WO2024082541A1 - 一种动力电池监测方法、装置、设备及存储介质 - Google Patents

一种动力电池监测方法、装置、设备及存储介质 Download PDF

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Publication number
WO2024082541A1
WO2024082541A1 PCT/CN2023/082870 CN2023082870W WO2024082541A1 WO 2024082541 A1 WO2024082541 A1 WO 2024082541A1 CN 2023082870 W CN2023082870 W CN 2023082870W WO 2024082541 A1 WO2024082541 A1 WO 2024082541A1
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Prior art keywords
battery
power battery
target power
value
recycling
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PCT/CN2023/082870
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English (en)
French (fr)
Inventor
况汶芳
余海军
谢英豪
黄逸嘉
李长东
Original Assignee
广东邦普循环科技有限公司
湖南邦普循环科技有限公司
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Publication of WO2024082541A1 publication Critical patent/WO2024082541A1/zh

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Classifications

    • 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]
    • 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
    • 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
    • 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
    • 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]
    • 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]
    • 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
    • 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
    • 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
    • B60L2260/40—Control modes
    • B60L2260/46—Control modes by self learning

Definitions

  • the present invention relates to the field of battery monitoring technology, and in particular to a power battery monitoring method, device, equipment and storage medium.
  • the purpose of the present invention is to address the deficiencies in the above-mentioned prior art.
  • the present application provides a power battery monitoring method, device, equipment and storage medium to solve the problems in the prior art such as low accuracy in determining the optimal retirement time of the battery.
  • an embodiment of the present application provides a power battery monitoring method, the method comprising:
  • a recycling assessment is performed on the target power battery to determine whether the target power battery meets the recycling conditions.
  • determining the economic value of the target power battery according to the remaining number of cycles includes:
  • a pre-trained battery value assessment model is used to obtain the economic value of the target power battery.
  • the method 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, the method further includes:
  • the battery recycling data set includes: multiple groups of battery recycling sample data, each group of battery recycling sample data includes: the number of remaining cycles of a power battery recycled historically and the historical recycling economic value of the power battery;
  • the training sample set is used to perform model training to obtain the battery value assessment model.
  • the method further comprises:
  • the recognition error is greater than the preset error threshold, the number of groups of battery recycling sample data in the training sample set is increased, and the model training is re-performed based on the increased training sample set until the error parameter of the trained battery value assessment model is less than or equal to the preset error threshold.
  • performing a recycling assessment on the target power battery according to the economic value and the remaining use value includes:
  • performing a recycling assessment on the target power battery according to the economic value and the remaining use value further includes:
  • the method further comprises:
  • an early warning message is sent to remind the user to check the usage of the target power battery.
  • an embodiment of the present application provides a power battery monitoring device, the device comprising:
  • An acquisition module used to acquire the remaining number of cycles of a target power battery in the electric vehicle from a battery pack of the electric vehicle;
  • a first determination module configured to determine the economic value of the target power battery according to the remaining number of cycles
  • a second determination module configured to determine a remaining use value of the target power battery according to an initial use value of the target power battery and a mileage of the electric vehicle based on the target power battery;
  • An evaluation module is used to evaluate the recycling of the target power battery according to the economic value and the remaining use value.
  • the target power battery is evaluated to determine whether it meets the recycling conditions.
  • an embodiment of the present application provides a monitoring device, comprising: a processor and a storage medium, wherein the processor and the storage medium are connected to each other via a bus communication, the storage medium stores program instructions executable by the processor, and the processor calls the program stored in the storage medium to execute the steps of the power battery monitoring method as described in any one of the first aspects.
  • an embodiment of the present application provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the power battery monitoring method as described in any one of the first aspects are executed.
  • the present application provides a power battery monitoring method, device, equipment and storage medium.
  • the method obtains the remaining number of cycles of the target power battery in the electric vehicle from the battery pack of the electric vehicle; determines the economic value of the target power battery according to the remaining number of cycles; determines the remaining use value of the target power battery according to the initial use value of the target power battery and the mileage of the electric vehicle based on the target power battery; and performs a recycling evaluation on the target power battery according to the economic value and the remaining use value to determine whether the target power battery meets the recycling conditions.
  • the power battery is monitored in real time, and the recycling evaluation is performed according to the actual value and the theoretical value, making the recycling evaluation of the power battery more accurate, and providing users with a convenient power battery evaluation service.
  • FIG1 is a schematic diagram of the structure of a power battery monitoring system provided by the present application.
  • FIG2 is a schematic diagram of a flow chart of a power battery monitoring method provided by the present application.
  • FIG3 is a flow chart of a method for determining a battery value assessment model provided in an embodiment of the present application
  • FIG4 is a flow chart of a method for verifying a battery value assessment model provided in an embodiment of the present application.
  • FIG5 is a schematic flow chart of a method for evaluating power battery recycling provided in an embodiment of the present application.
  • FIG6 is a flow chart of another method for evaluating power battery recycling provided in an embodiment of the present application.
  • FIG7 is a schematic diagram of a power battery monitoring device provided in an embodiment of the present application.
  • FIG8 is a schematic diagram of a monitoring device provided in an embodiment of the present application.
  • Icons 100 - monitoring device, 200 - cycle counter, 300 - mileage counter, 400 - vehicle-mounted device, 701 - acquisition module, 702 - first determination module, 703 - second determination module, 704 - evaluation module, 801 - processor, 802 - storage medium.
  • the present application provides a power battery monitoring method, device, equipment and storage medium.
  • Figure 1 is a schematic diagram of the structure of a power battery monitoring system provided by the present application. As shown in Figure 1, the system includes: a monitoring device 100, a cycle counter 200, a mileage counter 300, and a vehicle-mounted device 400.
  • the monitoring device 100 is connected to the cycle counter 200 , the mileage counter 300 , and the vehicle-mounted device 400 , respectively.
  • the cycle counter 200 is used to record the remaining number of cycles of the battery pack of the electric vehicle and transmit the remaining number of cycles to the monitoring device 100.
  • a battery cycle is a complete charge and discharge cycle of the battery pack.
  • the cycle counter 200 obtains the number of cycles that have been used for the battery pack and obtains the remaining number of cycles according to 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 and the number of cycles that have been used is 2000, then the remaining number of cycles is 3000.
  • 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 to evaluate the recycling of the target power battery according to the number of remaining cycles, the mileage traveled, and the property information of the power battery.
  • the monitoring device 100 may also transmit the recycling assessment information to the vehicle-mounted device 400 to remind the user.
  • the vehicle-mounted device 400 may be a display screen, a speaker, etc. of an electric vehicle, which is not limited here.
  • Figure 2 is a flow chart of a power battery monitoring method provided by the present application.
  • the execution subject of the method is a monitoring device, which can be a device with computing and processing functions. As shown in Figure 2, the method includes:
  • the battery pack of the electric vehicle is equipped with a cycle counter, and the remaining cycle count of the target power battery in the electric vehicle is obtained from the cycle counter.
  • Electric vehicles include: electric cars, electric bicycles, and electric vehicles. Other means of transportation.
  • a battery cycle is a complete charge and discharge cycle of a battery pack, and the remaining number of cycles represents the remaining usage time of the target power battery.
  • the remaining usage time corresponds to the value of the target power battery. The more remaining usage time, the higher the value of the target power battery; the less remaining usage time, the lower the value of the target power battery. Therefore, the economic value of the target power battery can be determined based on the remaining number of cycles and the historical recycling transaction data of power batteries of the same model as the target power battery. For example, the economic value is the recycling price of power batteries of the same model as the target power battery, which represents the actual market economic value of the target power battery.
  • S103 Determine the remaining use value of the target power battery according to the initial use value of the target power battery and the mileage of the electric vehicle based on the target power battery.
  • the initial use value of the target power battery refers to the selling price when it is sold.
  • the use value per unit mileage is calculated based on the initial use value and total mileage of the target power battery. Then, the remaining use value of the target power battery is determined based on the use value per unit mileage and the mileage traveled. That is, the mileage traveled is positively correlated with the remaining use value of the target power battery, and represents the theoretical remaining value of the target power battery based on the mileage traveled.
  • step S101 and step S102 there is no order relationship between determining the economic value of the target power battery in step S101 and step S102 and determining the remaining use value of the target power battery in step 103.
  • This embodiment and the flowchart shown in FIG2 are only examples, and the application does not limit the order between step S101 and step S102 and step 103.
  • S104 Perform a recycling assessment on the target power battery based on the economic value and the remaining use value to determine whether the target power battery meets the recycling conditions.
  • the economic value represents the actual market economic value of the target power battery.
  • the residual use value represents the theoretical residual value of the target power battery based on the mileage traveled.
  • the target power battery can be evaluated for recycling based on the economic value and the residual use value to determine whether the target power battery meets the recycling conditions.
  • the recycling evaluation based on the actual value and the theoretical value makes the recycling evaluation of the power battery more accurate, providing users with convenient power battery evaluation services and helping users determine the best time to retire the target power battery.
  • the specific recycling condition may be: the economic value is greater than the residual use value; or: the economic value and the residual use value are weighted, and the weighted economic value is greater than the residual use value.
  • the remaining number of cycles of the target power battery in the electric vehicle is obtained from the battery pack of the electric vehicle; the economic value of the target power battery is determined according to the remaining number of cycles; the remaining use value of the target power battery is determined according to the initial use value of the target power battery and the mileage of the electric vehicle based on the target power battery; the target power battery is recycled and evaluated according to the economic value and the remaining use value to determine whether the target power battery meets the recycling conditions.
  • the power battery is monitored in real time, and the recycling evaluation is performed according to the actual value and the theoretical value, making the recycling evaluation of the power battery more accurate, and providing users with a convenient power battery evaluation service.
  • the present application further provides a method for calculating the economic value of a target power battery.
  • the method of determining the economic value of the target power battery according to the number of remaining cycles in S102 includes:
  • the pre-trained battery value assessment model is used to obtain the economic value of the target power battery.
  • a pre-trained battery value assessment model can be used, and the current remaining number of cycles can be input into the battery value assessment model to calculate the economic value of the target power battery.
  • the pre-trained battery value assessment model includes a mapping relationship between the remaining number of cycles and the economic value of the target power battery.
  • the corresponding economic value can be calculated by inputting the remaining number of cycles.
  • the battery value assessment model can be a model built based on a neural network recognition model, or it can be other models, as long as it can complete the battery value assessment, which is not limited here.
  • the economic value of the target power battery is obtained according to the remaining number of cycles using a pre-trained battery value assessment model, thereby accurately obtaining the economic value of the target power battery.
  • FIG3 is a flow chart of a method for determining a battery value assessment model provided by the embodiment of the present application. As shown in FIG3, before obtaining the economic value of the target power battery by using a pre-trained battery value assessment model according to the remaining number of cycles, it also includes:
  • the battery recycling data set includes: multiple groups of battery recycling sample data, each group of battery recycling sample data includes: the remaining number of cycles of a power battery recycled historically and the historical recycling economic value of a power battery.
  • the historical recycling economic value may be the historical recycling price of the power battery.
  • the number of groups in the battery recycling dataset should be large enough, for example, the number of groups in the battery recycling dataset should be greater than 100.
  • S202 Randomly select a preset number of groups of battery recycling sample data from the battery recycling data set as a training sample set.
  • the preset number should be greater than half of the battery recycling dataset, for example, the preset number is 80% of the battery recycling dataset. Random selection from the battery recycling dataset makes the selected training sample set more representative of the battery recycling dataset.
  • the remaining number of cycles and historical recycling economic value in the training sample set are input into the model, and multiple trainings are performed to obtain a battery value assessment model.
  • the battery value assessment model is taken as an example of a neural network recognition model to explain the model training process.
  • the neural network recognition model consists of three layers: input layer, hidden layer and output layer.
  • the input layer has I nodes
  • the hidden layer has J nodes
  • the output layer has one node.
  • the error between the calculated result and the expected error is obtained through a multi-layer network of neuron back propagation.
  • the main learning process in the training of the neural network is: first input multiple residual cycles in the training sample set The number of times C1, C2, C3, ..., Ci, the corresponding multiple output historical recovery economic values D1, D2, D3, ..., Di are obtained; then the errors between the multiple output historical recovery economic values D1, D2, D3, ..., Di of the neural network and the historical recovery economic values A1, A2, A3, ..., Ai in the training sample set are used to continuously correct the weights of the neurons connected inside the neural network, and the output value is made close to the target value by continuously reducing the error.
  • the back propagation learning method will be transmitted layer by layer to each layer of neurons, and the error will be continuously corrected and adjusted to minimize the sum of squared errors.
  • the "Sigmoid” function is used as the learning and training function, and the function expression is as described in the following formula (1).
  • Softmax is used as the output layer transfer function of the model, and the function expression is as described in the following formula (2):
  • the model structure After determining the model structure, determine that the input value of the model is the number of remaining cycles; the number of hidden layers is 1, and the output value is the economic value of recovery. Start training the neural network recognition model until the sum of square errors between the output value of the neural network recognition model and the target value meets the preset requirements. If the sum of square errors still does not meet the requirements when the specified number of iterations is reached, the network can be repeatedly trained or the number of hidden layers can be appropriately increased.
  • the trained weights and thresholds need to be assigned to the neural network recognition model as initial values to achieve the purpose of adaptive control.
  • a battery recycling data set is obtained; a preset number of groups of battery recycling sample data are randomly selected from the battery recycling data set as a training sample set; and the training sample set is used to perform model training to obtain a battery value assessment model.
  • an accurate battery value assessment model is obtained.
  • FIG. 4 is a flow chart of a method for verifying a battery value assessment model provided by the embodiment of the present application. As shown in FIG. 4, the method further includes:
  • the number of validation sample sets is smaller than the number of training sample sets.
  • S302 Use a verification sample set to verify the battery value assessment model to obtain a recognition error of the battery value assessment model.
  • the remaining number of cycles in the verification sample set is input into the battery value assessment model to calculate the output economic value.
  • the recognition error of the battery value assessment model is calculated based on the actual economic value corresponding to the remaining number of cycles and the output economic value.
  • the specific calculation method is shown in the following formula (3):
  • the battery value assessment model can be used as a preset battery value assessment model.
  • the recognition error is greater than the preset error threshold, it indicates that the battery value assessment model does not meet the error requirements.
  • the number of battery recycling sample data in the training sample set is increased, and the model is retrained based on the increased training sample set, and the error parameters of the retrained battery value assessment model are calculated until the error parameters of the trained battery value assessment model are less than or equal to the preset error threshold. If the error parameters of the battery value assessment model are less than or equal to the preset error threshold, the battery value assessment model can be used as the preset battery value assessment model.
  • the battery recycling sample data outside the training sample set in the battery recycling data set is used as the verification sample set; the verification sample set is used to verify the battery value assessment model to obtain the recognition error of the battery value assessment model; if the recognition error is less than or equal to the preset error threshold, it is determined that the battery value assessment model training is completed; if the recognition error is greater than the preset error threshold, the number of groups of battery recycling sample data in the training sample set is increased, and the model training is re-performed based on the increased training sample set until the error parameter of the trained battery value assessment model is less than or equal to the preset error threshold.
  • a high-precision battery value assessment model is obtained through the verification sample set.
  • FIG. 5 is a flow chart of a method for evaluating the recycling of a power battery provided in the embodiment of the present application. As shown in FIG. 5 , in S104, the recycling evaluation of the target power battery is performed based on the economic value and the remaining use value, including:
  • the remaining use value is greater than or equal to the economic value, that is, 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 economically cost-effective. Then it is determined that the target power battery does not meet the recycling conditions.
  • S402 Output first instruction information to instruct to continue using the target power battery.
  • the first indication information may be output to the vehicle-mounted device to instruct to continue using the target power battery.
  • the first indication information may be text reminder information transmitted to a display screen; or may be voice reminder information transmitted to a speaker.
  • the power battery monitoring in the present application is carried out in real time. Therefore, in actual monitoring, text reminder information is mainly used, and the battery model display transmitted to the display screen can be viewed by entering the battery module without affecting the user's normal use of other functions in the display screen.
  • the remaining use value is greater than or equal to the economic value, it is determined that the target power battery has not reached Recycling conditions; outputting first instruction information to instruct to continue to use the target power battery.
  • the user is reminded to continue to use the target power battery in a timely manner.
  • FIG. 6 is a flow chart of another method for evaluating the recycling of power batteries provided in the embodiment of the present application. 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 also includes:
  • the remaining use value is less than the economic value, that is, 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 from an economic point of view. Then it is determined that the target power battery meets the recycling conditions.
  • S502 Output a second prompt message to remind the user to recycle the target power battery.
  • the second indication information may be output to the vehicle-mounted device to remind the user to recycle the target power battery.
  • the second indication information may be text reminder information transmitted to a display screen; or may be voice reminder information transmitted to a speaker.
  • the second prompt information is output to remind the user to recycle the target power battery.
  • the user is reminded to sell the target power battery in time.
  • the embodiment of the present application further provides another power battery monitoring method.
  • the method further includes:
  • an early warning message is sent to remind the user to check the usage of the target power battery.
  • the preset time period is set to one day, and the preset value is 1,000 yuan. If the change in the economic value of the target power battery is greater than 1,000 yuan within one day, it means that the user has unhealthy battery use or the battery has a fault, resulting in a large change in economic value. Then, an early warning message is sent to remind the user to check the use of the target power battery, which can increase the service life of the target power battery and extend the life cycle of the target power battery.
  • an early warning message is sent to remind the user to check the use of the target power battery, thereby extending the life cycle of the target power battery.
  • the embodiments of the present application also provide an example to demonstrate the process of power battery monitoring.
  • power batteries can be monitored in real time and recycling assessments can be conducted based on actual and theoretical values, making power battery recycling assessments more accurate and providing users with convenient power battery assessment services.
  • FIG7 is a schematic diagram of a power battery monitoring device provided in an embodiment of the present application. As shown in FIG7 , the device includes:
  • the acquisition module 701 is used to acquire the remaining number of cycles of a target power battery in the electric vehicle from a battery pack of the electric vehicle.
  • the first determination module 702 is used to determine the economic value of the target power battery according to the remaining number of cycles.
  • the second determination module 703 is used to determine the remaining use value of the target power battery according to the initial use value of the target power battery and the mileage of the electric vehicle based on the target power battery.
  • the evaluation module 704 is used to perform a recycling evaluation on the target power battery according to the economic value and the remaining use value to determine whether the target power battery meets the recycling conditions.
  • the first determination module 702 is specifically configured to obtain the economic value of the target power battery according to the remaining number of cycles by using a pre-trained battery value evaluation model.
  • the first determination module 702 is further configured to obtain a battery recycling data set, wherein the battery recycling data set includes: a plurality of battery recycling sample data sets, each of which includes: a remaining cycle of a power battery recycled in history; The number of cycles and the historical recycling economic value of a power battery; randomly select a preset number of groups of battery recycling sample data from the battery recycling data set as a training sample set; use the training sample set to train the model and obtain a battery value assessment model.
  • the battery recycling data set includes: a plurality of battery recycling sample data sets, each of which includes: a remaining cycle of a power battery recycled in history; The number of cycles and the historical recycling economic value of a power battery; randomly select a preset number of groups of battery recycling sample data from the battery recycling data set as a training sample set; use the training sample set to train the model and obtain a battery value assessment model.
  • the first determination module 702 is specifically used to use the battery recycling sample data outside the training sample set in the battery recycling data set as a verification sample set; use the verification sample set to verify the battery value assessment model to obtain the recognition error of the battery value assessment model; if the recognition error is less than or equal to the preset error threshold, it is determined that the training of the battery value assessment model is completed; if the recognition error is greater than the preset error threshold, the number of groups of battery recycling sample data in the training sample set is increased, and the model is re-trained based on the increased training sample set until the error parameter of the trained battery value assessment model is less than or equal to the preset error threshold.
  • the evaluation module 704 is specifically configured to determine that the target power battery does not meet the recycling conditions if the remaining use value is greater than or equal to the economic value; and output first indication information to indicate that the target power battery should continue to be used.
  • the evaluation module 704 is specifically configured to determine that the target power battery meets the recycling conditions if the remaining use value is less than the economic value; and output a second prompt message to remind the user to recycle the target power battery.
  • the evaluation module 704 is also used to send an early warning message to remind the user to check the use of the target power battery if it is monitored that the change value of the economic value of the target power battery within a preset time period is greater than a preset value.
  • FIG8 is a schematic diagram of a monitoring device provided in an embodiment of the present application, and the monitoring device may be a device with computing and processing functions.
  • the monitoring device includes: a processor 801 and a storage medium 802.
  • the processor 801 and the storage medium 802 are connected via a bus.
  • the storage medium 802 is used to store programs, and the processor 801 calls the programs stored in the storage medium 802 to execute the above method embodiment.
  • the specific implementation method and technical effect are similar and will not be repeated here.
  • the present invention also provides a storage medium, including a program, which is used to execute the above-mentioned method embodiment when executed by a processor.
  • a storage medium including a program, which is used to execute the above-mentioned method embodiment when executed by a processor.
  • the disclosed devices and methods can be implemented in other ways.
  • the device embodiments described above are merely schematic.
  • the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.
  • Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.
  • the above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a storage medium.
  • the above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (English: processor) to perform some steps of the method described in each embodiment of the present invention.
  • the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (English: Read-Only Memory, abbreviated: ROM), random access memory (English: Random Access Memory, abbreviated: RAM), disk or optical disk and other media that can store program codes.

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Abstract

一种动力电池监测方法，涉及电池监测技术领域。该方法通过从电动交通工具的电池包中获取电动交通工具中目标动力电池的剩余循环次数；根据剩余循环次数，确定目标动力电池的经济价值；根据目标动力电池的初始使用价值，以及电动交通工具基于目标动力电池的已行驶里程，确定目标动力电池的剩余使用价值；根据经济价值和剩余使用价值，对目标动力电池进行回收评估，以确定目标动力电池是否达到回收条件。从而，实时监测动力电池，根据实际价值与理论价值进行回收评估，使得动力电池的回收评估更加精准，为用户提供便捷的动力电池评估服务。还提供了一种装置、设备及存储介质。

Description

一种动力电池监测方法、装置、设备及存储介质 技术领域
本发明涉及电池监测技术领域，具体而言，涉及一种动力电池监测方法、装置、设备及存储介质。
背景技术
新能源汽车市场规模持续扩大，这意味着越来越多人购买电动汽车。而电动汽车性能则是所有车主关注的焦点问题。动力电池作为电动汽车的核心部件是体现车辆性能的决定性因素之一，为迎合低碳可循环发展，市场上高度重视新能源汽车动力电池回收利用。车主可将退役后的动力电池贩卖给回收企业进行梯次利用，以此达到将废旧电池资源化利用和增加用车经济性的目的。
在电动汽车使用过程中，其回收价格会随着锂电池循环次数的增加而降低，这时候，需要帮助车主判断电池最佳退役时间来达成以最优的价格售卖退役废旧电池的目标。而现有技术中多为人工判断电池最佳退役时间，精确度较低。
发明内容
本发明的目的在于，针对上述现有技术中的不足，本申请提供了一种动力电池监测方法、装置、设备及存储介质，以解决现有技术中判断电池最佳退役时间的精确度较低等问题。
为实现上述目的，本申请实施例采用的技术方案如下：
第一方面，本申请实施例提供一种动力电池监测方法，所述方法包括：
从电动交通工具的电池包中获取所述电动交通工具中目标动力电池的剩余循环次数；
根据所述剩余循环次数，确定所述目标动力电池的经济价值；
根据所述目标动力电池的初始使用价值，以及所述电动交通工具基于所述目标动力电池的已行驶里程，确定所述目标动力电池的剩余使用价值；
根据所述经济价值和所述剩余使用价值，对所述目标动力电池进行回收评估，以确定所述目标动力电池是否达到回收条件。
可选地，根据所述剩余循环次数，确定所述目标动力电池的经济价值，包括：
根据所述剩余循环次数，采用预先训练的电池价值评估模型，得到所述目标动力电池的经济价值。
可选地，在根据所述剩余循环次数，采用预先训练的电池价值评估模型，得到所述目标动力电池的经济价值之前，还包括：
获取电池回收数据集，其中，所述电池回收数据集包括：多组电池回收样本数据，每组电池回收样本数据包括：历史回收的一个动力电池的剩余循环次数以及所述一个动力电池的历史回收经济价值；
从所述电池回收数据集中随机选择预设数量组的电池回收样本数据作为训练样本集；
采用所述训练样本集进行模型训练，得到所述电池价值评估模型。
可选地，所述方法还包括：
将所述电池回收数据集中所述训练样本集之外的电池回收样本数据作为验证样本集；
采用所述验证样本集，对所述电池价值评估模型进行验证，得到所述电池价值评估模型的识别误差；
若识别误差小于或等于预设误差阈值，则确定所述电池价值评估模型训练完成；
若所述识别误差大于所述预设误差阈值，则增加所述训练样本集中电池回收样本数据的组数，并基于增加后的训练样本集重新进行模型训练，直至训练得到的电池价值评估模型的误差参数小于或等于所述预设误差阈值。
可选地，根据所述经济价值和所述剩余使用价值，对所述目标动力电池进行回收评估，包括：
若所述剩余使用价值大于或等于所述经济价值，则确定所述目标动力电池未达到回收条件；
输出第一指示信息，以指示继续使用所述目标动力电池。
可选地，根据所述经济价值和所述剩余使用价值，对所述目标动力电池进行回收评估，还包括：
若所述剩余使用价值小于所述经济价值，则确定所述目标动力电池达到回收条件；
输出第二提示信息，以提醒用户对所述目标动力电池进行回收。
可选地，所述方法还包括：
若监测到所述目标动力电池在预设时间段内的经济价值的变化值大于预设价值，则发送预警信息，以提醒用户检查所述目标动力电池的使用情况。
第二方面，本申请实施例提供一种动力电池监测装置，所述装置包括：
获取模块，用于从电动交通工具的电池包中获取所述电动交通工具中目标动力电池的剩余循环次数；
第一确定模块，用于根据所述剩余循环次数，确定所述目标动力电池的经济价值；
第二确定模块，用于根据所述目标动力电池的初始使用价值，以及所述电动交通工具基于所述目标动力电池的已行驶里程，确定所述目标动力电池的剩余使用价值；
评估模块，用于根据所述经济价值和所述剩余使用价值，对所述目标动力电池进行回收评 估，以确定所述目标动力电池是否达到回收条件。
第三方面，本申请实施例提供一种监测设备，包括：处理器、存储介质，所述处理器与所述存储介质之间通过总线通信连接，所述存储介质存储有所述处理器可执行的程序指令，所述处理器调用存储介质中存储的程序，以执行如第一方面任一所述的动力电池监测方法的步骤。
第四方面，本申请实施例提供一种存储介质，所述存储介质上存储有计算机程序，所述计算机程序被处理器运行时执行如第一方面任一所述的动力电池监测方法的步骤。
相对于现有技术而言，本申请具有以下有益效果：
本申请提供一种动力电池监测方法、装置、设备及存储介质，该方法通过从电动交通工具的电池包中获取电动交通工具中目标动力电池的剩余循环次数；根据剩余循环次数，确定目标动力电池的经济价值；根据目标动力电池的初始使用价值，以及电动交通工具基于目标动力电池的已行驶里程，确定目标动力电池的剩余使用价值；根据经济价值和剩余使用价值，对目标动力电池进行回收评估，以确定目标动力电池是否达到回收条件。从而，实时监测动力电池，根据实际价值与理论价值进行回收评估，使得动力电池的回收评估更加精准，为用户提供便捷的动力电池评估服务。
附图说明
为了更清楚地说明本发明实施例的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，应当理解，以下附图仅示出了本发明的某些实施例，因此不应被看作是对范围的限定，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他相关的附图。
图1为本申请提供的一种动力电池监测***的结构示意图；
图2为本申请提供的一种动力电池监测方法的流程示意图；
图3为本申请实施例提供的一种确定电池价值评估模型的方法的流程示意图；
图4为本申请实施例提供的一种验证电池价值评估模型的方法的流程示意图；
图5为本申请实施例提供的一种动力电池回收评估的方法的流程示意图；
图6为本申请实施例提供的另一种动力电池回收评估的方法的流程示意图；
图7为本申请实施例提供的一种动力电池监测装置的示意图；
图8为本申请实施例提供的一种监测设备的示意图。
图标：100-监测设备、200-循环次数计数器、300-里程计数器、400-车载设备、701-获取模块、702-第一确定模块、703-第二确定模块、704-评估模块、801-处理器、802-存储介质。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请一部分 实施例，而不是全部的实施例。通常在此处附图中描述和示出的本申请实施例的组件可以以各种不同的配置来布置和设计。
因此，以下对在附图中提供的本申请的实施例的详细描述并非旨在限制要求保护的本申请的范围，而是仅仅表示本申请的选定实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其它实施例，都属于本申请保护的范围。
应注意到：相似的标号和字母在下面的附图中表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行进一步定义和解释。
此外，若出现术语“第一”、“第二”等仅用于区分描述，而不能理解为指示或暗示相对重要性。
需要说明的是，在不冲突的情况下，本发明的实施例中的特征可以相互结合。
为精准地为用户提示动力电池的售卖时机，本申请提供了一种动力电池监测方法、装置、设备及存储介质。
如下通过具体示例对本申请提供的一种动力电池监测***进行解释说明。图1为本申请提供的一种动力电池监测***的结构示意图。如图1所示，该***包括：监测设备100、循环次数计数器200、里程计数器300、车载设备400。
监测设备100分别与循环次数计数器200、里程计数器300、车载设备400连接。
循环次数计数器200用于记录电动交通工具的电池包的剩余循环次数，并将剩余循环次数传输至监测设备100。电池循环为电池包一个完整的充放电周期。循环次数计数器200获取电池包的已经使用的循环次数，根据电池包的总循环次数以及已经使用的循环次数得到剩余循环次数。例如，总循环次数为5000次，已经使用的循环次数为2000次，则剩余循环次数为3000次。
里程计数器300用于记录电动交通工具的电动交通工具基于目标动力电池的已行驶里程，并将已行驶里程传输至监测设备100。
监测设备100用于根据剩余循环次数、已行驶里程以及动力电池的属性信息对目标动力电池进行回收评估。
监测设备100还可以将回收评估信息传输至车载设备400，用以提醒用户。示例地，车载设备400可以为电动交通工具的显示屏、扬声器等，此处并不限定。
如下通过具体示例对本申请提供的一种动力电池监测方法进行解释说明。图2为本申请提供的一种动力电池监测方法的流程示意图，该方法的执行主体为监测设备，该监测设备可以为具有计算处理功能的设备。如图2所示，该方法包括：
S101、从电动交通工具的电池包中获取电动交通工具中目标动力电池的剩余循环次数。
电动交通工具的电池包安装有循环次数计数器，从循环次数计数器中获取电动交通工具中目标动力电池的剩余循环次数。电动交通工具包括：电动汽车、电动自行车以及采用电动力运 行的其他交通工具。
S102、根据剩余循环次数，确定目标动力电池的经济价值。
电池循环为电池包一个完整的充放电周期，剩余循环次数表征了目标动力电池的剩余使用时间。而剩余使用时间对应着目标动力电池的价值，剩余使用时间越多，目标动力电池的价值越高；剩余使用时间越少，目标动力电池的价值越低。因此，可根据剩余循环次数以及历史与目标动力电池同型号动力电池的回收交易数据，确定目标动力电池的经济价值。示例地，经济价值为与目标动力电池同型号动力电池的回收价格，表征了目标动力电池的实际市场经济价值。
S103、根据目标动力电池的初始使用价值，以及电动交通工具基于目标动力电池的已行驶里程，确定目标动力电池的剩余使用价值。
目标动力电池的初始使用价值是指出售时的售价。对于每个动力电池的参数，除了初始使用价值，还有初始总里程，总里程表征了动力电池在整个生命周期内，该电动交通工具基于目标动力电池的形式总里程，例如：100000公里。
首先，根据目标动力电池的初始使用价值以及总里程，计算单位里程的使用价值。再根据单位里程的使用价值以及已行驶里程确定目标动力电池的剩余使用价值。即，已行驶里程与目标动力电池的剩余使用价值正相关，并表征了目标动力电池基于已行驶里程的理论剩余价值。
此处需要说明的是，步骤S101以及步骤S102中确定目标动力电池的经济价值，与步骤103中确定目标动力电池的剩余使用价值，并无先后顺序关系。本实施例以及图2所示的流程示意图只是一种示例，本申请不限定步骤S101以及步骤S102、步骤103之间的顺序。
S104、根据经济价值和剩余使用价值，对目标动力电池进行回收评估，以确定目标动力电池是否达到回收条件。
经济价值表征了目标动力电池的实际市场经济价值。剩余使用价值表征了目标动力电池基于已行驶里程的理论剩余价值。可根据经济价值和剩余使用价值，对目标动力电池进行回收评估，以确定目标动力电池是否达到回收条件。根据实际价值与理论价值进行回收评估，使得动力电池的回收评估更加精准，为用户提供便捷的动力电池评估服务，帮助用户判断目标动力电池的最佳退役时间。
示例地，具体的回收条件可以为：经济价值大于剩余使用价值；也可以为：对经济价值和剩余使用价值进行加权处理，加权之后的经济价值大于剩余使用价值。
综上，在本实施例中，通过从电动交通工具的电池包中获取电动交通工具中目标动力电池的剩余循环次数；根据剩余循环次数，确定目标动力电池的经济价值；根据目标动力电池的初始使用价值，以及电动交通工具基于目标动力电池的已行驶里程，确定目标动力电池的剩余使用价值；根据经济价值和剩余使用价值，对目标动力电池进行回收评估，以确定目标动力电池是否达到回收条件。从而，实时监测动力电池，根据实际价值与理论价值进行回收评估，使得动力电池的回收评估更加精准，为用户提供便捷的动力电池评估服务。
在上述图2对应的实施例的基础上，本申请还提供了一种计算目标动力电池的经济价值的方法，S102中的根据剩余循环次数，确定目标动力电池的经济价值，包括：
根据剩余循环次数，采用预先训练的电池价值评估模型，得到目标动力电池的经济价值。
为精准地根据剩余循环次数得到目标动力电池的经济价值。可采用预先训练的电池价值评估模型，将当前剩余循环次数输入至电池价值评估模型，计算得到目标动力电池的经济价值。
其中，预先训练的电池价值评估模型中包含有剩余循环次数与目标动力电池的经济价值之间的映射关系，输入剩余循环次数可计算得到对应的经济价值。示例地，电池价值评估模型可以为基于神经网络识别模型搭建的模型，也可以为其他模型，只要能完成电池价值评估，此处并不限定。
综上，在本实施例中，根据剩余循环次数，采用预先训练的电池价值评估模型，得到目标动力电池的经济价值。从而，精准地得到目标动力电池的经济价值。
在上述实施例的基础上，本申请实施例还提供了一种确定电池价值评估模型的方法。图3为本申请实施例提供的一种确定电池价值评估模型的方法的流程示意图。如图3所示，在根据剩余循环次数，采用预先训练的电池价值评估模型，得到目标动力电池的经济价值之前，还包括：
S201、获取电池回收数据集。
其中，电池回收数据集包括：多组电池回收样本数据，每组电池回收样本数据包括：历史回收的一个动力电池的剩余循环次数以及一个动力电池的历史回收经济价值。
示例地，历史回收经济价值可以为动力电池的历史回收价格。为确保模型质量，电池回收数据集的组数量应足够大，例如：电池回收数据集的组数量大于100个。
S202、从电池回收数据集中随机选择预设数量组的电池回收样本数据作为训练样本集。
为保证模型训练精度，预设数量应大于电池回收数据集的一半，例如，预设数量为电池回收数据集的80％。从电池回收数据集中随机选择，使得选择得到的训练样本集更能代表电池回收数据集。
S203、采用训练样本集进行模型训练，得到电池价值评估模型。
将训练样本集中的剩余循环次数、历史回收经济价值输入至模型中，进行多次训练，得到电池价值评估模型。
进一步地，以电池价值评估模型为神经网络识别模型作为示例，对模型训练的过程进行解释说明。
神经网络识别模型由输入层、隐含层以及输出层三层结构构成。输入层有I个节点，隐含层有J个节点，输出层有一个节点。通过神经元反向传播的多层网络来得到计算结果与期望误差之间的误差。在神经网络的训练中的主要学习流程为：首先输入训练样本集中的多个剩余循环 次数C1、C2、C3、……、Ci，得到对应的多个输出历史回收经济价值D1、D2、D3、……、Di；再通过神经网络的多个输出历史回收经济价值D1、D2、D3、……、Di与训练样本集中的历史回收经济价值A1、A2、A3、……、Ai之间的误差，来不断修正神经网络内部连接各神经元的权值，通过不断的减小误差从而使得输出值逼近目标值。同时反向传播的学***方和达到最小。
在构建神经网络识别模型中，采用“Sigmoid”函数作为学习和训练函数，函数表达式如下公式(1)所述，采用“Softmax”函数作为模型的输出层传递函数，函数表达式如下公式(2)所述：

确定模型结构后，确定模型的输入值为剩余循环次数；隐含层数为1层，输出值为回收经济价值。开始训练神经网络识别模型，直至神经网络识别模型的输出值与目标值的误差平方和满足预设要求为止。如果到达规定的迭代次数时，误差平方和仍不满足要求，可重复训练网络或适当增加隐含层层数。
在神经网络识别模型训练成功之后，需要将训练后的权值和阈值赋予神经网络识别模型作为初始值，以实现自适应控制的目的。
综上，在本实施例中，获取电池回收数据集；从电池回收数据集中随机选择预设数量组的电池回收样本数据作为训练样本集；采用训练样本集进行模型训练，得到电池价值评估模型。从而，精准地得到电池价值评估模型。
在上述图3对应的实施例的基础上，本申请实施例还提供了一种验证电池价值评估模型的方法。图4为本申请实施例提供的一种验证电池价值评估模型的方法的流程示意图。如图4所示，该方法还包括：
S301、将电池回收数据集中训练样本集之外的电池回收样本数据作为验证样本集。
其中，验证样本集的数量小于训练样本集的数量。
S302、采用验证样本集，对电池价值评估模型进行验证，得到电池价值评估模型的识别误差。
将验证样本集中的剩余循环次数输入至电池价值评估模型，计算得到输出经济价值。根据剩余循环次数对应的实际经济价值以及输出经济价值计算电池价值评估模型的识别误差，具体的计算方式如下公式(3)所示：
S303、若识别误差小于或等于预设误差阈值，则确定电池价值评估模型训练完成。
若识别误差小于或等于预设误差阈值，则表明电池价值评估模型满足误差要求，确定电池价值评估模型训练完成。可将该电池价值评估模型作为预设的电池价值评估模型。
S304、若识别误差大于预设误差阈值，则增加训练样本集中电池回收样本数据的组数，并基于增加后的训练样本集重新进行模型训练，直至训练得到的电池价值评估模型的误差参数小于或等于预设误差阈值。
若识别误差大于预设误差阈值，则表明电池价值评估模型不满足误差要求。为进一步地得到精度更高的模型，则增加训练样本集中电池回收样本数据的组数，并基于增加后的训练样本集重新进行模型训练，并计算重新训练之后的电池价值评估模型的误差参数，直至训练得到的电池价值评估模型的误差参数小于或等于预设误差阈值。满足电池价值评估模型的误差参数小于或等于预设误差阈值，可将该电池价值评估模型作为预设的电池价值评估模型。
综上，在本实施例中，将电池回收数据集中训练样本集之外的电池回收样本数据作为验证样本集；采用验证样本集，对电池价值评估模型进行验证，得到电池价值评估模型的识别误差；若识别误差小于或等于预设误差阈值，则确定电池价值评估模型训练完成；若识别误差大于预设误差阈值，则增加训练样本集中电池回收样本数据的组数，并基于增加后的训练样本集重新进行模型训练，直至训练得到的电池价值评估模型的误差参数小于或等于预设误差阈值。从而，通过验证样本集得到高精度的电池价值评估模型。
在上述图2对应的实施例的基础上，本申请实施例还提供了一种动力电池回收评估的方法。图5为本申请实施例提供的一种动力电池回收评估的方法的流程示意图。如图5所示，S104中的根据经济价值和剩余使用价值，对目标动力电池进行回收评估，包括：
S401、若剩余使用价值大于或等于经济价值，则确定目标动力电池未达到回收条件。
若剩余使用价值大于或等于经济价值，也就是说，目标动力电池当前的理论价值大于或等于实际价值。若用户此时出售目标动力电池，从经济意义上来看并不划算。则确定目标动力电池未达到回收条件。
S402、输出第一指示信息，以指示继续使用目标动力电池。
为及时提醒用关于目标动力电池的相关信息，可将输出第一指示信息至车载设备，以指示继续使用目标动力电池。
示例地，第一指示信息可以是文字提醒信息，传输至显示屏；也可以是语音提醒信息，传输至扬声器。
进一步地，本申请中的动力电池监测是实时进行的，因此，在实际监测中，多以文字提醒信息为主，传输至显示屏的电池模型显示，可进入电池模块查看，不影响用户正常使用显示屏中的其他功能。
综上，在本实施例中，若剩余使用价值大于或等于经济价值，则确定目标动力电池未达到 回收条件；输出第一指示信息，以指示继续使用目标动力电池。从而，及时提醒用户继续使用目标动力电池。
在上述图5对应的实施例的基础上，本申请实施例还提供了另一种动力电池回收评估的方法。图6为本申请实施例提供的另一种动力电池回收评估的方法的流程示意图。如图6所示，S104中的根据经济价值和剩余使用价值，对目标动力电池进行回收评估，还包括：
S501、若剩余使用价值小于经济价值，则确定目标动力电池达到回收条件。
若剩余使用价值小于经济价值，也就是说，目标动力电池当前的理论价值小于实际价值。若用户此时出售目标动力电池，从经济意义上来看非常划算。则确定目标动力电池达到回收条件。
S502、输出第二提示信息，以提醒用户对目标动力电池进行回收。
为及时提醒用关于目标动力电池的相关信息，可将输出第二指示信息至车载设备，以提醒用户对目标动力电池进行回收。
示例地，第二指示信息可以是文字提醒信息，传输至显示屏；也可以是语音提醒信息，传输至扬声器。
综上，在本实施例中，若剩余使用价值小于经济价值，则确定目标动力电池达到回收条件；输出第二提示信息，以提醒用户对目标动力电池进行回收。从而，及时提醒用户出售目标动力电池。
在上述图2对应的实施例的基础上，本申请实施例还提供了另一种动力电池监测方法。该方法还包括：
若监测到目标动力电池在预设时间段内的经济价值的变化值大于预设价值，则发送预警信息，以提醒用户检查目标动力电池的使用情况。
示例地，设定预设时间段为一天，预设价值为1000元。在一天时间内监测到目标动力电池的经济价值的变化值大于1000元，则说明用户存在不健康使用电池的情况，或电池出现故障，导致经济价值变化较大。则发送预警信息，以提醒用户检查目标动力电池的使用情况，可以增加目标动力电池的使用寿命，延长目标动力电池生命周期。
综上，在本实施例中，若监测到目标动力电池在预设时间段内的经济价值的变化值大于预设价值，则发送预警信息，以提醒用户检查目标动力电池的使用情况。从而，延长目标动力电池生命周期。
在上述实施例的基础上，本申请实施例还提供了一种实例，以展示动力电池监测的过程。
如下表1所示，以6组监测数据为例，根据6组剩余循环次数确定对应的目标动力电池的经济价值。

表1
如下表2所示，获取6组监测数据对应的剩余使用价值，并与经济价值进行比较，得到电池监测建议信息。
表2
以此，实时监测动力电池，根据实际价值与理论价值进行回收评估，使得动力电池的回收评估更加精准，为用户提供便捷的动力电池评估服务。
下述对用以执行的本申请所提供的动力电池监测装置、设备及存储介质等进行说明，其具体的实现过程以及技术效果参见上述，下述不再赘述。
图7为本申请实施例提供的一种动力电池监测装置的示意图。如图7所示，该装置包括：
获取模块701，用于从电动交通工具的电池包中获取电动交通工具中目标动力电池的剩余循环次数。
第一确定模块702，用于根据剩余循环次数，确定目标动力电池的经济价值。
第二确定模块703，用于根据目标动力电池的初始使用价值，以及电动交通工具基于目标动力电池的已行驶里程，确定目标动力电池的剩余使用价值。
评估模块704，用于根据经济价值和剩余使用价值，对目标动力电池进行回收评估，以确定目标动力电池是否达到回收条件。
进一步地，第一确定模块702，具体用于根据剩余循环次数，采用预先训练的电池价值评估模型，得到目标动力电池的经济价值。
进一步地，第一确定模块702，具体还用于获取电池回收数据集，其中，电池回收数据集包括：多组电池回收样本数据，每组电池回收样本数据包括：历史回收的一个动力电池的剩余循 环次数以及一个动力电池的历史回收经济价值；从电池回收数据集中随机选择预设数量组的电池回收样本数据作为训练样本集；采用训练样本集进行模型训练，得到电池价值评估模型。
进一步地，第一确定模块702，具体还用于将电池回收数据集中训练样本集之外的电池回收样本数据作为验证样本集；采用验证样本集，对电池价值评估模型进行验证，得到电池价值评估模型的识别误差；若识别误差小于或等于预设误差阈值，则确定电池价值评估模型训练完成；若识别误差大于预设误差阈值，则增加训练样本集中电池回收样本数据的组数，并基于增加后的训练样本集重新进行模型训练，直至训练得到的电池价值评估模型的误差参数小于或等于预设误差阈值。
进一步地，评估模块704，具体用于若剩余使用价值大于或等于经济价值，则确定目标动力电池未达到回收条件；输出第一指示信息，以指示继续使用目标动力电池。
进一步地，评估模块704，具体还用于若剩余使用价值小于经济价值，则确定目标动力电池达到回收条件；输出第二提示信息，以提醒用户对目标动力电池进行回收。
进一步地，评估模块704，还用于若监测到目标动力电池在预设时间段内的经济价值的变化值大于预设价值，则发送预警信息，以提醒用户检查目标动力电池的使用情况。
图8为本申请实施例提供的一种监测设备的示意图，该监测设备可以是具备计算处理功能的设备。
该监测设备包括：处理器801、存储介质802。处理器801和存储介质802通过总线连接。
存储介质802用于存储程序，处理器801调用存储介质802存储的程序，以执行上述方法实施例。具体实现方式和技术效果类似，这里不再赘述。
可选地，本发明还提供一种存储介质，包括程序，该程序在被处理器执行时用于执行上述方法实施例。在本发明所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个***，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本发明各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用硬件加软件功能单元的形式实现。
上述以软件功能单元的形式实现的集成的单元，可以存储在一个存储介质中。上述软件功能单元存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)或处理器(英文：processor)执行本发明各个实施例所述方法的部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(英文：Read-Only Memory，简称：ROM)、随机存取存储器(英文：Random Access Memory，简称：RAM)、磁碟或者光盘等各种可以存储程序代码的介质。

Claims (10)

  1. 一种动力电池监测方法，其特征在于，所述方法包括：
    从电动交通工具的电池包中获取所述电动交通工具中目标动力电池的剩余循环次数；
    根据所述剩余循环次数，确定所述目标动力电池的经济价值；
    根据所述目标动力电池的初始使用价值，以及所述电动交通工具基于所述目标动力电池的已行驶里程，确定所述目标动力电池的剩余使用价值；
    根据所述经济价值和所述剩余使用价值，对所述目标动力电池进行回收评估，以确定所述目标动力电池是否达到回收条件。
  2. 根据权利要求1所述的方法，其特征在于，根据所述剩余循环次数，确定所述目标动力电池的经济价值，包括：
    根据所述剩余循环次数，采用预先训练的电池价值评估模型，得到所述目标动力电池的经济价值。
  3. 根据权利要求2所述的方法，其特征在于，在根据所述剩余循环次数，采用预先训练的电池价值评估模型，得到所述目标动力电池的经济价值之前，还包括：
    获取电池回收数据集，其中，所述电池回收数据集包括：多组电池回收样本数据，每组电池回收样本数据包括：历史回收的一个动力电池的剩余循环次数以及所述一个动力电池的历史回收经济价值；
    从所述电池回收数据集中随机选择预设数量组的电池回收样本数据作为训练样本集；
    采用所述训练样本集进行模型训练，得到所述电池价值评估模型。
  4. 根据权利要求3所述的方法，其特征在于，所述方法还包括：
    将所述电池回收数据集中所述训练样本集之外的电池回收样本数据作为验证样本集；
    采用所述验证样本集，对所述电池价值评估模型进行验证，得到所述电池价值评估模型的识别误差；
    若识别误差小于或等于预设误差阈值，则确定所述电池价值评估模型训练完成；
    若所述识别误差大于所述预设误差阈值，则增加所述训练样本集中电池回收样本数据的组数，并基于增加后的训练样本集重新进行模型训练，直至训练得到的电池价值评估模型的误差参数小于或等于所述预设误差阈值。
  5. 根据权利要求1所述的方法，其特征在于，根据所述经济价值和所述剩余使用价值，对所述目标动力电池进行回收评估，包括：
    若所述剩余使用价值大于或等于所述经济价值，则确定所述目标动力电池未达到回收条件；
    输出第一指示信息，以指示继续使用所述目标动力电池。
  6. 根据权利要求5所述的方法，其特征在于，根据所述经济价值和所述剩余使用价值，对所述目标动力电池进行回收评估，还包括：
    若所述剩余使用价值小于所述经济价值，则确定所述目标动力电池达到回收条件；
    输出第二提示信息，以提醒用户对所述目标动力电池进行回收。
  7. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    若监测到所述目标动力电池在预设时间段内的经济价值的变化值大于预设价值，则发送预警信息，以提醒用户检查所述目标动力电池的使用情况。
  8. 一种动力电池监测装置，其特征在于，所述装置包括：
    获取模块，用于从电动交通工具的电池包中获取所述电动交通工具中目标动力电池的剩余循环次数；
    第一确定模块，用于根据所述剩余循环次数，确定所述目标动力电池的经济价值；
    第二确定模块，用于根据所述目标动力电池的初始使用价值，以及所述电动交通工具基于所述目标动力电池的已行驶里程，确定所述目标动力电池的剩余使用价值；
    评估模块，用于根据所述经济价值和所述剩余使用价值，对所述目标动力电池进行回收评估，以确定所述目标动力电池是否达到回收条件。
  9. 一种监测设备，其特征在于，包括：处理器、存储介质，所述处理器与所述存储介质之间通过总线通信连接，所述存储介质存储有所述处理器可执行的程序指令，所述处理器调用存储介质中存储的程序，以执行如权利要求1至7任一所述的方法的步骤。
  10. 一种存储介质，其特征在于，所述存储介质上存储有计算机程序，所述计算机程序被处理器运行时执行如权利要求1至7任一所述的方法的步骤。
PCT/CN2023/082870 2022-10-21 2023-03-21 一种动力电池监测方法、装置、设备及存储介质 WO2024082541A1 (zh)

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