CN115465152A - Power battery monitoring method, device, equipment and storage medium - Google Patents
Power battery monitoring method, device, equipment and storage medium 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
- 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
- B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
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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
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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
- B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
- B60L3/0046—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electric energy storage systems, e.g. batteries or capacitors
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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
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
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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
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
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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
- B60L2260/00—Operating Modes
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Abstract
The application provides a power battery monitoring method, a power battery monitoring device, power battery monitoring equipment and a storage medium, and relates to the technical field of battery monitoring. The method comprises the steps that the residual cycle number of a target power battery in the electric vehicle is obtained from a battery pack of the electric vehicle; determining the economic value of the target power battery according to the residual cycle times; determining the residual use value of the target power battery according to the initial use value of the target power battery and the traveled mileage of the electric vehicle based on the target power battery; and carrying out recovery evaluation on the target power battery according to the economic value and the residual use value so as to determine whether the target power battery reaches the recovery condition. Therefore, the power battery is monitored in real time, and recovery evaluation is carried out according to the actual value and the theoretical value, so that the recovery evaluation of the power battery is more accurate, and convenient power battery evaluation service is provided for users.
Description
Technical Field
The invention relates to the technical field of battery monitoring, in particular to a power battery monitoring method, a power battery monitoring device, power battery monitoring equipment and a storage medium.
Background
The market scale of new energy automobiles continues to expand, which means that more and more people buy electric automobiles. The performance of the electric vehicle is a focus of attention of all vehicle owners. The power battery is one of the decisive factors for embodying the performance of the electric automobile as a core component of the electric automobile, and the recycling of the power battery of the new energy automobile is highly emphasized in the market for catering to the low-carbon recyclable development. The vehicle owner can sell the retired power battery to a recycling enterprise for echelon utilization, so that the purposes of resource utilization of the waste battery and improvement of vehicle economy are achieved.
In the use process of the electric automobile, the recycling price of the electric automobile is reduced along with the increase of the cycle number of the lithium battery, and at this time, the automobile owner needs to be helped to judge the optimal decommissioning time of the battery so as to achieve the goal of selling the decommissioned waste battery at the optimal price. Most of the prior art judges the optimal retirement time of the battery manually, and the accuracy is low.
Disclosure of Invention
The present invention provides a method, an apparatus, a device and a storage medium for monitoring a power battery, so as to solve the problem of low accuracy of determining the optimal decommissioning time of the battery in the prior art.
In order to achieve the above purpose, the technical solutions adopted in the embodiments of the present application are as follows:
in a first aspect, an embodiment of the present application provides a power battery monitoring method, where the method includes:
acquiring the residual cycle number of a target power battery in an electric vehicle from a battery pack of the electric vehicle;
determining the economic value of the target power battery according to the residual cycle number;
determining a remaining use value of the target power battery according to the initial use value of the target power battery and the traveled mileage of the electric vehicle based on the target power battery;
and according to the economic value and the residual use value, carrying out recovery evaluation on the target power battery to determine whether the target power battery reaches a recovery condition.
Optionally, the determining the economic value of the target power battery according to the remaining cycle number includes:
and obtaining the economic value of the target power battery by adopting a pre-trained battery value evaluation model according to the residual cycle number.
Optionally, before obtaining the economic value of the target power battery by using a pre-trained battery value evaluation model according to the remaining cycle number, the method further includes:
obtaining a battery reclaim data set, wherein the battery reclaim data set comprises: the method comprises the following steps of (1) recycling sample data by multiple groups of batteries, wherein the sample data recycling of each group of batteries comprises the following steps: the historical recovery of the residual cycle number of one power battery and the historical recovery economic value of the one power battery;
recovering data from the battery randomly selecting a predetermined number in a set the battery of the group retrieves sample data as training sample set;
and performing model training by adopting the training sample set to obtain the battery value evaluation model.
Optionally, the method further comprises:
taking the battery recycling sample data outside the training sample set in the battery recycling data set as a verification sample set;
verifying the battery value evaluation model by adopting the verification sample set to obtain an identification error of the battery value evaluation model;
if the recognition error is smaller than or equal to a preset error threshold value, determining that the battery value evaluation model is trained;
and if the identification error is larger than the preset error threshold, increasing the group number of the sample data recovered by the battery in the training sample set, and performing model training again based on the increased training sample set until the error parameter of the battery value evaluation model obtained by training is smaller than or equal to the preset error threshold.
Optionally, the performing recovery evaluation on the target power battery according to the economic value and the remaining use value includes:
if the residual use value is greater than or equal to the economic value, determining that the target power battery does not reach a recovery condition;
and outputting first indication information to indicate that the target power battery continues to be used.
Optionally, the recycling evaluation of the target power battery according to the economic value and the remaining use value further includes:
if the residual use value is smaller than the economic value, determining that the target power battery reaches a recovery condition;
and outputting second prompt information to remind a user of recycling the target power battery.
Optionally, the method further comprises:
if the change value of the economic value of the target power battery in a preset time period is monitored to be larger than a preset value, sending early warning information to remind a user to check the service condition of the target power battery.
In a second aspect, an embodiment of the present application provides a power battery monitoring device, where the device includes:
the acquisition module is used for acquiring the residual cycle number of a target power battery in the electric vehicle from a battery pack of the electric vehicle;
the first determining module is used for determining the economic value of the target power battery according to the residual cycle number;
a second determination module for determining a remaining use value of the target power battery according to an initial use value of the target power battery and based on a traveled mileage of the target power battery by the electric vehicle;
and the evaluation module is used for carrying out recovery evaluation on the target power battery according to the economic value and the residual use value so as to determine whether the target power battery reaches a recovery condition.
In a third aspect, an embodiment of the present application provides a monitoring device, including: the power battery monitoring method comprises a processor and a storage medium, wherein the processor is in communication connection with the storage medium through a bus, the storage medium stores program instructions executable by the processor, and the processor calls a program stored in the storage medium to execute the steps of the power battery monitoring method according to any one of the first aspect.
In a fourth aspect, the present application provides a storage medium, where a computer program is stored, where the computer program is executed by a processor to perform the steps of the power battery monitoring method according to any one of the first aspect.
Compared with the prior art, the method has the following beneficial effects:
the application provides a power battery monitoring method, a device, equipment and a storage medium, wherein the method comprises the steps of obtaining the residual cycle number of a target power battery in an electric vehicle from a battery pack of the electric vehicle; determining the economic value of the target power battery according to the residual cycle times; determining the residual use value of the target power battery according to the initial use value of the target power battery and the traveled mileage of the electric vehicle based on the target power battery; and carrying out recovery evaluation on the target power battery according to the economic value and the residual use value so as to determine whether the target power battery reaches the recovery condition. Therefore, the power battery is monitored in real time, and recovery evaluation is carried out according to the actual value and the theoretical value, so that the recovery evaluation of the power battery is more accurate, and convenient power battery evaluation service is provided for users.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic structural diagram of a power battery monitoring system provided in the present application;
fig. 2 is a schematic flow chart of a power battery monitoring method provided in the present application;
FIG. 3 is a schematic flow chart diagram illustrating a method for determining a battery value evaluation model according to an embodiment of the present disclosure;
FIG. 4 is a schematic flow chart illustrating a method for validating a battery value evaluation model according to an embodiment of the present disclosure;
fig. 5 is a schematic flow chart of a method for power battery recycling evaluation according to an embodiment of the present disclosure;
FIG. 6 is a schematic flow chart of another method for power battery recycling evaluation according to an embodiment of the present disclosure;
fig. 7 is a schematic diagram of a power battery monitoring device according to an embodiment of the present disclosure;
fig. 8 is a schematic view of a monitoring device according to an embodiment of the present application.
Icon: 100-monitoring device, 200-cycle counter, 300-mileage counter, 400-vehicle device, 701-acquisition module, 702-first determination module, 703-second determination module, 704-evaluation module, 801-processor, 802-storage medium.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, as presented in the figures, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Furthermore, the appearances of the terms "first," "second," and the like, if any, are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
It should be noted that the features of the embodiments of the present invention may be combined with each other without conflict.
In order to accurately prompt the selling time of the power battery for a user, the application provides a power battery monitoring method, a device, equipment and a storage medium.
The following explains a power battery monitoring system provided by the present application by a specific example. Fig. 1 is a schematic structural diagram of a power battery monitoring system provided in the present application. As shown in fig. 1, the system includes: monitoring equipment 100, cycle number counter 200, mileage counter 300, and vehicle-mounted equipment 400.
The monitoring device 100 is connected to the cycle count counter 200, the mileage counter 300, and the in-vehicle device 400, respectively.
The cycle number counter 200 is used to record the remaining cycle number of the battery pack of the electric vehicle and transmit the remaining cycle number to the monitoring apparatus 100. The battery cycle is a complete charge-discharge cycle of the battery pack. The cycle count counter 200 acquires the number of cycles of the battery pack that have been used, and obtains the remaining number of cycles from the total number of cycles of the battery pack and the number of cycles that have been used. For example, if the total number of cycles is 5000 cycles, the number of cycles already used is 2000 cycles, and the remaining number of cycles is 3000 cycles.
The mileage counter 300 is used to record the traveled mileage of the electric vehicle based on the target power battery of the electric vehicle and transmit the traveled mileage to the monitoring device 100.
The monitoring device 100 is used for recycling and evaluating the target power battery according to the remaining cycle number, the traveled mileage and the attribute information of the power battery.
The monitoring apparatus 100 may also transmit the recycling evaluation information to the in-vehicle apparatus 400 to remind the user. By way of example, the in-vehicle device 400 may be a display screen, a speaker, etc. of an electric vehicle, and is not limited thereto.
The following explains a power battery monitoring method provided by the application by a specific example. Fig. 2 is a schematic flow diagram of a power battery monitoring method provided in the present application, where an execution subject of the method is a monitoring device, and the monitoring device may be a device with a calculation processing function. As shown in fig. 2, the method includes:
and S101, acquiring the residual cycle number of the target power battery in the electric vehicle from a battery pack of the electric vehicle.
A cycle counter is installed in a battery pack of the electric vehicle, and the remaining cycle of a target power battery in the electric vehicle is acquired from the cycle counter. An electric vehicle comprising: electric cars, electric bicycles, and other vehicles that operate using electric power.
And S102, determining the economic value of the target power battery according to the residual cycle number.
The battery cycle is a complete charge-discharge period of the battery pack, and the residual cycle number represents the residual service time of the target power battery. The remaining service time corresponds to the value of the target power battery, and the more the remaining service time is, the higher the value of the target power battery is; the less the remaining usage time, the lower the value of the target power cell. Therefore, the economic value of the target power battery can be determined according to the residual cycle number and the historical recovery transaction data of the power battery with the same type as the target power battery. Illustratively, the economic value is the recovery price of the power battery with the same type as the target power battery, and represents the actual market economic value of the target power battery.
S103, determining the residual use value of the target power battery according to the initial use value of the target power battery and the traveled distance of the electric vehicle based on the target power battery.
The initial use value of the target power battery refers to the selling price at the time of sale. For each power cell parameter, in addition to the initial value of use, there is an initial total mileage that characterizes the power cell throughout its life cycle, the electric vehicle being based on a form of total mileage of the target power cell, such as: 100000 km.
Firstly, calculating the use value of unit mileage according to the initial use value and the total mileage of the target power battery. And determining the residual use value of the target power battery according to the use value of the unit mileage and the traveled mileage. Namely, the traveled mileage is positively correlated with the remaining use value of the target power battery, and the theoretical remaining value of the target power battery based on the traveled mileage is represented.
Here, the economic value of the target power battery determined in step S101 and step S102 is not in sequence with the remaining use value of the target power battery determined in step S103. The present embodiment and the flowchart shown in fig. 2 are only examples, and the present application does not limit the sequence between step S101 and steps S102 and 103.
And S104, carrying out recovery evaluation on the target power battery according to the economic value and the residual use value so as to determine whether the target power battery reaches the recovery condition.
The economic value characterizes the actual market economic value of the target power cell. The residual use value represents the theoretical residual value of the target power battery based on the traveled mileage. And carrying out recovery evaluation on the target power battery according to the economic value and the residual use value so as to determine whether the target power battery reaches the recovery condition. And the recovery evaluation is carried out according to the actual value and the theoretical value, so that the recovery evaluation of the power battery is more accurate, the convenient power battery evaluation service is provided for users, and the users are helped to judge the optimal retirement time of the target power battery.
For example, the specific recovery conditions may be: the economic value is greater than the residual use value; the following steps can be also included: and weighting the economic value and the residual use value, wherein the weighted economic value is greater than the residual use value.
In summary, in the embodiment, the remaining cycle number of the target power battery in the electric vehicle is obtained from the battery pack of the electric vehicle; determining the economic value of the target power battery according to the residual cycle number; determining the residual use value of the target power battery according to the initial use value of the target power battery and the traveled mileage of the electric vehicle based on the target power battery; and carrying out recovery evaluation on the target power battery according to the economic value and the residual use value so as to determine whether the target power battery reaches the recovery condition. Therefore, the power battery is monitored in real time, and recovery evaluation is carried out according to the actual value and the theoretical value, so that the recovery evaluation of the power battery is more accurate, and convenient power battery evaluation service is provided for users.
On the basis of the embodiment corresponding to fig. 2, the present application further provides a method for calculating the economic value of the target power battery, wherein the determining the economic value of the target power battery according to the remaining cycle number in S102 includes:
and according to the residual cycle times, obtaining the economic value of the target power battery by adopting a pre-trained battery value evaluation model.
The economic value of the target power battery is accurately obtained according to the residual cycle number. And inputting the current residual cycle number into the battery value evaluation model by adopting a pre-trained battery value evaluation model, and calculating to obtain the economic value of the target power battery.
The pre-trained battery value evaluation model comprises a mapping relation between the residual cycle number and the economic value of the target power battery, and the corresponding economic value can be calculated by inputting the residual cycle number. For example, the battery value evaluation model may be a model built based on a neural network recognition model, and may also be other models, as long as the battery value evaluation can be completed, which is not limited herein.
In summary, in this embodiment, the economic value of the target power battery is obtained by using the pre-trained battery value evaluation model according to the remaining cycle number. Therefore, the economic value of the target power battery is accurately obtained.
On the basis of the above embodiments, the embodiments of the present application further provide a method for determining a battery value evaluation model. Fig. 3 is a schematic flowchart of a method for determining a battery value evaluation model according to an embodiment of the present disclosure. As shown in fig. 3, before obtaining the economic value of the target power battery by using the pre-trained battery value evaluation model according to the remaining cycle number, the method further includes:
s201, acquiring a battery recovery data set.
Wherein the battery recovery data set includes: the method comprises the following steps of recovering sample data of a plurality of groups of batteries, wherein the sample data recovery of each group of batteries comprises the following steps: historical recycling of the remaining cycle number of a power battery and the historical recycling economic value of a power battery.
For example, the historical reclamation economic value may be a historical reclamation price of the power cell. To ensure model quality, the number of groups of the battery recycle data set should be large enough, for example: the number of sets of battery recycle data sets is greater than 100.
S202, randomly selecting a preset number of groups of battery recycling sample data from the battery recycling data set as a training sample set.
To ensure the training accuracy of the model, the predetermined number is greater than half of the battery recycling data set, for example, the predetermined number is 80% of the battery recycling data set. And randomly selecting from the battery recovery data set, so that the selected training sample set is more representative of the battery recovery data set.
And S203, performing model training by adopting the training sample set to obtain a battery value evaluation model.
And inputting the residual cycle times and the historical recovery economic value in the training sample set into the model, and performing multiple training to obtain a battery value evaluation model.
Further, the battery value evaluation model is taken as a neural network recognition model as an example, and the process of model training is explained.
The neural network recognition model is composed of three layers of structures, namely an input layer, a hidden layer and an output layer. The input layer has I nodes, the hidden layer has J nodes, and the output layer has one node. The error between the calculated result and the expected error is obtained by a multi-layer network of neuron back propagation. The main learning process in the training of the neural network is: firstly, inputting a plurality of residual cycle times C1, C2, C3, … … and Ci in a training sample set to obtain a plurality of corresponding output historical recovery economic values D1, D2, D3, … … and Di; and continuously correcting the weight of each neuron connected inside the neural network through errors between a plurality of output historical recovery economic values D1, D2, D3, … … and Di of the neural network and historical recovery economic values A1, A2, A3, … … and Ai in a training sample set, and continuously reducing the errors to enable the output value to approach a target value. Meanwhile, the learning mode of back propagation transmits the data to each layer of the neuron layer by layer, and the error is continuously corrected and adjusted, so that the sum of squares of the errors is minimized.
In the construction of the neural network recognition model, a Sigmoid function is adopted as a learning and training function, a function expression is as shown in a formula (1), a Softmax function is adopted as an output layer transfer function of the model, and a function expression is as shown in a formula (2):
after the model structure is determined, determining the input value of the model as the residual cycle number; the number of hidden layers is 1, and the output value is the recycling economic value. And starting to train the neural network recognition model until the sum of squares of errors of the output value of the neural network recognition model and the target value meets the preset requirement. If the error sum of squares still does not meet the requirement when the specified number of iterations is reached, the training network can be repeated or the number of hidden layers can be increased appropriately.
After the neural network identification model is trained successfully, the trained weight and threshold value are required to be given to the neural network identification model as initial values, so as to achieve the purpose of self-adaptive control.
To sum up, in the present embodiment, a battery recycling data set is obtained; randomly selecting a preset number of groups of battery recycling sample data from a battery recycling data set as a training sample set; and performing model training by adopting a training sample set to obtain a battery value evaluation model. Thus, the battery value evaluation model is accurately obtained.
On the basis of the embodiment corresponding to fig. 3, the embodiment of the present application further provides a method for verifying a battery value evaluation model. Fig. 4 is a schematic flowchart of a method for verifying a battery value evaluation model according to an embodiment of the present disclosure. As shown in fig. 4, the method further comprises:
s301, taking the battery recycling sample data outside the training sample set in the battery recycling data set as a verification sample set.
Wherein the number of the verification sample sets is smaller than the number of the training sample sets.
And S302, verifying the battery value evaluation model by adopting the verification sample set to obtain the identification error of the battery value evaluation model.
And inputting the residual cycle times in the verification sample set into a battery value evaluation model, and calculating to obtain an output economic value. And calculating the identification error of the battery value evaluation model according to the actual economic value corresponding to the residual cycle number and the output economic value, wherein the specific calculation mode is shown in the following formula (3):
and S303, if the identification error is smaller than or equal to a preset error threshold value, determining that the training of the battery value evaluation model is finished.
And if the identification error is smaller than or equal to the preset error threshold, the battery value evaluation model meets the error requirement, and the battery value evaluation model is determined to be trained completely. The battery value evaluation model may be used as a preset battery value evaluation model.
And S304, if the identification error is larger than the preset error threshold, increasing the group number of the battery recycling sample data in the training sample set, and performing model training again based on the increased training sample set until the error parameter of the battery value evaluation model obtained by training is smaller than or equal to the preset error threshold.
And if the identification error is larger than the preset error threshold value, indicating that the battery value evaluation model does not meet the error requirement. In order to further obtain a model with higher precision, the number of groups of sample data recovered by the battery in the training sample set is increased, model training is carried out again on the basis of the increased training sample set, and error parameters of the battery value evaluation model after retraining are calculated until the error parameters of the battery value evaluation model obtained through training are smaller than or equal to a preset error threshold. And if the error parameter of the battery value evaluation model is less than or equal to the preset error threshold, the battery value evaluation model can be used as a preset battery value evaluation model.
In summary, in the embodiment, the battery recycling sample data outside the training sample set in the battery recycling data set is used as the verification sample set; verifying the battery value evaluation model by adopting a verification sample set to obtain the identification error of the battery value evaluation model; if the recognition error is smaller than or equal to a preset error threshold value, determining that the training of the battery value evaluation model is finished; and if the identification error is larger than the preset error threshold, increasing the group number of the battery recovery sample data in the training sample set, and performing model training again based on the increased training sample set until the error parameter of the battery value evaluation model obtained by training is smaller than or equal to the preset error threshold. Therefore, a high-precision battery value evaluation model is obtained by verifying the sample set.
On the basis of the embodiment corresponding to fig. 2, the embodiment of the present application further provides a method for evaluating recycling of a power battery. Fig. 5 is a schematic flowchart of a method for power battery recycling evaluation according to an embodiment of the present disclosure. As shown in fig. 5, the recycling evaluation of the target power battery according to the economic value and the remaining use value in S104 includes:
s401, if the residual use value is larger than or equal to the economic value, determining that the target power battery does not reach the recovery condition.
If the residual use value is greater than or equal to the economic value, namely, the current theoretical value of the target power battery is greater than or equal to the actual value. If the user sells the target power battery at this time, it is not cost effective in an economical sense. It is determined that the target power cell does not reach the recovery condition.
S402, outputting first indication information to indicate that the target power battery continues to be used.
In order to prompt the user to use the related information about the target power battery, first indication information can be output to the vehicle-mounted equipment so as to indicate that the target power battery is continuously used.
Exemplarily, the first indication information may be a text reminding information, which is transmitted to the display screen; or voice reminding information, and transmitting the information to the loudspeaker.
Further, the power battery monitoring in this application is gone on in real time, consequently, in actual monitoring, mostly use the characters to remind information to be the main, transmit to the battery model display of display screen, can get into battery module and look over, do not influence other functions in the user normal use display screen.
In summary, in this embodiment, if the remaining use value is greater than or equal to the economic value, it is determined that the target power battery does not reach the recovery condition; and outputting first indication information to indicate that the target power battery continues to be used. Therefore, the user is reminded to continue using the target power battery in time.
On the basis of the embodiment corresponding to fig. 5, the embodiment of the present application further provides another method for evaluating the recycling of the power battery. Fig. 6 is a schematic flow chart of another power battery recycling evaluation method according to an embodiment of the present disclosure. As shown in fig. 6, the recycling evaluation of the target power battery according to the economic value and the remaining use value in S104 further includes:
s501, if the residual use value is smaller than the economic value, determining that the target power battery reaches the recovery condition.
If the residual use value is less than the economic value, namely, the current theoretical value of the target power battery is less than the actual value. If the user sells the target power battery at this time, it is very cost-effective in an economic sense. It is determined that the target power cell reaches the recovery condition.
And S502, outputting second prompt information to remind a user to recycle the target power battery.
In order to remind the user of the relevant information about the target power battery in time, second indication information can be output to the vehicle-mounted equipment to remind the user of recovering the target power battery.
Illustratively, the second indication information may be a text reminding information, which is transmitted to the display screen; or voice reminding information, and transmitting the information to the loudspeaker.
In summary, in this embodiment, if the remaining use value is less than the economic value, it is determined that the target power battery reaches the recovery condition; and outputting second prompt information to remind the user to recover the target power battery. Therefore, the user is reminded to sell the target power battery in time.
On the basis of the embodiment corresponding to fig. 2, the embodiment of the present application further provides another power battery monitoring method. The method further comprises the following steps:
if the change value of the economic value of the target power battery in the preset time period is greater than the preset value, sending early warning information to remind a user to check the use condition of the target power battery.
Illustratively, the preset time period is set to one day, and the preset value is 1000 yuan. If the change value of the economic value of the target power battery is monitored to be larger than 1000 yuan within one day, the situation that the battery is used by a user in an unhealthy mode or the battery breaks down is indicated, and the economic value is greatly changed. And sending early warning information to remind a user to check the service condition of the target power battery, so that the service life of the target power battery can be prolonged, and the life cycle of the target power battery is prolonged.
In summary, in this embodiment, if it is monitored that the change value of the economic value of the target power battery in the preset time period is greater than the preset value, the warning message is sent to remind the user to check the use condition of the target power battery. Thus, the target power battery life cycle is prolonged.
On the basis of the above embodiments, the embodiments of the present application also provide an example to show the power battery monitoring process.
As shown in table 1 below, taking 6 sets of monitoring data as an example, the economic value of the corresponding target power battery is determined according to the 6 sets of remaining cycle times.
TABLE 1
As shown in table 2 below, the remaining use values corresponding to the 6 sets of monitoring data are obtained and compared with the economic value to obtain the battery monitoring recommendation information.
| Serial number | Number of remaining cycles/time | Economic value/yuan | Residual use value/dollar | Advice information |
| 1 | 4600 | 19031.6 | 184000 | Continue to use |
| 2 | 2100 | 16826.4 | 73000 | Continue to use |
| 3 | 1550 | 8216 | 7050 | Recycle and utilize |
| 4 | 1100 | 8216 | 6350 | Recycle and utilize |
| 5 | 350 | 97.03 | 70 | Recycling and utilizing |
| 6 | 100 | 98.4 | 63 | Recycling and utilizing |
TABLE 2
Therefore, the power battery is monitored in real time, and recovery evaluation is carried out according to the actual value and the theoretical value, so that the recovery evaluation of the power battery is more accurate, and convenient power battery evaluation service is provided for users.
The following describes a power battery monitoring apparatus, a device, a storage medium, and the like provided by the present application for implementation, and specific implementation processes and technical effects thereof are referred to above, and will not be described again below.
Fig. 7 is a schematic diagram of a power battery monitoring device according to an embodiment of the present application. As shown in fig. 7, the apparatus includes:
an obtaining module 701 is configured to obtain a remaining cycle number of a target power battery in an electric vehicle from a battery pack of the electric vehicle.
And a first determining module 702, configured to determine the economic value of the target power battery according to the remaining cycle number.
A second determination module 703 is used for determining the remaining use value of the target power battery according to the initial use value of the target power battery and the traveled distance of the electric vehicle based on the target power battery.
And the evaluation module 704 is used for performing recycling evaluation on the target power battery according to the economic value and the remaining use value so as to determine whether the target power battery reaches a recycling condition.
Further, the first determining module 702 is specifically configured to obtain the economic value of the target power battery by using a pre-trained battery value evaluation model according to the remaining cycle number.
Further, the first determining module 702 is specifically configured to obtain a battery recycling data set, where the battery recycling data set includes: the method comprises the following steps of recovering sample data of a plurality of groups of batteries, wherein the sample data recovery of each group of batteries comprises the following steps: historically recycling the residual cycle number of a power battery and the historical recycling economic value of the power battery; randomly selecting a preset number of groups of battery recycling sample data from a battery recycling data set as a training sample set; and carrying out model training by adopting the training sample set to obtain a battery value evaluation model.
Further, the first determining module 702 is specifically configured to use battery recycling sample data outside the training sample set in the battery recycling data set as a verification sample set; verifying the battery value evaluation model by adopting a verification sample set to obtain an identification error of the battery value evaluation model; if the identification error is smaller than or equal to a preset error threshold value, determining that the training of the battery value evaluation model is completed; and if the identification error is larger than the preset error threshold, increasing the group number of the sample data recovered by the battery in the training sample set, and performing model training again based on the increased training sample set until the error parameter of the battery value evaluation model obtained by training is smaller than or equal to the preset error threshold.
Further, the evaluation module 704 is specifically configured to determine that the target power battery does not reach the recycling condition if the remaining use value is greater than or equal to the economic value; and outputting first indication information to indicate that the target power battery is continuously used.
Further, the evaluation module 704 is specifically configured to determine that the target power battery reaches the recovery condition if the remaining use value is less than the economic value; and outputting second prompt information to remind the user to recover the target power battery.
Further, the evaluation module 704 is further configured to send an early warning message to remind a user to check the service condition of the target power battery if it is monitored that the change value of the economic value of the target power battery in the preset time period is greater than the preset value.
Fig. 8 is a schematic diagram of a monitoring device according to an embodiment of the present application, where the monitoring device may be a device with a computing processing function.
The monitoring device includes: a processor 801, a storage medium 802. The processor 801 and the storage medium 802 are connected by a bus.
The storage medium 802 is used for storing a program, and the processor 801 calls the program stored in the storage medium 802 to execute the above-described method embodiments. The specific implementation and technical effects are similar, and are not described herein again.
Optionally, the present invention also provides a storage medium comprising a program which, when executed by a processor, is adapted to perform the above-described method embodiments. In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
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, may be located in one place, or may be 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, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, 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.
Claims (10)
1. A power battery monitoring method, characterized in that the method comprises:
acquiring the residual cycle number of a target power battery in an electric vehicle from a battery pack of the electric vehicle;
determining the economic value of the target power battery according to the residual cycle times;
determining a remaining use value of the target power battery according to the initial use value of the target power battery and the traveled mileage of the electric vehicle based on the target power battery;
and according to the economic value and the residual use value, carrying out recovery evaluation on the target power battery to determine whether the target power battery reaches a recovery condition.
2. The method of claim 1, wherein said determining an economic value of said target power cell based on said number of remaining cycles comprises:
and obtaining the economic value of the target power battery by adopting a pre-trained battery value evaluation model according to the residual cycle number.
3. The method according to claim 2, further comprising, before obtaining the economic value of the target power battery by using a pre-trained battery value evaluation model according to the remaining number of cycles:
obtaining a battery reclaim data set, wherein the battery reclaim data set comprises: the method comprises the following steps of recovering sample data of a plurality of groups of batteries, wherein the sample data recovery of each group of batteries comprises the following steps: the historical recovery of the residual cycle number of one power battery and the historical recovery economic value of the one power battery;
randomly selecting a preset number of groups of battery recycling sample data from the battery recycling data set as a training sample set;
and performing model training by adopting the training sample set to obtain the battery value evaluation model.
4. The method of claim 3, further comprising:
taking the battery recycling sample data outside the training sample set in the battery recycling data set as a verification sample set;
verifying the battery value evaluation model by adopting the verification sample set to obtain an identification error of the battery value evaluation model;
if the identification error is smaller than or equal to a preset error threshold value, determining that the training of the battery value evaluation model is completed;
and if the identification error is larger than the preset error threshold, increasing the group number of the battery recovery sample data in the training sample set, and performing model training again based on the increased training sample set until the error parameter of the battery value evaluation model obtained by training is smaller than or equal to the preset error threshold.
5. The method of claim 1, wherein said performing a recovery assessment of said target power cell based on said economic value and said remaining use value comprises:
if the residual use value is greater than or equal to the economic value, determining that the target power battery does not reach the recovery condition;
and outputting first indication information to indicate that the target power battery continues to be used.
6. The method of claim 5, wherein the performing a recovery assessment of the target power cell based on the economic value and the remaining use value further comprises:
if the residual use value is smaller than the economic value, determining that the target power battery reaches a recovery condition;
and outputting second prompt information to remind a user to recover the target power battery.
7. The method of claim 1, further comprising:
if the change value of the economic value of the target power battery in a preset time period is monitored to be larger than a preset value, sending early warning information to remind a user to check the service condition of the target power battery.
8. A power cell monitoring device, the device comprising:
the acquisition module is used for acquiring the residual cycle number of a target power battery in the electric vehicle from a battery pack of the electric vehicle;
the first determining module is used for determining the economic value of the target power battery according to the residual cycle number;
a second determination module for determining a remaining use value of the target power battery according to an initial use value of the target power battery and based on a traveled mileage of the target power battery by the electric vehicle;
and the evaluation module is used for carrying out recovery evaluation on the target power battery according to the economic value and the residual use value so as to determine whether the target power battery reaches a recovery condition.
9. A monitoring device, comprising: the power battery monitoring method comprises a processor and a storage medium, wherein the processor is connected with the storage medium through bus communication, the storage medium stores program instructions executable by the processor, and the processor calls a program stored in the storage medium to execute the steps of the power battery monitoring method according to any one of claims 1 to 7.
10. A storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, performs the steps of the power cell monitoring method according to any one of claims 1 to 7.
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| CN202211294680.3A CN115465152A (en) | 2022-10-21 | 2022-10-21 | Power battery monitoring method, device, equipment and storage medium |
| PCT/CN2023/082870 WO2024082541A1 (en) | 2022-10-21 | 2023-03-21 | Traction battery monitoring method and apparatus, and device and storage medium |
| FR2311300A FR3141111A1 (en) | 2022-10-21 | 2023-10-19 | POWER BATTERY MONITORING METHOD AND APPARATUS, DEVICE AND STORAGE MEDIUM |
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| WO2024082541A1 (en) * | 2022-10-21 | 2024-04-25 | 广东邦普循环科技有限公司 | Traction battery monitoring method and apparatus, and device and storage medium |
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| CN110406427B (en) * | 2019-06-12 | 2023-04-07 | 四川野马汽车股份有限公司 | Self-learning method for remaining mileage of electric automobile |
| CN114103647B (en) * | 2021-11-12 | 2023-10-17 | 上汽通用五菱汽车股份有限公司 | Method and device for evaluating residual performance of electric automobile and computer-readable storage medium |
| CN114236396A (en) * | 2021-12-17 | 2022-03-25 | 北京交通大学 | Battery decline-based electric vehicle charging amount control method and system |
| CN115465152A (en) * | 2022-10-21 | 2022-12-13 | 广东邦普循环科技有限公司 | Power battery monitoring method, device, equipment and storage medium |
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| WO2024082541A1 (en) * | 2022-10-21 | 2024-04-25 | 广东邦普循环科技有限公司 | Traction battery monitoring method and apparatus, and device and storage medium |
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