CN115588242B - Energy storage battery testing system and method based on Internet of things - Google Patents
Energy storage battery testing system and method based on Internet of things Download PDFInfo
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Abstract
The invention relates to the technical field of energy storage battery testing, in particular to an energy storage battery testing system and method based on the Internet of things, which comprises the following steps: the system comprises a test data acquisition module, an Internet of things management cloud platform, a test data analysis module, a driving data analysis module and an automobile driving reminding module, wherein the test data acquisition module is used for acquiring power consumption test data of an energy storage battery, generating a current battery power consumption curve, simultaneously acquiring historical driving routes and historical battery power consumption curve data of the electric automobile, storing all acquired data through the Internet of things management cloud platform, analyzing the current operation state of the battery through the test data analysis module, predicting the current remaining time that the electric automobile can drive through the driving data analysis module, reminding and adjusting the driving routes through the automobile driving reminding module, simultaneously reminding a user of charging the automobile, saving the battery test time and improving the accuracy of a remaining time prediction result.
Description
Technical Field
The invention relates to the technical field of energy storage battery testing, in particular to an energy storage battery testing system and method based on the Internet of things.
Background
The energy storage battery mainly refers to a storage battery for storing energy for solar power generation equipment, wind power generation equipment and renewable energy, along with the continuous deepening of energy crisis and the gradual exhaustion of petroleum resources, energy conservation and emission reduction are main directions of future automobile technology development, an electric automobile is used as a new generation of transportation tool, and has incomparable advantages of the traditional automobile in the aspects of energy conservation and emission reduction, the condition of the vehicle energy storage battery is vital to the safety and reliability of the electric automobile, and the problem of the energy storage battery can be timely and effectively discovered by testing the condition of the vehicle energy storage battery;
however, the existing testing technology for the energy storage battery for the vehicle still has some problems: firstly, the commonly used energy storage battery testing method comprises a voltage testing method, a discharge testing method and the like, wherein the discharge testing is a commonly used means for testing the running condition of the energy storage battery, however, the battery electric quantity and the running condition are generally estimated through the discharge testing in the prior art, the energy storage battery is finally applied to an electric automobile, the battery testing can find the battery problem in time and give an early warning in time during the running process, and the prior art can not test the battery in the running process of the electric automobile to realize the early warning in time, so as to help a user to adjust the running mode and replace the running route in time to smoothly reach the destination; secondly, the prior art cannot save the test time; finally, because the driving environment of the electric vehicle has a certain influence on the test result in the operation process, the prior art cannot combine the driving environment factors with the test data to improve the accuracy of the test result.
Therefore, an energy storage battery testing system and method based on the internet of things are needed to solve the above problems.
Disclosure of Invention
The invention aims to provide an energy storage battery testing system and method based on the Internet of things, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an energy storage battery test system based on the internet of things, the system comprising: the system comprises a test data acquisition module, an Internet of things management cloud platform, a test data analysis module, a driving data analysis module and an automobile driving reminding module;
the output end of the test data acquisition module is connected with the input end of the Internet of things management cloud platform, the output end of the Internet of things management cloud platform is connected with the input end of the test data analysis module, the output end of the test data analysis module is connected with the input end of the driving data analysis module, and the output end of the driving data analysis module is connected with the input end of the automobile driving reminding module;
acquiring power consumption test data of an energy storage battery through the test data acquisition module, generating a current battery power consumption curve, acquiring historical driving routes and historical battery power consumption curve data of the electric automobile, and transmitting all acquired data to the Internet of things management cloud platform;
storing all collected data through the Internet of things management cloud platform;
analyzing the current running state of the battery through the test data analysis module;
analyzing the influence degree of the road condition of the current driving route on the power consumption of the battery and predicting the remaining time of the electric automobile which can be driven currently through the driving data analysis module;
and judging whether the vehicle can successfully reach the destination or not through the vehicle running reminding module, if not, reminding and adjusting the running route, and simultaneously reminding the user to charge the vehicle.
Furthermore, the test data acquisition module comprises an energy storage battery test unit, a battery data acquisition unit and a driving data acquisition unit;
the output ends of the energy storage battery testing unit, the battery data acquisition unit and the driving data acquisition unit are connected with the input end of the Internet of things management cloud platform;
the energy storage battery testing unit is used for testing the power consumption of an energy storage battery of the electric automobile in the driving process of the electric automobile and generating a battery power consumption curve of the current driving route according to a testing result;
the driving data acquisition unit is used for acquiring historical driving route data of a user;
the battery data acquisition unit is used for acquiring battery power consumption curve data corresponding to the historical driving route.
Further, the test data analysis module comprises a power consumption curve matching unit and a data screening unit;
the input end of the power consumption curve matching unit is connected with the output end of the Internet of things management cloud platform, and the output end of the power consumption curve matching unit is connected with the input end of the data screening unit;
the power consumption curve matching unit is used for generating a current battery power consumption curve according to the tested power consumption and matching the current battery power consumption curve with a historical battery power consumption curve in the Internet of things management cloud platform;
and the data screening unit is used for acquiring a matching result and screening a historical battery power consumption curve with the highest matching degree with the current battery power consumption curve.
Further, the driving data analysis module includes a driving route analysis unit and a remaining time prediction unit;
the input end of the driving route analysis unit is connected with the output end of the data screening unit, and the output end of the driving route analysis unit is connected with the input end of the residual time prediction unit;
the driving route analysis unit is used for acquiring influence factors of the road surface of the remaining distance on the current driving route on the power consumption of the battery;
the residual time prediction unit is used for predicting the residual time of the automobile capable of running at present.
Furthermore, the automobile driving reminding module comprises a comprehensive analysis unit, a route replacement reminding unit and a charging reminding unit;
the input end of the comprehensive analysis unit is connected with the output end of the residual time prediction unit, the output end of the comprehensive analysis unit is connected with the input end of the route replacement reminding unit, and the output end of the route replacement reminding unit is connected with the input end of the charging reminding unit;
the comprehensive analysis unit is used for judging whether the vehicle can successfully arrive at the destination according to the current driving route;
the route replacement reminding unit is used for reminding the driver of replacing the driving route when judging that the driver cannot successfully arrive at the destination according to the current driving route;
the charging reminding unit is used for planning an optimal driving route and reminding a user to charge the automobile after reminding to replace the driving route.
An energy storage battery testing method based on the Internet of things comprises the following steps:
s1: collecting power consumption test data of an energy storage battery, generating a current battery power consumption curve, and collecting historical driving routes and historical battery power consumption curve data of the electric automobile;
s2: analyzing the matching degree of the current battery power consumption curve and the historical battery power consumption curve, and screening out the historical battery power consumption curve with the highest matching degree with the current power consumption curve;
s3: obtaining influence factors of the road surface of the remaining distance on the current driving route on the power consumption of the battery, and predicting the remaining time of the automobile capable of driving currently by combining the matching degree and the influence factors;
s4: judging whether the vehicle can successfully reach the destination according to the current driving route according to the remaining time;
s5: and reminding the user to adjust the driving route, selecting the optimal driving route for the user and simultaneously reminding the user to charge the automobile.
Further, in step S1: in the driving process of the electric automobile, testing the residual capacity of an energy storage battery of the electric automobile in real time, generating a current battery power consumption curve in the driving process, acquiring the current driving time length as T, and acquiring the past driving route data of the electric automobile: the method comprises the steps of collecting the distance set from a departure point to a first charging point as d = { d1, d2, …, dn } when the automobile runs according to a corresponding running route in the past, wherein n represents the number of the running routes, the initial electric quantity of a battery when the automobile starts according to the current running route is the same as the initial electric quantity of the battery when the automobile starts according to n running routes, collecting historical battery power consumption curves when the automobile runs according to the corresponding running routes, extracting the historical battery power consumption curves of T duration of running, testing the battery in the running process of the electric automobile, and facilitating the promotion of the function and significance of battery testing;
in step S2: randomly extracting the residual electricity quantity of m time points on a current battery power consumption curve, wherein the residual electricity quantity sequence set is A = { A1, A2, …, am }, extracting the residual electricity quantity of the battery at the same time point on a historical battery power consumption curve, obtaining a random residual electricity quantity sequence set of a historical power consumption curve as B = { B1, B2, …, bm }, and calculating the matching degree wi of the random historical battery power consumption curve and the current battery power consumption curve according to the following formula:
the method comprises the steps that Aj represents the battery residual capacity at a random time point at present, bj represents the battery residual capacity at a random time point on a random historical power consumption curve, a matching degree set of n historical battery power consumption curves and the current power consumption curve is w = { w1, w2, …, wi, …, wn }, the historical battery power consumption curve with the highest matching degree with the current power consumption curve is screened out, the battery power consumption data in the past driving process of the electric automobile is directly called from an Internet of things management cloud platform, the current battery power consumption curve is directly matched with the called curve, the time that the battery or the automobile can run in the residual state is predicted according to the driving route data corresponding to the matched curve, the battery power is not required to be estimated firstly, the battery residual time is then estimated, the workload of the battery residual time prediction can be greatly reduced, the battery is not required to be tested through long-time discharging, and the testing time is effectively saved.
Further, in step S3: taking a driving route corresponding to a historical battery power consumption curve with the highest matching degree with the current power consumption curve as an optimal matching route, and obtaining the historical battery power consumption curve with the highest matching degree as w max Calling the current traveled distance as D, the current battery residual capacity as C, obtaining the traveled distance when the battery residual capacity on the best matching route is C as L, the interval duration from the battery residual capacity as C to the first charging time as t, and obtaining the influence factor of the road surface of the residual distance on the current traveled route on the battery power consumption as T
The road surface of the optimal matching route has an influence factor on the power consumption of the battery of
Predicting the remaining time t for the automobile to run currently ’ When is coming into contact with
When the temperature of the water is higher than the set temperature,
(ii) a When the temperature is higher than the set temperature
When the utility model is used, the water is discharged,
the reference data and the current automobile operation data have certain difference, the road environment can also have certain influence on the power consumption of the battery and further on the remaining time, when the automobile operation remaining time is predicted, the matching degree parameter of the reference data and the current automobile operation data is added, in addition, the influence degree parameter of the driving road surface on the power consumption of the battery is added, and the accuracy of the time prediction result is improved.
Further, in steps S4-S5: calling current driving route data: if no charging station exists on the current driving route, the obtained distance of the remaining destination is F, the average speed of the current driving of the automobile is v, and if no charging station exists on the current driving route, the obtained average speed of the current driving of the automobile is v
Judging that the vehicle can successfully reach the destination according to the current driving route without reminding; if it is
Judging that the current running route can not be used for successfully reaching the destination, reminding the user to adjust the running route, and selecting the time required for reaching the nearest charging station for the user to be less than that of the current running route
The driving route of (2) is taken as an optimal driving route;
if the current driving is performedA charging station exists on the route, the distance from the current position to the nearest charging station is acquired as f, and if the distance is f
Judging that the current running route can not be successfully reached to the nearest charging station, reminding the user to adjust the running route, and selecting the time required for reaching the nearest charging station to be less than that of the user
The driving route of (2) is taken as an optimal driving route; if it is
And reminding the user to charge the automobile after reaching the nearest charging station, and when the driving route is adjusted, considering whether the current driving route has the charging station or not, further analyzing whether the residual electric quantity of the automobile can reach the charging station for charging or not, or whether the automobile can reach the destination, adjusting the driving route according to the situation, so that the method is beneficial to helping the user to make the best scheme for adjusting the driving route, and helps the user to smoothly reach the destination and improve the driving experience.
Compared with the prior art, the invention has the following beneficial effects:
the energy storage battery is tested in real time in the running process of the automobile to realize timely early warning, so that a user is helped to adjust a running scheme in time; the battery power consumption data of the electric automobile in the past driving process is directly called, the current battery power consumption curve is directly matched with the called curve, the time that the battery or the automobile can run in the residual state is predicted according to the driving route data corresponding to the matched curve, the workload for predicting the residual time of the battery is reduced to a great extent, the battery is not required to be tested through long-time discharging, and the testing time is effectively saved; when the remaining driving time of the automobile is predicted according to the battery test result, the matching degree parameter of the reference data and the current automobile operation data is added, and in addition, the influence degree parameter of the driving road surface on the power consumption of the battery is added, so that the accuracy of the time prediction result is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a structural diagram of an energy storage battery testing system based on the Internet of things;
fig. 2 is a flow chart of the energy storage battery testing method based on the internet of things.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The invention will be further described with reference to fig. 1-2 and the specific embodiments.
The first embodiment is as follows:
as shown in fig. 1, the embodiment provides an energy storage battery test system based on the internet of things, and the system includes: the system comprises a test data acquisition module, an Internet of things management cloud platform, a test data analysis module, a driving data analysis module and an automobile driving reminding module;
the output end of the test data acquisition module is connected with the input end of the Internet of things management cloud platform, the output end of the Internet of things management cloud platform is connected with the input end of the test data analysis module, the output end of the test data analysis module is connected with the input end of the driving data analysis module, and the output end of the driving data analysis module is connected with the input end of the automobile driving reminding module;
acquiring power consumption test data of an energy storage battery through the test data acquisition module, generating a current battery power consumption curve, acquiring historical driving routes and historical battery power consumption curve data of the electric automobile, and transmitting all acquired data to the Internet of things management cloud platform;
storing all collected data through the Internet of things management cloud platform;
analyzing the current running state of the battery through the test data analysis module;
analyzing the influence degree of the current driving route road condition on the power consumption of the battery and predicting the remaining time of the electric automobile which can be driven currently by the driving data analysis module;
and judging whether the current destination can be successfully reached or not through the automobile driving reminding module, if not, reminding and adjusting a driving route, and reminding a user to charge the automobile.
The test data acquisition module comprises an energy storage battery test unit, a battery data acquisition unit and a driving data acquisition unit;
the output ends of the energy storage battery testing unit, the battery data acquisition unit and the driving data acquisition unit are connected with the input end of the Internet of things management cloud platform;
the energy storage battery testing unit is used for testing the power consumption of an energy storage battery of the electric automobile in the driving process of the electric automobile and generating a battery power consumption curve of the current driving route according to a testing result;
the driving data acquisition unit is used for acquiring historical driving route data of a user;
the battery data acquisition unit is used for acquiring battery power consumption curve data corresponding to the historical driving route.
The test data analysis module comprises a power consumption curve matching unit and a data screening unit;
the input end of the power consumption curve matching unit is connected with the output end of the Internet of things management cloud platform, and the output end of the power consumption curve matching unit is connected with the input end of the data screening unit;
the power consumption curve matching unit is used for generating a current battery power consumption curve according to the tested power consumption and matching the current battery power consumption curve with a historical battery power consumption curve in the Internet of things management cloud platform;
and the data screening unit is used for acquiring a matching result and screening a historical battery power consumption curve with the highest matching degree with the current battery power consumption curve.
The driving data analysis module comprises a driving route analysis unit and a residual time prediction unit;
the input end of the driving route analysis unit is connected with the output end of the data screening unit, and the output end of the driving route analysis unit is connected with the input end of the residual time prediction unit;
the driving route analysis unit is used for acquiring influence factors of the road surface of the remaining distance on the current driving route on the power consumption of the battery;
the residual time prediction unit is used for predicting the residual time of the automobile capable of running at present.
The automobile driving reminding module comprises a comprehensive analysis unit, a route replacement reminding unit and a charging reminding unit;
the input end of the comprehensive analysis unit is connected with the output end of the residual time prediction unit, the output end of the comprehensive analysis unit is connected with the input end of the route replacement reminding unit, and the output end of the route replacement reminding unit is connected with the input end of the charging reminding unit;
the comprehensive analysis unit is used for judging whether the vehicle can successfully arrive at the destination according to the current driving route;
the route replacement reminding unit is used for reminding the driver of replacing the driving route when judging that the driver can not successfully arrive at the destination according to the current driving route;
the charging reminding unit is used for planning an optimal driving route and reminding a user to charge the automobile after reminding to replace the driving route.
Example two:
as shown in fig. 2, the embodiment provides an energy storage battery testing method based on the internet of things, which is implemented based on a testing system in the embodiment and specifically includes the following steps:
s1: the method comprises the following steps of collecting power consumption test data of an energy storage battery, generating a current battery power consumption curve, collecting historical driving routes and historical battery power consumption curve data of the electric automobile, testing the residual electric quantity of the energy storage battery of the electric automobile in real time in the driving process of the electric automobile, generating a current battery power consumption curve in the driving process, and collecting the current driving time length as T =3, wherein the unit is as follows: acquiring the past driving route data of the electric automobile in hours: the method comprises the following steps of collecting a distance set from a departure point to a first charging point as d = { d1, d2, d3} when the vehicle travels according to a corresponding travel route in the past, wherein n represents the number of the travel routes, the initial electric quantity of a battery when the vehicle departs according to the current travel route is the same as the initial electric quantity of the battery when the vehicle departs according to n =3 travel routes, and the initial electric quantity of the battery is as follows: acquiring a historical battery power consumption curve when the vehicle runs according to a corresponding running route by 100%, and extracting the historical battery power consumption curve of the previous 3 hours in n =3 running routes;
s2: analyzing the matching degree of the current battery power consumption curve and the historical battery power consumption curve, screening out the historical battery power consumption curve with the highest matching degree with the current power consumption curve, randomly extracting the residual electric quantity of m =5 time points on the current battery power consumption curve, wherein the residual electric quantity sequence set is A = { A1, A2, A3, A4, A5} = {80, 75, 64, 60, 53}, and the unit is: percentage, extracting the remaining capacity of the battery at the same time point on a historical battery power consumption curve, obtaining a random remaining capacity sequence set of the historical power consumption curve as B = { B1, B2, B3, B4, B5} = {82, 72, 62, 58, 50}, and obtaining the percentage according to a formula
Calculating the matching degree wi =0.91 of a random historical battery power consumption curve and the current battery power consumption curve, obtaining a matching degree set of n =3 historical battery power consumption curves and the current power consumption curve in the same calculation mode, wherein the matching degree set is w = { w1, w2, w3} = {0.91,0.85,0.62}, and screening out the historical battery power consumption curve with the highest matching degree with the current power consumption curve: a historical battery power consumption curve corresponding to w 1;
s3: obtaining influence factors of the road surface of the remaining distance on the current driving route on the battery power consumption, predicting the remaining time of the automobile capable of driving at present by combining the matching degree and the influence factors, taking the driving route corresponding to the historical battery power consumption curve with the highest matching degree with the current power consumption curve as the optimal matching route, and obtaining the highest matching degree as w max =0.91, the current traveled distance is called as D =200, and the unit is: kilometers, the current battery remaining capacity isC =53, acquiring that a distance traveled when the remaining battery capacity on the best matching route is C =53 is L =220, and an interval duration from the time when the remaining battery capacity is C =53 to the time of the first charging is t =3, where the unit is: when the current driving route is small, the influence factor of the road surface of the remaining route on the battery power consumption is acquired as
The road surface of the optimal matching route has an influence factor on the power consumption of the battery
Predicting the remaining time t for the automobile to run currently ’ ，
，
，
Predicting that the automobile can run for 3.1 hours at present;
s4: judging whether the current driving route can be successfully reached to the destination according to the remaining time, and calling current driving route data: inquiring that no charging station exists on the current driving route, and obtaining that the distance of the residual destination is F =300, wherein the unit is: the average speed of the current driving of the automobile is v =60 in kilometers as: the length of the kilometer per hour,
judging that the vehicle cannot successfully reach the destination according to the current driving route;
s5: and reminding the user of adjusting the driving route and selecting the optimal driving route for the user, reminding the user of charging the automobile, reminding the user of adjusting the driving route, selecting the driving route which can reach the nearest charging station within 3.1 hours for the user as the optimal driving route, and enabling the selected driving route to reach the destination.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. An energy storage battery testing method based on the Internet of things is characterized in that: the method comprises the following steps:
s1: collecting power consumption test data of an energy storage battery, generating a current battery power consumption curve, and collecting historical driving routes and historical battery power consumption curve data of the electric automobile;
s2: analyzing the matching degree of the current battery power consumption curve and the historical battery power consumption curve, and screening out the historical battery power consumption curve with the highest matching degree with the current power consumption curve;
s3: acquiring influence factors of the road surface of the remaining distance on the current driving route on the power consumption of the battery, and predicting the remaining time that the automobile can currently drive by combining the matching degree and the influence factors;
s4: judging whether the vehicle can successfully reach the destination according to the current driving route according to the remaining time;
s5: reminding a user to adjust a driving route, selecting an optimal driving route for the user, and simultaneously reminding the user to charge the automobile;
in step S1: in the driving process of the electric automobile, testing the residual capacity of an energy storage battery of the electric automobile in real time, generating a current battery power consumption curve in the driving process, acquiring the current driving time length as T, and acquiring the past driving route data of the electric automobile: collecting the distance set from a departure point to a first charging point as d = { d1, d2, …, dn } when the vehicle runs according to a corresponding running route in the past, wherein n represents the number of the running routes, the initial electric quantity of the battery when the vehicle starts according to the current running route is the same as the initial electric quantity of the battery when the vehicle starts according to n running routes, collecting a historical battery power consumption curve when the vehicle runs according to the corresponding running route, and extracting the historical battery power consumption curve of T duration of running;
in step S2: randomly extracting the residual electricity quantity of m time points on a current battery power consumption curve, wherein the residual electricity quantity sequence set is A = { A1, A2, …, am }, extracting the residual electricity quantity of the battery at the same time point on a historical battery power consumption curve, obtaining a random residual electricity quantity sequence set of a historical power consumption curve as B = { B1, B2, …, bm }, and calculating the matching degree wi of the random historical battery power consumption curve and the current battery power consumption curve according to the following formula:
the method comprises the steps that Aj represents the battery residual capacity at a random time point at present, bj represents the battery residual capacity at a random time point on a random historical power consumption curve, the matching degree set of n historical battery power consumption curves and the current power consumption curve obtained through the same calculation mode is w = { w1, w2, …, wi, …, wn }, and the historical battery power consumption curve with the highest matching degree with the current power consumption curve is screened out;
in step S3: taking a driving route corresponding to a historical battery power consumption curve with the highest matching degree with the current power consumption curve as an optimal matching route, and obtaining the highest matching degree as w max Calling the current traveled distance as D, the current battery residual capacity as C, obtaining the traveled distance when the battery residual capacity on the best matching route is C as L, the interval duration from the battery residual capacity as C to the first charging time as t, and obtaining the influence factor of the road surface of the residual distance on the current traveled route on the battery power consumption as T
The road surface of the optimal matching route has an influence factor on the power consumption of the battery
Predicting the remaining time that the vehicle can run at presentIs m between t ’ When it comes to
When the utility model is used, the water is discharged,
(ii) a When in use
When the temperature of the water is higher than the set temperature,
。
2. the energy storage battery testing method based on the Internet of things according to claim 1, characterized in that: in steps S4-S5: calling current driving route data: if no charging station exists on the current driving route, acquiring that the distance to the destination is F, the average speed of the current driving of the automobile is v, and if no charging station exists on the current driving route, acquiring that the distance to the destination is F, and the average speed of the current driving of the automobile is v
Judging that the vehicle can successfully reach the destination according to the current driving route without reminding; if it is
Judging that the current running route can not be used for successfully reaching the destination, reminding the user to adjust the running route, and selecting the time required for reaching the nearest charging station for the user to be less than that of the current running route
The driving route of (2) is taken as an optimal driving route;
if a charging station exists on the current driving route, acquiring the distance from the current position to the nearest charging station as f, and if the charging station exists on the current driving route, acquiring the distance from the current position to the nearest charging station as f
Judging that the current running route can not be successfully reached to the nearest charging station, reminding the user to adjust the running route, and selecting the time required for reaching the nearest charging station to be less than that of the user
The driving route of (2) is taken as an optimal driving route; if it is
And reminding the user to charge the automobile after the automobile arrives at the nearest charging station.
3. An energy storage battery testing system based on the Internet of things is applied to the energy storage battery testing method based on the Internet of things as claimed in claim 1, and is characterized in that: the system comprises: the system comprises a test data acquisition module, an Internet of things management cloud platform, a test data analysis module, a driving data analysis module and an automobile driving reminding module;
the output end of the test data acquisition module is connected with the input end of the Internet of things management cloud platform, the output end of the Internet of things management cloud platform is connected with the input end of the test data analysis module, the output end of the test data analysis module is connected with the input end of the driving data analysis module, and the output end of the driving data analysis module is connected with the input end of the automobile driving reminding module;
acquiring power consumption test data of an energy storage battery through the test data acquisition module, generating a current battery power consumption curve, acquiring historical driving routes and historical battery power consumption curve data of the electric automobile, and transmitting all acquired data to the Internet of things management cloud platform;
the method comprises the steps that test data of a battery and all collected data are received and stored through the Internet of things management cloud platform;
analyzing the current running state of the battery through the test data analysis module;
analyzing the influence degree of the road condition of the current driving route on the power consumption of the battery and predicting the remaining time of the electric automobile which can be driven currently through the driving data analysis module;
and judging whether the vehicle can successfully reach the destination or not through the vehicle running reminding module, if not, reminding and adjusting the running route, and simultaneously reminding the user to charge the vehicle.
4. The energy storage battery test system based on the Internet of things as claimed in claim 3, wherein: the test data acquisition module comprises an energy storage battery test unit, a battery data acquisition unit and a driving data acquisition unit;
the output ends of the energy storage battery testing unit, the battery data acquisition unit and the driving data acquisition unit are connected with the input end of the Internet of things management cloud platform;
the energy storage battery testing unit is used for testing the power consumption of an energy storage battery of the electric automobile in the driving process of the electric automobile and generating a battery power consumption curve of the current driving route according to a testing result;
the driving data acquisition unit is used for acquiring historical driving route data of a user;
the battery data acquisition unit is used for acquiring battery power consumption curve data corresponding to the historical driving route.
5. The energy storage battery test system based on the Internet of things as claimed in claim 3, wherein: the test data analysis module comprises a power consumption curve matching unit and a data screening unit;
the input end of the power consumption curve matching unit is connected with the output end of the Internet of things management cloud platform, and the output end of the power consumption curve matching unit is connected with the input end of the data screening unit;
the power consumption curve matching unit is used for generating a current battery power consumption curve according to the tested power consumption and matching the current battery power consumption curve with a historical battery power consumption curve in the Internet of things management cloud platform;
and the data screening unit is used for acquiring a matching result and screening a historical battery power consumption curve with the highest matching degree with the current battery power consumption curve.
6. The energy storage battery test system based on the Internet of things as claimed in claim 5, wherein: the driving data analysis module comprises a driving route analysis unit and a residual time prediction unit;
the input end of the driving route analysis unit is connected with the output end of the data screening unit, and the output end of the driving route analysis unit is connected with the input end of the remaining time prediction unit;
the driving route analysis unit is used for acquiring influence factors of the road surface of the remaining distance on the current driving route on the power consumption of the battery;
the residual time prediction unit is used for predicting the residual time of the automobile capable of running at present.
7. The energy storage battery test system based on the Internet of things as claimed in claim 6, wherein: the automobile driving reminding module comprises a comprehensive analysis unit, a route replacement reminding unit and a charging reminding unit;
the input end of the comprehensive analysis unit is connected with the output end of the residual time prediction unit, the output end of the comprehensive analysis unit is connected with the input end of the route replacement reminding unit, and the output end of the route replacement reminding unit is connected with the input end of the charging reminding unit;
the comprehensive analysis unit is used for judging whether the vehicle can successfully arrive at the destination according to the current driving route;
the route replacement reminding unit is used for reminding the driver of replacing the driving route when judging that the driver cannot successfully arrive at the destination according to the current driving route;
the charging reminding unit is used for planning an optimal driving route and reminding a user to charge the automobile after reminding to replace the driving route.
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