Power battery state prediction method and device, electronic equipment, medium and product
By building a digital twin model of the power battery, the problem of BMS being unable to predict the entire life cycle is solved, accurate prediction and safety assessment of the battery status are achieved, and the accuracy and efficiency of battery management are improved.
Patent Information
- Application Number
- CN202410324510.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-03-20
- Publication Date
- 2025-09-23
AI Technical Summary
Existing battery management systems (BMS) are unable to predict the entire life cycle of power batteries, resulting in poor accuracy in battery life estimation and safety assessment.
By establishing physical models, geometric simulation models and rule models, a digital twin model of the power battery is constructed. The digital twin model is used to simulate the battery operation process. Combined with model reduction algorithm and real-time data calibration, the battery status can be predicted.
The accuracy of battery life estimation and safety assessment is improved, the complexity of the model is reduced, and the prediction speed is increased.
Smart Images
Figure CN120688199A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of digital modeling of power batteries, and in particular to a method, device, electronic equipment, medium, and product for predicting the state of a power battery. Background Art
[0002] With the development of technology, power batteries, as a key branch of the new energy sector, have attracted the attention of major manufacturers in recent years, and power battery status prediction has become a focus of these manufacturers. Related technologies typically use battery management systems (BMS) to manage power battery status. However, BMS systems cannot predict the entire life cycle of power batteries, resulting in poor accuracy in battery life estimation and battery safety assessment. Summary of the Invention
[0003] The present disclosure provides a method, device, electronic device, medium and product for predicting the state of a power battery, which can realize remote monitoring, fault prediction and fault location of the power battery by predicting the state of the power battery, and at the same time improve the accuracy of battery life estimation and battery safety assessment.
[0004] The first aspect of the present disclosure provides a method for predicting the state of a power battery, the method comprising: establishing a physical model based on a first battery assembly of the power battery, the physical model being used to indicate the structure of the first battery assembly and the data relationship of the first battery assembly, the first battery assembly comprising at least one of the following: a battery module, a battery thermal management accessory and a battery electrical accessory; establishing a geometric simulation model based on the physical model and a first data set of the power battery, the geometric model being a geometric form of the physical model, the first data set comprising at least two of the following: battery material data, battery structure data and mechanical state data; establishing a rule model based on a second data set of the power battery, the rule model being used to restrict the geometric simulation model, the second data set comprising at least one of the following: historical battery data and real-time battery data; establishing a digital twin model of the power battery based on the geometric simulation model, the rule model and the physical model; simulating the operation process of the power battery using the digital twin model to obtain predicted data of the power battery, the predicted data being used to predict the operation state of the power battery.
[0005] In some embodiments of the present disclosure, historical battery data includes at least one of the following: historical battery voltage data, historical battery current data, and historical battery pressure data; real-time battery data includes at least one of the following: real-time battery voltage data, real-time battery current data, and real-time battery pressure data; battery thermal management accessories include at least one of the following: a battery pack liquid cooling system and a battery pack insulation system; battery electrical accessories include at least one of the following: a battery low-voltage wiring harness, a battery high-voltage wiring harness, and a battery drive connector.
[0006] In some embodiments of the present disclosure, establishing a digital twin model of a power battery based on a geometric simulation model, a rule model and a physical model includes: establishing a reduced-order model based on the geometric simulation model, the rule model and the physical model using a model reduction algorithm; establishing a target equation based on the reduced-order model, and determining the target equation as the digital twin model of the power battery.
[0007] In some embodiments of the present disclosure, after using the digital twin model to predict the current state of the power battery, it also includes: obtaining a third data set of the power battery, the third data set is used to indicate the real-time data of the power battery; and calibrating the digital twin model based on the third data set and the predicted data.
[0008] In some embodiments of the present disclosure, the third data set includes at least one of the following: real-time battery voltage data, real-time battery current data, and real-time battery temperature data.
[0009] In some embodiments of the present disclosure, calibrating the digital twin model based on the third battery data set and the predicted data includes: determining that the error value between the third battery data set and the predicted data is greater than an error threshold; and calibrating the digital twin model based on the error value.
[0010] A second embodiment of the present disclosure provides a state prediction device for a power battery, the device comprising:
[0011] a first creation module, configured to establish a physical model based on first battery assembly data of a power battery, the physical model being configured to indicate a relationship between data in the first battery assembly, the first battery assembly data including at least one of the following: data of a battery module, data of a battery thermal management accessory, and data of a battery electrical accessory;
[0012] a second creation module, configured to establish a geometric simulation model based on the physical model and a first data set of the power battery, wherein the geometric model is a geometric form of the physical model, and the first data set includes at least two items of the following: battery material data, battery structure data, and mechanical state data;
[0013] a third creation module, configured to establish a rule model based on a second data set of the power battery, the rule model being configured to constrain the geometric simulation model, the second data set comprising at least one of the following: historical battery data and real-time battery data;
[0014] The fourth creation module is used to establish a digital twin model of the power battery based on the geometric simulation model, the rule model and the physical model;
[0015] The prediction module is used to use the digital twin model to simulate the operation process of the power battery and obtain the predicted data of the power battery. The predicted data is used to reflect the real-time operation result data of the power battery or to predict the operating status of the power battery.
[0016] The third aspect embodiment of the present disclosure proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute any one of the methods in the first aspect embodiment of the present disclosure.
[0017] The fourth aspect embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, characterized in that the computer instructions are used to enable a computer to execute the method in the first aspect embodiment of the present disclosure.
[0018] The fifth aspect of the present disclosure provides a computer program product, characterized in that it includes a computer program, and when the computer program is executed by a processor, it implements any one of the methods in the first aspect of the present disclosure.
[0019] In summary, the state prediction method of a power battery proposed in the present disclosure includes: establishing a physical model based on the first battery assembly of the power battery, the physical model is used to indicate the structure of the first battery assembly and the data relationship of the first battery assembly, the first battery assembly includes at least one of the following: battery module, battery thermal management accessories and battery electrical accessories: battery module data, battery thermal management accessories data and battery electrical accessories data; establishing a geometric simulation model based on the physical model and the first data set of the power battery, the geometric model is the geometric form of the physical model, the first data set includes at least two of the following: battery material data, battery structure data and mechanical state data; establishing a rule model based on the second data set of the power battery, the rule model is used to limit the geometric simulation model, the second data set includes at least one of the following: historical battery data and real-time battery data; establishing a digital twin model of the power battery based on the geometric simulation model, the rule model and the physical model; using the digital twin model to simulate the operation process of the power battery to obtain predicted data of the power battery, the predicted data is used to reflect the real-time operation result data of the power battery or to predict the operation state of the power battery. The method disclosed herein realizes a model representation of the parameters of a power battery by establishing a physical model, a geometric model and a rule model, and constructs a digital twin model of the power battery through the physical model, the geometric model and the rule model. By using the digital twin model to simulate the operation process of the power battery, the entire life cycle of the power battery can be predicted. The operating status of the power battery is predicted by the predicted data of the power battery obtained by simulation, which can improve the accuracy of the battery life estimation and the accuracy of the battery safety assessment.
[0020] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute an improper limitation of the present disclosure.
[0022] Figure 1 This is a flow chart of a method for predicting the state of a power battery according to an embodiment of the present disclosure;
[0023] Figure 2 This is a flow chart of another method for predicting the state of a power battery according to an embodiment of the present disclosure;
[0024] Figure 3 This is a schematic structural diagram of a power battery state prediction device according to an embodiment of the present disclosure;
[0025] Figure 4 The present invention is a block diagram of an electronic device for implementing the method for predicting the state of a power battery disclosed herein, according to an exemplary embodiment. DETAILED DESCRIPTION
[0026] The following describes in detail embodiments of the present disclosure, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout identify the same or similar components or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.
[0027] In the new energy sector, power batteries, as a key component, have attracted significant attention from major manufacturers in recent years, with power battery status prediction becoming a key focus. Related technologies typically utilize BMS systems to manage power battery status. These systems intelligently manage and maintain individual battery cells, preventing overcharging and over-discharging to extend battery life. However, BMS systems cannot predict the entire lifecycle of a power battery.
[0028] The present disclosure aims to predict the entire life cycle of power batteries through a twin model, thereby realizing remote monitoring, fault prediction, and fault location of power batteries, while improving the accuracy of battery life estimation and battery safety assessment.
[0029] The method proposed in this disclosure can be widely applied to code development and maintenance in aspects such as vehicle driving, vehicle assisted driving, unmanned driving, and vehicle electronic control. The application scenarios are not limited in the embodiments of this disclosure.
[0030] The power battery state prediction method provided in this application is described in detail below with reference to the accompanying drawings.
[0031] Figure 1 FIG. 1 is a flow chart of a method for predicting the state of a power battery according to an embodiment of the present disclosure. Figure 1 In the embodiment shown, the power battery state prediction method includes:
[0032] Step 101: Establish a physical model based on a first battery assembly of a power battery.
[0033] In some embodiments, the first battery assembly of the power battery includes at least one of the following: a battery module, a battery thermal management accessory, and a battery electrical accessory.
[0034] In some embodiments, the physical model can be used to indicate the structure of the first battery assembly, that is, the physical model can indicate the connection relationship between the battery module, the battery thermal management accessory, and the battery electrical accessory.
[0035] In some embodiments, the physical model can be used to indicate the data relationship of the first battery assembly, that is, the physical model can indicate the data relationship between the data of the battery module, the data of the battery thermal management accessories, and the data of the battery electrical accessories.
[0036] In some optional embodiments, the battery thermal management accessories may include a battery pack (PACK) liquid cooling system and a battery pack insulation system, but are not limited thereto.
[0037] In some optional embodiments, the battery electrical accessories may include: a battery high-voltage wiring harness, a battery low-voltage wiring harness, and a battery drive connector, but are not limited thereto.
[0038] In some optional embodiments, a model framework of a physical model can be constructed through the connection relationship between the battery module, the battery thermal management accessories and the battery electrical accessories. Then, based on the above model framework, based on the data of the battery module, the data of the battery thermal management accessories and the data of the battery electrical accessories, a physical model that can realize physical structure and data flow can be created using machine learning methods.
[0039] In some embodiments, a physical model is constructed by using a battery thermal management accessory and a battery electrical accessory, which lays the foundation for the established twin model to be used for predictions on battery thermal management, battery charge estimation, and other aspects.
[0040] Step 102: Establish a geometric simulation model based on the physical model and the first data set of the power battery.
[0041] In some embodiments, the first data set includes at least two of the following: battery material data, battery structure data, and mechanical state data.
[0042] In some embodiments, the geometric simulation model is a geometric form of the physical model. In other words, the geometric simulation model is a multi-dimensional representation of the physical model.
[0043] For example, when the first data set only includes battery material data, battery structure data, and mechanical state data, the geometric simulation model is a three-dimensional representation of the physical model.
[0044] For example, when the first data set only includes battery material data and mechanical state data, the geometric simulation model is a two-dimensional representation of the physical model.
[0045] In an optional embodiment, the geometric shape of the geometric simulation model can be determined by the model parameters in the physical model, and then the material properties, boundary conditions, etc. in the geometric simulation model can be defined by the first data set, thereby realizing the construction of the geometric simulation model, ensuring that the geometric simulation model can accurately reflect the structure and characteristics of the physical model.
[0046] In an optional embodiment, the physical model can mirror model parameters and the like into a geometric simulation model based on finite element analysis, and the geometric simulation model optimizes the physical model in a feedback manner through methods such as structural finite element analysis.
[0047] In an optional embodiment, the objective equation of the geometric simulation model can be expressed as G(m, s, a), where m represents battery material data, s represents battery structure data, a represents mechanical state data, and G represents a model function.
[0048] Step 103: Establish a rule model based on the second data set of the power battery.
[0049] In some embodiments, the second data set includes at least one of: historical battery data and real-time battery data.
[0050] The historical battery data includes at least one of the following: historical battery voltage data, historical battery current data, and historical battery pressure data; the real-time battery data includes at least one of the following: real-time battery voltage data, real-time battery current data, and real-time battery pressure data;
[0051] In some embodiments, power battery rules in a rule-based model are formulated using historical and current battery data to constrain the state of the geometric simulation model, such as the range of battery voltage and current. In particular, the rules in the rule-based model should adhere to the physical rules of the physical model to avoid model conflicts between models.
[0052] Furthermore, the rules in the rule model can be adjusted in a timely manner through real-time battery data.
[0053] In other words, the rule model is a model composed of rules constructed from the data in the second dataset.
[0054] In some embodiments, by constructing the data of the rule model and the rules set by the rule model, the foundation is laid for the digital twin model to determine the fault location and realize functions such as fault prediction.
[0055] In an optional embodiment, the objective equation of the rule model can be expressed as R(p,d), where p represents real-time battery pressure data, d represents real-time battery voltage data, real-time battery current data, etc., and R represents the model function.
[0056] Step 104: Establish a digital twin model of the power battery based on the geometric simulation model, the rule model, and the physical model.
[0057] In some embodiments, a reduced-order model can be established based on a geometric simulation model, a rule model, and a physical model using a model reduction algorithm; based on the reduced-order model, a target equation is established, and the target equation is determined as a digital twin model of the power battery, thereby realizing the establishment of a digital twin model.
[0058] In some embodiments, the geometric simulation model, the rule model, and the physical model can be understood as models used to extract parameters and convert the parameters into model representations.
[0059] In an optional embodiment, the digital twin model can be expressed as F(i,u,t,G,R), where i represents the real-time current of the power battery, u represents the real-time voltage of the power battery, t represents the real-time temperature of the power battery, G represents the target equation in the geometric simulation model, R represents the target equation in the rule model, and F represents the model function.
[0060] Step 105: Use the digital twin model to simulate the operation process of the power battery to obtain predicted data of the power battery.
[0061] In some embodiments, a digital twin model can be used to simulate the operation process of the power battery to obtain predicted data of the power battery. The predicted data is used to reflect the real-time operation result data of the power battery, where the predicted data includes, for example: predicted battery temperature, predicted battery voltage, predicted battery current, etc. of the power battery.
[0062] Furthermore, based on the above prediction data, the status of the power battery can be evaluated (for example, estimating the remaining power of the power battery, whether there is a fault in the power battery, etc.), thereby predicting the current status of the power battery.
[0063] In other words, the prediction data is used to reflect the real-time operating result data of the power battery or to predict the operating status of the power battery.
[0064] In summary, the state prediction method of a power battery proposed in the present disclosure includes: establishing a physical model based on the first battery assembly of the power battery, the physical model is used to indicate the structure of the first battery assembly and the data relationship of the first battery assembly, and the first battery assembly includes at least one of the following: a battery module, a battery thermal management accessory and a battery electrical accessory; establishing a geometric simulation model based on the physical model and a first data set of the power battery, the geometric model is a geometric form of the physical model, and the first data set includes at least two of the following: battery material data, battery structure data and mechanical state data; establishing a rule model based on the second data set of the power battery, the rule model is used to limit the geometric simulation model, and the second data set includes at least one of the following: historical battery data and real-time battery data; establishing a digital twin model of the power battery based on the geometric simulation model, the rule model and the physical model; using the digital twin model to simulate the operation process of the power battery to obtain predicted data of the power battery, and the predicted data is used to reflect the real-time operation result data of the power battery or to predict the operation state of the power battery. The method disclosed herein realizes a model representation of the parameters of a power battery by establishing a physical model, a geometric model and a rule model, and constructs a digital twin model of the power battery through the physical model, the geometric model and the rule model. By using the digital twin model to simulate the operation process of the power battery, the entire life cycle of the power battery can be predicted. The operating status of the power battery is predicted by the predicted data of the power battery obtained by simulation, which can improve the accuracy of the battery life estimation and the accuracy of the battery safety assessment.
[0065] Figure 2 This is a flow chart of a method for predicting the state of a power battery according to an embodiment of the present disclosure. Figure 1 The embodiment shown is further explained as Figure 2 In the embodiment shown, the power battery state prediction method includes:
[0066] Step 201: Establish a physical model based on a first battery assembly of a power battery.
[0067] In this disclosure, the principle of step 201 is the same as Figure 1 The step 101 in the embodiment shown is the same as that in the embodiment shown. Figure 1 The relevant description will not be repeated here.
[0068] Step 202 : establishing a geometric simulation model based on the physical model and the first data set of the power battery.
[0069] In this disclosure, the principle of step 202 is the same as Figure 1 The step 102 in the embodiment shown is the same as that in the embodiment shown. Figure 1 The relevant description will not be repeated here.
[0070] Step 203: Establish a rule model based on the second data set of the power battery.
[0071] In this disclosure, the principle of step 203 is the same as Figure 1 The step 103 in the embodiment shown is the same as that in the embodiment shown. Figure 1 The relevant description will not be repeated here.
[0072] Step 204 : Based on the geometric simulation model, the rule model and the physical model, a reduced-order model is established using a model reduction algorithm.
[0073] In some embodiments, a model reduction algorithm is used to reduce the model order of the entire model constructed by the geometric simulation model, the rule model and the physical model, thereby greatly reducing the running time and storage requirements of the digital twin model and improving the prediction efficiency while retaining the key information of the waveform and the main influencing factors.
[0074] In some embodiments, the model reduction algorithm is, for example, a random forest algorithm, a feature mapping algorithm, etc., which is not limited in this disclosure.
[0075] Step 205: Establish a target equation based on the reduced-order model, and determine the target equation as the digital twin model of the power battery.
[0076] In some embodiments, a target equation can be established by using a machine learning method and a reduced-order model. The target equation can be expressed as F(i,u,t,G,R), where i represents the predicted current of the power battery, u represents the predicted voltage of the power battery, t represents the predicted temperature of the power battery, G represents the target equation in the geometric simulation model, R represents the target equation in the rule model, and F represents the model function.
[0077] Step 206 : Acquire a third data set of the power battery.
[0078] In some embodiments, the third data set includes at least one of the following: real-time battery voltage data, real-time battery current data, and real-time battery temperature data.
[0079] In some embodiments, the third data set is used to indicate real-time data of the power battery.
[0080] In some embodiments, the third data set may be obtained by collecting data from the power battery entity using a sensor, but the method is not limited thereto. The present disclosure does not limit the method for obtaining the third data set.
[0081] In some embodiments, step 206 is optional, and whether the predicted structure of the digital twin model is accurate can be determined by other means.
[0082] Step 207: calibrate the digital twin model based on the third data set and the predicted data.
[0083] In some embodiments, when the error value between the third battery data set and the predicted data is greater than the error threshold, it indicates that the error in prediction using the digital twin model is large. At this time, the digital twin model can be calibrated based on the error value.
[0084] The present disclosure does not limit the specific method of calibrating the digital twin model. For example, the digital twin model can be calibrated by adjusting the coefficients of each parameter in the target equation.
[0085] It should be understood that step 207 is optional. In some embodiments, when the error value between the third data set and the predicted data is less than the error threshold, step 207 can be omitted.
[0086] In summary, the state prediction method of the power battery provided by the embodiment of the present disclosure includes: establishing a physical model based on the first battery assembly of the power battery; establishing a geometric simulation model based on the physical model and the first data set of the power battery; establishing a rule model based on the second data set of the power battery; establishing a reduced-order model based on the geometric simulation model, the rule model and the physical model using a model reduction algorithm; establishing a target equation based on the reduced-order model, and determining the target equation as a digital twin model of the power battery; obtaining a third data set of the power battery, the third data set being used to indicate real-time data of the power battery; and calibrating the digital twin model based on the third data set and the predicted data. The method disclosed herein realizes the model representation of the parameters of the power battery by establishing a physical model, a geometric model and a rule model, and then constructs a digital twin model that can predict the status of the power battery through the physical model, the geometric model and the rule model, and then uses the digital twin model to predict the entire life cycle of the power battery, thereby improving the accuracy of the battery life estimation and the accuracy of the battery safety assessment; at the same time, by utilizing the model reduction algorithm, the model complexity of the digital twin model is reduced, the model prediction rate is improved, and by calibrating the digital twin model using a third data set, the accuracy of the model prediction is further improved.
[0087] Therefore, the present disclosure has the following beneficial effects:
[0088] 1. By establishing physical models, geometric models and rule models, the model representation of the power battery parameters is realized. Then, a digital twin model that can predict the power battery status is constructed through the physical models, geometric models and rule models. Then, the digital twin model is used to predict the entire life cycle of the power battery.
[0089] 2. By utilizing the model reduction algorithm, the model complexity of the digital twin model is reduced and the model prediction rate is improved.
[0090] 3. By calibrating the digital twin model using a third data set, the accuracy of battery life estimation and battery safety assessment is improved.
[0091] Corresponding to the methods provided in the above-mentioned embodiments, the present disclosure also provides a state prediction device for a power battery. Since the device provided in the embodiment of the present disclosure corresponds to the methods provided in the above-mentioned embodiments, the implementation method is also applicable to the device provided in this embodiment and will not be described in detail in this embodiment.
[0092] Figure 3 FIG. 3 is a schematic diagram of a state prediction device 300 for a power battery according to an embodiment of the present disclosure. Figure 3 As shown, the state prediction device of the power battery includes:
[0093] A first creation module 310 is configured to establish a physical model based on first battery assembly data of a power battery, where the physical model is configured to indicate a relationship of the first battery assembly data, where the first battery assembly data includes at least one of the following: battery module data, battery thermal management accessory data, and battery electrical accessory data;
[0094] A second creation module 320 is configured to establish a geometric simulation model based on the physical model and a first data set of the power battery, where the geometric model is a geometric form of the physical model, and the first data set includes at least two of the following: battery material data, battery structure data, and mechanical state data;
[0095] A third creation module 330 is configured to establish a rule model based on a second data set of the power battery, the rule model being configured to constrain the geometric simulation model, wherein the second data set includes at least one of the following: historical battery data and real-time battery data;
[0096] A fourth creation module 340 is configured to establish a digital twin model of the power battery based on the geometric simulation model, the rule model, and the physical model;
[0097] The prediction module 350 is used to simulate the operation process of the power battery using the digital twin model to obtain prediction data of the power battery. The prediction data is used to reflect the real-time operation result data of the power battery or to predict the operating status of the power battery.
[0098] In some embodiments, historical battery data includes at least one of the following: historical battery voltage data, historical battery current data, and historical battery pressure data; real-time battery data includes at least one of the following: real-time battery voltage data, real-time battery current data, and real-time battery pressure data; battery thermal management accessories include at least one of the following: a battery pack liquid cooling system and a battery pack insulation system; battery electrical accessories include at least one of the following: a battery low-voltage wiring harness, a battery high-voltage wiring harness, and a battery drive connector.
[0099] In some embodiments, the fourth creation module 340 is also used to: establish a reduced-order model based on the geometric simulation model, the rule model and the physical model using a model reduction algorithm; establish a target equation based on the reduced-order model, and determine the target equation as a digital twin model of the power battery.
[0100] In some embodiments, the prediction module 350 is further used to: obtain a third data set of the power battery, where the third data set is used to indicate real-time data of the power battery; and calibrate the digital twin model based on the third data set and the prediction data.
[0101] In some embodiments, the prediction module 350 is further used to: determine whether the error value between the third battery data set and the predicted data is greater than an error threshold; and calibrate the digital twin model based on the error value.
[0102] In some embodiments, the third data set includes at least one of the following: real-time battery voltage data, real-time battery current data, and real-time battery temperature data.
[0103] In summary, the state prediction device of the power battery includes: a first creation module, which is used to establish a physical model based on the first battery assembly data of the power battery, and the physical model is used to indicate the relationship of the first battery assembly data. The first battery assembly data includes at least one of the following: data of the battery module, data of the battery thermal management accessories and data of the battery electrical accessories; a second creation module, which is used to establish a geometric simulation model based on the physical model and the first data set of the power battery. The geometric model is the geometric form of the physical model. The first data set includes at least two of the following: battery material data, battery structure data and mechanical state data; a third creation module, which is used to establish a rule model based on the second data set of the power battery. The rule model is used to limit the geometric simulation model. The second data set includes at least one of the following: historical battery data and real-time battery data; a fourth creation module, which is used to establish a digital twin model of the power battery based on the geometric simulation model, the rule model and the physical model; a prediction module, which is used to simulate the operation process of the power battery using the digital twin model to obtain prediction data of the power battery. The prediction data is used to reflect the real-time operation result data of the power battery or to predict the operation state of the power battery. The device disclosed herein realizes a model representation of the parameters of the power battery by establishing a physical model, a geometric model and a rule model, and then constructs a digital twin model that can predict the status of the power battery through the physical model, the geometric model and the rule model, and then uses the digital twin model to realize the prediction of the entire life cycle of the power battery, thereby improving the accuracy of the battery life estimation and the accuracy of the battery safety assessment.
[0104] In the embodiments provided above, the methods and devices provided in the embodiments of the present application are introduced. In order to implement the various functions of the methods provided in the embodiments of the present application, the electronic device may include a hardware structure and a software module, and implement the aforementioned functions in the form of a hardware structure, a software module, or a hardware structure plus a software module. One of the aforementioned functions may be executed in the form of a hardware structure, a software module, or a hardware structure plus a software module.
[0105] Figure 4 is a block diagram of an electronic device 400 for implementing the above-mentioned power battery state prediction method according to an exemplary embodiment.
[0106] For example, the electronic device 400 may be a mobile phone, a computer, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like.
[0107] Reference Figure 4, electronic device 400 may include one or more of the following components: a processing component 402 , a memory 404 , a power component 406 , a multimedia component 408 , an audio component 410 , an input / output (I / O) interface 412 , a sensor component 414 , and a communication component 416 .
[0108] The processing component 402 generally controls the overall operation of the electronic device 400, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 402 may include one or more processors 420 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 402 may include one or more modules to facilitate interaction between the processing component 402 and other components. For example, the processing component 402 may include a multimedia module to facilitate interaction between the multimedia component 408 and the processing component 402.
[0109] The memory 404 is configured to store various types of data to support operations on the electronic device 600. Examples of such data include instructions for any application or method operating on the electronic device 400, contact data, phone book data, messages, pictures, videos, etc. The memory 404 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.
[0110] The power supply assembly 406 provides power to the various components of the electronic device 400. The power supply assembly 406 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 400.
[0111] The multimedia component 408 includes a screen that provides an output interface between the electronic device 400 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensor can not only sense the boundaries of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 408 includes a front camera and / or a rear camera. When the electronic device 400 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.
[0112] The audio component 410 is configured to output and / or input audio signals. For example, the audio component 410 includes a microphone (MIC), which is configured to receive external audio signals when the electronic device 400 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 404 or transmitted via the communication component 416. In some embodiments, the audio component 410 also includes a speaker for outputting audio signals.
[0113] I / O interface 412 provides an interface between processing component 402 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.
[0114] The sensor assembly 414 includes one or more sensors for providing various aspects of status assessment for the electronic device 400. For example, the sensor assembly 414 can detect the open / closed state of the electronic device 400, the relative positioning of components, such as the display and keypad of the electronic device 400. The sensor assembly 414 can also detect changes in the position of the electronic device 400 or a component of the electronic device 400, the presence or absence of user contact with the electronic device 400, the orientation or acceleration / deceleration of the electronic device 400, and temperature changes of the electronic device 400. The sensor assembly 414 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 414 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 414 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
[0115] The communication component 416 is configured to facilitate wired or wireless communication between the electronic device 400 and other devices. The electronic device 400 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, 4G LTE, 5G NR (NewRadio) or a combination thereof. In an exemplary embodiment, the communication component 416 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 416 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
[0116] In an exemplary embodiment, the electronic device 400 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described methods.
[0117] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 404 including instructions, which can be executed by the processor 420 of the electronic device 400 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.
[0118] An embodiment of the present disclosure further provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the power battery state prediction method described in the above embodiment of the present disclosure.
[0119] An embodiment of the present disclosure further provides a computer program product, including a computer program. When a processor executes the method for predicting the state of a power battery described in the above embodiment of the present disclosure, the computer program executes the method.
[0120] It should be noted that the terms "first," "second," and the like in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this manner are interchangeable where appropriate so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure as detailed in the appended claims.
[0121] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with an embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the illustrative use of the above terms does not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
[0122] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present disclosure belong.
[0123] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processing module, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection having one or more wires (control method), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.
[0124] It should be understood that the various parts of the embodiments of the present disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.
[0125] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.
[0126] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disk, etc.
[0127] Although the embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and are not to be construed as limitations on the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.
Claims
1. A method for predicting the state of a power battery, characterized in that: The method comprises: Establishing a physical model based on a first battery assembly of a power battery and data of the first battery assembly, the physical model being used to indicate a structure of the first battery assembly and a data relationship of the first battery assembly, the first battery assembly including at least one of the following: a battery module, a battery thermal management accessory, and a battery electrical accessory; Establishing a geometric simulation model based on the physical model and a first data set of the power battery, wherein the geometric model is a geometric form of the physical model, and the first data set includes at least two items of the following: battery material data, battery structure data, and mechanical state data; Establishing a rule model based on a second data set of the power battery, wherein the rule model is used to constrain the geometric simulation model, wherein the second data set includes at least one of the following: historical battery data and real-time battery data; Establishing a digital twin model of the power battery based on the geometric simulation model, the rule model, and the physical model; The digital twin model is used to simulate the operation process of the power battery to obtain prediction data of the power battery, and the prediction data is used to predict the operating state of the power battery.
2. The method according to claim 1, characterized in that The historical battery data includes at least one of the following: historical battery voltage data, historical battery current data, and historical battery pressure data; The real-time battery data includes at least one of the following: real-time battery voltage data, real-time battery current data, and real-time battery pressure data; The battery thermal management accessories include at least one of the following: a battery pack liquid cooling system and a battery pack insulation system; The battery electrical accessories include at least one of the following: a battery low-voltage wiring harness, a battery high-voltage wiring harness, and a battery drive connector.
3. The method according to claim 1, characterized in that The establishing of the digital twin model of the power battery based on the geometric simulation model, the rule model and the physical model includes: Based on the geometric simulation model, the rule model and the physical model, a reduced-order model is established by using a model reduction algorithm; Based on the reduced-order model, a target equation is established, and the target equation is determined as the digital twin model of the power battery.
4. The method according to claim 1, wherein After predicting the current state of the power battery using the digital twin model, the method further includes: Acquire a third data set of the power battery, where the third data set is used to indicate real-time data of the power battery; The digital twin model is calibrated based on the third data set and the predicted data.
5. The method according to claim 4, characterized in that The third data set includes at least one of the following: real-time battery voltage data, real-time battery current data, and real-time battery temperature data.
6. The method according to claim 4, characterized in that The calibrating the digital twin model based on the third battery data set and the predicted data includes: Determining that an error value between the third battery data set and the predicted data is greater than an error threshold; Based on the error value, the digital twin model is calibrated.
7. A power battery state prediction device, characterized in that: The device comprises: a first creation module, configured to establish a physical model based on a first battery assembly of a power battery and data of the first battery assembly, wherein the physical model is configured to indicate a relationship between a structure of the first battery assembly and the data of the first battery assembly, wherein the first battery assembly includes at least one of the following: a battery module, a battery thermal management accessory, and a battery electrical accessory; a second creation module, configured to establish a geometric simulation model based on the physical model and a first data set of the power battery, wherein the geometric model is a geometric form of the physical model, and the first data set includes at least two items of the following: battery material data, battery structure data, and mechanical state data; a third creation module, configured to establish a rule model based on a second data set of the power battery, wherein the rule model is configured to constrain the geometric simulation model, wherein the second data set includes at least one of the following: historical battery data and real-time battery data; a fourth creation module, configured to establish a digital twin model of the power battery based on the geometric simulation model, the rule model, and the physical model; A prediction module is used to use the digital twin model to simulate the operation process of the power battery to obtain prediction data of the power battery. The prediction data is used to reflect the real-time operation result data of the power battery or to predict the operating status of the power battery.
8. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1 to 6.
9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 6.
10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.