Battery task scheduling method and system based on Internet of Things

Abstract

The invention relates to a battery task scheduling method and system based on the Internet of Things, a computer readable storage medium and electronic equipment. The battery task scheduling method based on the Internet of Things comprises the following steps that one or more pieces of information about a plurality of battery modules are obtained through a plurality of edge units, and each edge unit in the plurality of edge units corresponds to one or more battery modules in the plurality of battery modules; transmitting the acquired one or more pieces of information to a central unit; analyzing, via the central unit, based on the acquired one or more information; and determining a battery task scheduling strategy about the plurality of battery modules based on the analysis.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle batteries, and in particular to a battery task scheduling method and system based on the Internet of Things. Background Art

[0002] In terms of electric vehicle battery management, the current common preset scheduling strategies and regular maintenance methods rely on static preset parameters and are difficult to adapt to dynamic changes in ambient temperature, load conditions, user behavior, etc. in actual applications, resulting in poor accuracy of battery status monitoring data, affecting the operating efficiency and safety of the battery system. In addition, when battery production capacity is in excess and poor sales of new energy vehicle manufacturers lead to a large backlog of battery modules, there is a lack of effective monitoring methods for battery pack storage in the natural environment, which can easily cause thermal runaway of the battery and damage the battery quality. In addition, the existing system has a high reliance on manual operation, low maintenance efficiency, and complex system integration. These problems need to be solved urgently. Summary of the invention

[0003] In view of the above problems, the present disclosure aims to provide a battery task scheduling method and system based on the Internet of Things, a computer-readable storage medium and an electronic device.

[0004] The first aspect of the present disclosure is an IoT-based battery task scheduling method, comprising the following steps: acquiring one or more information about multiple battery modules via multiple edge units, wherein each of the multiple edge units corresponds to one or more battery modules among the multiple battery modules; transmitting the acquired one or more information to a central unit; performing analysis based on the acquired one or more information via the central unit; and determining a strategy for battery task scheduling for the multiple battery modules based on the analysis.

[0005] According to the battery task scheduling method based on the Internet of Things in one or more embodiments, optionally, the method further includes: transmitting the determined battery task scheduling strategy to one or more edge units among the multiple edge units; and scheduling the battery task according to the battery task scheduling strategy via one or more edge units.

[0006] According to the battery task scheduling method based on the Internet of Things in one or more embodiments, optionally, each of the one or more information includes one of the following: voltage information, temperature information, SOC information and battery firmware version information, and operation information of the edge unit.

[0007] According to one or more embodiments of the battery task scheduling method based on the Internet of Things, optionally, the strategy for scheduling battery tasks for multiple battery modules further includes: determining the execution priority of the battery tasks to be executed based on one or more information.

[0008] An Internet of Things-based battery task scheduling method according to one or more embodiments. Optionally, a strategy for battery task scheduling regarding multiple battery modules further includes: determining the edge unit allocation of battery tasks to be executed based on one or more pieces of information.

[0009] An Internet of Things-based battery task scheduling method according to one or more embodiments. Optionally, the method further includes: via a central unit, adopting a dynamic partitioning algorithm to adjust the number of battery modules corresponding to one or more edge units among multiple edge units according to one or more pieces of information.

[0010] An Internet of Things-based battery task scheduling method according to one or more embodiments. Optionally, the method further includes: via each of the multiple edge units, obtaining one or more pieces of information corresponding to one or more battery modules at a preset time interval.

[0011] An Internet of Things-based battery task scheduling method according to one or more embodiments. Optionally, transmitting the obtained one or more pieces of information to the central unit is performed by wireless transmission through the Message Queuing Telemetry Transport (MQTT) protocol.

[0012] An Internet of Things-based battery task scheduling method according to one or more embodiments. Optionally, analysis is performed via the central unit based on the obtained one or more pieces of information, and further includes: analyzing the priority and resource constraints of battery tasks using linear programming and integer programming.

[0013] An Internet of Things-based battery task scheduling method according to one or more embodiments. Optionally, determining a strategy for battery task scheduling regarding multiple battery modules based on the analysis further includes: when it is analyzed that the firmware version of a battery module has expired, setting the firmware upgrade task to the highest execution priority.

[0014] An Internet of Things-based battery task scheduling method according to one or more embodiments. Optionally, the strategy for battery task scheduling further includes: adjusting the load distribution of multiple battery modules or starting a heat dissipation device according to the temperature situation distribution of the multiple battery modules indicated by one or more pieces of information.

[0015] A computer-readable storage medium according to a second aspect of the present disclosure, which stores instructions that, when executed by a processor, implement the method according to any one of the foregoing embodiments.

[0016] An electronic device according to a third aspect of the present disclosure, which includes a memory and a processor, and the memory stores instructions that, when executed by the processor, implement the method according to any one of the foregoing embodiments.

[0017] The battery task scheduling system based on the Internet of Things according to the fourth aspect of the present disclosure includes: a plurality of edge units and a central unit. Each edge corresponds to one or more battery modules among the plurality of battery modules, and the plurality of edge units are configured to: acquire one or more pieces of information about the plurality of battery modules; transmit the acquired one or more pieces of information to the following central unit. The central unit is configured to perform analysis based on the acquired one or more pieces of information; determine a strategy for battery task scheduling for the plurality of battery modules based on the analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 FIG. shows a schematic flowchart of an Internet-of-Things-based battery task scheduling method 100 according to some embodiments. DETAILED DESCRIPTION

[0019] The following describes some of the multiple embodiments of the present disclosure, aiming to provide a basic understanding of the present disclosure. It is not intended to identify the key or decisive elements of the present disclosure or to limit the scope to be protected.

[0020] For the sake of brevity and illustrative purposes, the principles of the present disclosure are mainly described herein with reference to its exemplary embodiments. However, those skilled in the art will readily recognize that the same principles can be equivalently applied to all types of Internet-of-Things-based battery task scheduling methods and systems, computer-readable storage media, and electronic devices, and these same principles can be implemented therein, and any such variations do not depart from the true spirit and scope of this patent application.

[0021] Moreover, in the following description, reference is made to the drawings, which illustrate specific exemplary embodiments. Electrical, mechanical, logical, and structural changes can be made to these embodiments without departing from the spirit and scope of the present disclosure. In addition, although a feature of the present disclosure is disclosed in combination with only one of several embodiments / embodiments, this feature can be combined with one or more other features of other embodiments / embodiments as may be desired and / or advantageous for any given or identifiable function. Therefore, the following description should not be construed in a limiting sense, and the scope of the present disclosure is defined by the appended claims and their equivalents.

[0022] Terms such as "comprising" and "including" indicate that in addition to having units (modules) and steps directly and explicitly stated in the specification and claims, the technical solutions of the present disclosure do not exclude the situation of having other units (modules) and steps not directly or explicitly stated.

[0023] Figure 1 FIG. shows a schematic flowchart of an Internet-of-Things-based battery task scheduling method 100 according to some embodiments. The Internet-of-Things-based battery task scheduling method 100 may include the following steps:

[0024] In step 110, one or more pieces of information about a plurality of battery modules are obtained via a plurality of edge units, where each edge unit among the plurality of edge units corresponds to one or more battery modules among the plurality of battery modules. In step 120, the one or more pieces of information obtained are transmitted to a central unit. In step 130, analysis is performed based on the one or more pieces of information obtained via the central unit. In step 140, a strategy for battery task scheduling regarding the plurality of battery modules is determined based on the analysis.

[0025] Specifically, in the present disclosure, an edge unit refers to an intelligent device or node operating in an edge computing environment. These units are responsible for processing computing tasks close to the data source, reducing data transmission latency, and optimizing system performance. As an intelligent device (or agent) close to the battery module, the edge unit undertakes the task of data acquisition. Each edge unit is connected to a specific one or more battery modules and can sense the status of these battery modules in real time and acquire one or more pieces of information. The information it acquires covers multiple aspects, such as the operating parameters of the battery module, environmental data, status data, voltage, temperature, firmware version, SOC, etc. After these information are monitored and collected in real time, they can be transmitted to the central unit through, for example, Internet of Things communication technology. After receiving the data, the central unit analyzes this information using specific algorithms and models. For example, analyze the performance trend of the battery module, potential failure risks, etc. Based on these analysis results, the central unit formulates a battery task scheduling strategy for the plurality of battery modules, such as the type of task, priority, the edge unit to be called, etc. This battery task scheduling strategy aims to optimize the operating status of the battery module and improve the overall efficiency of the battery management system.

[0026] In some examples, the battery pack of each electric vehicle is divided into a plurality of battery modules, and each module is equipped with an edge unit. The edge unit acquires information such as the voltage and temperature of the battery module in real time and transmits it to a central unit such as the vehicle battery management system. Based on this information, the central unit can, for example, analyze the health status of each battery module, grasp the actual status of the battery module in real time, and then determine the scheduling strategy for tasks such as charging, discharging, or balancing of each battery module.

[0027] During actual use, the battery module is affected by various external factors, such as ambient temperature, changes in charging / discharging rate, load fluctuations, storage time, software version iteration and upgrade, etc. Fixed scheduling strategies cannot adapt to these dynamic changes, resulting in inaccurate state monitoring of the battery module and the inability to optimize scheduling tasks in real time, thereby affecting the performance and lifespan of the battery module. Through the present disclosure, centralized collection and analysis of battery module information are achieved, and based on this, a scheduling strategy is formulated, and the maintenance strategy is dynamically adjusted according to the real-time data of the battery module, so that the battery can be maintained in a timely manner when its state deteriorates rapidly, thus obtaining the best timing for repair or replacement. In addition, the problem of low efficiency and susceptibility to human factors in manual operation is also avoided, reducing the probability of system errors.

[0028] In some embodiments, method 100 may further include: transmitting the determined battery task scheduling strategy to one or more of the multiple edge units; and scheduling the battery tasks according to the battery task scheduling strategy via one or more edge units. After the central unit determines the battery task scheduling strategy, it accurately transmits these strategies to the corresponding one or more edge units through the Internet of Things communication network. After receiving the scheduling strategy, the edge unit performs the corresponding battery task scheduling operation according to the strategy content. For example, if the scheduling strategy requires charging a certain battery module, the edge unit will control the relevant charging equipment to charge the battery module according to the set parameters. This process from the central unit formulating the strategy to the edge unit executing ensures the coordinated operation of the entire battery management system. In an example, the central unit formulates a scheduling strategy based on the real-time status of each battery module, which requires discharging some battery modules to meet the current power demand. The central unit sends this strategy to the corresponding edge unit. After receiving the instruction, the edge unit controls the discharge switch and related circuits of the battery module to perform the discharge operation at the discharge rate and time specified by the strategy. The effective execution of the battery task scheduling strategy is achieved, enabling the battery management system to perform real-time and accurate task scheduling for the battery module according to the actual situation, improving the execution efficiency and actual operation effect of the battery management system.

[0029] In some embodiments, each of the above one or more pieces of information includes one of the following: voltage information, temperature information, SOC information, battery firmware version information, and operating information of the edge unit. The information collected by the edge unit is crucial for accurately evaluating the state of the battery module and formulating a reasonable scheduling strategy. Among them, the voltage information reflects the instantaneous power state of the battery module, and different voltage values can indicate the charging level of the battery and whether there are any abnormalities. The temperature information is related to the safety and performance stability of the battery. Excessive or too low temperature may affect the battery life and performance. The SOC (State of Charge) information represents the charge state of the battery, intuitively reflecting the remaining battery power. The battery firmware version information is used to judge the software state of the battery module. An outdated firmware version may affect the battery performance and functions. In addition, the operating information of the edge unit can reflect its own working state, ensuring the reliability of data collection and task execution. In the actual operation of, for example, an electric vehicle, the edge unit continuously collects the voltage information of the battery module. When detecting abnormal voltage fluctuations, it timely transmits the data to the central unit. The central unit conducts comprehensive analysis by combining the simultaneously collected temperature, SOC and other information to judge whether there are potential faults in the battery module, and then adjusts the scheduling strategy, such as suspending the charge and discharge operations of the module, to protect the battery module. In this way, the edge unit collects more types of information, providing a more comprehensive data basis for the central unit, making the analysis results more accurate, and thus enabling a more reasonable battery task scheduling strategy to be formulated to ensure the stable operation of the battery module.

[0030] In some embodiments, the strategy for battery task scheduling regarding multiple battery modules further includes: determining the execution priority of the battery tasks to be executed based on one or more pieces of information. After analyzing the information obtained from the edge units, the central unit determines the execution priority of each battery task according to the actual situation of the battery modules. For example, when it is detected that the temperature of a certain battery module is too high, which may cause safety problems, the cooling task for this module will be given a higher priority. If the firmware version of the battery module is severely outdated, affecting its performance and safety, the firmware upgrade task will be arranged with priority. By reasonably determining the task priority, it is ensured that under limited resources, tasks that have a greater impact on the safety and performance of the battery modules are processed first. For example, by analyzing the information of each battery module, the central unit finds that the temperature of one module is close to the danger threshold, while the firmware version of another module is relatively low. At this time, the central unit sets the cooling task for the battery module with too high temperature as the highest priority and starts the cooling device to cool it first; while the firmware upgrade task is arranged according to the system resources, either after the cooling task is completed or when the system resources permit. In this way, the execution priority of the battery tasks can be reasonably determined, enabling the battery management system to give priority to processing critical tasks when facing multiple tasks, ensuring the safe operation of the battery modules, and improving the overall performance and reliability of the battery management system.

[0031] In some embodiments, the strategy for battery task scheduling regarding multiple battery modules further includes: determining the edge unit allocation of the battery tasks to be executed based on one or more pieces of information. When formulating the battery task scheduling strategy, the central unit will, based on the information obtained from the edge units, consider factors such as the computing power, load conditions, and connection relationships with the battery modules of each edge unit, and reasonably allocate the battery tasks. For example, for battery data analysis tasks with a large amount of computation, they will be allocated to edge units with stronger computing power and lower current load; for tasks that require real-time control of specific battery modules, they will be allocated to the edge units directly connected to them. Such an allocation method can make full use of the resources of each edge unit and improve the task execution efficiency. For example, there are multiple edge units and a large number of battery modules. When a batch of battery modules need to perform firmware upgrade tasks, the central unit, according to the current load and computing power information of each edge unit, allocates the upgrade tasks to the edge units with lighter load and stronger computing power. After receiving the tasks, these edge units perform firmware upgrade operations on the battery modules they are responsible for respectively, ensuring the efficient completion of the upgrade tasks. By reasonably allocating battery tasks to appropriate edge units, the utilization of system resources is optimized, the execution efficiency of battery tasks is improved, and the overall operation efficiency of the battery management system is enhanced.

[0032] In some embodiments, method 100 may further include: via a central unit, using a dynamic partitioning algorithm to adjust the number of battery modules corresponding to one or more of the multiple edge units according to one or more pieces of information. The central unit, with the help of the dynamic partitioning algorithm, dynamically adjusts the number of battery modules responsible for each edge unit according to the battery module information collected by the edge unit, such as the working state of the battery module, load changes, etc. When the load of the battery modules in a certain area suddenly increases, causing excessive pressure on the corresponding edge unit, the central unit can, through the dynamic partitioning algorithm, allocate some battery modules to other edge units with lighter loads to balance the system load. Such dynamic adjustment can adapt to the real-time changes of the battery management system, improving the stability and resource utilization rate of the system. For example, in a battery energy storage system, as the power consumption load changes, the usage frequency and load conditions of battery modules in different areas vary greatly. By real-time monitoring the battery module information transmitted by each edge unit, the central unit finds that the load of the battery modules responsible for an edge unit in a certain area is too high, while the load of the edge unit in the adjacent area is low. Then, the central unit uses the dynamic partitioning algorithm to divide some battery modules from the area corresponding to the high-load edge unit to the area corresponding to the low-load edge unit, achieving an even distribution of the load. In this way, by dynamically adjusting the number of battery modules corresponding to the edge units, the system load is effectively balanced, the stability and resource utilization efficiency of the system are improved, and the battery management system can better adapt to different working scenarios and changing requirements.

[0033] In some embodiments, method 100 may further include: obtaining, via each of the plurality of edge units, one or more information corresponding to one or more battery modules at a preset time interval. In order to grasp the state changes of the battery modules in real time, the edge units collect the information of the corresponding battery modules at the preset time interval. This preset time interval is set according to the characteristics of the battery modules and system requirements, which can not only ensure obtaining sufficiently timely data but also avoid wasting resources due to overly frequent collection. For example, for the battery modules on an electric vehicle, since their operating states change relatively quickly, a shorter collection time interval may be set; while for some relatively stable energy storage battery modules, the collection time interval can be appropriately extended. The edge units periodically collect information such as voltage, temperature, and SOC, providing continuous battery state data for the central unit to facilitate trend analysis and decision-making. For example, the edge unit corresponding to each battery module is set to collect the voltage, temperature, and SOC information of the battery every 10 seconds. This information is transmitted to the central control unit of the vehicle in real time through the in-vehicle network. The central control unit monitors the battery state in real time based on the data collected at high frequencies. When it detects that the battery temperature rises abnormally, it takes timely cooling measures to ensure battery safety. Thus, collecting information at a preset time interval ensures that the state changes of the battery modules can be obtained in a timely manner, provides continuous and stable data support for the central unit, and helps to achieve real-time monitoring and precise management of the battery modules.

[0034] In some embodiments, transmitting the obtained one or more information to the central unit is performed by wireless transmission through the Message Queuing Telemetry Transport (MQTT) protocol. In the battery Internet of Things system of the present invention, the data transmission between the edge units and the central unit uses the MQTT protocol for wireless transmission. The MQTT protocol has characteristics such as lightweight, low power consumption, and a message publish / subscribe mechanism, and is very suitable for use in scenarios with a large amount of data and certain real-time requirements such as battery management. The edge units encapsulate the collected battery module information according to the specifications of the MQTT protocol and then send it out through the wireless communication network. The central unit acts as the subscriber of the MQTT protocol, receiving and parsing this data. This transmission method can ensure stable and efficient data transmission in a complex wireless environment, reducing data loss and latency. Even in cases where the signal is weak in some areas, the retransmission mechanism and low power consumption characteristics of the MQTT protocol ensure that the data can be accurately and timely transmitted to the central unit, enabling the central unit to grasp the states of each battery module in real time.

[0035] In some embodiments, the analysis performed by the central unit based on the acquired one or more pieces of information further includes: analyzing the priorities and resource constraints of battery tasks using linear programming and integer programming. After receiving the battery module information transmitted by the edge unit, the central unit uses linear programming and integer programming methods to deeply analyze the priorities and resource constraints of battery tasks. Linear programming can help determine how to optimally arrange battery tasks to maximize system performance while satisfying various resource limitations (such as computing resources, power resources, etc.). Integer programming, on the other hand, optimizes discrete variables in task allocation (such as whether a task is assigned to a certain edge unit). For example, when considering the execution order and resource allocation of multiple battery tasks, through linear programming and integer programming, factors such as the status of battery modules and the resource situation of edge units are comprehensively analyzed to determine the priority of each task, while ensuring that the system's resource limitations are not exceeded during task execution.

[0036] For example, after the central unit receives the information of each battery module, it may face multiple tasks, such as charging, discharging, maintenance, etc., while being subject to resource constraints such as power supply and the computing power of edge units. The central unit uses linear programming and integer programming methods to sort the priorities of these tasks and reasonably allocate resources. For example, it gives priority to ensuring the charging task of the backup battery of critical servers, and at the same time arranges the maintenance tasks of other battery modules while satisfying the power supply limit. In this way, by using linear programming and integer programming for analysis, it is possible to more scientifically and reasonably determine the priorities and resource allocation schemes of battery tasks, optimize the task scheduling of the battery management system, and improve the system resource utilization rate and overall operation efficiency.

[0037] In some embodiments, determining a strategy for battery task scheduling for multiple battery modules based on the analysis further includes: when it is analyzed that the firmware version of a battery module has expired, setting the firmware upgrade task to the highest execution priority. When analyzing the battery module information collected by the edge unit, the central unit will focus on the firmware version of the battery module. If it is found that the firmware version of a certain battery module has expired, it may lead to problems such as performance degradation and reduced security. To ensure the normal operation of the battery module, at this time, the central unit will set the firmware upgrade task to the highest execution priority. This means that when formulating the battery task scheduling strategy, the firmware upgrade operation of this battery module is given priority, and other tasks need to make way for it to complete the firmware upgrade as soon as possible to restore and improve the performance and security of the battery module. In this way, it is possible to timely solve the problems of battery module performance and security caused by the expired firmware version, ensure the stable operation of the battery module, and improve the reliability and security of the battery management system.

[0038] In some embodiments, the strategy of battery task scheduling further includes: adjusting the load distribution of multiple battery modules or starting a heat dissipation device according to the temperature distribution of multiple battery modules indicated by one or more pieces of information. The central unit analyzes the temperature distribution of multiple battery modules based on the battery module temperature information collected by the edge unit. When it is found that the temperature of some battery modules is too high, in order to prevent the battery from being damaged due to overheating and affecting its performance and lifespan, corresponding measures will be taken. On the one hand, the load distribution of the battery modules can be adjusted to reduce the workload of the modules with too high temperature and transfer the load to the modules with lower temperature to achieve temperature balance. On the other hand, if the overheating situation is relatively serious, the heat dissipation device is directly started to cool down the battery modules with too high temperature. Through these two methods, it is ensured that the battery modules operate within an appropriate temperature range.

[0039] For example, when the central unit analyzes the temperature information of each battery module and finds that the temperature of the battery modules in a certain area is relatively high. Then, the central unit can adjust the load distribution of these battery modules, reduce their discharge or charge current, and at the same time start the cooling fans in the corresponding area to forcibly dissipate heat from the battery modules with too high temperature, so that the temperature of the battery modules gradually returns to the normal range. In this way, by adjusting the load or starting the heat dissipation device according to the temperature of the battery modules, the temperature of the battery modules is effectively controlled, the performance and service life of the battery modules are guaranteed, and the stability and reliability of the battery management system are improved.

[0040] Based on the present disclosure, by introducing Internet of Things technology, multiple agents (central unit and edge unit) in the battery management system are networked to form an adaptive monitoring and scheduling network. When the edge unit senses environmental changes or battery state changes, it can transmit data to the central unit in real time through, for example, the MQTT protocol. The central unit then dynamically adjusts the task scheduling strategy according to this real-time data, so as to ensure that the state of the battery modules is always at the best management level. In addition, in some examples, the edge units can be controlled in zones by the central unit to achieve real-time monitoring and automated management of the state of the battery modules. Each edge unit operates independently, responsible for collecting data and executing tasks, while the central unit is responsible for global coordination and task allocation. Through this distributed management architecture, the system can automatically initiate maintenance tasks when detecting abnormal battery states without manual intervention, thus greatly improving the maintenance efficiency. In other examples, based on the unified Internet of Things architecture, the present disclosure significantly reduces the integration difficulty with other vehicle electronic control units and power systems in system design. Through standardized data interfaces and communication protocols (such as MQTT), seamless docking with other external systems can be achieved, improving the compatibility and scalability of the system, and enabling the entire battery management system to operate efficiently and reliably.

[0041] In one embodiment, for the battery Internet of Things environment, the battery management system may include multiple battery modules and multiple edge units (edge devices). Each battery module is composed of 8 or 12 battery cells connected in series, and the edge unit is responsible for information collection and task execution. The main tasks of the battery management system include monitoring tasks and refreshing tasks. The monitoring task periodically collects the status data of the battery module, such as voltage, temperature, firmware version, SOC, etc.; the refreshing task is used to automatically upgrade the battery module with a lower firmware version.

[0042] In this regard, in another embodiment, the solution of the present disclosure can be formulated. Specifically, the state space of the battery management system is represented by a vector S, which covers multiple dimensions such as computing power C, storage capacity S, and the number of tasks T. The edge unit interacts with the battery module to perform monitoring and upgrade tasks. Each task has an earliest start time and a latest end time, and the execution time of the task is determined by the task time interval. At the same time, a variety of constraint conditions are set, such as: the non - negative time constraint is used to ensure that the execution time of all tasks is non - negative, to avoid tasks ending before they start; the task indication variable is used to clarify whether the edge device is executing a certain task; the resource constraint ensures that the total resource requirements of the monitoring and upgrade tasks do not exceed the available resources of the edge device; the task priority constraint stipulates that when the firmware version is seriously overdue, the upgrade task is preferentially executed; the non - conflict constraint of task resource occupancy prevents the same edge device from executing the monitoring and upgrade tasks simultaneously, to avoid over - occupancy of resources. The objective function of the battery management system can also be set, which can be defined as the total execution time of the monitoring task and the update task, and the optimization objective of the entire method 100 can be, for example, to minimize this total time.

[0043] Based on the above description and formulation, the present disclosure may also involve a series of step - by - step processes.

[0044] First, the processing step - by - step process design can be carried out for the edge unit. The model corresponding to the process steps of the edge unit can run in the form of a finite - state machine, which is responsible for managing the operation states and their transitions in the system, and undertakes important tasks such as data collection, task execution, and status reporting, so as to ensure the best performance and reliability of the battery system by continuously monitoring and managing the resources at the edge level. The specific steps are as follows: 1) Initially, set the state S to idle; 2) Enter a loop and continuously listen for tasks. When receiving task T: Convert the state S to data collection, and collect data such as the voltage (V), temperature (T), SOC, firmware version (F), etc. of the battery module; the state S changes to task execution. If the task type is data processing, perform data processing operations; if it is firmware upgrade, perform firmware upgrade operations; after the task execution is completed, the state S becomes status reporting, and report the task result and the current state of the system to the system; finally, set the state S back to idle, waiting for the next task.

[0045] Then, the processing step flow can also be designed for the central unit. The central unit can achieve the coordinated work of task management and resource optimization with the help of a well-defined state machine model. Among them, the key states include data aggregation, global scheduling, task allocation, and monitoring feedback. The specific work process is as follows: 1) The initial state is data summarization; 2) Enter a loop. When data T from the edge agent is received: First, perform data aggregation to integrate the data transmitted by the edge devices; the state changes to global scheduling, and use linear programming and integer programming methods to optimize task allocation; then enter the task allocation state, and allocate the optimized tasks to the corresponding edge agents; then change to the monitoring feedback state to monitor the task execution process and whether there are any abnormalities in the system.

[0046] The processing step flow of the dynamic partitioning algorithm can be designed for the partitioning described above. Among them, this algorithm is applied to a distributed system to adjust the resource and task distribution. It dynamically adjusts the size of each partition and task allocation based on the real-time monitored data, so as to achieve the efficient use of resources and the balance of system load, and ensure that the system can maintain high performance under different workloads and environmental changes. The specific execution steps are as follows: 1) First, use dynamic programming for initial partitioning and establish a Bayesian model; 2) During the operation of the system, continuously collect real-time task and resource data; 3) Update the Bayesian model according to the collected data; 4) Optimize the partitioning using the updated Bayesian model; 5) Adjust the partitioning and reallocate resources according to the optimization results.

[0047] The processing step flow of negotiation between partitions can be designed for the partitioning described above: This step flow is used to ensure effective cooperation and resource sharing between different partitions in a distributed system. When the system has uneven load, each partition dynamically adjusts the resource allocation through a negotiation mechanism, thereby improving the overall efficiency and resource utilization rate of the system and optimizing the performance of the entire system. Its specific work steps are: 1) Work based on a game model and a contract; 2) During the operation of the system, continuously monitor the resource usage and task execution progress; 3) Negotiate a resource sharing agreement according to the monitoring results; 4) Execute the contract to optimize the resource allocation.

[0048] The processing flow steps of the partition-based enhanced MDP (Markov decision process) scheduling algorithm can be designed for the partitioning described above. This algorithm optimizes the task scheduling with the help of the partitioning mechanism. The MDP model uses the state space S and the action space A to describe the possible states and decision-making actions of the system. Through value iteration, the algorithm can dynamically adjust the state transition probability and the reward function, and then optimize the task scheduling strategy. The specific operation steps are as follows: 1) Set the MDP parameters; 2) Enter an iterative loop. Each time an iteration is performed: Execute value iteration; Update the state transition probability and the reward according to the partitioning adjustment; Optimize the scheduling strategy according to the update results.

[0049] On the other hand, the present disclosure uses decentralized control and automated management technology to achieve real-time monitoring, task scheduling, and efficient management of battery modules. The overall system architecture covers the object layer, collection layer, and application layer. The decentralized control and automated management technologies used are as follows:

[0050] First, decentralized control and automated management: A decentralized control architecture can be adopted, and multiple distributed modules (such as BRFM, VICM, BDSB, CMU) can work together to achieve autonomous collaborative work of chips, and tasks can be adjusted online in real time. Under this architecture, task allocation and execution are freed from dependence on a single central control node, and multiple distributed modules collaborate with each other to achieve seamless connection and efficient execution of tasks. This architecture significantly enhances the system's ability to respond to emergencies, reduces manual intervention, and improves the robustness of the system. In addition, at the object layer, RFID tags are used to identify and manage module bins. Each bin has a unique identifier, which is transmitted to the collection layer for data processing through ULTIUM wireless communication technology.

[0051] Second, multi-level data collection and processing can be performed: for example, the main processing unit of the system is the business server, which obtains data from the logistics center in real time through API calls and uses the AMQP protocol for data flow. After the data enters the business server, data access, data analysis, and data storage operations are performed in sequence, followed by data indicator calculation and analysis. Finally, the processing results are presented to the user through the application end developed based on Vue and Bootstrap. With the help of the data information server, the collection layer analyzes the matching of the battery script body information and the vehicle breakpoints in real time, and stores the battery parameter information in the database. The collected data is transmitted to the PC application via the local area network, which is convenient for users to remotely monitor and analyze.

[0052] Third, task scheduling and system integration: At the application layer, the system is connected to the local area network through a PC-side Web application to display the working status of the battery system in real time, including key parameters such as capacity and voltage. At the same time, the system dynamically adjusts the task scheduling strategy based on the real-time status of the battery module to ensure optimal performance under different time periods and conditions. The data center is responsible for managing the firmware upgrade tasks of the battery refresh center, using the MQTT protocol for task publishing and subscription to ensure data synchronization and system stability during the firmware upgrade process. MySQL is used as the database, which has a high access speed and data processing capability. In addition, the system establishes an interface between the battery and the workshop logistics management system, opens up the connection between the vehicle's software and hardware breakpoint requirements and the warehouse logistics management system, realizes the coordinated breakpoint, autonomous upgrade and inventory functions of the storage area materials, and monitors the battery's power, voltage, temperature and other states, optimizing the online management process.

[0053] Fourth, design and functional optimization of high-performance monitoring devices (edge units): The monitoring devices adopted in this system have many advantages. It adopts a square structure design, which is convenient for installation on walls or the ground and can effectively reduce the risk of falling. The device shell is made of ABS high molecular composite material, which has extremely high flexibility and impact resistance, ensuring that the device is not easily damaged in various environments. The device comes with a dust-proof net, which can effectively maintain the cleanliness of internal components, thereby extending the service life of the device. In addition, the device adopts an external independent antenna design, enhancing the networking communication ability; it is equipped with a dual-fan system, which can ensure the stable operation of the device in a high-temperature environment of up to 90°C. There is a USB expansion interface on the back of the device, supporting more application expansions. It integrates a high-computing-power processor, a wireless communication module, and a 4G module inside, and through a lean circuit layout, it ensures the efficient operation of the chip.

[0054] Exemplarily, the battery task scheduling system based on the Internet of Things according to the present disclosure may include multiple edge units. Among them, each edge corresponds to one or more battery modules among multiple battery modules, and the multiple edge units are configured to: obtain one or more pieces of information about the multiple battery modules; transmit the obtained one or more pieces of information to the following central unit. The battery task scheduling system based on the Internet of Things may further include a central unit, which is configured to: analyze based on the obtained one or more pieces of information; determine a strategy for battery task scheduling of the multiple battery modules based on the analysis. It can be understood that each part and module of the battery task scheduling system based on the Internet of Things according to the present disclosure can be designed with reference to each step of the method 100 described above and has similar functions, which will not be elaborated here.

[0055] According to another aspect of the present disclosure, there is also provided a computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed by a processor, implement the battery task scheduling method 100 based on the Internet of Things according to any one of the foregoing embodiments.

[0056] According to still another aspect of the present disclosure, there is also provided an electronic device. The electronic device includes a memory and a processor, and the memory stores instructions, which, when executed by the processor, implement the battery task scheduling method 100 based on the Internet of Things according to any one of the foregoing embodiments.

[0057] Among them, the computer-readable storage medium, memory, storage unit, storage module, etc. referred to in the present disclosure include various types of computer-readable storage media, which can be any available media accessible by a general or special computer. For example, the computer-readable medium may include RAM, ROM, EPROM, E 2A PROM, register, hard disk, removable disk, CD-ROM, or other optical disk storage, magnetic disk storage, or any other transient or non-transitory medium that can be used to carry or store desired program code units in the form of instructions or data structures and can be accessed by a general or special-purpose computer or a general or special-purpose processor. The above combinations should also be included within the scope of protection of computer-readable media. An exemplary storage medium is coupled to the processor so that the processor can read from and write to the storage medium. In an alternative, the storage medium can be integrated into the processor. The processor and the storage medium can, for example, reside in an ASIC. The ASIC can, for example, reside in vehicle systems, MCUs, ECUs, or battery management system-related hardware. In an alternative, the processor and the storage medium can reside as discrete components in vehicle systems, MCUs, ECUs, or battery management system-related hardware.

[0058] According to another aspect of the present disclosure, there is also provided a vehicle including an electronic device and / or a computer-readable storage medium according to any one of the embodiments of the present disclosure. The vehicle referred to in the present disclosure is intended to represent any suitable vehicle having a drive system, for example, a fuel-powered vehicle, a hybrid vehicle, an electric vehicle, a plug-in hybrid electric vehicle, and so on.

[0059] The above mainly describes the battery task scheduling method and system, computer-readable storage medium, and electronic device based on the Internet of Things of the present disclosure. Although only some specific embodiments of the present disclosure have been described, those of ordinary skill in the art should understand that the present disclosure can be implemented in many other forms without departing from its gist and scope. Therefore, the examples and embodiments shown are regarded as illustrative rather than restrictive, and the present disclosure may cover various modifications and substitutions without departing from the spirit and scope of the present disclosure as defined by the appended claims.

Claims

1. A battery task scheduling method based on the Internet of Things, characterized in that: The method comprises the following steps: Acquire one or more information about a plurality of battery modules via a plurality of edge units, wherein each edge unit in the plurality of edge units corresponds to one or more battery modules in the plurality of battery modules; transmitting the acquired one or more information to a central unit; performing, via the central unit, an analysis based on the acquired one or more information; and A strategy for battery task scheduling for the plurality of battery modules is determined based on the analysis.

2. The method according to claim 1, characterized in that The method further comprises: transmitting the determined battery task scheduling strategy to one or more edge units among the plurality of edge units; and The battery task is scheduled via the one or more edge units according to the battery task scheduling policy.

3. The method according to claim 1, characterized in that Each of the one or more information includes one of the following: voltage information, temperature information, SOC information and battery firmware version information, and operation information of the edge unit.

4. The method according to claim 1, characterized in that: The strategy for scheduling battery tasks for the multiple battery modules further includes: Based on the one or more information, an execution priority of the battery task to be executed is determined.

5. The method according to claim 1, characterized in that: The strategy for scheduling battery tasks for the multiple battery modules further includes: Based on the one or more information, an edge unit allocation for battery tasks to be performed is determined.

6. The method according to claim 1, characterized in that The method further comprises: The central unit uses a dynamic partitioning algorithm to adjust the number of battery modules corresponding to one or more edge units in the plurality of edge units according to the one or more information.

7. The method according to claim 1, characterized in that The method further comprises: The one or more information corresponding to the one or more battery modules is obtained at a preset time interval via each edge unit of the plurality of edge units.

8. The method according to claim 1, characterized in that: The transmission of the acquired one or more information to the central unit is performed wirelessly via the message queue telemetry transmission MQTT protocol.

9. The method according to claim 1, characterized in that: The performing analysis based on the one or more information obtained by the central unit further includes: analyzing the priority and resource constraints of the battery task using linear programming and integer programming.

10. The method according to claim 1, characterized in that The strategy of determining battery task scheduling for multiple battery modules based on analysis further includes: when it is analyzed that the firmware version of the battery module is expired, setting the firmware upgrade task to the highest execution priority.

11. The method according to claim 1, characterized in that The strategy for scheduling battery tasks further includes: adjusting the load distribution of the multiple battery modules or starting a heat dissipation device according to the temperature distribution of the multiple battery modules indicated by the one or more information.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, which, when executed by a processor, implement the method according to any one of claims 1-11.

13. A battery task scheduling system based on the Internet of Things, characterized in that: The battery task scheduling system based on the Internet of Things includes: A plurality of edge units, each edge corresponding to one or more battery modules in the plurality of battery modules, the plurality of edge units being configured as follows: obtaining one or more information about the plurality of battery modules; transmitting the acquired one or more information to the central unit as described below; A central unit configured to: performing analysis based on the acquired one or more information; A strategy for battery task scheduling for the plurality of battery modules is determined based on the analysis.

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