Walking control method and device of battery sampling line breakage fault-tolerant control system and electronic equipment

Abstract

The invention provides a walking control method and device of a battery sampling line breakage fault-tolerant control system and electronic equipment. In the method, after a voltage or temperature acquisition line is broken, a vehicle cloud double-source dynamic weight model is utilized to calculate the fault-tolerant fault-tolerant control system; according to the method, battery discharge curve (namely fused discharge curve) simulation is achieved based on local data timeliness and cloud data diversity self-adaptive weighting, normal running of the vehicle is supported, the actual state of the battery (namely historical charge-discharge cycle data is considered) is fully considered in the process, and compared with a traditional fixed power reduction mode, the thermal runaway risk is reduced, and the safety of the vehicle is improved. The safety performance is improved, the vehicle can be maintained to run to the maintenance station, additional standby hardware does not need to be added, meanwhile, the trailer cost caused by sampling line faults is reduced, the battery over-discharge damage probability is reduced, and the cost is saved.

Description

Technical Field

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

[0002] In new energy electric vehicles, disconnection of battery sampling lines (such as voltage and temperature acquisition wiring harnesses) can prevent the battery management system (BMS) from obtaining real-time data. In this situation, traditional processing strategies each have their limitations: Immediate power-off and parking: 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 pose a potential threat to the driver and passengers.

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

[0004] Redundant hardware backup: Increase system reliability by adding additional backup hardware. Although this approach can provide additional safety in some cases, it significantly increases costs and is difficult to cover all possible wiring harness failure scenarios due to physical space limitations and technical complexity.

[0005] In summary, the traditional processing strategy after the battery sampling line is broken is difficult to meet the requirements of safety and economy. 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 equipment for a battery sampling line break fault-tolerant control system to alleviate the technical problem that traditional processing strategies after battery sampling line break are difficult to meet safety and economic requirements.

[0007] In a first aspect, an embodiment of the present invention provides a running 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 and 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; Obtaining a cloud-based predicted discharge curve obtained by a battery mechanism-data fusion model in the cloud based on the current operating condition data, the historical charge and discharge cycle data, historical charge and discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters; Calculating a dynamic weight based on the variance of the historical charge and discharge cycle data and the variance of the cloud-based predicted discharge curve data; The local simulated discharge curve and the cloud-predicted discharge curve are weightedly 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.

[0008] Furthermore, a local simulated discharge curve is generated based on the historical charge and discharge cycle data and current operating condition data stored in the battery management system, including: Extracting 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 according to the target historical charge and discharge cycle data.

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

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

[0011] Furthermore, weighted fusion of the local simulated discharge curve and the cloud-based predicted discharge curve is performed according to the dynamic weight, including: According to the weighted fusion formula The local simulated discharge curve and the cloud-predicted discharge curve are weightedly fused, wherein: represents the fusion discharge curve, represents the cloud-side predicted discharge curve, represents the dynamic weight, represents the local simulated discharge curve.

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

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

[0014] In a second aspect, an embodiment of the present invention further provides a travel control device for a battery sampling line disconnection fault-tolerant control system, comprising: a generating unit, configured to generate a local simulated discharge curve based on historical charge and 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, wherein the current operating condition data includes: SOC, ambient temperature, total voltage, and total current; an acquisition unit, configured to acquire a cloud-based predicted discharge curve obtained by a cloud-based battery mechanism-data fusion model based on the current operating condition data, the historical charge and discharge cycle data, historical charge and discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters; a calculation unit, configured to calculate a dynamic weight based on a variance of the historical charge and discharge cycle data and a variance of the cloud-based predicted discharge curve data; A fusion unit is used to perform weighted fusion on 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] In a third aspect, an embodiment of the present invention further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0016] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to execute any method described in the first aspect above.

[0017] In an embodiment of the present invention, a running control method for a battery sampling line disconnection fault-tolerant control system is provided, comprising: when a voltage or temperature acquisition line disconnection is detected, generating a local simulated discharge curve based on historical charge and discharge cycle data and current operating condition data stored in a battery management system, wherein the current operating condition data includes: SOC, ambient temperature, total voltage, and total current; obtaining a cloud-based predicted discharge curve predicted by a battery mechanism-data fusion model in the cloud based on current operating condition data, historical charge and discharge cycle data, historical charge and discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters; calculating a dynamic weight based on the variance of the historical charge and discharge cycle data and the variance of the cloud-based predicted discharge curve data; performing weighted fusion of the local simulated discharge curve and the cloud-based predicted discharge curve based on the dynamic weight to obtain a fused discharge curve, and controlling the motor power based on the fused discharge curve to maintain vehicle running. As can be seen from the foregoing description, in the driving control method of the battery sampling line break fault-tolerant control system of the present invention, after a voltage or temperature acquisition line is broken, a vehicle-cloud dual-source dynamic weight model is utilized. That is, adaptive weighting is performed based on the timeliness of local data and the diversity of cloud data to simulate the battery discharge curve (i.e., a fused discharge curve), thereby supporting normal vehicle operation. The foregoing process fully considers the actual state of the battery (i.e., historical charge and discharge cycle data). Compared with the traditional fixed power reduction mode, the risk of thermal runaway is reduced, safety performance is improved, and the vehicle can continue driving to the maintenance station without the need for additional backup hardware. At the same time, towing costs caused by sampling line failures are reduced, the probability of battery over-discharge damage is reduced, and costs are saved. This alleviates the technical problem that traditional processing strategies for battery sampling line breakages are difficult to meet safety and economic requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 A flow chart of a travel control method for a battery sampling line disconnection fault-tolerant control system provided by an embodiment of the present invention; Figure 2 A schematic diagram of a travel control device of a battery sampling line disconnection fault-tolerant control system provided by an embodiment of the present invention; Figure 3 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] The traditional processing strategy after the battery sampling line is broken is difficult to meet the requirements of safety and economy.

[0022] Based on this, in the running control method of the battery sampling line break fault-tolerant control system of the present invention, after the voltage or temperature acquisition line is broken, the vehicle-cloud dual-source dynamic weight model is used, that is, adaptive weighting is performed based on the timeliness of local data and the diversity of cloud data to realize the simulation of the battery discharge curve (that is, the fused discharge curve), supporting the normal running of the vehicle. The above process fully considers the actual state of the battery (that is, considering the historical charge and discharge cycle data). Compared with the traditional fixed power reduction mode, the risk of thermal runaway is reduced and the safety performance is improved. The vehicle can continue to travel to the maintenance station without adding additional backup hardware. At the same time, the towing costs caused by sampling line failure are reduced, the probability of battery over-discharge damage is reduced, and costs are saved.

[0023] To facilitate understanding of this embodiment, a running control method of a battery sampling line disconnection fault-tolerant control system disclosed in an embodiment of the present invention is first introduced in detail.

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

[0025] Figure 1 FIG. 1 is a flow chart of a travel control method of a battery sampling line disconnection fault-tolerant control system according to an embodiment of the present invention. 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 historical charge and discharge cycle data and current operating condition data stored in the battery management system, where the current operating condition data includes: SOC, ambient temperature, total voltage, and total current; Specifically, conventional electric vehicle BMSs (Battery Management Systems) all have a disconnection detection module. When this module detects a disconnection in the voltage or temperature collection line, it reports a fault. The BMS then generates a local simulated discharge curve based on historical charge and discharge cycle data stored in the BMS and current operating condition data.

[0026] The above historical charge and discharge cycle data includes: cell voltage, temperature, total current, total voltage, charge and discharge curve, ambient temperature, SOC-SOH mapping table, etc. Specifically, the BMS locally stores the complete charge and discharge cycle data for the last 30 days (based on normal operating conditions without any disconnections between the two vehicles).

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

[0028] Step S104, obtaining a cloud-based predicted discharge curve obtained by the battery mechanism-data fusion model in the cloud based on current operating condition data, historical charge and discharge cycle data, historical charge and discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters; Specifically, the current operating condition data and historical charge and discharge cycle data mentioned above are all vehicle-specific data. The process of obtaining the cloud-based predicted discharge curve is described in detail below.

[0029] Step S106, calculating a dynamic weight based on the variance of the historical charge and discharge cycle data and the variance of the cloud-based predicted discharge curve data; In step S108 , the local simulated discharge curve and the cloud-predicted discharge curve are weightedly 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.

[0030] In an embodiment of the present invention, a running control method for a battery sampling line disconnection fault-tolerant control system is provided, comprising: when a voltage or temperature acquisition line disconnection is detected, generating a local simulated discharge curve based on historical charge and discharge cycle data and current operating condition data stored in a battery management system, wherein the current operating condition data includes: SOC, ambient temperature, total voltage, and total current; obtaining a cloud-based predicted discharge curve predicted by a battery mechanism-data fusion model in the cloud based on current operating condition data, historical charge and discharge cycle data, historical charge and discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters; calculating a dynamic weight based on the variance of the historical charge and discharge cycle data and the variance of the cloud-based predicted discharge curve data; performing weighted fusion of the local simulated discharge curve and the cloud-based predicted discharge curve based on the dynamic weight to obtain a fused discharge curve, and controlling the motor power based on the fused discharge curve to maintain vehicle running. As can be seen from the foregoing description, in the driving control method of the battery sampling line break fault-tolerant control system of the present invention, after a voltage or temperature acquisition line is broken, a vehicle-cloud dual-source dynamic weight model is utilized. That is, adaptive weighting is performed based on the timeliness of local data and the diversity of cloud data to simulate the battery discharge curve (i.e., a fused discharge curve), thereby supporting normal vehicle operation. The foregoing process fully considers the actual state of the battery (i.e., historical charge and discharge cycle data). Compared with the traditional fixed power reduction mode, the risk of thermal runaway is reduced, safety performance is improved, and the vehicle can continue driving to the maintenance station without the need for additional backup hardware. At the same time, towing costs caused by sampling line failures are reduced, the probability of battery over-discharge damage is reduced, and costs are saved. This alleviates the technical problem that traditional processing strategies for battery sampling line breakages are difficult to meet safety and economic requirements.

[0031] The above content briefly introduces the travel control method of the battery sampling line breakage fault-tolerant control system of the present invention. The specific contents involved are described in detail below.

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

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

[0034] Construction of a local digital twin model: Under normal vehicle operating conditions (with no disconnections), the BMS collects and stores the following data in real time: Single cell voltage ,temperature , total current , total voltage , charge and discharge curves, temperature distribution, SOC-SOH mapping table (i.e. historical charge and discharge cycle data), and compress and store the historical charge and discharge cycle data through piecewise linear encoding (PLE), saving only the key inflection points ( , , ).

[0035] Compression algorithm formula: .

[0036] in: is a step function ( , activated), , are the slope and intercept of the linear segment passing through adjacent inflection points and Fitting obtained.

[0037] When the line is broken, the generation of the simulated discharge curve is: When a disconnection is detected, the local digital twin model outputs a local simulated discharge curve according to the following process: Step 1: Load local historical inflection point data The turning point from compressed storage The historical fragment closest to the current operating condition data (such as SOC, ambient temperature) is extracted (that is, the closest historical inflection point, and then the corresponding target historical charge and discharge cycle data is obtained).

[0038] Step 2: Dynamic interpolation to generate local simulated discharge curve

[0039] Based on the closest inflection point data, the voltage is calculated in real time by 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. Obtaining the cloud-based predicted discharge curve predicted by the cloud-based battery mechanism-data fusion model based on current operating condition data, historical charge and discharge cycle data, historical charge and discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters specifically 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 and discharge cycle data, historical charge and discharge cycle data of other vehicles, and environmental parameters to obtain the cloud-based predicted discharge curve.

[0041] Specifically, the real-time battery information is transmitted to the big data platform using the vehicle RTM remote message, and a dynamic discharge curve library of the two vehicles is constructed through the cloud, and a battery mechanism-data fusion model is constructed, namely: .

[0042] This enables the storage of a three-dimensional VIT relationship graph for a large number of vehicles. This enables multi-scale time series prediction, combining a long short-term memory network (LSTM) with an equivalent circuit model (ECM) to simulate the battery's charge and discharge processes, describing the battery's dynamic behavior, such as voltage, current, and temperature, and thus predicting real-time data such as battery voltage, capacity (SOC, State of Charge), and state of health (SOH).

[0043] The input of the equivalent circuit model includes: battery calibration parameters (internal resistance , polarized capacitor , rated capacitance , these parameters are the physical modeling basis of ECM) and current working condition data, the output is the voltage response curve, the long short-term memory network is used to calculate the voltage response curve, historical charge and discharge cycle data, historical charge and discharge cycle data of other vehicles, and environmental parameters (ambient temperature). , and the vehicle's operating status (acceleration / deceleration / climbing) are analyzed to obtain the cloud-based predicted discharge curve.

[0044] In an optional embodiment of the present invention, the dynamic weight is calculated based on the variance of the historical charge and discharge cycle data and the variance of the cloud-based predicted discharge curve data, specifically including the following steps: Calculation formula based on dynamic weight Calculate the dynamic weight, where represents the dynamic weight, Represents the variance of the data of the cloud-side predicted discharge curve, Represents the variance of historical charge and discharge cycle data.

[0045] Specifically, the voltage (V) dispersion is extracted from the locally stored historical charge and discharge cycle data. Calculation method: Calculate the variance of V(t) for the most recent complete charge and discharge cycle in the historical charge and discharge cycle data.

[0046] The discreteness (i.e., variance) of the voltage data is extracted from the similar operating condition curve Dcloud (i.e., the cloud-based predicted discharge curve) obtained from the cloud, and then calculated according to the above formula.

[0047] Low variance data has small fluctuations, indicating that the curve is more stable and reliable (for example, the voltage platform in the constant current discharge stage).

[0048] High-variance data fluctuates greatly and may be less reliable due to noise, complex operating conditions, or battery aging.

[0049] If the cloud data variance is smaller (the data is more stable), then the weight Approaching 1, cloud data is trusted first; if the local data variance If the historical data is smaller (such as the historical data is highly consistent with the current working conditions), the weight Approaching 0, depending on local data.

[0050] In an optional embodiment of the present invention, weighted fusion of the local simulated discharge curve and the cloud-based predicted discharge curve is performed according to dynamic weights, specifically including the following steps: According to the weighted fusion formula Perform weighted fusion of the local simulated discharge curve and the cloud-based predicted discharge curve, where: represents the fusion discharge curve, represents the cloud-side predicted discharge curve, represents the dynamic weight, Represents the local simulated discharge curve.

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

[0052] Specifically, piecewise linear coding is used to reduce the amount of storage, so that only the inflection points can be saved.

[0053] In an optional embodiment of the present invention, the method further comprises 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%], that is, the battery needs to be in the battery plateau period before disconnection before this strategy can be implemented.

[0055] The following is a specific example to illustrate: When the vehicle's original SOC is at 70%, it is detected that the battery voltage and temperature collection lines are disconnected.

[0056] BMS implements countermeasures: First, it generates a local simulated discharge curve and matches it with similar vehicle data (same batch battery + same ambient temperature) from the cloud, obtains the cloud-based predicted discharge curve, and calculates the fusion weight. =0.65 (cloud data has smaller variance) output =0.65 +0.35 In this way, VCU (Vehicle Control Unit) can be supported according to Control the motor power to keep the vehicle running normally until repairs are performed.

[0057] This method, using a vehicle-cloud collaborative data fusion model, maintains normal vehicle operation even when a voltage / temperature sampling line fails. This reduces the risk of thermal runaway (compared to traditional fixed power reduction methods); allows the vehicle to continue driving to a repair station after a line failure; reduces towing costs associated with sampling line failures; and reduces the probability of battery over-discharge damage. This solution combines lightweight on-board storage, high-precision cloud-based modeling, and a hierarchical control strategy (limiting the battery SOC operating range to [20% to 80%]), addressing safety challenges during line failures.

[0058] Example 2: An embodiment of the present invention also provides a running control device for a battery sampling line break fault-tolerant control system. The running control device for the battery sampling line break fault-tolerant control system is mainly used to execute the running control method for the battery sampling line break fault-tolerant control system provided in the first embodiment of the present invention. The following is a detailed introduction to the running control device for the battery sampling line break fault-tolerant control system provided in the embodiment of the present invention.

[0059] Figure 2 Schematic diagram of a travel control device of a battery sampling line disconnection fault-tolerant control system according to an embodiment of the present invention. Figure 2 As shown, the device mainly includes: a generating unit 10, an acquiring unit 20, a calculating unit 30 and a fusion unit 40, wherein: A generation unit is configured to generate a local simulated discharge curve based on historical charge and 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, wherein the current operating condition data includes: SOC, ambient temperature, total voltage, and total current; An acquisition unit is used to obtain the cloud-based predicted discharge curve obtained by the battery mechanism-data fusion model based on current operating condition data, historical charge and discharge cycle data, historical charge and discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters; A calculation unit, configured to calculate a dynamic weight based on the variance of historical charge and discharge cycle data and the variance of cloud-based 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 control the motor power according to the fused discharge curve to maintain vehicle movement.

[0060] In an embodiment of the present invention, a running control device of a battery sampling line break fault-tolerant control system is provided, comprising: when a voltage or temperature acquisition line break is detected, generating a local simulated discharge curve based on historical charge and discharge cycle data and current operating condition data stored in a battery management system, wherein the current operating condition data includes: SOC, ambient temperature, total voltage, and total current; obtaining a cloud-based predicted discharge curve predicted by a battery mechanism-data fusion model in the cloud based on current operating condition data, historical charge and discharge cycle data, historical charge and discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters; calculating a dynamic weight based on the variance of the historical charge and discharge cycle data and the variance of the cloud-based predicted discharge curve data; performing weighted fusion of the local simulated discharge curve and the cloud-based predicted discharge curve based on the dynamic weight to obtain a fused discharge curve, and controlling the motor power based on the fused discharge curve to maintain vehicle running. As can be seen from the above description, in the running control device of the battery sampling line break fault-tolerant control system of the present invention, after the voltage or temperature acquisition line is broken, the vehicle-cloud dual-source dynamic weight model is used, that is, adaptive weighting is performed based on the timeliness of local data and the diversity of cloud data to realize battery discharge curve (i.e., fused discharge curve) simulation to support normal vehicle operation. The above process fully considers the actual state of the battery (i.e., 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 travel to the maintenance station without adding additional backup hardware. At the same time, the towing costs caused by sampling line failure are reduced, the probability of battery over-discharge damage is reduced, and costs are saved. This alleviates the technical problem that the traditional processing strategy after the battery sampling line is broken is difficult to meet the safety and economic requirements.

[0061] Optionally, the generating unit is further configured to: extract target historical charge and discharge cycle data corresponding to the current operating condition data from the historical charge and discharge cycle data; and generate a local simulated discharge curve according to the target historical charge and discharge cycle data.

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

[0063] Optionally, the calculation unit is further configured to: calculate the formula according to the dynamic weight Calculate the dynamic weight, where represents the dynamic weight, Represents the variance of the data of the cloud-side predicted discharge curve, Represents the variance of historical charge and discharge cycle data.

[0064] Optionally, the fusion unit is further configured to: Perform weighted fusion of the local simulated discharge curve and the cloud-based predicted discharge curve, where: represents the fusion discharge curve, represents the cloud-side predicted discharge curve, represents the dynamic weight, Represents the local simulated discharge curve.

[0065] Optionally, the historical charge and discharge cycle data is compressed and stored using a piecewise linear encoding 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 the embodiment of the present invention has the same implementation principle and technical effects as those in the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference can be made to the corresponding content in the aforementioned method embodiment.

[0068] like Figure 3 As shown, an electronic device 600 provided in an embodiment of the present application includes: a processor 601, a memory 602 and a bus, wherein the memory 602 stores machine-readable instructions executable by the processor 601. When the electronic device is running, the processor 601 communicates with the memory 602 through the bus, and the processor 601 executes the machine-readable instructions to perform the steps of the running control method of the battery sampling line break fault-tolerant control system as described above.

[0069] Specifically, the above-mentioned memory 602 and processor 601 can be general-purpose memories and processors, which are not specifically limited here. When the processor 601 runs the computer program stored in the memory 602, it can execute the running control method of the above-mentioned battery sampling line break fault-tolerant control system.

[0070] The processor 601 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 601 or by instructions in the form of software. The above-mentioned processor 601 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can 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 gates or transistor logic devices, discrete hardware components. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in memory 602, and processor 601 reads the information in memory 602 and performs the steps of the above method in conjunction with its hardware.

[0071] Corresponding to the above-mentioned running control method of the battery sampling line break fault-tolerant control system, an embodiment of the present application also provides a computer-readable storage medium, which stores machine-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to execute the steps of the running control method of the above-mentioned battery sampling line break fault-tolerant control system.

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

[0073] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0074] For another example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0075] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

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

[0077] If the functions are implemented in the form of 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 the present application, or the part that contributes to the existing technology, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling an electronic device (which can 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 each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0078] It should be noted that similar numbers and letters represent similar items in the following figures. 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 only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0079] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The scope of protection of the present application is not limited thereto. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-mentioned embodiments within the technical scope disclosed in the present application, or perform equivalent replacements for some of the technical features thereof. However, these modifications, changes, or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application. They should all be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection 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 and 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; Obtaining a cloud-based predicted discharge curve obtained by a battery mechanism-data fusion model in the cloud based on the current operating condition data, the historical charge and discharge cycle data, historical charge and discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters; Calculating a dynamic weight based on the variance of the historical charge and discharge cycle data and the variance of the cloud-based predicted discharge curve data; The local simulated discharge curve and the cloud-predicted discharge curve are weightedly 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.

2. The method according to claim 1, characterized in that Generates a local simulated discharge curve based on the historical charge and discharge cycle data and current operating condition data stored in the battery management system, including: Extracting 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 according to the target historical charge and discharge cycle data.

3. The method according to claim 1, characterized in that The battery mechanism-data fusion model includes: an equivalent circuit model and a long short-term memory network, and obtains a cloud-based predicted discharge curve obtained by the cloud-based battery mechanism-data fusion model based on the current operating condition data, the historical charge and discharge cycle data, the historical charge and discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters, including: The equivalent circuit model processes the current operating condition data based on the battery calibration parameters to obtain a voltage response curve; The long short-term memory network analyzes the voltage response curve, the historical charge and discharge cycle data, the historical charge and discharge cycle data of other vehicles, and environmental parameters to obtain the cloud-based predicted discharge curve.

4. The method according to claim 1, wherein Calculating a dynamic weight based on the variance of the historical charge and discharge cycle data and the variance of the cloud-based predicted discharge curve data includes: Calculation formula based on dynamic weight Calculate the dynamic weight, where represents the dynamic weight, represents the variance of the data of the cloud-side predicted discharge curve, represents the variance of the historical charge-discharge cycle data.

5. The method according to claim 1, wherein The method further comprises: performing weighted fusion on the local simulated discharge curve and the cloud-based predicted discharge curve according to the dynamic weight, including: According to the weighted fusion formula The local simulated discharge curve and the cloud-predicted discharge curve are weightedly fused, wherein: represents the fusion discharge curve, represents the cloud-side predicted discharge curve, represents the dynamic weight, represents the local simulated discharge curve.

6. The method according to claim 1, wherein The historical charge and discharge cycle data is compressed and stored using a piecewise linear encoding algorithm.

7. The method according to claim 1, characterized in that The method further comprises: After power failure, the battery SOC operating range is limited to [20%, 80%].

8. A travel control device for a battery sampling line break fault-tolerant control system, characterized in that: include: a generating unit, configured to generate a local simulated discharge curve based on historical charge and 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, wherein the current operating condition data includes: SOC, ambient temperature, total voltage, and total current; an acquisition unit, configured to acquire a cloud-based predicted discharge curve obtained by a cloud-based battery mechanism-data fusion model based on the current operating condition data, the historical charge and discharge cycle data, historical charge and discharge cycle data of other vehicles, environmental parameters, and battery calibration parameters; a calculation unit, configured to calculate a dynamic weight based on a variance of the historical charge and discharge cycle data and a variance of the cloud-based predicted discharge curve data; A fusion unit is used to perform weighted fusion on 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.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to execute the method according to any one of claims 1 to 7.

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