Battery test scheduling method and device based on multi-objective optimization driving

Abstract

The present application relates to the technical field of battery testing, solves the problem that the prior art cannot uniformly schedule and comprehensively balance battery testing tasks under multi-factor constraints, and provides a battery testing scheduling method and device based on multi-objective optimization driving, the method comprising: in response to a preset scheduling trigger instruction, obtaining real-time temperature in a battery testing environment, real-time charge and discharge parameters of a testing battery, and task attribute information of each task in a to-be-scheduled task sequence; constructing a multi-objective optimization model according to decision variables, a multi-objective cost function, and target constraint conditions; inputting the real-time temperature, the real-time charge and discharge parameters, and the task attribute information into the multi-objective optimization model to obtain a target task sequence; and controlling a handling module and a testing environment module to perform testing scheduling on to-be-tested batteries at each battery testing station according to the target task sequence. The present application achieves unified scheduling and comprehensive balancing of battery testing tasks under multi-factor constraints.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of battery testing, in particular to a battery testing scheduling method and device based on multi-objective optimization driving. BACKGROUND

[0002] The charge-discharge characteristics, cycle performance and consistency performance of lithium batteries under multiple temperature points are significantly different, so in the production, quality detection and research and development verification process, multiple temperature point charge-discharge tests in the range of-40℃ to +80℃ are usually required. A typical battery test system includes a temperature control box, multiple test stations, charge-discharge channels, and a mechanical handling device for feeding and discharging. As the test scale expands, dozens to hundreds of batteries need to be continuously switched between different stations and different temperature points, making the scheduling efficiency, temperature control energy consumption control and system power management of the test system particularly important.

[0003] The existing battery test system generally includes a temperature control box, multiple battery test stations, charge-discharge equipment, and a handling mechanism for feeding and discharging. Test tasks are usually queued according to first-in-first-out or fixed order, and the controller independently controls the temperature of the temperature control box, the movement path of the mechanical arm, and the charge-discharge equipment. Due to the separation of task scheduling logic and temperature control process, handling path planning and power load distribution, the existing technology generally has the following situations in actual operation: on the one hand, test tasks are switched between different target temperature points in disorder, causing the temperature control box to frequently raise and lower the temperature between multiple temperature points, and the temperature control process lacks overall planning, thereby causing the test cycle to be lengthened and the energy consumption level to be increased; on the other hand, the mechanical arm only optimizes the path around shortening the moving distance or reducing the single handling time, without considering the temperature state or subsequent test plan in the scheduling perspective, so that the device utilization and station turnover rhythm are difficult to form synergy with the temperature control process; in addition, the arrangement of charge-discharge tasks on the time axis is mostly static or locally controlled by rules, without planned timing distribution adjustment of tasks with different current levels and different charge-discharge states, which is easy to form instantaneous power load aggregation in some time period, causing great impact on the test power supply system or the plant distribution system, increasing the operation cost and operation risk.

[0004] Therefore, how to uniformly schedule and comprehensively balance the battery test tasks under multiple factor constraints is a technical problem to be solved. SUMMARY

[0005] Therefore, the embodiments of the present application provide a battery test scheduling method and device based on multi-objective optimization driving, to solve the problem that the prior art cannot uniformly schedule and comprehensively balance the battery test tasks under multiple factor constraints.

[0006] In a first aspect, an embodiment of the present application provides a battery test scheduling method based on multi-objective optimization driving, applied to a battery test scheduling device based on multi-objective optimization driving. The device comprises a controller, a test environment module connected to the controller, for providing required temperature conditions for battery testing, a plurality of battery test stations arranged in the test environment, and a data acquisition module for acquiring data for battery test scheduling in real time and transmitting the acquired data to the controller. A handling module is connected to the controller and is used to handle batteries between the loading and unloading area and the battery test stations.

[0007] The method comprises:

[0008] In response to a preset scheduling trigger instruction, the data acquisition module is used to acquire real-time temperature in a battery test environment, real-time charge and discharge parameters of a test battery, and task attribute information of each task in a to-be-scheduled task sequence.

[0009] A multi-objective optimization model is constructed according to a preset decision variable, a multi-objective cost function, and a target constraint condition.

[0010] The real-time temperature, the real-time charge and discharge parameters, and the task attribute information are input into the multi-objective optimization model to obtain a target task sequence.

[0011] According to the target task sequence, the handling module and the test environment module are controlled to perform test scheduling on the to-be-tested batteries in each battery test station.

[0012] In an optional embodiment, the response to the preset scheduling trigger instruction and the acquisition of the real-time temperature in the battery test environment, the real-time charge and discharge parameters of the test battery, and the task attribute information of each task in the to-be-scheduled task sequence by the data acquisition module comprise:

[0013] According to a preset scheduling trigger event, a scheduling trigger condition is acquired, wherein the scheduling trigger event comprises completion of a previous battery test task, addition of a new battery test task, and test time timeout.

[0014] The scheduling trigger condition is monitored, and when it is monitored that the scheduling trigger condition is met, the scheduling trigger instruction is acquired.

[0015] According to the scheduling trigger instruction, the test environment temperature, the charge and discharge current, and the charge and discharge voltage of the test battery are acquired by the data acquisition module to obtain the real-time temperature and the real-time charge and discharge parameters.

[0016] The attribute information of each task in the to-be-scheduled task sequence is analyzed to obtain the task attribute information.

[0017] In an optional embodiment, the constructing a multi-objective optimization model according to a preset decision variable, a multi-objective cost function and a target constraint condition comprises:

[0018] taking a task execution order in a task sequence as the decision variable;

[0019] constructing a total temperature control energy consumption cost function, a total test time cost function and a power penalty cost function according to preset constant parameters and initial state parameters, to obtain each target cost function;

[0020] performing weighted calculation on each target cost function according to a preset weight coefficient, to obtain the multi-objective cost function;

[0021] determining the target constraint condition according to a preset task execution frequency constraint condition, a carrying path continuity constraint condition and a power constraint condition;

[0022] constructing the multi-objective optimization model according to the decision variable, the multi-objective cost function and the target constraint condition.

[0023] In an optional embodiment, the constructing a total temperature control energy consumption cost function, a total test time cost function and a power penalty cost function according to preset constant parameters and initial state parameters, to obtain each target cost function comprises:

[0024] obtaining the constant parameters and the initial state parameters, wherein the constant parameters comprise a unit temperature difference energy consumption constant and a power threshold, and the initial state parameters comprise an initial test environment temperature, an initial carrying module position and an initial total charging and discharging power;

[0025] constructing the total temperature control energy consumption cost function according to the unit temperature difference energy consumption constant and the initial test environment temperature, in combination with target temperatures of adjacent tasks in a task sequence;

[0026] constructing the total test time cost function according to a carrying module moving time, a test environment stabilizing time and a task test duration;

[0027] constructing the power penalty cost function according to the initial total charging and discharging power, the power threshold and a total power curve in a task sequence execution process, in combination with a preset penalty coefficient;

[0028] determining each target cost function according to the total temperature control energy consumption cost function, the total test time cost function and the power penalty cost function.

[0029] In an optional embodiment, the constructing the total temperature control energy consumption cost function according to the unit temperature difference energy consumption constant and the initial test environment temperature, in combination with target temperatures of adjacent tasks in a task sequence comprises:

[0030] According to the task execution order in the task sequence, a first target temperature of a first task and a second target temperature of a neighboring task are obtained;

[0031] According to the first target temperature, the second target temperature and the initial test environment temperature, a plurality of temperature difference values are calculated;

[0032] The temperature difference values and the unit temperature difference energy consumption constant are integrated to obtain the total temperature control energy consumption cost function.

[0033] In an optional embodiment, the construction of the total test time cost function according to the transport module moving time, the test environment stabilization time and the task test duration includes:

[0034] According to the initial transport module position and the battery test station position corresponding to the first task in the task sequence, a target moving time required by the transport module is calculated;

[0035] According to the battery test station positions corresponding to the neighboring tasks in the task sequence and a preset moving time matrix, a total sequence moving time is calculated;

[0036] According to the initial test environment temperature and the target temperature corresponding to the first task, a target stabilization time is calculated;

[0037] According to the target temperatures corresponding to the neighboring tasks in the task sequence, a total sequence stabilization time is calculated;

[0038] The test durations of the tasks in the task sequence are summed to obtain the task test duration;

[0039] The target moving time, the total sequence moving time, the target stabilization time, the total sequence stabilization time and the task test duration are summed to obtain the total test time cost function.

[0040] In an optional embodiment, the construction of the power penalty cost function according to the initial total charging and discharging power, the power threshold and the total power curve in the task sequence execution process, combined with a preset penalty coefficient, includes:

[0041] The penalty coefficient is obtained, wherein the penalty coefficient includes a first penalty coefficient and a second penalty coefficient;

[0042] According to the initial total charging and discharging power, the charging and discharging state of each task in the task sequence, the average charging and discharging power and the preset test duration, a curve of the total power changing with time in the task sequence execution process is simulated and constructed to obtain the total power curve;

[0043] According to the total power curve, the first penalty coefficient and the power threshold, a power over-standard penalty term is calculated;

[0044] According to the total power curve, a power variance in a preset time interval is calculated;

[0045] According to the power variance and the second penalty coefficient, a power fluctuation penalty term is calculated;

[0046] The power over-standard penalty term and the power fluctuation penalty term are summed to obtain the power penalty cost function.

[0047] In an optional embodiment, the inputting the real-time temperature, real-time charging and discharging parameters and the task attribute information into the multi-objective optimization model to obtain a target task sequence comprises:

[0048] According to the real-time charging and discharging parameters, a real-time charging and discharging total power is calculated;

[0049] According to the task attribute information, real-time target temperatures, real-time test durations, real-time workstation positions, real-time charging and discharging states and real-time charging and discharging average powers of tasks in a to-be-scheduled task sequence are obtained;

[0050] According to the real-time charging and discharging total power, real-time target temperatures, real-time test durations, real-time workstation positions, real-time charging and discharging states and real-time charging and discharging average powers, a multi-objective optimization function in the multi-objective optimization model is parameterized and updated to obtain an updated multi-objective optimization model;

[0051] According to the updated multi-objective optimization model, a global search is performed on a solution space with a task execution sequence as the decision variable by using a differential evolution algorithm, and multi-objective cost function values of a plurality of candidate solutions representing different task execution sequences are calculated and evaluated to obtain a candidate task sequence;

[0052] The candidate task sequence is substituted into a preset integer programming solving model as an initial solution, and the task execution sequence under the target constraint condition is optimized and solved to obtain the target task sequence.

[0053] In an optional embodiment, if the current battery test scenario is a preset scheduling time delay limited scenario, the method further comprises:

[0054] According to the waiting time of each task in the to-be-scheduled task sequence, the temperature difference between the current test environment temperature and the real-time target temperature of each task, and the deviation between the system total power predicted according to the real-time charging and discharging total power, real-time charging and discharging state and real-time charging and discharging average power after executing each task and the preset ideal average power, evaluation index values representing time factors, temperature factors and power factors are respectively determined.

[0055] According to the preset evaluation weight coefficient, the evaluation index values are weighted and calculated to obtain priority score values corresponding to each task in the to-be-scheduled task sequence;

[0056] According to the priority score values, each task in the to-be-scheduled task sequence is sorted, and a target task is selected in turn according to the order from high to low of the priority score values to generate a fast scheduling task sequence for the current scheduling period;

[0057] According to the fast scheduling task sequence, the test scheduling of the to-be-tested battery corresponding to each target task in the fast scheduling task sequence is controlled by the carrying module and the test environment module, so that the battery test under the constraint of limited scheduling time delay is realized.

[0058] In a second aspect, the embodiments of the present application provide a test device, comprising at least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method of the first aspect in the above-mentioned embodiments.

[0059] In summary, the beneficial effects of the present application are as follows:

[0060] The battery test scheduling method and device based on multi-objective optimization driving provided by the embodiments of the present application, the method comprises: in response to a preset scheduling trigger instruction, acquiring real-time temperature in a battery test environment, real-time charge and discharge parameters of a test battery, and task attribute information of each task in a to-be-scheduled task sequence through the data acquisition module; constructing a multi-objective optimization model according to a preset decision variable, a multi-objective cost function, and a target constraint condition; inputting the real-time temperature, the real-time charge and discharge parameters, and the task attribute information into the multi-objective optimization model to obtain a target task sequence; and controlling the carrying module and the test environment module to perform test scheduling of the to-be-tested battery on each battery test station according to the target task sequence. The present application synchronously acquires the real-time temperature of the test environment, the charge and discharge states and power parameters of each battery, and the target temperature, test duration, station position, and other task attribute information of the to-be-scheduled task at the scheduling trigger time, takes the task execution order as the decision variable, constructs a multi-objective optimization model that simultaneously constrains the temperature control energy consumption, the test beat, and the system power load, and uniformly solves and generates the task sequence based on the model, so that the temperature control box temperature switching, the mechanical arm carrying movement, and the charge and discharge power distribution are no longer independently decided, but are cooperatively weighed and overall arranged under the same optimization framework, and then the carrying module and the test environment module are coordinately controlled through the target task sequence in the actual execution stage, which relieves the problems such as disordered temperature regulation, disconnection between path planning and temperature control, and unbalanced charge and discharge power timing in the prior art, and realizes comprehensive scheduling and overall optimization of the battery test process. BRIEF DESCRIPTION OF DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly introduced as follows, and other drawings can also be obtained by those of ordinary skill in the art without any creative effort on the basis of these drawings, and these drawings are within the protection scope of the present application.

[0062] Figure 1 is the overall flowchart of the battery test scheduling method based on multi-objective optimization driving in the embodiment 1 of the present application;

[0063] Figure 2 is the flowchart of constructing the multi-objective optimization model in the embodiment 1 of the present application;

[0064] Figure 3 is the flowchart of respectively constructing the total temperature control energy consumption cost function, the total test time cost function and the power penalty cost function in the embodiment 1 of the present application;

[0065] Figure 4 is the structural schematic diagram of the test equipment in the embodiment 2 of the present application. DETAILED DESCRIPTION

[0066] The features and exemplary embodiments of various aspects of the present application will be described in detail below, in order to make the purposes, technical solutions and advantages of the present application more clear and apparent, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only configured to explain the present application, and are not configured to limit the present application. The present application can be implemented without some of these specific details by those skilled in the art. The following description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0067] It should be noted that, in this paper, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitations, the elements defined by the statement "include" do not exclude the presence of other identical elements in the process, method, article or equipment including the elements.

[0068] It should be noted that all the actions of obtaining signals, information or data in the present application are carried out in accordance with the corresponding data protection regulations and policies of the place, and with the authorization given by the owner of the corresponding device.

[0069] Embodiment 1

[0070] Please refer to Figure 1 The embodiment of the present application provides a battery test scheduling method based on multi-objective optimization driving, which is applied to a battery test scheduling device based on multi-objective optimization driving. The device comprises a controller, a test environment module connected with the controller, used to provide the required temperature conditions for battery test, a plurality of battery test stations arranged in the test environment, and a data acquisition module used to acquire data for battery test scheduling in real time and transmit the acquired data to the controller. A carrying module is connected with the controller and used to carry the battery between the loading and unloading area and the battery test stations.

[0071] Specifically, the battery test scheduling device based on multi-objective optimization driving forms a multi-objective optimization scheduling system capable of dynamically adjusting the test sequence according to the real-time working condition through the controller to uniformly coordinate each functional module. The test environment module comprises a temperature control box located in the core area of the device and capable of providing the required target temperature range for different test tasks according to the temperature setting instructions issued by the controller and maintaining a stable temperature control environment among the plurality of battery test stations. The plurality of test stations are respectively used to carry the battery to be tested to complete the charge and discharge test under the specified temperature conditions. The data acquisition module is connected with the test environment module, the charge and discharge channel and the task management unit, and is used to acquire the current temperature, the voltage and current parameters of each battery and the task attribute data in real time and transmit these information to the controller, so that the scheduling logic can be based on the latest state. The carrying module comprises a mechanical arm responsible for transferring the battery between the loading and unloading area and each test station and accurately executing the path movement according to the scheduling instructions given by the controller. Each module is cooperatively operated under the centralized management of the controller, so that the scheduling system can be optimized in the three dimensions of energy consumption, test rhythm and power load, thereby maintaining the efficient, safe and grid-friendly battery test capability under the dynamic working condition.

[0072] The method comprises:

[0073] In response to a preset scheduling trigger instruction, the real-time temperature in the battery test environment, the real-time charge and discharge parameters of the test battery and the task attribute information of each task in the task sequence to be scheduled are acquired through the data acquisition module.

[0074] Specifically, when a trigger event of starting a new scheduling cycle is triggered, a scheduling trigger instruction corresponding to the trigger event is obtained, for example, a certain test task ends, a new test task is added to the queue or a preset time point is reached; the data acquisition module includes a sensing and communication interface connected with the temperature control box, the charging and discharging device and the task management unit, for acquiring the current temperature of the temperature control box, the voltage and current parameters of each test channel, and the target temperature, test duration, work position, charging and discharging state and average power of the task attribute information of the task to be scheduled. After receiving the scheduling trigger instruction, the controller drives the data acquisition module to read the temperature sensor data, the real-time voltage and current of the charging and discharging channel in turn, and calculates the current instantaneous total power by combining the preset algorithm, at the same time, reads the tasks not yet executed and their attributes from the task queue, stores the above information in the memory in a unified format, and uses it as the input of subsequent optimization modeling, so as to be able to grasp the temperature control box state, battery operating condition and task demand in real time, and provide reliable data basis for reducing temperature control energy consumption, avoiding power impact on the power grid and improving test resource utilization.

[0075] According to the preset decision variable, the multi-objective cost function and the target constraint condition, a multi-objective optimization model is constructed;

[0076] Specifically, the decision variable is used to describe the scheduling scheme to be optimized, for example, the task execution order, binary variable describing the relationship between tasks, etc. The multi-objective cost function quantifies the temperature control energy consumption, total test time and power smoothness, etc. The corresponding weight coefficient is introduced to reflect the importance of different targets. The target constraint condition includes that each task is executed only once, the mechanical arm movement path is continuous, the physical capacity limit of the temperature control box and the charging and discharging system, and the instantaneous power does not exceed the maximum allowed power threshold, etc. Through this modeling process, the actual battery test scheduling problem is transformed into a multi-objective optimization problem with clear objectives and boundary conditions, which is convenient for subsequent solution by intelligent algorithm. In specific implementation, the controller establishes a temperature switching energy consumption model according to the relationship between temperature change and energy consumption, constructs a total time model combining the movement time between each work position of the mechanical arm, the temperature control stabilization time and the test duration, defines the super power penalty term and the power fluctuation penalty term according to the system total power curve, and integrates the above models into a weighted cost function, and takes the decision variable as the independent variable, and takes the task integrity constraint, the path constraint and the power constraint as the condition, thereby forming a multi-objective optimization model. With the help of the model, the scheduling strategy is no longer dependent on experience rules, but comprehensively considers energy consumption, efficiency and power grid friendliness at the numerical level, laying a foundation for subsequent optimization.

[0077] The real-time temperature, real-time charging and discharging parameters and the task attribute information are input into the multi-objective optimization model to obtain a target task sequence;

[0078] Specifically, the real-time temperature is used to reflect the actual operating temperature of the current temperature control box, the real-time charging and discharging parameters include the voltage, current and power calculated therefrom of each test channel, and the task attribute information is used to describe the demand characteristics of the to-be-scheduled task for the temperature resource and the power resource. After introducing the above data as input into the multi-objective optimization model, the model can evaluate the energy consumption, time and power cost of different task permutations and combinations under the constraints of the current system state, and then select the task sequence with better comprehensive performance. In the implementation process, the controller first arranges the temperature, total power and task attributes collected in the first step into the data structure required by the model, substitutes the temperature switching path, mechanical arm moving path and power superposition of each task under different sorting into the multi-objective cost function, generates multiple candidate sequences by using global search algorithms such as differential evolution and iteratively filters them, and then uses an integer programming model to finely solve the optimal solution in the vicinity of the preferred solution, and finally obtains the task permutation with the minimum cost under the given constraints, and determines the permutation as the target task sequence. With the help of this real-time data-based solving process, the scheduling system can adaptively adjust the task order under different working conditions, reduce unnecessary temperature back-and-forth adjustment, shorten the waiting time, suppress the charging and discharging power peak value, and achieve more reasonable resource allocation.

[0079] According to the target task sequence, the conveying module and the test environment module are controlled to perform test scheduling on the to-be-tested batteries at each battery test station.

[0080] Specifically, the conveying module is usually a mechanical arm or other automatic conveying mechanism that conveys the batteries between the loading and unloading area and each test station, the test environment module mainly consists of a temperature control box that provides and maintains a set temperature condition and its control unit, and the target task sequence provides the execution order of each task and information such as the target temperature, station position, etc. According to the task sequence, the controller can perform integrated scheduling of the conveying and temperature control processes, so that the battery loading and unloading, station switching and temperature adjustment are coordinated with each other. In specific execution, the controller parses the next to-be-executed task according to the target task sequence, issues temperature setting instructions to the test environment module in advance, so that the temperature control box completes the temperature rising or falling and reaches stability before the target task starts, then issues path and grabbing instructions to the conveying module to send the corresponding battery into the specified station or transfer it between stations, and configures the working current and voltage of the charging and discharging equipment according to the charging and discharging state and average power parameters of the task, until the task test is completed; then the subsequent task information in the sequence is continuously called until the end of the current scheduling period. Through this execution link, the task sequence output by the optimization algorithm is strictly implemented at the physical level, the temperature switching frequency of the temperature control box is reduced, the mechanical arm idle stroke is compressed, and the total power curve of the system is smoother, which supports the technical effects of energy consumption reduction, power grid impact reduction and test efficiency improvement at the system operation level.

[0081] In an optional embodiment, the real-time temperature in the battery test environment, the real-time charge-discharge parameters of the test battery, and the task attribute information of each task in the task sequence to be scheduled are acquired by the data acquisition module in response to the preset scheduling trigger instruction.

[0082] According to a preset scheduling trigger event, a scheduling trigger condition is acquired, wherein the scheduling trigger event includes completion of a previous battery test task, addition of a new battery test task, and test time timeout;

[0083] Specifically, the scheduling trigger event is a business scenario condition for starting a new round of scheduling calculation, typically including completion of a previous battery test task, addition of a new battery test task to the test queue, and current test running time reaching or exceeding a preset timeout time. These events jointly constitute the scheduling trigger condition, which is used to define when the current scheduling scheme needs to be updated. By predefining the type and determination rule of the scheduling trigger event, the controller can automatically identify whether there is a scheduling demand during the test process, thereby avoiding rigid polling according to a fixed time interval. In implementation, a group of scheduling trigger event configurations can be maintained by the upper computer or the scheduling controller, the scheduling trigger condition is generated by logically combining the task completion signal, the task queue change signal, and the timer state, and is used as the basis for subsequent monitoring and judgment. With this sub-step, the scheduling mechanism is coupled with the actual test progress and task dynamics, the scheduling timing is more suitable for the field conditions, which is conducive to reducing invalid scheduling calculation and improving the real-time performance and resource utilization of the overall scheduling system.

[0084] The scheduling trigger condition is monitored, and when the scheduling trigger condition is met, the scheduling trigger instruction is acquired;

[0085] Specifically, the scheduling trigger instruction is generated by continuously monitoring the scheduling trigger condition when the preset condition is met. The scheduling trigger condition can be regarded as the combination result of the events configured in the previous sub-step, for example, any end of a test task or number of new tasks in the queue reaching a certain threshold. The scheduling trigger instruction is an execution signal sent to the scheduling module or the controller, which is used to formally start a new data acquisition and optimization modeling process. The system can monitor the task state, queue change, and timer state in the background at a high frequency, and when the logical judgment result is true, the scheduling trigger instruction is generated and written into the internal message queue or the interrupt processing flow is triggered, so that scheduling is carried out in time when the business state changes. By separating monitoring and instruction generation, continuous attention to the field state is ensured, and frequent triggering of large-scale calculation is avoided, which has obvious effect on improving the scheduling response speed, taking into account the calculation overhead and system stability.

[0086] According to the scheduling trigger instruction, the test environment temperature, the charge-discharge current and the charge-discharge voltage of the test battery are collected by the data acquisition module to obtain the real-time temperature and the real-time charge-discharge parameter;

[0087] Specifically, once the scheduling trigger instruction takes effect, the controller calls the data acquisition module to collect the test environment temperature and the charge-discharge current and voltage of each test battery once to obtain the real-time temperature and the real-time charge-discharge parameter for scheduling calculation. The test environment temperature is usually given by a temperature sensor arranged in a temperature control box, and the charge-discharge current and voltage are provided by a measurement module connected to the test channel. The controller can read these data through an analog quantity acquisition board card or a digital communication interface, and combine the current and voltage to be smoothed or filtered to improve the stability and reliability of the parameters. The instantaneous power or short-time average power of each channel can be further calculated from the voltage and current data to characterize the current system power level. The trigger collection method makes the model input data strictly aligned with the scheduling trigger time, which can accurately reflect the real working condition of the system at the time of scheduling decision, helping the subsequent multi-objective model to more accurately evaluate the temperature control energy consumption and power distribution, and reducing the decision deviation caused by measurement lag.

[0088] The attribute information of each task in the to-be-scheduled task sequence is parsed to obtain the task attribute information.

[0089] Specifically, for the to-be-scheduled task sequence, the attribute information of each task is uniformly parsed to form a structured task attribute information set. The task attribute information usually includes target temperature, test duration, corresponding station position, charge-discharge state, and average power fields, which are used to describe the demand characteristics of a single test task for temperature resources, time resources, and power resources. The controller can read the original task description from the task management module or the task queue, extract the parameter fields according to the predefined data format, type conversion and validity check, filter out the completed or cancelled tasks, and generate a task attribute array or list for algorithm processing. After this process, all to-be-scheduled tasks are uniformly expressed at the parameter level, which facilitates the subsequent multi-objective optimization model to calculate the cost of different task combinations, and also facilitates the introduction of temperature switching energy consumption and power penalty indicators for batch operation, so as to better balance energy consumption control, power grid impact suppression, and test beat arrangement at the algorithm level.

[0090] In an optional embodiment, referring to Figure 2 , the multi-objective optimization model is constructed according to the preset decision variable, multi-objective cost function and target constraint condition, which includes:

[0091] The task execution order in the task sequence is taken as the decision variable;

[0092] Specifically, the execution order of tasks in the task sequence is taken as a decision variable, that is, the position of each to-be-tested task in the sequence is used to depict the form of the scheduling scheme itself. The scheduling system does not directly modify the physical properties of individual tasks in the solving process, but indirectly affects the temperature switching path of the temperature control box, the movement path of the mechanical arm, and the superposition of the total system power at each time by adjusting the arrangement order of the tasks in the sequence. In practice, the execution order can be encoded in the form of a vector or an arrangement, for example, a sequence of length equal to the number of tasks is used to represent the execution order, and the sequence is taken as the search object of the optimization algorithm. By taking the execution order as a decision variable, the variable size is controllable, and it is convenient for algorithms such as differential evolution and integer programming to encode, mutate and select, which is conducive to obtaining an optimal scheduling scheme that takes into account the temperature control energy consumption, test time and power smoothness under limited computing resources.

[0093] According to the preset constant parameters and the initialization state parameters, the total temperature control energy consumption cost function, the total test time cost function and the power penalty cost function are constructed respectively to obtain the target cost functions;

[0094] Specifically, the total temperature control energy consumption cost function , the total test time cost function and the power penalty cost function are constructed respectively in combination with the preset constant parameters and the initialization state parameters. The constant parameters include the temperature control box unit temperature difference energy consumption constant , the system maximum allowable power threshold and the penalty coefficient for describing the power penalty intensity, and the initialization state parameters include the current temperature control box temperature, the initial position of the mechanical arm and the current total system power. The total temperature control energy consumption cost function is based on the temperature switching path corresponding to the task order, and the temperature control energy consumption is accumulated according to the relationship between the temperature difference and the energy consumption constant; the total test time cost function considers the moving time of the mechanical arm between the workstations, the time required for the temperature control box to stabilize from one target temperature to the next target temperature, and the test time of each task itself; the power penalty cost function is based on the total system power curve, and quantitatively punishes the part exceeding the power threshold and the power fluctuation degree. This multi-objective modeling method makes the energy consumption, efficiency and grid friendliness have clear mathematical depiction forms, which is convenient for unified trade-off in the subsequent steps.

[0095] According to the preset weight coefficient, the target cost functions are weighted and calculated to obtain the multi-objective cost function;

[0096] Specifically, the preset weight coefficient for dynamic priority calculation is used, wherein The multi-objective cost functions are weighted to form a multi-objective cost function that comprehensively reflects multiple objectives The multi-objective cost function needs to be minimized That is:

[0097]

[0098] wherein, represents a minimization process, and the weight coefficients are generally set according to the importance of temperature control energy consumption, total test time and power smoothness in actual application, for example, in a scenario where the power grid constraint is relatively tight, the weight of the power penalty term can be appropriately increased; in a scenario where the test cycle requirement is strict, the weight of the time cost can be increased. In implementation, the controller normalizes or scales the three cost functions according to the same dimension or comparable scale, then multiplies them by the corresponding weight coefficients and sums them up to obtain a single scalar form of the cost value, which is used to evaluate the overall pros and cons of a certain task order. Through weighted combination, the multi-objective problem is converted into a single-objective optimization problem, which is convenient for optimization algorithms to sort and select, while retaining differentiated attention to each technical index, avoiding the sacrifice of other key performance for the friendliness of the scheduling result to a single target.

[0099] According to the preset task execution frequency constraint condition, the carrying path continuity constraint condition and the power constraint condition, the target constraint condition is determined.

[0100] Specifically, the task execution frequency constraint condition, the carrying path continuity constraint condition and the power constraint condition are explicitly defined as the target constraint conditions that must be met in the optimization process. The task execution frequency constraint is used to ensure that each to-be-scheduled task appears exactly once in the final task sequence, neither missing nor repeatedly arranged; the carrying path continuity constraint is used to ensure that the next carrying of the mechanical arm always starts from the last resting station, so that the generated scheduling scheme is physically executable and avoids jumping station switching; the power constraint condition limits or soft-constraints the total system power at any time based on the maximum allowable power threshold, to prevent excessive instantaneous impact on the plant power grid during the test process. These constraints are usually written in the form of inequalities or equations in the optimization model, which work together with the decision variables and cost functions to make the task sequence obtained by solving implementable and safe in engineering.

[0101] According to the decision variables, the multi-objective cost function and the target constraint condition, the multi-objective optimization model is constructed.

[0102] Specifically, the decision variables, multi-objective cost functions and objective constraints are incorporated into the same mathematical framework to form a complete multi-objective optimization model. The model takes the task execution sequence as the independent variable, the weighted comprehensive cost function as the optimization objective, and the task number, carrying path and power boundary as the constraint set, forming a standard form of constrained optimization problem. Based on the model, the scheduling controller can call the differential evolution algorithm for global search, and then combine the integer programming method for local fine solution, to find a task sequence with smaller comprehensive cost while ensuring that all constraints are met. The model thus constructed changes the battery test scheduling from an empirical schedule to a computable, comparable and iterative optimization process, which is conducive to maintaining an optimal energy consumption level, test efficiency and grid friendliness under different operating conditions.

[0103] In an optional embodiment, referring to Figure 3 , the total temperature control energy consumption cost function, the total test time cost function and the power penalty cost function are constructed according to preset constant parameters and initialization state parameters, to obtain each objective cost function, including:

[0104] The constant parameters and the initialization state parameters are obtained, wherein the constant parameters include a unit temperature difference energy consumption constant and a power threshold, and the initialization state parameters include an initial test environment temperature, an initial carrying module position and an initial total charging and discharging power;

[0105] Specifically, the constant parameters are used to depict the physical properties of the system that do not change with the task in the entire scheduling period, such as the unit temperature difference energy consumption constant , which is used to describe the energy required by the temperature control box for each 1℃ lifting, and the maximum allowed power threshold of the system , which is used to limit the maximum total power allowed by the system at any time; the initialization state parameters are used to describe the immediate state of the system at the beginning of scheduling, including the initial test environment temperature , the initial carrying module position and the current initial total charging and discharging power . Before starting to construct the cost function, the scheduling controller needs to read all these two types of parameters, so that the cost function can reflect the actual running basis of the temperature control box, the mechanical arm and the power system. By uniformly collecting these parameters at the starting point of scheduling, the model can accurately establish the baseline temperature for energy consumption calculation, the starting position for path calculation and the initial point for power calculation, so that the subsequent cost function construction has a complete and calculable starting coordinate system.

[0106] The total temperature control energy consumption cost function is constructed according to the unit temperature difference energy consumption constant and the initial test environment temperature, in combination with the target temperature of adjacent tasks in the task sequence;

[0107] Specifically, in the modeling process of temperature control energy consumption, the unit temperature difference energy consumption constant reflects the energy consumption of temperature switching, and the initial test environment temperature is the starting temperature of the temperature control box. By associating these two parameters with the target temperature change between adjacent tasks in the task sequence, the temperature switching trajectory of the temperature control box during the entire execution of the task sequence can be obtained, and then the corresponding energy consumption can be accumulated according to the temperature difference. When constructing the cost function, the controller traverses the task sequence, and for the difference between the target temperatures of each adjacent two tasks , the product is accumulated, and the temperature difference energy consumption from the initial temperature to the target temperature of the first task is added at the first item of the sequence, thereby forming the total temperature control energy consumption cost function . This function can quantify the amplitude and frequency of temperature changes of the temperature control box under different task orders, and intuitively depict the energy consumption, providing a comparable index basis for the optimization algorithm.

[0108] According to the moving time of the carrying module, the test environment stabilization time and the task test time length, the total test time cost function is constructed;

[0109] Specifically, the total test time cost function is composed of multiple components, including the moving time of the carrying module between different stations, the time required for the temperature control box to stabilize between target temperatures, and the test time length of a single task itself. The moving time is given by the time matrix between stations, the temperature stabilization time can be calculated according to the temperature control box's temperature rising and falling ability and the target temperature difference, and the test time length is a parameter of the task attribute itself. When constructing this cost function, the controller calculates the moving time between the current task station and the next task station in sequence according to the order of the task sequence, adds the temperature stabilization time to the test time length of the corresponding task, and accumulates these time components into the total test time cost function. This function reflects the core device occupation time and the additional waiting time caused by scheduling, and has key significance for optimizing the overall cycle and reducing idle time.

[0110] According to the initial charge and discharge total power, the power threshold and the total power curve during the execution of the task sequence, and in combination with a preset penalty coefficient, the power penalty cost function is constructed;

[0111] Specifically, the power penalty cost function The initial total charging and discharging power, the power threshold, and the total power curve of the system during the task execution process are integrated. The power curve is calculated by superimposing the current channel current and voltage, and is used to describe the real-time power load of the system during the execution of the scheduling sequence. The power penalty model generally considers both the part exceeding the power threshold and the impact of power fluctuation, and the penalty coefficient is used to adjust the sensitivity of power safety in different scenarios. The cost function integrates or accumulates the penalty of the part exceeding the power threshold, and additionally punishes the variance or fluctuation amplitude of the total power curve, so as to ensure that the scheduling scheme maintains a relatively smooth and controlled power distribution during the execution process, thereby avoiding the problem of instantaneous peak impact on the power grid.

[0112] According to the total temperature control energy consumption cost function, the total test time cost function and the power penalty cost function, each target cost function is determined.

[0113] Specifically, after constructing the total temperature control energy consumption cost function , the total test time cost function and the power penalty cost function , the scheduling controller encapsulates the three cost functions, so that each cost function maintains an independent mathematical structure and can be called alone, and is used as three optimization objectives of the multi-objective optimization model. This step arranges the three cost functions into the form of a standardized objective function that can be directly involved in weighted sum and solution, laying a foundation for the generation of subsequent multi-objective cost functions and the construction of overall optimization models. In this process, the controller ensures that each cost function has relevance with the decision variables and can successfully participate in model solution under the constraint condition, so that the performance of different task sequences in the three dimensions of energy consumption, efficiency and power can be completely and accurately evaluated.

[0114] In an optional embodiment, constructing the total temperature control energy consumption cost function according to the unit temperature difference energy consumption constant and the initial test environment temperature, in combination with the target temperature of the adjacent task in the task sequence comprises:

[0115] According to the execution order of the tasks in the task sequence, a first target temperature of a first task and a second target temperature of an adjacent task are obtained;

[0116] Specifically, the task execution sequence determines the target temperatures that the temperature control box needs to reach in turn during the test process, so when building the temperature control energy consumption model, these temperature information must be extracted according to the task sequence first. The target temperature of the first task constitutes the first temperature control target of the temperature control box starting from the initial state, and the target temperatures of adjacent tasks constitute the subsequent nodes of the temperature switching path. After reading the task sequence, the controller extracts the target temperature of the first task as the first target temperature according to the sequence order, and then extracts the target temperature of each pair of adjacent tasks in the sequence as the second target temperature, so that the temperature change path forms a chain that can be continuously calculated in mathematics. Through this process, the temperature change trajectory of the temperature control box when executing different task schemes is clear, providing a structured input for subsequent energy consumption calculation, so that the energy consumption difference caused by different temperature jump amplitudes between different task sequences can be quantified and presented.

[0117] According to the first target temperature, the second target temperature and the initial test environment temperature, a plurality of temperature difference values are calculated;

[0118] Specifically, after mastering each node in the temperature path, the controller needs to calculate the temperature difference value from the initial temperature to the first target temperature, and the temperature difference value between the target temperatures of adjacent tasks. The initial test environment temperature is used to describe the actual temperature state of the temperature control box at present, which is the starting point of the temperature control energy consumption calculation; the first target temperature and the second target temperature correspond to the temperature target points in the sequence respectively. The controller calculates the difference value between the initial temperature and the first target temperature according to the temperature path order, and then calculates the difference value between adjacent target temperatures in segments, thereby forming a plurality of temperature difference value arrays. The temperature difference value can be positive or negative, and energy consumption will be generated whether the temperature is rising or falling. By mapping the real task sequence to continuous temperature difference values, the model can accurately reflect the temperature jump quantization of the temperature control box under different task sequences, so that the energy consumption evaluation can be based on the actual temperature change amplitude.

[0119] Integrating each of the temperature difference values and the unit temperature difference energy consumption constant, a total temperature control energy consumption cost function is obtained.

[0120] Specifically, the temperature control energy consumption is essentially in a linear correspondence with the temperature change amount, so after obtaining each segment of temperature difference value, it is multiplied and accumulated with the unit temperature difference energy consumption constant in segments, thereby constructing the total temperature control energy consumption cost function. The integral operation is used here to describe the summation of the energy consumption of all temperature difference value segments, which covers the temperature control energy consumption from the initial temperature to the first target temperature and between all adjacent tasks. If the temperature change amplitude is large or the number of temperature jumps is large, the cumulative energy consumption value will be higher, so it will be given a higher cost weight in the optimization process. The specific calculation formula is:

[0121]

[0122] wherein, Tj represents the target temperature of the jth task; Tj+1 represents the target temperature of the task adjacent to the jth task, Tj+1 represents the target temperature of the task adjacent to the jth task, Tj+1 represents the target temperature of the task adjacent to the jth task, segment temperature switching in the temperature difference calculation. Through this calculation process, the system can completely quantify the impact of the task sequence on the temperature control energy consumption, making the advantages and disadvantages of different scheduling schemes in the dimension of temperature control energy consumption comparable, and providing a clear energy consumption evaluation basis for the optimization algorithm.

[0123] In an optional embodiment, constructing the total test time cost function according to the moving time of the carrying module, the test environment stabilization time and the task test duration includes:

[0124] According to the initial carrying module position and the battery test station position corresponding to the first task in the task sequence, the target moving time required by the carrying module is calculated;

[0125] Specifically, the initial position of the carrying module is usually the standby station or the center position set by the system, which is the actual position of the mechanical arm at the beginning of the scheduling period; the battery test station position corresponding to the first task represents the first operation target position of the execution sequence required in this scheduling. After obtaining the task sequence, the controller reads the two position parameters, and calculates the moving time from the initial position to the station according to the preset moving time matrix or geometric path model. Since this moving occurs at the beginning of the scheduling sequence, there is no predecessor task to reuse the path, so its time consumption directly determines the starting delay of the entire test sequence. By explicitly calculating this initial moving time, the total test time model can accurately reflect the inevitable consumption required for the mechanical arm to enter the working state, and provide a reliable reference for the real test rhythm.

[0126] According to the battery test station positions corresponding to adjacent tasks in the task sequence and the preset moving time matrix, the total sequence moving time is calculated;

[0127] Specifically, the change of station position between adjacent tasks determines the path consumption of the robot arm in the entire scheduling sequence, so it is necessary to sum up all adjacent task pairs according to the preset movement time matrix. The movement time matrix is usually determined by the spatial layout between stations and the movement ability of the robot arm, and can directly give the shortest movement time between any two stations. The controller reads the station positions of each pair of adjacent tasks in the order of the task sequence, finds the corresponding movement time from the matrix and accumulates it, forming the total sequence movement time. This calculation process can record all the displacement of the robot arm during the execution of the task sequence, so that the scheduling model can accurately compare the influence of different task orders on the movement burden of the robot arm, and then reduce unnecessary migration by adjusting the task order and improve the overall execution efficiency.

[0128] According to the initial test environment temperature and the target temperature corresponding to the first task, a target stabilization time is calculated;

[0129] Specifically, the temperature control box needs to adjust from the current actual temperature to the target temperature of the first task before executing the first task, so the difference between the initial test environment temperature and the first target temperature determines the time required for temperature stabilization. According to the heating or cooling capacity of the temperature control box, the temperature difference can be converted to obtain the corresponding stabilization time, and this period of time is used as the preparation time required by the temperature control system before the start of the test sequence. Since the adjustment of the temperature from the initial point to the first target temperature cannot be avoided by task ordering, this time overhead also has a basic impact on the overall test rhythm. By explicitly calculating this stabilization time, the scheduling model can accurately present the time cost required by the temperature control system to enter the working state of the first task, providing a clear starting point for the subsequent accumulation of stabilization time considering the temperature changes of adjacent tasks.

[0130] According to the target temperatures corresponding to adjacent tasks in the task sequence, a total sequence stabilization time is calculated;

[0131] Specifically, the temperature control system needs to complete a temperature adjustment from the target temperature of the previous task to the target temperature of the next task between every two adjacent tasks in the execution of the task sequence, so the temperature difference between the target temperatures of adjacent tasks determines the temperature stabilization time required for this section. After reading the task sequence, the controller compares the target temperatures of each pair of adjacent tasks in the sequence, converts the temperature difference to the corresponding temperature control stabilization time according to the heating and cooling capacity of the temperature control box, and accumulates it to form the total sequence stabilization time. Since the temperature control stabilization time is linearly or nearly linearly related to the temperature difference, different task orders will significantly change the amplitude and number of temperature jumps, thereby directly affecting the total test time; therefore, through this calculation, the scheduling model can accurately evaluate the delay of the temperature control system caused by task ordering, and provide a quantitative basis for the subsequent optimization algorithm to reduce unnecessary back-and-forth adjustment in the temperature dimension.

[0132] summing up the test duration of each task in the task sequence to obtain the task test duration;

[0133] Specifically, the task test duration refers to the inherent execution time required by each battery test task itself, including the duration of the entire charging and discharging cycle or other set test process, which is an incompressible time component in the scheduling process. The controller traverses the task sequence, sequentially takes out the preset test duration of each task and accumulates it to form the sum of the task test duration. This part of the time is not affected by the scheduling order, but must be combined with the handling time and the temperature control stabilization time to form the total test time, so the separate summation in the cost function can clearly present the size of the task inherent workload, providing a complete basis for the composition of the overall time cost. By summarizing all the task durations, the scheduling system can accurately reflect the unavoidable basic cycle of the test process, which is a necessary component for subsequent total time optimization.

[0134] summing up the target moving time, the total sequence moving time, the target stabilization time, the total sequence stabilization time and the task test duration to obtain the total test time cost function.

[0135] Specifically, after the calculation of the aforementioned time parameters is completed, the controller summarizes the target moving time of the initial handling stage, the total sequence moving time formed by the cumulative displacement of the mechanical arm during the entire test process, the target stabilization time required by the temperature control system to enter the first task, the total sequence stabilization time formed by the temperature switching of adjacent tasks, and the test duration of all tasks themselves. The above time components jointly constitute the complete total test time cost function, providing the core index for the scheduling model to measure the differences in execution efficiency of different task sequences. The calculation method of the total test time can comprehensively cover all time-consuming links in the task execution process, so that the scheduling system not only considers the execution time of the task itself when evaluating different task arrangements, but also takes into account the additional delay caused by the handling system and the temperature control system, thereby helping to generate a scheduling scheme with more compact overall execution rhythm and less idling.

[0136] In an optional embodiment, the constructing the power penalty cost function according to the initial charging and discharging total power, the power threshold and the total power curve in the task sequence execution process, in combination with a preset penalty coefficient comprises:

[0137] obtaining the penalty coefficient, wherein the penalty coefficient includes a first penalty coefficient and a second penalty coefficient;

[0138] Specifically, the penalty coefficient is used to adjust the role intensity of the power penalty in the overall cost function, wherein the first penalty coefficient preferentially corresponds to the penalty of the part of the power exceeding the threshold, and the second penalty coefficient The two penalties correspond to the degree of power fluctuation, which together reflect the importance of power grid safety and power smoothness. The controller needs to read these two penalty coefficients from the parameter configuration before building the power penalty cost function, or set reasonable values in advance according to different test scenarios, which are used for subsequent weighting of the excessive power and power variance. Once the penalty coefficients are determined, it provides an adjustable means for soft control of power constraints, so that the scheduling algorithm can tolerate the power approaching the threshold for a short time when necessary, and can increase the penalty strength in scenarios that are more sensitive to power grid impact, and enhance the weight of power constraints in the comprehensive cost, to achieve adaptive matching of different application requirements.

[0139] According to the initial charging and discharging total power and the charging and discharging state, the charging and discharging average power and the preset test duration of each task in the task sequence, a total power curve changing with time during the execution of the task sequence is simulated and constructed, to obtain the total power curve;

[0140] Specifically, the total power curve is used to describe the process of the total charging and discharging power of the system changing with time under a given task sequence, and is the basis for subsequent judgment of whether the power is excessive and evaluation of power fluctuation. Under the premise of knowing the initial charging and discharging total power, the controller combines the charging and discharging state, the charging and discharging average power and the corresponding preset test duration of each task in the task sequence, adds or deducts the tasks newly added or ended in each time period, to simulate the change trajectory of the total power with time in the entire scheduling period. For charging tasks, the average power is counted in the positive direction into the total power; for discharging tasks, according to the actual measurement method, it is considered as the power contribution in the corresponding direction to be added, so that the total power at any time can be obtained by summing the measured task power. Through this power curve construction method based on task attributes, the power peak position, duration and overall load level under different scheduling schemes can be finely described without increasing additional hardware measurement burden, to provide accurate input for subsequent calculation of the power penalty term.

[0141] According to the total power curve, the first penalty coefficient and the power threshold, a power excessive penalty term is calculated;

[0142] Specifically, the power over-limit penalty term is used to quantify the degree and duration of the total power exceeding the power threshold during the task sequence execution process, so as to suppress the scheduling scheme that may impact the power grid in the optimization process. After obtaining the total power curve, the controller discretely or continuously scans the time axis in the entire scheduling period, compares the total power value at each time point with the power threshold, takes the part exceeding the threshold as the over-limit power interval, and combines the first penalty coefficient for accumulation or integration processing to obtain the penalty value reflecting the over-limit severity. The greater the over-limit amplitude and the longer the duration, the higher the penalty value, and the corresponding cost in the multi-objective cost function also increases, thereby guiding the optimization algorithm to preferentially select the task ordering with lower power peak and smaller over-limit risk, so that the scheduling result is more in line with the requirements of power grid friendliness and safe operation.

[0143] According to the total power curve, the power variance in the preset time interval is calculated;

[0144] Specifically, the power variance is used to measure the fluctuation degree of the total power curve during the scheduling process, and is an important indicator reflecting the power stability of the system. After the task sequence is determined, the total power curve is clear, and therefore the controller can divide the entire scheduling period into continuous or discrete time intervals, and calculate the variance of the power values in the time interval according to statistical methods. The greater the power variance, the more intense the power change during the execution of the task, the more obvious the peak-valley difference, and the more unnecessary dynamic impact on the power grid. Through the calculation of the variance, the model can quantify the smoothness of the power curve, so that the scheduling algorithm can identify and avoid schemes with too large fluctuation amplitude, thereby guiding the overall power to be more smooth, which helps to improve the safety and power consumption coordination of system operation.

[0145] According to the power variance and the second penalty coefficient, a power fluctuation penalty term is calculated;

[0146] Specifically, after obtaining the power variance, the controller performs weighting processing on the variance according to the preset second penalty coefficient, thereby forming the power fluctuation penalty term. The second penalty coefficient is essentially used to adjust the importance of power fluctuation in the total target cost, and can be set according to the load characteristics or power smoothing requirements of the power grid in different scenarios. By multiplying the power variance and the second penalty coefficient, the statistical quantity originally used to describe the fluctuation amplitude can be converted into a penalty value that can directly participate in the optimization model, so that the model can be more sensitive to the severity of power change when evaluating different task sequences. Such processing method can effectively suppress the task combination in the high fluctuation state, and promote the generation of a scheduling scheme that meets the requirements of power grid friendliness.

[0147] The power over-limit penalty term and the power fluctuation penalty term are summed to obtain the power penalty cost function.

[0148] Specifically, after obtaining the power overshoot penalty term and the power fluctuation penalty term respectively, the controller directly sums the two to form a final power penalty cost function, so that the overshoot problem and the fluctuation problem are uniformly embodied in the same function. Since the overshoot penalty term reflects the severity of the system breaking through the power constraint, and the fluctuation penalty term reflects the smoothness of the power curve, the two can achieve comprehensive constraint of the grid friendliness requirement through summation, and the specific calculation formula of the power penalty cost function is as follows:

[0149]

[0150] Wherein, The power fluctuation penalty term, i.e. the power variance, is used to measure the fluctuation degree of the power; The power overshoot penalty term is used to calculate the power overshoot amplitude; the obtained power penalty cost function can jointly consider the power safety risk and the operation stability of the system when evaluating different scheduling schemes, and provides complete and quantifiable power level basis for multi-objective optimization, so that the task sequence finally generated by the scheduling algorithm can meet the test requirements while giving maximum consideration to the grid safety and stable operation of the system.

[0151] In an optional embodiment, the inputting the real-time temperature, the real-time charging and discharging parameter and the task attribute information into the multi-objective optimization model to obtain a target task sequence comprises:

[0152] According to the real-time charging and discharging parameter, a real-time total charging and discharging power is calculated;

[0153] Specifically, the real-time charging and discharging parameter usually includes the current current and voltage data collected by each test channel, and after obtaining these parameters, the controller sums the product of the voltage and current of each channel to obtain the real-time total charging and discharging power at the current time. The total power reflects the instantaneous load level of the system on the power grid under the existing task execution state, and is a basic quantity for subsequent judgment of whether the power is close to or exceeds the power threshold and evaluation of the power friendliness of the scheduling scheme. By converting the real-time charging and discharging parameter into a total power scalar, the scheduling model can accurately grasp the current working condition in the power dimension, and can make the subsequent task sorting not only consider the task demand to be executed, but also consider the power background formed by the tasks that are currently being executed, thereby improving the consistency of the scheduling result and the field operation state.

[0154] According to the task attribute information, the real-time target temperature, the real-time test duration, the real-time station position, the real-time charging and discharging state and the real-time average charging and discharging power of each task in the to-be-scheduled task sequence are obtained;

[0155] Specifically, the task attribute information needs to be updated in real time in combination with the current time and the latest task queue, and therefore the controller obtains the real-time target temperature, real-time test duration, real-time station position, real-time charging and discharging state, and real-time charging and discharging average power of each task in the task sequence to be scheduled according to the latest task attribute information. The above real-time parameters can be understood as the instantaneous demand description of the temperature control system, test time, and power resource when each task is arranged to be executed at the current scheduling time, for example, the remaining test duration and subsequent power level of part of the tasks that have been partially executed in the previous scheduling period need to be recalculated. By arranging and updating the real-time attributes of each task in this way, the task data used by the optimization model can accurately reflect the current remaining workload and resource demand, so that the subsequent solving result is more in line with the actual test progress, instead of being based on the initial static task parameters to make decisions, which is beneficial to improve the accuracy and practicality of the overall scheduling.

[0156] According to the real-time total charging and discharging power, real-time target temperature, real-time test duration, real-time station position, real-time charging and discharging state, and real-time charging and discharging average power, the multi-objective optimization function in the multi-objective optimization model is updated by parameter assignment, to obtain an updated multi-objective optimization model;

[0157] Specifically, the real-time total charging and discharging power, real-time target temperature, real-time test duration, real-time station position, real-time charging and discharging state, and real-time charging and discharging average power jointly constitute the key input parameters of the multi-objective optimization model, and the controller updates the multi-objective optimization function in the model by parameter assignment, so that the energy consumption, time, and power penalty cost functions are recalculated based on the current system state. The total temperature control energy consumption cost function will adjust the temperature switching path according to the latest temperature state and task temperature demand, the total test time cost function will re-estimate the time overhead according to the spatial relationship between the current mechanical arm position and the task station and the remaining test duration, and the power penalty cost function will update the simulation result of the total power curve using the real-time total power and the average power of each task. Through this overall parameter updating process, the originally statically constructed multi-objective optimization model is converted into a dynamic model reflecting the real working conditions at the current time, so that the subsequent optimization process is evaluated based on the latest state, and the adaptive ability of the scheduling strategy to environmental changes and task progress changes is enhanced.

[0158] According to the updated multi-objective optimization model, a differential evolution algorithm is used to perform global search on a solution space taking the task execution order as the decision variable, and the multi-objective cost function values of a plurality of candidate solutions representing different task execution orders are calculated and evaluated, to obtain a candidate task sequence;

[0159] Specifically, after the parameter update of the multi-objective optimization model is completed, the model has been able to accurately depict the temperature control energy consumption, test time and power characteristics at the scheduling moment. At this time, the differential evolution algorithm is used to search for all possible combinations of the decision variable "task execution order" in the entire solution space. The differential evolution algorithm updates the sorting schemes of the tasks by random initialization, and combines mutation, crossover and selection mechanisms to constantly update the sorting schemes, so that each generation of candidate solutions evolves towards a lower cost. Each candidate sorting is input into the optimization model as an independent solution, and the multi-objective cost function value of each candidate sorting is calculated by the model to reflect the comprehensive performance of the sorting in terms of energy consumption, time and power. By evaluating the candidate value, the algorithm can select candidate task sequences that perform better than other solutions, so that the scheduling scheme avoids local optimal traps in the global range and provides a high-quality initial solution basis for subsequent fine solution.

[0160] The candidate task sequence is substituted into the preset integer programming solution model as an initial solution to optimize and solve the task execution order under the target constraint condition, and the target task sequence is obtained.

[0161] Specifically, after the differential evolution algorithm obtains the candidate task sequence, the scheduling system further inputs the candidate task sequence into the integer programming solution model as an initial solution. The integer programming model can further optimize the task execution order under the premise of strictly complying with the target constraint condition, and is particularly suitable for handling strong constraint conditions such as task execution frequency constraint, continuous path constraint and power constraint. During the solution process, the model further searches the feasible solution space near the candidate sequence, and adjusts or rearranges the task order to make the final obtained task sequence achieve better performance in the comprehensive cost function. Since the initial solution is derived from the global search stage, the quality is high, and the integer programming can quickly find a high-quality optimal solution or an approximate optimal solution that meets the constraint in a smaller search range, and finally form a target task sequence that can be directly used for scheduling execution. The sequence takes into account the minimization of temperature control energy consumption, the improvement of execution efficiency and the control of power stability, and provides a reliable decision basis for the actual operation of the scheduling system.

[0162] In an optional embodiment, if the current battery test scenario is a preset scheduling delay limited scenario, the method further comprises:

[0163] According to the waiting time of each task in the to-be-scheduled task sequence, the temperature difference between the current test environment temperature and the real-time target temperature of each task, and the deviation between the total power of the system predicted according to the real-time total power, the real-time charging and discharging state and the real-time average power of charging and discharging after each task is executed and the preset ideal average power, the evaluation index values for representing the time factor, the temperature factor and the power factor are respectively determined;

[0164] Specifically, in the test scenario with limited scheduling delay, the system needs to give a scheduling decision within a short time, so the evaluation index values of time factor, temperature factor and power factor are respectively constructed by using the waiting time of each task in the task sequence to be scheduled, the temperature difference between the current test environment temperature and the real-time target temperature of each task, and the deviation between the predicted total power of the system and the preset ideal average power according to the real-time total charging and discharging power, the real-time charging and discharging state and the real-time average charging and discharging power. The longer the waiting time is, the greater the risk of the task being delayed is; the smaller the temperature difference is, the lower the temperature adjustment cost required for the task to be executed at the current temperature is; the closer the predicted total power is to the ideal average power, the more beneficial the task is to maintain the smoothness of the power curve. In this way, the priority of the task in the time effectiveness, temperature control cost and power friendliness dimensions can be quantified without complex global optimization, forming an evaluation basis suitable for rapid scheduling.

[0165] According to the preset evaluation weight coefficient, the evaluation index values are weighted and calculated to obtain the priority score values corresponding to each task in the task sequence to be scheduled;

[0166] Specifically, after obtaining the evaluation indexes of the time factor, the temperature factor and the power factor, the three indexes are weighted and calculated in combination with the preset evaluation weight coefficient, so as to obtain the priority score values corresponding to each task in the task sequence to be scheduled. The evaluation weight coefficient can be set according to the emphasis of the current test scenario, for example, the weight of the time factor is appropriately increased in the case of more stringent scheduling delay constraint, and the weight of the power factor is appropriately increased in the case of more sensitive power grid constraint. The calculation formula of the priority score value is as follows:

[0167]

[0168] wherein, priority score value of the i th task in the task sequence; , evaluation weight coefficients corresponding to the time factor, the temperature factor and the power factor, respectively; waiting time of task i, that is, the accumulated waiting time of the task from entering the task queue to the current scheduling time.

[0169] The longer the waiting time is, the higher the priority is; temperature difference between the current test environment temperature and the target temperature of task i, the smaller the temperature difference is, the less the temperature control adjustment required for executing the task is, and the lower the cost is; charging and discharging state factor of task i, generally taking the value of +1 (charging) or -1 (discharging), for reflecting the influence of the task execution on the direction of the total power; represents a preset ideal average power, used to guide the entire scheduling process to maintain a relatively stable, not too high or too low power load; represents the power-friendliness of task i. By weighting the scores, the indicators of different tasks in three dimensions are unified into a single priority value, enabling the scheduling system to measure the urgency and scheduling value of each task with an intuitive and comparable score, providing a direct basis for subsequent quick sorting and selection.

[0170] According to the priority score value of each task in the to-be-scheduled task sequence, the target task is selected in turn according to the order from high to low of the priority score value, and a quick scheduling task sequence for the current scheduling period is generated;

[0171] Specifically, after obtaining the priority score of each task, the system sorts the tasks in the to-be-scheduled task sequence according to the score from high to low, and selects the tasks with higher scores in turn to construct the quick scheduling task sequence for the current scheduling period. Tasks with high priority are placed at the front of the sequence for priority execution, and tasks with low priority are postponed for subsequent execution, so that under the constraint of limited scheduling delay, tasks with longer waiting time, better temperature matching, and more power-friendly are prioritized for timely processing. This sorting process replaces complex global search and integer programming solution with simple rule operations, significantly reducing scheduling computation, while retaining comprehensive consideration of time, temperature and power, making it suitable as a quick scheduling strategy for time-sensitive scenarios.

[0172] According to the quick scheduling task sequence, the handling module and the test environment module are controlled to test and schedule the to-be-tested battery corresponding to each target task in the quick scheduling task sequence, to realize battery testing under the constraint of limited scheduling delay.

[0173] Specifically, after the quick scheduling task sequence is determined, the controller controls the handling module and the test environment module in turn according to the sequence to implement specific test scheduling operations on the to-be-tested battery corresponding to each target task, including driving the handling module to deliver the corresponding battery to the designated test station, adjusting the test environment temperature, and configuring the corresponding charging and discharging state and power parameters. The entire execution process is developed along the time axis of the quick scheduling task sequence, without the need for complex optimization iteration, and can issue execution instructions in a timely manner under the constraint of limited scheduling delay, ensuring that the system can orderly advance each test task according to the priority. In this mode, even if the global optimum cannot be completely achieved, a reasonable compromise between time constraints, temperature control costs and power stability can be achieved, meeting the battery testing needs under the constraint of limited scheduling delay.

[0174] In a specific embodiment, the embodiment of the present application successfully controls the power peak within a safe range with slightly increased energy consumption, significantly smooths the power curve, and achieves the goal of grid load optimization. For example, as shown below:

[0175] Table 1 Battery test task parameter setting table

[0176]

[0177] Referring to Table 1, the battery test task parameter setting table gives the specific working condition configuration of five battery test tasks T1-T5, including: the target temperature corresponding to each task is-20℃, 25℃, 60℃, -10℃ and 25℃, respectively, and the test duration is 40 min, 20 min, 25 min, 35 min and 15 min, respectively; the work position is P1, P3, P2, P4 and P1, respectively; the temperature control stabilization time required to reach the corresponding target temperature and stabilize is 3.2 min, 1.5 min, 2 min, 2.8 min and 1.5 min, respectively; the charge and discharge state is alternately set between discharging and charging, wherein T1 and T4 are discharging conditions, and T2, T3 and T5 are charging conditions; the corresponding average power is 5 W, 3 W, 4 W, 4.5 W and 2.5 W, respectively, which is used to verify the performance of the battery test scheduling method based on multi-objective optimization driving under different temperature, time, position and power combinations in experiments.

[0178] Table 2 Constant parameter setting table

[0179]

[0180] Referring to Table 2, Table 2 gives the constant parameter settings used in the battery test scheduling method based on multi-objective optimization driving: the unit temperature difference energy consumption constant C_energy is 1.92 kJ / °C, which is used to quantify the temperature switching energy consumption; the system maximum allowed power P_max is set to 10 W, which is used to constrain the upper limit of the total power in the test process; the weight coefficients α, β, γ of the energy consumption, time and power in the multi-objective cost function are 0.62, 0.08 and 0.38, respectively; the time factor, temperature factor and power factor weights ω_e, ω_t, ω_l used for fast scheduling priority scoring are set to 0.65, 0.35 and 0.5, respectively.

[0181] In this experimental scenario, the initial conditions were set as follows: the current temperature of the temperature control box is 25℃, and the initial position of the robotic arm is at the central workstation P0. A one-way movement time matrix from P0 to each test workstation P1 to P4 was provided to quantify the time consumption of subsequent scheduling schemes on the transport path. In comparison, the traditional FIFO scheduling executes in the order T1→T2→T3→T4→T5, causing the temperature path of the temperature control box to repeatedly switch between 25℃, -20℃, 60℃, and -10℃, resulting in numerous temperature switching times and high energy consumption. In contrast, the temperature-power collaborative optimization scheduling of this invention automatically generates the execution order T2→T5→T3→T4→T1 through a hybrid optimization algorithm, ensuring that the temperature control path starts at 25℃, maintains it at 25℃, and then sequentially adjusts to 60℃, -10℃, and -20℃, with only two substantial temperature switching events. Under this optimized sequence, the total energy consumption was 225.5 kJ, the total test time was 153.5 min, and the maximum instantaneous power was controlled at 8.0 kW within a safe range, indicating that the temperature control energy consumption was significantly reduced and the power peak was effectively suppressed while taking into account the test duration.

[0182] Table 3 Performance Index Comparison Table

[0183]

[0184] Please refer to Table 3, which compares the performance indicators of different scheduling strategies: In terms of total energy consumption, the traditional FIFO scheduling consumes 441.6 kJ, while the temperature-optimized scheme of this invention reduces it to 220.8 kJ, and the temperature-power co-optimization scheme consumes 225.5 kJ, resulting in approximately half the energy consumption; the number of temperature switching times is reduced from 5 for FIFO to 2 for this invention; in terms of scheduling time, FIFO takes 20.6 min, while the temperature-optimized and temperature-power co-optimized schemes of this invention take 18.5 min and 19.1 min respectively, showing an overall improvement in scheduling efficiency; in terms of power characteristics, the maximum instantaneous power of FIFO is 12.5 kW and exceeds the limit, while the two schemes of this invention control it to 9.5 kW and 8.0 kW respectively, and the power variance is reduced from "high" to "medium" and "low", indicating that under the premise of slightly increasing energy consumption, the temperature-power co-optimization scheme further smooths the power curve and significantly improves grid friendliness.

[0185] Example 2

[0186] In addition, combined Figure 1 The battery test scheduling method based on multi-objective optimization driven by the embodiments of the present invention described herein can be implemented by test equipment. Figure 4 A schematic diagram of the hardware structure of the test equipment provided in an embodiment of the present invention is shown.

[0187] The testing device can include a processor and a memory having stored computer program instructions.

[0188] In particular, the processor can include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement one or more embodiments of the present application.

[0189] The memory can include non-persistent memory in computer readable media, random access memory (RAM), and / or non-volatile memory such as read only memory (ROM) or flash memory. The memory is an example of computer readable media.

[0190] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented using any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer readable media does not include transitory media such as modulated data signals and carrier waves.

[0191] The processor implements any one of the above-mentioned battery test scheduling methods based on multi-objective optimization driving by reading and executing the computer program instructions stored in the memory.

[0192] In one example, the testing device can further include a communication interface and a bus. As shown in Figure 4 The processor 401, the memory 402 and the communication interface 403 are connected through the bus 410 and complete communication with each other.

[0193] The communication interface is mainly used to realize the communication between the modules, devices, units and / or equipment in the embodiments of the present application.

[0194] The bus includes hardware, software, or both, to couple components of the test device to each other and to couple components to other systems. For example, but not limited to, the bus can include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infmiband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable bus or a combination of two or more of these. Where suitable, the bus can include one or more buses. Although specific buses are described and shown, the present application contemplates any suitable bus or interconnect.

[0195] In summary, the embodiment of the present application provides a battery test scheduling method and device based on multi-objective optimization driving.

[0196] It is to be understood that the present application is not limited to the particular configurations and processes described and illustrated herein, and that the detailed description is not to be limited to only the specifically enumerated configurations and processes. For the sake of clarity, conventional methods will not be described in detail. In the above embodiments, several specific steps are described and illustrated as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and one skilled in the art can make various changes, modifications and additions, or change the order of the steps, after understanding the spirit of the present application.

[0197] Those skilled in the art will appreciate that embodiments of the present application can be supplied as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) having computer-usable program code embodied therein.

[0198] The present application is described with reference to the flowcharts and / or block diagrams of the methods, apparatus (systems) and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce the functions described in the flowcharts and / or block diagrams. Figure 1 one flow or multiple flows and / or blocks Figure 1apparatuses that perform functions specified in one or more blocks or multiple blocks.

[0199] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 one or more flows and / or blocks Figure 1 apparatuses that perform functions specified in one or more blocks or multiple blocks.

[0200] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 one or more flows and / or blocks Figure 1 apparatuses that perform functions specified in one or more blocks or multiple blocks.

[0201] It should also be noted that the exemplary embodiments mentioned in the present application describe some methods or systems based on a series of steps or apparatuses. However, the present application is not limited to the order of the above steps, that is, the steps can be executed in the order mentioned in the embodiments, or in an order different from the embodiments, or several steps can be executed simultaneously.

[0202] The above merely illustrates the specific implementation of the present application. Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, module and unit can refer to the corresponding process in the foregoing method embodiments, which will not be described herein. It should be understood that the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application.

Claims

1. A battery test scheduling method based on multi-objective optimization driving, characterized in that, The application is applied to a battery test scheduling device based on multi-objective optimization driving, and the device comprises a controller, a test environment module connected with the controller, a plurality of battery test stations arranged in the test environment, a data acquisition module for acquiring data for battery test scheduling in real time and transmitting the acquired data to the controller, and a carrying module connected with the controller for carrying batteries between the loading and unloading area and the battery test stations. The method comprises: In response to a preset scheduling trigger instruction, the data acquisition module is used to acquire real-time temperature in a battery test environment, real-time charge and discharge parameters of a test battery, and task attribute information of each task in a to-be-scheduled task sequence; A multi-objective optimization model is constructed according to preset decision variables, a multi-objective cost function and target constraint conditions; The real-time temperature, real-time charge and discharge parameters and the task attribute information are input into the multi-objective optimization model to obtain a target task sequence; According to the target task sequence, the carrying module and the test environment module are controlled to perform test scheduling on the to-be-tested batteries on each battery test station; The construction of the multi-objective optimization model according to the preset decision variables, the multi-objective cost function and the target constraint conditions comprises: The task execution order in the task sequence is taken as the decision variable; According to preset constant parameters and initialization state parameters, a total temperature control energy consumption cost function, a total test time cost function and a power penalty cost function are constructed respectively to obtain each target cost function; According to preset weight coefficients, each target cost function is weighted and calculated to obtain the multi-objective cost function; According to preset task execution frequency constraint conditions, carrying path continuity constraint conditions and power constraint conditions, the target constraint conditions are determined; The multi-objective optimization model is constructed according to the decision variables, the multi-objective cost function and the target constraint conditions.

2. The battery test scheduling method based on multi-objective optimization driving according to claim 1, wherein, The method comprises: According to a preset scheduling trigger event, a scheduling trigger condition is acquired, wherein the scheduling trigger event comprises completion of a previous battery test task, addition of a new battery test task and test time timeout; The scheduling trigger condition is monitored, and when it is monitored that the scheduling trigger condition is met, the scheduling trigger instruction is acquired; According to the scheduling trigger instruction, the test environment temperature, the charge and discharge current and the charge and discharge voltage of the test battery are acquired by the data acquisition module to obtain the real-time temperature and the real-time charge and discharge parameters; The attribute information of each task in the to-be-scheduled task sequence is analyzed to obtain each task attribute information. 3.The battery test scheduling method based on multi-objective optimization driving of claim 1, wherein, The method comprises: acquire the constant parameters and the initialization state parameters, wherein the constant parameters include a unit temperature difference energy consumption constant and a power threshold, and the initialization state parameters include an initial test environment temperature, an initial carrying module position, and an initial total charging and discharging power; construct a total temperature control energy consumption cost function according to the unit temperature difference energy consumption constant and the initial test environment temperature, in combination with target temperatures of adjacent tasks in a task sequence; construct a total test time cost function according to a carrying module moving time, a test environment stabilizing time, and a task test duration; construct a power penalty cost function according to the initial total charging and discharging power, the power threshold, and a total power curve in a task sequence execution process, in combination with a preset penalty coefficient; determine each target cost function according to the total temperature control energy consumption cost function, the total test time cost function, and the power penalty cost function.

4. The battery test scheduling method based on multi-objective optimization driving according to claim 3, characterized in that, The constructing of the total temperature control energy consumption cost function according to the unit temperature difference energy consumption constant and the initial test environment temperature, in combination with target temperatures of adjacent tasks in a task sequence includes: acquiring a first target temperature of a first task and a second target temperature of an adjacent task according to a task execution order in a task sequence; calculating a plurality of temperature difference values according to the first target temperature, the second target temperature, and the initial test environment temperature; performing integral operation on each temperature difference value and the unit temperature difference energy consumption constant to obtain the total temperature control energy consumption cost function.

5. The battery test scheduling method based on multi-objective optimization driving of claim 3, wherein, The constructing of the total test time cost function according to a carrying module moving time, a test environment stabilizing time, and a task test duration includes: calculating a target moving time required by a carrying module according to the initial carrying module position and a battery test station position corresponding to a first task in a task sequence; calculating a total sequence moving time according to battery test station positions corresponding to adjacent tasks in a task sequence and a preset moving time matrix; calculating a target stabilizing time according to the initial test environment temperature and a target temperature corresponding to the first task; calculating a total sequence stabilizing time according to target temperatures corresponding to adjacent tasks in a task sequence; performing summation calculation on test durations of each task in a task sequence to obtain the task test duration; performing summation calculation on the target moving time, the total sequence moving time, the target stabilizing time, the total sequence stabilizing time, and the task test duration to obtain the total test time cost function.

6. The battery test scheduling method based on multi-objective optimization driving of claim 3, wherein, The constructing of the power penalty cost function according to the initial total charging and discharging power, the power threshold, and a total power curve in a task sequence execution process, in combination with a preset penalty coefficient includes: acquiring the penalty coefficient, wherein the penalty coefficient includes a first penalty coefficient and a second penalty coefficient; simulating and constructing a curve of total power changing over time in a task sequence execution process according to the initial total charging and discharging power, charging and discharging states of each task in a task sequence, charging and discharging average power, and a preset test duration to obtain the total power curve; calculating a power over-limit penalty term according to the total power curve, the first penalty coefficient, and the power threshold. According to the total power curve, a power variance in a preset time interval is calculated; According to the power variance and the second penalty coefficient, a power fluctuation penalty term is calculated; The power fluctuation penalty term and the power fluctuation penalty term are summed to obtain the power penalty cost function.

7. The battery test scheduling method based on multi-objective optimization driving of claim 1, wherein, The inputting the real-time temperature, real-time charging and discharging parameters and the task attribute information into the multi-objective optimization model to obtain a target task sequence comprises: According to the real-time charging and discharging parameters, a real-time total charging and discharging power is calculated; According to the task attribute information, the real-time target temperature, real-time test duration, real-time station position, real-time charging and discharging state and real-time average charging and discharging power of each task in the to-be-scheduled task sequence are obtained; According to the real-time total charging and discharging power, real-time target temperature, real-time test duration, real-time station position, real-time charging and discharging state and real-time average charging and discharging power, the multi-objective optimization function in the multi-objective optimization model is parameterized and updated to obtain an updated multi-objective optimization model; According to the updated multi-objective optimization model, a differential evolution algorithm is used to perform global search on a solution space with task execution order as the decision variable, and a multi-objective cost function value of a plurality of candidate solutions representing different task execution orders is calculated and evaluated to obtain a candidate task sequence; The candidate task sequence is substituted into a preset integer programming solution model as an initial solution, and the task execution order under the target constraint condition is optimized and solved to obtain the target task sequence.

8. The battery test scheduling method based on multi-objective optimization driving according to any one of claims 1-7, characterized in that, If the current battery test scenario is a preset scheduling time delay limited scenario, the method further comprises: According to the waiting time of each task in the to-be-scheduled task sequence, the temperature difference between the current test environment temperature and the real-time target temperature of each task, and the deviation between the system total power predicted according to the real-time total charging and discharging power, real-time charging and discharging state and real-time average charging and discharging power after executing each task and the preset ideal average power, the evaluation index values representing time factor, temperature factor and power factor are respectively determined; According to the preset evaluation weight coefficient, the evaluation index values are weighted to obtain the priority score values corresponding to each task in the to-be-scheduled task sequence; According to the priority score values, each task in the to-be-scheduled task sequence is sorted, and target tasks are selected in order of priority score values from high to low to generate a fast scheduling task sequence for the current scheduling period; According to the fast scheduling task sequence, the handling module and the test environment module are controlled to test and schedule the to-be-tested batteries corresponding to each target task in the fast scheduling task sequence, so as to realize battery testing under the constraint of scheduling time delay limitation.

9. A test apparatus, characterized by, Comprise: At least one processor, at least one memory and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, realize the method as claimed in any one of claims 1-8.

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