Hybrid heat dissipation system and dynamic adjustment method of multi-chemical system battery partial charging equipment

Abstract

The application belongs to the technical field of battery capacity detection, and particularly relates to a mixed heat dissipation system and a dynamic adjustment method of a multi-chemical system battery capacity detection device, wherein the system comprises a cabinet body, a stepped natural air inlet module, a variable-diameter forced air extraction module, a temperature sensing module and a control module arranged in the cabinet body. In the method, data are obtained through multi-point temperature sensing, and a median value average filtering algorithm is used for processing. The core is that the thermal time constant parameters and the graded temperature intervals of different battery types are preset, the EWMA-AR mixed model is used to predict the trend of the battery micro-environment temperature, and the operation power of the air extraction component is dynamically adjusted in advance. Through the combination of structural design and control algorithm, the application realizes accurate temperature control and energy consumption optimization of various battery capacity detection processes such as lithium ion, lead acid and nickel hydrogen, and effectively solves the problems of poor adaptability, response lag and high energy consumption of the traditional heat dissipation scheme.

Description

Technical Field

[0001] This invention belongs to the field of battery capacity testing technology, specifically relating to a hybrid heat dissipation system and dynamic adjustment method for multi-chemical system battery capacity testing equipment. Background Technology

[0002] In battery manufacturing, capacity grading (i.e., capacity classification and screening) is a crucial process, as the accuracy of its test results directly affects the consistency, lifespan, and safety of the assembled batteries. During capacity grading, batteries generate a significant amount of heat due to continuous charge and discharge testing. If this heat cannot be dissipated promptly and effectively, the operating temperature of the battery will rise, severely impacting the accuracy of capacity grading and potentially triggering thermal runaway, posing a safety hazard. Therefore, the performance of the heat dissipation system in capacity grading equipment is of paramount importance.

[0003] Currently, most mainstream battery capacity testing equipment on the market adopts traditional air-cooling solutions, which generally rely on fixed-structure air ducts and a constant-power forced ventilation mode. However, with the increasing demand for capacity testing of various battery systems such as lithium-ion, lead-acid, and nickel-metal hydride batteries, the limitations of these traditional heat dissipation solutions are becoming increasingly apparent, specifically in the following aspects:

[0004] First, the heat dissipation methods have poor adaptability. Different types of batteries have significantly different heat dissipation characteristics: for example, lithium-ion batteries generate concentrated heat at the end of charging and discharging, lead-acid batteries generate relatively stable heat throughout the process, while nickel-metal hydride batteries are sensitive to excessive heat dissipation at low temperatures. Traditional single, fixed air duct designs cannot intelligently guide airflow according to the physical size and heat dissipation characteristics of the batteries, resulting in uneven airflow distribution within the cabinet, which easily leads to localized overheating or "overheating," making it difficult to meet the common temperature stability requirements of various types of batteries.

[0005] Secondly, there is a lack of energy consumption control methods. The existing constant power ventilation mode maintains full or high power operation of the heat dissipation system regardless of whether the battery is in a low-heat or high-heat stage. This results in serious energy waste when the battery is not generating much heat, which is inconsistent with the industrial development trend of green manufacturing and energy conservation.

[0006] Finally, the temperature regulation method suffers from lag. Traditional control methods are mostly based on simple temperature thresholds for feedback control, lacking forward-looking prediction and dynamic response mechanisms for different battery thermal time constants. When the battery heating power changes abruptly or the external ambient temperature fluctuates, the system response is slow, exhibiting significant control lag. This leads to large temperature fluctuations inside the equipment, affecting the accuracy and consistency of capacity testing. Summary of the Invention

[0007] The purpose of this invention is to provide a hybrid heat dissipation system and dynamic adjustment method for multi-chemical system battery capacity testing equipment, so as to solve the problems mentioned in the background art.

[0008] The present invention achieves the above objectives through the following technical solutions:

[0009] Firstly, this invention proposes a hybrid heat dissipation system for a multi-chemical system battery capacity testing device. The capacity testing device includes a cabinet and multiple testing stations located inside it for supporting batteries to be tested. The system includes:

[0010] An air intake module, located at the bottom of the cabinet, includes an air inlet and is used to guide natural air to diffuse evenly to each of the testing stations;

[0011] An exhaust module, located at the top of the cabinet, includes a variable-diameter air duct and an adjustable-power exhaust component disposed within the variable-diameter air duct;

[0012] The temperature sensing module includes a first temperature sensor located in the air intake area inside the cabinet, a second temperature sensor located at the battery station, and a third temperature sensor located in the top exhaust area.

[0013] The control module is electrically connected to the temperature sensing module and the adjustable power exhaust assembly, respectively.

[0014] The control module is configured as follows:

[0015] Determine the target battery type corresponding to the current capacity grading operation, and call the thermal time constant parameter and grading temperature range corresponding to the target battery type;

[0016] Based on the thermal time constant parameter, an exponentially weighted moving average-autoregressive hybrid calculation is performed on the temperature data sequence at the battery station to generate an advanced prediction value of the microenvironment temperature at the battery station.

[0017] The advanced predicted value is compared with the threshold of the graded temperature range, and the target operating power of the adjustable power exhaust component is dynamically determined based on the comparison result.

[0018] Furthermore, in the air intake module, the air intake direction of the air inlet is tilted towards the detection station, and a drainage slit is opened below the air inlet, with a waterproof and breathable membrane attached to the inside of the drainage slit.

[0019] Furthermore, the exhaust module also includes a negative pressure upper limit protection device, which includes a pressure sensor and an electric air supply valve. When the pressure sensor detects that the absolute value of the negative pressure inside the cabinet exceeds a set threshold, it controls the electric air supply valve to open, so that the negative pressure inside the cabinet is maintained within a preset range.

[0020] Furthermore, the air intake module is provided with several openings facing the detection station to alleviate airflow collision between the air intake module and the exhaust module.

[0021] Furthermore, the battery to be tested includes at least one of lithium-ion batteries, lead-acid batteries, and nickel-metal hydride batteries.

[0022] Secondly, the present invention proposes a dynamic adjustment method based on the above-mentioned hybrid heat dissipation system, comprising the following steps:

[0023] S1. The temperature T1 of the air inlet area, the micro-environment temperature T2 of the battery station, and the temperature T3 of the top exhaust area detected by the temperature sensing module are acquired in real time and filtered.

[0024] S2. Determine the type of battery to be sized in the current capacity grading operation, and call the pre-stored graded temperature range and thermal time constant parameters corresponding to the type of battery to be sized;

[0025] S3. Based on the thermal time constant parameter, perform exponential weighted moving average-autoregressive hybrid calculation on the filtered battery station microenvironment temperature T2 data sequence to generate a temperature prediction value leading k seconds;

[0026] S4. Compare the predicted temperature value with the threshold of the graded temperature range, dynamically determine the target operating power of the adjustable power exhaust component based on the comparison result, and output a control signal.

[0027] Furthermore, the process of performing an exponentially weighted moving average-autoregressive hybrid calculation on the filtered battery workstation microenvironment temperature T2 data sequence to generate a temperature prediction value leading k seconds includes:

[0028] Calculate the exponentially weighted moving average at the current time. ; Calculate the first-order rate of temperature change at the current moment. ;

[0029] Based on the moving average and the first-order temperature change rate, calculate the k-step advance prediction value. ; in, This is the filtered temperature value at the current moment. This is the filtered temperature value from the previous moment. α is the exponentially weighted moving average of the previous time step, β is the smoothing coefficient, β is the trend gain coefficient, and k is the prediction step size.

[0030] Furthermore, in step S2, the thermal time constant parameters called include the pre-stored values ​​of the smoothing coefficient α, the trend gain coefficient β, and the prediction step size k corresponding to the battery type.

[0031] The pre-stored value is:

[0032] For nickel-metal hydride batteries: α=0.04, β=0.25, k=3;

[0033] For lead-acid batteries: α=0.07, β=0.45, k=5;

[0034] For lithium-ion batteries: α=0.125, β=0.70, k=7.

[0035] Furthermore, step S4 specifically includes:

[0036] When the temperature prediction value t+k When the temperature is below the lower limit of the medium temperature range of the graded temperature range, the target operating power is determined to be low power. ;

[0037] When the temperature prediction value t+k When the temperature is in the middle range of the graded temperature range, combined with the temperature prediction value t+k The target operating power of the adjustable power exhaust assembly is determined through a linear mapping relationship. ;

[0038] When the temperature prediction value t+k When the temperature exceeds the upper limit of the high-temperature range within the defined temperature range, the target operating power is determined to be full power. And triggers auxiliary air intake inside the cabinet.

[0039] Furthermore, the combined temperature prediction value t+k The target operating power of the adjustable power exhaust assembly is determined through a linear mapping relationship. This can be achieved through the following formula:

[0040] ;

[0041] in,[ , [This refers to the intermediate temperature range.] , These are the minimum and maximum power limits for the adjustable power exhaust assembly to operate within the medium temperature range, respectively.

[0042] The beneficial effects of this invention are as follows:

[0043] 1. This invention, by pre-setting graded temperature ranges and thermal time constant parameters corresponding to different battery chemical systems, enables the same heat dissipation system to be adapted to the capacity testing process of various batteries such as lithium-ion batteries, lead-acid batteries, and nickel-metal hydride batteries without hardware modification, effectively addressing the engineering challenge of significant differences in the heating characteristics of different batteries.

[0044] 2. The stepped natural air intake structure in this invention promotes uniform airflow distribution and reduces localized overheating or undercooling areas. Combined with an EWMA-AR hybrid prediction model based on filtered temperature sequences, it can predict the temperature change trend of the battery station in advance and adjust the exhaust power linearly accordingly. This method reduces the control lag caused by mechanical inertia of the exhaust components, achieving more stable and precise temperature control.

[0045] 3. This invention abandons the traditional mode of continuous full-power operation and instead implements graded power consumption adjustment based on the actual heat generation stage of the battery. It maintains low-power operation in the low-temperature range, linearly adjusts power based on predicted values ​​only in the medium-temperature range, and activates full-power cooling only in the high-temperature range. This on-demand power allocation strategy avoids energy waste during periods of low heat generation and reduces the overall energy consumption of the device. Attached Figure Description

[0046] Figure 1 This is a structural diagram of a hybrid heat dissipation system for a multi-chemical system battery capacity assessment device in one embodiment of the present invention;

[0047] Figure 2 This is a structural diagram of the air intake module in one embodiment of the present invention;

[0048] Figure 3 This is a structural diagram of the exhaust module in one embodiment of the present invention;

[0049] Figure 4 This is a flowchart illustrating a dynamic adjustment method in another embodiment of the present invention;

[0050] Figure 5 This is a schematic diagram of another dynamic adjustment method in yet another embodiment of the present invention.

[0051] Figures 1-3 In the middle, 1. Cabinet; 2. Air inlet module; 3. Air outlet module; 4. Testing station; 5. Variable diameter air duct; 6. First temperature sensor; 7. Second temperature sensor; 8. Third temperature sensor; 9. Control module; 10. Outlet; 11. Battery to be tested; 12. Drainage seam; 13. Air inlet. Detailed Implementation

[0052] The present application will now be described in further detail with reference to the accompanying drawings. It should be noted that the following specific embodiments are only used to further illustrate the present application and should not be construed as limiting the scope of protection of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application based on the above application content.

[0053] It is worth noting that precise temperature control of the working environment is crucial for ensuring the accuracy of test data, improving battery grouping consistency, and guaranteeing the safe and stable operation of equipment during battery production and capacity testing. Currently, traditional heat dissipation methods for battery capacity testing equipment mostly employ fixed air ducts and constant power exhaust modes, failing to fully consider the significant differences in heat dissipation characteristics, thermal inertia, and temperature sensitivity among different battery types (such as lithium-ion, lead-acid, and nickel-metal hydride batteries). This "one-size-fits-all" heat dissipation solution has obvious limitations: it cannot dynamically adjust the heat dissipation intensity according to the actual heat dissipation state of the battery, easily leading to excessively high local temperatures or overheating, which not only affects capacity testing accuracy but also causes serious energy waste; furthermore, it lacks the ability to predict temperature trends, resulting in a lag in control system response and difficulty in coping with the dynamic changes in battery heat dissipation during charging and discharging. Therefore, there is an urgent need to develop a heat dissipation solution that can intelligently adapt to multiple chemical systems and achieve precise temperature control and energy consumption optimization.

[0054] To address the aforementioned problems, this invention provides a hybrid heat dissipation system and dynamic adjustment method for multi-chemical system battery capacity assessment equipment. Please refer to the appendix. Figure 1 and Figure 4 The system and method described herein can be applied to various battery capacity testing equipment, as well as corresponding control units and computer-readable storage media. The system architecture and control flow will be described in detail below. The hybrid heat dissipation and dynamic adjustment scheme mainly includes the following core components:

[0055] Example 1

[0056] Please see Figure 1 and Figure 2 This embodiment proposes a hybrid heat dissipation system for a multi-chemical system battery capacity testing device. The capacity testing device includes a cabinet 1 and multiple testing stations 4 inside the cabinet to support the batteries 11 to be tested. The system consists of the following parts:

[0057] Air intake module 2: This module is located at the bottom of cabinet 1, combined with... Figure 2 The unit has a square structure with several evenly distributed square air inlets 13 on its sides to guide natural airflow evenly to each testing station 4. The air inlets 13 are not simple openings, but rather upward-sloping structures to direct airflow upwards. In a preferred embodiment, the air inlets 13 are also connected to a stepped air guide structure, which consists of multiple levels of inclined air guide plates facing upwards towards the testing stations 4. Drainage slits 12 are formed on the surface of the plate below the air inlets 13, with a waterproof and breathable membrane attached to the inside of the slits. This design ensures that natural airflow entering from the bottom is guided and evenly diffused to all testing stations 4 at all heights within the cabinet, avoiding dead airflow zones.

[0058] The waterproof and breathable membrane used in this solution has a microporous structure with pore sizes typically between 0.1 and 1 micrometer. This size is much larger than water molecule clusters but much smaller than typical dust particles (particle size typically ≥10 micrometers). Therefore, the membrane can achieve selective permeability: dust particles are effectively blocked due to their large size, preventing backflow; the membrane layer is attached to the inner bottom of the drainage slit 12, and liquid condensate collects at the drainage slit under gravity. When the liquid film forms and contacts the membrane layer, its surface tension and gravity work together to overcome the microporous resistance, allowing water to slowly permeate or drip down the membrane surface, thereby achieving passive drainage of condensate.

[0059] Exhaust Module 3: This module is located at the top of cabinet 1. Its core includes a variable diameter air duct 5, combined with... Figure 3 The cross-sectional area of ​​the duct is designed to gradually decrease from the connection point with the cabinet towards the outlet, forming a tapered structure to enhance the suction capacity of hot air inside the cabinet. An adjustable power exhaust component (such as a variable frequency fan or a PWM controlled fan) is installed in the duct. In a preferred embodiment, the exhaust module 3 also integrates a negative pressure upper limit protection device, which consists of a pressure sensor and an electric air supply valve. When the pressure sensor detects that the absolute value of the negative pressure inside the cabinet exceeds a set threshold (e.g., -50Pa) due to excessive suction, it controls the electric air supply valve to open, introducing external air to maintain the negative pressure inside the cabinet within a preset range for safe operation of the equipment.

[0060] Temperature sensing module: To achieve precise control, multiple temperature monitoring points are set up inside the cabinet. These include: a first temperature sensor 6 (located in the air intake area) for monitoring the temperature of the incoming air; a second temperature sensor 7 (located at the battery station) for directly monitoring the critical temperature around the battery; and a third temperature sensor 8 (located in the top exhaust area) for monitoring the final exhaust temperature of the system.

[0061] Control Module 9: This module is the core of the system's processing and can be implemented using a PLC, microcontroller, or industrial computer. It is electrically connected to the temperature sensing module and the adjustable power exhaust assembly, respectively. Control Module 9 internally stores thermal time constant parameters (specifically embodied in the parameters α, β, k of the prediction model described below) and graded temperature ranges (including low-temperature, medium-temperature, and high-temperature ranges) corresponding to different battery types (such as lithium-ion batteries, lead-acid batteries, and nickel-metal hydride batteries).

[0062] The control module 9 is configured to execute the following implementable control logic:

[0063] Step 1: After system startup, control module 9 first acquires raw temperature data in real time from the first temperature sensor 6, the second temperature sensor 7, and the third temperature sensor 8 via the temperature sensing module. Simultaneously, the operator can specify the current battery type (e.g., lithium-ion battery) through the human-machine interface, or the system can automatically identify it by scanning the battery barcode. Control module 9 then calls up pre-stored thermal time constant parameters and graded temperature ranges corresponding to the target battery type.

[0064] The second step involves control module 9 performing median averaging filtering on the raw data, particularly the temperature data sequence from the battery station (to suppress pulse interference). Next, based on the called thermal time constant parameter, it performs an exponentially weighted moving average-autoregressive (EWMA-AR) hybrid calculation on the filtered data sequence. Specifically, it calculates the result according to the formula... (in Generate temperature prediction values ​​k seconds ahead. t+k .

[0065] in, This is the filtered temperature value at the current moment. This is the filtered temperature value from the previous moment. α is the exponentially weighted moving average of the previous time step, β is the smoothing coefficient, β is the trend gain coefficient, and k is the prediction step size.

[0066] Step 3: Control module 9 calculates the advanced prediction value. t+k The target operating power of the exhaust system is dynamically determined based on the comparison results, comparing it with the threshold values ​​of the graded temperature ranges called in the second step. For example: when t+k When within the intermediate temperature range, a target power percentage is calculated using a linear mapping based on its specific location within that range; when t+k If the high temperature range is predicted to be exceeded, the target operating power will be immediately set to 100% full power, and the auxiliary air intake component can be activated in conjunction with it.

[0067] As a preferred solution, in order to alleviate the problem that the difference in airflow speed and direction between the bottom air intake module 2 and the top exhaust module 3 may cause direct collision and thus form turbulence affecting the uniformity of heat dissipation, a number of evenly distributed circular or rectangular openings 10 are provided on the vertical or side wall of the stepped air guide plate in the direction of the air intake module 2 toward the detection station 4.

[0068] In practice, these openings 10 collectively form an airflow buffer layer. Their working principle is as follows: when natural wind flows upwards under the guidance of the stepped air guide plate, and the top exhaust assembly generates a strong upward suction force, some airflow can be pre-dissipated and mixed laterally and longitudinally through these openings 10. This design effectively weakens the rigid velocity gradient in the main airflow direction, allowing the rising airflow from the bottom and the downward-seeping negative pressure airflow from the top to transition smoothly in this area, avoiding the formation of severe airflow shearing and vortices in the middle of the cabinet, thus ensuring a more stable and uniform airflow across the surfaces of each battery station 4.

[0069] As a preferred embodiment, the battery to be tested 11 includes, but is not limited to, at least one of lithium-ion batteries, lead-acid batteries, and nickel-metal hydride batteries. The internal database of the control module 9 stores a dedicated set of control parameters for each of these battery types to achieve precise adaptation of the heat dissipation strategy.

[0070] For lithium-ion batteries, the characteristic is that heat generation is concentrated and significant at the end of the charge and discharge cycle (especially at high rates). Therefore, the system presets a low upper limit threshold for the high temperature range, and the prediction step size k in the prediction model is relatively large (e.g., k=7) to ensure that heat dissipation can be enhanced in advance before the temperature rises rapidly.

[0071] For lead-acid batteries, the heating process is relatively stable and continuous. The system has a wide preset medium temperature range, and the control strategy focuses on smooth linear power regulation within this range to maintain a stable heat dissipation intensity.

[0072] Nickel-metal hydride batteries are sensitive to low temperatures, and excessive heat dissipation can inhibit their chemical activity. Therefore, the system sets a high lower limit threshold for the low temperature range (e.g., 15°C) and controls the ventilation components to operate at extremely low sustaining power near this temperature to prevent the battery microenvironment temperature from becoming too low.

[0073] Example 2

[0074] This embodiment proposes a dynamic adjustment method based on the hybrid heat dissipation system described in Embodiment 1. This method is executed by the control module 9 in the system, and its core lies in achieving proactive adjustment of the heat dissipation intensity by predicting the temperature forecast value k seconds in advance, thereby overcoming the system's thermal inertia and mechanical lag. The method includes the following specific steps:

[0075] S1. Control module 9 acquires raw temperature data of three key points detected by the temperature sensing module in real time at a fixed sampling period (e.g., once per second): air inlet area temperature T1, battery station micro-environment temperature T2, and top exhaust area temperature T3.

[0076] Due to the complex industrial environment, the raw temperature data may contain random noise and transient pulse interference, which can affect control accuracy if used directly. Therefore, this step first applies a median average filtering algorithm to the acquired raw temperature data, especially the critical battery station microenvironment temperature T2.

[0077] The specific filtering steps are as follows:

[0078] S101. Data Sampling: Perform N consecutive samplings at each detection point (N is an odd number, dynamically configured according to the battery type: for example, N=7 for nickel-metal hydride batteries, N=5 for lead-acid batteries, and N=9 for lithium-ion batteries), forming a sampling array T=[T1,T2,...,T... n ].

[0079] S102. Pulse Removal: Sort the sampled array and remove the maximum value T. max With minimum value T min Retain the middle N-2 data to form a valid array T valid .

[0080] S103. Smoothing Calculation: Perform an arithmetic mean operation on the effective array to obtain the filtered temperature value T. filtered The calculation formula is as follows:

[0081] ;

[0082] in, Form a valid array from i data points.

[0083] This algorithm can simultaneously suppress random pulse interference and periodic noise. Its filtering effect on slowly changing signals such as temperature is better than that of the single moving average method. The data error after filtering can be controlled within ±0.1℃.

[0084] S2. Control module 9 determines the type of battery 11 to be tested in the current capacity testing operation. This process can be achieved by the operator manually selecting the battery through the human-machine interface, scanning the battery identification mark (such as a QR code), or by automatic detection by the equipment.

[0085] Subsequently, control module 9 retrieves a pre-stored set of control parameters that strictly corresponds to the battery type from its internal non-volatile memory. This parameter set includes:

[0086] Graded temperature range: Defines the low temperature range and medium temperature range for this type of battery. , ] and high temperature range.

[0087] Thermal time constant parameters: Specifically, these are the smoothing coefficient α, trend gain coefficient β, and prediction step size k required by the EWMA-AR prediction model.

[0088] For example, this embodiment provides a specific set of pre-stored parameter values:

[0089] For nickel-metal hydride batteries (high thermal inertia, slow temperature change): α=0.04, β=0.25, k=3.

[0090] For lead-acid batteries (moderate thermal inertia): α=0.07, β=0.45, k=5.

[0091] For lithium-ion batteries (low thermal inertia, rapid temperature change): α=0.125, β=0.70, k=7.

[0092] S3. Based on the thermal time constant parameters (α, β, k) called in step S2, control module 9 performs exponentially weighted moving average-autoregressive (EWMA-AR) hybrid calculation on the filtered battery station microenvironment temperature T2 data sequence obtained in step S1 to generate a temperature prediction value k seconds ahead. t+k .

[0093] The specific iterative prediction sub-steps are as follows:

[0094] Calculate the exponentially weighted moving average at the current time. ; Calculate the first-order rate of temperature change at the current moment. ;

[0095] Calculate the k-step advance prediction value based on the moving average and the first-order rate of temperature change. ; in, This is the filtered temperature value at the current moment. This is the filtered temperature value from the previous moment. α is the exponentially weighted moving average of the previous time step, β is the smoothing coefficient, β is the trend gain coefficient, and k is the prediction step size.

[0096] This model uses S t Smoothing historical data, through β*ΔT t By capturing changing trends and then extrapolating them to the future using k, we can predict temperature trends.

[0097] S4. Control module 9 will use the advanced predicted temperature value obtained in step S3. t+k The threshold of the graded temperature range called in step S2 is compared with the threshold, and based on the comparison result, a graded dynamic power consumption adjustment strategy is executed, specifically including:

[0098] when t+k < (Low Temperature Range): The target operating power is determined to be low power. (For example, 20% of the rated power) Only maintain the necessary basic ventilation to prevent "excessive heat dissipation" of low-temperature sensitive batteries such as nickel-metal hydride batteries.

[0099] when ≤ t+k ≤ (Medium temperature range): Combined with predicted values t+k The target operating power is determined through a linear mapping relationship. This achieves a match between heat dissipation intensity and expected heat load.

[0100] Linear mapping is achieved by the following formula:

[0101] ;

[0102] in,[ , [This refers to the medium temperature range.] , These are the minimum and maximum power limits for the adjustable power exhaust assembly to operate within the medium temperature range. (For example) =30%, =70%). Ultimately, it will... Constraints on [ , Within the range.

[0103] when t+k > (High-temperature range): Immediately determine the target operating power to be full power. (100%), and simultaneously triggers the auxiliary air intake components (such as an additional low-power fan) in cabinet 1 to cool down quickly at maximum capacity and ensure battery safety.

[0104] Finally, control module 9 converts the determined target operating power into a corresponding control signal (such as a PWM wave or analog signal) and outputs it to the adjustable power ventilation component, completing one control cycle. This process repeats continuously, forming a prediction-based closed-loop dynamic adjustment system.

[0105] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0106] In addition, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0107] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A dynamic adjustment method for the hybrid heat dissipation system of a multi-chemical system battery capacity assessment device, characterized in that, The capacity testing equipment includes a cabinet (1) and multiple testing stations (4) inside it for carrying the batteries (11) to be tested. The system includes: An air intake module (2) is located at the bottom of the cabinet (1) and includes an air inlet (13) for guiding natural wind to diffuse evenly to each of the testing stations (4). The exhaust module (3) is located on the top of the cabinet (1) and includes a variable diameter air duct (5) and an adjustable power exhaust component located in the variable diameter air duct (5). The temperature sensing module includes a first temperature sensor (6) located in the air intake area inside the cabinet (1), a second temperature sensor (7) located at the battery station, and a third temperature sensor (8) located in the top exhaust area. The control module (9) is electrically connected to the temperature sensing module and the adjustable power exhaust assembly, respectively. The dynamic adjustment method includes: S1. The temperature T1 of the air inlet area, the micro-environment temperature T2 of the battery station, and the temperature T3 of the top exhaust area detected by the temperature sensing module are acquired in real time and filtered. S2. Determine the type of the battery (11) to be sized in the current capacity grading operation, and call the pre-stored graded temperature range and thermal time constant parameters corresponding to the type of battery (11) to be sized; S3. Based on the thermal time constant parameter, perform exponential weighted moving average-autoregressive hybrid calculation on the filtered battery station microenvironment temperature T2 data sequence to generate a temperature prediction value leading k seconds; S4. Compare the predicted temperature value with the threshold of the graded temperature range, dynamically determine the target operating power of the adjustable power exhaust component based on the comparison result, and output a control signal; The process of performing an exponentially weighted moving average-autoregressive hybrid calculation on the filtered battery workstation microenvironment temperature T2 data sequence to generate a temperature prediction value leading k seconds includes: Calculate the exponentially weighted moving average at the current time. ; Calculate the first-order rate of temperature change at the current moment. ; Based on the moving average and the first-order temperature change rate, calculate the k-step advance prediction value. ; in, T is the filtered temperature value at the current moment. t−1 This is the filtered temperature value from the previous moment. α is the exponentially weighted moving average of the previous time step, β is the smoothing coefficient, β is the trend gain coefficient, and k is the prediction step size.

2. The dynamic adjustment method for the hybrid heat dissipation system of the multi-chemical system battery capacity assessment device according to claim 1, characterized in that, In the air intake module (2), the air intake port (13) is tilted towards the detection station (4), and a drainage slit (12) is opened below the air intake port (13). A waterproof and breathable membrane is attached to the inside of the drainage slit (12).

3. The dynamic adjustment method for the hybrid heat dissipation system of the multi-chemical system battery capacity assessment device according to claim 1, characterized in that, The exhaust module (3) also includes a negative pressure upper limit protection device, which includes a pressure sensor and an electric air supply valve. When the pressure sensor detects that the absolute value of the negative pressure inside the cabinet exceeds the set threshold, it controls the electric air supply valve to open so that the negative pressure inside the cabinet is maintained within the preset range.

4. The dynamic adjustment method for the hybrid heat dissipation system of the multi-chemical system battery capacity assessment device according to claim 1, characterized in that, The air intake module (2) is provided with several openings (10) facing the detection station (4) to alleviate the airflow collision between the air intake module (2) and the exhaust module (3).

5. The dynamic adjustment method for the hybrid heat dissipation system of the multi-chemical system battery capacity assessment device according to claim 1, characterized in that, The battery to be tested (11) includes at least one of lithium-ion batteries, lead-acid batteries and nickel-metal hydride batteries.

6. The dynamic adjustment method for the hybrid heat dissipation system of the multi-chemical system battery capacity assessment device according to claim 1, characterized in that, In step S2, the thermal time constant parameters called include the pre-stored values ​​of the smoothing coefficient α, the trend gain coefficient β, and the prediction step size k corresponding to the battery type. The pre-stored value is: For nickel-metal hydride batteries: α=0.04, β=0.25, k=3; For lead-acid batteries: α=0.07, β=0.45, k=5; For lithium-ion batteries: α=0.125, β=0.70, k=7.

7. The dynamic adjustment method for the hybrid heat dissipation system of the multi-chemical system battery capacity assessment device according to claim 6, characterized in that, Step S4 specifically includes: When the temperature prediction value t+k When the temperature is below the lower limit of the medium temperature range of the graded temperature range, the target operating power is determined to be low power. ; When the temperature prediction value t+k When the temperature is in the middle range of the graded temperature range, combined with the temperature prediction value t+k The target operating power of the adjustable power exhaust assembly is determined through a linear mapping relationship. ; When the temperature prediction value t+k When the temperature exceeds the upper limit of the high-temperature range within the defined temperature range, the target operating power is determined to be full power. And trigger the auxiliary air intake inside the cabinet (1).

8. The dynamic adjustment method for the hybrid heat dissipation system of the multi-chemical system battery capacity assessment device according to claim 7, characterized in that, The combined temperature prediction value t+k The target operating power of the adjustable power exhaust assembly is determined through a linear mapping relationship. This can be achieved through the following formula: ； in,[ , [This refers to the intermediate temperature range.] , These are the minimum and maximum power limits for the adjustable power exhaust assembly to operate within the specified medium temperature range.

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