Battery management method, storage medium and edge computing BMS controller

Abstract

The embodiment of the invention provides a battery management method, a storage medium and an edge computing BMS controller. The method comprises the following steps: acquiring battery cell data of each battery cell acquired by a wireless sensing node in real time; calculating a battery state according to the battery cell data; obtaining an anomaly detection result according to the battery state, and performing anomaly processing through a corresponding local control strategy; acquiring the processed cell data after the exception processing; and uploading the processed battery cell data or the battery cell data and the battery state to a cloud server for the cloud server to carry out trend prediction and fault early warning. According to the technical scheme provided by the embodiment of the invention, the real-time performance and the reliability of the wireless BMS can be remarkably improved, the powerful computing power of the cloud server is fully utilized, accurate battery state evaluation and fault early warning functions are provided, and the expandability, the flexibility, the real-time performance and the reliability of the battery management system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery management system, and particularly relates to a battery management method, a storage medium and an edge computing BMS controller. BACKGROUND

[0002] With the wide application of electric vehicles and energy storage systems, the importance of battery management system (BMS) is increasingly prominent. The traditional BMS usually relies on a centralized architecture, and data processing and decision-making are mainly completed on the central controller. This architecture has problems of delay and excessive computational load when dealing with complex battery state estimation and fault diagnosis in large systems with multiple battery modules. In addition, due to the complex cable connection and data transmission burden, the scalability and flexibility of the existing BMS are limited, and the real-time performance and reliability are low. SUMMARY

[0003] Therefore, the embodiments of the present application provide a battery management method, a storage medium and an edge computing BMS controller to improve the scalability, flexibility, real-time performance and reliability of the battery management system.

[0004] In one aspect, the embodiments of the present application provide a battery management method applied to an edge computing BMS controller of a vehicle, comprising: obtaining cell data of each cell collected in real time by a wireless sensing node, wherein the wireless sensing node is deployed on each cell of an automobile battery; calculating a battery state according to the cell data; obtaining an abnormality detection result according to the battery state, and performing abnormality processing through a corresponding local control strategy based on the abnormality detection result; obtaining processed cell data after the abnormality processing; uploading the processed cell data or the cell data and the battery state to a cloud server, so that the cloud server performs trend prediction and fault early warning according to the processed cell data or the cell data and the battery state.

[0005] Optionally, the cell data includes voltage data and current data, and the battery state includes a state of charge and a state of health; and the calculation of the battery state according to the cell data comprises: calculating the voltage data based on a discrete state equation, a discrete output equation and an extended Kalman filtering algorithm to generate the state of charge; comparing a charging capacity calculated according to the current data and the state of charge with an initial capacity to obtain the state of health.

[0006] Optionally, the abnormality processing includes a pre-warning prompt, the abnormality detection result is obtained according to the battery state, and abnormality processing is performed based on the abnormality detection result through a corresponding local control strategy, including: When the cell data includes voltage data, if the abnormality detection result represents that the voltage data of one cell drops by more than a first set threshold value of the voltage data of its adjacent cell, an internal short-circuit pre-warning prompt is triggered; or, When the cell data includes voltage data, the voltage change rate of each cell is calculated according to a sliding window algorithm, and if the abnormality detection result represents that the voltage change rate is greater than a second set threshold value, a hard short-circuit fault pre-warning prompt is triggered; or, When the cell data includes current data, an improved Hampel filter of 3σ rule is used to obtain the current data, and if the abnormality detection result represents that the instantaneous fluctuation of the current data exceeds a third set threshold value of the baseline value and lasts for a set number of sampling periods, a current abnormality pre-warning prompt is triggered; or, When the battery state includes a health state, the ratio of the actual capacity to the rated capacity is calculated by a coulomb counting method, and if the abnormality detection result represents that the ratio of the continuous set number of cycles is less than a fourth set threshold value, a health state degradation pre-warning prompt is triggered.

[0007] Optionally, after the hard short-circuit fault pre-warning prompt is triggered, the method further includes: The metal oxide semiconductor field effect transistor (MOSFET) is driven to cut off the fault module for a set time, and a discharge resistor is started to consume residual electric energy.

[0008] Optionally, the abnormality processing includes abnormality control, the abnormality detection result is obtained according to the battery state, and abnormality processing is performed based on the abnormality detection result through a corresponding local control strategy, including: When the battery state includes a state of charge, if the abnormality detection result represents that the difference of the state of charge is greater than a fifth set threshold value, a bidirectional DC-DC equalization circuit is started for battery equalization control; or, When the cell data includes temperature data, if the abnormality detection result represents that the temperature data is in a first temperature interval, the charging current is reduced and a liquid cooling pump is started; or, if the abnormality detection result represents that the temperature data is in a second temperature interval, a charging circuit is cut off and a positive temperature coefficient (PTC) heater is turned on for reverse refrigeration; or, if the abnormality detection result represents that the temperature data is greater than a first temperature threshold value, a fuse protection is triggered, and an emergency power-down instruction is sent through a controller area network (CAN) bus; or, if the abnormality detection result represents that the temperature data is less than a second temperature threshold value, a silica gel heating film is activated, and a fuzzy proportional-integral-derivative (PID) control algorithm is used to heat the cell at a set temperature rise rate.

[0009] In another aspect, the embodiment of the present application provides a battery management method, applied to a cloud server, the cloud server being in communication connection with an edge computing BMS controller through a wireless network, and the method comprising: obtaining battery data sent by the edge computing BMS controller, the battery data being battery states and cell data of a plurality of cells, or being processed cell data after abnormality processing; performing trend prediction and fault early warning according to the battery data.

[0010] Optionally, the cell data or the processed cell data both comprise a cell internal resistance matrix and surface temperature field data. The performing trend prediction and fault early warning according to the battery data comprises: performing three-dimensional thermal-electric coupling simulation through a finite element algorithm in a high-performance computing cluster according to the cell internal resistance matrix, the surface temperature field data and obtained historical battery thermal runaway data, to generate a simulation result; generating a three-dimensional thermal field suppression scheme according to the simulation result; sending the three-dimensional thermal field suppression scheme to the edge computing BMS controller.

[0011] Optionally, the cell data or the processed cell data both comprise a cell internal resistance matrix and temperature field data. The performing trend prediction and fault early warning according to the battery data and the battery states comprises: performing calculation on the cell internal resistance matrix and the temperature field data based on an interior point method, to generate a charging current curve; generating a charging strategy according to the charging current curve, the charging strategy comprising charging function parameter encoding; sending the charging function parameter encoding to the edge computing BMS controller, so that the edge computing BMS controller performs charging control according to the charging function parameter encoding.

[0012] In another aspect, the embodiment of the present application provides a storage medium, comprising a stored program, wherein when the program runs, the storage medium controls a device where the storage medium is located to execute the above-mentioned battery management method.

[0013] In another aspect, the embodiment of the present application provides an edge computing BMS controller, comprising a memory and a processor, the memory being used for storing information comprising program instructions, and the processor being used for controlling execution of the program instructions, wherein the program instructions are loaded and executed by the processor to realize steps of the above-mentioned battery management method.

[0014] Thus, the technical solution provided by the embodiments of the present invention can significantly improve the real-time performance and reliability of wireless BMS, make full use of the powerful computing capabilities of cloud servers, provide accurate battery status assessment and fault warning functions, and improve the scalability, flexibility, real-time performance and reliability of battery management system. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of a battery management system provided in an embodiment of the present invention; Figure 2 A flowchart illustrating a battery management method according to an embodiment of the present invention; Figure 3 A flowchart illustrating another battery management method provided in an embodiment of the present invention; Figure 4 A schematic diagram of an edge computing BMS controller provided in an embodiment of the present invention. Detailed Implementation

[0017] To better understand the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0019] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0020] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0021] Wireless BMS, as an emerging technology, reduces the cabling complexity of traditional wired systems, but it also brings issues such as data transmission latency and reliability. Furthermore, with the development of cloud computing technology, BMS is beginning to leverage the powerful computing and storage capabilities of the cloud to achieve more advanced data analysis and fault prediction.

[0022] Therefore, this invention provides a wireless battery management system that fully leverages the advantages of edge computing and cloud computing to improve system real-time performance, reliability, and scalability. The combination of edge computing and cloud computing offers a new approach to solving these problems. Edge computing enables real-time computation close to the data source, while cloud computing provides powerful data analysis and global optimization capabilities. Through edge-cloud collaboration, the response speed, processing power, and overall efficiency of the battery management system can be effectively improved.

[0023] Specifically, one embodiment of the present invention provides a battery management system. Figure 1 This is a schematic diagram of a battery management system provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the system includes: a wireless sensor node 10, an edge computing BMS controller 20, and a cloud server 30. The wireless sensor node 10 and the edge computing BMS controller 20 are installed in the vehicle and connected to each other. The edge computing BMS controller 20 is connected to the cloud server 30. Each wireless sensor node 10 includes a Bluetooth chip corresponding to each battery cell. Each Bluetooth chip can be used to form a Bluetooth communication matrix network for communication with the edge computing BMS controller 20. Specifically, the edge computing BMS controller 20 and the cloud server 30 are connected via mobile communication technology, such as fourth-generation mobile communication technology (4G) or fifth-generation mobile communication technology (5G).

[0024] In this embodiment of the invention, the cloud server 30 can adopt a Spark + Hadoop hybrid architecture and use Kafka stream processing for real-time data (throughput > 1M records / second). Historical data such as historical battery thermal runaway data are stored in an HBase cluster (PB-level capacity), and real-time analysis uses the Flink stream computing engine.

[0025] The edge computing BMS controller 20 is used to acquire the cell data of each cell collected in real time by the wireless sensor node 10; calculate the battery status based on the cell data; obtain the anomaly detection result based on the battery status, and perform anomaly handling through the corresponding local control strategy based on the anomaly detection result; and upload the processed cell data or cell data and battery status to the cloud server 30.

[0026] The cloud server 30 is used to perform trend prediction and fault warning based on the processed cell data or cell data and battery status.

[0027] In this embodiment of the invention, the cell data includes voltage data and current data. The battery state includes State of Charge (SOC) and State of Health (SOH). The edge computing BMS controller 20 is specifically used to calculate the voltage data based on discrete state equations, discrete output equations, and the Extended Kalman Filter (EKF) algorithm to generate the State of Charge. The charging capacity calculated based on the current data and the State of Charge is compared with the obtained initial capacity to obtain the State of Health.

[0028] In this embodiment of the invention, the anomaly handling includes early warning prompts. Specifically, the edge computing BMS controller 20 is used to trigger an internal short circuit early warning prompt when the cell data includes voltage data and the voltage data of a cell suddenly drops above a first set threshold of the voltage data of its adjacent cells; or, when the cell data includes voltage data, it calculates the voltage change rate of each cell according to the sliding window algorithm and the voltage change rate is greater than a second set threshold, triggering a hard short circuit fault early warning prompt; or, when the cell data includes current data, it uses an improved Hampel filter based on the 3σ rule to obtain current data and the instantaneous fluctuation of the current data exceeds a third set threshold of the baseline value, and continues for a set number of sampling periods, triggering a current anomaly early warning prompt; or, when the battery status includes a healthy state, it calculates the ratio of the actual capacity to the rated capacity using the coulomb counting method and the ratio is less than a fourth set threshold for a set number of consecutive cycles, triggering a health status deterioration early warning prompt.

[0029] In this embodiment of the invention, the edge computing BMS controller 20 is specifically used to trigger a hard short-circuit fault warning prompt, and then drive the metal-oxide-semiconductor field-effect transistor (MOSFET) to cut off the fault module within a set time, while simultaneously activating the discharge resistor to consume the residual power.

[0030] In this embodiment of the invention, the anomaly handling includes anomaly control. Specifically, the edge computing BMS controller 20 is used to: when the battery state includes the state of charge, and the difference in the state of charge is greater than a fifth set threshold, activate the bidirectional DC-DC balancing circuit for battery balancing control; or, when the cell data includes temperature data, if the temperature data is within a first temperature range, reduce the charging current and activate the liquid cooling pump; or, if the temperature data is within a second temperature range, disconnect the charging circuit and activate the positive temperature coefficient (PTC) heater for reverse cooling; or, if the temperature data is greater than the first temperature threshold, trigger the fuse protection and send an emergency power-down command via the Controller Area Network (CAN) bus; or, if the temperature data is less than the second temperature threshold, activate the silicone heating film and use a fuzzy proportional-integral-derivative (PID) control algorithm to heat the cell at a set temperature rise rate.

[0031] In one embodiment of the present invention, the cloud server communicates with the edge computing BMS controller via a wireless network to obtain battery data sent by the edge computing BMS controller. The battery data includes battery status and cell data of multiple cells, or processed cell data after anomaly handling. Trend prediction and fault warning are performed based on the battery data.

[0032] The cell data or processed cell data includes the cell internal resistance matrix and surface temperature field data. The cloud server 30 is specifically used to perform three-dimensional thermoelectric coupling simulation on a high-performance computing cluster using the finite element algorithm based on the cell internal resistance matrix, surface temperature field data and acquired historical battery thermal runaway data, and generate simulation results; generate a three-dimensional thermal field suppression scheme based on the simulation results; and send the three-dimensional thermal field suppression scheme to the edge computing BMS controller 20.

[0033] In this embodiment of the invention, the cell data or the processed cell data includes the cell internal resistance matrix and temperature field data. The cloud server 30 is specifically used to calculate the cell internal resistance matrix and temperature field data based on the interior point method to generate a charging current curve; generate a charging strategy based on the charging current curve, the charging strategy including charging function parameter encoding; and transmit the charging function parameter encoding to the edge computing BMS controller 20 so that the edge computing BMS controller 20 can perform charging control based on the charging function parameter encoding.

[0034] In this embodiment of the invention, the battery management system may further include a mobile terminal 40. The mobile terminal 40 is connected to the cloud server 30. Specifically, the mobile terminal 40 and the cloud server 30 are connected via mobile communication technology, for example, the mobile communication technology may include 4G or 5G.

[0035] In this embodiment of the invention, a user can access a cloud server 30 via a mobile terminal 40 to monitor the battery status in real time, receive alarms, and perform remote control when necessary. For example, the user can send a power-off control command to the cloud server 30 via the mobile terminal 40. The cloud server 30 then sends an encrypted power-off control command to the edge computing BMS controller 20, which can respond to the power-off control command by cutting off the high-voltage relay within 50ms. As another example, the user can set the vehicle's SOC limit (e.g., 80%) on the cloud server 30 via the mobile terminal 40. The cloud server 30 then sends an encrypted SOC setting command to the edge computing BMS controller 20, which can respond to the SOC setting command by dynamically adjusting the pulse width modulation (PWM) charging current.

[0036] As can be seen from the above, the technical solution provided by the embodiments of the present invention can significantly improve the real-time performance and reliability of the wireless BMS, fully utilize the powerful computing capabilities of the cloud server, provide accurate battery status assessment and fault warning functions, and improve the scalability, flexibility, real-time performance, and reliability of the battery management system. Specifically, by deploying a wireless Bluetooth chip on each cell, the battery management system can more accurately monitor and manage the current, voltage, and temperature data of each cell, rather than just focusing on the overall state of the battery pack, thereby improving the safety and accuracy of the battery management system; by setting up a wireless Bluetooth chip, the cable complexity of the traditional BMS system is reduced, improving the system's flexibility and scalability; the edge computing BMS controller 20 integrates battery management and edge computing functions, improving the system's real-time response capability and processing efficiency, and reducing data transmission burden and system latency; combining the advantages of the edge computing BMS controller 20 and the cloud server 30, an organic combination of local rapid response and cloud-based global optimization is achieved, enhancing the system's intelligence level.

[0037] Based on the above-described battery management system, one embodiment of the present invention provides a battery management method. Figure 2 A flowchart of a battery management method provided in an embodiment of the present invention is shown below. Figure 2 As shown, the method includes: Step 101: Obtain the cell data of each cell collected in real time by the wireless sensor node.

[0038] In this embodiment of the invention, each step is executed by the edge computing BMS controller.

[0039] In this embodiment of the invention, the cell data may include voltage data, current data, or temperature data.

[0040] Step 102: Calculate the battery status based on the cell data.

[0041] In this embodiment of the invention, the edge computing BMS controller can preprocess the cell data before calculating the battery state. For example, the preprocessing includes noise reduction and / or filtering.

[0042] In this embodiment of the invention, the edge computing BMS controller can perform state estimation (SOX calculation) based on cell data to generate the battery state. For example, the battery state may include SOC, SOH, or State of Power (SOP).

[0043] Step 103: Obtain the anomaly detection result based on the battery status, and perform anomaly handling based on the corresponding local control strategy according to the anomaly detection result.

[0044] Step 104: Obtain the processed cell data after anomaly handling.

[0045] In this embodiment of the invention, after an abnormality is detected in the battery, the abnormality detection result can be directly used to control the local battery module, and the re-acquired processed cell data is uploaded to the cloud server; if no abnormality is detected, the cell data and battery status are uploaded to the cloud server. For example, abnormality handling may include battery equalization, over-temperature protection, etc.

[0046] Step 105: Upload the processed cell data or cell data and battery status to the cloud server so that the cloud server can perform trend prediction and fault warning based on the processed cell data or cell data and battery status.

[0047] In this embodiment of the invention, the cloud server can also generate a global control strategy based on trend prediction and feed the optimized global control strategy back to the edge computing BMS controller. The cloud server can integrate and perform in-depth analysis on the data from the edge computing BMS controller, including advanced functions such as trend prediction, SOH assessment, and fault warning. The analysis results from the cloud server can be fed back to the edge computing BMS controller to optimize the vehicle's local control strategy.

[0048] In this embodiment of the invention, the battery management system can achieve globally optimal control strategy adjustments based on the calculation results of the cloud server and the edge computing BMS controller. For example, when an edge computing BMS controller detects a battery anomaly, it can handle the anomaly through the cloud server or issue an early warning through the cloud server. Battery anomalies may include capacity degradation, voltage imbalance, precursors to thermal runaway, low-temperature risks, insulation failure, and overcharge failure.

[0049] As can be seen from the above, the technical solution provided by the embodiments of the present invention can significantly improve the real-time performance and reliability of wireless BMS, make full use of the powerful computing power of cloud servers, provide accurate battery status assessment and fault warning functions, and improve the scalability, flexibility, real-time performance and reliability of battery management system.

[0050] One embodiment of the present invention provides another battery management method. Figure 3 A flowchart of another battery management method provided in an embodiment of the present invention is shown below. Figure 3 As shown, the method includes: Step 201: The edge computing BMS controller acquires the cell data of each cell collected in real time by the wireless sensor node.

[0051] Step 202: The edge computing BMS controller calculates the battery status based on the cell data.

[0052] In this embodiment of the invention, the cell data includes voltage data and current data, the battery state includes state of charge and state of health, and step 202 includes: Step S1: Calculate the voltage data based on the discrete state equation, discrete output equation, and extended Kalman filter algorithm to generate the state of charge.

[0053] In this embodiment of the invention, based on the first-order RC equivalent circuit model, the discrete state equation can be: Where x is the state variable, A and B are coefficient matrices, and u is the voltage data. The polarization voltage is SOC, which stands for State of Charge. Sampling time, and For polarization resistors and polarization capacitors, Q represents the charge / discharge efficiency, and Q represents the battery capacity.

[0054] The discrete output equation can be: in, Let C be the terminal voltage, and D be the coefficient matrix. Open circuit voltage, The internal resistance is ohmic. The discrete state equation and the discrete output equation together form the SOC estimation equation, which, combined with the EKF algorithm, can generate the battery's SOC.

[0055] Step S2: Compare the charging capacity calculated based on the current data and state of charge with the obtained initial capacity to obtain the health status.

[0056] In this embodiment of the invention, the initial capacity and charging capacity are calculated using the following formula.

[0057] in, Indicates the charging section number; Indicates the first The start time of each charging segment; Indicates the first The end time of each charging segment; Indicates the first The corresponding time for each charging segment; Indicates the first The current of each charging segment; It is the first Initial charging capacity; It is the first SOC at the start of charging for each charging segment; It is the first SOC at the end of each charging segment; It is the first Initial capacity; It is the first Current charging level; It is the first The current capacity; M and N are the filter coefficients.

[0058] Step 203: The edge computing BMS controller obtains the anomaly detection result based on the cell data or battery status, and performs anomaly handling based on the anomaly detection result through the corresponding local control strategy.

[0059] In this embodiment of the invention, when the anomaly handling includes an early warning notification, step 203 includes: When the cell data includes voltage data, an internal short circuit warning is triggered when the voltage data of a cell suddenly drops below a first set threshold of the voltage data of its adjacent cells. The first set threshold can be set according to the actual situation. For example, if the first set threshold is 30%, an internal short circuit warning is triggered when the voltage data of a cell suddenly drops from 4.2V to 2.9V.

[0060] When the cell data includes voltage data, the voltage change rate of each cell is calculated according to the sliding window algorithm (window size 10 seconds). When the voltage change rate is greater than the second set threshold, a hard short circuit fault warning is triggered. The second set threshold can be set according to the actual situation. For example, the second set threshold is 0.5V / s.

[0061] When cell data includes current data, an improved Hampel filter (window width 5 seconds) based on the 3σ rule is used to acquire the current data. When the instantaneous fluctuation of the current data exceeds the third set threshold of the baseline value and continues for a set number of sampling periods, a current abnormality warning is triggered. The third set threshold and the set number can be set according to the actual situation. For example, the third set threshold is 3 times the standard deviation and the set number is 3.

[0062] When the battery status includes a healthy state, the ratio of the actual capacity to the rated capacity is calculated using the coulomb counting method. When the ratio is less than the fourth set threshold after a set number of consecutive cycles, a health status deterioration warning is triggered. The fourth set threshold and the set number of cycles can be set according to the actual situation. For example, the fourth set threshold is 80%, and the set number of cycles is 3.

[0063] The edge computing BMS controller can provide other warnings based on cell data or battery status, such as warnings of local overheating of the battery or abnormal internal resistance. This embodiment of the invention does not limit this.

[0064] In this embodiment of the invention, after triggering a hard short-circuit fault warning, the MOSFET can be driven to cut off the fault module within a set time, and the bleeder resistor can be activated to consume the residual power.

[0065] In this embodiment of the invention, when exception handling includes exception control, step 206 includes: When the battery state includes the state of charge, if the difference in state of charge is greater than the fifth set threshold, the bidirectional DC-DC equalization circuit is activated to perform battery equalization control. Energy is transferred between adjacent cells through transformer energy storage. The maximum equalization current can reach 5A. The fifth set threshold can be set according to the actual situation. For example, the fifth set threshold is 5%.

[0066] When cell data includes temperature data, if the temperature is within the first temperature range, reduce the charging current to 0.5C and start the liquid cooling pump (flow rate 3L / min); or, if the temperature is within the second temperature range, disconnect the charging circuit and activate the PTC heater for reverse cooling; or, if the temperature is greater than the first temperature threshold, trigger the fuse protection and send an emergency power-down command via the CAN bus; or, if the temperature is less than the second temperature threshold, activate the silicone heating film (power density 0.5W / cm²).2 It employs a fuzzy PID control algorithm to heat the battery cell at a set temperature rise rate, which can heat it to above 5°C.

[0067] In this embodiment of the invention, the first temperature range, the second temperature range, the first temperature threshold, the second temperature threshold, and the set temperature rise rate can be set according to actual conditions. For example, the first temperature range is 50-55℃, the second temperature range is 55-60℃, the first temperature threshold is 60℃, the second temperature threshold is 0℃, and the set temperature rise rate is less than or equal to 1℃ / min.

[0068] In this embodiment of the invention, the edge computing BMS controller performs other abnormal control based on cell data or battery status, such as short circuit emergency handling and self-healing control. This embodiment of the invention does not limit this.

[0069] Step 204: Obtain the processed cell data after anomaly handling.

[0070] In this embodiment of the invention, after the battery is abnormally processed, it is necessary to obtain the real-time data of the processed battery cell. If no abnormality is detected, the battery cell data obtained afterward, as well as the corresponding battery status, are used.

[0071] Step 205: The edge computing BMS controller uploads the processed cell data or cell data and battery status to the cloud server.

[0072] Step 206: The cloud server performs trend prediction and fault warning based on the processed cell data or cell data and battery status.

[0073] In this embodiment of the invention, the cloud server can obtain processed cell data or cell data and battery status sent by multiple edge computing BMS controllers, and integrate the cell data and battery status (e.g., SOC cycle curve, temperature distribution matrix, charging rate distribution, etc.) sent by multiple edge computing BMS controllers.

[0074] In this embodiment of the invention, the cell data (or processed cell data) includes the cell internal resistance matrix and surface temperature field data. The cloud server can perform three-dimensional thermoelectric coupling simulation using the finite element method on a high-performance computing cluster based on the cell internal resistance matrix, surface temperature field data, and acquired historical battery thermal runaway data, generating simulation results. A three-dimensional thermal field suppression scheme is then generated based on the simulation results and sent to the edge computing BMS controller. A three-level warning is triggered when the simulation results meet the following conditions: maximum temperature > critical temperature (180℃); temperature gradient > 50℃ / cm; heat generation rate > 10℃ / s. The three-dimensional thermal field suppression scheme may include adjusting the liquid cooling pipe flow rate distribution parameters (accurate to each cooling branch).

[0075] In this embodiment of the invention, the cell data (or processed cell data) includes the cell internal resistance matrix (e.g., the cell internal resistance matrix is ​​Rcell=[0.25,0.23,...,0.27]mΩ) and temperature field data (e.g., the temperature field data is T(x,y,z)=35±2℃). The cloud server can calculate the cell internal resistance matrix and temperature field data based on the interior-point method to generate a charging current curve; a charging strategy is generated based on the charging current curve (e.g., the charging strategy can be: 2C constant current charging for the first 30 minutes, and charging at a decreasing slope of 0.1C / minute for 30-45 minutes). The charging strategy includes the encoding of charging function parameters (e.g., the charging function parameter encoding is a 16-byte instruction: CMD_OPT_CHG:). [0x01,0x1E,0x02,0x00,0x1E,0x2D,0xFE,0x...]); The charging function parameter encoding is sent to the edge computing BMS controller so that the edge computing BMS controller can perform charging control according to the charging function parameter encoding. For example, the charging control can be to dynamically adjust the PWM duty cycle with a control accuracy of ±0.5%.

[0076] In this embodiment of the invention, the cloud server can also perform other trend predictions and fault warnings based on the cell data (or processed cell data) and the corresponding battery status, such as battery life prediction based on transfer learning, global SOC calibration based on federated learning, spatiotemporal correlation fault diagnosis, and computing resource scheduling.

[0077] As can be seen from the above, the technical solution provided by the embodiments of the present invention can significantly improve the real-time performance and reliability of wireless BMS, fully utilize the powerful computing capabilities of cloud servers, provide accurate battery status assessment and fault early warning functions, and improve the scalability, flexibility, real-time performance, and reliability of the battery management system. Furthermore, through the end-cloud collaborative architecture design, the embodiments of the present invention can effectively cope with complex and ever-changing application scenarios and are suitable for large-scale multi-module battery management systems.

[0078] This invention provides a storage medium that includes a stored program. When the program runs, it controls the device where the storage medium is located to execute the steps of the above-described battery management method. For a detailed description, please refer to the embodiments of the above-described battery management method.

[0079] This invention provides an edge computing BMS controller, including a memory and a processor. The memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions. When the program instructions are loaded and executed by the processor, they implement the steps of the above-described battery management method. For a detailed description, please refer to the above-described battery management method embodiments.

[0080] Figure 4 This invention provides a schematic diagram of an edge computing BMS controller. (See diagram below.) Figure 4 As shown, the edge computing BMS controller 20 of this embodiment includes: a processor 21, a memory 22, and a computer program 23 stored in the memory 22 and executable on the processor 21. When the computer program 23 is executed by the processor 21, it implements the battery management method in this embodiment. To avoid repetition, it will not be described in detail here.

[0081] The edge computing BMS controller 20 includes, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will understand that... Figure 4 This is merely an example of the edge computing BMS controller 20 and does not constitute a limitation on the edge computing BMS controller 20. It may include more or fewer components than shown, or combine certain components, or different components. For example, the edge computing BMS controller may also include input / output devices, network access devices, buses, etc.

[0082] The processor 21 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0083] The memory 22 can be an internal storage unit of the edge computing BMS controller 20, such as a hard drive or RAM of the edge computing BMS controller 20. The memory 22 can also be an external storage device of the edge computing BMS controller 20, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the edge computing BMS controller 20. Furthermore, the memory 22 can include both internal storage units and external storage devices of the edge computing BMS controller 20. The memory 22 is used to store computer programs and other programs and data required by the edge computing BMS controller. The memory 22 can also be used to temporarily store data that has been output or will be output.

[0084] 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 be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0085] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0086] 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.

[0087] Furthermore, the functional units in the various embodiments of the present invention 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. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0088] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute partial steps of the methods of the various embodiments of the present invention. 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.

[0089] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A battery management method, characterized in that, An edge computing BMS controller for vehicles includes: The system acquires real-time data of each battery cell from wireless sensor nodes deployed on each battery cell of the automotive battery. The battery status is calculated based on the cell data; Anomaly detection results are obtained based on the battery status, and anomaly handling is performed based on the corresponding local control strategy according to the anomaly detection results. Obtain the processed cell data after the aforementioned anomaly handling; The processed cell data, or the cell data and battery status, are uploaded to a cloud server so that the cloud server can perform trend prediction and fault warning based on the processed cell data or the cell data and battery status.

2. The method according to claim 1, characterized in that, The cell data includes voltage data and current data, and the battery status includes state of charge and state of health; the calculation of the battery status based on the cell data includes: The voltage data is calculated based on the discrete state equation, the discrete output equation, and the extended Kalman filter algorithm to generate the state of charge. The charging capacity calculated based on the current data and the state of charge is compared with the obtained initial capacity to obtain the health status.

3. The method according to claim 1, characterized in that, The anomaly handling includes early warning prompts, obtaining anomaly detection results based on the battery status, and performing anomaly handling based on the anomaly detection results using corresponding local control strategies, including: When the cell data includes voltage data, if the anomaly detection result indicates that the voltage data of a cell suddenly drops beyond a first preset threshold of the voltage data of its adjacent cells, an internal short circuit warning is triggered; or, When the cell data includes voltage data, the voltage change rate of each cell is calculated according to the sliding window algorithm. If the anomaly detection result indicates that the voltage change rate is greater than a second preset threshold, a hard short-circuit fault warning is triggered; or, When the cell data includes current data, an improved Hamper filter based on the 3σ rule is used to acquire the current data. If the anomaly detection result indicates that the instantaneous fluctuation of the current data exceeds a third preset threshold of the baseline value and continues for a set number of sampling periods, a current anomaly warning is triggered; or, When the battery status includes a healthy state, the ratio of the actual capacity to the rated capacity is calculated using the coulomb counting method. If the abnormal detection result indicates that the ratio is less than a fourth set threshold after a set number of consecutive cycles, a health status deterioration warning is triggered.

4. The method according to claim 3, characterized in that, After triggering the hard short-circuit fault warning, the method further includes: Within a set time, the metal-oxide-semiconductor field-effect transistor is driven to cut off the faulty module, while the bleeder resistor is activated to consume the residual power.

5. The method according to claim 1, characterized in that, The anomaly handling includes anomaly control, wherein the anomaly detection result is obtained based on the battery state, and anomaly handling is performed based on the anomaly detection result using a corresponding local control strategy, including: When the battery state includes the state of charge, if the anomaly detection result indicates that the difference in the state of charge is greater than a fifth preset threshold, then the bidirectional DC-DC equalization circuit is activated for battery equalization control; or... When the cell data includes temperature data, if the anomaly detection result indicates that the temperature data is within a first temperature range, the charging current is reduced and the liquid cooling pump is started; or, if the anomaly detection result indicates that the temperature data is within a second temperature range, the charging circuit is cut off and the positive temperature coefficient heater is turned on for reverse cooling; or, if the anomaly detection result indicates that the temperature data is greater than a first temperature threshold, the fuse protection is triggered, and an emergency power-down command is sent through the controller local area network bus; or, if the anomaly detection result indicates that the temperature data is less than a second temperature threshold, the silicone heating film is activated, and a fuzzy proportional-integral-derivative control algorithm is used to heat the cell at a set temperature rise rate.

6. A battery management method, characterized in that, Applied to a cloud server, the cloud server is communicatively connected to an edge computing BMS controller via a wireless network, including: Obtain battery data sent by the edge computing BMS controller. The battery data includes battery status and cell data of multiple cells, or processed cell data after anomaly handling. Trend prediction and fault warning are performed based on the battery data.

7. The method according to claim 6, characterized in that, The cell data or the processed cell data both include the cell internal resistance matrix and surface temperature field data. The trend prediction and fault warning based on the battery data includes: Based on the cell internal resistance matrix, the surface temperature field data, and the acquired historical battery thermal runaway data, a three-dimensional thermoelectric coupling simulation is performed on a high-performance computing cluster using the finite element algorithm to generate simulation results. A three-dimensional thermal field suppression scheme is generated based on the simulation results; The three-dimensional thermal field suppression scheme is sent to the edge computing BMS controller.

8. The method according to claim 6, characterized in that, The cell data or the processed cell data both include the cell internal resistance matrix and temperature field data. The trend prediction and fault warning based on the cell data and the battery status include: The charging current curve is generated by calculating the internal resistance matrix of the battery cell and the temperature field data based on the interior point method. A charging strategy is generated based on the charging current curve, and the charging strategy includes the encoding of charging function parameters; The charging function parameter encoding is sent to the edge computing BMS controller so that the edge computing BMS controller can perform charging control according to the charging function parameter encoding.

9. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the storage medium to perform the battery management method according to any one of claims 1 to 5.

10. An edge computing BMS controller, comprising a memory and a processor, wherein the memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions, characterized in that, When the program instructions are loaded and executed by the processor, they implement the steps of the battery management method according to any one of claims 1 to 5.

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