Wherein the traction parameters may include at least a traction direction and a traction magnitude. The traction force can be expressed in tons (t), cattle (N) or kilograms (Kg). Illustratively, the traction parameter may include traction tonnage.
Wherein the traction parameter may be provided by a monitoring system on the rail vehicle. The monitoring system may establish a communication connection with the engine control unit from which traction parameters of the rail vehicle are obtained.
Step S303, acquiring operation interval parameters corresponding to the operation interval of the railway vehicle in real time.
The operation interval parameter may be acquired according to geographic information and related control information of an operation interval in the track system, and may be stored in a locomotive control system of the track vehicle or a server establishing remote communication with the track vehicle in advance.
Considering that the power of the traction engine is mainly influencing the power consumption speed of the storage battery, and the ramp and the vehicle speed influence the power of the engine, the ramp and the speed limit of the operation interval influence the cruising of the storage battery. To this end, in an alternative embodiment, the operating interval parameters include at least one of: slope information of the operation interval and speed limit information of the operation interval.
For example, assuming that a certain section of mileage in an operation section is an uphill section, under the condition of keeping the speed unchanged, the power consumed by the traction engine of the section is increased, the traction force is increased, the power consumption speed of a storage battery is increased, and the cruising mileage is reduced; under the condition that the power consumed by the engine is kept unchanged, the running speed of the railway vehicle in the section is reduced, the power consumption time of the storage battery is prolonged, and the endurance mileage is also reduced.
Taking a lead-acid battery as an example, the activity of the battery is easily affected by the ambient temperature, and therefore, the temperature of the region where the operation interval is located may also affect the endurance mileage. To this end, in an alternative embodiment, the operating interval parameters may further include: and the environment temperature information of the operation interval.
And step S304, obtaining the predicted value of the endurance mileage of the railway vehicle in real time according to the residual electric quantity information, the traction force parameter and the running interval parameter.
Specifically, the model of the neural network can be trained by using the sample value to obtain a prediction model, and the residual electric quantity information, the traction force parameter and the running interval parameter are input to obtain a predicted value of the endurance mileage of the railway vehicle.
In some alternative embodiments, predictive calculations may be performed by a locomotive control system to obtain a range prediction for the rail vehicle.
In yet other alternative embodiments, to reduce costs and increase the sample size of the model so that the model is fully trained in use, the locomotive control system may send the remaining capacity information, the traction parameters, and the operating interval parameters to a computing server with which to establish a remote communication connection, which may be provided with a trained predictive model, with the computing server making predictions of range of a plurality of rail vehicles throughout the rail system.
The higher the remaining power, the larger the range prediction value may be. The greater the traction, the less the range forecast of the rail vehicle may be. The steeper the ramp or the lower the ambient temperature or the lower the speed limit in the running interval parameters, the smaller the range forecast value may be.
Through the embodiment, the characteristics that the rail vehicle runs on the fixed line are fully utilized, the residual electric quantity information, the traction force parameters and the running interval parameters of the electric rail vehicle are obtained in real time, the continuous voyage mileage of the electric rail vehicle is accurately and in real time predicted by taking the line to be run subsequently of the locomotive as a reference, the situation that the rail vehicle is anchored on the running line due to insufficient electric quantity of the rail vehicle is avoided, the anxiety of workers is reduced, and the potential safety hazard that the workers ride the electric rail vehicle on line is reduced.
The running interval can be updated in real time according to the current position of the railway vehicle. To this end, in an alternative embodiment, the present application further provides a method for obtaining an operation interval parameter, including:
and step S401, acquiring current position information of the railway vehicle in real time under the condition that the railway vehicle is detected to be in a starting state.
Wherein the current location information may be provided by a monitoring system on the rail vehicle. The monitoring system can be in communication connection with a preset positioning system, and current position information of the railway vehicle is obtained from the preset positioning system. The preset positioning system can be a Beidou satellite positioning system or a GPS positioning system.
And step S402, acquiring destination position information of the railway vehicle in real time.
Step S403, generating the running section of the rail vehicle according to the current position information and the destination position information of the rail vehicle.
And step S404, acquiring an operation interval parameter corresponding to the operation interval of the railway vehicle in real time.
In some alternative embodiments, the present application contemplates using neural network models for prediction to improve the accuracy of the prediction. To this end, in an alternative embodiment, the present application further provides a method for obtaining a range prediction value of a rail vehicle, including:
and inputting the residual electric quantity information, the traction force parameter and the running interval parameter which are acquired in real time into a prediction model, and acquiring a range predicted value of the railway vehicle in real time.
The prediction model is obtained by inputting preset quantity of sample residual electric quantity information, sample traction force parameters and sample operation interval parameters into an initial neural network model for training.
In order to improve the prediction accuracy, the embodiment of the application also considers that the actual power consumption is utilized to correct the predicted value of the endurance mileage of the railway vehicle because the predicted value can only be infinitely close to the actual situation. To this end, in an alternative embodiment, the present application further provides a method for correcting a range prediction value of a rail vehicle, including:
step S501, after the rail vehicle travels a first preset mileage in the operation interval, obtaining an actual power consumption of the rail vehicle traveling in the first preset mileage.
Step S502, obtaining a predicted power consumption of the rail vehicle in the first preset mileage according to the traction parameter of the traction engine and the running interval parameter of the rail vehicle in the first preset mileage.
Step S503, comparing the actual power consumption of the rail vehicle running in the first preset mileage with the predicted power consumption, and correcting the predicted value of the range of the rail vehicle in the remaining running interval.
Although the traction parameters can reflect the power consumption of the storage battery to a certain extent, the weight of the railway vehicle also affects the running speed of the railway vehicle in a set running interval, and the weight of the railway vehicle obviously affects the running time of the railway vehicle in the set running interval even though the running interval may have speed limit, so that the weight of the railway vehicle also affects the predicted range value of the railway vehicle. To this end, in an alternative embodiment, the present application further provides a method for obtaining a range prediction value of a rail vehicle, including:
step S601, when it is detected that the rail vehicle is in a start state, acquiring the total weight of the rail vehicle.
Step S602, obtaining a predicted speed curve of the rail vehicle running in the running interval according to the total weight of the rail vehicle, the traction parameter and the running interval parameter.
And step 603, obtaining a predicted value of the endurance mileage of the railway vehicle in real time according to the residual electric quantity information, the traction force parameter, the running interval parameter and the predicted speed curve.
Referring to fig. 2, fig. 2 is a flow chart illustrating a method for predicting a range of a rail vehicle according to another embodiment provided in the present application. As shown in fig. 2, in some optional embodiments, the embodiments of the present application further provide a method for predicting a range of a rail vehicle, where the rail vehicle further includes: traction battery management system, locomotive monitoring system and locomotive control system. The method comprises the following steps:
step S701, after the rail vehicle is started, the traction storage battery management system self-checks the SOC state information of the traction storage battery and sends the SOC state information to the locomotive control system.
Step S702 obtains weight, traction parameters, and operating intervals of the rail vehicle from the locomotive monitoring system.
The traction force parameter and the running interval can also be input by a driver through a locomotive monitoring system.
In step S703, the locomotive control system predicts the cruising mileage of the locomotive in the current state according to the weight of the rail vehicle, the traction force parameter, the running interval and the SOC state information of the traction battery.
Step S704, after the rail vehicle runs for a period of time, the real-time position information, speed, traction battery discharging current and SOC state information of the locomotive can be collected through the monitoring system and the battery management system, so as to correct the endurance mileage of the rail vehicle.
Referring to fig. 3, fig. 3 is a block diagram illustrating a prediction apparatus for a range of a rail vehicle according to an embodiment of the present application. As shown in fig. 3, based on the same inventive concept, an embodiment of the present application further provides a prediction system for a range of a rail vehicle, where the apparatus includes:
and a power metering unit 801, configured to obtain information on the remaining power of the traction battery.
And the traction parameter obtaining unit 802 is configured to obtain, in real time, a traction parameter of the traction engine when the railway vehicle is detected to be in a start state.
An operation interval parameter obtaining unit 803, configured to obtain an operation interval parameter corresponding to an operation interval of the rail vehicle in real time.
And the prediction unit 804 is configured to obtain a range prediction value of the railway vehicle in real time according to the remaining power information, the traction parameter and the running interval parameter.
Based on the same inventive concept, another embodiment of the present application provides a rail vehicle, comprising:
a traction engine.
And the traction storage battery is used for supplying power to the traction engine so that the traction engine drives the railway vehicle to run.
Prediction means for implementing the steps in a method according to any of the embodiments described herein.
Based on the same inventive concept, another embodiment of the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements steps in a method as in any of the embodiments described herein.
Based on the same inventive concept, another embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, which when executed realizes the steps in the method of any of the above embodiments of the present application.
For the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described by differences from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.
It will be apparent to those skilled in the art that embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, the present embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present application may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present embodiments have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the present application.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or terminal device comprising the element.
Claims (10)
1. The method for predicting the endurance mileage of the railway vehicle is characterized in that the railway vehicle comprises the following steps: traction engine and traction battery; the traction storage battery is used for supplying power to the traction engine so that the traction engine drives the railway vehicle to run; the method comprises the following steps:
under the condition that the rail vehicle is detected to be in a starting state, obtaining the information of the residual electric quantity of the traction storage battery in real time;
acquiring traction parameters of the traction engine in real time;
acquiring operation interval parameters corresponding to the operation interval of the railway vehicle in real time;
and obtaining the endurance mileage predicted value of the railway vehicle in real time according to the residual electric quantity information, the traction force parameter and the running interval parameter.
2. The method for predicting the range of a railway vehicle according to claim 1, wherein the step of acquiring in real time an operation interval parameter corresponding to the operation interval of the railway vehicle comprises:
the method comprises the steps that under the condition that the fact that the railway vehicle is in a starting state is detected, current position information of the railway vehicle is obtained in real time;
acquiring destination position information of the railway vehicle in real time;
generating the running interval of the railway vehicle according to the current position information and the destination position information of the railway vehicle;
and acquiring an operation interval parameter corresponding to the operation interval of the railway vehicle in real time.
3. The method of claim 1, wherein the operating interval parameters include at least one of: slope information of the operation interval and speed limit information of the operation interval.
4. The method for predicting the range of the railway vehicle according to claim 1, wherein obtaining the range prediction value of the railway vehicle in real time according to the remaining power information, the traction parameter and the operation interval parameter comprises:
inputting the residual electric quantity information, the traction force parameter and the running interval parameter which are acquired in real time into a prediction model, and acquiring a range predicted value of the railway vehicle in real time;
the prediction model is obtained by inputting preset quantity of sample residual electric quantity information, sample traction force parameters and sample operation interval parameters into an initial neural network model for training.
5. The method for predicting range of a rail vehicle of claim 1, further comprising:
after the rail vehicle runs a first preset mileage in an operation interval, obtaining the actual power consumption of the rail vehicle running in the first preset mileage;
obtaining the predicted electricity consumption of the railway vehicle in the first preset mileage according to the traction parameters of the traction engine and the running interval parameters of the railway vehicle in the first preset mileage;
and comparing the actual power consumption of the railway vehicle running in the first preset mileage with the predicted power consumption, and correcting the predicted value of the endurance mileage of the railway vehicle in the residual running interval.
6. The method for predicting range of a rail vehicle of claim 1, further comprising:
under the condition that the rail vehicle is detected to be in a starting state, acquiring the total weight of the rail vehicle;
obtaining a predicted speed curve of the rail vehicle running in the running interval according to the total weight of the rail vehicle, the traction force parameter and the running interval parameter;
and obtaining the predicted value of the endurance mileage of the railway vehicle in real time according to the residual electric quantity information, the traction force parameter, the running interval parameter and the predicted speed curve.
7. A prediction apparatus for a range of a railway vehicle, wherein the railway vehicle comprises: traction engine and traction battery; the traction storage battery can supply power to the traction engine so that the traction engine drives the railway vehicle to run; the device comprises:
the electricity metering unit is used for obtaining the information of the residual electric quantity of the traction storage battery;
the traction parameter acquisition unit is used for acquiring traction parameters of the traction engine in real time under the condition that the railway vehicle is detected to be in a starting state;
an operation interval parameter obtaining unit, configured to obtain an operation interval parameter corresponding to an operation interval of the rail vehicle in real time;
and the prediction unit is used for obtaining the endurance mileage predicted value of the railway vehicle in real time according to the residual electric quantity information, the traction force parameter and the running interval parameter.
8. A rail vehicle, comprising:
a traction engine;
a traction battery for powering the traction engine so that the traction engine drives the rail vehicle to travel;
prediction means for implementing the steps in the method according to any one of claims 1 to 6.
9. A computer readable storage medium, characterized in that a computer program is stored thereon, which program, when being executed by a processor, realizes the steps in the method according to any of claims 1 to 6.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executed implementing the steps in the method of any one of claims 1 to 6.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310179321.1A CN116279601A (en) | 2023-02-28 | 2023-02-28 | Prediction method, device and product for endurance mileage of railway vehicle |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310179321.1A CN116279601A (en) | 2023-02-28 | 2023-02-28 | Prediction method, device and product for endurance mileage of railway vehicle |
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| CN116279601A true CN116279601A (en) | 2023-06-23 |
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| CN202310179321.1A Pending CN116279601A (en) | 2023-02-28 | 2023-02-28 | Prediction method, device and product for endurance mileage of railway vehicle |
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