Travel control methods, devices, and electronic equipment for a battery sampling line disconnection fault-tolerant control system

Abstract

This invention provides a driving control method, device, and electronic device for a battery sampling line disconnection fault-tolerant control system. In this method, after the voltage or temperature sampling line is disconnected, a vehicle-cloud dual-source dynamic weight model is used to simulate the battery discharge curve (i.e., the fused discharge curve) based on adaptive weighting of local data timeliness and cloud data diversity, supporting normal vehicle operation. The above process fully considers the actual state of the battery (i.e., considers historical charge and discharge cycle data). Compared with the traditional fixed power reduction mode, the risk of thermal runaway is reduced, the safety performance is improved, the vehicle can continue to drive to the repair station without the need for additional spare hardware, and the towing costs caused by sampling line failure are reduced. The probability of battery over-discharge damage is also reduced, saving costs.

Description

Technical Field

[0001] This invention relates to the technical field of new energy vehicles, and in particular to a driving control method, device, and electronic equipment for a battery sampling line disconnection fault-tolerant control system. Background Technology

[0002] In new energy electric vehicles, a break in the battery sampling lines (such as voltage and temperature acquisition harnesses) can prevent the Battery Management System (BMS) from acquiring real-time data. Traditional solutions to this problem each have their limitations: Immediate Power Cut-off and Vehicle Stop: Once a sampling line fault is detected, the system will take emergency power-off measures and require the vehicle to stop. However, at high speeds, sudden stalling may pose a safety risk and a potential threat to the driver and passengers.

[0003] Fixed power reduction mode: Another common method is to automatically switch to a preset low-power operating state. While this method can protect the battery to some extent, it may lead to over-discharge or thermal runaway because it does not fully consider the actual state of the battery.

[0004] Redundant hardware backup: This method improves system reliability by adding extra backup hardware. While it can provide additional security in certain situations, it significantly increases costs and, due to limitations in physical space and technical complexity, struggles to cover all possible harness failure scenarios.

[0005] In summary, traditional handling strategies for broken battery sampling lines are insufficient to meet both safety and cost requirements. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a running control method, device and electronic device for a battery sampling line disconnection fault-tolerant control system, so as to alleviate the technical problem that the traditional battery sampling line disconnection handling strategy is difficult to meet the requirements of safety and economy.

[0007] In a first aspect, embodiments of the present invention provide a travel control method for a battery sampling line disconnection fault-tolerant control system, comprising: When a voltage or temperature acquisition line is detected to be disconnected, a local simulated discharge curve is generated based on the historical charge-discharge cycle data and current operating condition data stored in the battery management system. The current operating condition data includes: SOC, ambient temperature, total voltage, and total current. The cloud-based battery mechanism-data fusion model obtains a cloud-predicted discharge curve based on the current operating condition data, the historical charge-discharge cycle data, the historical charge-discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters. Dynamic weights are calculated based on the variance of the historical charge-discharge cycle data and the variance of the cloud-predicted discharge curve data. The local simulated discharge curve and the cloud-predicted discharge curve are weighted and fused according to the dynamic weights to obtain a fused discharge curve. The motor power is then controlled according to the fused discharge curve to maintain vehicle movement.

[0008] Furthermore, a local simulated discharge curve is generated based on historical charge-discharge cycle data and current operating condition data stored in the battery management system, including: Extract the target historical charge-discharge cycle data corresponding to the current operating condition data from the historical charge-discharge cycle data; The local simulated discharge curve is generated based on the target's historical charge-discharge cycle data.

[0009] Furthermore, the battery mechanism-data fusion model includes: an equivalent circuit model and a long short-term memory network. The cloud-based battery mechanism-data fusion model obtains a cloud-predicted discharge curve based on the current operating condition data, the historical charge-discharge cycle data, historical charge-discharge cycle data from other vehicles, environmental parameters, and battery calibration parameters. This includes: The equivalent circuit model processes the current operating condition data based on the battery calibration parameters to obtain the voltage response curve; The long short-term memory network analyzes the voltage response curve, the historical charge-discharge cycle data, the historical charge-discharge cycle data of other vehicles, and environmental parameters to obtain the cloud-based predicted discharge curve.

[0010] Furthermore, dynamic weights are calculated based on the variance of the historical charge-discharge cycle data and the variance of the cloud-predicted discharge curve data, including: Calculation formula based on dynamic weights Calculate the dynamic weights, where, This represents the dynamic weight. This represents the variance of the data for the cloud-predicted discharge curve. This represents the variance of the historical charge-discharge cycle data.

[0011] Furthermore, the local simulated discharge curve and the cloud-predicted discharge curve are weighted and fused according to the dynamic weights, including: According to the weighted fusion formula The local simulated discharge curve and the cloud-predicted discharge curve are weighted and fused, wherein, This represents the fusion discharge curve. This represents the cloud-predicted discharge curve. This represents the dynamic weight. This represents the local simulated discharge curve.

[0012] Furthermore, the historical charge-discharge cycle data is compressed and stored using a piecewise linear encoding algorithm.

[0013] Furthermore, the method also includes: After power failure, the battery SOC operating range is limited to [20%, 80%].

[0014] Secondly, embodiments of the present invention also provide a travel control device for a battery sampling line disconnection fault-tolerant control system, comprising: The generation unit is used to generate a local simulated discharge curve based on the historical charge-discharge cycle data and current operating condition data stored in the battery management system when a voltage or temperature acquisition line is detected to be disconnected. The current operating condition data includes: SOC, ambient temperature, total voltage and total current. The acquisition unit is used to acquire the cloud-based predicted discharge curve obtained by the battery mechanism-data fusion model based on the current operating condition data, the historical charge-discharge cycle data, the historical charge-discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters. The calculation unit is used to calculate dynamic weights based on the variance of the historical charge-discharge cycle data and the variance of the cloud-predicted discharge curve data. The fusion unit is used to perform weighted fusion of the local simulated discharge curve and the cloud-predicted discharge curve according to the dynamic weight to obtain a fused discharge curve, and control the motor power according to the fused discharge curve to maintain vehicle movement.

[0015] Thirdly, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described in any of the first aspects above.

[0016] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing machine-executable instructions, which, when invoked and executed by a processor, cause the processor to perform the method described in any of the first aspects above.

[0017] In this embodiment of the invention, a driving control method for a battery sampling line disconnection fault-tolerant control system is provided, comprising: when a voltage or temperature sampling line disconnection is detected, generating a local simulated discharge curve based on historical charge-discharge cycle data and current operating condition data stored in the battery management system, wherein the current operating condition data includes: SOC, ambient temperature, total voltage, and total current; acquiring a cloud-based predicted discharge curve obtained by a battery mechanism-data fusion model based on the current operating condition data, historical charge-discharge cycle data, historical charge-discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters; calculating dynamic weights based on the variance of the historical charge-discharge cycle data and the variance of the cloud-based predicted discharge curve; weighting and fusing the local simulated discharge curve and the cloud-based predicted discharge curve according to the dynamic weights to obtain a fused discharge curve, and controlling the motor power according to the fused discharge curve to maintain vehicle driving. As described above, in the driving control method of the battery sampling line disconnection fault-tolerant control system of the present invention, after the voltage or temperature acquisition line is disconnected, the vehicle-cloud dual-source dynamic weight model is used to simulate the battery discharge curve (i.e., the fused discharge curve) based on the timeliness of local data and the diversity of cloud data. This supports normal vehicle driving. The above process fully considers the actual state of the battery (i.e., considers historical charge and discharge cycle data). Compared with the traditional fixed power reduction mode, the risk of thermal runaway is reduced, the safety performance is improved, and the vehicle can continue to drive to the repair station without the need for additional spare hardware. At the same time, it reduces the towing costs caused by sampling line failure, reduces the probability of battery over-discharge damage, saves costs, and alleviates the technical problem that the traditional battery sampling line disconnection handling strategy is difficult to meet the requirements of safety and economy. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 A flowchart of a travel control method for a battery sampling line disconnection fault-tolerant control system provided in an embodiment of the present invention; Figure 2 A schematic diagram of a travel control device for a battery sampling line disconnection fault-tolerant control system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0020] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Traditional handling strategies for broken battery sampling lines are insufficient to meet safety and cost requirements.

[0022] Based on this, in the driving control method of the battery sampling line disconnection fault-tolerant control system of the present invention, after the voltage or temperature acquisition line is disconnected, the vehicle-cloud dual-source dynamic weight model is used to simulate the battery discharge curve (i.e., the fused discharge curve) based on the timeliness of local data and the diversity of cloud data, so as to support the normal driving of the vehicle. The above process fully considers the actual state of the battery (i.e., considers historical charge and discharge cycle data). Compared with the traditional fixed power reduction mode, the risk of thermal runaway is reduced, the safety performance is improved, the vehicle can continue to drive to the repair station without the need to add additional spare hardware, and at the same time, the towing cost caused by sampling line failure is reduced, the probability of battery over-discharge damage is reduced, and the cost is saved.

[0023] To facilitate understanding of this embodiment, a detailed description of the travel control method of a battery sampling line disconnection fault-tolerant control system disclosed in this embodiment of the invention will be provided first.

[0024] Example 1: According to an embodiment of the present invention, an embodiment of a travel control method for a battery sampling line disconnection fault-tolerant control system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0025] Figure 1 This is a flowchart of a travel control method for a battery sampling line disconnection fault-tolerant control system according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes the following steps: Step S102: When a voltage or temperature acquisition line is detected to be disconnected, a local simulated discharge curve is generated based on the historical charge-discharge cycle data and current operating condition data stored in the battery management system. The current operating condition data includes: SOC, ambient temperature, total voltage, and total current. Specifically, conventional electric vehicle BMS (Battery Management System) has a disconnection detection module. When the disconnection detection module detects a break in the voltage or temperature acquisition line, it will report a fault. At this time, the BMS will generate a local simulated discharge curve based on the historical charge-discharge cycle data stored in the battery management system and the current operating condition data.

[0026] The aforementioned historical charge-discharge cycle data includes: individual cell voltage, temperature, total current, total voltage, charge-discharge curves, ambient temperature, and SOC-SOH mapping table. Specifically, the BMS locally stores complete charge-discharge cycle data for the most recent 30 days (based on normal operating conditions where no wire breakage occurred).

[0027] The process of generating local simulated discharge curves will be described in detail below, and will not be repeated here.

[0028] Step S104: Obtain the cloud-based battery mechanism-data fusion model's predicted discharge curve based on current operating condition data, historical charge-discharge cycle data, historical charge-discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters. Specifically, the current operating condition data and historical charge / discharge cycle data mentioned above are all vehicle-specific data. The process of obtaining the cloud-based predicted discharge curve will be described in detail below.

[0029] Step S106: Calculate dynamic weights based on the variance of historical charge-discharge cycle data and the variance of cloud-predicted discharge curve data; Step S108: The local simulated discharge curve and the cloud-predicted discharge curve are weighted and fused according to the dynamic weight to obtain the fused discharge curve, and the motor power is controlled according to the fused discharge curve to maintain vehicle movement.

[0030] In this embodiment of the invention, a driving control method for a battery sampling line disconnection fault-tolerant control system is provided, comprising: when a voltage or temperature sampling line disconnection is detected, generating a local simulated discharge curve based on historical charge-discharge cycle data and current operating condition data stored in the battery management system, wherein the current operating condition data includes: SOC, ambient temperature, total voltage, and total current; acquiring a cloud-based predicted discharge curve obtained by a battery mechanism-data fusion model based on the current operating condition data, historical charge-discharge cycle data, historical charge-discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters; calculating dynamic weights based on the variance of the historical charge-discharge cycle data and the variance of the cloud-based predicted discharge curve; weighting and fusing the local simulated discharge curve and the cloud-based predicted discharge curve according to the dynamic weights to obtain a fused discharge curve, and controlling the motor power according to the fused discharge curve to maintain vehicle driving. As described above, in the driving control method of the battery sampling line disconnection fault-tolerant control system of the present invention, after the voltage or temperature acquisition line is disconnected, the vehicle-cloud dual-source dynamic weight model is used to simulate the battery discharge curve (i.e., the fused discharge curve) based on the timeliness of local data and the diversity of cloud data. This supports normal vehicle driving. The above process fully considers the actual state of the battery (i.e., considers historical charge and discharge cycle data). Compared with the traditional fixed power reduction mode, the risk of thermal runaway is reduced, the safety performance is improved, and the vehicle can continue to drive to the repair station without the need for additional spare hardware. At the same time, it reduces the towing costs caused by sampling line failure, reduces the probability of battery over-discharge damage, saves costs, and alleviates the technical problem that the traditional battery sampling line disconnection handling strategy is difficult to meet the requirements of safety and economy.

[0031] The above provides a brief overview of the travel control method of the battery sampling line disconnection fault-tolerant control system of the present invention. The specific details involved are described in detail below.

[0032] In an optional embodiment of the present invention, a local simulated discharge curve is generated based on historical charge-discharge cycle data and current operating condition data stored in the battery management system, specifically including the following steps: (1) Extract the target historical charge-discharge cycle data corresponding to the current operating condition data from the historical charge-discharge cycle data; (2) Generate a local simulated discharge curve based on the target’s historical charge and discharge cycle data.

[0033] Specifically, after the battery sampling line (i.e., the voltage or temperature acquisition line) is disconnected, the system outputs a simulated discharge curve through the following steps. .

[0034] Construction of the local digital twin model: Under normal vehicle operating conditions (without disconnection), the BMS collects and stores the following data in real time: Individual voltage ,temperature Total current Total voltage Charge-discharge curves, temperature distribution, and SOC-SOH mapping table (i.e., historical charge-discharge cycle data) are compressed and stored using piecewise linear encoding (PLE), preserving only key inflection points. , , ).

[0035] Compression algorithm formula: .

[0036] in: It is a step function ( (When activated) , The slope and intercept of the linear segment are the values ​​passing through adjacent inflection points. and The results were obtained through fitting.

[0037] When the wire is broken, the simulated discharge curve is generated: Upon detection of a disconnection, the local digital twin model outputs a local simulated discharge curve according to the following process: Step 1: Load local historical inflection point data From the inflection point of compressed storage Extract the historical segment that is closest to the current operating condition data (such as SOC and ambient temperature) (i.e. the closest historical inflection point, and then obtain the corresponding target historical charge and discharge cycle data).

[0038] Step 2: Dynamically interpolate to generate local simulated discharge curves

[0039] Based on the closest inflection point data, the voltage is calculated in real time using linear interpolation: .

[0040] In an optional embodiment of the present invention, the battery mechanism-data fusion model includes: an equivalent circuit model and a long short-term memory network. The cloud-based battery mechanism-data fusion model is used to obtain a cloud-predicted discharge curve based on current operating condition data, historical charge-discharge cycle data, historical charge-discharge cycle data from other vehicles, environmental parameters, and battery calibration parameters. Specifically, this includes the following steps: (1) The equivalent circuit model processes the current operating condition data based on the battery calibration parameters to obtain the voltage response curve; (2) The long short-term memory network analyzes the voltage response curve, historical charge-discharge cycle data, historical charge-discharge cycle data of other vehicles, and environmental parameters to obtain the cloud-based predicted discharge curve.

[0041] Specifically, real-time battery information is transmitted to a big data platform using vehicle RTM remote messages. A dynamic discharge curve library for the vehicle is then constructed in the cloud, leading to the creation of a battery mechanism-data fusion model. .

[0042] Achieving V-type storage for massive numbers of vehicles I The T-dimensional relationship map enables multi-scale time series prediction, which combines a Long Short-Term Memory (LSTM) network with an Equivalent Circuit Model (ECM) to simulate the charging and discharging process of the battery, describe the dynamic behavior of the battery such as voltage, current and temperature, and thus achieve the prediction of real-time data such as battery voltage, capacity (SOC, State of Charge) and state of health (SOH).

[0043] The inputs to the equivalent circuit model include: battery calibration parameters (internal resistance) Polarized capacitors Rated capacitor These parameters form the basis of ECM's physical modeling, along with current operating condition data. The output is a voltage response curve. The Long Short-Term Memory (LSTM) network processes the voltage response curve, historical charge-discharge cycle data, historical charge-discharge cycle data from other vehicles, and environmental parameters (ambient temperature). The vehicle's operating status (acceleration / deceleration / climbing) is analyzed to obtain the cloud-based predicted discharge curve.

[0044] In an optional embodiment of the present invention, dynamic weights are calculated based on the variance of historical charge-discharge cycle data and the variance of cloud-predicted discharge curve data, specifically including the following steps: Calculation formula based on dynamic weights Calculate the dynamic weights, where, Indicates dynamic weights. This represents the variance of the data used to predict the discharge curve in the cloud. This represents the variance of historical charge-discharge cycle data.

[0045] Specifically, the dispersion of voltage (V) is extracted from locally stored historical charge-discharge cycle data. The calculation method is to calculate the variance of V(t) for the most recent complete charge-discharge cycle in the historical charge-discharge cycle data.

[0046] Extract the dispersion (i.e., variance) of voltage data from the similar operating condition curve Dcloud (i.e., cloud-predicted discharge curve) obtained from the cloud, and then calculate it according to the above formula.

[0047] Low variance data with small fluctuations indicate that the curve is more stable and reliable (e.g., the voltage plateau during the constant current discharge stage).

[0048] High variance data fluctuates greatly and may have reduced reliability due to noise, complex operating conditions, or battery aging.

[0049] If the variance of cloud data Smaller (more stable data) weight If the variance approaches 1, prioritize trusting cloud data; if the local data variance is low... If the weight is relatively small (e.g., historical data closely matches current operating conditions), then the weight will be lower. Approaching 0, relying on local data.

[0050] In an optional embodiment of the present invention, the local simulated discharge curve and the cloud-predicted discharge curve are weighted and fused according to dynamic weights, specifically including the following steps: According to the weighted fusion formula The local simulated discharge curve and the cloud-predicted discharge curve are weighted and fused, whereby... This represents the fusion discharge curve. This represents the cloud-predicted discharge curve. Indicates dynamic weights. This represents the local simulated discharge curve.

[0051] In an optional embodiment of the present invention, historical charge-discharge cycle data is compressed and stored using a piecewise linear coding algorithm.

[0052] Specifically, by using segmented linear encoding to reduce storage requirements, it is possible to save only the inflection points.

[0053] In an optional embodiment of the present invention, the method further includes the following steps: After power failure, the battery SOC operating range is limited to [20%, 80%].

[0054] Specifically, in disconnection mode, the SOC working range is compressed to [20%, 80%], meaning that the battery needs to be at its plateau period before this strategy can be implemented.

[0055] The following is a specific example to illustrate this: When the vehicle's initial SOC was 70%, a break in the battery voltage and temperature acquisition lines was detected.

[0056] BMS's countermeasures: First, generate a local simulated discharge curve and match it with similar vehicle data (same batch of batteries + same ambient temperature) from the cloud to obtain the cloud-predicted discharge curve, and calculate the fusion weight. =0.65 (cloud data has smaller variance) Output =0.65 +0.35 This allows the VCU (Vehicle Control Unit) to support [the following]: Control the motor power to keep the vehicle running normally until repairs are needed.

[0057] The method of this invention maintains normal vehicle operation in the event of a voltage / temperature sampling line disconnection fault through a vehicle-cloud collaborative data fusion model. This reduces the risk of thermal runaway (compared to the traditional fixed power reduction mode); the vehicle can continue driving to a repair shop after a disconnection; reduces towing costs due to sampling line failure; and lowers the probability of battery over-discharge damage. This solution integrates lightweight vehicle-side storage, high-precision cloud-based modeling, and a hierarchical control strategy (limiting the battery SOC operating range to [20%, 80%]), solving the safety challenges under disconnection conditions.

[0058] Example 2: This invention also provides a travel control device for a battery sampling line disconnection fault-tolerant control system. This travel control device is mainly used to execute the travel control method of the battery sampling line disconnection fault-tolerant control system provided in Embodiment 1 of this invention. The following is a detailed description of the travel control device of the battery sampling line disconnection fault-tolerant control system provided in this invention.

[0059] Figure 2 This is a schematic diagram of a travel control device for a battery sampling line disconnection fault-tolerant control system according to an embodiment of the present invention, as shown below. Figure 2 As shown, the device mainly includes: a generation unit 10, an acquisition unit 20, a calculation unit 30, and a fusion unit 40, wherein: The generation unit is used to generate a local simulated discharge curve based on the historical charge-discharge cycle data and current operating condition data stored in the battery management system when a voltage or temperature acquisition line is detected to be disconnected. The current operating condition data includes: SOC, ambient temperature, total voltage and total current. The acquisition unit is used to acquire the cloud-predicted discharge curves predicted by the battery mechanism-data fusion model based on current operating condition data, historical charge-discharge cycle data, historical charge-discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters. The calculation unit is used to calculate dynamic weights based on the variance of historical charge-discharge cycle data and the variance of cloud-predicted discharge curve data. The fusion unit is used to perform weighted fusion of the local simulated discharge curve and the cloud-predicted discharge curve according to dynamic weights to obtain a fused discharge curve, and to control the motor power according to the fused discharge curve to maintain vehicle movement.

[0060] In this embodiment of the invention, a driving control device for a battery sampling line disconnection fault-tolerant control system is provided, comprising: when a voltage or temperature sampling line disconnection is detected, generating a local simulated discharge curve based on historical charge-discharge cycle data and current operating condition data stored in the battery management system, wherein the current operating condition data includes: SOC, ambient temperature, total voltage, and total current; acquiring a cloud-based predicted discharge curve obtained by a battery mechanism-data fusion model based on the current operating condition data, historical charge-discharge cycle data, historical charge-discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters; calculating dynamic weights based on the variance of the historical charge-discharge cycle data and the variance of the cloud-based predicted discharge curve; weighting and fusing the local simulated discharge curve and the cloud-based predicted discharge curve according to the dynamic weights to obtain a fused discharge curve, and controlling the motor power according to the fused discharge curve to maintain vehicle driving. As described above, in the driving control device of the battery sampling line disconnection fault-tolerant control system of the present invention, after the voltage or temperature acquisition line is disconnected, the vehicle-cloud dual-source dynamic weight model is used to simulate the battery discharge curve (i.e., the fused discharge curve) based on the timeliness of local data and the diversity of cloud data. This supports normal vehicle driving. The above process fully considers the actual state of the battery (i.e., considers historical charge and discharge cycle data). Compared with the traditional fixed power reduction mode, the risk of thermal runaway is reduced, the safety performance is improved, and the vehicle can continue to drive to the repair station without the need for additional spare hardware. At the same time, it reduces the towing costs caused by sampling line failure, reduces the probability of battery over-discharge damage, saves costs, and alleviates the technical problem that the traditional battery sampling line disconnection handling strategy is difficult to meet the requirements of safety and economy.

[0061] Optionally, the generation unit is also used to: extract target historical charge-discharge cycle data corresponding to the current operating condition data from historical charge-discharge cycle data; and generate a local simulated discharge curve based on the target historical charge-discharge cycle data.

[0062] Optionally, the battery mechanism-data fusion model includes an equivalent circuit model and a long short-term memory network. The acquisition unit is also used for: the equivalent circuit model to process the current operating condition data based on the battery calibration parameters to obtain the voltage response curve; and the long short-term memory network to analyze the voltage response curve, historical charge-discharge cycle data, historical charge-discharge cycle data of other vehicles, and environmental parameters to obtain the cloud-predicted discharge curve.

[0063] Optionally, the calculation unit is also used to: calculate the formula based on the dynamic weights. Calculate the dynamic weights, where, Indicates dynamic weights. This represents the variance of the data used to predict the discharge curve in the cloud. This represents the variance of historical charge-discharge cycle data.

[0064] Optionally, the fusion unit is also used to: [according to the weighted fusion formula] The local simulated discharge curve and the cloud-predicted discharge curve are weighted and fused, whereby... This represents the fusion discharge curve. This represents the cloud-predicted discharge curve. Indicates dynamic weights. This represents the local simulated discharge curve.

[0065] Optionally, historical charge-discharge cycle data is compressed and stored using a piecewise linear coding algorithm.

[0066] Optionally, the device is also used to: limit the battery SOC operating range to [20%, 80%] after power failure.

[0067] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0068] like Figure 3 As shown in the embodiment of this application, an electronic device 600 includes a processor 601, a memory 602, and a bus. The memory 602 stores machine-readable instructions that can be executed by the processor 601. When the electronic device is running, the processor 601 communicates with the memory 602 through the bus. The processor 601 executes the machine-readable instructions to perform the steps of the travel control method of the battery sampling line disconnection fault-tolerant control system described above.

[0069] Specifically, the memory 602 and processor 601 mentioned above can be general-purpose memory and processor, without any specific limitations. When the processor 601 runs the computer program stored in the memory 602, it can execute the travel control method of the battery sampling line disconnection fault-tolerant control system mentioned above.

[0070] The processor 601 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 601 or by instructions in software form. The processor 601 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 602, and processor 601 reads the information from memory 602 and, in conjunction with its hardware, completes the steps of the above method.

[0071] Corresponding to the above-described walking control method of the battery sampling line disconnection fault-tolerant control system, this application embodiment also provides a computer-readable storage medium storing machine-executable instructions. When the machine-executable instructions are called and run by a processor, the machine-executable instructions cause the processor to perform the steps of the above-described walking control method of the battery sampling line disconnection fault-tolerant control system.

[0072] The travel control device of the battery sampling line disconnection fault-tolerant control system provided in this application embodiment can be specific hardware on the device or software or firmware installed on the device. The device provided in this application embodiment has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0073] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0074] For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0075] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0077] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the travel control method of the battery sampling line disconnection fault-tolerant control system described in various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0078] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0079] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A travel control method for a battery sampling line disconnection fault-tolerant control system, characterized in that, include: When a voltage or temperature acquisition line is detected to be disconnected, a local simulated discharge curve is generated based on the historical charge-discharge cycle data and current operating condition data stored in the battery management system. The current operating condition data includes: SOC, ambient temperature, total voltage, and total current. The cloud-based battery mechanism-data fusion model obtains the cloud-predicted discharge curve based on the current operating condition data, the historical charge-discharge cycle data, the historical charge-discharge cycle data of other vehicles, the vehicle operating status, and the battery calibration parameters. Dynamic weights are calculated based on the variance of voltage data in the historical charge-discharge cycle data and the variance of voltage data in the cloud-predicted discharge curve. The local simulated discharge curve and the cloud-predicted discharge curve are weighted and fused according to the dynamic weight to obtain a fused discharge curve, and the motor power is controlled according to the fused discharge curve to maintain vehicle movement. The battery mechanism-data fusion model includes an equivalent circuit model and a long short-term memory network. It acquires a cloud-based predicted discharge curve based on the current operating condition data, historical charge-discharge cycle data, historical charge-discharge cycle data from other vehicles, vehicle operating status, and battery calibration parameters. This prediction includes: The equivalent circuit model processes the current operating condition data based on the battery calibration parameters to obtain the voltage response curve; The Long Short-Term Memory (LSTM) network analyzes the voltage response curve, the historical charge-discharge cycle data, the historical charge-discharge cycle data of other vehicles, the ambient temperature, and the vehicle operating status to obtain the cloud-predicted discharge curve.

2. The method according to claim 1, characterized in that, A local simulated discharge curve is generated based on historical charge-discharge cycle data and current operating condition data stored in the battery management system, including: Extract the target historical charge-discharge cycle data corresponding to the current operating condition data from the historical charge-discharge cycle data; The local simulated discharge curve is generated based on the target's historical charge-discharge cycle data.

3. The method according to claim 1, characterized in that, Dynamic weights are calculated based on the variance of the historical charge-discharge cycle data and the variance of the cloud-predicted discharge curve data, including: Calculation formula based on dynamic weights Calculate the dynamic weights, where, This represents the dynamic weight. This represents the variance of the data for the cloud-predicted discharge curve. This represents the variance of the historical charge-discharge cycle data.

4. The method according to claim 1, characterized in that, The local simulated discharge curve and the cloud-predicted discharge curve are weighted and fused according to the dynamic weights, including: According to the weighted fusion formula The local simulated discharge curve and the cloud-predicted discharge curve are weighted and fused, wherein, This represents the fusion discharge curve. This represents the cloud-predicted discharge curve. This represents the dynamic weight. This represents the local simulated discharge curve.

5. The method according to claim 1, characterized in that, The historical charge-discharge cycle data is compressed and stored using a piecewise linear encoding algorithm.

6. The method according to claim 1, characterized in that, The method further includes: After the circuit is disconnected, the battery SOC operating range is limited to [20%, 80%].

7. A travel control device for a battery sampling line disconnection fault-tolerant control system, characterized in that, include: The generation unit is used to generate a local simulated discharge curve based on the historical charge-discharge cycle data and current operating condition data stored in the battery management system when a voltage or temperature acquisition line is detected to be disconnected. The current operating condition data includes: SOC, ambient temperature, total voltage and total current. The acquisition unit is used to acquire the cloud-predicted discharge curve obtained by the battery mechanism-data fusion model based on the current operating condition data, the historical charge-discharge cycle data, the historical charge-discharge cycle data of other vehicles, the vehicle operating status and battery calibration parameters. The calculation unit is used to calculate dynamic weights based on the variance of voltage data in the historical charge-discharge cycle data and the variance of voltage data in the cloud-predicted discharge curve; The fusion unit is used to perform weighted fusion of the local simulated discharge curve and the cloud predicted discharge curve according to the dynamic weight to obtain a fused discharge curve, and to control the motor power according to the fused discharge curve to maintain vehicle movement. The battery mechanism-data fusion model includes an equivalent circuit model and a long short-term memory network. The acquisition unit is further configured to: process the current operating condition data based on the battery calibration parameters using the equivalent circuit model to obtain a voltage response curve; and analyze the voltage response curve, the historical charge-discharge cycle data, the historical charge-discharge cycle data of other vehicles, the ambient temperature, and the vehicle operating status using the long short-term memory network to obtain the cloud-predicted discharge curve.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores machine-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 6.

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