CN113158141A - New energy automobile overload detection method based on big data - Google Patents

New energy automobile overload detection method based on big data Download PDF

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CN113158141A
CN113158141A CN202110372170.2A CN202110372170A CN113158141A CN 113158141 A CN113158141 A CN 113158141A CN 202110372170 A CN202110372170 A CN 202110372170A CN 113158141 A CN113158141 A CN 113158141A
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王震坡
刘鹏
张普琛
张照生
武烨
曲昌辉
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Beijing Institute of Technology BIT
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Abstract

The invention provides a new energy automobile overload detection method based on big data, which can determine whether a target new energy automobile is overloaded or not by calculating the overload energy consumption probability of similar automobile models on the same road section and comparing the overload energy consumption probability with processed traffic management historical data, overcomes the defect of complex single-automobile single-time automobile inspection mode in the prior art, fully exerts the advantages of new energy automobiles and big data platforms, obviously improves the efficiency of overload inspection and reduces the cost. The overload is divided into different conditions of overload and overspeed and not overspeed through combining the calculation of the average speed probability of the target vehicle, so that the overload target vehicle in a low-speed and low-energy-consumption state can be effectively found, the omission of the inspection result is avoided or the target which is only overspeed is mistakenly identified as overload, and the accuracy of the method is ensured. Those skilled in the art can more flexibly choose whether to adjust the accuracy of the result in conjunction with the subsequent detection of the average velocity based on the teachings provided by the present invention.

Description

New energy automobile overload detection method based on big data
Technical Field
The invention belongs to the technical field of big data of new energy automobiles, and particularly relates to a method for detecting an overload condition of a vehicle by using the big data of the new energy automobile.
Background
Due to continuous improvement of road conditions and improvement of various performances of the new energy automobile, the overload problem of the new energy automobile is also getting worse. The overload problem not only damages public roads, but also damages the life and property safety of people. The overload problem has become one of the main causes of traffic accidents.
In order to solve the problem of vehicle overload, the traffic management department and the related departments consume a large amount of manpower, material resources and financial resources, however, under the current conditions, the method for detecting the overload of the traditional fuel vehicle or the new energy vehicle mainly comprises the following steps: the method of setting an overrun station for detection, additionally installing an acceleration sensor for a vehicle for detection, carrying out routine inspection on vehicle overload by personnel in a traffic control department and the like is still limited to single detection of a single vehicle, has serious defects in the aspects of real-time performance and detection efficiency, and has extremely high cost of consumed manpower, material resources and financial resources.
Disclosure of Invention
In view of this, the present invention aims to solve the technical problems of low overload detection efficiency and high cost in the prior art, and provides a new energy vehicle overload detection method based on big data by using the advantages of new energy vehicles and big data, internet of vehicles and other technologies, and specifically includes the following steps:
(1) acquiring running condition data corresponding to a plurality of continuous segments of a new energy automobile type running on a specific road section;
(2) calculating the energy consumed by the target vehicle on each segment by using the acquired running condition data corresponding to each segment; obtaining the total energy consumed by the target vehicle on the road section based on the energy consumed on each segment;
(3) repeating the steps (1) and (2) for other vehicles of the same vehicle type, and counting to obtain a probability density function of total energy consumed by the vehicle type on the road section;
(4) and calculating the probability of the total energy consumed by the target vehicle to be detected of the vehicle type when the target vehicle runs on the road section based on the probability density function of the total energy, and comparing the probability with an overload energy probability threshold determined according to historical statistical data of a traffic control department so as to determine whether the target vehicle is overloaded.
Further, the step (2) is specifically as follows: acquiring voltage and current data in the driving condition data corresponding to the continuous segments, and calculating the total energy E consumed by a certain specific vehicle type on the road section by the following formula:
Figure BDA0003009712510000011
wherein n is the number of times of fragment collection; u shapekIs the voltage sampled at the kth time and has the unit of V; i iskThe current sampled at the kth time is in A; delta t is the time interval of two adjacent sampling, and the unit is s; the unit of E is J.
Further, the specific calculation method for determining the overload energy probability threshold according to the historical statistical data of the traffic management department is as follows:
Figure BDA0003009712510000021
wherein n is1The total number of the vehicles which are checked on the same road section and are of the same type as the target vehicle; n is2The total number of overloaded and overspeed vehicles of the same type which are checked;
comparing the probability P (E) of the total energy consumed by the target vehicle when traveling on the road sectionMeasuring) And said P1If P (E)Measuring)>P1It is confirmed that overload occurs.
Further, after performing the steps (1) to (3), the following steps (5) to (6) are also performed:
(5) calculating the average speed of the target vehicle on each segment by using the acquired running condition data corresponding to each segment; obtaining the total average speed of the target vehicle on the road section based on the average speed on each segment;
(6) repeating the step (5) for other vehicles of the same vehicle type, and counting to obtain a probability density function of the total average speed of the vehicle type on the road section;
when the content is 50 percent<P(EMeasuring)<P1And if so, calculating the probability of the average speed of the target vehicle by using the probability density function of the total average speed, and comparing the probability with a speed probability threshold determined according to historical statistical data of the traffic control department so as to determine whether the target vehicle is overloaded or not.
Further, the step (6) is specifically: obtaining average speed v corresponding to a plurality of continuous segmentskThe total average speed v of a particular vehicle type over the route is calculated by the following formula:
Figure BDA0003009712510000022
further, the specific calculation method for determining the overspeed probability threshold according to the historical statistical data of the traffic management department is as follows:
Figure BDA0003009712510000023
wherein n is1The total number of the vehicles which are checked on the same road section and are of the same type as the target vehicle; n is3The total number of overloaded but not overspeed vehicles of the same type which have been checked;
comparing the probability P (v) of the total average speed of the target vehicle when traveling on the road sectionMeasuring) And said P2If P (v)Measuring)<P2Confirming that overload has occurred, otherwise, not having occurred。
According to the method provided by the invention, whether the target new energy automobile is overloaded or not can be determined by calculating the overload energy consumption probability of the same type of automobile on the same road section and comparing the overload energy consumption probability with the processed traffic management historical data, so that the defect that in the prior art, a complicated single-automobile single-time inspection mode is overcome, the advantages of the new energy automobile and a large data platform are fully exerted, the overload inspection efficiency is obviously improved, and the cost is reduced. The calculation of the overspeed probability of the target vehicle is combined, the overload is divided into different conditions of overload and overspeed-free overload, so that the overload target vehicle in a low-speed and low-energy-consumption state can be effectively found, the omission of the inspection result is avoided or the overspeed-only target is mistakenly identified as overload, and the accuracy of the method is ensured. Those skilled in the art can more flexibly choose whether to adjust the accuracy of the result in conjunction with the subsequent detection of the average velocity based on the teachings provided by the present invention.
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FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a new energy automobile overload detection method based on big data, which specifically comprises the following steps:
(1) acquiring running condition data corresponding to a plurality of continuous segments of a new energy automobile type running on a specific road section;
(2) calculating the energy consumed by the target vehicle on each segment by using the acquired running condition data corresponding to each segment; obtaining the total energy consumed by the target vehicle on the road section based on the energy consumed on each segment;
(3) repeating the steps (1) and (2) for other vehicles of the same vehicle type, and counting to obtain a probability density function of total energy consumed by the vehicle type on the road section;
(4) and calculating the probability of the total energy consumed by the target vehicle to be detected of the vehicle type when the target vehicle runs on the road section based on the probability density function of the total energy, and comparing the probability with an overload energy probability threshold determined according to historical statistical data of a traffic control department so as to determine whether the target vehicle is overloaded.
In a preferred embodiment of the present invention, step (2) is specifically: acquiring voltage and current data in the driving condition data corresponding to the plurality of continuous segments, and calculating the total energy E consumed by a vehicle of a certain vehicle type on the road section by the following formula:
Figure BDA0003009712510000031
wherein n is the number of times of fragment collection; u shapekIs the voltage sampled at the kth time and has the unit of V; i iskThe current sampled at the kth time is in A; delta t is the time interval of two adjacent sampling, and the unit is s; the unit of E is J.
The specific calculation method for determining the overload energy probability threshold according to the historical statistical data of the traffic management department comprises the following steps:
Figure BDA0003009712510000032
wherein n is1The total number of the vehicles which are checked on the same road section and are of the same type as the target vehicle; n is2The total number of overloaded and overspeed vehicles of the same type which are checked;
using probability density function f of total energyE(x) By the formula
Figure BDA0003009712510000033
The probability of the total energy of the target vehicle can be obtained. Comparing the probability P (E) of the total energy consumed by the target vehicle when traveling on the road sectionMeasuring) Andthe P is1If P (E)Measuring)＞P1It is confirmed that overload occurs.
When the target vehicle is in a low speed state, because the energy consumed at this time is less, it may not be able to correctly judge whether the target vehicle is overloaded according to the energy probability alone. Since the overloaded vehicle is less harmful at low speed, the inspection can be terminated after the inspection for the more dangerous condition of overload and overspeed is realized in the above steps, and the technical problem proposed by the present invention is solved. And for the scene needing to more comprehensively check the overload, the following steps (5) to (6) can be synchronously executed when the steps (1) to (3) are executed:
(5) calculating the average speed of the target vehicle on each segment by using the acquired running condition data corresponding to each segment; obtaining the total average speed of the target vehicle on the road section based on the average speed on each segment;
(6) repeating the step (5) for other vehicles of the same vehicle type, and counting to obtain a probability density function of the total average speed of the vehicle type on the road section;
using probability density function f of total average velocityv(x) By the formula
Figure BDA0003009712510000041
The probability of the overall average speed of the target vehicle can be obtained. When the content is 50 percent<P(EMeasuring)<P1And calculating the average speed probability of the target vehicle by using the probability density function of the total average speed because the consumed energy is still higher, and comparing the average speed probability with an overspeed probability threshold determined according to historical statistical data of a traffic control department so as to determine whether the target vehicle is overloaded or not.
In step (6), the average speed v corresponding to a plurality of continuous segments is obtainedkCalculating the total average speed v of the vehicle of a specific vehicle type on the road section by the following formula:
Figure BDA0003009712510000042
the specific calculation method for determining the overspeed probability threshold according to the historical statistical data of the traffic management department comprises the following steps:
Figure BDA0003009712510000043
wherein n is1The total number of the vehicles which are checked on the same road section and are of the same type as the target vehicle; n is3The total number of overloaded but not overspeed vehicles of the same type which have been checked;
comparing the probability P (v) of the total average speed of the target vehicle when traveling on the road sectionMeasuring) And said P2If P (v)Measuring)＜P2It means that the target vehicle still consumes a high amount of energy in a low speed state, and thus it can be confirmed that overload has occurred, otherwise it can be confirmed that overload has not occurred.
It should be understood that, the sequence numbers of the steps in the embodiments of the present invention do not mean the execution sequence, and the execution sequence of each process should be determined by the function and the inherent logic of the process, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (6)

1. New energy automobile overload detection method based on big data, its characterized in that: the method specifically comprises the following steps:
(1) acquiring running condition data corresponding to a plurality of continuous segments of a certain new energy automobile type running on a certain specific road section;
(2) calculating the energy consumed by the target vehicle on each segment by using the acquired running condition data corresponding to each segment; obtaining the total energy consumed by the target vehicle on the road section based on the energy consumed on each segment;
(3) repeating the steps (1) and (2) for other vehicles of the same vehicle type, and counting to obtain a probability density function of total energy consumed by the vehicle type on the road section;
(4) and calculating the probability of the total energy consumed by the target vehicle to be detected of the vehicle type when the target vehicle runs on the road section based on the probability density function of the total energy, and comparing the probability with an overload energy probability threshold determined according to historical statistical data of a traffic control department so as to determine whether the target vehicle is overloaded.
2. The method of claim 1, wherein: the step (2) is specifically as follows: acquiring voltage and current data in the driving condition data corresponding to the plurality of continuous segments, and calculating the total energy E consumed by a target vehicle of a specific vehicle type on the road section by the following formula:
Figure FDA0003009712500000011
wherein n is the number of times of fragment collection; u shapekIs the voltage sampled at the kth time and has the unit of V; i iskThe current sampled at the kth time is in A; delta t is the time interval of two adjacent sampling, and the unit is s; the unit of E is J.
3. The method of claim 1, wherein: the specific calculation method for determining the overload energy probability threshold according to the historical statistical data of the traffic management department comprises the following steps:
Figure FDA0003009712500000012
wherein n is1The total number of the vehicles which are checked on the same road section and are of the same type as the target vehicle; n is2The total number of overloaded and overspeed vehicles of the same type which are checked;
comparing the probability P (E) of the total energy consumed by the target vehicle when traveling on the road sectionMeasuring) And said P1If P (E)Measuring)>P1It is confirmed that overload occurs.
4. The method of claim 3, wherein: after performing the steps (1) to (3), the following steps (5) to (6) are also performed:
(5) calculating the average speed of the target vehicle on each segment by using the acquired running condition data corresponding to each segment; obtaining the total average speed of the target vehicle on the road section based on the average speed on each segment;
(6) repeating the step (5) for other vehicles of the same vehicle type, and counting to obtain a probability density function of the total average speed of the vehicle type on the road section;
when the content is 50 percent<P(EMeasuring)<P1And if so, calculating the probability of the average speed of the target vehicle by using the probability density function of the total average speed, and comparing the probability with a speed probability threshold determined according to historical statistical data of the traffic control department so as to determine whether the target vehicle is overloaded or not.
5. The method of claim 4, wherein: the step (6) is specifically as follows: obtaining average speed v corresponding to a plurality of continuous segmentskThe total average speed v of a particular vehicle type over the route is calculated by the following formula:
Figure FDA0003009712500000021
6. the method of claim 5, wherein: the specific calculation method for determining the speed probability threshold according to the historical statistical data of the traffic management department comprises the following steps:
Figure FDA0003009712500000022
wherein n is1For the same vehicle as the target vehicle which has been checked on the same road sectionTotal number of vehicles of type; n is3The total number of overloaded but not overspeed vehicles of the same type which have been checked;
comparing the probability P (v) of the total average speed of the target vehicle when traveling on the road sectionMeasuring) And said P2If P (v)Measuring)<P2It is confirmed that overload has occurred, otherwise no overload has occurred.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022213596A1 (en) * 2021-04-07 2022-10-13 北京理工大学 Big data-based new energy vehicle overload detection method

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116152757B (en) * 2023-04-18 2023-07-07 深圳亿维锐创科技股份有限公司 Weighing data analysis method and related device based on multiple points

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101887639A (en) * 2010-06-25 2010-11-17 苏州位置科技有限公司 Vehicle overload detecting system and method based on CAN (Controller Area Network) bus
CN108860011A (en) * 2018-04-17 2018-11-23 北京理工大学 A kind of overload of vehicle recognition methods and system
CN111027146A (en) * 2019-12-30 2020-04-17 行蜂科技(深圳)有限责任公司 Vehicle load dynamic real-time calculation method
CN111833604A (en) * 2020-07-10 2020-10-27 北京交通大学 Vehicle load state identification method and device based on driving behavior feature extraction

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016074608A2 (en) * 2014-11-11 2016-05-19 冯春魁 Methods and systems for vehicle operation monitoring and control, video monitoring, data processing, and overload monitoring and control
CN111762096A (en) * 2020-06-27 2020-10-13 王亚鹏 New energy automobile safety early warning method and system based on artificial intelligence
CN113158141B (en) * 2021-04-07 2023-02-28 北京理工大学 New energy automobile overload detection method based on big data

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101887639A (en) * 2010-06-25 2010-11-17 苏州位置科技有限公司 Vehicle overload detecting system and method based on CAN (Controller Area Network) bus
CN108860011A (en) * 2018-04-17 2018-11-23 北京理工大学 A kind of overload of vehicle recognition methods and system
CN111027146A (en) * 2019-12-30 2020-04-17 行蜂科技(深圳)有限责任公司 Vehicle load dynamic real-time calculation method
CN111833604A (en) * 2020-07-10 2020-10-27 北京交通大学 Vehicle load state identification method and device based on driving behavior feature extraction

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022213596A1 (en) * 2021-04-07 2022-10-13 北京理工大学 Big data-based new energy vehicle overload detection method

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