CN116853015B - Automatic power supply control method, system and storage medium based on artificial intelligence - Google Patents
Automatic power supply control method, system and storage medium based on artificial intelligence Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
- B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
- B60L15/2045—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed for optimising the use of energy
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/12—Recording operating variables ; Monitoring of operating variables
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0063—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with circuits adapted for supplying loads from the battery
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/10—Vehicle control parameters
- B60L2240/12—Speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/10—Vehicle control parameters
- B60L2240/14—Acceleration
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- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/50—Control modes by future state prediction
- B60L2260/54—Energy consumption estimation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The application discloses an automatic power supply control method, a system and a storage medium based on artificial intelligence, which relate to the technical field of power supply control, wherein a power supply system can aim at a mine explosion-proof lithium ion storage battery power supply, the power supply can be carried in a mine car, and the power supply control system comprises: the system comprises a data acquisition module, a data analysis module, a power supply control module, an electric energy calculation module and a battery monitoring module; the technical key points are as follows: through the mode of the constant speed running of design interval, automatic power supply regulation and control work is carried out in this mode, whether to carry out power supply drive to the electric motor car according to the regulation and control of two extreme points of speed decay interval, can save the consumption of electric energy and guarantee the stability of car operation, then utilize artificial intelligence technique to monitor to intermittent power supply's battery stability, judge whether power supply control module can continue the operation according to the stability degree of battery, guarantee that power supply control system can adapt to battery state and use.
Description
Technical Field
The application relates to the technical field of power supply control, in particular to an automatic power supply control method, system and storage medium based on artificial intelligence.
Background
The power supply control is to control and regulate a power supply in the power system so as to meet power requirements and ensure safe and stable operation of the system, and specifically comprises power supply selection, power supply switch control, power supply regulation, load management, power supply protection and energy management, wherein the power supply control is usually realized by professional equipment and systems in the power system, the professional equipment and the systems comprise switching equipment, an automatic control system, a protection device, monitoring equipment and the like, the equipment and the systems realize accurate control and management of power supply through mutual coordination and cooperation, and a mining explosion-proof lithium ion battery power supply can be adopted for a power supply used by the power supply system and can be mounted in a mine car.
The prior application publication number is CN107599859A, the name is a power supply system of an electric automobile, a control method and a Chinese patent of the electric automobile, the power supply system of the electric automobile comprises: the battery system consists of a battery pack or a plurality of battery packs connected in parallel, and is used for providing power for the electric automobile; the range extender unit is used for generating direct current, charging the battery system and/or providing power for the electric automobile; the controller is used for controlling the power generation state of the range extender unit, controlling the connection state of each battery pack and the high-voltage direct-current bus of the electric automobile and/or controlling the connection state of the range extender unit and the high-voltage direct-current bus of the electric automobile; in addition, the patent with the application publication number of CN 108008648A indicates that the power supply is only connected with the control module, and the control module is used for controlling the power supply and the power failure of the detection module and the emission module, so that the idle power consumption of the detection module and the emission module is effectively reduced.
However, according to the above-mentioned matters in combination with the prior art, for a power supply control system in an electric vehicle, which is generally composed of a battery management system BMS, a charging system, a motor control system ECU, and an energy recovery system, an energy saving form for the electric vehicle is performed by the energy recovery system, and the electric vehicle can convert braking energy into electric energy to be stored in a battery through the energy recovery system during braking or decelerating, the energy recovery system includes a braking energy recovery device and an energy conversion controller, which is responsible for controlling the degree of energy recovery and the efficiency of energy conversion, thus resulting in a single expression form of energy saving effect; in addition, the electric automobile driving on the expressway is higher in normal speed, the automobile can continue to move forward due to inertia force when the accelerator pedal is stopped, the automobile speed can be slowly reduced, the automobile can start to accelerate after the accelerator pedal is continuously stepped on, and in the process that the automobile is slowly reduced to start to accelerate, electric energy can be saved to a certain extent due to a certain distance of the inertia force, but the automobile is required to be operated by manually and frequently controlling the automobile speed, so that the automobile speed change interval is larger, and the stability of the battery state of the automobile can be influenced due to long-term operation while the automobile is not stable enough.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the application provides an automatic power supply control method, an automatic power supply control system and a storage medium based on artificial intelligence, which automatically carry out power supply regulation and control work in a mode of constant-speed running in a design interval, regulate and control whether power supply driving is carried out on an electric automobile according to two endpoints of a speed attenuation interval, save consumption of electric energy and ensure the running stability of the automobile, monitor the stability of a battery which is intermittently powered by utilizing the artificial intelligence technology, judge whether a power supply control module can continue to run according to the stability degree of the battery, ensure that the power supply control system can adapt to the use of the battery state, and solve the problems in the background technology.
(II) technical scheme
In order to achieve the above purpose, the application is realized by the following technical scheme:
an artificial intelligence based automated power supply control system comprising:
the data acquisition module is used for acquiring real-time operation data of the electric automobile, wherein the real-time operation data comprise real-time speed, acceleration and vehicle load of the electric automobile, and preprocessing the real-time operation data;
the data analysis module is used for formulating a speed attenuation interval based on real-time operation data through a preset rule engine, namelyV is more than or equal to 80, and a prediction model is built to obtain the predicted speed attenuation intervalActual electric energy consumption of the electric automobile;
the power supply control module is connected with a BMS battery system carried by the electric automobile according to the analysis process and the result of the data analysis module so as to realize power supply control actions, wherein the control actions comprise the operations of respectively powering off and powering on the front end point and the rear end point of the speed attenuation section;
the battery monitoring module comprises a characteristic selecting unit, a state analyzing unit and a threshold value comparing unit;
the method comprises the steps of collecting evaluation parameters through a feature selection unit, inputting the collected evaluation parameters as features to a state analysis unit, constructing a data analysis model in the state analysis unit by using an artificial intelligence technology to generate a battery state evaluation value Bsev, comparing a state threshold set by a threshold comparison unit with the battery state evaluation value Bsev, and selecting whether to continue to operate a power supply control module according to a comparison result.
Further, the process of constructing the prediction model is as follows:
s1, collecting data: extracting real-time operation data and recording the data of each time point;
s2, data processing and analysis: processing and analyzing the acquired data, determining a time period during the attenuation period, and extracting the real-time vehicle speed, the vehicle load and the total time consumption of the attenuation period end at each time point from the time period;
s3, calculating running power: from the extracted data, the running power at each time point during the decay period is calculated, and the running power can be calculated by the following formula: running power = vehicle load × real-time vehicle speed per time point;
s4, actual electric energy consumption: integrating the running power data to calculate the actual electric energy consumption during the attenuation period, integrating the curve of the power changing along with time by adopting a numerical integration method, and accumulating the result, wherein a specific calculation formula is as follows: actual electric energy consumption= ≡ (running power x dt), wherein the integration interval is a period of time during which the attenuation is performed.
Further, the vehicle load: representing the vehicle load level, dividing the actual cargo weight or number of passengers by the maximum load or maximum passenger capacity of the vehicle, and converting the quotient to a percentage to obtain the percentage of the vehicle load level.
Further, the specific steps of the power supply control action are as follows:
s101, determining a current initial vehicle speed V;
s102, when the actual speed of the automobile is in a speed attenuation interval, controlling the power supply of the electric automobile to be in a power-off state;
and S103, when the actual speed of the automobile is attenuated to a moment beyond the speed attenuation interval, recovering power supply to the electric automobile, uniformly lifting the actual speed of the automobile to an initial speed V through a speed-increasing unit, recording the moment as a power-off moment, continuously repeating the operations from S102 to S103, and obtaining the number N of repeated operations through a counting unit.
Further, the system also comprises an electric energy calculation module, which is used for calculating the total electric energy saving amount when the power supply control system operates and feeding back the total electric energy saving amount to a central control screen of the electric automobile, wherein the formula for calculating the total electric energy saving amount is as follows: total amount of electric energy savings = actual amount of electric energy consumption N.
Further, the evaluation parameters acquired in the feature selection unit include: real-time temperature, discharge rate, discharge time, average current and average voltage during discharge, and vehicle load of the electric vehicle battery at the same time.
Further, the artificial intelligence technique applied in the state analysis unit is a machine learning algorithm, and the formula according to which the battery state evaluation value Bsev is generated is as follows:
in the method, in the process of the application,for loading the vehicle->For the real-time temperature of the battery of the electric vehicle, +.>Ar, ae, ac are respectively the vehicle load, the real-time temperature of the electric car battery and the preset proportionality coefficient of the discharge index, and +.>，/>，/>Is a constant correction coefficient;
vehicle load: representing the load degree of the vehicle, and acquiring the load degree from real-time operation data in a data acquisition module;
real-time temperature of electric automobile battery: the actual temperature of the surface of the battery of the electric automobile is represented, and the actual temperature can be directly obtained by installing a temperature sensor on the surface of the electric automobile;
discharge index: an index for evaluating energy conversion efficiency of a battery during discharging is represented, wherein +.>Indicating the discharge rate, the amount of electrical energy released by the battery per unit time,/or%>Indicating the discharge time, the time the battery is discharged, < >>Represents the average current of the battery during discharge, < >>The average voltage, the discharge rate, the discharge time, the average current and the average voltage of the battery during the discharge process are all directly obtained through the BMS battery system.
Further, the state threshold in the threshold comparison unit includes a first state thresholdAnd a second state thresholdAnd->＜/>，/>，/>，/>As a standard threshold, hz is a correction value; as a standard threshold, hz is a correction value;
wherein, the step of obtaining the correction value Hz is as follows:
s201, collecting the working environment temperature Lw and the humidity Ls of an electric automobile battery;
s202, building a data processing model, and generating a correction value Hz, wherein the formula is as follows:
，
in the method, in the process of the application,for a preset proportionality coefficient of working environment temperature and working environment humidity +.>＞/>＞0，/>Is a constant correction coefficient;
after comparing the state threshold value with the battery state evaluation value Bsev:
if the battery state evaluation value Bsev is smaller than the first state threshold valueThe method includes the steps that the battery state stability is low, a power supply control system sends out primary early warning, and a first execution strategy is executed;
if it is the first state thresholdBattery state evaluation value Bsev < second state threshold +.>The battery state stability is medium, the power supply control system sends out a second-level early warning, and a second execution strategy is executed;
if it is the second state thresholdThe battery state evaluation value Bsev is less than or equal to the battery state evaluation value Bsev, the battery state stability is high, and the power supply control system does not respond;
the first execution strategy is to stop running the power supply control module, so that the electric automobile normally runs, the second execution strategy is to periodically start the power supply control module, the times of which the power supply control module can be started in a fixed time period are periodically represented according to the following formula:
。
an automatic power supply control method based on artificial intelligence comprises the following steps:
step one, acquiring real-time operation data of an electric automobile, wherein the real-time operation data comprise real-time speed, acceleration and vehicle load of the electric automobile, and preprocessing the real-time operation data;
step two, formulating a speed attenuation interval based on real-time operation data through a preset rule engine, namelyV is more than or equal to 80, and a prediction model is built to obtain the actual electric energy consumption of the electric automobile in a predicted speed attenuation interval;
and step three, performing power supply control actions according to the analysis process and the result of the step two, wherein the control actions comprise the operations of respectively powering off and powering on the front end point and the rear end point of the speed attenuation interval, and the specific operations are as follows:
s101, determining a current initial vehicle speed V;
s102, when the actual speed of the automobile is in a speed attenuation interval, controlling the power supply of the electric automobile to be in a power-off state;
s103, when the actual speed of the automobile is attenuated to a moment beyond the speed attenuation interval, recovering power supply to the electric automobile, uniformly lifting the actual speed of the automobile to an initial speed V through a speed-increasing unit, recording the moment as a power-off moment, continuously repeating the operations of S102 to S103, obtaining the times N of repeated operations through a counting unit, and calculating the total energy saving amount, wherein the formula is as follows: the total electric energy saving amount=actual electric energy consumption amount is fed back to a central control screen of the electric automobile for times of N;
and step four, collecting evaluation parameters, taking the collected evaluation parameters as characteristics, constructing a data analysis model by using an artificial intelligence technology according to the characteristics to generate a battery state evaluation value Bsev, comparing a set state threshold with the battery state evaluation value Bsev, and selecting whether to continue to execute the operation of the step three according to the comparison result.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of an artificial intelligence based automated power supply control method.
(III) beneficial effects
The application provides an automatic power supply control method, system and storage medium based on artificial intelligence, which have the following beneficial effects:
1. by combining the data analysis module and the power supply control module, in a scene that the electric automobile runs on a highway, when a certain speed is reached, the electric automobile can be switched to a mode of running at a constant speed in an interval, power supply regulation and control work is automatically carried out in the mode, whether the electric automobile is powered and driven is regulated and controlled according to two endpoints of a speed attenuation interval, and the electric automobile is in a power-off state in the speed attenuation interval, so that on one hand, the consumption of electric energy can be saved, and the predicted actual electric energy consumption is obtained, and on the other hand, the electric automobile is different from a traditional constant-speed cruising mode, the speed can be increased or decreased in an interval in a circulating mode, and the running stability of the automobile can be ensured to a certain extent;
2. the battery monitoring module is additionally arranged on the basis of the power supply control module, the stability of the intermittent power supply battery is monitored by utilizing an artificial intelligence technology, the battery state evaluation value Bsev can be generated by collecting various evaluation parameters and building a data analysis model, the factors of the environment where the battery is located are comprehensively considered when the battery state evaluation value Bsev is compared with a set state threshold value, the accuracy of a comparison result can be further ensured, the stability degree of the battery state can be intuitively known according to the comparison result, whether the power supply control module can continue to operate can be judged according to the stability degree, the power supply control system can adapt to the battery state for use, and the applicability of the whole system is enhanced.
Drawings
FIG. 1 is a block diagram of an artificial intelligence based automated power supply control system of the present application;
FIG. 2 is a flow chart of the steps of an artificial intelligence based automated power control method of the present application.
Description of the embodiments
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1: referring to fig. 1, the present application provides an artificial intelligence based automatic power supply control system, comprising:
the system comprises a data acquisition module, a control module and a load sensor, wherein the data acquisition module acquires real-time operation data of the electric automobile, the real-time operation data comprise real-time speed, acceleration and vehicle load of the electric automobile, the real-time operation data are preprocessed, the real-time operation data are directly acquired through a control system of the automobile, and the control system comprises a speed sensor, a power control unit PCM and the load sensor;
the vehicle speed sensor is usually arranged on a transmission shaft or wheels of the electric vehicle and is used for measuring the real-time speed of the vehicle, namely the real-time vehicle speed; the power control unit is called PCM for short, which is an electronic control unit on the vehicle and is responsible for monitoring and controlling the operation of the engine and the power system, and the acceleration of the vehicle can be obtained through the data of different sensors, such as a vehicle speed sensor and an acceleration sensor; the load sensor is also arranged on the electric automobile and used for measuring the current load condition of the automobile, and the load sensor can detect the deformation of the chassis and the suspension system of the automobile so as to indirectly estimate the load condition of the automobile and obtain the load of the automobile;
when the real-time operation data is preprocessed, the data analysis and processing technology is utilized to carry out data cleaning and format conversion on the collected data, for example, the speed and acceleration data can be subjected to smooth processing to reduce the influence of noise, and the operations of removing duplicates, deletions and abnormal values can be carried out on each data in the real-time operation data to ensure the quality and accuracy of the data.
The data analysis module is used for formulating a speed attenuation interval based on real-time operation data through a preset rule engine, constructing a prediction model and obtaining the actual electric energy consumption of the electric vehicle in the predicted speed attenuation interval;
the specific value of the speed decay interval is formulated in the rule engine as followsAnd V is more than or equal to 80, the unit is kilometers per hour, so the whole control system is usually required to be used when running on a highway, and the running at high speed or uniform speed cannot be realized on a urban road; it should be noted that: v mentioned in the application is the initial speed of the corresponding electric automobile under the condition of starting the whole power supply control system, 5 in V-5 is 5km/h, if the initial speed is 80km/h, the speed attenuation interval is +.>I.e. 80km/h to 75km/h, the setting of the section speed can also be adjusted subsequently in the actual design, e.g. +.>。
The process of constructing the prediction model is as follows:
s1, collecting data: extracting real-time operation data and recording the data of each time point; the real-time operation data are directly extracted by the data acquisition module, and the time point is an interval per minute;
s2, data processing and analysis: processing and analyzing the acquired data, determining a time period during the attenuation period, namely a time period from V to V-5 of the speed of the electric automobile, and extracting real-time vehicle speed, vehicle load and total time consumption of an attenuation period end of each time point from the time period;
wherein, vehicle load: the method comprises the steps of representing the vehicle load degree, dividing the actual cargo weight or the number of passengers by the maximum load weight or the maximum passenger capacity of the vehicle, multiplying the maximum load weight or the maximum passenger capacity by 100 (or converting the quotient into the percentage) to obtain the percentage of the vehicle load degree, wherein the maximum load weight or the maximum passenger capacity can be directly obtained through a vehicle-mounted technical manual, and the actual cargo weight or the number of passengers can be obtained through weighing and direct observation; the real-time vehicle speed can be directly obtained from a central control screen of the electric vehicle, the total time consumption of the attenuation period end can be obtained through a timer, the time t1 of the initial vehicle speed V is recorded, the time t2 shown by the vehicle speed V-5 is recorded, and the total time consumption of the attenuation period end can be obtained by t2-t 1;
for example, if the maximum load of an electric vehicle is 4 tons and the actual load is 2 tons, the calculation formula is: (2/4) ×100=50% indicating that the load degree of the electric vehicle is 50%; if the passenger capacity of an electric automobile is 4 people and the actual passenger number is 2 people, the calculation formula is: (2/4) ×100=50% and represents that the load of the electric vehicle is 50%
S3, calculating running power: from the extracted data, the running power at each time point during the decay period is calculated, and the running power can be calculated by the following formula: running power = vehicle load × real-time vehicle speed per time point;
s4, actual electric energy consumption: the running power data is integrated to calculate the actual electric energy consumption during the decay period, a numerical integration method, such as a trapezoidal rule or a simpson rule, can be adopted to integrate the curves of the power changing along with time, and the results are accumulated, wherein a specific calculation formula is as follows: actual electric energy consumption= ≡ (running power x dt), wherein the integration interval is a period of time during which the attenuation is performed.
The power supply control module determines that the current initial vehicle speed is V, and is connected with a BMS battery system carried by the electric vehicle according to the analysis process and the result of the data analysis module so as to realize power supply control actions, wherein the control actions comprise operations of respectively powering off and powering on the front end point and the rear end point of the speed attenuation interval.
The electric control brake is used as:
s101, determining the current initial vehicle speed V, namelyV of (a);
s102, when the actual speed of the automobile is in a speed attenuation interval, controlling the power supply of the electric automobile to be in a power-off state;
s103, when the actual speed of the automobile is attenuated to a moment beyond the speed attenuation interval, recovering power supply to the electric automobile, uniformly lifting the actual speed of the automobile to an initial speed V through a speed-increasing unit, recording the moment as a power-off moment, continuously repeating the operations from S102 to S103, and obtaining the times N of repeated operations through a counting unit so as to achieve the aim of uniform-speed running in the interval;
it should be noted that: the speed increasing unit and the counting unit are built-in units of the power supply control module.
By combining the data analysis module and the power supply control module, in a scene that the electric automobile runs on a highway, when a certain speed is reached, the electric automobile can be switched to a mode of running at a constant speed in an interval, power supply regulation and control work is automatically carried out in the mode, whether the electric automobile is powered and driven is regulated and controlled according to two endpoints of a speed attenuation interval, and the electric automobile is in a power-off state in the speed attenuation interval, so that on one hand, the consumption of electric energy can be saved, and the predicted actual electric energy consumption is obtained, and on the other hand, the electric automobile is different from a traditional constant-speed cruising mode, the speed can be increased or decreased in an interval in a circulating mode, and the running stability of the automobile can be ensured to a certain extent; since the interval is small, the perception of the speed is not obvious to the driver, and the instantaneous speed change is not large, so that the running of the rear vehicle is not influenced.
The electric energy calculation module calculates the total electric energy saving amount when the power supply control system operates, and the formula is as follows: the total electric energy saving amount=actual electric energy consumption amount N, and since the counting unit built in the power supply control module is connected with the central control screen of the electric automobile, the predicted total electric energy saving amount during continuous operation of the power supply control system can be fed back to the central control screen of the electric automobile for a user or a maintenance personnel to obtain.
In the continuous power-off and power-on processes of the power supply control module, the stability of the battery needs to be considered, so that the battery monitoring module is additionally arranged in the power supply control system.
The battery monitoring module comprises a characteristic selecting unit, a state analyzing unit and a threshold value comparing unit;
the feature selecting unit collects the evaluation parameters and inputs the collected evaluation parameters as features to the state analyzing unit, wherein the collected evaluation parameters comprise: real-time temperature, discharge rate, discharge time, average current and average voltage in the discharge process of the electric automobile battery at the same moment, and vehicle load;
the state analysis unit builds a data analysis model by using an artificial intelligence technology to generate a battery state evaluation value Bsev, wherein the applied artificial intelligence technology is a machine learning algorithm, and a formula according to which the battery state evaluation value Bsev is generated is as follows:
,
in the method, in the process of the application,for loading the vehicle->For the real-time temperature of the battery of the electric vehicle, +.>For discharge index>Preset proportionality coefficients of vehicle load, real-time temperature of electric vehicle battery and discharge index respectively, and +.>，/>0，/>The constant correction coefficient is specifically 1.375.
It should be noted that: a person skilled in the art collects a plurality of groups of sample data and sets a corresponding preset scaling factor for each group of sample data; substituting the preset proportionality coefficient which is set and can be the preset proportionality coefficient and the collected sample data into a formula;
any three formulas form a binary once equation set, and the calculated coefficients are screened and averaged to obtainThe magnitude of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, the magnitude of the coefficient depends on the number of sample data and the corresponding preset proportional coefficient preliminarily set by a person skilled in the art for each group of sample data, so to speak, the coefficient is preset according to the actual practice, as long as the proportional relation between the parameter and the quantized numerical value is not influenced, and the above description is also adopted for the preset proportional coefficient and the constant correction coefficient described in other formulas.
Vehicle load: representing the load degree of the vehicle, and acquiring the load degree from real-time operation data in a data acquisition module;
real-time temperature of electric automobile battery: the actual temperature of the surface of the battery of the electric automobile is represented, and the actual temperature can be directly obtained by installing a temperature sensor on the surface of the electric automobile;
discharge index: an index for evaluating energy conversion efficiency of a battery during discharging is represented, wherein +.>Indicating the discharge rate, the amount of electrical energy released by the battery per unit time,/or%>Indicating the discharge time, the time the battery is discharged, < >>Represents the average current of the battery during discharge, < >>The average voltage, the discharging rate, the discharging time, the average current and the average voltage of the battery in the discharging process can be directly obtained through a BMS battery system, if no BMS is available or the BMS can not provide required data, the data can be obtained or calculated through other methods, and the batteries are all automobile batteries;
for example, the current and voltage of the battery may be measured in real time using an ammeter and a voltmeter, and then an average value is calculated, the discharge time may be calculated by measuring the time when the battery starts discharging and the time when the discharging is ended, and the discharge rate may be calculated by a known ratio of the electric energy and the discharge time; it should be noted that: the closer the value of the discharge index is to 100%, the higher the energy conversion efficiency of the battery during discharge.
A threshold value comparing unit that compares the battery state evaluation value Bsev by setting a state threshold value;
wherein the status threshold comprises a first status thresholdAnd a second state threshold +.>And->＜/>，，/>，/>As a standard threshold, hz is a correction value; it should be noted that: wherein the standard threshold->The iterative adjustment is generally completed by performing specific setting according to specific safety standards, specifications and industry requirements and referencing according to historical data and actual conditions, and the specific setting process is not described herein.
Wherein, the step of obtaining the correction value Hz is as follows:
s201, collecting the working environment temperature Lw and the humidity Ls of an electric automobile battery;
temperature and humidity values of a working environment can be directly obtained by installing temperature and humidity sensors around the battery of the electric automobile;
s202, building a data processing model, and generating a correction value Hz, wherein the formula is as follows:
,
in the method, in the process of the application,for a preset proportionality coefficient of working environment temperature and working environment humidity +.>＞/>＞0，/>The constant correction coefficient is specifically 3, and the construction of the data processing model is the same as the construction of the data analysis model;
then, the state threshold value is compared with the battery state evaluation value Bsev;
if the battery state evaluation value Bsev is smaller than the first state threshold valueThe method includes the steps that the battery state stability is low, a power supply control system sends out primary early warning, and a first execution strategy is executed;
if it is the first state thresholdBattery state evaluation value Bsev < second state threshold +.>The battery state stability is medium, the power supply control system sends out a second-level early warning, and a second execution strategy is executed;
if it is the second state thresholdAnd if the battery state evaluation value Bsev is less than or equal to the battery state evaluation value Bsev, the battery state stability is high, and the power supply control system does not respond.
The power supply system sends out early warning through the mode that the automobile central control screen carries out stroboscopic + characters suggestion and carries out early warning, and the stroboscopic frequency that one-level early warning was faster than second grade early warning, for example: the central control screen displays a character sample with low battery stability during primary early warning, a red character is displayed, and the character sample performs 20 stroboscopic actions in unit time; the medium control screen displays the character with medium stability of the battery during the second-level early warning, the yellow character is displayed, and the character performs 10 stroboscopic actions in unit time.
The battery monitoring module is additionally arranged on the basis of the power supply control module, the stability of the intermittent power supply battery is monitored by utilizing an artificial intelligence technology, the battery state evaluation value Bsev can be generated by collecting various evaluation parameters and building a data analysis model, the factors of the environment where the battery is located are comprehensively considered when the battery state evaluation value Bsev is compared with a set state threshold value, the accuracy of a comparison result can be further ensured, the stability degree of the battery state can be intuitively known according to the comparison result, whether the power supply control module can continue to operate can be judged according to the stability degree, the power supply control system can adapt to the battery state for use, and the applicability of the whole system is enhanced.
The first execution strategy is to stop running the power supply control module so that the electric automobile can run normally, the second execution strategy is to periodically start the power supply control module, the number of times the power supply control module can be started in a fixed time period is periodically represented, and the following formula is adopted:
。
for example: the fixed time period is generally one day, if the battery state evaluation value Bsev is 53.274, the number of times that the power supply control module can be turned on in one day can be 10.655 times, namely, 10-11 times, according to the above formula, and specific times can be prompted on the central control screen, and the next day is cleared.
The method also comprises a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, the computer program realizes the steps of an automatic power supply control method based on artificial intelligence when being executed by a processor, the processor is a processing chip built in an electric automobile, and the specific model is selected as follows: gao Tongxiao Dragon SA8155P.
Example 2: as shown in fig. 2, the present application provides an automatic power supply control method based on artificial intelligence, comprising the following steps:
step one, acquiring real-time operation data of an electric automobile, wherein the real-time operation data comprise real-time speed, acceleration and vehicle load of the electric automobile, and preprocessing the real-time operation data;
step two, formulating a speed attenuation interval based on real-time operation data through a preset rule engine, namelyV is more than or equal to 80, and a prediction model is built to obtain the actual electric energy consumption of the electric automobile in a predicted speed attenuation interval;
and step three, performing power supply control actions according to the analysis process and the result of the step two, wherein the control actions comprise the operations of respectively powering off and powering on the front end point and the rear end point of the speed attenuation interval, and the specific operations are as follows:
s101, determining a current initial vehicle speed V;
s102, when the actual speed of the automobile is in a speed attenuation interval, controlling the power supply of the electric automobile to be in a power-off state;
s103, when the actual speed of the automobile is attenuated to a moment beyond the speed attenuation interval, recovering power supply to the electric automobile, uniformly lifting the actual speed of the automobile to an initial speed V through a speed-increasing unit, recording the moment as a power-off moment, continuously repeating the operations of S102 to S103, obtaining the times N of repeated operations through a counting unit, and calculating the total energy saving amount, wherein the formula is as follows: the total electric energy saving amount=actual electric energy consumption amount is fed back to a central control screen of the electric automobile for times of N;
and step four, collecting evaluation parameters, taking the collected evaluation parameters as characteristics, constructing a data analysis model by using an artificial intelligence technology according to the characteristics to generate a battery state evaluation value Bsev, comparing a set state threshold with the battery state evaluation value Bsev, and selecting whether to continue to execute the operation of the step three according to the comparison result.
Example 3: based on embodiment 1, the whole power supply system can be applied to a full electric vehicle system, including a mine car carrying a power supply or an explosion-proof transport vehicle, and runs in a mine tunnel to realize subsequent power supply control processing work, the power supply mentioned in this embodiment is a power core of a mine, in order to ensure normal power supply of the mine car running in the mine tunnel in the power supply process, and meet the safety production requirement, the power supply equipped in the mine car must be a mine lithium ion battery power supply, specifically a DXBL series mine explosion-proof lithium ion battery power supply, which comprises a starting power supply and a power supply, and allows power supply or starting to mine intrinsically safe vehicles or equipment in an environment with gas and coal dust explosion hazard, the whole power supply uses a copper pipe radiator, has a 10 inch color screen and carries BMS technology, and simultaneously has 18 paths of independent output and 18 paths of independent protection.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application.
Claims (9)
1. An artificial intelligence based automated power supply control system, comprising:
the data acquisition module is used for acquiring real-time operation data of the electric automobile, wherein the real-time operation data comprise real-time speed, acceleration and vehicle load of the electric automobile, and preprocessing the real-time operation data;
the data analysis module is used for formulating a speed attenuation interval based on real-time operation data through a preset rule engine, namelyV is more than or equal to 80, and a prediction model is built to obtain the actual electric energy consumption of the electric automobile in a predicted speed attenuation interval;
the power supply control module is connected with a BMS battery system carried by the electric automobile according to the analysis process and the result of the data analysis module so as to realize power supply control actions, wherein the control actions comprise the operations of respectively powering off and powering on the front end point and the rear end point of the speed attenuation section;
the battery monitoring module comprises a characteristic selecting unit, a state analyzing unit and a threshold value comparing unit;
collecting evaluation parameters through a characteristic selection unit, inputting the collected evaluation parameters as characteristics into a state analysis unit, constructing a data analysis model in the state analysis unit by using an artificial intelligence technology to generate a battery state evaluation value Bsev, comparing a state threshold set by a threshold comparison unit with the battery state evaluation value Bsev, and selecting whether to continue to operate the power supply control module according to a comparison result;
the state threshold in the threshold comparison unit comprises a first state thresholdAnd a second state threshold +.>And->＜/>，/>，/>，/>As a standard threshold, hz is a correction value;
wherein, the step of obtaining the correction value Hz is as follows:
s201, collecting the working environment temperature Lw and the humidity Ls of an electric automobile battery;
s202, building a data processing model, and generating a correction value Hz, wherein the formula is as follows:
,
in the method, in the process of the application,for a preset proportionality coefficient of working environment temperature and working environment humidity +.>＞/>＞0，/>Is a constant correction coefficient;
after comparing the state threshold value with the battery state evaluation value Bsev:
if the battery state evaluation value Bsev is smaller than the first state threshold valueThe method includes the steps that the battery state stability is low, a power supply control system sends out primary early warning, and a first execution strategy is executed;
if it is the first state thresholdBattery state evaluation value Bsev < second state threshold +.>The battery state stability is medium, the power supply control system sends out a second-level early warning, and a second execution strategy is executed;
if it is the second state thresholdThe battery state evaluation value Bsev is less than or equal to the battery state evaluation value Bsev, the battery state stability is high, and the power supply control system does not respond;
the first execution strategy is to stop running the power supply control module, so that the electric automobile normally runs, the second execution strategy is to periodically start the power supply control module, the times of which the power supply control module can be started in a fixed time period are periodically represented according to the following formula:
。
2. an artificial intelligence based automated power supply control system according to claim 1 wherein: the process of constructing the prediction model is as follows:
s1, collecting data: extracting real-time operation data and recording the data of each time point;
s2, data processing and analysis: processing and analyzing the acquired data, determining a time period during the attenuation period, and extracting the real-time vehicle speed, the vehicle load and the total time consumption of the attenuation period end at each time point from the time period;
s3, calculating running power: from the extracted data, the running power at each time point during the decay period is calculated, and the running power can be calculated by the following formula: running power = vehicle load × real-time vehicle speed per time point;
s4, actual electric energy consumption: integrating the running power data to calculate the actual electric energy consumption during the attenuation period, integrating the curve of the power changing along with time by adopting a numerical integration method, and accumulating the result, wherein a specific calculation formula is as follows: actual electric energy consumption= ≡ (running power x dt), wherein the integration interval is a period of time during which the attenuation is performed.
3. An artificial intelligence based automated power supply control system according to claim 2 wherein: vehicle load: representing the vehicle load level, dividing the actual cargo weight or number of passengers by the maximum load or maximum passenger capacity of the vehicle, and converting the quotient to a percentage to obtain the percentage of the vehicle load level.
4. An artificial intelligence based automated power supply control system according to claim 3 wherein: the specific steps of the power supply control action are as follows:
s101, determining a current initial vehicle speed V;
s102, when the actual speed of the automobile is in a speed attenuation interval, controlling the power supply of the electric automobile to be in a power-off state;
and S103, when the actual speed of the automobile is attenuated to a moment beyond the speed attenuation interval, recovering power supply to the electric automobile, uniformly lifting the actual speed of the automobile to an initial speed V through a speed-increasing unit, recording the moment as a power-off moment, continuously repeating the operations from S102 to S103, and obtaining the number N of repeated operations through a counting unit.
5. An artificial intelligence based automated power supply control system according to claim 4 and wherein: the system also comprises an electric energy calculation module which is used for calculating the total electric energy saving amount when the power supply control system operates and feeding back the total electric energy saving amount to a central control screen of the electric automobile, wherein the formula for calculating the total electric energy saving amount is as follows: total amount of electric energy savings = actual amount of electric energy consumption N.
6. An artificial intelligence based automated power supply control system according to claim 5 and wherein: the evaluation parameters acquired in the feature selection unit include: real-time temperature, discharge rate, discharge time, average current and average voltage during discharge, and vehicle load of the electric vehicle battery at the same time.
7. An artificial intelligence based automated power supply control system according to claim 6 and wherein: the artificial intelligence technique applied in the state analysis unit is a machine learning algorithm, and the formula from which the battery state evaluation value Bsev is generated is as follows:
in the method, in the process of the application,for loading the vehicle->For the real-time temperature of the battery of the electric vehicle, +.>In order to be a discharge index,the real-time temperature and the discharge index of the vehicle load and the electric vehicle battery are respectively preset proportional coefficients, and，/>0，/>is a constant correction coefficient;
vehicle load: representing the load degree of the vehicle, and acquiring the load degree from real-time operation data in a data acquisition module;
real-time temperature of electric automobile battery: the actual temperature of the surface of the battery of the electric automobile is represented, and the actual temperature can be directly obtained by installing a temperature sensor on the surface of the electric automobile;
discharge index: an index for evaluating energy conversion efficiency of a battery during discharging is represented, wherein +.>Indicating the discharge rate, the amount of electrical energy released by the battery per unit time,/or%>Indicating the discharge time, the time the battery is discharged, < >>Represents the average current of the battery during discharge, < >>The average voltage, the discharge rate, the discharge time, the average current and the average voltage of the battery during the discharge process are all directly obtained through the BMS battery system.
8. An artificial intelligence based automated power control method using the system of any one of claims 1 to 7, characterized in that: the method comprises the following steps:
step one, acquiring real-time operation data of an electric automobile, wherein the real-time operation data comprise real-time speed, acceleration and vehicle load of the electric automobile, and preprocessing the real-time operation data;
step two, formulating a speed attenuation interval based on real-time operation data through a preset rule engine, namelyV is more than or equal to 80, and a prediction model is built to obtain the actual electric energy consumption of the electric automobile in a predicted speed attenuation interval;
and step three, performing power supply control actions according to the analysis process and the result of the step two, wherein the control actions comprise the operations of respectively powering off and powering on the front end point and the rear end point of the speed attenuation interval, and the specific operations are as follows:
s101, determining a current initial vehicle speed V;
s102, when the actual speed of the automobile is in a speed attenuation interval, controlling the power supply of the electric automobile to be in a power-off state;
s103, when the actual speed of the automobile is attenuated to a moment beyond the speed attenuation interval, recovering power supply to the electric automobile, uniformly lifting the actual speed of the automobile to an initial speed V through a speed-increasing unit, recording the moment as a power-off moment, continuously repeating the operations of S102 to S103, obtaining the times N of repeated operations through a counting unit, and calculating the total energy saving amount, wherein the formula is as follows: the total electric energy saving amount=actual electric energy consumption amount is fed back to a central control screen of the electric automobile for times of N;
step four, collecting evaluation parameters, taking the collected evaluation parameters as characteristics, then constructing a data analysis model by using an artificial intelligence technology according to the characteristics to generate a battery state evaluation value Bsev, comparing a set state threshold with the battery state evaluation value Bsev, and selecting whether to continue to execute the operation of the step three according to a comparison result;
wherein the status threshold comprises a first status thresholdAnd a second state threshold +.>And->＜/>，，/>，/>As a standard threshold, hz is a correction value;
the step of acquiring the correction value Hz is as follows:
s201, collecting the working environment temperature Lw and the humidity Ls of an electric automobile battery;
s202, building a data processing model, and generating a correction value Hz, wherein the formula is as follows:
in the method, in the process of the application,for a preset proportionality coefficient of working environment temperature and working environment humidity +.>＞/>＞0，/>Is a constant correction coefficient;
after comparing the state threshold value with the battery state evaluation value Bsev:
if the battery state evaluation value Bsev is smaller than the first state threshold valueThe method includes the steps that the battery state stability is low, a power supply control system sends out primary early warning, and a first execution strategy is executed;
if it is the first state thresholdBattery state evaluation value Bsev < second state threshold +.>The battery state stability is medium, the power supply control system sends out a second-level early warning, and a second execution strategy is executed;
if it is the second state thresholdThe battery state evaluation value Bsev is less than or equal to the battery state evaluation value Bsev, the battery state stability is high, and the power supply control system does not respond;
the first execution strategy is to stop executing the operation of the third step, so that the electric automobile normally runs, the second execution strategy is to periodically execute the third step, and periodically indicates the number of times the third step can be executed in a fixed time period according to the following formula:
。
9. a computer-readable storage medium having stored thereon a computer program, characterized by: the computer program realizes the steps of the method according to claim 8 when executed by a processor.
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