CN110702130A - New energy automobile remaining mileage estimation system - Google Patents
New energy automobile remaining mileage estimation system Download PDFInfo
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- CN110702130A CN110702130A CN201910837360.XA CN201910837360A CN110702130A CN 110702130 A CN110702130 A CN 110702130A CN 201910837360 A CN201910837360 A CN 201910837360A CN 110702130 A CN110702130 A CN 110702130A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3469—Fuel consumption; Energy use; Emission aspects
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3407—Route searching; Route guidance specially adapted for specific applications
- G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C22/00—Measuring distance traversed on the ground by vehicles, persons, animals or other moving solid bodies, e.g. using odometers, using pedometers
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L17/00—Devices or apparatus for measuring tyre pressure or the pressure in other inflated bodies
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
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- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
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- Automation & Control Theory (AREA)
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Abstract
The invention provides a system for estimating remaining mileage of a new energy automobile, which comprises: the system comprises a server, a vehicle-mounted client, a gyroscope and a tire pressure weight measuring system. The vehicle-mounted client side is electrically connected with the power supply module, measures the SOC of the power supply module, measures the pitching of the vehicle, the speed of the vehicle, the steering of the vehicle and the acceleration change of the vehicle through the gyroscope, measures the weight of the vehicle through the tire pressure weight measuring system, and transmits the data to the server. And planning a route on the vehicle-mounted client, wherein the vehicle-mounted client acquires meteorological information, road condition information (determining vehicle acceleration) and geographic information (vehicle body pitching) on the planned route from the server, and determines the remaining mileage by using the current SOC in combination with the weight of the vehicle acquired from the tire pressure weight measuring system and substituting the weight into the relation between the SOC change and the vehicle distance.
Description
Technical Field
The invention relates to the field of new energy automobiles, in particular to a system for estimating remaining mileage of a new energy automobile.
Background
Compared with the traditional internal combustion engine automobile, the pure electric automobile has obvious advantages in the aspects of energy consumption and emission, such as good dynamic property, low driving noise, energy conservation, zero emission and the like.
However, due to the limitation of the development of battery technology, the driving range of the electric vehicle is often limited, and a driver judges whether the vehicle can reach a destination or not according to the predicted 'remaining driving range', and plans a journey and a charging place in advance. The method for predicting the remaining mileage of the commercially-operated pure electric vehicle mainly comprises the following steps: and counting historical energy consumption data of a period of time, calculating the current energy consumption rate on the assumption that the future energy consumption is similar to the current energy consumption, estimating the residual energy according to the charge state of the battery, and finally obtaining the predicted residual driving mileage. The method has the advantages of simple calculation, good real-time performance and easy realization. Therefore, the method is adopted by the remaining mileage prediction of most electric automobiles. However, the disadvantages of this approach are: for a path which is traveled by the automobile for the first time, the automobile enters a new environment, environmental factors are changed, the energy consumption of the automobile in the new environment may have a larger difference from a historical environment, but the historical data is still adopted to predict the remaining mileage, so that a larger deviation occurs, and the driving and charging planning of a driver is influenced.
Disclosure of Invention
In order to solve the technical problem, the invention provides a system for estimating the remaining mileage of a new energy automobile.
A system for estimating remaining mileage of a new energy automobile comprises a server and a vehicle-mounted client, wherein,
the vehicle-mounted client is arranged in front of a driving position of the new energy automobile, the vehicle-mounted client is in data connection with a tire pressure weight measuring system, and the tire pressure weight measuring system is arranged at a wheel of the new energy automobile;
the vehicle-mounted client is connected with a gyroscope in a data mode, and the gyroscope is arranged at the head of the new energy automobile;
the vehicle-mounted client is electrically connected with a power module of the new energy automobile, and monitors the SOC of the power module;
the vehicle is internally provided with a GPS positioning system in the client;
the vehicle-mounted client is connected with the server through a network, and the server is provided with a database;
the server is connected with the geographic information data through the api interface data, the server is connected with the real-time road condition data through the api interface data, and the server is connected with the real-time meteorological condition data through the api interface data; the vehicle-mounted client is connected with the server through a network, the vehicle-mounted client calls the geographic information data through the server to plan the route, the client obtains the longitude and latitude and the altitude of the planned route from the geographic information data, the client calls the implementation road condition data of the planned route from the server, and the client calls the meteorological condition data of the planned route from the server.
Preferably, the client can be wirelessly connected with an intelligent device, and the intelligent device can be connected with the server through a network.
Preferably, the vehicle-mounted client measures the SOC of the automobile power supply module by combining a Kalman filtering algorithm and a three-layer neural network.
Compared with the related art, the system for estimating the remaining mileage of the new energy automobile has the following beneficial effects:
the system for estimating the remaining mileage of the new energy automobile accurately acquires the SOC of the automobile power module by utilizing the vehicle-mounted client through the Kalman algorithm in combination with the three-layer neural network, learns and identifies the parameter characteristics of the battery through the three-layer neural network, determines the weight of the SOC influenced by the parameters through learning, establishes a mathematical model of the power module, acquires a state equation of the estimated SOC, and determines the minimum variance of the SOC through the Kalman algorithm in a recursion manner, so that the SOC is accurately estimated.
The vehicle-mounted client plans a route by acquiring geographic information data from the server and combining a GPS positioning system, extracts geographic information, road condition information and meteorological information on the planned route from the server, and measures the weight of the vehicle by using the tire pressure weight measuring system; and acquiring influence factors of the relation between the SOC variable and the driving distance.
The vehicle-mounted client positions the vehicle by using a GPS positioning system, the server retrieves meteorological information of the position of the vehicle according to the positioning, the server determines the relation between the SOC variation and the vehicle driving distance by using the measured self weight of the vehicle, the acceleration and the steering of the vehicle and the pitching factors of the vehicle body, and the server substitutes the geographic information (the slope fluctuation corresponds to the pitching of the vehicle body), the road condition information (corresponding to the vehicle acceleration variation) and the meteorological information on the planned route and the weight of the vehicle into the relation between the SOC variation and the vehicle driving distance, and then estimates the obtained remaining mileage by using the measured current SOC more accurately.
Drawings
Fig. 1 is a schematic structural diagram of a system for estimating remaining mileage of a new energy vehicle according to a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of a connection relationship of the system for estimating the remaining mileage of the new energy vehicle provided by the invention;
FIG. 3 is a schematic diagram of a neural network for determining a relationship between an SOC variable and mileage.
Reference numbers in the figures: 1. the system comprises a server, 2, a vehicle-mounted client, 3, a tire pressure weight measuring system, 4, a gyroscope, 5 and a power supply module.
Detailed Description
The invention is further described with reference to the following figures and embodiments.
Please refer to fig. 1, fig. 2 and fig. 3 in combination, wherein fig. 1 is a schematic structural diagram of a system for estimating remaining mileage of a new energy vehicle according to a preferred embodiment of the present invention; FIG. 2 is a schematic diagram of a connection relationship of the system for estimating the remaining mileage of the new energy vehicle provided by the invention; FIG. 3 is a schematic diagram of a neural network for determining a relationship between an SOC variable and mileage.
Example one
With reference to fig. 1 and fig. 2, a system for estimating remaining mileage of a new energy automobile comprises a server 1 and a vehicle-mounted client 2, wherein the vehicle-mounted client 2 is arranged in front of a driving position of the new energy automobile, the vehicle-mounted client 2 comprises a display and a single chip microcomputer, a network card interface, a processor and a GPS positioning system are arranged on the single chip microcomputer, the network card interface is inserted with a network card, the vehicle-mounted client is connected with the server 1 through a network card network, the single chip microcomputer is electrically connected with the display, the display is a touch screen capable of being touched, the processor of the vehicle-mounted client 2 is in data connection with a tire pressure weight measuring system 3 arranged at a tire of the new energy automobile, the tire pressure measuring system 3 measures tire pressure of the automobile tire, and the tire pressure measuring system 3 determines the weight of the new energy automobile after loading by using the weight of the empty automobile, the new energy automobile and the, the loaded weight is equal to the loaded tire pressure multiplied by the weight of the new energy automobile divided by the empty tire pressure, and the tire pressure weight measuring system 3 transmits the measured automobile weight to the vehicle-mounted client 2 through a data line.
The data of the processor of the vehicle-mounted client 2 is connected with a gyroscope 4 arranged at the head of the new energy automobile, and the gyroscope 4 transmits the measured pitch angle, steering condition, speed and acceleration data of the new energy automobile to the vehicle-mounted client 2.
The vehicle-mounted client 2 is electrically connected with a power module 5 of an automobile, the vehicle-mounted client 2 simulates aging of the power module 5 through a mathematical model by measuring internal resistance, current, voltage and temperature of the power module 5, the vehicle-mounted client 2 inputs the aging, the internal resistance, the current, the voltage and the temperature into a three-layer neural network, and the three-layer neural network is a BP neural network and comprises an input layer, a hidden layer and an output layer. The aging, the internal resistance, the current, the voltage and the temperature are input into the input layer, transmitted to the hidden layer from the input layer, and the operation result of the hidden layer is transmitted to the output layer, so that the weight between the layers is trained. The transmission process of the error is opposite, and the error is reversely transmitted from the output layer to the hidden layer and then to the input layer to be used as the basis for the next transmission weight value adjustment. And establishing an SOC state equation of the power module 5 by using the adjusted parameter weight, and determining the minimum variance estimation of the SOC through recursion by using a Kalman algorithm, thereby accurately estimating the SOC.
The server 1 is connected with geographic information data through api interface data, the vehicle-mounted client determines the position of a vehicle through the GPS, a user can conveniently plan a route by combining the geographic information data, specifically, the vehicle-mounted client 2 acquires the geographic information data through the server 1, the vehicle-mounted client 2 determines the position of the vehicle-mounted client 2 through the GPS, the user inputs a destination to the vehicle-mounted client 2, and the vehicle-mounted client 2 plans an optimal route through an A-x algorithm.
The server 1 is connected with the real-time road condition data through the api interface data, and the server 1 is connected with the real-time meteorological condition data through the api interface data. The vehicle-mounted client 2 acquires the altitude of a point on the planned route from the server 1, and implements road condition data and meteorological condition data. And in combination with the GPS, the vehicle-mounted client 2 acquires the meteorological condition data of the position of the vehicle from the server 1.
Referring to fig. 3, the server 1 includes a database, the vehicle-mounted client 2 transmits the measured pitch angle, steering condition, speed, and acceleration data of the new energy vehicle to the database through the network via the network card connected to the network card interface, and transmits the measured weight of the new energy vehicle, weather condition data (rain, snow, wind condition) of the vehicle location, and the estimated SOC to the database through the network. In fact, the weather in the meteorological conditions causes the change of the friction between the vehicle and the road surface, the thrust or resistance brought by the wind, the acceleration, deceleration (acceleration), turning and climbing processes in the vehicle running process affect the relation between the SOC change and the vehicle running distance under the corresponding change, the database utilizes the pitch angle, the steering condition, the acceleration, the vehicle weight and the meteorological condition data of the new energy vehicle obtained in real time to be input into an input layer of a neural network for calculating the relation between the SOC change and the running distance, the data is transmitted to a hidden layer from the input layer, the operation result of the hidden layer is transmitted to an output layer, and the weight between each layer is trained. The transmission process of the error is opposite, the error is reversely transmitted from the output layer to the hidden layer and then to the input layer, and the error is used as the basis for the next transmission weight value adjustment to make a relation equation between the SOC variation and the running distance (the vehicle speed measured by the gyroscope is integrated with the time).
The vehicle-mounted client 2 calculates the remaining mileage according to the currently measured SOC value by introducing the weather conditions on the planned route, the implementation road condition (acceleration and deceleration during the driving process of the vehicle is influenced), the fluctuation condition of the route and the self weight of the vehicle into a relational equation between the SOC variation and the driving distance.
Example two
The difference between the second embodiment and the first embodiment is that: the single chip microcomputer is provided with a Bluetooth module, the Bluetooth module is in wireless connection with intelligent equipment, and the intelligent equipment is in network connection with the server 1.
The principle of the system for estimating the remaining mileage of the new energy automobile provided by the invention is as follows:
the vehicle-mounted client 2 is electrically connected with the power module 5, the vehicle-mounted client 2 measures the SOC of the power module 5, the vehicle-mounted client 2 measures the pitching of the vehicle, the speed of the vehicle, the steering of the vehicle and the acceleration of the vehicle through the gyroscope 4, and the vehicle-mounted client 2 measures the weight of the vehicle through the tire pressure weight measuring system 3. The vehicle-mounted client 2 transmits the SOC, the vehicle weight, the vehicle pitching, the vehicle steering and the vehicle acceleration to a database of the server 1 through a network, the database calls meteorological information according to the vehicle position, and data in the database is input into a neural network algorithm to determine the relation between the SOC change and the vehicle distance. The server transmits the relationship between the SOC change and the vehicle distance to the in-vehicle client 2.
And the vehicle-mounted client 2 is positioned by using a GPS (global positioning system), and the geographic information is called from the server for route planning.
The vehicle-mounted client 2 acquires weather information, road condition information (vehicle acceleration determination) and geographic information (vehicle body pitching) on a planned route from the server 1, substitutes the weight of the vehicle acquired from the tire pressure weight measuring system into the relation between the SOC change and the vehicle distance, and determines the remaining mileage by using the current SOC.
The system for estimating the remaining mileage of the new energy automobile accurately acquires the SOC of the power module 5 of the automobile by utilizing the vehicle-mounted client 22 through a Kalman algorithm in combination with a three-layer neural network, learns and identifies the characteristics of battery parameters through the three-layer neural network, determines the weight of the SOC influenced by the parameters through learning, establishes a mathematical model of the power module 5, acquires a state equation of the estimated SOC, and determines the minimum variance of the SOC through the Kalman algorithm in a recursion manner, so that the SOC is accurately estimated.
The vehicle-mounted client 2 plans a route by acquiring geographic information data from the server 1 and combining a GPS positioning system, the vehicle-mounted client 2 acquires geographic information, road condition information and meteorological information on the planned route from the server 1, and the vehicle-mounted client 2 measures the weight of a vehicle by using the tire pressure weight measuring system 3; and acquiring influence factors of the relation between the SOC variable and the driving distance.
The vehicle-mounted client 2 utilizes a GPS positioning system to position the vehicle, the server 1 retrieves meteorological information of the position of the vehicle according to the positioning, the server 1 utilizes the measured self weight of the vehicle, the acceleration and the steering of the vehicle and the pitching factors of the vehicle body to determine the relation between the SOC variation and the vehicle driving distance by utilizing a neural network, the server 1 substitutes the geographic information (the gradient fluctuation corresponds to the pitching of the vehicle body), the road condition information (corresponding to the vehicle acceleration variation) and the meteorological information on the planned route and the weight of the vehicle into the relation between the SOC variation and the vehicle driving distance, and the measured current SOC is utilized to estimate the obtained remaining mileage more accurately.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (3)
1. The system for estimating the remaining mileage of the new energy automobile is characterized by comprising a server (1) and an on-vehicle client (2),
the vehicle-mounted client (2) is arranged in front of a driving position of the new energy automobile, the vehicle-mounted client (2) is in data connection with the tire pressure weight measuring system (3), and the tire pressure weight measuring system (3) is arranged at a wheel of the new energy automobile;
the vehicle-mounted client (2) is in data connection with a gyroscope (4), and the gyroscope (4) is arranged at the head of the new energy automobile;
the vehicle-mounted client (2) is electrically connected with a power module (5) of the new energy automobile, and the vehicle-mounted client (2) monitors the SOC of the power module (5);
the vehicle is internally provided with a GPS positioning system in the client (2);
the vehicle-mounted client (2) is connected with the server (1) through a network, and the server (1) is provided with a database;
the server (1) is connected with geographic information data through api interface data, the server (1) is connected with real-time road condition data through the api interface data, and the server (1) is connected with real-time meteorological condition data through the api interface data; the vehicle-mounted client (2) is connected with the server (1) through a network, the vehicle-mounted client (2) calls geographic information data to plan a route through the server (1), the client (2) obtains longitude and latitude and altitude of the planned route from the geographic information data, the client (2) calls implementation road condition data of the planned route from the server (1), and the client (2) calls meteorological condition data of the planned route from the server (1).
2. The system for estimating remaining mileage of a new energy vehicle as claimed in claim 1, wherein the client (2) is capable of wirelessly connecting to a smart device, and the smart device is capable of network-connecting to the server (1).
3. The system for estimating the remaining mileage of a new energy vehicle according to claim 1, wherein the vehicle-mounted client (2) measures the SOC of the vehicle power module by using a kalman filter algorithm in combination with a three-layer neural network.
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| CN201910837360.XA CN110702130A (en) | 2019-09-05 | 2019-09-05 | New energy automobile remaining mileage estimation system |
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| CN201910837360.XA CN110702130A (en) | 2019-09-05 | 2019-09-05 | New energy automobile remaining mileage estimation system |
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| CN113696784A (en) * | 2021-10-26 | 2021-11-26 | 深圳市乐骑智能科技有限公司 | Electric scooter residual capacity prediction method and device based on Internet of things |
| CN114370918A (en) * | 2022-02-23 | 2022-04-19 | 浙江吉利控股集团有限公司 | Vehicle load monitoring method, device and system |
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