Battery management system of coal mine underground unmanned vehicle

Abstract

The invention discloses a battery management system of an underground coal mine unmanned vehicle, which relates to the technical field of vehicle data analysis, and comprises the following steps: determining a transportation path matched with transportation task information on an underground coal mine map, screening the transportation path according to a vehicle power condition, a road condition and a battery condition, a path with the highest vehicle and road adaptation degree is selected, and a reliable basis is provided for prediction of a subsequent vehicle battery discharge parameter curve and comparison of battery discharge parameters. The method comprises the following steps: setting a standard curve of vehicle battery discharge related parameters in the transportation process of an underground coal mine driverless vehicle, comparing the difference between the standard curve and an actual curve to evaluate the health of the vehicle battery, and evaluating the health change condition of the vehicle battery by taking a single task as a unit. The reliability and adaptability of battery management of the unmanned vehicle are improved, and the maintenance and management conditions of the battery are optimized, so that the availability and safety of the vehicle battery are continued.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle data analysis, and in particular to a battery management system for an unmanned vehicle in a coal mine. Background Art

[0002] The battery management solution for unmanned vehicles in coal mines is mainly based on the current development trend of intelligent and automated mines and the continuous advancement of battery technology. With the accelerated application of new generation information technologies such as artificial intelligence and 5G, unmanned driving technology in mines has reached a fairly high level of automation, and L4 level unmanned driving has become possible in mines. The underground environment of coal mines is complex, which puts higher requirements on the performance and safety of vehicle batteries. Therefore, the battery management solution needs to comprehensively consider multiple factors such as the health status, charging and discharging efficiency, safety and service life of the battery. By real-time monitoring of key battery parameters such as voltage, current, temperature, etc., combined with the degree of battery health changes, the charging and discharging strategy is dynamically adjusted to optimize battery performance, extend battery life, and ensure the safe and stable operation of the vehicle. In addition, with the continuous development of battery technology, the research and development of high-capacity, high-power, and long-life power batteries has become the key, providing more possibilities for battery management of unmanned vehicles in coal mines.

[0003] In the prior art, the vehicle battery health is monitored by checking the vehicle battery at a fixed period. However, each vehicle transportation mission is different, and regular battery monitoring cannot meet the complex battery management needs, resulting in poor reliability and adaptability of unmanned vehicle battery management, which is not conducive to vehicle battery maintenance.

[0004] Therefore, how to improve the reliability and adaptability of battery management for driverless vehicles is a technical problem that needs to be solved. Summary of the invention

[0005] The purpose of the present invention is to solve the problem of poor reliability and adaptability of battery management of unmanned vehicles in the prior art due to the fixed periodic inspection of vehicle batteries, and propose a battery management system for unmanned vehicles in coal mines, which includes: A determination module is used to obtain basic information of unmanned vehicles in coal mines, transportation task information and a map of coal mines, and determine a transportation path that matches the transportation task information on the map of coal mines; A prediction module, used to set a standard curve of vehicle battery discharge-related parameters of the unmanned vehicle in the coal mine during transportation according to the basic information, transportation task information and transportation route of the unmanned vehicle in the coal mine; The analysis module is used to collect the actual curve of the vehicle battery discharge-related parameters of the unmanned vehicle in the coal mine during transportation, compare the difference between the standard curve and the actual curve to evaluate the vehicle battery health, and obtain the battery health change degree; The optimization module is used to set the next vehicle battery charging and discharging strategy based on the battery health change, so as to optimize the battery management of unmanned vehicles in coal mines.

[0006] In some embodiments of the present application, the determination module is specifically used to: The transport task information includes the starting point, the end point and the weight of the transported object, and the starting point and the end point are marked on the coal mine underground map. The path planning algorithm is used to search for multiple optional paths based on the starting point and the end point, and the road features on each optional path are extracted, and the road features are divided into vehicle road features and non-vehicle road features. The basic information of the unmanned vehicle in the coal mine includes the vehicle power information and the vehicle battery information. The extreme value, average value and vehicle power consumption of the vehicle power characteristics of the unmanned vehicle on the optional path are predicted according to the vehicle power information and the weight of the transported object. The road suitability of each optional path is calculated based on the extreme value, average value, vehicle power consumption, vehicle road characteristics and non-vehicle road characteristics of the vehicle power characteristics. The vehicle power characteristics correspond to the types of vehicle road characteristics. ; in, For the The road suitability of the optional paths, , are the number of types of vehicle dynamic characteristics and non-vehicle road characteristics, , Respectively The combined weights of the extreme values ​​and average values ​​of the vehicle dynamic characteristics, , Respectively The first of the optional paths The extreme and average values ​​of the vehicle dynamic characteristics, For the The size of the vehicle road feature, For the The combined weights of non-vehicle road features, For the The first of the optional paths The size of non-vehicle road features, For the The vehicle power consumption of the optional routes, For the Constants corresponding to optional paths; The optimal optional path is found among multiple optional paths through road suitability, and the optimal optional path is used as the transportation path matching the transportation task information.

[0007] In some embodiments of the present application, the prediction module is specifically used to: Extract features based on the basic information of unmanned vehicles in coal mines, transportation mission information, and transportation routes on coal mine maps, and build a prediction model for vehicle battery discharge parameters; The vehicle battery discharge parameter prediction model outputs a change curve of the vehicle battery discharge related parameters of the unmanned vehicle in the coal mine during transportation. The horizontal axis of the change curve is the distance of the transportation path, and the vertical axis is the vehicle battery discharge related parameters; The planning information of all unmanned vehicles involved in the transportation route is collected, and the change curve of the vehicle battery discharge-related parameters is adjusted according to the planning information to generate a standard curve of the vehicle battery discharge-related parameters.

[0008] In some embodiments of the present application, a traffic congestion module is further included, and the traffic congestion module is used to adjust the change curve of the vehicle battery discharge-related parameters according to the planning information, which is specifically: All unmanned vehicles are marked on the coal mine underground map, and vehicle driving simulation is performed on the coal mine underground map based on the planning information on a time scale; The unmanned vehicle to be analyzed is recorded as the target vehicle, and the other unmanned vehicles are recorded as the remaining vehicles; The transport path of the target vehicle is divided into multiple sections, and the vehicle density around the target vehicle is calculated during the vehicle driving simulation on the coal mine underground map, and the driving speeds of the target vehicle and other vehicles within the preset vehicle density are counted to determine the average driving speed, and the number of vehicle intersections in each section is determined based on the transport path of the target vehicle and the transport paths of other vehicles; Determine potential congested sections and section locations among multiple sections based on the number of vehicle crossings, the average driving speed, and the vehicle density around the target vehicle, and capture congestion information on the potential congested sections; The potential congested road section is marked at the corresponding position of the change curve of the vehicle battery discharge related parameters, and the change curve of the vehicle battery discharge related parameters is adjusted according to the congestion information.

[0009] In some embodiments of the present application, the analysis module includes a collection management unit and a difference calculation unit.

[0010] In some embodiments of the present application, the collection management unit is used to: The BMS system on the unmanned vehicle is used to collect the actual values ​​of the vehicle battery discharge related parameters of the unmanned vehicle in the coal mine during transportation, and the vehicle positioning of the unmanned vehicle during transportation is monitored, so as to establish the actual curve of the vehicle battery discharge related parameters of the unmanned vehicle during transportation.

[0011] In some embodiments of the present application, the difference calculation unit is used to: Obtain the actual driving conditions of unmanned vehicles in coal mines, coordinate statistics on the actual driving conditions of all unmanned vehicles in coal mines, determine congested sections, and mark the congested sections on the actual curves; When comparing the difference between the standard curve and the actual curve, the standard curve and the actual curve are equally divided into a congested portion and a non-congested portion; The curve features of the congested part and the non-congested part of the standard curve and the actual curve are extracted, and the curve features of the congested part and the non-congested part of the standard curve and the actual curve are compared respectively, and the degree of change of the vehicle battery health after the completion of this task is determined after comprehensive analysis.

[0012] In some embodiments of the present application, the optimization module is specifically used to: Analyze the changes in vehicle battery health to set the next vehicle battery charging and discharging strategy, and help formulate vehicle battery maintenance plans and safety management.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. Determine the transportation route that matches the transportation task information on the coal mine underground map, select the transportation route according to the vehicle power, road and battery conditions, and select a route with the highest degree of adaptability between the vehicle and the road to ensure the rationality of the route of the unmanned vehicle, and provide a reliable basis for the prediction of the subsequent vehicle battery discharge parameter curve and the comparison of battery discharge parameters.

[0014] 2. Set a standard curve for the battery discharge-related parameters of unmanned vehicles in coal mines during transportation. Compare the difference between the standard curve and the actual curve to evaluate the health of the vehicle battery, and obtain the changes in battery health caused by this mission. Evaluate the changes in vehicle battery health based on a single mission, which improves the reliability and adaptability of battery management for unmanned vehicles, assists subsequent charging and discharging strategies, optimizes battery maintenance and management, thereby extending the availability and safety of vehicle batteries and ensuring the normal transportation of unmanned vehicles in coal mines. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic diagram of the structure of a battery management system for an unmanned vehicle in a coal mine proposed by the present invention; Figure 2 The present invention is a schematic diagram of the structure of an analysis module in a battery management system for an unmanned vehicle in a coal mine. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0017] Reference Figure 1 , a battery management system for unmanned vehicles in coal mines, including the following modules and units, The determination module is used to obtain basic information of unmanned vehicles in coal mines, transportation task information and coal mine underground maps, and determine a transportation path that matches the transportation task information on the coal mine underground maps.

[0018] In this embodiment, the basic information of the unmanned vehicle includes the model of the vehicle, battery capacity, maximum mileage, battery health status, etc. This information can be obtained through the vehicle management system or on-board sensors. Transportation task information includes the starting point, end point, cargo weight, transportation time, etc. This information is usually provided by the mine's dispatching system. Coal mine underground map includes the layout, slope, curves, obstacles and other information of the tunnel. This information can be obtained through a three-dimensional geographic information system (3DGIS) or a lidar scan. After obtaining this data, preprocessing is required to ensure the accuracy and consistency of the data. For example, the map data is cleaned and calibrated, and the vehicle and transportation task information is formatted and standardized.

[0019] In this embodiment, the path planning algorithm: based on the coal mine underground map and transportation task information, the path planning algorithm (such as A* algorithm, Dijkstra algorithm, etc.) is used to calculate the optional transportation path from the starting point to the end point. Path feasibility check: feasibility check is performed on the planned path to ensure that the path meets the vehicle's driving requirements (such as maximum slope, minimum turning radius, etc.), and the road suitability of the vehicle for each optional path is defined, which describes the suitability between the vehicle and the road.

[0020] In some embodiments of the present application, the determination module is specifically used to: The transport task information includes the starting point, the end point and the weight of the transported object, and the starting point and the end point are marked on the coal mine underground map. The path planning algorithm is used to search for multiple optional paths based on the starting point and the end point, and the road features on each optional path are extracted, and the road features are divided into vehicle road features and non-vehicle road features. The basic information of the unmanned vehicle in the coal mine includes the vehicle power information and the vehicle battery information. The extreme value, average value and vehicle power consumption of the vehicle power characteristics of the unmanned vehicle on the optional path are predicted according to the vehicle power information and the weight of the transported object. The road suitability of each optional path is calculated based on the extreme value, average value, vehicle power consumption, vehicle road characteristics and non-vehicle road characteristics of the vehicle power characteristics. The vehicle power characteristics correspond to the types of vehicle road characteristics. The optimal optional path is found among multiple optional paths through road suitability, and the optimal optional path is used as the transportation path matching the transportation task information.

[0021] In this embodiment, road suitability. Map modeling: The map information of the coal mine underground is converted into a graph structure in graph theory, where nodes represent key locations (such as intersections, loading and unloading points, etc.), and edges represent road sections and their attributes (such as length, slope, turning radius, etc.). Using the selected path planning algorithm (such as the A algorithm), search for all optional paths from the starting point to the end point in the graph structure. The A algorithm evaluates the pros and cons of each node by combining the actual cost (the path length from the starting point to the current node) and the estimated cost (the estimated value from the current node to the target node), thereby guiding the search to expand to the target node. After obtaining all optional paths, it is necessary to comprehensively consider influencing factors such as road section conditions, vehicle battery conditions, and cargo load conditions to screen out the most reliable optional path.

[0022] In this embodiment, the vehicle road characteristics are similar road characteristics such as slope, turning radius, and road section speed limit that can be compared with vehicle power, and the non-vehicle road characteristics are road characteristics such as road flatness and road congestion that cannot be compared with vehicle power. A vehicle dynamics model is established, and the vehicle dynamics model is the basis for predicting vehicle dynamics characteristics and power consumption. The model needs to comprehensively consider factors such as the mass, speed, acceleration, and friction between the tire and the ground of the vehicle. For unmanned vehicles, it is also necessary to consider the impact of the control strategy of the automatic driving system on the vehicle dynamic characteristics. The weight of the transported object will directly affect the dynamic characteristics and power consumption of the vehicle. Heavier transported objects will increase the load of the vehicle, causing the vehicle to require more power to overcome friction, thereby increasing power consumption. Therefore, in the prediction process, the weight of the transported object needs to be used as an input parameter and its impact on the vehicle dynamic characteristics is considered. For each optional path, the vehicle dynamics model and the weight information of the transported object are used to predict the dynamic characteristics of the vehicle on the path. This includes the extreme values ​​(maximum or minimum values) and average values ​​of parameters such as the vehicle's speed, traction, and turning radius. Based on the vehicle dynamic characteristics and the weight of the transported object, the power consumption of the vehicle on the path can be further predicted. This typically involves converting the vehicle's power demand into electrical energy consumption, taking into account factors such as the battery's charging and discharging efficiency.

[0023] In this embodiment, the types of vehicle power characteristics and vehicle road characteristics correspond to each other (they are of the same type), and the ratio of the two describes the adaptability of the vehicle power and road conditions. The adaptability of the extreme value and the average value describes the different stability conditions of the vehicle, for example, the ratio of the vehicle's maximum climbing grade to the road section slope, and the ratio of the vehicle's maximum traction to the road's required traction. It represents the correction of the vehicle power consumption to the sum of the vehicle road characteristics and non-vehicle road characteristics adaptation, This is to balance the size of the correction function.

[0024] The prediction module is used to set a standard curve of vehicle battery discharge-related parameters of the unmanned vehicle in the coal mine during transportation according to the basic information, transportation task information and transportation route of the unmanned vehicle in the coal mine.

[0025] In this embodiment, a standard variation curve of the vehicle battery discharge parameters is set according to the basic information of the unmanned vehicle, the transportation task information and the transportation route, including the variation relationship of the battery voltage, current, capacity and other parameters over time.

[0026] In some embodiments of the present application, the prediction module is specifically used to: Extract features based on the basic information of unmanned vehicles in coal mines, transportation mission information, and transportation routes on coal mine maps, and build a prediction model for vehicle battery discharge parameters; The vehicle battery discharge parameter prediction model outputs a change curve of the vehicle battery discharge related parameters of the unmanned vehicle in the coal mine during transportation. The horizontal axis of the change curve is the distance of the transportation path, and the vertical axis is the vehicle battery discharge related parameters; The planning information of all unmanned vehicles involved in the transportation route is collected, and the change curve of the vehicle battery discharge-related parameters is adjusted according to the planning information to generate a standard curve of the vehicle battery discharge-related parameters.

[0027] In this embodiment, the vehicle battery discharge parameter prediction model uses statistical methods to analyze historical data and identify key factors related to battery discharge parameters (such as voltage, current, capacity, etc.). (Load, power conditions, road conditions) perform correlation analysis to determine which factors have a significant impact on battery discharge parameters. Visual tools (such as charts, scatter plots, etc.) are used to display the relationship between data to help understand data characteristics and trends. Based on the data analysis results, select the appropriate model type. This can be a simple empirical formula to describe the linear or nonlinear relationship between battery discharge parameters and key influencing factors.

[0028] Model building process: Data preprocessing: Clean, denoise, and normalize the collected data to ensure data quality.

[0029] Feature selection: Based on the data analysis results, features that have a significant impact on battery discharge parameters are selected as model inputs.

[0030] Model training: Use the processed data to train the model and adjust the model parameters to optimize the prediction performance.

[0031] Model Validation: Validate model performance using an independent dataset to ensure the model can accurately predict battery discharge parameters.

[0032] The model generates a standard variation curve of battery discharge parameters based on the input data.

[0033] The curve uses the path length as the horizontal axis and the changes in battery-related discharge parameters (such as voltage, current, capacity, etc.) as the vertical axis.

[0034] The curve can show the changing trend of battery discharge parameters with path length, helping to understand the discharge behavior of the battery under different transportation conditions.

[0035] It should be noted that the battery discharge parameter change curve predicted by this model does not include the state of the vehicle in congestion, so it is necessary to consider the impact of congestion on the curve and adjust the curve. The frequent start and stop of vehicles in congestion will affect the discharge parameters and battery performance.

[0036] In some embodiments of the present application, a traffic congestion module is further included, and the traffic congestion module is used to adjust the change curve of the vehicle battery discharge-related parameters according to the planning information, which is specifically: All unmanned vehicles are marked on the coal mine underground map, and vehicle driving simulation is performed on the coal mine underground map based on the planning information on a time scale; The unmanned vehicle to be analyzed is recorded as the target vehicle, and the other unmanned vehicles are recorded as the remaining vehicles; The transport path of the target vehicle is divided into multiple sections, and the vehicle density around the target vehicle is calculated during the vehicle driving simulation on the coal mine underground map, and the driving speeds of the target vehicle and other vehicles within the preset vehicle density are counted to determine the average driving speed, and the number of vehicle intersections in each section is determined based on the transport path of the target vehicle and the transport paths of other vehicles; Determine potential congested sections and section locations among multiple sections based on the number of vehicle crossings, the average driving speed, and the vehicle density around the target vehicle, and capture congestion information on the potential congested sections; The potential congested road section is marked at the corresponding position of the change curve of the vehicle battery discharge related parameters, and the change curve of the vehicle battery discharge related parameters is adjusted according to the congestion information.

[0037] In this embodiment, vehicle density refers to the number of vehicles traveling simultaneously on the road near the target vehicle, and driving speed refers to the average speed of vehicles traveling on the road. The number of vehicle intersections refers to the intersections between different vehicle driving paths. Potential congested sections are determined based on the number of vehicle intersections, the average driving speed, and the vehicle density around the target vehicle. Congestion information on potential congested sections and congested sections are input into the vehicle battery discharge parameter prediction model to adjust the change curve of vehicle battery discharge related parameters.

[0038] It is understandable that the standard curve of the discharge parameter is predicted based on the current state of the vehicle battery and the road conditions.

[0039] The analysis module is used to collect the actual curve of the vehicle battery discharge-related parameters of the unmanned vehicle in the coal mine during transportation, compare the difference between the standard curve and the actual curve to evaluate the vehicle battery health, and obtain the battery health change degree.

[0040] In some embodiments of the present application, Figure 2 As shown, the analysis module includes a collection management unit and a difference calculation unit.

[0041] In some embodiments of the present application, the collection management unit is used to: The BMS system on the unmanned vehicle is used to collect the actual values ​​of the vehicle battery discharge related parameters of the unmanned vehicle in the coal mine during transportation, and the vehicle positioning of the unmanned vehicle during transportation is monitored, so as to establish the actual curve of the vehicle battery discharge related parameters of the unmanned vehicle during transportation.

[0042] In this embodiment, the battery management system (BMS) is the core component that controls the battery discharge strategy of the unmanned vehicle. It is responsible for monitoring the status of the battery, including key parameters such as voltage, current, and temperature, and adjusting the battery discharge strategy based on these parameters. The main functions of the BMS include: Status monitoring: Real-time monitoring of battery parameters such as voltage, current, and temperature through sensors to ensure that the battery operates within a safe range. Balance control: Since the performance of each single cell in the battery pack may vary, the BMS needs to implement balanced control of the battery pack to ensure that each single cell can be fully discharged to avoid overcharging or over-discharging. Protection mechanism: When the battery has abnormal conditions such as overvoltage, undervoltage, overcurrent, and overtemperature, the BMS needs to quickly activate the protection mechanism to cut off the battery's charge and discharge circuit to prevent battery damage or safety accidents.

[0043] In some embodiments of the present application, the difference calculation unit is used to: Obtain the actual driving conditions of unmanned vehicles in coal mines, coordinate statistics on the actual driving conditions of all unmanned vehicles in coal mines, determine congested sections, and mark the congested sections on the actual curves; When comparing the difference between the standard curve and the actual curve, the standard curve and the actual curve are equally divided into a congested portion and a non-congested portion; The curve features of the congested part and the non-congested part of the standard curve and the actual curve are extracted, and the curve features of the congested part and the non-congested part of the standard curve and the actual curve are compared respectively, and the degree of change of the vehicle battery health after the completion of this task is determined after comprehensive analysis.

[0044] In this embodiment, the difference between the standard curve and the actual curve describes the change in battery health after completing the task (this change will be set very small to capture the change in battery health after a single task). For example, if the voltage drop rate of the actual curve is significantly faster than that of the standard curve, it may indicate that the battery capacity is severely attenuated; if the current fluctuation amplitude of the actual curve is large, it may indicate that there is a fault or aging phenomenon inside the battery.

[0045] In this embodiment, the two curves are divided into a congested part and a non-congested part, and the same part is compared. The curve characteristics include slope characteristics, fluctuation amplitude characteristics, etc. For example, the slope difference may indicate that the discharge rate of the battery at different path distances is different. For example, a steeper slope of the actual curve may mean that the battery discharges faster on this path, which may be caused by battery capacity attenuation, increased internal resistance, or load changes.

[0046] In this embodiment, the curve characteristics of the congested part and the non-congested part of the standard curve and the actual curve are compared respectively, and the difference in curve characteristics of each type of discharge parameter in the congested part and the non-congested part is compared. Since the congested part has frequent starts and stops compared with the non-congested part, the slope of the curve may be steeper, etc., and the thresholds of the two parts are different, the overall change degree of each discharge parameter of the two parts is determined after comprehensive analysis, and the change degree of vehicle battery health after the completion of this task is determined.

[0047] ; in, This is the change in vehicle battery health after the mission is completed. , They are the conversion coefficients of the congested part and the non-congested part respectively. , The number of discharge parameters for the congested part and the non-congested part, respectively. , Respectively The discharge parameters and The influence weight of each discharge parameter, , Respectively The discharge parameters and The overall variation of the discharge parameters, express The sum of their respective maximum values, is a constant determined by the battery health before the mission. In order to balance the size of the correction function, express The correction of the sum of the congested part and the non-congested part by the sum of the respective maximum values.

[0048] The optimization module is used to set the next vehicle battery charging and discharging strategy based on the battery health change, so as to optimize the battery management of unmanned vehicles in coal mines.

[0049] Reference Figure 2 In some embodiments of the present application, the optimization module is specifically used to: Analyze the changes in vehicle battery health to set the next vehicle battery charging and discharging strategy, and help formulate vehicle battery maintenance plans and safety management.

[0050] In this embodiment, the battery health change, that is, the change in the battery health state (SOH), is an important indicator to measure the remaining life and performance of the battery. As the number of battery cycles increases, and affected by factors such as temperature and charge and discharge rate, physical and chemical changes will occur inside the battery, causing the SOH value to gradually decrease and the battery's available capacity to decrease. This change not only affects the battery's range, but is also directly related to the battery's charge and discharge efficiency and safety.

[0051] Charging strategy: When the battery health change shows that the battery performance has declined, the charging strategy can be adjusted appropriately, such as reducing the charging rate and extending the charging time, to reduce damage to the battery and extend the battery life.

[0052] For batteries with lower health, a gentler charging method, such as slow charging, can be used to avoid overheating and damage to the battery due to excessively fast charging rates.

[0053] Discharge strategy: According to the change in battery health, the driving strategy of the driverless vehicle can be optimized, such as planning the route reasonably and avoiding unnecessary sudden acceleration and braking to reduce energy loss during battery discharge.

[0054] When the battery health is low, the vehicle's power output can be adjusted to reduce the load requirements on the battery, thereby extending the battery life.

[0055] In addition to helping adjust the charging and discharging strategies, battery health variability can also provide strong support for vehicle battery management in the following aspects: Battery Maintenance Program: According to the battery health change, a more scientific battery maintenance plan can be formulated, including regular inspections, performance tests and replacement plans. This helps to timely detect and deal with potential battery problems and extend battery life.

[0056] Battery performance evaluation: By monitoring the battery health change, the battery performance status can be evaluated in real time, providing an important reference for vehicle scheduling and operation. For example, when the battery health is low, the vehicle can be given priority for charging or battery replacement to ensure the normal operation of the vehicle.

[0057] Security Management: Battery health changes can also help identify potential safety hazards of the battery. When the battery health drops to a certain level, it may mean that the battery is at risk of overheating, short circuit, etc. At this time, timely measures can be taken, such as reducing the vehicle speed, limiting the battery discharge depth, etc., to ensure the safety of the vehicle and personnel.

[0058] Compared with the prior art, the present invention has the following beneficial effects: 1. Determine the transportation route that matches the transportation task information on the coal mine underground map, select the transportation route according to the vehicle power, road and battery conditions, and select a route with the highest degree of adaptability between the vehicle and the road to ensure the rationality of the route of the unmanned vehicle, and provide a reliable basis for the prediction of the subsequent vehicle battery discharge parameter curve and the comparison of battery discharge parameters.

[0059] 2. Set a standard curve for the battery discharge-related parameters of unmanned vehicles in coal mines during transportation. Compare the difference between the standard curve and the actual curve to evaluate the health of the vehicle battery, and obtain the changes in battery health caused by this mission. Evaluate the changes in vehicle battery health based on a single mission, which improves the reliability and adaptability of battery management for unmanned vehicles, assists subsequent charging and discharging strategies, optimizes battery maintenance and management, thereby extending the availability and safety of vehicle batteries and ensuring the normal transportation of unmanned vehicles in coal mines.

[0060] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0061] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present invention.

[0062] Those skilled in the art will appreciate that the modules in the system in the implementation scenario can be distributed in the system of the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more systems different from the implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0063] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A battery management system for unmanned vehicles in coal mines, characterized in that: include, A determination module is used to obtain basic information of unmanned vehicles in coal mines, transportation task information and a map of coal mines, and determine a transportation path that matches the transportation task information on the map of coal mines; A prediction module, used to set a standard curve of vehicle battery discharge-related parameters of the unmanned vehicle in the coal mine during transportation according to the basic information, transportation task information and transportation route of the unmanned vehicle in the coal mine; The analysis module is used to collect the actual curve of the vehicle battery discharge-related parameters of the unmanned vehicle in the coal mine during transportation, compare the difference between the standard curve and the actual curve to evaluate the vehicle battery health, and obtain the battery health change degree; The optimization module is used to set the next vehicle battery charging and discharging strategy based on the battery health change, so as to optimize the battery management of unmanned vehicles in coal mines.

2. The battery management system for unmanned vehicles in coal mines according to claim 1, characterized in that: The determination module is specifically used to: The transport task information includes the starting point, the end point and the weight of the transported object, and the starting point and the end point are marked on the coal mine underground map. The path planning algorithm is used to search for multiple optional paths based on the starting point and the end point, and the road features on each optional path are extracted, and the road features are divided into vehicle road features and non-vehicle road features. The basic information of the unmanned vehicle in the coal mine includes the vehicle power information and the vehicle battery information. The extreme value, average value and vehicle power consumption of the vehicle power characteristics of the unmanned vehicle on the optional path are predicted according to the vehicle power information and the weight of the transported object. The road suitability of each optional path is calculated based on the extreme value, average value, vehicle power consumption, vehicle road characteristics and non-vehicle road characteristics of the vehicle power characteristics. The vehicle power characteristics correspond to the types of vehicle road characteristics. ； in, For the The road suitability of the optional paths, , are the number of types of vehicle dynamic characteristics and non-vehicle road characteristics, , Respectively The combined weights of the extreme values ​​and average values ​​of the vehicle dynamic characteristics, , Respectively The first of the optional paths The extreme and average values ​​of the vehicle dynamic characteristics, For the The size of the vehicle road feature, For the The combined weights of non-vehicle road features, For the The first of the optional paths The size of non-vehicle road features, For the The vehicle power consumption of the optional routes, For the Constants corresponding to optional paths; The optimal optional path is found among multiple optional paths through road suitability, and the optimal optional path is used as the transportation path matching the transportation task information.

3. The battery management system for unmanned vehicles in coal mines according to claim 1, characterized in that: The prediction module is specifically used to: Extract features based on the basic information of unmanned vehicles in coal mines, transportation mission information, and transportation routes on coal mine maps, and build a prediction model for vehicle battery discharge parameters; The vehicle battery discharge parameter prediction model outputs a change curve of the vehicle battery discharge related parameters of the unmanned vehicle in the coal mine during transportation. The horizontal axis of the change curve is the distance of the transportation path, and the vertical axis is the vehicle battery discharge related parameters; The planning information of all unmanned vehicles involved in the transportation route is collected, and the change curve of the vehicle battery discharge-related parameters is adjusted according to the planning information to generate a standard curve of the vehicle battery discharge-related parameters.

4. The battery management system for unmanned vehicles in coal mines according to claim 3, characterized in that: It also includes a traffic congestion module, which is used to adjust the change curve of the vehicle battery discharge related parameters according to the planning information, which is specifically: All unmanned vehicles are marked on the coal mine underground map, and vehicle driving simulation is performed on the coal mine underground map based on the planning information on a time scale; The unmanned vehicle to be analyzed is recorded as the target vehicle, and the other unmanned vehicles are recorded as the remaining vehicles; The transport path of the target vehicle is divided into multiple sections, and the vehicle density around the target vehicle is calculated during the vehicle driving simulation on the coal mine underground map, and the driving speeds of the target vehicle and other vehicles within the preset vehicle density are counted to determine the average driving speed, and the number of vehicle intersections in each section is determined based on the transport path of the target vehicle and the transport paths of other vehicles; Determine potential congested sections and section locations among multiple sections based on the number of vehicle crossings, the average driving speed, and the vehicle density around the target vehicle, and capture congestion information on the potential congested sections; The potential congested road section is marked at the corresponding position of the change curve of the vehicle battery discharge related parameters, and the change curve of the vehicle battery discharge related parameters is adjusted according to the congestion information.

5. The battery management system for unmanned vehicles in coal mines according to claim 1, characterized in that: The analysis module includes a collection management unit and a difference calculation unit.

6. The battery management system for unmanned vehicles in coal mines according to claim 5, characterized in that: The collection management unit is used to: The BMS system on the unmanned vehicle is used to collect the actual values ​​of the vehicle battery discharge related parameters of the unmanned vehicle in the coal mine during transportation, and the vehicle positioning of the unmanned vehicle during transportation is monitored, so as to establish the actual curve of the vehicle battery discharge related parameters of the unmanned vehicle during transportation.

7. The battery management system for unmanned vehicles in coal mines according to claim 5, characterized in that: The difference calculation unit is used to: Obtain the actual driving conditions of unmanned vehicles in coal mines, coordinate statistics on the actual driving conditions of all unmanned vehicles in coal mines, determine congested sections, and mark the congested sections on the actual curves; When comparing the difference between the standard curve and the actual curve, the standard curve and the actual curve are equally divided into a congested portion and a non-congested portion; The curve features of the congested part and the non-congested part of the standard curve and the actual curve are extracted, and the curve features of the congested part and the non-congested part of the standard curve and the actual curve are compared respectively, and the degree of change of the vehicle battery health after the completion of this task is determined after comprehensive analysis.

8. The battery management system for unmanned vehicles in coal mines according to claim 1, characterized in that: The optimization module is specifically used to: Analyze the changes in vehicle battery health to set the next vehicle battery charging and discharging strategy, and help formulate vehicle battery maintenance plans and safety management.

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