Lithium battery pack applied to robot

Abstract

The invention relates to the technical field of lithium battery management, in particular to a lithium battery pack applied to a robot. A plurality of functional modules are integrated in the lithium battery pack, the discharging strategy of the battery is dynamically adjusted by monitoring and analyzing the working state of the robot in real time, and firstly, the data acquisition module collects the real-time working load data of the robot and the discharging data of the battery pack; then, a mode recognition module judges which working mode the robot is currently in according to the data, and a strategy matching module selects a discharging strategy model most suitable for the current working mode from a preset strategy database; then the control parameter generation module inputs the working load data and the discharge data into a selected discharge strategy model to generate corresponding discharge control parameters; and finally, the discharge control unit adjusts the discharge behavior of the battery pack according to the control parameters, so that the energy utilization rate is improved, the cruising ability of the battery is improved, and the intelligent level of battery use is increased.

Description

Technical Field

[0001] The present application relates to the technical field of lithium battery management, and in particular to a lithium battery pack for use in robots. Background Art

[0002] With the rapid development of intelligent robots, the demand for battery performance is increasing. High-voltage, high-rate lithium batteries, due to their high energy density and excellent discharge performance, are widely used in various robots, especially in applications requiring high power output. These batteries not only provide sufficient power support, but also meet the needs of long-term operation.

[0003] Currently, most intelligent robots' lithium-ion battery management systems typically use a fixed discharge strategy. While this strategy is simple and easy to implement, it struggles to achieve precise energy management in a changing operating environment. This fixed strategy often leads to inefficient energy utilization, particularly in applications with complex tasks and varying operating modes. This situation warrants further improvement. Summary of the Invention

[0004] In order to solve the problem of low energy utilization efficiency caused by the existing fixed strategy of intelligent robots, this application provides a lithium battery pack for robots, which adopts the following technical solutions: In a first aspect, the present application provides a lithium battery pack for a robot, comprising: Data acquisition module, used to obtain the robot's real-time workload data and battery pack discharge data; a mode recognition module, configured to determine a current working mode of the robot based on the workload data; A strategy matching module is used to match a corresponding discharge strategy model in a preset strategy database according to the determined working mode; a control parameter generating module, configured to input the workload data and the battery pack discharge data into the discharge strategy model to obtain a discharge control parameter; The discharge control unit is used to adjust the discharge data of the battery pack based on the discharge control parameters.

[0005] By adopting the above technical solution, in existing intelligent robot applications, lithium batteries generally adopt a fixed discharge strategy, which leads to low energy utilization efficiency in different working modes and difficulty in coping with complex and changing working environments. For example, when the robot performs a high-intensity task, if it still operates according to the conventional discharge strategy, it may cause the battery to be exhausted prematurely, affecting the completion of the task; and during low-intensity tasks, if the discharge rate is too high, it will cause energy waste. The present application integrates multiple functional modules in the lithium battery pack to dynamically adjust the battery discharge strategy by real-time monitoring and analysis of the robot's working status. First, the data acquisition module collects the robot's real-time workload data and the battery pack's discharge data. Then, the pattern recognition module determines the robot's current working mode based on this data. The strategy matching module selects the discharge strategy model that best suits the current working mode from a preset strategy database. Then, the control parameter generation module inputs the workload data and discharge data into the selected discharge strategy model to generate corresponding discharge control parameters. Finally, the discharge control unit adjusts the discharge behavior of the battery pack based on these control parameters. The present application can dynamically adjust the discharge strategy according to the actual workload of the robot, thereby improving energy utilization, increasing battery life, and increasing the level of intelligent battery use.

[0006] Optionally, the pattern recognition module specifically includes: a motion and power data acquisition unit, configured to acquire motion data and power data of the robot based on the workload data; An environmental data acquisition unit, used to acquire environmental data of the robot's environment; The working mode recognition unit is used to input the environmental data, motion data and power data into a preset working mode recognition model to determine the current working mode of the robot.

[0007] By adopting the above technical solution, the motion state and energy consumption of robots working on complex outdoor terrain may change frequently. If the battery discharge strategy cannot be adjusted in time, it may lead to power waste or premature exhaustion, affecting the smooth completion of the task; this application is responsible for extracting the robot's motion data and power data from the workload data through the motion and power data acquisition unit; the environmental data acquisition unit collects relevant information about the robot's environment, such as temperature, humidity and other environmental data; then, the working mode recognition unit inputs this data into the preset working mode recognition model, and determines the specific working mode of the robot through analysis, which can more accurately identify the robot's working mode and thus optimize the discharge strategy.

[0008] Optionally, the working mode includes a patrol mode, a maneuvering mode, and a low power consumption mode, and the working mode identification unit further includes: A data preprocessing subunit, configured to preprocess the environmental data, motion data, and power data; An input subunit, used to input the pre-processed environmental data, motion data and power data into a preset working mode recognition model; a matching degree calculation subunit, configured to calculate the matching degree between the input data and each working mode in the working mode recognition model, obtain a matching score for each working mode, and determine the working mode with the highest matching score as the target working mode; The mode confirmation subunit is used to determine that the target working mode is the current working mode of the robot when the matching score of the target working mode is greater than a preset threshold; otherwise, perform working mode determination failure processing.

[0009] By adopting the above technical solution, when performing patrol tasks, the robot needs to move for a long time and maintain a certain speed. In the maneuvering mode, it may need to accelerate or decelerate suddenly, and in the low-power mode, it is necessary to minimize energy consumption. If the battery discharge strategy cannot be adjusted in time, it may lead to power waste or premature exhaustion, affecting the successful completion of the task. The present application pre-processes the received environmental data, motion data and power data through the data pre-processing subunit, and inputs the pre-processed data into the preset working mode recognition model through the input subunit. Then, the matching degree calculation subunit calculates the matching degree between the input data and each working mode in the working mode recognition model in turn, obtains the matching score of each working mode, and determines the working mode with the highest matching score as the target working mode. Finally, the mode confirmation subunit checks whether the matching score of the target working mode is greater than a preset threshold. If it is greater than the threshold, the target working mode is determined to be the current working mode of the robot. Otherwise, the working mode judgment failure processing is performed, thereby determining the working mode that best meets the current working conditions by analyzing the environmental data, motion data and power data. By setting the matching score threshold, the reliability of pattern recognition is ensured.

[0010] Optionally, the control parameter generation module specifically includes: a target discharge parameter estimation unit, configured to input the workload data and the battery pack discharge data into the discharge strategy model, and estimate the target discharge power and allowed discharge time of the battery pack under the corresponding discharge strategy; The output unit is configured to output the target discharge power and the allowed discharge time as discharge control parameters.

[0011] By adopting the above-mentioned technical solution, the present application integrates a control parameter generation module in the lithium battery pack, and dynamically adjusts the battery discharge strategy by real-time monitoring and analysis of the robot's working status. Specifically, the target discharge parameter estimation unit is first used to estimate the target discharge power and allowable discharge time of the battery pack under different working modes; then, the output unit uses these estimation results as discharge control parameters for use by the discharge control unit; the target discharge parameter estimation unit can provide personalized discharge strategies according to the specific working mode to ensure the optimal performance of the battery under different working conditions; the output unit directly converts the estimation results into discharge control parameters, which can simplify the design of the control system and improve the response speed and accuracy of the system.

[0012] Optionally, also include: A trigger condition setting module is used to obtain the preset battery management method trigger condition; A status monitoring module is used to monitor whether the current battery status meets the trigger condition; an execution control module, configured to control the data acquisition module to acquire real-time workload data and battery pack discharge data of the robot when the trigger condition is met; The trigger conditions include: the battery power or health status changes within a preset range; reaching a preset management method execution cycle; and receiving an instruction to perform a charging operation on the battery pack.

[0013] By adopting the above-mentioned technical solution, the present application dynamically adjusts the battery discharge strategy by real-time monitoring and analyzing the working status of the robot, and starts optimization management when specific trigger conditions are met. The trigger conditions include but are not limited to changes in the battery pack power or health status within a preset range, reaching a preset management method execution cycle, and receiving an instruction to perform a charging operation on the battery pack; when the status monitoring module detects that the current battery status meets the preset trigger conditions, the execution control module controls the data acquisition module to obtain the robot's real-time workload data and battery pack discharge data, thereby timely adjusting the battery discharge strategy.

[0014] Optionally, also include: A state feedback module is used to obtain state feedback data of the battery pack after discharge adjustment; A comparison and analysis module is used to compare the state feedback data with the expected state data to obtain state deviation data; An early warning trigger module is used to trigger an early warning mechanism if the state deviation data exceeds a preset safety threshold range; A parameter optimization module is used to generate optimized discharge control parameters based on the state deviation data and update the discharge strategy model.

[0015] By adopting the above-mentioned technical solution, the present application integrates a state feedback module, a comparative analysis module, a warning trigger module and a parameter optimization module into the lithium battery pack, dynamically adjusts the battery discharge strategy by real-time monitoring and analysis of the robot's working status, and continuously optimizes the discharge strategy through the feedback mechanism after the discharge adjustment to ensure that the battery pack is always in the best working state. This intelligent management method not only improves the robot's working efficiency, but also enhances its adaptability and reliability in complex environments, and extends the battery life.

[0016] Optionally, also include: an adaptive learning module, configured to collect and analyze historical workload data, discharge data, and environmental data, and optimize the work pattern recognition model and the discharge strategy model; The update interface is used to allow external devices to upload updated model parameters, realize remote upgrade and maintenance of the working mode recognition model and the discharge strategy model, improve the performance and safety of the lithium battery pack, and enhance the reliability and adaptability of the system.

[0017] By adopting the above technical solution, this application continuously optimizes the working mode recognition model and discharge strategy model by collecting and analyzing historical data, and at the same time allows external devices to upload updated model parameters to achieve remote upgrades and maintenance.

[0018] In a second aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor realizes the functions of the lithium battery pack applied to the robot when executing the computer program.

[0019] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the functions of the above-mentioned lithium battery pack applied to the robot when executed by a processor.

[0020] In summary, this application includes at least one of the following beneficial technical effects: 1. This application integrates multiple functional modules into a lithium battery pack, dynamically adjusting the battery discharge strategy by real-time monitoring and analyzing the robot's operating status. First, a data acquisition module collects the robot's real-time workload data and battery pack discharge data. Then, a pattern recognition module uses this data to determine the robot's current operating mode. A strategy matching module selects the discharge strategy model that best suits the current operating mode from a preset strategy database. A control parameter generation module then inputs the workload data and discharge data into the selected discharge strategy model to generate corresponding discharge control parameters. Finally, a discharge control unit adjusts the battery pack's discharge behavior based on these control parameters. This application can dynamically adjust the discharge strategy based on the robot's actual workload, thereby improving energy utilization, extending battery life, and increasing the level of intelligent battery use. 2. When performing patrol tasks, the robot needs to move for a long time and maintain a certain speed. In the maneuvering mode, it may need to accelerate or decelerate suddenly, and in the low-power mode, it is necessary to minimize energy consumption. If the battery discharge strategy cannot be adjusted in time, it may lead to power waste or premature exhaustion, affecting the successful completion of the task. The present application pre-processes the received environmental data, motion data and power data through the data pre-processing subunit, and inputs the pre-processed data into the preset working mode recognition model through the input subunit. Then, the matching degree calculation subunit calculates the matching degree between the input data and each working mode in the working mode recognition model in turn, obtains the matching score of each working mode, and determines the working mode with the highest matching score as the target working mode. Finally, the mode confirmation subunit checks whether the matching score of the target working mode is greater than a preset threshold. If it is greater than the threshold, the target working mode is determined to be the current working mode of the robot. Otherwise, the working mode judgment failure processing is performed, thereby determining the working mode that best meets the current working conditions by analyzing the environmental data, motion data and power data. By setting the matching score threshold, the reliability of pattern recognition is ensured. 3. This application integrates a status feedback module, a comparative analysis module, an early warning trigger module, and a parameter optimization module into the lithium battery pack. It dynamically adjusts the battery discharge strategy by real-time monitoring and analyzing the robot's working status, and continuously optimizes the discharge strategy through a feedback mechanism after the discharge adjustment to ensure that the battery pack is always in the best working condition. This intelligent management method not only improves the robot's working efficiency, but also enhances its adaptability and reliability in complex environments, and extends the battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a schematic diagram of a module of a lithium battery pack applied to a robot according to an embodiment of the present application; Figure 2This is a schematic diagram of a pattern recognition module in a lithium battery pack used in a robot according to an embodiment of the present application; Figure 3 This is a schematic diagram of three operating modes of a lithium battery pack used in a robot according to an embodiment of the present application; Figure 4 This is a schematic diagram of a control parameter generation module in a lithium battery pack applied to a robot according to an embodiment of the present application; Figure 5 This is a schematic diagram of three trigger conditions in a lithium battery pack applied to a robot according to an embodiment of the present application; Figure 6 This is another module schematic diagram of a lithium battery pack applied to a robot according to an embodiment of the present application; Figure 7 This is another module schematic diagram of a lithium battery pack applied to a robot according to an embodiment of the present application; Figure 8 This is a diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations comprising one or more of the listed items.

[0023] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0024] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0025] In the first aspect, the present application provides a lithium battery pack for a robot, referring to Figure 1 , including data acquisition module, pattern recognition module, strategy matching module, control parameter generation module and discharge control unit.

[0026] Among them, the data acquisition module is used to obtain the robot's real-time workload data and battery pack discharge data.

[0027] In this embodiment, the data acquisition module includes sensors, a data processor, and memory. The sensors collect the robot's motion data, load data, and the battery pack's current, voltage, and temperature information. The data processor performs preliminary processing on the collected data, such as filtering and data format conversion, before storing it in the memory.

[0028] Specifically, the sensors in the data acquisition module may include accelerometers, gyroscopes, current sensors, voltage sensors, and temperature sensors. The accelerometers and gyroscopes are used to detect the robot's motion status, the current sensor measures the battery pack's discharge current, the voltage sensor measures the battery pack's terminal voltage, and the temperature sensor monitors the battery pack's operating temperature. The data processor processes the data from these sensors, removing noise and performing necessary data conversions. The processed data is then stored for use by subsequent modules.

[0029] The mode recognition module is used to determine the current working mode of the robot based on the workload data.

[0030] In this embodiment, the data obtained from the data acquisition module is denoised and smoothed to improve data quality. Then, the robot's motion characteristics, load characteristics, and battery characteristics are extracted from the processed data, and a pre-trained classification model is used to identify the robot's current working mode, such as patrol mode, maneuvering mode, or low power mode.

[0031] The strategy matching module is used to match the corresponding discharge strategy model in the preset strategy database according to the determined working mode.

[0032] In this embodiment, the strategy matching module includes a pattern matching unit and a strategy retrieval unit. The pattern matching unit matches the working mode identified by the pattern recognition module with the pattern in the strategy database, and the strategy retrieval unit retrieves the corresponding discharge strategy model from the database.

[0033] Specifically, the pattern matching unit compares the operating mode output by the pattern recognition module with the patterns in the policy database to find the entry that most closely matches the current operating mode. Based on the matching results, the policy retrieval unit retrieves the corresponding discharge policy model from the database. These models are typically pre-trained based on extensive experimental data and contain optimal discharge policy parameters for different operating modes.

[0034] The control parameter generation module is used to input workload data and battery pack discharge data into the discharge strategy model to obtain discharge control parameters.

[0035] In this embodiment, the control parameter generation module includes a parameter calculation unit and a parameter output unit. The parameter calculation unit calculates the required discharge control parameters according to the discharge strategy model, and the parameter output unit outputs these parameters to the discharge control unit.

[0036] Specifically, the parameter calculation unit inputs workload data and battery pack discharge data into a matching discharge strategy model. Based on this input data, the model calculates optimal discharge control parameters, such as target discharge power and allowed discharge duration. The parameter output unit sends the calculated discharge control parameters to the discharge control unit for battery pack adjustment.

[0037] The discharge control unit is used to adjust the discharge data of the battery pack based on the discharge control parameters.

[0038] In this embodiment, the discharge control unit includes a control algorithm subunit and an execution subunit. The control algorithm subunit generates a specific control signal according to the discharge control parameter, and the execution subunit adjusts the discharge behavior of the battery pack according to the control signal.

[0039] Specifically, the control algorithm subunit receives discharge control parameters from the control parameter generation module and generates corresponding control signals based on these parameters, such as adjusting the battery pack's discharge current or limiting the discharge duration. The execution subunit then adjusts the battery pack's discharge behavior based on the control signals, such as by adjusting the battery management system's parameter settings. This ensures that the battery pack discharges according to the optimal strategy, thereby improving energy efficiency and extending battery life.

[0040] In one embodiment, referring to Figure 2 , the pattern recognition module specifically includes: The motion and power data acquisition unit is used to acquire the motion data and power data of the robot based on the workload data.

[0041] In this embodiment, the motion and power data acquisition unit obtains real-time workload data from the data acquisition module, and extracts data related to the robot motion, such as speed, acceleration, direction, etc., as well as data related to power consumption, such as motor power, drive current, etc.

[0042] Specifically, the motion and power data acquisition unit includes a signal separator and a data parser. The signal separator is responsible for separating the motion signal and power signal from the mixed workload data. The data parser further analyzes these signals to extract specific motion parameters (such as velocity and acceleration) and power parameters (such as motor power and drive current). For example, when the robot is performing a patrol mission, the signal separator separates the robot's motion data, such as average velocity and instantaneous acceleration, from the overall workload data, as well as the drive motor's power data, such as average power consumption and peak power.

[0043] The environmental data acquisition unit is used to acquire environmental data of the environment in which the robot is located.

[0044] In this embodiment, the data acquisition module also collects environmental data, such as temperature, humidity, light intensity, etc. The environmental data acquisition unit obtains the data of the environmental sensor from the data acquisition module and performs preliminary processing.

[0045] Specifically, the environmental data acquisition unit includes an environmental sensor interface and a data preprocessor. The environmental sensor interface connects to various environmental sensors, such as temperature, humidity, and light sensors. The data preprocessor performs preliminary processing on the sensor data, such as formatting and removing outliers. For example, the environmental data acquisition unit can obtain the current ambient temperature through a temperature sensor and the ambient humidity through a humidity sensor. This can then determine whether the robot is operating in a high-temperature or high-humidity environment, further confirming its operating mode.

[0046] The working mode recognition unit is used to input environmental data, motion data and power data into a preset working mode recognition model to determine the current working mode of the robot.

[0047] In this embodiment, the operating mode recognition unit includes a data fusion module and a pattern recognition algorithm. The data fusion module integrates data obtained from the motion and power data acquisition unit and the environmental data acquisition unit to form a unified data set. The pattern recognition algorithm uses a pre-trained classification model to identify the current operating mode. For example, if the data received by the data fusion module indicates that the robot is moving at high speed and the motor power is high, combined with the temperature information in the environmental data, the pattern recognition algorithm may determine that the robot is in maneuver mode. If the data indicates that the robot is moving slowly, the motor power is low, and the ambient temperature is moderate, the algorithm may determine that the robot is in low-power mode. Ambient temperature has a significant impact on battery performance. In high-temperature environments, the battery's discharge efficiency and capacity will decrease, while in low-temperature environments, the battery's internal resistance increases, affecting discharge efficiency. Therefore, combining ambient temperature data can better understand the actual operating state of the battery pack and more accurately identify the operating mode. For example, in high-temperature environments, even if the robot is in low-power mode, it may require more cooling system work, resulting in increased power consumption.

[0048] In one embodiment, there may be a problem of misjudgment in the working mode identification. By determining the specific working mode and setting a preset threshold, the risk of misjudgment can be reduced to ensure the stability and security of the system; Figure 3 , the working modes include patrol mode, maneuver mode and low power mode, and the working mode recognition unit further includes: The data preprocessing subunit is used to preprocess environmental data, motion data and power data.

[0049] Among them, the data preprocessing subunit includes data cleaning, data conversion and data standardization.

[0050] The input subunit is used to input the pre-processed environmental data, motion data and power data into the preset working mode recognition model.

[0051] In this embodiment, the input subunit is responsible for transmitting the pre-processed data to the working mode recognition model in an appropriate manner to ensure that the model can correctly receive and process the data.

[0052] Specifically, the input subunit includes a data formatting module and a data transmission module. The data formatting module converts preprocessed data into the format required by the model, such as combining different types of sensor data into a vector or matrix. The data transmission module is responsible for transmitting the formatted data to the working mode recognition model. For example, the input subunit combines preprocessed ambient temperature, humidity, robot speed and acceleration, and motor power data into a feature vector and inputs it into the working mode recognition model for processing.

[0053] The matching degree calculation subunit is used to calculate the matching degree between the input data and each working mode in the working mode recognition model, obtain the matching score of each working mode, and determine the working mode with the highest matching score as the target working mode.

[0054] In this embodiment, the matching degree calculation subunit is used to sequentially calculate the matching degree between the input data and each operating mode in the operating mode recognition model, obtain a matching score for each operating mode, and determine the operating mode with the highest matching score as the target operating mode. The matching degree calculation subunit identifies the current operating mode by calculating the similarity between the input data and different operating modes.

[0055] Specifically, the matching degree calculation subunit includes a similarity calculation module and a highest score determination module. The similarity calculation module uses a preset algorithm (such as Euclidean distance or cosine similarity) to calculate the similarity between the input data and each preset operating mode, obtaining a matching score for each mode. The highest score determination module identifies the highest score among all calculated matching scores and selects the corresponding operating mode as the target operating mode. For example, if the input data indicates a high robot speed and high acceleration, the matching degree calculation subunit may calculate that the maneuvering mode has the highest matching score and thus determine it as the current operating mode.

[0056] The mode confirmation subunit is used to determine that the target working mode is the current working mode of the robot when the matching score of the target working mode is greater than a preset threshold; otherwise, the working mode determination fails.

[0057] In this embodiment, the mode confirmation subunit ensures that the working mode is confirmed only when the matching degree is high enough, thereby improving the accuracy and reliability of pattern recognition.

[0058] In one embodiment, referring to Figure 4 , the control parameter generation module specifically includes: The target discharge parameter estimation unit is used to input the workload data and the battery pack discharge data into the discharge strategy model to estimate the target discharge power and the allowed discharge time of the battery pack under the corresponding discharge strategy.

[0059] In this embodiment, the target discharge parameter estimation unit analyzes input data and uses a pre-trained discharge strategy model to predict appropriate discharge parameters to ensure optimal performance of the battery pack in the current operating mode.

[0060] Specifically, the target discharge parameter estimation unit includes a data input interface, a model application module, and a parameter calculation module. The data input interface is responsible for receiving real-time workload data and battery pack discharge data from the data acquisition module; the model application module inputs this data into a pre-trained discharge strategy model; and the parameter calculation module calculates the target discharge power and allowable discharge duration of the battery pack based on the model output. For example, when the robot is operating in patrol mode, the target discharge parameter estimation unit uses the discharge strategy model to calculate the target discharge power and allowable discharge duration suitable for patrol mode based on the current load and battery status.

[0061] The output unit is used to output the target discharge power and the allowed discharge time as discharge control parameters.

[0062] Specifically, the output unit packages the target discharge power and the allowed discharge time in a specific protocol format and adjusts the discharge strategy of the battery pack according to these parameters.

[0063] In one embodiment, referring to Figure 5 , also includes: The trigger condition setting module is used to obtain the preset battery management method trigger condition.

[0064] In this embodiment, the trigger condition setting module allows a user or system administrator to define a set of trigger conditions that are used to determine when to initiate the battery management method. In this way, the battery management method is ensured to be initiated at the appropriate time, thereby optimizing the battery's efficiency and lifespan.

[0065] The status monitoring module is used to monitor whether the current battery status meets the trigger conditions.

[0066] The execution control module is used to control the data acquisition module to obtain the real-time workload data and battery pack discharge data of the robot when the trigger condition is met.

[0067] The triggering conditions include: the battery pack power or health status changes within a preset range; reaching a preset management method execution cycle; and receiving an instruction to perform a charging operation on the battery pack.

[0068] By setting trigger conditions, the system can intelligently decide when to start the battery management method, avoiding blind management under fixed times or conditions, improving the targeted management, and optimizing battery usage while ensuring the normal operation of the robot, saving energy and ensuring safety.

[0069] In one embodiment, referring to Figure 6 , also includes: The state feedback module is used to obtain state feedback data of the battery pack after discharge adjustment.

[0070] Specifically, the state feedback module obtains battery pack status information, such as remaining capacity, voltage, current, and temperature, from the BMS or other sensors. For example, after the discharge control unit adjusts the battery pack's discharge behavior based on new discharge control parameters, the state feedback module obtains the new battery pack status data, such as the new remaining capacity and temperature, from the BMS and processes it to prepare for subsequent comparative analysis.

[0071] The comparison and analysis module is used to compare the state feedback data with the expected state data to obtain state deviation data.

[0072] In this embodiment, the comparison and analysis module evaluates the effectiveness of the current discharge strategy by comparing the feedback data with the expected data, and calculates the difference between the actual state and the expected state.

[0073] Specifically, the comparative analysis module compares the actual state data provided by the state feedback module with the preset or theoretical expected state data, identifying any discrepancies. Based on these discrepancies, it calculates state deviation data, which measures the degree of deviation between the actual state and the expected state. For example, if the battery pack is expected to have an 80% remaining charge after adjusting the discharge strategy, but the actual state feedback data indicates a remaining charge of 75%, the comparative analysis module will calculate a 5% state deviation and record it for use by the parameter optimization module.

[0074] The early warning trigger module is used to trigger the early warning mechanism if the state deviation data exceeds the preset safety threshold range.

[0075] Specifically, the early warning trigger module compares the state deviation data with a preset safety threshold to determine whether it exceeds the safety range. Based on the comparison result, it generates an early warning message and alerts the user or system through visual, audible, or other means. For example, if the state deviation data indicates that the actual remaining charge of the battery pack differs from the expected value by more than 10%, the early warning trigger module will generate an early warning message and display it on the display or sound an alarm to remind the user to check the battery status or adjust the discharge strategy.

[0076] The parameter optimization module is used to generate optimized discharge control parameters based on state deviation data and update the discharge strategy model.

[0077] Specifically, the parameter optimization module may include a deviation analysis unit and a parameter adjustment unit. The deviation analysis unit analyzes the state deviation data to identify the main factors causing the deviation. Based on the results of the deviation analysis, the parameter adjustment unit adjusts the relevant parameters in the discharge strategy model to generate new discharge control parameters. For example, if the analysis finds that the actual remaining charge of the battery pack is always lower than the expected value, the parameter optimization module may adjust the discharge power parameter in the discharge strategy model to make it more conservative during future discharge processes to ensure that the remaining charge is closer to the expected value.

[0078] In one embodiment, referring to Figure 7 , also includes: Adaptive learning module, which collects and analyzes historical workload data, discharge data, and environmental data to optimize the work pattern recognition model and discharge strategy model; The update interface is used to allow external devices to upload updated model parameters, realize remote upgrade and maintenance of the working mode recognition model and discharge strategy model, improve the performance and safety of the lithium battery pack, and enhance the reliability and adaptability of the system.

[0079] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0080] In one embodiment, the present application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 8As shown. The electronic device includes a processor, a memory and a network interface connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the functions of the lithium battery pack applied to the robot are realized.

[0081] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0082] In one embodiment, an electronic device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0083] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The above-described computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0084] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A lithium battery pack for a robot, characterized in that: include: Data acquisition module, used to obtain the robot's real-time workload data and battery pack discharge data; a mode recognition module, configured to determine a current working mode of the robot based on the workload data; A strategy matching module is used to match a corresponding discharge strategy model in a preset strategy database according to the determined working mode; a control parameter generating module, configured to input the workload data and the battery pack discharge data into the discharge strategy model to obtain a discharge control parameter; The discharge control unit is used to adjust the discharge data of the battery pack based on the discharge control parameters.

2. The lithium battery pack for robots according to claim 1, characterized in that: The pattern recognition module specifically includes: a motion and power data acquisition unit, configured to acquire motion data and power data of the robot based on the workload data; An environmental data acquisition unit, used to acquire environmental data of the robot's environment; The working mode recognition unit is used to input the environmental data, motion data and power data into a preset working mode recognition model to determine the current working mode of the robot.

3. The lithium battery pack for robots according to claim 2, characterized in that: The working modes include patrol mode, maneuvering mode and low power consumption mode, and the working mode identification unit further includes: A data preprocessing subunit, configured to preprocess the environmental data, motion data, and power data; An input subunit, used to input the pre-processed environmental data, motion data and power data into a preset working mode recognition model; a matching degree calculation subunit, configured to calculate the matching degree between the input data and each working mode in the working mode recognition model, obtain a matching score for each working mode, and determine the working mode with the highest matching score as the target working mode; The mode confirmation subunit is used to determine that the target working mode is the current working mode of the robot when the matching score of the target working mode is greater than a preset threshold; otherwise, perform working mode determination failure processing.

4. The lithium battery pack for robots according to claim 1, characterized in that: The control parameter generation module specifically includes: a target discharge parameter estimation unit, configured to input the workload data and the battery pack discharge data into the discharge strategy model, and estimate the target discharge power and allowed discharge time of the battery pack under the corresponding discharge strategy; The output unit is configured to output the target discharge power and the allowed discharge time as discharge control parameters.

5. The lithium battery pack for robots according to claim 1, characterized in that: Also includes: A trigger condition setting module is used to obtain the preset battery management method trigger condition; A status monitoring module is used to monitor whether the current battery status meets the trigger condition; an execution control module, configured to control the data acquisition module to acquire real-time workload data and battery pack discharge data of the robot when the trigger condition is met; The trigger conditions include: the battery power or health status changes within a preset range; reaching a preset management method execution cycle; and receiving an instruction to perform a charging operation on the battery pack.

6. The lithium battery pack for robots according to claim 1, characterized in that: Also includes: A state feedback module is used to obtain state feedback data of the battery pack after discharge adjustment; A comparison and analysis module is used to compare the state feedback data with the expected state data to obtain state deviation data; An early warning trigger module is used to trigger an early warning mechanism if the state deviation data exceeds a preset safety threshold range; A parameter optimization module is used to generate optimized discharge control parameters based on the state deviation data and update the discharge strategy model.

7. The lithium battery pack for robots according to claim 2, characterized in that: Also includes: an adaptive learning module, configured to collect and analyze historical workload data, discharge data, and environmental data, and optimize the work pattern recognition model and the discharge strategy model; The update interface is used to allow an external device to upload updated model parameters to achieve remote upgrade and maintenance of the working mode recognition model and the discharge strategy model.

8. An electronic device, characterized in that: The invention comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor realizes the functions of the lithium battery pack applied to a robot according to any one of claims 1 to 7 when executing the computer program.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the functions of the lithium battery pack applied to a robot according to any one of claims 1 to 7 are realized.

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