A hybrid agricultural machine battery state detection method, system, device and storage medium

Abstract

The present application relates to the technical field of battery management, and relates to a hybrid agricultural machine battery state detection method, system, device and storage medium; wherein the hybrid agricultural machine battery state detection method comprises: synchronously collecting battery state observation data of a hybrid agricultural machine battery at a preset sampling frequency; constructing a state vector of an extended Kalman filter algorithm and initializing; constructing a state equation based on a nonlinear equivalent circuit model of the battery, predicting a battery state quantity at a current time k, and obtaining a battery state prediction value at the current time k; calculating an optimal state vector at the current time k and updating the state vector according to the battery state prediction value at the current time k and a battery state observation value at the current time k in the battery state observation data; comparing information of the updated state vector at the current time k with a preset reference value to obtain a comparison result, and determining a state detection result of the battery according to the comparison result. The present application can improve the state prediction reliability under different working conditions.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a method, system, device, and storage medium for detecting the state of hybrid agricultural machinery batteries. Background Technology

[0002] When hybrid agricultural machinery operates in the field, its power battery faces complex conditions such as drastic fluctuations in charging and discharging current, high-temperature and bumpy working environments, and strong electromagnetic interference. Accurate and real-time monitoring of battery status, especially the state of charge (SOC), is crucial for the energy management and stable operation of the agricultural machinery.

[0003] Currently, the most mainstream technology in the field of hybrid agricultural machinery battery testing is the ampere-hour integration method. Its core principle is to calculate the remaining capacity (State of Charge, or SOC) by accumulating the battery's charge and discharge current and combining it with the battery's rated capacity. The ampere-hour integration method has the advantages of simple calculation and good real-time performance. However, the ampere-hour integration method has the following inherent drawbacks: First, the ampere-hour integration method is an open-loop calculation with no error correction mechanism. Errors caused by current measurement deviations, noise interference, etc., will continue to accumulate, resulting in a SOC estimation deviation of more than 10% after long-term operation, which leads to low accuracy.

[0004] Second, this method mainly relies on linear filtering to process signals, which cannot effectively filter out nonlinear non-Gaussian noise under hybrid agricultural machinery operating conditions, and cannot adapt to the nonlinear working characteristics of batteries, resulting in poor adaptability to operating conditions.

[0005] Third, the detection function is limited, usually only able to calculate SOC, and cannot comprehensively monitor key states such as battery state of health (SOH), internal resistance, and cell voltage balance.

[0006] Fourth, the fault diagnosis mechanism is simple, mostly relying on post-event threshold alarms, and lacks the ability to provide early warnings based on changes in multiple state variables, which can easily lead to sudden shutdowns. Summary of the Invention

[0007] This application aims to at least solve the technical problems existing in the prior art, and to provide a method, system, device and storage medium for detecting the state of hybrid agricultural machinery batteries.

[0008] In a first aspect, the present invention provides a method for detecting the state of a hybrid agricultural machinery battery, the method comprising: Battery status observation data of hybrid agricultural machinery batteries are collected synchronously at a preset sampling frequency; Construct the state vector of the extended Kalman filter algorithm; initialize the initial values ​​of the state vector, the state estimation error covariance matrix, the prediction error noise covariance matrix, and the observation noise covariance matrix; A state equation is constructed based on a nonlinear equivalent circuit model of the battery. The battery state variables at the previous time k-1 are mapped to the observation space through a nonlinear observation function to predict the battery state variables at the current time k, thus obtaining the predicted battery state value at the current time k, where k is a positive integer greater than 1. The optimal state vector for the current time k is calculated and updated based on the predicted battery state value at the current time k and the observed battery state value at the current time k in the battery state observation data. The updated state vector information at the current time k is compared with the preset reference value to obtain the comparison result, and the battery state detection result is determined based on the comparison result.

[0009] Secondly, the present invention provides a hybrid agricultural machinery battery status detection system, the system comprising: The data acquisition module is used to synchronously acquire battery status observation data of hybrid agricultural machinery batteries at a preset sampling frequency; An initialization module is used to construct the state vector of the extended Kalman filter algorithm; and to initialize the initial values ​​of the state vector, the state estimation error covariance matrix, the prediction error noise covariance matrix, and the observation noise covariance matrix. The state prediction module is used to construct state equations based on the nonlinear equivalent circuit model of the battery. It maps the battery state variables at the previous time k-1 to the observation space through a nonlinear observation function to predict the battery state variables at the current time k, and obtains the predicted battery state value at the current time k, where k is a positive integer greater than 1. The state update module is used to calculate the optimal state vector at the current time k based on the predicted battery state value at the current time k and the observed battery state value at the current time k in the battery state observation data, and then update the state vector. The output module is used to compare the updated state vector information of the current time k with the preset reference value to obtain the comparison result, and determine the battery state detection result based on the comparison result.

[0010] Thirdly, the present invention provides an electronic device, the electronic device comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the hybrid agricultural machinery battery status detection method described above.

[0011] Fourthly, the present invention also provides a computer-readable storage medium storing at least one computer program, which is executed by a processor in an electronic device to implement the hybrid agricultural machinery battery state detection method described above.

[0012] In summary, this application includes the following beneficial technical effects: The hybrid agricultural machinery battery state detection method provided in this application calculates and updates the optimal state vector based on the predicted battery state value and the observed battery state value at the current time k, forming a closed-loop correction mechanism. This reduces the accumulation of errors over operating time and improves the long-term stability of the detection. Furthermore, the method explicitly constructs state equations for prediction based on the nonlinear equivalent circuit model of the battery, which can more accurately adapt to the nonlinear dynamic characteristics of the battery under complex operating conditions of hybrid agricultural machinery, improving the reliability of state prediction under different operating conditions. In addition, by comparing the updated state vector information with a preset reference value to determine the state detection result, the detection conclusion is based on the fused predicted and updated state vector, rather than solely relying on the instantaneous threshold of the original signal, which helps to more objectively identify whether the battery state deviates from the expected range. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating a method for detecting the state of a hybrid agricultural machinery battery according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of an electronic device for implementing the hybrid agricultural machinery battery state detection method according to an embodiment of the present invention.

[0014] Reference numerals: 10, processor; 11, memory; 12, communication bus; 13, communication interface.

[0015] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0016] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0017] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "up", "down", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0018] In the description of this invention, unless otherwise specified and limited, it should be noted that the terms "installation", "connection" and "linking" should be interpreted broadly. For example, they can refer to mechanical or electrical connections, or internal connections between two components. They can be direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0019] Reference Figure 1 The diagram shown is a flowchart illustrating a hybrid agricultural machinery battery status detection method according to an embodiment of the present invention. In this embodiment, the hybrid agricultural machinery battery status detection method includes: S1. Synchronously collect battery status observation data of hybrid agricultural machinery batteries at a preset sampling frequency.

[0020] Battery status monitoring data includes information on the terminal voltage, charging and discharging current, battery temperature, and individual cell voltage of the hybrid agricultural machinery battery.

[0021] Specifically, in this embodiment, the sensors of the hybrid agricultural machinery vehicle battery management system synchronously collect the core physical quantities of the battery at a sampling frequency of 80~100Hz, including: battery terminal voltage, charging and discharging current, battery temperature, single cell voltage, battery surface temperature, etc. The collected data covers the dynamic changes in current and voltage during agricultural machinery operation. In a preferred embodiment of this example, the 3σ principle is used to identify outliers in the collected raw data. Extreme outliers caused by sensor vibration, electromagnetic interference, or poor line contact in the initial battery state observation data are removed to avoid outliers affecting the model estimation accuracy. Subsequently, the dataset after outlier removal is subjected to Min-Max normalization to uniformly map physical quantities of different dimensions and numerical ranges to the [0,1] interval to obtain the final battery state observation data. This eliminates the influence of dimensional differences on the subsequent calculation of extended Kalman filter algorithm parameters and ensures the stability of the subsequent state equation and observation equation calculations.

[0022] S2. Construct the state vector of the extended Kalman filter algorithm; initialize the initial values ​​of the state vector, the state estimation error covariance matrix, the prediction error noise covariance matrix, and the observation noise covariance matrix.

[0023] The Extended Kalman Filter (EKF) is a type of battery filter. Its state vector includes the battery's state of charge (SOC), state of health (SOH), internal resistance, and cell voltage equalization. SOC refers to the battery's current remaining capacity, usually expressed as a percentage (e.g., 80% means 80% of the rated capacity). SOH refers to the degree of performance degradation of the battery relative to a brand-new battery, also usually expressed as a percentage (e.g., 90% means the battery capacity or internal resistance has degraded to 90% of its initial value), and is used to assess the battery's aging and lifespan.

[0024] Initialization is completed before the first run of the extended Kalman filter algorithm to set the basic parameters and initial state for subsequent prediction-update stages. Subsequent loops do not require repeated initialization; only the parameters updated in the previous round are used. The core initialization content revolves around state variables, covariance matrix, and core algorithm parameters, all calibrated in conjunction with the actual operating characteristics of hybrid agricultural machinery batteries. The specific initialization steps in step 2 are as follows: S21. Initial value setting of state variables: The four core state variables of battery remaining charge (SOC), state of health (SOH), internal resistance, and single cell voltage balance are constructed into the state vector X0 of EKF. The initial values ​​are set according to the factory parameters and initial working state of the hybrid agricultural machinery battery. For example, typical initial values ​​are: SOC0=0.8, SOH0=0.95, the initial value of internal resistance is calibrated according to the battery rated parameters, and the initial value of single cell voltage balance is 0 (no imbalance). S22. Initialization of covariance matrix: Set the process noise covariance matrix Q and the observation noise covariance matrix R. The typical value of the process noise covariance matrix is ​​set to 0.01. The observation noise covariance matrix is ​​matched according to the sensor accuracy (e.g., 0.001 for voltage dimension and 0.005 for current dimension) to reflect the noise level of the system model and sensor measurements.

[0025] S23. Initialization of the state estimation error covariance matrix: Set the initial state estimation error covariance matrix P0 as a diagonal matrix. The diagonal elements are set according to the initial estimation error of each state quantity, reflecting the estimation uncertainty of the initial state quantity. S24. Initialization of other core parameters: Set the Kalman gain dynamic adjustment coefficient (typical value 0.8~0.9), second-order RC equivalent circuit model parameters (such as capacitance and resistance values, calibrated according to the characteristics of hybrid agricultural machinery batteries), and determine the algorithm convergence criteria.

[0026] S3. Based on the nonlinear equivalent circuit model of the battery, construct the state equation, map the battery state variables of the previous time k-1 to the observation space through a nonlinear observation function, and predict the battery state variables of the current time k to obtain the predicted battery state value of the current time k. k is a positive integer greater than 1.

[0027] The core objective of step S3 is to predict the battery's state variables and state estimation error covariance matrix at the current moment based on the battery state variables from the previous moment and the charging / discharging current at the current moment, using the constructed battery nonlinear state equation, thus adapting to the nonlinear operating characteristics of the hybrid agricultural machinery battery. The specific operation method in step S3 is as follows: S31. Invoking Nonlinear State Equations: Based on the second-order RC equivalent circuit model of the battery optimized for hybrid agricultural machinery operating conditions, a nonlinear state equation model suitable for agricultural machinery under varying loads is constructed. S32. One-step prediction of state variables: Substitute the optimal state vector at time k-1 and the preprocessed charging and discharging current at time k into the nonlinear state equation to complete the one-step prediction of the four major state variables at time k: SOC, SOH, internal resistance, and single-cell voltage balance, and obtain the predicted values ​​of each state variable. S33. The prediction of the state estimation error covariance matrix involves linearizing the state function f of the nonlinear state equation by performing a first-order Taylor expansion to obtain the Jacobian matrix Fk. Then, the prediction state estimation error covariance matrix at time k is calculated using the EKF prediction formula P, which reflects the uncertainty of the predicted state quantity. S34. Prediction Result Output: Output the predicted state vector Xk and the prediction state estimation error covariance matrix Pk at time k, to prepare for subsequent update steps.

[0028] S4. Calculate the optimal state vector for the current time k based on the predicted battery state value at the current time k and the observed battery state value at the current time k in the battery state observation data, and update the state vector.

[0029] This step is the core of achieving real-time error correction and adaptive nonlinear filtering. It optimally fuses the observed values ​​collected by the sensor with the predicted values ​​from the prediction step to calculate the optimal state quantity at the current moment. At the same time, it performs adaptive filtering of voltage and current signals to eliminate nonlinear non-Gaussian noise in hybrid agricultural machinery operations.

[0030] Using the preprocessed battery terminal voltage and charging / discharging current at time k as observation vectors, the predicted observation vector at time k is calculated through a constructed nonlinear observation equation. Specifically, the optimal state vector at time k is calculated and updated based on the predicted battery state value at current time k and the observed battery state value at current time k in the battery state observation data, including: S41. Calculate the residual between the predicted battery state and the observed battery state at the current time k; the residual reflects the degree of inconsistency between the model prediction and the sensor measurement, and the formula for calculating the residual is: ; in, Let the residual be at time k. Let be the actual observed value at time k. For nonlinear observation functions, This is the prior state estimate at time k (i.e., the state predicted based on information from the previous time).

[0031] S42. Take the first-order partial derivative of the nonlinear observation function to obtain the observation Jacobian matrix, and calculate the observation covariance matrix based on the observation Jacobian matrix. S43. Determine the Kalman gain based on the observation covariance matrix; Kalman gain reflects the weighting of predicted and observed values ​​in optimal state quantity estimation. The EKF algorithm dynamically adjusts the value of Kalman gain according to the operating conditions of hybrid agricultural machinery. S44. The predicted state vector is weighted and corrected based on the residual and Kalman gain to determine the optimal state vector at the current time k, and the state vector and the state estimation error covariance matrix are updated.

[0032] The optimal state vector at time k is calculated, enabling real-time error correction for state of charge, health state, internal resistance, and single-cell voltage balance. This yields the optimal estimates of each state variable at the current time, which is the core result of battery state detection. During the update of the state vector, the prediction error covariance matrix and the observation error covariance matrix are dynamically adjusted, and the collected battery state observation data (collected voltage and current signals) are adaptively filtered to remove electromagnetic interference, environmental noise, and nonlinear non-Gaussian noise caused by sensor vibration, thereby improving the accuracy of state quantity estimation.

[0033] S5. Compare the updated state vector information of the current time k with the preset reference value to obtain the comparison result, and determine the battery state detection result based on the comparison result.

[0034] The updated state vector information at the current time k is compared with a preset reference value to obtain a comparison result. Based on the comparison result, the battery state detection result is determined, including: S51. Compare the updated state of charge data with the preset state of charge reference threshold, determine whether the updated state of charge data exceeds the preset state of charge reference threshold range, and obtain the state of charge comparison result.

[0035] S52. Compare the updated health status data with the preset health status reference threshold, determine whether the updated health status data exceeds the preset health status reference threshold range, and obtain the health status comparison result.

[0036] S53. Compare the updated internal resistance with the preset internal resistance reference threshold, determine whether the updated internal resistance exceeds the preset internal resistance reference threshold range, and obtain the internal resistance state comparison result.

[0037] S54. Compare the updated individual voltage equalization with the preset individual voltage equalization reference threshold, determine whether the updated individual voltage equalization exceeds the preset individual voltage equalization reference threshold range, and obtain the individual voltage equalization status comparison result.

[0038] S55. Determine the battery's state detection results based on the comparison results of state of charge, health status, internal resistance status, and single-cell voltage balance status.

[0039] This step is the application output stage of the algorithm. The optimal state quantity obtained by the update step of the extended Kalman filter algorithm is used as the core input. Through the constructed hybrid agricultural machinery working condition adaptive fault early warning model, the identification, classification and early warning of potential battery faults are realized, transforming the traditional post-fault alarm into a pre-fault potential early warning, and at the same time outputting the battery full state quantity detection results.

[0040] 1. Full-state quantity output and storage: The system outputs the state of charge data, health data, internal resistance, and optimal estimate of single-cell voltage balance from the optimal state vector at time k in real time, and stores them synchronously to the vehicle BMS system. This provides data support for energy management and battery maintenance of hybrid agricultural machinery. The output accuracy meets the following requirements: absolute error of state of charge (SOC) ≤ 2%, relative error of state of health (SOH) ≤ 5%, absolute deviation of internal resistance estimation ≤ 5mΩ, and single-cell voltage balance identification error ≤ 0.03V. 2. Working condition matching and threshold retrieval: Based on the real-time working conditions of the hybrid agricultural machinery (identified by the on-board controller for tillage, climbing, transportation, and no-load) and environmental conditions (such as battery temperature), the threshold range of the state quantity under the corresponding working condition is retrieved from the fault warning model. The threshold range is a dynamic threshold calibrated in combination with the actual operating characteristics of the agricultural machinery, rather than a fixed threshold. 3. Multi-state quantity correlation analysis: Correlation analysis is performed on SOC, SOH, internal resistance, and individual cell voltage balance to capture the intrinsic relationship between each state quantity (such as the positive correlation between SOH decrease and internal resistance increase, and the chain reaction between internal resistance abnormality and individual cell voltage imbalance), and to identify the situation of single state quantity abnormality or multi-state quantity coordinated abnormality. 4. Potential Fault Identification: By comparing the real-time optimal state quantity with the threshold range of the corresponding operating condition, potential battery faults such as abnormal increase in internal resistance, inconsistent cell voltage, sudden change in SOC, and rapid decay of SOH are identified. At the same time, the fault type is determined by combining the fault duration and abnormality. 5. Graded early warning and signal output: Graded early warning is implemented according to the severity of the fault (such as mild warning, moderate warning, severe warning) and the warning signal is output to the hybrid agricultural machinery on-board controller. Mild warning only provides data reminders, moderate warning prompts the driver to check in time, and severe warning triggers the agricultural machinery to reduce load or stop. 6. Fault source tracing data retention: Data such as battery status, operating conditions, and environmental conditions at the time of fault identification will be retained to provide a basis for subsequent battery fault tracing and maintenance.

[0041] In a preferred embodiment of this application, the hybrid agricultural machinery battery state detection method further includes: S6. Dynamically optimize the parameters of the nonlinear equivalent circuit model, the process noise covariance matrix, and the observation noise covariance matrix based on the battery's historical operating data and the battery's full life cycle characteristics. This algorithm operates in a continuously looping, closed-loop process. After completing all steps at time k, the optimal state vector and optimal state estimation error covariance matrix at time k are used as the initial values ​​for time k+1. The algorithm then returns to step S3 to re-execute the prediction-update process, achieving 24-hour real-time continuous monitoring of the battery status. Simultaneously, the algorithm dynamically optimizes the parameters of the second-order RC equivalent circuit model, process noise covariance matrix, and observation noise covariance matrix based on long-term operating data of the hybrid agricultural machinery and the characteristics of the battery's entire lifecycle. This adapts to long-term characteristics such as battery capacity decay and internal resistance changes, ensuring the algorithm's detection accuracy and adaptability throughout the battery's lifecycle without requiring manual parameter adjustments.

[0042] Based on the same inventive concept, one embodiment of the present invention provides a hybrid agricultural machinery battery status detection system.

[0043] The hybrid agricultural machinery battery status detection system of the present invention can be installed in an electronic device. According to the functions implemented, the hybrid agricultural machinery battery status detection system includes: The data acquisition module is used to synchronously acquire battery status observation data of hybrid agricultural machinery batteries at a preset sampling frequency; The initialization module is used to construct the state vector of the extended Kalman filter algorithm; it initializes the initial values ​​of the state vector, the state estimation error covariance matrix, the prediction error noise covariance matrix, and the observation noise covariance matrix. The state prediction module is used to construct state equations based on the nonlinear equivalent circuit model of the battery. It maps the battery state variables at the previous time k-1 to the observation space through a nonlinear observation function to predict the battery state variables at the current time k, and obtains the predicted battery state value at the current time k, where k is a positive integer greater than 1. The state update module is used to calculate the optimal state vector at the current time k based on the predicted battery state value at the current time k and the observed battery state value at the current time k in the battery state observation data, and then update the state vector. The output module is used to compare the updated state vector information of the current time k with the preset reference value to obtain the comparison result, and determine the battery state detection result based on the comparison result.

[0044] The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0045] The various variations and specific examples of the hybrid agricultural machinery battery status detection method provided in the above embodiments are also applicable to the hybrid agricultural machinery battery status detection system of this embodiment. Through the foregoing detailed description of the hybrid agricultural machinery battery status detection method, those skilled in the art can clearly understand the implementation method of the hybrid agricultural machinery battery status detection system of this embodiment. For the sake of brevity, it will not be described in detail here.

[0046] This application also discloses an electronic device, such as Figure 2 The diagram shown is a schematic representation of an electronic device for a method of detecting the state of a hybrid agricultural machinery battery according to an embodiment of the present invention. The electronic device may include at least one processor 10, a memory 11 communicatively connected to the at least one processor, a communication bus 12, and a communication interface 13. It may also include a computer program, such as a method program for detecting the state of a hybrid agricultural machinery battery, stored in the memory 11 and executable on the processor 10.

[0047] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., methods for detecting the state of hybrid agricultural machinery batteries), and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0048] The memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 11 can include both internal and external storage units of the electronic device. The memory 11 can be used not only to store application software and various types of data installed in the electronic device, such as the code of a method program for detecting the state of a hybrid agricultural machinery battery, but also to temporarily store data that has been output or will be output.

[0049] The communication bus 12 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0050] Communication interface 13 is used for communication between the aforementioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), and optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.

[0051] Figure 2 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 2 The structure shown does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0052] For example, although not shown, the electronic device may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to at least one processor 10 via a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be elaborated further here.

[0053] It should be understood that the embodiments are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0054] Furthermore, if the modules / units integrated into the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile.

[0055] This application provides a computer-readable storage medium, including, for example, any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard disk, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM). The computer-readable storage medium stores a computer program that can be loaded by a processor and execute the hybrid agricultural machinery battery state detection method described in the above embodiments.

[0056] In the description of this specification, the references to terms such as "an embodiment," "some embodiments," "example," "specific example," "a implementation," "a preferred implementation," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0057] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for detecting the state of a hybrid agricultural machinery battery, characterized in that, The method includes: Battery status observation data of hybrid agricultural machinery batteries are collected synchronously at a preset sampling frequency; Construct the state vector of the extended Kalman filter algorithm; initialize the initial values ​​of the state vector, the state estimation error covariance matrix, the prediction error noise covariance matrix, and the observation noise covariance matrix; A state equation is constructed based on a nonlinear equivalent circuit model of the battery. The battery state variables at the previous time k-1 are mapped to the observation space through a nonlinear observation function to predict the battery state variables at the current time k, thus obtaining the predicted battery state value at the current time k, where k is a positive integer greater than 1. The optimal state vector for the current time k is calculated and updated based on the predicted battery state value at the current time k and the observed battery state value at the current time k in the battery state observation data. The updated state vector information at the current time k is compared with the preset reference value to obtain the comparison result, and the battery state detection result is determined based on the comparison result.

2. The method for detecting the battery status of hybrid agricultural machinery as described in claim 1, characterized in that, The state vector includes the battery's state of charge data, state of health data, internal resistance, and single-cell voltage balance.

3. The method for detecting the battery status of hybrid agricultural machinery as described in claim 2, characterized in that, The step of comparing the updated state vector information at the current time k with a preset reference value to obtain a comparison result, and determining the battery state detection result based on the comparison result, includes: The updated state of charge data is compared with the preset state of charge reference threshold to determine whether the updated state of charge data exceeds the preset state of charge reference threshold range, and the state of charge comparison result is obtained. The updated health status data is compared with the preset health status reference threshold to determine whether the updated health status data exceeds the preset health status reference threshold range, and the health status comparison result is obtained. The updated internal resistance is compared with the preset internal resistance reference threshold to determine whether the updated internal resistance exceeds the preset internal resistance reference threshold range, and the internal resistance state comparison result is obtained. The updated individual voltage equalization is compared with the preset individual voltage equalization reference threshold to determine whether the updated individual voltage equalization exceeds the preset individual voltage equalization reference threshold range, and the individual voltage equalization status comparison result is obtained. The battery's state detection results are determined based on the comparison results of state of charge, state of health, internal resistance, and individual cell voltage balance.

4. The method for detecting the battery status of hybrid agricultural machinery as described in claim 1, characterized in that, During the update of the state vector, the prediction error covariance matrix and the observation error covariance matrix are dynamically adjusted to perform adaptive filtering on the collected battery state observation data.

5. The method for detecting the battery status of hybrid agricultural machinery as described in claim 1, characterized in that, The step of calculating the optimal state vector at current time k based on the predicted battery state value at current time k and the observed battery state value at current time k in the battery state observation data, and updating the state vector, includes: Calculate the residual between the predicted battery state and the observed battery state at the current time k; The observation Jacobian matrix is ​​obtained by taking the first-order partial derivative of the nonlinear observation function, and the observation covariance matrix is ​​calculated based on the observation Jacobian matrix. Determine the Kalman gain based on the observation covariance matrix; The predicted state vector is weighted and corrected based on the residuals and Kalman gain to determine the optimal state vector at the current time k, and the state vector and the state estimation error covariance matrix are updated.

6. The method for detecting the battery status of hybrid agricultural machinery as described in claim 1, characterized in that, Battery status monitoring data includes information on the terminal voltage, charging and discharging current, battery temperature, and individual cell voltage of the hybrid agricultural machinery battery.

7. The method for detecting the battery status of hybrid agricultural machinery as described in claim 1, characterized in that, The method further includes: dynamically optimizing the parameters of the nonlinear equivalent circuit model, the process noise covariance matrix, and the observation noise covariance matrix based on the battery's historical operating data and the battery's full life cycle characteristics.

8. A hybrid agricultural machinery battery status detection system, used to implement the hybrid agricultural machinery battery status detection method according to any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to synchronously acquire battery status observation data of hybrid agricultural machinery batteries at a preset sampling frequency; The initialization module is used to construct the state vector of the extended Kalman filter algorithm; Initialize the initial values ​​of the state vector, the state estimation error covariance matrix, the prediction error noise covariance matrix, and the observation noise covariance matrix; The state prediction module is used to construct state equations based on the nonlinear equivalent circuit model of the battery. It maps the battery state variables at the previous time k-1 to the observation space through a nonlinear observation function to predict the battery state variables at the current time k, and obtains the predicted battery state value at the current time k, where k is a positive integer greater than 1. The state update module is used to calculate the optimal state vector at the current time k based on the predicted battery state value at the current time k and the observed battery state value at the current time k in the battery state observation data, and then update the state vector. The output module is used to compare the updated state vector information of the current time k with the preset reference value to obtain the comparison result, and determine the battery state detection result based on the comparison result.

9. An electronic device, characterized in that, The electronic device includes: At least one processor (10); and, A memory (11) communicatively connected to the at least one processor (10); The memory (11) stores a computer program that can be executed by the at least one processor (10), which is executed by the at least one processor (10) to enable the at least one processor (10) to perform the hybrid agricultural machinery battery status detection method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program; when the computer program is executed by a processor, it implements the hybrid agricultural machinery battery status detection method as described in any one of claims 1 to 7.

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