An adaptive regulation method for battery cell drying

Abstract

This invention relates to the field of waste battery recycling and dismantling technology, and proposes an adaptive adjustment method for cell drying. The method includes: acquiring an individual feature dataset of the batteries to be dried using an online monitoring station, including a first feature characterizing the total amount of residual electrolyte and a second feature characterizing the differences in its spatial distribution within the battery; based on this dataset, using a preset feature-process mapping relationship, generating a customized drying process parameter sequence associated with the battery's position within the drying channel, including temperature, air velocity, and flow line speed; determining a target channel from multiple parallel drying channels based on the real-time operating status and expected energy load of each drying channel, and controlling a distribution device to allocate batteries to that channel; and dynamically adjusting the temperature, air velocity, and flow line speed of the corresponding drying unit based on the battery's real-time position within the target channel until the drying process is completed. This invention achieves differentiated adaptive control of cell drying, improving drying quality consistency and reducing energy consumption.

Description

Technical Field

[0001] This invention relates to the field of waste battery recycling and dismantling technology, and in particular to an adaptive adjustment method for drying battery cells. Background Technology

[0002] During the recycling and dismantling of waste lithium batteries, retired batteries need to undergo discharge and electrolyte extraction pretreatment before dismantling. After removing the outer casing, terminals, and tabs of the lithium battery, bare cells are obtained. Because the electrolyte inside the casing cannot be completely removed during the electrolyte extraction process, some electrolyte remains in the core of the bare cell. In subsequent processes, the cells need to be unwound to obtain the electrodes. Before unwound, a drying process is required to completely remove the residual electrolyte from the core of the bare cell to ensure the safety of the subsequent dismantling process and the purity of the recycled electrodes, ensuring that the electrodes are not contaminated.

[0003] However, in large-scale dismantling and recycling scenarios, the residual electrolyte content and internal spatial distribution of different retired batteries vary significantly. Existing drying processes struggle to dynamically adjust to these individual differences, leading to the following main problems: 1. Traditional drying methods typically employ fixed parameters, such as uniformly setting drying temperature, air velocity, and conveyor speed. This fails to differentiate based on the total residual electrolyte content of individual batteries, resulting in insufficient drying of batteries with large residual electrolyte and over-drying of batteries with small residual electrolyte, affecting subsequent dismantling safety and causing energy waste. 2. The uneven distribution of residual electrolyte within the battery, with some areas rich and others sparse, makes it impossible for fixed-parameter drying processes to match drying intensity spatially. This can easily lead to localized under-drying or overheating, reducing the consistency of recycled materials. 3. In scenarios with multiple drying channels operating in parallel, existing technologies typically employ simple polling or random allocation mechanisms. This lack of comprehensive assessment of real-time energy load and expected battery energy consumption for each channel makes it difficult to achieve load balancing between channels, further exacerbating energy waste and limiting overall processing efficiency. In summary, existing technologies in the drying process of battery dismantling and recycling are insufficient to adaptively adjust based on the differentiated characteristics of individual cells. There is an urgent need for a multi-channel collaborative drying control method that can detect individual battery characteristics in real time, generate customized processes, and execute them dynamically. Summary of the Invention

[0004] To address the aforementioned shortcomings, the present invention aims to propose an adaptive adjustment method for battery cell drying. This method aims to detect individual characteristics of each battery to be dried and generate a customized drying process parameter sequence based on these individual characteristics, thereby achieving differentiated drying control for different batteries, improving the consistency of drying quality and reducing drying energy consumption.

[0005] To achieve this objective, the present invention adopts the following technical solution: An adaptive adjustment method for drying battery cells is applied to a drying control system. The drying control system is communicatively connected to an online detection station, a production line with multiple parallel drying channels, and a diversion device. Each of the drying channels is composed of multiple independently adjustable drying units connected in series. The adaptive adjustment method includes: The online testing station acquires an individual feature dataset of the batteries to be dried. The individual feature dataset includes a first feature for characterizing the total amount of residual electrolyte and a second feature for characterizing the differences in the spatial distribution of residual electrolyte inside the battery. Based on the individual feature dataset, a customized drying process parameter sequence is generated for the battery to be dried using a preset feature-process mapping relationship. The customized drying process parameter sequence is used to define the combination of parameters such as temperature, wind speed and flow line speed associated with the battery's position in the drying channel. Based on the real-time operating status of each drying channel and the expected energy consumption load of the customized drying process parameter sequence, a target drying channel is determined from the multiple parallel drying channels, and the shunt device is controlled to distribute the battery to be dried to the target drying channel. Based on the real-time position information of the battery to be dried in the target drying channel, the drying unit corresponding to the real-time position information is dynamically adjusted to execute the corresponding temperature and wind speed parameters according to the customized drying process parameter sequence, and the production line is controlled to execute the corresponding production line speed parameters until the battery to be dried completes the drying process.

[0006] Preferably, the individual feature dataset of the batteries to be dried obtained by the online testing station includes: The current weight of the battery to be dried is obtained by weighing device, and the current weight is compared with the preset standard weight to obtain a first feature characterizing the total amount of residual electrolyte. An image of the internal structure of the battery to be dried is acquired using an imaging device. The internal structure image is then segmented and analyzed for region to extract a second feature that characterizes the differences in the spatial distribution of residual electrolyte inside the battery.

[0007] Preferably, based on the individual feature dataset, a customized drying process parameter sequence for the battery to be dried is generated using a preset feature-process mapping relationship, including: Based on the total amount of residual electrolyte characterized by the first feature, the target total drying time is determined by using a preset table corresponding to the residual amount and drying time. Based on the spatial distribution differences of residual electrolyte inside the battery characterized by the second feature, the target heating rate of each segment along the travel direction of the drying channel is determined by using a preset table corresponding to the distribution differences and heating rates. Based on the target total drying time and the target heating rate of each segment, combined with the total number of segments and the length of each segment of the drying channel, the target temperature value, target wind speed and target travel speed of the battery to be dried in each segment are calculated, and a customized drying process parameter sequence of temperature, wind speed and flow line speed along the travel position of the drying channel is obtained.

[0008] Preferably, determining a target drying channel from the multiple parallel drying channels based on the real-time operating status of each drying channel and the expected energy consumption load of the customized drying process parameter sequence includes: Based on the temperature, wind speed, and flow line speed parameters in the customized drying process parameter sequence, the total heating power and total fan power required to complete the drying of the battery to be dried are calculated as the expected energy consumption load; Obtain the number of batteries currently assigned to each drying channel but not yet dried, and the current operating temperature and airflow of each drying unit in each drying channel; For each drying channel, the current energy load is calculated based on its current operating temperature and wind speed, and the expected energy load is superimposed with the current energy load to calculate the expected total energy consumption of the channel after allocating the batteries to be dried. The drying channel with the lowest expected total energy consumption is determined as the target drying channel; if there are multiple channels with the same expected total energy consumption, the channel with the fewest batteries in transit is further selected as the target drying channel.

[0009] Preferably, controlling the shunt device to distribute the battery to be dried to the target drying channel includes: Obtain the channel identifier of the target drying channel, and query the target position parameter corresponding to the target drying channel from the preset mapping table of channel identifier and diversion device position parameter; Generate a diversion control command including the target position parameters, and send the diversion control command to the drive controller of the diversion device; The diversion control command is used to instruct the drive controller to drive the diversion actuator of the diversion device to move to the position indicated by the target position parameter, and to return a position confirmation signal after the movement is in place; After receiving the position confirmation signal returned by the shunt device, the system controls the battery to be dried to enter the shunt device for distribution.

[0010] Preferably, dynamically adjusting the drying unit corresponding to the real-time location information to execute the corresponding temperature and wind speed parameters, and controlling the production line to execute the corresponding production line speed parameters includes: A first position sensor is installed at the entrance of the target drying channel, and a second position sensor is installed at the junction of each adjacent drying unit. When the first position sensor detects that the battery to be dried has entered the target drying channel, the first set of temperature and wind speed parameters in the customized drying process parameter sequence is activated, and the flow line speed is adjusted to the corresponding initial speed in the customized drying process parameter sequence. During the process of the battery to be dried passing through the second position sensor, the dwell time of the battery to be dried in the current drying unit is acquired in real time. The dwell time is compared with the preset target dwell time of the current drying unit in the customized drying process parameter sequence to obtain the dwell time deviation. Based on the time deviation, within the allowable fluctuation range of the set temperature value of the current drying unit as defined by the customized drying process parameter sequence, the temperature set value of the current drying unit is adjusted. When the battery to be dried passes the second position sensor and enters the next drying unit, the set temperature value of the next drying unit is restored to the original set value in the customized drying process parameter sequence, and the adjustment of the next drying unit is repeated.

[0011] Preferably, the method for establishing the preset feature-process mapping relationship includes: Obtain individual feature datasets for multiple sample batteries, wherein each sample battery's individual feature dataset includes the first feature and the second feature; For each sample battery, the first feature and the second feature are used as input variables, and the temperature setting value, wind speed setting value and production line speed setting value of each segment of the drying channel are used as output variables. In the two-dimensional feature space formed by the first feature and the second feature, a continuous mapping function between the input variables and the output variables is established by an interpolation algorithm. The continuous mapping function is pre-set in the drying control system as the preset feature-process mapping relationship; when the individual feature dataset of the battery to be dried is obtained, the corresponding customized drying process parameter sequence is directly calculated through the continuous mapping function.

[0012] Preferably, the process of drying the battery to be dried according to the customized drying process parameter sequence includes: The cumulative actual heat supply of the battery to be dried is obtained in real time from each drying unit that has been completed in the target drying channel. The cumulative actual heat supply is calculated based on the actual operating temperature and actual operating wind speed of each drying unit. The cumulative actual heat supply is compared with the cumulative target heat supply of each drying unit in the customized drying process parameter sequence to obtain the cumulative heat deviation; Based on the cumulative heat deviation, the set temperature and set wind speed of the downstream drying unit that the battery to be dried has not yet passed through are feedforward corrected so that the total cumulative heat supply of the battery to be dried when all drying processes are completed is close to the total target heat supply defined by the customized drying process parameter sequence. The allocation weight of the feedforward correction decreases sequentially along the remaining drying units.

[0013] One of the above technical solutions has the following advantages or beneficial effects: This invention acquires individual characteristic datasets of batteries to be dried through an online testing station. This dataset includes characteristic information on both the total amount of residual electrolyte and the differences in the spatial distribution of residual electrolyte within the battery. This incorporates the significant individual differences of retired batteries into the drying decision-making process. Based on these individual characteristic datasets, a customized drying process parameter sequence matching the individual characteristics of each battery is generated through a pre-defined feature process mapping relationship. By defining the combination and variation of temperature, air velocity, and flow line speed along the drying channel, the drying process can accurately match the electrolyte residual status of different batteries, avoiding insufficient or excessive drying due to rigid process parameters, and ensuring the safety of subsequent disassembly processes. In the channel allocation stage, considering the real-time operating status of each drying channel and the expected energy load of the current battery, the batteries to be dried are allocated to the target drying channel with the lowest expected total energy consumption. This achieves load balancing across channels under multi-channel parallel recycling processing, improving parallel processing efficiency and overall energy efficiency. After the battery enters the target drying channel, the temperature and wind speed parameters of the corresponding drying unit are dynamically adjusted based on real-time location information, and the production line speed is controlled simultaneously to ensure that the customized process parameter sequence is precisely executed in the spatial dimension. This ensures that the battery is always in a hot air environment adapted to its own characteristics throughout the drying process, guaranteeing the consistency of drying quality and the purity of recycled materials. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0015] Figure 1 This is a flowchart of the adaptive adjustment method for cell drying provided in an embodiment of the present invention; Figure 2This is a schematic diagram of the drying production line provided in an embodiment of the present invention. Detailed Implementation

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

[0017] In this invention, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0018] An adaptive adjustment method for battery cell drying is applied to a drying control system, such as... Figure 2 As shown, the drying control system is communicatively connected to an online monitoring station, a production line with multiple parallel drying channels, and a diversion device. Each drying channel consists of multiple independently controllable drying units connected in series. The production line is a drying production line, including an upstream material conveying section, multiple parallel drying channels in the middle, and a downstream converging section at the end of each drying channel. The online monitoring station is located at the upstream end of the material conveying section and is used to acquire individual feature datasets before the batteries enter the drying channels. The diversion device is located at the intersection of the material conveying section and the multiple parallel drying channels and is used to guide the batteries to be dried to the corresponding drying channel entrance according to the channel identifier of the target drying channel. Each drying channel consists of multiple drying units connected in series along the battery travel direction. Adjacent drying units are separated by physical partitions or air curtains. Each drying unit is equipped with an independent heating device, a fan system, and a temperature sensor to achieve independent control of the process parameters of each unit.

[0019] like Figure 1 As shown, the adaptive adjustment method includes: S1: The online testing station acquires an individual feature dataset of the batteries to be dried. The individual feature dataset includes a first feature for characterizing the total amount of residual electrolyte and a second feature for characterizing the differences in the spatial distribution of residual electrolyte inside the battery. It should be noted that the online testing station is a testing station located upstream of the drying channel. It is used for non-contact measurement of the batteries before they enter the drying process, acquiring parameter information reflecting the residual state of the electrolyte inside the battery. The online testing station can include one or more of the following: weighing sensors, X-ray imaging devices, ultrasonic testing devices, or visual inspection devices. The individual feature dataset is a unique set of parameters collected independently for each battery to be dried, distinguishing it from other batteries, rather than uniform preset values ​​applicable to all batteries. The first feature characterizes the total amount of residual electrolyte, referring to a detection index that can quantitatively or semi-quantitatively reflect the overall amount of residual electrolyte inside the battery, such as the difference between the current weight and the standard weight of the battery, X-ray transmittance, ultrasonic attenuation coefficient, etc. The second feature characterizes the spatial distribution differences of residual electrolyte inside the battery, referring to a detection index that can reflect the degree of uneven distribution of electrolyte in different areas inside the battery, such as the grayscale distribution characteristics of different areas in the battery's internal image, the conductivity difference at different locations, thermal imaging temperature gradient, etc.

[0020] Understandably, the main reason for using uniform parameters in existing drying processes is the lack of effective detection methods and ways to utilize individual battery differences. This makes it impossible to know the electrolyte residue status of each battery, thus necessitating a one-size-fits-all drying approach. By acquiring individual characteristic datasets for each battery to be dried through an online testing station, information on the total amount of residual electrolyte and its spatial distribution differences can be quantified into detection indicators that can be used for process decisions. This breaks the limitation of treating all batteries indiscriminately in traditional processes, allowing subsequent process parameters to be adjusted specifically according to the actual situation of each battery.

[0021] S2: Based on the individual feature dataset, a customized drying process parameter sequence is generated for the battery to be dried using a preset feature-process mapping relationship. The parameter sequence is used to define the parameter combination of temperature, wind speed and flow line speed associated with the battery's travel position in the drying channel. It should be noted that the feature-process mapping relationship is a pre-established rule stored in the drying control system, used to transform the values ​​of the first and second features into a set of specific drying process parameters. This mapping relationship can be implemented in various forms, such as multidimensional lookup tables, interpolation functions, and piecewise rule matching functions. The customized drying process parameter sequence is a set of serialized data generated for a single battery to be dried, corresponding to different process parameters at different positions along the length of the drying channel. Each position index in this sequence corresponds to a set of parameter combinations, including the target temperature, target air velocity, and target conveyor speed of the production line at that position. The position index in the parameter sequence is associated with the battery's position within the drying channel, meaning that when the battery reaches a certain drying unit in the drying channel, the corresponding parameter combination is activated and executed. The entire parameter sequence is executed sequentially as the battery moves forward.

[0022] Understandably, the main reason for using fixed parameters in existing drying processes is the lack of a decision-making mechanism to convert individual battery characteristics into differentiated process parameters. Even if differences between different batteries can be detected, it is impossible to automatically generate an adaptive drying scheme. By using a preset feature-process mapping relationship, the first and second features obtained in step S1 are used as decision inputs to generate a set of parameter sequences corresponding to the battery's position. This establishes a deterministic correspondence between individual battery characteristics and drying process parameters, enabling the drying process to accurately match the specific electrolyte residue status of different batteries. Batteries with a large amount of residual electrolyte are automatically matched with a longer drying time or a higher drying intensity, while batteries with a small amount are matched with a shorter drying time or a lower drying intensity. Batteries with uneven electrolyte distribution are matched with a temperature gradient distribution that is appropriate for them, thereby solving the problems of insufficient and excessive drying caused by fixed parameters at the source.

[0023] S3: Based on the real-time operating status of each drying channel and the expected energy consumption load of the customized drying process parameter sequence, determine a target drying channel from the multiple parallel drying channels, and control the shunt device to allocate the battery to be dried to the target drying channel; It should be noted that real-time operating status refers to the current working status information of each drying channel, reflecting the current load level of each channel. This can include the number of batteries currently being processed in each channel, the current temperature and airflow of each drying unit, and the cumulative running time of each channel. Expected energy consumption load refers to the additional energy required to execute the customized drying process parameter sequence corresponding to a given battery after it has been assigned to a specific channel. This is determined by the temperature setpoint, airflow setpoint, and operating speed parameters in the parameter sequence. The target drying channel is the most suitable channel for handling the current battery drying task, selected from multiple parallel channels according to preset rules. A diversion device is a mechanical device located upstream of the inlet of each drying channel, used to guide the batteries to be dried to the designated drying channel inlet according to control commands. This can include a oscillating guide plate, a diversion conveyor belt, a shifting fork mechanism, etc.

[0024] Understandably, in recycling and dismantling operations with multiple parallel drying channels, the real-time load status of each channel varies dynamically. The incoming retired batteries exhibit significant individual differences and varying specifications. If a fixed polling or random allocation method is used to distribute batteries to each channel, batteries may be assigned to channels with currently heavy loads, leading to overload in those channels while other channels remain idle, impacting overall drying efficiency and causing uneven energy consumption. By acquiring the real-time operating status of each drying channel and the expected energy load of the batteries to be dried, and comprehensively comparing the two, the optimal target drying channel is determined. This achieves a dynamic and balanced distribution of batteries across multiple channels, making the load distribution more uniform, avoiding localized overload or idling, improving the overall utilization and drying efficiency of the parallel production line, and simultaneously helping to reduce overall energy consumption.

[0025] S4: Based on the real-time position information of the battery to be dried in the target drying channel, according to the customized drying process parameter sequence, dynamically adjust the drying unit corresponding to the real-time position information to execute the corresponding temperature and wind speed parameters, and control the production line to execute the corresponding production line speed parameters until the battery to be dried completes the drying process.

[0026] It should be noted that real-time location information refers to the detection data of the battery's current specific position within the target drying channel. This data is used to determine which drying unit or its inlet / outlet position the battery is located in. It can be obtained through position sensors installed within the channel, such as photoelectric sensors, proximity switches, and encoders. The drying unit is the basic functional section constituting the drying channel. Each drying unit has an independent heating device and fan system, and can independently receive control commands and adjust its output temperature and airflow. Adjacent drying units are separated by physical partitions or air curtains to reduce mutual interference. Dynamic control refers to the real-time switching and adjustment of the execution parameters of the corresponding drying unit based on changes in the battery's position, rather than setting all parameters once before the battery enters the channel and then not adjusting them again. The timing and values ​​of the control are based on a customized drying process parameter sequence.

[0027] Understandably, the customized drying process parameter sequence defines the drying intensity that the battery should receive at different locations. Since the battery moves continuously within the drying channel, the position index in the parameter sequence must precisely correspond to the actual position of the battery and be executed synchronously to ensure the customized process design is effectively implemented. By acquiring the battery's position information within the target drying channel in real time and dynamically adjusting the corresponding drying unit based on the temperature and wind speed settings in the position index parameter sequence, while simultaneously adjusting the conveyor speed of the production line, the parameter sequence is executed precisely in both time and space. This ensures that each position of the battery within the drying channel receives hot air treatment matching its electrolyte residue characteristics, ultimately keeping the battery under optimal process conditions throughout the entire drying process, thus guaranteeing the consistency and stability of the drying quality.

[0028] Preferably, the individual feature dataset of the batteries to be dried obtained by the online testing station includes: The current weight of the battery to be dried is obtained by weighing device, and the current weight is compared with the preset standard weight to obtain a first feature characterizing the total amount of residual electrolyte. An image of the internal structure of the battery to be dried is acquired using an imaging device. The internal structure image is then segmented and analyzed for region to extract a second feature that characterizes the differences in the spatial distribution of residual electrolyte inside the battery.

[0029] It should be noted that the weighing device is a high-precision weight sensor installed in the online testing station. It is used to dynamically measure the overall weight of the battery in real time before it enters the drying channel. Its function is to indirectly reflect the total amount of residual electrolyte inside the battery through weight difference. The preset standard weight is a reference weight value pre-calibrated for this model of battery in a fully dried state. This value is stored in the memory of the drying control system and serves as a reference for judging the total amount of residual electrolyte. The imaging device is a transmission-type or reflection-type imaging device installed in the online testing station. It is used to acquire structural images of the battery's interior. X-ray imaging devices or ultrasonic imaging devices can be used. Its function is to convert the electrolyte distribution state inside the battery into a visualized grayscale image. Region segmentation is the process of dividing the acquired internal structural image into multiple sub-regions according to preset grid or region division rules. By dividing different regions, the residual electrolyte in each part can be evaluated separately. Gray-scale analysis is the process of statistically calculating the gray-scale values ​​of each sub-region of the image. The extracted statistics include the mean, standard deviation, coefficient of variation, or gray-scale gradient between regions, which are used to quantitatively characterize the non-uniformity of electrolyte distribution within the battery. The greater the difference in gray-scale values ​​between different regions, the more non-uniform the electrolyte distribution.

[0030] Understandably, the total amount and spatial distribution of residual electrolyte are the two core factors influencing the selection of drying process parameters. The residual amount determines the overall drying intensity requirement, while the spatial distribution differences determine the temperature and wind speed gradient distribution requirements during the drying process. The first feature is obtained by comparing the total residual amount with a standard weight using a weighing device, converting the overall residual amount into a quantifiable weight difference, providing a direct basis for determining the total drying time and total heat supply. The second feature is obtained by acquiring internal structural images using an imaging device and performing region segmentation and grayscale analysis, converting the spatial distribution differences into grayscale statistical characteristics of each region, providing spatial reference information for determining the temperature gradient and wind speed distribution.

[0031] Specifically, the online testing station is equipped with a conveyor track, through which the batteries to be dried sequentially enter the testing station. A weighing device is installed at the front of the testing station, consisting of a support platform and a high-precision weighing sensor. The batteries are conveyed to the support platform and briefly pause for weighing. The weighing sensor transmits the collected weight signal to the processor of the online testing station. The processor calculates the difference between the current weight and the standard weight of the battery model retrieved from memory. The difference is used as the first characteristic; a positive difference and a larger value indicate a greater amount of residual electrolyte. After weighing, the battery is transferred to the imaging detection area. The imaging device scans the battery from above or the side to acquire cross-sectional or projected images of the battery's internal structure. The processor segments the image according to a preset grid template. The grid template sets the segmentation density based on the battery's size and shape. For regularly shaped square batteries, a rectangular grid with evenly divided rows and columns is used for segmentation. For cylindrical batteries, a polar coordinate grid is used for segmentation along the radial and circumferential directions. After segmentation, the processor calculates the gray-scale mean of each sub-region and further calculates the standard deviation of the gray-scale mean of all sub-regions. The standard deviation is used as a second feature. The calculation of the gray-scale mean and standard deviation can be implemented using techniques in this field, which will not be elaborated here. If the standard deviation is small, it indicates that the electrolyte is spatially more uniformly distributed. If the standard deviation is large, it indicates that the electrolyte is concentrated in some areas while it is sparse in others.

[0032] Preferably, based on the individual feature dataset, a customized drying process parameter sequence for the battery to be dried is generated using a preset feature-process mapping relationship, including: Based on the total amount of residual electrolyte characterized by the first feature, the target total drying time is determined by using a preset table corresponding to the residual amount and drying time. Based on the spatial distribution differences of residual electrolyte inside the battery characterized by the second feature, the target heating rate of each segment along the travel direction of the drying channel is determined by using a preset table corresponding to the distribution differences and heating rates. Based on the target total drying time and the target heating rate of each segment, combined with the total number of segments and the length of each segment of the drying channel, the target temperature value, target wind speed and target travel speed of the battery to be dried in each segment are calculated, and the parameter sequence of the temperature, wind speed and flow line speed along the travel position of the drying channel is obtained.

[0033] It should be noted that the residual electrolyte content and drying time correspondence table is a pre-established mapping table stored in the drying control system. This table was established through previous experiments, mapping different residual electrolyte total amounts or ranges to corresponding total drying times; the larger the residual electrolyte content, the longer the corresponding drying time. The distribution difference and heating rate correspondence table is another pre-established mapping table, mapping different distribution difference indicators to heating rate values ​​for each segment along the channel direction. The heating rate refers to the temperature increase per unit travel distance; the more uneven the distribution, the higher the heating rate needs to be in areas with concentrated electrolyte. The target total drying time is the estimated total time from the battery entering the drying channel to completion of drying, determined by looking up the table based on the first feature, used to constrain the total time length of the entire parameter sequence. The target heating rate for each segment is the rate of temperature change with distance within each drying segment, determined by looking up the table based on the second feature, used to determine the temperature gradient distribution characteristics along the channel. The total number of segments refers to how many independent drying units a complete drying channel is equally divided into, and the length of each segment refers to the physical length of each drying unit along the battery's travel direction.

[0034] Understandably, existing drying process parameters are typically fixed values, making it impossible to adjust drying time and temperature gradient distribution based on individual battery differences. By pre-establishing a correspondence table between residual electrolyte and drying time, and a correspondence table between distribution differences and heating rates, the first characteristic is directly mapped to the target total drying time. Batteries with higher residual electrolyte automatically receive longer drying times to ensure sufficient electrolyte removal, while batteries with lower residual electrolyte automatically have shorter drying times to avoid over-drying and energy waste. The second characteristic is directly mapped to the target heating rate for each segment, ensuring that areas with concentrated electrolyte correspond to higher heating rates to accelerate electrolyte evaporation in those areas, while areas with thinner electrolyte correspond to lower heating rates to avoid localized overheating. After determining the target total time and segmented heating rates, the specific temperature, wind speed, and travel speed values ​​for each segment are calculated using the channel's physical parameters. This transforms the abstract residual electrolyte and distribution difference information into a sequence of specific process parameters that can be executed segment by segment, achieving a complete mapping from individual battery characteristics to drying action commands.

[0035] Specifically, the drying control system executes the following calculation process: First, based on the value of the first feature, a lookup is performed in a preset table corresponding to residual amount and drying time. This table uses the residual amount interval as the index key, and each interval corresponds to a total drying time value. If the value of the first feature falls into a certain interval, the time corresponding to that interval is taken as the target total drying time. If the value of the first feature is exactly at the boundary between two intervals, the target total drying time is determined by linear interpolation. Second, based on the value of the second feature, a lookup is performed in a preset table corresponding to distribution difference and heating rate. This table uses the interval of the distribution difference index as the index key, and each interval corresponds to a set of target heating rate vectors for each segment. If the battery is divided into multiple drying segments, a sequence containing multiple heating rate values ​​consistent with the number of segments is output. Then, using the total drying time as a constraint, the target heating rate of each segment is discretized by integrating over the total time and total length. The target temperature value of each segment is calculated sequentially, starting from the ambient temperature, by accumulating the temperature increment of each segment by multiplying the heating rate by the segment length to obtain the target temperature value at the outlet of each segment. The target wind speed of each segment is calculated based on the heating rate of that segment and the power characteristics of the heating device. Segments with higher heating rates correspond to higher wind speeds to enhance convective heat transfer. The target travel speed of each segment is calculated based on the proportion of the residence time of that segment in the total time and the segment length. Finally, the target temperature value, target wind speed value, and target travel speed value of each segment are arranged according to the segment number to form a parameter sequence of the travel position along the channel.

[0036] Preferably, determining a target drying channel from the multiple parallel drying channels based on the real-time operating status of each drying channel and the expected energy consumption load of the customized drying process parameter sequence includes: Based on the temperature, wind speed, and flow line speed parameters in the customized drying process parameter sequence, the total heating power and total fan power required to complete the drying of the battery to be dried are calculated as the expected energy consumption load; Obtain the number of batteries currently assigned to each drying channel but not yet dried, and the current operating temperature and airflow of each drying unit in each drying channel; For each drying channel, the current energy load is calculated based on its current operating temperature and wind speed, and the expected energy load is superimposed with the current energy load to calculate the expected total energy consumption of the channel after allocating the batteries to be dried. The drying channel with the lowest expected total energy consumption is determined as the target drying channel; if there are multiple channels with the same expected total energy consumption, the channel with the fewest batteries in transit is further selected as the target drying channel.

[0037] It should be noted that total heating power refers to the sum of electrical power consumed by the heating devices of each drying unit to execute the temperature value set in the customized drying process parameter sequence. It is determined by the difference between the target temperature and the initial temperature, the battery thermal capacity, and the heating efficiency. Total fan power refers to the sum of electrical power consumed by the fans of each drying unit to execute the wind speed value set in the parameter sequence. It is calculated from the wind speed and fan power curve. The number of batteries in transit refers to the number of batteries that have been allocated to the entrance of a drying channel or have entered the channel but have not yet completed all drying processes, reflecting the current queue load of that channel. Current execution temperature and wind speed refer to the actual temperature and wind speed set values ​​that each drying unit is currently operating at. Current energy consumption load is the electrical power consumed by the channel calculated based on the current execution temperature and wind speed. Estimated total energy consumption is the estimated value obtained by superimposing the current energy consumption load and the expected energy consumption load, reflecting the total power demand of the channel after the allocation of new batteries.

[0038] Understandably, by calculating the expected energy load of the current battery based on a customized parameter sequence and obtaining the real-time operating status of each channel, the estimated total energy consumption of each channel after allocation is obtained by superimposing the two. The channel with the lowest total energy consumption is selected as the target channel, so that new batteries are preferentially allocated to channels with lighter current loads, avoiding the addition of tasks to high-load channels, which could lead to local overload and excessively high energy consumption peaks. At the same time, when the estimated total energy consumption is equal, the number of batteries in transit is used as a secondary screening condition to select channels with fewer queued batteries, reducing the waiting time of batteries at the channel entrance and improving the overall line flow efficiency. This allocation strategy combines energy consumption balancing with task queue balancing to achieve optimized allocation of multi-channel resources.

[0039] Specifically, the calculation process for the expected energy load is as follows: Based on the target temperature and target wind speed values ​​corresponding to each drying unit in the customized drying process parameter sequence, the heating power requirement and fan power requirement for each drying unit are calculated. The heating power requirement can be calculated using the temperature difference between the target temperature of the unit and the initial temperature of the battery when it enters the unit, the heat capacity of the battery body, and the thermal efficiency coefficient of the heating device. The fan power requirement is obtained by finding the corresponding value on the power characteristic curve of the fan model based on the target wind speed. For example, the heating power requirement and fan power requirement for each unit are calculated using the following formula: ; ,in, For the specific heat capacity of the battery, For battery quality, For the first The target temperature of each unit The initial temperature when the battery enters the cell. To preset the dwell time, The heating efficiency coefficient; For the target wind speed, This is the power coefficient of the wind turbine. This is the power index.

[0040] The total heating power is obtained by summing the heating power requirements of all drying units unit by unit, and the total fan power is obtained by summing the fan power requirements of all drying units unit by unit. The sum of the two is taken as the expected energy load, such as the total heating power. Total fan power The current energy load calculation process is as follows: For each drying channel, the current temperature and wind speed settings of each drying unit in that channel are read. The current heating power and current fan power of each unit are calculated using the same method as the expected energy load. All units are summed to obtain the current energy load for that channel. The expected energy load is added to the current energy load to obtain the total estimated energy consumption. The total estimated energy consumption values ​​of each channel are then compared, and the channel with the lowest value is selected as the target drying channel. If there is a tie, the number of batteries in transit for each channel is further compared, and the channel with the fewest batteries in transit is selected.

[0041] Preferably, controlling the shunt device to distribute the battery to be dried to the target drying channel includes: Obtain the channel identifier of the target drying channel, and query the target position parameter corresponding to the target drying channel from the preset mapping table of channel identifier and diversion device position parameter; Generate a diversion control command including the target position parameters, and send the diversion control command to the drive controller of the diversion device; The diversion control command is used to instruct the drive controller to drive the diversion actuator of the diversion device to move to the position indicated by the target position parameter, and to return a position confirmation signal after the movement is in place; After receiving the position confirmation signal returned by the shunt device, the system controls the battery to be dried to enter the shunt device for distribution.

[0042] It should be noted that the channel identifier is a unique code or number assigned to each drying channel by the drying control system. This is used to distinguish different channels in a multi-channel system and can take the form of a numerical code, address code, etc. The mapping table between channel identifiers and the position parameters of the shunt device is a pre-stored correspondence table in the drying control system. This table maps each channel identifier to a specific position parameter value of the shunt actuator; for example, channel one corresponds to position parameter value A, and channel two corresponds to position parameter value B. The target position parameter is the target coordinate or angle value that controls the movement of the shunt actuator, directly determining which channel's entrance the shunt actuator will guide the battery to. The shunt control command is a data message generated by the drying control system and sent to the shunt device drive controller. It contains the target position parameter and is used to instruct the shunt device to perform the corresponding mechanical action. The drive controller is the control unit inside the shunt device, responsible for receiving commands and driving actuators such as motors or cylinders to complete the movement. The position confirmation signal is a feedback signal returned by the shunt device to the drying control system after the movement is completed, indicating that the shunt actuator has reached the designated position and the battery can be safely released into the channel.

[0043] Understandably, the reliable operation of the shunt device is crucial for ensuring the accurate entry of batteries into the target channel. Releasing batteries before the shunt mechanism is in place can lead to battery jamming, falling into the wrong channel, or equipment damage. By establishing a mapping table between channel identifiers and position parameters, the target channel is converted into target position parameters recognizable by the shunt device. Standardized shunt control commands are generated and sent to the drive controller for execution. The drive controller then returns a confirmation signal after the shunt mechanism has reached its designated position, forming a request-execution-confirmation closed-loop control logic. Batteries are only released after the control system confirms that the shunt mechanism is in place, ensuring the accuracy and safety of the distribution process.

[0044] Specifically, after determining the target drying channel in step S3, the drying control system extracts the channel identifier from the target channel information and uses the channel identifier as an index key to search in a preset mapping table of channel identifiers and diversion device position parameters. This mapping table is stored in key-value pairs, and the search process is either direct matching or hash lookup to obtain the corresponding target position parameters. Subsequently, the drying control system generates a diversion control command including the target position parameters according to the communication protocol format agreed upon with the diversion device. The command message includes a frame header, a target position parameter field, a check bit, and a frame tail. The drying control system sends this command to the drive controller of the diversion device via a communication bus, which can be an RS-485 bus, a CAN bus, or an industrial Ethernet. After receiving the command, the drive controller parses the message to extract the target position parameters, calculates the motion trajectory and the required number of pulses or displacement based on the current position and the target position, and drives a stepper motor or servo motor to move the diversion actuator. If the diversion actuator is a swing guide plate, the drive motor rotates at the corresponding angle to align it with the target channel entrance; if the diversion actuator is a slide rail type distributor, the slide rail is moved to the feeding position above the corresponding channel. The drive controller monitors the position feedback signal in real time. When the deviation between the actual position and the target position is within the allowable error range, it determines that the movement is in place, generates a position confirmation signal, and returns it to the drying control system via the communication bus. After receiving the position confirmation signal, the drying control system controls the conveyor belt or pushing mechanism to send the batteries to be dried into the distribution device. Under the action of gravity or conveying force, the batteries slide into the entrance of the target drying channel along the distribution actuator. If the drying control system does not receive a confirmation signal after a preset timeout period following the issuance of the command, it determines that the distribution device is abnormal, triggers an alarm, and suspends the distribution process.

[0045] Preferably, dynamically adjusting the drying unit corresponding to the real-time location information to execute the corresponding temperature and wind speed parameters, and controlling the production line to execute the corresponding production line speed parameters includes: A first position sensor is installed at the entrance of the target drying channel, and a second position sensor is installed at the junction of each adjacent drying unit. When the first position sensor detects that the battery to be dried has entered the target drying channel, the first set of temperature and wind speed parameters in the customized drying process parameter sequence is activated, and the flow line speed is adjusted to the corresponding initial speed in the customized drying process parameter sequence. During the process of the battery to be dried passing through the second position sensor, the dwell time of the battery to be dried in the current drying unit is acquired in real time. The dwell time is compared with the preset target dwell time of the current drying unit in the customized drying process parameter sequence to obtain the dwell time deviation. Based on the time deviation, within the allowable fluctuation range of the set temperature value of the current drying unit as defined by the customized drying process parameter sequence, the temperature set value of the current drying unit is adjusted. When the battery to be dried passes the second position sensor and enters the next drying unit, the set temperature value of the next drying unit is restored to the original set value in the customized drying process parameter sequence, and the adjustment of the next drying unit is repeated.

[0046] It should be noted that the first position sensor is a detection element set at the entrance of the target drying channel to sense whether the battery has entered the channel. It can be a photoelectric through-beam sensor or a proximity sensor. The second position sensor is a detection element set at the boundary between two adjacent drying units. One second position sensor is set at each boundary to detect the arrival and departure of the battery, thereby determining the current position of the battery in the drying unit. It can also be a photoelectric through-beam sensor. The dwell time refers to the actual time elapsed from when the battery enters the current drying unit to the current moment, recorded by a timer. The preset target dwell time refers to the dwell time that the battery should spend in this drying unit as set in the customized drying process parameter sequence. This value originates from the dwell time of each segment calculated in step S3 based on the total target drying time and the target travel speed of each segment. The dwell time deviation is the difference between the dwell time and the target dwell time. A positive deviation indicates that the actual dwell time exceeds expectations, while a negative deviation indicates that the actual dwell time is insufficient. The allowable fluctuation range refers to the range of adjustment that is allowed above and below the temperature reference value set in the parameter sequence. Exceeding this range may affect the drying quality or equipment safety. This fluctuation range is set comprehensively based on the lower limit of the control accuracy of the heating device of the drying unit and the upper limit of the temperature resistance of the battery material.

[0047] It is understandable that during the drying process, the actual performance of each drying unit is affected by factors such as ambient temperature fluctuations, voltage fluctuations, and individual battery thermal conductivity differences, which may cause the actual drying progress of the battery in the current unit to be inconsistent with the preset parameter sequence. By setting position sensors at the boundaries of each unit, the actual dwell time of the battery in the current unit is obtained and compared with the preset target dwell time in the parameter sequence. If there is a deviation between the actual dwell time and the target value, temperature compensation adjustment is performed within the allowable temperature fluctuation range of the current unit to make up for the deviation in drying effect as much as possible before the battery leaves the current unit, ensuring that the cumulative heat supply of the current unit is close to the preset value. This method of performing closed-loop correction at each unit boundary decomposes the drying process of the entire channel into multiple independent but continuously connected local closed-loop control segments. Deviation detection and compensation are performed at the end of each unit, which improves the tracking accuracy of the preset parameter sequence of the entire drying process and avoids the accumulation and amplification of errors along the channel.

[0048] Specifically, the position sensors are arranged in the channel as follows: a first position sensor, a reflective photoelectric sensor, is installed at the inlet flow channel of the drying channel. The emitting beam and receiver are mounted on the same side. When the front end of the battery blocks the beam, a rising edge signal is generated to indicate that the battery has entered. A second position sensor, also a reflective photoelectric sensor, is installed at the junction of the outlet end of each drying unit and the inlet end of the next drying unit. When the front end of the battery reaches the junction, a signal is triggered. When the first position sensor detects that the battery has entered the channel, the timer module inside the drying control system starts timing. At the same time, it reads the target temperature value and target wind speed value with the index number of the first group from the customized drying process parameter sequence, and sends them to the temperature controller and fan frequency converter of the first drying unit through the communication interface for execution. The production line speed register is updated to the initial speed value in the parameter sequence, and the production line motor drive module runs at this speed. When the battery travels along the production line to the junction of the first and second drying units, the second position sensor detects the arrival signal of the battery's front end. The drying control system reads the dwell time value of that unit from the timer and reads the preset target dwell time of that drying unit from the parameter sequence. The two are subtracted to obtain the dwell time deviation. If the dwell time deviation is positive, it indicates that the actual dwell time exceeds the preset value, and the unit has provided excess drying heat. In this case, the temperature setting value of the current unit is reduced within the allowable fluctuation range of the unit's set temperature value. The reduction magnitude is positively correlated with the deviation magnitude but does not exceed the lower limit of the fluctuation. If the dwell time deviation is negative, it indicates that the actual dwell time is insufficient. In this case, the temperature setting value of the current unit is increased to provide more heat compensation in the remaining time. The increase magnitude is positively correlated with the absolute value of the deviation but does not exceed the upper limit of the fluctuation. The adjusted temperature setting value is immediately sent to the temperature controller of the current drying unit for execution. When the front end of the battery reaches the same second position sensor, triggering the signal to enter the next drying unit, the drying control system restores the temperature setpoint of the next drying unit to the original setpoint of that unit in the parameter sequence. Simultaneously, the timer is reset to start recording the dwell time of the next unit, and deviation detection and temperature compensation are performed in the same manner as described above. Specifically: ,in For compensation coefficient, Set the unit to its original temperature. The ambient reference temperature is used. Adjusted temperature. Limited to the floating range Within, exceeding the limit takes the boundary value, duration deviation. , This refers to the actual duration of stay. The preset target dwell time. For example, a battery in the third drying unit... , Floating range , , .like ,but , After adjustment, it is: ;like ,but After adjustment, it is: The above process is repeated at each cell junction until the battery leaves the second position sensor at the outlet of the last drying cell.

[0049] Preferably, the method for establishing the preset feature-process mapping relationship includes: Obtain individual feature datasets for multiple sample batteries, wherein each sample battery's individual feature dataset includes the first feature and the second feature; For each sample battery, the first feature and the second feature are used as input variables, and the temperature setting value, wind speed setting value and production line speed setting value of each segment of the drying channel are used as output variables. In the two-dimensional feature space formed by the first feature and the second feature, a continuous mapping function between the input variables and the output variables is established by an interpolation algorithm. The continuous mapping function is pre-set in the drying control system as the preset feature-process mapping relationship; when the individual feature dataset of the battery to be dried is obtained, the corresponding customized drying process parameter sequence is directly calculated through the continuous mapping function.

[0050] It should be noted that the sample batteries are representative individual batteries selected during the early process testing phase. The sample batteries should cover different combinations of residual electrolyte total amount from low to high and spatial distribution differences from small to large, ensuring that the subsequently established mapping function has sufficient coverage and accuracy across the entire feature space. The two-dimensional feature space is a planar coordinate system with the first feature as the horizontal axis and the second feature as the vertical axis, where each sample battery corresponds to a coordinate point. The interpolation algorithm is a numerical calculation method that infers the output value corresponding to any position in the feature space after knowing the input-output correspondence of several discrete sample points. Algorithms such as bilinear interpolation, spline interpolation, or radial basis function interpolation can be used. The continuous mapping function is a mathematical function established through interpolation algorithms from two-dimensional input to multi-dimensional output. The inputs are the first and second features, and the outputs are the temperature, wind speed, and velocity setpoints for each segment. The continuity of this function ensures that the output parameters can smoothly transition even when the input features change slightly, rather than abruptly.

[0051] Understandably, the accuracy and generalization ability of the mapping relationship directly determine the quality of the customized process parameter sequence. If only a discrete point-to-point lookup table method is used, it cannot cover the blank areas between sample points in the feature space. When the characteristics of the battery to be dried are not completely consistent with the existing samples, reasonable process parameters cannot be generated. By selecting multiple sample batteries covering different combinations of features in the preliminary experiment and recording the optimal process parameters corresponding to each sample, an interpolation function is constructed in the feature space using these sample points, forming a continuous mapping relationship from the feature space to the process parameter space. This ensures that for any value of the first and second features, even if the value has not appeared directly in the samples, a smooth and reasonable process parameter sequence can be obtained through interpolation calculation, guaranteeing the generalization and stability of the mapping relationship.

[0052] Specifically, the process of establishing the mapping relationship includes three stages: sample collection, optimal process determination, and interpolation function construction. In the sample collection stage, a large number of retired batteries that have undergone the liquid extraction process are randomly selected from the disassembled and recycled materials. The first and second features of each sample battery are obtained sequentially through an online testing station. The coordinate positions of each sample in the feature space are recorded to ensure that the sample points are evenly distributed within the feature space, covering the main feature value ranges. In the optimal process determination stage, each sample battery undergoes multiple drying tests on a test drying channel. Each test uses different combinations of temperature, wind speed, and velocity distribution parameters. After drying, the residual electrolyte content and battery performance indicators of each battery are measured. The set of process parameters with the best drying quality is taken as the optimal output for that sample battery. The temperature, wind speed, and velocity values ​​of each segment of this optimal output are recorded, forming the input-output pair for the sample point. In the interpolation function construction stage, all input-output pairs of sample points are used as known data. A suitable interpolation algorithm is selected to establish a continuous mapping function in the two-dimensional feature space. When using bilinear interpolation, the feature space is first divided into a triangular or rectangular grid composed of adjacent sample points. For any input point, its cell in the grid is first located, and then the output value of the sample at the vertex of that cell is used for linear weighted interpolation to obtain the output. When using spline interpolation, the spline surface equation is solved to fit the surface while ensuring second-order continuity and differentiability, resulting in a smoother mapping relationship. For example, in the two-dimensional feature space, for the point to be calculated... First, locate the triangular mesh cell containing the point, whose three vertices are known sample points. , , The corresponding output parameter vectors are respectively , , .point barycentric coordinates , , Solve the following system of linear equations: ; ; ; point The output parameter vector is obtained by weighted summation: Each output parameter vector contains multi-segment temperature, wind speed, and velocity setpoints, and interpolation calculations are performed independently for each parameter component.

[0053] The completed continuous mapping function is compiled into executable calculation code or parameterized formulas and pre-written into the program storage area of ​​the drying control system. When a new battery enters, the drying control system reads its first and second characteristics, substitutes them into the continuous mapping function for a calculation, and directly outputs the target temperature value, wind speed value, and velocity value for each segment, forming a complete sequence of customized drying process parameters.

[0054] Preferably, the process of drying the battery to be dried according to the customized drying process parameter sequence includes: The cumulative actual heat supply of the battery to be dried is obtained in real time from each drying unit that has been completed in the target drying channel. The cumulative actual heat supply is calculated based on the actual operating temperature and actual operating wind speed of each drying unit. The cumulative actual heat supply is compared with the cumulative target heat supply of each drying unit in the customized drying process parameter sequence to obtain the cumulative heat deviation; Based on the cumulative heat deviation, the set temperature and set wind speed of the downstream drying unit that the battery to be dried has not yet passed through are feedforward corrected so that the total cumulative heat supply of the battery to be dried when all drying processes are completed is close to the total target heat supply defined by the customized drying process parameter sequence. The allocation weight of the feedforward correction decreases sequentially along the remaining drying units.

[0055] It should be noted that the cumulative actual heat supply refers to the total heat actually provided by each completed drying unit from the moment the battery enters the drying channel to its current position. The actual heat supply of each drying unit is determined by the temperature difference between the actual operating temperature of the unit and the initial temperature of the battery when it enters the unit, the actual operating wind speed, and the actual residence time of the battery in the unit. The cumulative target heat supply refers to the sum of the target heat supply corresponding to each completed drying unit in the customized drying process parameter sequence. The target heat supply of each unit is calculated from the set temperature, set wind speed, and set residence time of that unit in the parameter sequence. The cumulative heat deviation is the difference between the cumulative actual heat supply and the cumulative target heat supply. When the cumulative actual heat supply is lower than the cumulative target heat supply, the deviation is negative, indicating that the completed drying process has insufficient heat supply; when the cumulative actual heat supply is higher than the cumulative target heat supply, the deviation is positive, indicating that the completed drying process has excessive heat supply. Feedforward correction refers to compensating for the detected heat supply deviation by pre-adjusting the set parameters of downstream units that have not yet been executed, rather than correcting the current unit through feedback, so that the total heat supply of the entire drying process tends to the target value. The allocation weight refers to the proportion of correction share that each downstream drying unit bears when the cumulative heat deviation is allocated to each downstream drying unit. Decreasing allocation means that the downstream unit closer to the current position bears a larger correction amount, and the downstream unit farther from the current position bears a smaller correction amount.

[0056] Understandably, as the battery travels along the channel, the heat supply to the drying units already passed cannot be adjusted. If the accumulated heat supply deviates from the target value, this deviation can only be compensated for by adjusting the heat supply of subsequent unexecuted drying units. By calculating the actual accumulated heat supply of completed units in real time and comparing it with the cumulative target heat supply to obtain the accumulated deviation, this deviation is distributed to the set temperature and set wind speed of each downstream unit in a feedforward manner for correction. This ensures that even if there is a deviation in the heat supply in the first half, the final total heat supply can be dynamically compensated in the second half to return to the preset total target value, ensuring that the overall drying effect is not affected by local deviations. At the same time, a decreasing weighting of the correction amount is adopted, with downstream units closer to the current position bearing a larger proportion of the correction. The rationale for this is that the current thermal state of the battery is closely related to the units it has just passed. Correcting adjacent units has the most direct impact on the cumulative thermal effect of the battery. The correction effect of distant units is attenuated because the battery will experience heat transfer changes from multiple units along the way. Therefore, the decreasing allocation strategy is more in line with the physical laws of heat conduction, avoiding excessive adjustment of distant units and causing new deviations.

[0057] Specifically, the calculation and feedforward correction of the cumulative actual heat supply and cumulative target heat supply are performed as follows: When the battery passes through the boundary of each drying unit, the drying control system reads the average temperature value, average wind speed value, and actual residence time of that unit during the battery's residence period. Simultaneously, it reads the preset temperature value, preset wind speed value, and preset residence time of that unit from the customized drying process parameter sequence. The actual heat supply and target heat supply are then accumulated into the corresponding cumulative registers according to the heat supply calculation model. This heat supply calculation model uses a convective heat transfer formula, where the heat supply is proportional to the product of the temperature difference, wind speed index, and residence time, satisfying the following relationship: Let the total number of drying units be... The battery has passed the first After one drying unit, the previous The cumulative actual heat supply of each unit and cumulative target heat supply Calculate using the following formulas respectively: ; ; in, To sum the sequence number ( ), The convective heat transfer coefficient, This represents the effective heat exchange area of ​​the battery. The wind speed impact index, For reference wind speed (take) ), This is a dimensionless wind speed correction factor; For the first The actual operating temperature of each unit For the first The preset target temperature of each unit, For the battery in the first Real-time temperature at the entrance of each unit For the first The actual wind speed of each unit For the first The preset target wind speed for each unit, For the battery in the first The actual dwell time of each unit For the battery in the first The preset target dwell time for each unit.

[0058] After each update of the two cumulative values, the cumulative actual heat supply is subtracted from the cumulative target heat supply to obtain the cumulative heat supply deviation. ,like Indicates the preceding If the cumulative oversupply of a unit is... This indicates insufficient cumulative heat supply; the sign and absolute value of the deviation reflect the nature and extent of the current heat shortage. For the allocation of cumulative heat deviation, the remaining quantity of each downstream drying unit that has not yet been executed is used as the allocation base. The allocation weight of each unit is determined in a decreasing arithmetic progression. If there are multiple downstream units... If there are remaining drying units, the unit closest to the current position is assigned a higher weight, the next unit has a lower weight, and so on, with the sum of all weights being... Specifically, let the number of remaining downstream units be... Then the downstream The remaining units ( Decreasing weights Calculate using the following formula: ; in, This represents the total number of drying units. The global sequence number of the remaining downstream units (from) Increment to ),molecular The denominator reflects a decreasing trend, where the closer the unit is to the current position, the smaller its index and the greater its weight. This is the formula for the summation of an arithmetic series, used to normalize the weights so that... .

[0059] The corrected heat supply allocated to each unit is as follows: Calculate the cumulative heat deviation. After inverting, the heat is distributed to downstream units in decreasing order of weight: if the upstream heat supply is insufficient ( ),but Downstream units receive positive compensation according to their weights, achieved by increasing the set temperature or the set wind speed; if upstream supply exceeds ( If the set temperature or airflow is reduced, the downstream units will receive a negative reduction, achieved by lowering the set temperature or reducing the set airflow. The corrected temperature and airflow values ​​must be within the adjustable range of the drying unit and the temperature tolerance limit of the battery materials. If they exceed these limits, a limiting process will be implemented, and the excess will be redistributed to the next downstream unit. After the correction is completed, the drying control system will send the corrected set temperature and airflow to the controller of the corresponding downstream drying unit, and the unit will execute the process according to the corrected parameters when the battery arrives.

[0060] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0061] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. An adaptive adjustment method for drying battery cells, characterized in that, It is applied to a drying control system, which is communicatively connected to an online monitoring station, a production line with multiple parallel drying channels, and a diversion device. Each of the drying channels is composed of multiple independently adjustable drying units connected in series. The adaptive adjustment method includes: The online testing station acquires an individual feature dataset of the batteries to be dried. The individual feature dataset includes a first feature for characterizing the total amount of residual electrolyte and a second feature for characterizing the differences in the spatial distribution of residual electrolyte inside the battery. Based on the individual feature dataset, a customized drying process parameter sequence is generated for the battery to be dried using a preset feature-process mapping relationship. The customized drying process parameter sequence is used to define the combination of parameters such as temperature, wind speed and flow line speed associated with the battery's position in the drying channel. Based on the real-time operating status of each drying channel and the expected energy consumption load of the customized drying process parameter sequence, a target drying channel is determined from the multiple parallel drying channels, and the shunt device is controlled to distribute the battery to be dried to the target drying channel. Based on the real-time position information of the battery to be dried in the target drying channel, the drying unit corresponding to the real-time position information is dynamically adjusted to execute the corresponding temperature and wind speed parameters according to the customized drying process parameter sequence, and the production line is controlled to execute the corresponding production line speed parameters until the battery to be dried completes the drying process.

2. The adaptive adjustment method for cell drying according to claim 1, characterized in that, The online testing station obtains individual feature datasets of batteries awaiting drying, including: The current weight of the battery to be dried is obtained by weighing device, and the current weight is compared with the preset standard weight to obtain a first feature characterizing the total amount of residual electrolyte. An image of the internal structure of the battery to be dried is acquired using an imaging device. The internal structure image is then segmented and analyzed for region to extract a second feature that characterizes the differences in the spatial distribution of residual electrolyte inside the battery.

3. The adaptive adjustment method for cell drying according to claim 1, characterized in that, Based on the individual feature dataset, and utilizing a preset feature-process mapping relationship, a customized drying process parameter sequence for the batteries to be dried is generated, including: Based on the total amount of residual electrolyte characterized by the first feature, the target total drying time is determined by using a preset table corresponding to the residual amount and drying time. Based on the spatial distribution differences of residual electrolyte inside the battery characterized by the second feature, the target heating rate of each segment along the travel direction of the drying channel is determined by using a preset table corresponding to the distribution differences and heating rates. Based on the target total drying time and the target heating rate of each segment, combined with the total number of segments and the length of each segment of the drying channel, the target temperature value, target wind speed and target travel speed of the battery to be dried in each segment are calculated, and a customized drying process parameter sequence of temperature, wind speed and flow line speed along the travel position of the drying channel is obtained.

4. The adaptive adjustment method for cell drying according to claim 1, characterized in that, Based on the real-time operating status of each drying channel and the expected energy consumption load of the customized drying process parameter sequence, a target drying channel is determined from the multiple parallel drying channels, including: Based on the temperature, wind speed, and flow line speed parameters in the customized drying process parameter sequence, the total heating power and total fan power required to complete the drying of the battery to be dried are calculated as the expected energy consumption load; Obtain the number of batteries currently assigned to each drying channel but not yet dried, and the current operating temperature and airflow of each drying unit in each drying channel; For each drying channel, the current energy load is calculated based on its current operating temperature and wind speed, and the expected energy load is superimposed with the current energy load to calculate the expected total energy consumption of the channel after allocating the batteries to be dried. The drying channel with the lowest expected total energy consumption is determined as the target drying channel; if there are multiple channels with the same expected total energy consumption, the channel with the fewest batteries in transit is further selected as the target drying channel.

5. The adaptive adjustment method for cell drying according to claim 1, characterized in that, Controlling the shunt device to distribute the batteries to be dried to the target drying channel includes: Obtain the channel identifier of the target drying channel, and query the target position parameter corresponding to the target drying channel from the preset mapping table of channel identifier and diversion device position parameter; Generate a diversion control command including the target position parameters, and send the diversion control command to the drive controller of the diversion device; The diversion control command is used to instruct the drive controller to drive the diversion actuator of the diversion device to move to the position indicated by the target position parameter, and to return a position confirmation signal after the movement is in place; After receiving the position confirmation signal returned by the shunt device, the system controls the battery to be dried to enter the shunt device for distribution.

6. The adaptive adjustment method for cell drying according to claim 1, characterized in that, Dynamically adjusting the drying unit corresponding to the real-time location information to execute the corresponding temperature and air speed parameters, and controlling the production line to execute the corresponding production line speed parameters, includes: A first position sensor is installed at the entrance of the target drying channel, and a second position sensor is installed at the junction of each adjacent drying unit. When the first position sensor detects that the battery to be dried has entered the target drying channel, the first set of temperature and wind speed parameters in the customized drying process parameter sequence is activated, and the flow line speed is adjusted to the corresponding initial speed in the customized drying process parameter sequence. During the process of the battery to be dried passing through the second position sensor, the dwell time of the battery to be dried in the current drying unit is acquired in real time. The dwell time is compared with the preset target dwell time of the current drying unit in the customized drying process parameter sequence to obtain the dwell time deviation. Based on the time deviation, within the allowable fluctuation range of the set temperature value of the current drying unit as defined by the customized drying process parameter sequence, the temperature set value of the current drying unit is adjusted. When the battery to be dried passes the second position sensor and enters the next drying unit, the set temperature value of the next drying unit is restored to the original set value in the customized drying process parameter sequence, and the adjustment of the next drying unit is repeated.

7. The adaptive adjustment method for cell drying according to claim 1, characterized in that, The preset feature-process mapping relationship is established in the following ways: Obtain individual feature datasets for multiple sample batteries, wherein each sample battery's individual feature dataset includes the first feature and the second feature; For each sample battery, the first feature and the second feature are used as input variables, and the temperature setting value, wind speed setting value and production line speed setting value of each segment of the drying channel are used as output variables. In the two-dimensional feature space formed by the first feature and the second feature, a continuous mapping function between the input variables and the output variables is established by an interpolation algorithm. The continuous mapping function is pre-set in the drying control system as the preset feature-process mapping relationship; when the individual feature dataset of the battery to be dried is obtained, the corresponding customized drying process parameter sequence is directly calculated through the continuous mapping function.

8. The adaptive adjustment method for cell drying according to claim 1, characterized in that, The process of drying the battery to be dried according to the customized drying process parameter sequence includes: The cumulative actual heat supply of the battery to be dried is obtained in real time from each drying unit that has been completed in the target drying channel. The cumulative actual heat supply is calculated based on the actual operating temperature and actual operating wind speed of each drying unit. The cumulative actual heat supply is compared with the cumulative target heat supply of each drying unit in the customized drying process parameter sequence to obtain the cumulative heat deviation; Based on the cumulative heat deviation, the set temperature and set wind speed of the downstream drying unit that the battery to be dried has not yet passed through are feedforward corrected so that the total cumulative heat supply of the battery to be dried when all drying processes are completed is close to the total target heat supply defined by the customized drying process parameter sequence. The allocation weight of the feedforward correction decreases sequentially along the remaining drying units.

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