CN115622203B - Analysis reminding method and system based on charging data of vehicle-mounted wireless charger - Google Patents
Analysis reminding method and system based on charging data of vehicle-mounted wireless charger Download PDFInfo
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- CN115622203B CN115622203B CN202211612238.0A CN202211612238A CN115622203B CN 115622203 B CN115622203 B CN 115622203B CN 202211612238 A CN202211612238 A CN 202211612238A CN 115622203 B CN115622203 B CN 115622203B
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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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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- Y02T10/60—Other road transportation technologies with climate change mitigation effect
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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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
Abstract
The invention discloses an analysis reminding method and system based on charging data of a vehicle-mounted wireless charger, which comprises the following steps: acquiring charging data of a vehicle-mounted wireless charger, and extracting characteristics according to the charging data to acquire health characteristics of a battery of the mobile equipment; performing health assessment on the mobile equipment battery according to a preset health index through the health characteristic pair; an adaptive charging strategy is formulated according to the health evaluation result of the mobile equipment battery, charging is carried out according to the adaptive charging strategy, and a charging curve is generated; and acquiring a charging abnormal condition according to the charging curve, and sending the health characteristic, the charging curve and the charging abnormal condition of the battery to an on-board display system for display according to a preset mode. According to the invention, the charging data of the wireless charger is analyzed and evaluated for the health of the battery, intelligent reminding is carried out, the charging process of the mobile equipment is visualized, the mobile equipment is prevented from being continuously charged under abnormal conditions through the intelligent reminding, and the user experience is enhanced.
Description
Technical Field
The invention relates to the field of vehicle-mounted wireless charging, in particular to an analysis reminding method and system based on charging data of a vehicle-mounted wireless charger.
Background
Along with the establishment of the wireless charging standard of the mobile phone, the wireless charging function becomes the standard of the high-end mobile phone, and the demand of the wireless charger is increased. In addition, due to rapid development of the automobile industry, automobiles become the most main travel mode of modern people during work and travel, and are gradually equipped with mobile phone wireless charging equipment, and the automobile-mounted wireless charging equipment is used as an excellent wireless charging application scene, so that frequent plugging and unplugging of charging wires are not needed, the automobile-mounted wireless charging equipment is a great tool for increasing driving safety and improving life quality of an automobile owner, and the use and charging experience of mobile phones in the automobile is greatly improved.
The existing vehicle-mounted wireless charging equipment is divided into a front-mounted wireless charging device and a rear-mounted wireless charging device, wherein the front-mounted wireless charging device is arranged before a vehicle leaves a factory, and the rear-mounted wireless charging device is formed by additionally arranging a vehicle-mounted bracket and other devices on the vehicle to realize wireless charging. However, no matter the front-loading or rear-loading wireless charging device brings convenience to passengers, a common problem exists, that is, an intelligent reminding system is lacked, and the existing reminding system of the wireless charging device only can provide simple reminding that sundries exist on a charging panel, a mobile device of a user is left or the wireless charging device has faults, and cannot carry out intelligent reminding according to the charging state of the mobile device. Therefore, how to analyze the charging state according to the charging data of the wireless charging device and perform intelligent reminding according to the charging state of the mobile device is an urgent problem that needs to be solved.
Disclosure of Invention
In order to solve the technical problem, the invention provides an analysis reminding method and system based on charging data of a vehicle-mounted wireless charger.
The invention provides an analysis reminding method based on charging data of a vehicle-mounted wireless charger, which comprises the following steps:
acquiring charging data of a vehicle-mounted wireless charger, and extracting characteristics according to the charging data to acquire health characteristics of a battery of the mobile equipment;
performing health assessment on the mobile equipment battery according to a preset health index through the health characteristics;
an adaptive charging strategy is formulated according to the health evaluation result of the mobile equipment battery, charging is carried out according to the adaptive charging strategy, and a charging curve is generated;
and acquiring a charging abnormal condition according to the charging curve, and sending the health characteristic, the charging curve and the charging abnormal condition of the battery to an on-board display system for display according to a preset mode.
In the scheme, charging data of the vehicle-mounted wireless charger are acquired, and according to the charging data, feature extraction is carried out to acquire the health features of the battery of the mobile device, and the method specifically comprises the following steps:
acquiring equipment ID information of mobile equipment on a vehicle-mounted wireless charger, acquiring historical charging data according to the equipment ID information, and acquiring charging curves of a constant-current charging stage and a constant-voltage charging stage of the wireless charger according to the historical charging data;
acquiring peak points of each interval through a preset time interval according to the charging curve, and extracting the position of the peak point on the charging curve, the height of the peak point and the height difference between the peak points;
performing correlation analysis by combining the curve type of the charging curve with the peak point position, the peak point height and the height difference between the peak points;
and acquiring a characteristic point with the correlation degree larger than a preset correlation degree threshold value according to the correlation coefficient as a health characteristic of the mobile equipment battery.
In this scheme, carry out health assessment to the mobile device battery according to predetermineeing healthy index through healthy characteristic, specifically do:
acquiring a health characteristic sequence of a mobile equipment battery within preset time, constructing a health evaluation model of the mobile equipment battery based on an LSTM network, carrying out standardization processing on the health characteristic sequence, and dividing the health characteristic sequence into a training set and a test set through difference;
carrying out hyper-parameter optimization on an LSTM layer in the health evaluation model through a particle optimization algorithm, taking a loss function of the health evaluation model as a fitness function, updating a speed parameter and a position parameter of a particle through iteration, and obtaining an optimal particle position according to a fitness minimum principle;
acquiring an optimal hyper-parameter of an LSTM layer according to the optimal particle position, importing the optimal hyper-parameter into a health assessment model for training and forecasting according to a training set and a test set, training the health assessment model until a loss function is converged, and then verifying through a verification set;
and generating a characteristic matrix from the current health characteristic sequence of the mobile equipment battery, inputting the characteristic matrix into the trained health evaluation model for prediction, and outputting the current health state evaluation result of the mobile equipment battery.
In this scheme, the abnormal charging condition is obtained according to the charging curve, which specifically includes:
determining a charging reference curve of the mobile equipment according to a battery health evaluation result of the mobile equipment and an adaptive charging strategy, segmenting according to the charging reference curve, and acquiring curve characteristics of each segment of the charging reference curve to generate a charging reference curve characteristic sequence;
acquiring a charging curve of the mobile equipment in the process of travel, segmenting the charging curve according to a charging reference curve characteristic sequence, calculating characteristic deviation, comparing the characteristic deviation with a deviation threshold value according to the characteristic deviation, and marking the charging abnormal condition if the characteristic deviation is greater than the deviation threshold value;
acquiring abnormal characteristics of abnormal charging conditions according to the charging strategy and the corresponding characteristic deviation, and calculating and acquiring abnormal charging data meeting a similarity standard according to the abnormal characteristics through similarity;
selecting the charging abnormal data with the maximum similarity, determining the type of the charging abnormal situation according to the charging abnormal data with the maximum similarity, marking the type of the charging abnormal situation, and displaying the type of the charging abnormal situation according to a preset mode.
In the scheme, an adaptive charging strategy is formulated according to the health evaluation result of the mobile equipment battery, and the adaptive charging strategy specifically comprises the following steps:
acquiring current health state information of a mobile equipment battery, initializing state variables, and acquiring current, voltage and thermal safety constraint information according to parameter information of a wireless charger;
setting a target function of a charging strategy according to the battery health state and the charging time of the mobile equipment battery, acquiring the charging strategy of the mobile equipment battery through a particle swarm algorithm, taking the current in a constant current stage as an optimization variable, and substituting the initialization state variable;
when the constraint information reaches a preset threshold value, charging the mobile equipment battery in a constant current stage, taking a target function of a charging strategy as a fitness function, calculating an optimal solution of particles through the fitness function, acquiring current in the constant current charging stage according to the optimal solution, and setting the charging strategy of the mobile equipment battery;
acquiring a distance according to the travel information of passengers in the vehicle, and judging whether the distance is greater than a preset distance threshold value or not according to the distance;
if the distance is larger than the preset distance threshold, an adaptive charging strategy is preferentially formulated according to the health of the mobile equipment battery, and if the distance is not larger than the preset distance threshold, the adaptive charging strategy is preferentially formulated according to the charging speed of the mobile equipment battery.
In this scheme, further comprising obtaining the adjustment of the battery charging strategy of the mobile device according to the user feedback, specifically:
acquiring capacity information of a battery according to the health evaluation of the battery of the mobile equipment, and acquiring the estimated full charge time of the mobile equipment according to the current electric quantity and a corresponding charging strategy;
obtaining the route information of a destination to judge the predicted arrival time, obtaining the charging capacity of the mobile equipment when the mobile equipment arrives according to the predicted arrival time and the predicted full-filling time, and displaying reminding information on the charging capacity according to a preset mode;
obtaining the feedback of the passengers in the vehicle on the reminding information, and obtaining whether the charging capacity meets the use requirements of the passengers in the vehicle according to the feedback;
and when the feedback of the passengers in the vehicle is that the use cannot be met, adjusting the charging strategy according to the expected charging capacity of the passengers in the vehicle, and wirelessly charging the mobile equipment according to the adjusted charging strategy.
The second aspect of the present invention further provides an analysis reminding system based on charging data of a vehicle-mounted wireless charger, the system comprising: the analysis reminding method based on the charging data of the vehicle-mounted wireless charger comprises a memory and a processor, wherein the memory comprises an analysis reminding method program based on the charging data of the vehicle-mounted wireless charger, and the analysis reminding method program based on the charging data of the vehicle-mounted wireless charger realizes the following steps when being executed by the processor:
acquiring charging data of a vehicle-mounted wireless charger, and extracting characteristics according to the charging data to acquire health characteristics of a battery of mobile equipment;
performing health assessment on the mobile equipment battery according to a preset health index through the health characteristics;
an adaptive charging strategy is formulated according to the health evaluation result of the mobile equipment battery, charging is carried out according to the adaptive charging strategy, and a charging curve is generated;
and acquiring a charging abnormal condition according to the charging curve, and sending the health characteristic, the charging curve and the charging abnormal condition of the battery to an on-board display system for display according to a preset mode.
The invention discloses an analysis reminding method and system based on charging data of a vehicle-mounted wireless charger, which comprises the following steps: acquiring charging data of a vehicle-mounted wireless charger, and extracting characteristics according to the charging data to acquire health characteristics of a battery of the mobile equipment; performing health assessment on the mobile equipment battery according to a preset health index through the health characteristics; an adaptive charging strategy is formulated according to the health evaluation result of the mobile equipment battery, charging is carried out according to the adaptive charging strategy, and a charging curve is generated; and acquiring a charging abnormal condition according to the charging curve, and sending the health characteristic, the charging curve and the charging abnormal condition of the battery to an on-board display system for display according to a preset mode. According to the invention, the charging data of the wireless charger is analyzed and evaluated for the health of the battery, intelligent reminding is carried out, the charging process of the mobile equipment is visualized, the mobile equipment is prevented from being continuously charged under abnormal conditions through the intelligent reminding, and the user experience is enhanced.
Drawings
FIG. 1 is a flow chart of an analysis reminding method based on charging data of an on-board wireless charger according to the invention;
FIG. 2 is a flow chart of a method of the present invention for health assessment of a mobile device battery;
FIG. 3 is a flow chart illustrating a method of developing an adaptive charging strategy based on the health assessment of a mobile device battery in accordance with the present invention;
fig. 4 shows a block diagram of an analysis reminding system based on charging data of an on-vehicle wireless charger according to the invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
Fig. 1 shows a flowchart of an analysis reminding method based on charging data of a vehicle-mounted wireless charger according to the present invention.
As shown in fig. 1, a first aspect of the present invention provides an analysis reminding method based on charging data of an on-vehicle wireless charger, including:
s102, acquiring charging data of the vehicle-mounted wireless charger, and extracting the characteristics according to the charging data to acquire the health characteristics of the battery of the mobile equipment;
s104, performing health assessment on the mobile equipment battery according to a preset health index through the health characteristics;
s106, an adaptive charging strategy is formulated according to the health evaluation result of the mobile equipment battery, charging is carried out according to the adaptive charging strategy, and a charging curve is generated;
and S108, acquiring the abnormal charging condition according to the charging curve, and sending the health characteristics, the charging curve and the abnormal charging condition of the battery to the vehicle display system for display according to a preset mode.
The wireless charger charges through a magnetic coupling induction type wireless power transmission technology, and comprises a direct current source, a magnetic coupling induction type power transmission converter with a series-series compensation topology, a clamping auxiliary circuit based on a coupling inductor, a battery load, a filter capacitor and the like, when the vehicle-mounted wireless charger charges, the device ID information of mobile devices on the vehicle-mounted wireless charger is acquired, charging data is matched and stored to a cloud database, historical charging data is acquired according to the device ID information, and charging curves of a constant current charging stage and a constant voltage charging stage of the wireless charger are acquired according to the historical charging data; acquiring peak points of each interval through a preset time interval according to the charging curve, and extracting the position of the peak point on the charging curve, the height of the peak point and the height difference between the peak points; performing correlation analysis by combining the curve type of the charging curve with the peak point position, the peak point height and the height difference between the peak points, wherein the correlation analysis is realized by Pearson correlation analysis to obtain Spearman rank correlation analysis and the like; and acquiring a characteristic point with the correlation degree larger than a preset correlation degree threshold value according to the correlation coefficient as a health characteristic of the mobile equipment battery.
FIG. 2 is a flow chart of a method for health assessment of a mobile device battery according to the present invention.
According to the embodiment of the invention, the health evaluation of the mobile equipment battery according to the preset health index is carried out through the health characteristics, and the method specifically comprises the following steps:
s202, acquiring a health characteristic sequence of the mobile equipment battery within preset time, constructing a health evaluation model of the mobile equipment battery based on an LSTM network, carrying out standardization processing on the health characteristic sequence, and dividing the health characteristic sequence into a training set and a test set through difference;
s204, carrying out hyperparametric optimization on an LSTM layer in the health evaluation model through a particle optimization algorithm, taking a loss function of the health evaluation model as a fitness function, updating a speed parameter and a position parameter of a particle through iteration, and obtaining an optimal particle position according to a fitness minimum principle;
s206, obtaining the optimal hyper-parameter of the LSTM layer according to the optimal particle position, importing the optimal hyper-parameter into a health assessment model for training and predicting according to a training set and a test set, training the health assessment model until a loss function is converged, and then verifying through a verification set;
and S208, generating a feature matrix from the current health feature sequence of the mobile equipment battery, inputting the feature matrix into the trained health evaluation model for prediction, and outputting the current health state evaluation result of the mobile equipment battery.
It should be noted that, the over-parameters such as the sample time step number, the hidden layer neuron number, the hidden layer number, the training batch size, the learning rate, the training round number, the initialization weight bias and the like are optimally set through the particle swarm algorithm, the LSTM unit structure controls the transmission state mainly through a forgetting gate, a memory gate and an output gate, and finally converts the output dimension into the time step number of the preset time through the full connection layer, and the output layer outputs the health state of the mobile device battery.
Determining a charging reference curve of the mobile equipment according to a battery health evaluation result of the mobile equipment and an adaptive charging strategy, segmenting according to the charging reference curve, and acquiring curve characteristics of each segment of the charging reference curve to generate a charging reference curve characteristic sequence; acquiring a charging curve of the mobile equipment in the process of travel, segmenting the charging curve according to a charging reference curve characteristic sequence, calculating characteristic deviation, comparing the characteristic deviation with a deviation threshold value according to the characteristic deviation, and marking the charging abnormal condition if the characteristic deviation is greater than the deviation threshold value; acquiring abnormal characteristics of abnormal charging conditions according to the charging strategy and the corresponding characteristic deviation, and calculating and acquiring abnormal charging data meeting a similarity standard according to the abnormal characteristics through similarity; selecting the charging abnormal data with the maximum similarity, determining the type of the charging abnormal situation according to the charging abnormal data with the maximum similarity, marking the type of the charging abnormal situation, and displaying the type of the charging abnormal situation according to a preset mode.
Fig. 3 is a flow chart illustrating a method for formulating an adaptive charging strategy according to the health assessment result of a mobile device battery.
According to the embodiment of the invention, an adaptive charging strategy is formulated according to the health evaluation result of the mobile equipment battery, and the adaptive charging strategy specifically comprises the following steps:
s302, acquiring current health state information of a battery of the mobile equipment, initializing state variables, and acquiring current, voltage and thermal safety constraint information according to parameter information of the wireless charger;
s304, setting a target function of a charging strategy according to the battery health state and the charging time of the mobile equipment battery, acquiring the charging strategy of the mobile equipment battery through a particle swarm algorithm, taking the current in a constant current stage as an optimization variable, and substituting the initialization state variable;
s306, when the constraint information reaches a preset threshold value, charging the mobile equipment battery in a constant current stage, taking a target function of the charging strategy as a fitness function, calculating the optimal solution of the particles through the fitness function, acquiring the current in the constant current charging stage according to the optimal solution, and setting the charging strategy of the mobile equipment battery;
s308, obtaining a distance according to the travel information of passengers in the vehicle, and judging whether the distance is larger than a preset distance threshold value or not according to the distance;
and S310, if the current charging speed is greater than the preset distance threshold, preferentially making an adaptive charging strategy according to the health of the mobile equipment battery, and if the current charging speed is not greater than the preset distance threshold, preferentially making the adaptive charging strategy according to the charging speed of the mobile equipment battery.
The method comprises the following steps of initializing the number of particle groups, and randomly giving particle speed and position information; obtaining the current speed of the particles, comparing the current speed with the constraint, judging the advantages and disadvantages of the particles according to the fitness value if the constraint is met, removing the particles if the constraint is not met, and performing iterative training on the removed particles until the constraint is met; and after the particle speed and position information is updated for a plurality of times, the optimal position searched by each particle and the optimal positions in all the particles are obtained. Wherein, an objective function of the charging strategy is set according to the battery health state and the charging time of the mobile equipment battery
Comprises the following steps:
wherein the content of the first and second substances,
in order to preset the weight coefficient,
to predict the time for charging based on the distance taken,
in order to preset the maximum charging time,
in order to start the charging time,
for the end of chargingThe state of health of the mobile device battery,
the state of health of the mobile device battery at the end of charging;
the state variables comprise variable information such as charging current and charging voltage, when an adaptive charging strategy is preferentially formulated according to the health of a mobile device battery, an objective function only comprises a latter half part, the setting of a constant current charging stage is carried out through the smaller charging current, the voltage cannot reach a constraint condition, the temperature change of the battery is small in a non-constant voltage stage, therefore, the damage of the health state of the battery is small, when the adaptive charging strategy is preferentially formulated according to the charging speed of the mobile device battery, the maximum constraint current is obtained according to the constraint condition to set an initial charging current, and the ohmic voltage and the polarization voltage on the internal resistance of the battery are rapidly increased due to the larger charging current, so that the voltage of the battery rapidly reaches the constraint voltage and is switched to the next constant current stage for charging.
Meanwhile, the adjustment of the mobile device battery charging strategy is obtained according to the user feedback, which specifically comprises the following steps: acquiring capacity information of a battery according to the health evaluation of the battery of the mobile equipment, and acquiring the estimated full charge time of the mobile equipment according to the current electric quantity and a corresponding charging strategy; obtaining the route information of a destination to judge the predicted arrival time, obtaining the charging capacity of the mobile equipment when the mobile equipment arrives according to the predicted arrival time and the predicted full-filling time, and displaying reminding information on the charging capacity according to a preset mode; obtaining the feedback of the passengers in the vehicle on the reminding information, and obtaining whether the charging capacity meets the use requirements of the passengers in the vehicle according to the feedback; and when the feedback of the passengers in the vehicle is that the use cannot be met, adjusting the charging strategy according to the expected charging capacity of the passengers in the vehicle as one of the constraint conditions, and performing wireless charging on the mobile equipment according to the adjusted charging strategy.
According to the embodiment of the invention, when the charging curve of the mobile equipment is incomplete, the charging curve is repaired through data completion and stored in a corresponding database, and the method specifically comprises the following steps:
according to the charging start-stop time of a mobile device battery, acquiring a partial charging curve of the device according to the start-stop time, wherein the charging curve comprises but is not limited to a voltage curve and a current curve;
acquiring the geometric characteristics of partial sufficient curves, screening inflection point characteristics of a charging curve according to the geometric characteristics, and establishing a data index according to the inflection point characteristics and equipment ID information of mobile equipment;
acquiring authorized charging data of a plurality of charging platforms through big data retrieval, and performing data similarity calculation according to the acquired charging data to acquire charging data with similarity meeting a preset similarity standard;
acquiring a corresponding charging curve through charging data meeting a preset similarity standard, and performing curve fitting on the charging curve and the partial charging curve to realize charging curve completion of the partial charging curve;
and matching the health state of the battery of the mobile equipment with the supplemented charging curve and storing the health state into a preset storage path of the cloud database.
Fig. 4 shows a block diagram of an analysis reminding system based on charging data of an on-board wireless charger according to the invention.
The second aspect of the present invention also provides an analysis reminding system 4 based on charging data of a vehicle-mounted wireless charger, which includes: the device comprises a memory 41 and a processor 42, wherein the memory comprises an analysis reminding method program based on charging data of the vehicle-mounted wireless charger, and when the analysis reminding method program based on the charging data of the vehicle-mounted wireless charger is executed by the processor, the following steps are realized:
acquiring charging data of a vehicle-mounted wireless charger, and extracting characteristics according to the charging data to acquire health characteristics of a battery of mobile equipment;
performing health assessment on the mobile equipment battery according to a preset health index through the health characteristics;
an adaptive charging strategy is formulated according to the health evaluation result of the mobile equipment battery, charging is carried out according to the adaptive charging strategy, and a charging curve is generated;
and acquiring a charging abnormal condition according to the charging curve, and sending the health characteristic, the charging curve and the charging abnormal condition of the battery to an on-board display system for display according to a preset mode.
The wireless charger charges through a magnetic coupling induction type wireless power transmission technology, and comprises a direct current source, a magnetic coupling induction type power transmission converter with a series-series compensation topology, a clamping auxiliary circuit based on a coupling inductor, a battery load, a filter capacitor and the like, when the vehicle-mounted wireless charger charges, the device ID information of mobile devices on the vehicle-mounted wireless charger is acquired, charging data is matched and stored to a cloud database, historical charging data is acquired according to the device ID information, and charging curves of a constant current charging stage and a constant voltage charging stage of the wireless charger are acquired according to the historical charging data; acquiring peak points of each interval through a preset time interval according to the charging curve, and extracting the position of the peak point on the charging curve, the height of the peak point and the height difference between the peak points; performing correlation analysis according to the curve type of the charging curve, the position of a peak point of the charging curve, the height of the peak point and the height difference between the peak points, wherein the correlation analysis is realized by methods such as Pearson correlation analysis and Spearman rank correlation analysis; and acquiring a characteristic point with the correlation degree larger than a preset correlation degree threshold value according to the correlation coefficient as a health characteristic of the mobile equipment battery.
According to the embodiment of the invention, the health evaluation of the mobile equipment battery according to the preset health index is carried out through the health characteristics, and the method specifically comprises the following steps:
acquiring a health characteristic sequence of a mobile equipment battery within preset time, constructing a health evaluation model of the mobile equipment battery based on an LSTM network, carrying out standardization processing on the health characteristic sequence, and dividing the health characteristic sequence into a training set and a test set through difference;
carrying out hyper-parameter optimization on an LSTM layer in the health assessment model through a particle optimization algorithm, taking a loss function of the health assessment model as a fitness function, updating a speed parameter and a position parameter of a particle through iteration, and obtaining an optimal particle position according to a fitness minimum principle;
acquiring an optimal hyper-parameter of an LSTM layer according to an optimal particle position, importing the optimal hyper-parameter into a health assessment model for training and prediction according to a training set and a test set, training the health assessment model until a loss function is converged, and then verifying the health assessment model through a verification set;
and generating a characteristic matrix from the current health characteristic sequence of the mobile equipment battery, inputting the characteristic matrix into the trained health evaluation model for prediction, and outputting the current health state evaluation result of the mobile equipment battery.
It should be noted that, the over-parameters such as the sample time step number, the hidden layer neuron number, the hidden layer number, the training batch size, the learning rate, the training round number, the initialization weight bias and the like are optimally set through the particle swarm algorithm, the LSTM unit structure controls the transmission state mainly through a forgetting gate, a memory gate and an output gate, and finally converts the output dimension into the time step number of the preset time through the full connection layer, and the output layer outputs the health state of the mobile device battery.
Determining a charging reference curve of the mobile equipment according to a battery health evaluation result of the mobile equipment and an adaptive charging strategy, segmenting according to the charging reference curve, and acquiring curve characteristics of each segment of the charging reference curve to generate a charging reference curve characteristic sequence; acquiring a charging curve of the mobile equipment in the process of travel, segmenting the charging curve according to a charging reference curve characteristic sequence, calculating characteristic deviation, comparing the characteristic deviation with a deviation threshold value according to the characteristic deviation, and marking the charging abnormal condition if the characteristic deviation is greater than the deviation threshold value; acquiring abnormal characteristics of abnormal charging conditions according to the charging strategy and the corresponding characteristic deviation, and calculating and acquiring abnormal charging data meeting a similarity standard according to the abnormal characteristics through similarity; selecting the charging abnormal data with the maximum similarity, determining the type of the charging abnormal situation according to the charging abnormal data with the maximum similarity, marking the type of the charging abnormal situation, and displaying the type of the charging abnormal situation according to a preset mode.
According to the embodiment of the invention, an adaptive charging strategy is formulated according to the health evaluation result of the mobile equipment battery, and the adaptive charging strategy specifically comprises the following steps:
acquiring current health state information of a mobile equipment battery, initializing state variables, and acquiring current, voltage and thermal safety constraint information according to parameter information of a wireless charger;
setting a target function of a charging strategy according to the battery health state and the charging time of the mobile equipment battery, acquiring the charging strategy of the mobile equipment battery through a particle swarm algorithm, taking the current in a constant current stage as an optimization variable, and substituting the initialization state variable;
when the constraint information reaches a preset threshold value, charging the mobile equipment battery in a constant current stage, taking a target function of a charging strategy as a fitness function, calculating an optimal solution of the particles through the fitness function, acquiring current in the constant current charging stage according to the optimal solution, and setting the charging strategy of the mobile equipment battery;
acquiring a distance according to the travel information of passengers in the vehicle, and judging whether the distance is greater than a preset distance threshold value or not according to the distance;
if the current charging speed is not greater than the preset distance threshold, the adaptive charging strategy is preferentially formulated according to the charging speed of the mobile equipment battery.
The number of particle groups is initialized, and the speed and position information of the particles are randomly given; obtaining the current speed of the particles, comparing the current speed with the constraint, if the current speed meets the constraint, judging the advantages and disadvantages of the particles according to the fitness value, if the current speed does not meet the constraint, excluding the particles, and performing iterative training on the excluded particles until the constraint is met; and after the particle speed and position information is updated for a plurality of times, the optimal position searched by each particle and the optimal position in all the particles are obtained. Wherein, an objective function of the charging strategy is set according to the battery health state and the charging time of the mobile equipment battery
Comprises the following steps:
wherein the content of the first and second substances,
in order to preset the weight coefficient,
to predict the time for charging based on the trip,
in order to preset the maximum charging time,
in order to start the charging time,
to the state of health of the mobile device battery at the end of charging,
the state of health of the mobile device battery at the end of charging;
the state variables comprise variable information such as charging current and charging voltage, when an adaptive charging strategy is preferentially formulated according to the health of a mobile device battery, an objective function only comprises a latter half part, the setting of a constant current charging stage is carried out through the smaller charging current, the voltage cannot reach a constraint condition, the temperature change of the battery is small in a non-constant voltage stage, therefore, the damage of the health state of the battery is small, when the adaptive charging strategy is preferentially formulated according to the charging speed of the mobile device battery, the maximum constraint current is obtained according to the constraint condition to set an initial charging current, and the ohmic voltage and the polarization voltage on the internal resistance of the battery are rapidly increased due to the larger charging current, so that the voltage of the battery rapidly reaches the constraint voltage and is switched to the next constant current stage for charging.
Meanwhile, the adjustment of the battery charging strategy of the mobile equipment is obtained according to the user feedback, which specifically comprises the following steps: acquiring capacity information of a battery according to the health evaluation of the battery of the mobile equipment, and acquiring the estimated full charge time of the mobile equipment according to the current electric quantity and a corresponding charging strategy; obtaining the route information of a destination to judge the predicted arrival time, obtaining the charging capacity of the mobile equipment when the mobile equipment arrives according to the predicted arrival time and the predicted full-filling time, and displaying reminding information on the charging capacity according to a preset mode; obtaining the feedback of the passengers in the vehicle on the reminding information, and obtaining whether the charging capacity meets the use requirements of the passengers in the vehicle according to the feedback; and when the feedback of the passengers in the vehicle is that the use cannot be met, adjusting the charging strategy according to the expected charging capacity of the passengers in the vehicle, and wirelessly charging the mobile equipment according to the adjusted charging strategy.
The third aspect of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a program of an analysis reminding method based on charging data of a vehicle-mounted wireless charger, and when the program of the analysis reminding method based on charging data of a vehicle-mounted wireless charger is executed by a processor, the steps of the analysis reminding method based on charging data of a vehicle-mounted wireless charger as described in any one of the above are implemented.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit may be implemented in the form of hardware, or in the form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media capable of storing program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (6)
1. The analysis reminding method based on the charging data of the vehicle-mounted wireless charger is characterized by comprising the following steps of:
acquiring charging data of a vehicle-mounted wireless charger, and extracting characteristics according to the charging data to acquire health characteristics of a battery of the mobile equipment;
performing health assessment on the mobile equipment battery according to the health characteristics and preset health indexes;
an adaptive charging strategy is formulated according to the health evaluation result of the mobile equipment battery, charging is carried out according to the adaptive charging strategy, and a charging curve is generated;
acquiring a charging abnormal condition according to the charging curve, and sending the health characteristic of the battery, the charging curve and the charging abnormal condition to an on-board display system for displaying according to a preset mode;
carry out health assessment to the mobile device battery through healthy characteristic and according to presetting healthy index, specifically do:
acquiring a health characteristic sequence of a mobile equipment battery within preset time, constructing a health evaluation model of the mobile equipment battery based on an LSTM network, carrying out standardization processing on the health characteristic sequence, and dividing the health characteristic sequence into a training set and a test set through difference;
carrying out hyper-parameter optimization on an LSTM layer in the health evaluation model through a particle optimization algorithm, taking a loss function of the health evaluation model as a fitness function, updating a speed parameter and a position parameter of a particle through iteration, and obtaining an optimal particle position according to a fitness minimum principle;
acquiring an optimal hyper-parameter of an LSTM layer according to an optimal particle position, importing the optimal hyper-parameter into a health assessment model for training and prediction according to a training set and a test set, training the health assessment model until a loss function is converged, and then verifying the health assessment model through a verification set;
generating a characteristic matrix from a current health characteristic sequence of the mobile equipment battery, inputting the characteristic matrix into a trained health evaluation model for prediction, and outputting a current health state evaluation result of the mobile equipment battery;
an adaptive charging strategy is formulated according to the health evaluation result of the mobile equipment battery, and the adaptive charging strategy specifically comprises the following steps:
acquiring current health state information of a mobile equipment battery, initializing state variables, and acquiring current, voltage and thermal safety constraint information according to parameter information of a wireless charger;
setting a target function of a charging strategy according to the battery health state and the charging time of the mobile equipment battery, acquiring the charging strategy of the mobile equipment battery through a particle swarm algorithm, taking the current in a constant current stage as an optimization variable, and substituting the initialization state variable;
when the thermal safety constraint information reaches a preset threshold value, charging the mobile equipment battery in a constant current stage, taking a target function of a charging strategy as a fitness function, calculating an optimal solution of particles through the fitness function, acquiring current in the constant current charging stage according to the optimal solution, and setting the charging strategy of the mobile equipment battery;
acquiring a distance according to the travel information of passengers in the vehicle, and judging whether the distance is greater than a preset distance threshold value or not according to the distance;
if the current charging speed is not greater than the preset distance threshold, the adaptive charging strategy is preferentially formulated according to the charging speed of the mobile equipment battery.
2. The method for analyzing and reminding the charging data based on the vehicle-mounted wireless charger according to claim 1, wherein the charging data of the vehicle-mounted wireless charger is acquired, and the health characteristics of the battery of the mobile device are acquired by performing characteristic extraction according to the charging data, specifically:
acquiring equipment ID information of mobile equipment on a vehicle-mounted wireless charger, acquiring historical charging data according to the equipment ID information, and acquiring charging curves of a constant-current charging stage and a constant-voltage charging stage of the wireless charger according to the historical charging data;
acquiring peak points of each interval through a preset time interval according to the charging curve, and extracting the position of the peak point on the charging curve, the height of the peak point and the height difference between the peak points;
performing correlation analysis by combining the curve type of the charging curve with the peak point position, the peak point height and the height difference between the peak points;
and acquiring a characteristic point with the correlation degree larger than a preset correlation degree threshold value according to the correlation coefficient as a health characteristic of the mobile equipment battery.
3. The analysis reminding method based on the charging data of the vehicle-mounted wireless charger according to claim 1, wherein the charging abnormal condition is obtained according to the charging curve, and specifically comprises the following steps:
determining a charging reference curve of the mobile equipment according to a battery health evaluation result of the mobile equipment and an adaptive charging strategy, segmenting according to the charging reference curve, and acquiring curve characteristics of each segment of the charging reference curve to generate a charging reference curve characteristic sequence;
acquiring a charging curve of the mobile equipment in the process of travel, segmenting the charging curve according to a charging reference curve characteristic sequence, calculating characteristic deviation, comparing the characteristic deviation with a deviation threshold value according to the characteristic deviation, and marking the charging abnormal condition if the characteristic deviation is greater than the deviation threshold value;
acquiring abnormal characteristics of abnormal charging conditions according to the charging strategy and the corresponding characteristic deviation, and calculating and acquiring abnormal charging data meeting a similarity standard according to the abnormal characteristics through similarity;
selecting the charging abnormal data with the maximum similarity, determining the type of the charging abnormal situation according to the charging abnormal data with the maximum similarity, marking the type of the charging abnormal situation, and displaying the type of the charging abnormal situation according to a preset mode.
4. The analysis reminding method based on the charging data of the vehicle-mounted wireless charger according to claim 1, further comprising:
acquiring capacity information of a battery according to the health evaluation of the battery of the mobile equipment, and acquiring the estimated full charge time of the mobile equipment according to the current electric quantity and a corresponding charging strategy;
obtaining the route information of a destination to judge the predicted arrival time, obtaining the charging capacity of the mobile equipment when the mobile equipment arrives according to the predicted arrival time and the predicted full-filling time, and displaying reminding information on the charging capacity according to a preset mode;
obtaining the feedback of the passengers in the vehicle on the reminding information, and obtaining whether the charging capacity meets the use requirements of the passengers in the vehicle according to the feedback;
and when the feedback of the passengers in the vehicle is that the use cannot be met, adjusting the charging strategy according to the expected charging capacity of the passengers in the vehicle, and wirelessly charging the mobile equipment according to the adjusted charging strategy.
5. The utility model provides an analysis warning system based on-vehicle wireless charger charging data which characterized in that, this system includes: the analysis reminding method based on the charging data of the vehicle-mounted wireless charger comprises a memory and a processor, wherein the memory comprises an analysis reminding method program based on the charging data of the vehicle-mounted wireless charger, and the analysis reminding method program based on the charging data of the vehicle-mounted wireless charger realizes the following steps when being executed by the processor:
acquiring charging data of a vehicle-mounted wireless charger, and extracting characteristics according to the charging data to acquire health characteristics of a battery of the mobile equipment;
performing health assessment on the mobile equipment battery according to the health characteristics and preset health indexes;
an adaptive charging strategy is formulated according to the health evaluation result of the mobile equipment battery, charging is carried out according to the adaptive charging strategy, and a charging curve is generated;
acquiring a charging abnormal condition according to the charging curve, and sending the health characteristic of the battery, the charging curve and the charging abnormal condition to an on-board display system for displaying according to a preset mode;
carry out health assessment to the mobile device battery through healthy characteristic and according to predetermineeing healthy index, specifically do:
acquiring a health characteristic sequence of a mobile equipment battery within preset time, constructing a health evaluation model of the mobile equipment battery based on an LSTM network, carrying out standardization processing on the health characteristic sequence, and dividing the health characteristic sequence into a training set and a test set through difference;
carrying out hyper-parameter optimization on an LSTM layer in the health assessment model through a particle optimization algorithm, taking a loss function of the health assessment model as a fitness function, updating a speed parameter and a position parameter of a particle through iteration, and obtaining an optimal particle position according to a fitness minimum principle;
acquiring an optimal hyper-parameter of an LSTM layer according to an optimal particle position, importing the optimal hyper-parameter into a health assessment model for training and prediction according to a training set and a test set, training the health assessment model until a loss function is converged, and then verifying the health assessment model through a verification set;
generating a characteristic matrix from a current health characteristic sequence of the mobile equipment battery, inputting the characteristic matrix into a trained health evaluation model for prediction, and outputting a current health state evaluation result of the mobile equipment battery;
an adaptive charging strategy is formulated according to the health evaluation result of the mobile equipment battery, and specifically comprises the following steps:
acquiring current health state information of a mobile equipment battery, initializing state variables, and acquiring current, voltage and thermal safety constraint information according to parameter information of a wireless charger;
setting a target function of a charging strategy according to the battery health state and the charging time of the mobile equipment battery, acquiring the charging strategy of the mobile equipment battery through a particle swarm algorithm, taking the current in a constant current stage as an optimization variable, and substituting the initialization state variable;
when the thermal safety constraint information reaches a preset threshold value, charging the mobile equipment battery in a constant current stage, taking a target function of a charging strategy as a fitness function, calculating an optimal solution of particles through the fitness function, acquiring current in the constant current charging stage according to the optimal solution, and setting the charging strategy of the mobile equipment battery;
acquiring a distance according to the travel information of passengers in the vehicle, and judging whether the distance is greater than a preset distance threshold value or not according to the distance;
if the current charging speed is not greater than the preset distance threshold, the adaptive charging strategy is preferentially formulated according to the charging speed of the mobile equipment battery.
6. The system of claim 5, wherein the charging abnormal condition is obtained according to the charging curve, and specifically comprises:
determining a charging reference curve of the mobile equipment according to a battery health evaluation result of the mobile equipment and an adaptive charging strategy, segmenting according to the charging reference curve, and acquiring curve characteristics of each segment of the charging reference curve to generate a charging reference curve characteristic sequence;
acquiring a charging curve of the mobile equipment in the process of travel, segmenting the charging curve according to a charging reference curve characteristic sequence, calculating characteristic deviation, comparing the characteristic deviation with a deviation threshold value according to the characteristic deviation, and marking the charging abnormal condition if the characteristic deviation is greater than the deviation threshold value;
acquiring abnormal characteristics of abnormal charging conditions according to the charging strategy and the corresponding characteristic deviation, and calculating and acquiring abnormal charging data meeting a similarity standard according to the abnormal characteristics through similarity;
selecting the charging abnormal data with the maximum similarity, determining the type of the charging abnormal situation according to the charging abnormal data with the maximum similarity, marking the type of the charging abnormal situation, and displaying the type of the charging abnormal situation according to a preset mode.
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| CN116683588B (en) * | 2023-08-02 | 2024-01-26 | 深圳市神通天下科技有限公司 | Lithium ion battery charge and discharge control method and system |
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| CN117375266B (en) * | 2023-12-05 | 2024-02-09 | 深圳市亿控电子科技有限公司 | Intelligent analysis method and system based on wireless charger charging data |
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