A power battery monitoring method, device, equipment and storage medium

Abstract

The present application provides a power battery monitoring method, device, equipment and storage medium, which relates to the field of battery monitoring technology. The method obtains the remaining number of cycles of a target power battery in an 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. Thus, 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.

Description

Technical Field

[0001] 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. Background Art

[0002] The market size of new energy vehicles continues to expand, which means that more and more people are buying electric vehicles. The performance of electric vehicles is the focus of all car owners. As the core component of electric vehicles, power batteries are one of the decisive factors that reflect vehicle performance. In order to cater to low-carbon and recyclable development, the market attaches great importance to the recycling of power batteries for new energy vehicles. Car owners can sell retired power batteries to recycling companies for cascade utilization, so as to achieve the purpose of resource utilization of waste batteries and increase the economic efficiency of car use.

[0003] During the use of electric vehicles, the recycling price will decrease as the number of lithium battery cycles increases. At this time, it is necessary to help car owners determine the best time to retire the battery in order to achieve the goal of selling retired batteries at the best price. However, the existing technology mostly manually determines the best time to retire the battery, which has low accuracy. Summary of the invention

[0004] The purpose of the present invention is to address the deficiencies in the above-mentioned prior art and provide 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.

[0005] To achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:

[0006] In a first aspect, an embodiment of the present application provides a power battery monitoring method, the method comprising:

[0007] Obtaining the remaining number of cycles of a target power battery in the electric vehicle from a battery pack of the electric vehicle;

[0008] Determining the economic value of the target power battery according to the remaining number of cycles;

[0009] Determining 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;

[0010] According to the economic value and the remaining use value, a recycling assessment is performed on the target power battery to determine whether the target power battery meets the recycling conditions.

[0011] Optionally, determining the economic value of the target power battery according to the remaining number of cycles includes:

[0012] According to the remaining number of cycles, a pre-trained battery value assessment model is used to obtain the economic value of the target power battery.

[0013] Optionally, 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:

[0014] Acquire a battery recycling data set, wherein 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;

[0015] Randomly selecting a preset number of groups of battery recycling sample data from the battery recycling data set as a training sample set;

[0016] The training sample set is used to perform model training to obtain the battery value assessment model.

[0017] Optionally, the method further comprises:

[0018] Using the battery recycling sample data outside the training sample set in the battery recycling data set as a verification sample set;

[0019] Using the verification sample set to verify the battery value assessment model, and obtain the recognition error of the battery value assessment model;

[0020] 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;

[0021] 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 retrained 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.

[0022] Optionally, the recycling evaluation of the target power battery according to the economic value and the remaining use value includes:

[0023] If the remaining use value is greater than or equal to the economic value, it is determined that the target power battery does not meet the recycling conditions;

[0024] Outputting first instruction information to instruct to continue using the target power battery.

[0025] Optionally, the recycling evaluation of the target power battery according to the economic value and the remaining use value further includes:

[0026] If the remaining use value is less than the economic value, determining that the target power battery meets the recycling conditions;

[0027] Output a second prompt message to remind the user to recycle the target power battery.

[0028] Optionally, the method further comprises:

[0029] If it is monitored that the change in the economic value of the target power battery within a preset time period is greater than a preset value, an early warning message is sent to remind the user to check the usage of the target power battery.

[0030] In a second aspect, an embodiment of the present application provides a power battery monitoring device, the device comprising:

[0031] 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;

[0032] A first determination module, configured to determine the economic value of the target power battery according to the remaining number of cycles;

[0033] 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;

[0034] An evaluation module 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.

[0035] In a third aspect, 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.

[0036] In a fourth aspect, 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.

[0037] Compared with the prior art, this application has the following beneficial effects:

[0038] 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. Thus, 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 A schematic diagram of the structure of a power battery monitoring system provided in this application;

[0041] Figure 2 A schematic diagram of a power battery monitoring method provided in this application;

[0042] Figure 3 A flowchart of a method for determining a battery value assessment model provided in an embodiment of the present application;

[0043] Figure 4 A flowchart of a method for verifying a battery value assessment model provided in an embodiment of the present application;

[0044] Figure 5 A schematic diagram of a flow chart of a method for evaluating power battery recycling provided in an embodiment of the present application;

[0045] Figure 6 A schematic flow chart of another method for evaluating power battery recycling provided in an embodiment of the present application;

[0046] Figure 7 A schematic diagram of a power battery monitoring device provided in an embodiment of the present application;

[0047] Figure 8 A schematic diagram of a monitoring device provided in an embodiment of the present application.

[0048] 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. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0050] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for protection, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0051] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0052] In addition, the terms “first”, “second”, etc., if used, are merely used to distinguish between the descriptions and should not be understood as indicating or implying relative importance.

[0053] It should be noted that, in the absence of conflict, the features in the embodiments of the present invention may be combined with each other.

[0054] In order to accurately inform users of the best time to sell power batteries, the present application provides a power battery monitoring method, device, equipment and storage medium.

[0055] A power battery monitoring system provided by the present application is explained below through a specific example. Figure 1 This is a schematic diagram of the structure of a power battery monitoring system provided in this application. Figure 1 As shown, the system includes: a monitoring device 100 , a cycle counter 200 , a mileage counter 300 , and a vehicle-mounted device 400 .

[0056] The monitoring device 100 is connected to the cycle counter 200 , the mileage counter 300 , and the vehicle-mounted device 400 respectively.

[0057] 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 based on 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.

[0058] 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 .

[0059] The monitoring device 100 is used to evaluate the recycling of the target power battery according to the remaining number of cycles, the mileage traveled, and the property information of the power battery.

[0060] The monitoring device 100 may also transmit the recycling assessment information to the vehicle-mounted device 400 to remind the user. For example, the vehicle-mounted device 400 may be a display screen, a speaker, etc. of an electric vehicle, which is not limited here.

[0061] The following is an explanation of a power battery monitoring method provided in the present application through a specific example. Figure 2 This is a flow chart of a power battery monitoring method provided in this application. The execution subject of this method is a monitoring device, which can be a device with computing and processing functions. Figure 2 As shown, the method includes:

[0062] S101. Obtain the remaining number of cycles of a target power battery in the electric vehicle from a battery pack of the electric vehicle.

[0063] 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. The electric vehicles include: electric cars, electric bicycles and other vehicles that use electric power.

[0064] S102. Determine the economic value of the target power battery according to the remaining number of cycles.

[0065] 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.

[0066] 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.

[0067] The initial use value of the target power battery refers to the selling price when it is sold. For each power battery parameter, in addition to the initial use value, there is also an initial total mileage, which represents the total mileage of the electric vehicle based on the target power battery during the entire life cycle of the power battery, for example: 100,000 kilometers.

[0068] First, 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.

[0069] It should be noted that 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. Figure 2 The flowchart shown is only an example, and the present application does not limit the order between step S101 and step S102 and step S103.

[0070] 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.

[0071] 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.

[0072] For example, 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.

[0073] In summary, in this embodiment, 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. Thus, 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.

[0074] In the above Figure 2 On the basis of the corresponding embodiment, 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 remaining number of cycles in S102 includes:

[0075] According to the remaining number of cycles, the pre-trained battery value assessment model is used to obtain the economic value of the target power battery.

[0076] In order to accurately obtain the economic value of the target power battery according to the remaining number of cycles, 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.

[0077] 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. For example, 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.

[0078] In summary, in this embodiment, 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.

[0079] Based on the above embodiments, the embodiments of the present application also provide a method for determining a battery value assessment model. Figure 3 A flow chart of a method for determining a battery value assessment model provided in an embodiment of the present application. Figure 3 As shown, 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:

[0080] S201. Obtain a battery recycling dataset.

[0081] 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.

[0082] For example, the historical recycling economic value may be the historical recycling price of the power battery. To ensure the quality of the model, 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.

[0083] S202: Randomly select a preset number of groups of battery recycling sample data from the battery recycling data set as a training sample set.

[0084] To ensure the accuracy of model training, 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.

[0085] S203: Use the training sample set to perform model training to obtain a battery value assessment model.

[0086] 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.

[0087] Furthermore, the battery value assessment model is taken as an example of a neural network recognition model to explain the model training process.

[0088] The neural network recognition model consists of a three-layer structure of input layer, hidden layer and 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 calculation 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 remaining cycle times C1, C2, C3, ..., Ci in the training sample set, and obtain the corresponding multiple output historical recovery economic values ​​D1, D2, D3, ..., Di; then, through 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, the weights of the neurons connected inside the neural network are continuously corrected, and the output value is approached to the target value by continuously reducing the error. At the same time, the back propagation learning method will be transmitted to each layer of neurons layer by layer, and the error will be continuously corrected and adjusted to minimize the sum of squared errors.

[0089] In constructing the neural network recognition model, the "Sigmoid" function is used as the learning and training function, and the function expression is as described in the following formula (1). The "Softmax" function is used as the output layer transfer function of the model, and the function expression is as described in the following formula (2):

[0090]

[0091]

[0092] 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.

[0093] After the neural network recognition model is successfully trained, 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.

[0094] In summary, in this embodiment, 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. Thus, an accurate battery value assessment model is obtained.

[0095] In the above Figure 3 Based on the corresponding embodiments, the embodiments of the present application also provide a method for verifying a battery value assessment model. Figure 4A flow chart of a method for verifying a battery value assessment model provided in an embodiment of the present application. Figure 4 As shown, the method also includes:

[0096] S301. Use the battery recycling sample data outside the training sample set in the battery recycling data set as a verification sample set.

[0097] Among them, the number of validation sample sets is smaller than the number of training sample sets.

[0098] S302: Use a verification sample set to verify the battery value assessment model to obtain a recognition error of the battery value assessment model.

[0099] 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):

[0100]

[0101] S303: 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.

[0102] If the recognition error is less than or equal to the preset error threshold, it indicates that the battery value assessment model meets the error requirement, and it is determined that the battery value assessment model training is completed. The battery value assessment model can be used as a preset battery value assessment model.

[0103] S304: If the recognition error is greater than a preset error threshold, the number of groups of battery recycling sample data in the training sample set is increased, and the model is retrained 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.

[0104] If the recognition error is greater than the preset error threshold, it indicates that the battery value assessment model does not meet the error requirements. In order to further obtain a more accurate model, the number of groups 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.

[0105] In summary, in this embodiment, 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. Thus, a high-precision battery value assessment model is obtained through the verification sample set.

[0106] In the above Figure 2 On the basis of the corresponding embodiments, the embodiments of the present application also provide a method for evaluating power battery recycling. Figure 5 A schematic diagram of a method for evaluating the recycling of a power battery provided in an embodiment of the present application. Figure 5 As shown, in S104, the target power battery is evaluated for recycling based on the economic value and the remaining use value, including:

[0107] S401. If the remaining use value is greater than or equal to the economic value, it is determined that the target power battery does not meet the recycling conditions.

[0108] If 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.

[0109] S402: Output first instruction information to instruct to continue using the target power battery.

[0110] In order to timely remind the user of relevant information about 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.

[0111] For example, the first indication information may be text reminder information transmitted to a display screen; or may be voice reminder information transmitted to a speaker.

[0112] Furthermore, 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.

[0113] 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 meet the recycling conditions; the first indication information is output to indicate that the target power battery should continue to be used. Thus, the user is reminded in time to continue to use the target power battery.

[0114] In the above Figure 5 On the basis of the corresponding embodiments, the embodiments of the present application also provide another method for evaluating power battery recycling. Figure 6 A flow chart of another method for evaluating power battery recycling provided in an embodiment of the present application. Figure 6 As shown, the recycling evaluation of the target power battery according to the economic value and the remaining use value in S104 also includes:

[0115] S501. If the remaining use value is less than the economic value, it is determined that the target power battery meets the recycling conditions.

[0116] If 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.

[0117] S502: Output a second prompt message to remind the user to recycle the target power battery.

[0118] In order to timely remind the user of relevant information about 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.

[0119] For example, the second indication information may be text reminder information transmitted to a display screen; or may be voice reminder information transmitted to a speaker.

[0120] In summary, in this embodiment, if the remaining use value is less than the economic value, it is determined that the target power battery meets the recycling conditions, and the second prompt information is output to remind the user to recycle the target power battery. Thus, the user is reminded to sell the target power battery in time.

[0121] In the above Figure 2 On the basis of the corresponding embodiment, the embodiment of the present application also provides another power battery monitoring method. The method also includes:

[0122] If it is monitored that the change in the economic value of the target power battery within a preset time period is greater than the preset value, an early warning message is sent to remind the user to check the usage of the target power battery.

[0123] For example, 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.

[0124] In summary, in this embodiment, if the change in the economic value of the target power battery within the preset time period is monitored to be greater than the preset value, an early warning message is sent to remind the user to check the usage of the target power battery, thereby extending the life cycle of the target power battery.

[0125] Based on the above embodiments, the embodiments of the present application also provide an example to demonstrate the process of power battery monitoring.

[0126] As shown in Table 1 below, taking 6 groups of monitoring data as an example, the economic value of the corresponding target power battery is determined according to the 6 groups of remaining cycle numbers.

[0127]

[0128] Table 1

[0129] As shown in Table 2 below, the remaining use values ​​corresponding to the six sets of monitoring data are obtained and compared with the economic values ​​to obtain battery monitoring recommendation information.

[0130] Serial number Remaining cycles / times Economic value / yuan Remaining use value / yuan Recommended information 1 4600 19031.6 184000 Continue to use 2 2100 16826.4 73000 Continue to use 3 1550 8216 7050 Recycling 4 1100 8216 6350 Recycling 5 350 97.03 70 Recycling 6 100 98.4 63 Recycling

[0131] Table 2

[0132] In this way, power batteries can be monitored in real time and recycling assessments can be conducted based on actual and theoretical values, making the recycling assessment of power batteries more accurate and providing users with convenient power battery assessment services.

[0133] The following is a description of the power battery monitoring device, equipment, storage medium, etc. provided in this application for execution. The specific implementation process and technical effects are described above and will not be repeated below.

[0134] Figure 7 This is a schematic diagram of a power battery monitoring device provided in an embodiment of the present application. Figure 7 As shown, the device comprises:

[0135] 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.

[0136] The first determination module 702 is used to determine the economic value of the target power battery according to the remaining number of cycles.

[0137] 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.

[0138] 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.

[0139] Furthermore, 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.

[0140] Furthermore, the first determination module 702 is specifically used to obtain a battery recycling data set, wherein 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; 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 perform model training to obtain a battery value assessment model.

[0141] Furthermore, 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.

[0142] Furthermore, 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.

[0143] Furthermore, 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.

[0144] Furthermore, 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.

[0145] Figure 8 A schematic diagram of a monitoring device provided in an embodiment of the present application, wherein the monitoring device may be a device with computing and processing functions.

[0146] 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.

[0147] 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.

[0148] Optionally, 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. In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, 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.

[0149] 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.

[0150] In addition, 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.

[0151] 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.

Claims

1. A power battery monitoring method, characterized in that: The method comprises: Obtaining the remaining number of cycles of a target power battery in the electric vehicle from a battery pack of the electric vehicle; Determining the economic value of the target power battery according to the remaining number of cycles; Determining 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; According to the economic value and the remaining use value, performing a recycling evaluation on the target power battery to determine whether the target power battery meets the recycling conditions; If it is monitored that the change in the economic value of the target power battery within a preset time period is greater than a preset value, an early warning message is sent to remind the user to check the usage of the target power battery.

2. The method according to claim 1, characterized in that: The determining the economic value of the target power battery according to the remaining number of cycles includes: According to the remaining number of cycles, a pre-trained battery value assessment model is used to obtain the economic value of the target power battery.

3. The method according to claim 2, characterized in that 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: Acquire a battery recycling data set, wherein 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; Randomly selecting a preset number of groups of battery recycling sample data from the battery recycling data set as a training sample set; The training sample set is used to perform model training to obtain the battery value assessment model.

4. The method according to claim 3, characterized in that The method further comprises: Using the battery recycling sample data outside the training sample set in the battery recycling data set as a verification sample set; Using the verification sample set to verify the battery value assessment model, and 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 is retrained 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.

5. The method according to claim 1, characterized in that: The recycling evaluation of the target power battery according to the economic value and the remaining use value includes: If the remaining use value is greater than or equal to the economic value, it is determined that the target power battery does not meet the recycling conditions; Outputting first instruction information to instruct to continue using the target power battery.

6. The method according to claim 5, characterized in that The recycling evaluation of the target power battery according to the economic value and the remaining use value further includes: If the remaining use value is less than the economic value, determining that the target power battery meets the recycling conditions; Output a second prompt message to remind the user to recycle the target power battery.

7. A power battery monitoring device, characterized in that: The device comprises: 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, configured to perform a 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 meets the recycling conditions; The evaluation module is further configured to send an early warning message to remind the user to check the usage 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.

8. A monitoring device, characterized in that: include: A processor and a storage medium, wherein the processor and the storage medium are communicatively connected via a bus, 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 claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the power battery monitoring method according to any one of claims 1 to 6 are executed.

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