Hybrid heat dissipation system of multi-chemical-system battery capacity grading equipment and dynamic adjustment method

Abstract

The invention belongs to the technical field of battery capacity grading detection, and particularly relates to a mixed heat dissipation system of multi-chemical-system battery capacity grading equipment and a dynamic adjustment method, the system comprises a cabinet body, and a stepped natural air inlet module, a variable-diameter forced air draft module, a temperature sensing module and a control module which are arranged in the cabinet body. According to the method, data are obtained through multi-point temperature sensing, and a median average filtering algorithm is adopted for processing; the method is characterized in that thermal time constant parameters and grading temperature intervals of different battery types are preset, the trend of the microenvironment temperature of the battery is predicted through an EWMA-AR hybrid model, and then the operation power of an air draft assembly is dynamically adjusted in advance. Through combination of structural design and a control algorithm, accurate temperature control and energy consumption optimization in the capacity grading process of various batteries such as lithium ion batteries, lead acid batteries and nickel-metal hydride batteries are achieved, and the problems that a traditional heat dissipation scheme is poor in adaptability, lagged in response and high in energy consumption are effectively solved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of battery capacity grading detection, and particularly relates to a hybrid heat dissipation system and a dynamic adjustment method for a multi-chemical system battery capacity grading device. BACKGROUND

[0002] In the battery production and manufacturing process, capacity grading (i.e. capacity grading and screening) is a crucial process, and the accuracy of the detection results directly affects the consistency, life and safety of the battery after grouping. During the capacity grading process, the battery will generate a large amount of heat due to continuous charging and discharging tests. If the heat cannot be dissipated in time and effectively, the battery operating environment temperature will rise, which not only seriously affects the capacity grading accuracy, but also may cause battery thermal runaway and safety hazards. Therefore, the performance of the heat dissipation system of the capacity grading device is crucial.

[0003] At present, the mainstream battery capacity grading devices on the market mostly adopt traditional air-cooled heat dissipation schemes, which generally rely on fixed structure air ducts and constant power forced air extraction mode. However, with the increasing demand for capacity grading of various different chemical system batteries such as lithium ion batteries, lead-acid batteries and nickel-hydrogen batteries, the limitations of such traditional heat dissipation schemes have become increasingly apparent, specifically in the following aspects: Firstly, the adaptability of the heat dissipation method is poor. Different types of batteries have significantly different heat generation 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, and nickel-hydrogen batteries are sensitive to excessive heat dissipation at low temperatures. The traditional single and fixed air duct design cannot intelligently guide the air flow according to the physical size and heat generation characteristics of the battery, resulting in uneven air distribution in the cabinet, which is prone to local temperature overheating or "excessive heat dissipation", and is difficult to meet the common demand of various batteries for temperature stability.

[0004] Secondly, the energy consumption control method is missing. The existing fixed power air extraction mode maintains full power or high power operation of the heat dissipation system regardless of whether the battery is in a small heat generation stage or a large heat generation stage, which causes serious waste of electrical energy under conditions where the battery generates little heat, and does not conform to the industry development trend of green manufacturing and energy saving.

[0005] Finally, the temperature regulation method is lagging. The traditional control method is mostly based on simple temperature threshold feedback control, lacking forward-looking prediction and dynamic response mechanism for different battery thermal time constants. When the battery heat power suddenly changes or the external environment temperature fluctuates, the system response is slow, there is obvious control lag, the internal temperature of the device fluctuates greatly, and the accuracy and consistency of the capacity grading detection are affected. SUMMARY

[0006] The purpose of the present application is to provide a hybrid heat dissipation system and a dynamic adjustment method for a multi-chemical system battery capacity grading device to solve the problems raised in the background.

[0007] The application achieves the above-mentioned purpose by the following technical solutions: In a first aspect, the application provides a hybrid heat dissipation system of a multi-chemical system battery capacity grading device, the capacity grading device comprising a cabinet and a plurality of layers of detection stations for carrying to-be-capacity-graded batteries arranged in the cabinet, the system comprising: An air inlet module arranged at the bottom of the cabinet, comprising an air inlet for guiding natural wind to uniformly diffuse to each detection station; An air outlet module arranged at the top of the cabinet, comprising a variable-diameter air duct and an adjustable power air extraction assembly arranged in the variable-diameter air duct; A temperature sensing module comprising a first temperature sensor arranged at an air inlet area in the cabinet, a second temperature sensor arranged at a battery station, and a third temperature sensor arranged at a top air outlet area; A control module electrically connected with the temperature sensing module and the adjustable power air extraction assembly; The control module is configured to: determine a target battery type corresponding to a current capacity grading operation, and call a thermal time constant parameter and a hierarchical temperature interval corresponding to the target battery type; based on the thermal time constant parameter, perform an exponential weighted moving average-autoregressive hybrid calculation on a temperature data sequence at the battery station to generate a leading prediction value of the micro-environment temperature of the battery station; compare the leading prediction value with a threshold value of the hierarchical temperature interval, and dynamically determine a target operating power of the adjustable power air extraction assembly according to a comparison result.

[0008] Further, in the air inlet module, the air inlet direction of the air inlet is inclined towards the detection station, a drainage gap is opened below the air inlet, and a waterproof and breathable film is attached inside the drainage gap.

[0009] Further, the air outlet module further comprises a negative pressure upper limit protection device, the negative pressure upper limit protection device comprising a pressure sensor and an electric air supplement valve, when the pressure sensor detects that the absolute value of the negative pressure in the cabinet exceeds a set threshold value, the electric air supplement valve is controlled to be opened, so that the negative pressure in the cabinet is maintained in a preset interval.

[0010] Further, the air inlet module is provided with a plurality of openings in the direction towards the detection station, for relieving the airflow collision between the air inlet module and the air outlet module.

[0011] Further, the to-be-capacity-graded battery comprises at least one of a lithium ion battery, a lead-acid battery and a nickel-hydrogen battery.

[0012] In a second aspect, the application provides a dynamic adjustment method based on the hybrid heat dissipation system, comprising the following steps: S1. Real-time acquisition of the temperature T1 of the air inlet area, the temperature T2 of the battery station micro-environment and the temperature T3 of the top air outlet area detected by the temperature sensing module, and filtering; S2. Determining the type of the battery to be sorted in the current sorting operation, and calling the pre-stored temperature interval and thermal time constant parameters corresponding to the type of the battery to be sorted; S3. Based on the thermal time constant parameter, performing an exponential weighted moving average-autoregressive hybrid calculation on the filtered battery station micro-environment temperature T2 data sequence to generate a temperature prediction value k seconds in advance; S4. Comparing the temperature prediction value with the threshold value of the temperature interval, and dynamically determining the target operating power of the adjustable power exhaust assembly and outputting a control signal according to the comparison result.

[0013] Further, the exponential weighted moving average-autoregressive hybrid calculation on the filtered battery station micro-environment temperature T2 data sequence to generate a temperature prediction value k seconds in advance comprises: calculating the exponential weighted moving average value at the current time ; calculating the first-order temperature change rate at the current time ; based on the moving average value and the first-order temperature change rate, calculating a k-step ahead prediction value ; wherein, is the filtered temperature value at the current time, is the filtered temperature value at the last time, is the exponential weighted moving average value at the last time, α is the smoothing coefficient, β is the trend gain coefficient, and k is the prediction step length.

[0014] Further, in step S2, the thermal time constant parameter called includes pre-stored values of the smoothing coefficient α, the trend gain coefficient β and the prediction step length k corresponding to the battery type; The pre-stored values are: for nickel-hydrogen 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.

[0015] Further, step S4 specifically comprises: when the temperature prediction value t+k is lower than the lower limit of the medium temperature interval of the temperature interval, the target operating power is determined to be low power ; when the temperature prediction value t+k is in a middle temperature interval of the hierarchical temperature interval, the target running power of the adjustable power exhaust assembly is determined in combination with the temperature prediction value t+k by a linear mapping relationship ; when the temperature prediction value t+k is higher than an upper limit of a high temperature interval of the hierarchical temperature interval, the target running power is determined as full power and auxiliary air intake in the cabinet is triggered.

[0016] Further, the target running power of the adjustable power exhaust assembly is determined in combination with the temperature prediction value t+k by a linear mapping relationship by the following formula: ; wherein, , ] is the middle temperature interval, 、 are minimum and maximum power limits of the adjustable power exhaust assembly running in the middle temperature interval, respectively.

[0017] The present application has the following beneficial effects: 1. The present application presets hierarchical temperature intervals and thermal time constant parameters corresponding to different battery chemical systems, so that the same set of heat dissipation system can adapt to the capacity grading process of lithium ion batteries, lead-acid batteries, nickel-hydrogen batteries and other batteries without hardware modification, effectively solving the engineering problem of significant differences in different battery heating characteristics.

[0018] 2. The stepped natural air intake structure in the present application promotes uniform distribution of airflow and reduces local overheating or overcooling areas; in combination with the EWMA-AR hybrid prediction model based on the filtered temperature sequence, the temperature change trend of the battery station can be predicted in advance, and the exhaust power is linearly adjusted accordingly. This method weakens the control lag caused by mechanical inertia of the exhaust assembly, and realizes more stable and accurate temperature control.

[0019] 3. The present application discards the traditional mode of continuous full power running, and instead implements hierarchical power consumption adjustment according to the actual heating stage of the battery. In the low temperature interval, low power running is maintained, only in the middle temperature interval, the power is linearly adjusted according to the prediction value, and only in the high temperature interval, full power dissipation is started. This on-demand power strategy avoids the waste of electric energy in the small heating stage, and reduces the overall energy consumption of the equipment. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 A structural diagram of a hybrid heat dissipation system of a multi-chemical system battery capacity grading device in an embodiment of the present application; Figure 2 A structural diagram of an air inlet module in an embodiment of the present application; Figure 3 A structural diagram of an air outlet module in an embodiment of the present application; Figure 4 A flowchart of a dynamic adjustment method in another embodiment of the present application; Figure 5 Another flowchart of a dynamic adjustment method in another embodiment of the present application.

[0021] Figures 1-3 In the figure, 1, cabinet body; 2, air inlet module; 3, air outlet module; 4, detection station; 5, variable-diameter air duct; 6, first temperature sensor; 7, second temperature sensor; 8, third temperature sensor; 9, control module; 10, through opening; 11, battery to be graded; 12, drainage gap; 13, air inlet. DETAILED DESCRIPTION

[0022] It is necessary to point out here that the following detailed description is only used to further illustrate the present application and cannot be understood as limiting the protection scope of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application according to the above application content.

[0023] It is worth noting that in the process of battery production and capacity grading detection, accurate temperature control of the working environment is a key link to ensure the accuracy of test data, improve the consistency of battery grouping, and ensure the safe and stable operation of the equipment. At present, the traditional battery capacity grading equipment heat dissipation method mostly adopts a fixed air duct and a constant power air extraction mode, which does not fully consider the significant differences in heat generation characteristics, thermal inertia, and temperature sensitivity of different types of batteries (such as lithium ion batteries, lead-acid batteries, nickel-hydrogen batteries, etc.). This kind of "one-size-fits-all" heat dissipation scheme has obvious limitations: it cannot dynamically adjust the heat dissipation intensity according to the actual heat generation state of the battery, which easily leads to local temperature being too high or excessive heat dissipation, not only affecting the capacity grading accuracy, but also causing serious energy waste; at the same time, it lacks the ability to predict temperature trends, and the control system response is lagging, making it difficult to respond to the dynamic changes in battery heating during charging and discharging. Therefore, it is urgent to build a heat dissipation solution that can intelligently adapt to multiple chemical systems and achieve precise temperature control and energy consumption optimization.

[0024] To solve the above problems, the present application provides a hybrid heat dissipation system and a dynamic adjustment method for a multi-chemical system battery capacity grading device. Please refer to the accompanying Figure 1 and Figure 4, the system and method can be applied to various battery capacity detection equipment, and the corresponding control unit and computer readable storage medium. The system architecture and control process will be described in detail below. The mixed heat dissipation and dynamic adjustment scheme mainly includes the following core parts: Embodiment 1 Please refer to Figure 1 and Figure 2 The embodiment proposes a mixed heat dissipation system for a multi-chemical system battery capacity detection device. The capacity detection device includes a cabinet 1 and multiple layers of detection workstations 4 inside the cabinet for carrying the batteries to be detected 11. The system includes the following parts: Air inlet module 2: The module is arranged at the bottom of the cabinet 1, combined with Figure 2 It is a square structure, and the side surface has a plurality of evenly distributed square air inlets 13 for guiding the natural wind to diffuse uniformly to each detection workstation 4. The air inlet 13 is not a simple opening, but an upwardly inclined air inlet structure for guiding the wind upward. In a preferred way, the air inlet 13 is also connected with a stepped air guide structure, which is composed of multiple levels of inclined air guide plates, which are directed to the upper detection workstations 4. The plate surface below the air inlet 13 is provided with a drainage slot 12, and the inner side of the slot is attached with a waterproof and breathable film. This design allows the ambient natural wind to enter from the bottom and be guided and uniformly diffused to all detection workstations 4 at different heights in the cabinet, avoiding air flow dead angles.

[0025] The waterproof and breathable film used in this scheme has a microporous structure, and the pore size is usually between 0.1 and 1 microns. This size is much larger than the water molecule group but much smaller than the typical dust particles (particle size is usually ≥10 microns). Therefore, the film can realize selective permeation: dust particles are effectively blocked outside due to their large size, preventing backflow; the film layer is attached to the inner side of the drainage slot 12, and the liquid condensed water collects at the drainage slot under the action of gravity. When the liquid film forms and contacts the film layer, the surface tension and gravity together can overcome the micropore resistance, allowing water to slowly penetrate or drip along the film surface, thereby passively discharging the condensed water.

[0026] Air outlet module 3: The module is arranged at the top of the cabinet 1. Its core includes a variable-diameter air duct 5, combined with Figure 3 The cross-sectional area of the air duct is designed to gradually decrease from the connection with the cabinet to the outlet, forming a tapered structure to enhance the suction capacity of the hot air in the cabinet. An adjustable power air suction assembly (such as a variable frequency fan or a PWM controlled fan) is arranged in the air duct. In a preferred way, the air outlet module 3 also integrates a negative pressure upper limit protection device, which is composed of a pressure sensor and an electric air supplement valve. When the pressure sensor detects that the absolute value of the negative pressure in the cabinet exceeds the set threshold (for example, -50 Pa) due to excessive air suction, the electric air supplement valve is controlled to open to introduce external air, so that the negative pressure in the cabinet is maintained within the preset interval for safe operation of the equipment.

[0027] Temperature sensing module: To achieve precise control, multiple temperature monitoring points are set inside the cabinet. Including: a first temperature sensor 6 (set in the air inlet area) for monitoring the inlet air temperature; a second temperature sensor 7 (set at the battery station) for directly monitoring the key temperature around the battery; and a third temperature sensor 8 (set in the top exhaust area) for monitoring the final exhaust air temperature of the system.

[0028] Control module 9: This module is the processing core of the system, which can be realized by PLC, microcontroller or industrial computer, and is electrically connected with the temperature sensing module and the adjustable power exhaust assembly. The control module 9 internally pre-stores the thermal time constant parameters (which are embodied as the parameters a, b, k of the following prediction model) and the graded temperature intervals (including low temperature interval, medium temperature interval and high temperature interval) corresponding to different battery types (such as lithium ion battery, lead-acid battery, nickel-hydrogen battery).

[0029] The control module 9 is configured to perform the following implementable control logic: First step, after the system starts, the control module 9 first obtains the original temperature data of the first temperature sensor 6, the second temperature sensor 7 and the third temperature sensor 8 in real time through the temperature sensing module. At the same time, the operator can specify the battery type (such as lithium ion battery) of the current grading through the human-machine interface, or the system can automatically identify it by scanning the battery barcode. The control module 9 then calls the pre-stored thermal time constant parameters and graded temperature intervals corresponding to the target battery type.

[0030] Second step, the control module 9 first performs median value average filtering on the collected original data, especially on the temperature data sequence at the battery station (to suppress pulse interference). Then, based on the called thermal time constant parameters, the filtered data sequence is executed by the Exponential Weighted Moving Average-Autoregressive (EWMA-AR) hybrid calculation. Specifically, it generates a k-second-ahead temperature prediction value T according to the formula (Where ). t+k .

[0031] Where, is the filtered temperature value at the current time, is the filtered temperature value at the last time, is the exponential weighted moving average value at the last time, a is the smoothing coefficient, b is the trend gain coefficient, and k is the prediction step.

[0032] Third step, the control module 9 calculates the k-second-ahead prediction value T t+kThe threshold of the hierarchical temperature interval called in the second step is compared, and the target operating power of the air exhaust assembly is dynamically determined according to the comparison result. For example: t+k When in the medium temperature interval, a target power percentage is calculated through linear mapping according to its specific position in the interval; when t+k When it is predicted that the upper limit of the high temperature interval will be exceeded, the target operating power is immediately determined as 100% full power, and the auxiliary air inlet assembly can be started.

[0033] As a preferred solution, to alleviate the problem of direct collision between the bottom air inlet module 2 and the top air exhaust module 3 due to the difference in airflow speed and direction, and to form turbulence affecting the uniformity of heat dissipation, a plurality of uniformly distributed circular or rectangular openings 10 are provided at the position of the vertical surface or side wall of the stepped air guide plate in the direction of the air inlet module 2 towards the detection station 4.

[0034] In specific implementation, these openings 10 collectively form an airflow buffer layer. Its working principle is that when the natural wind flows upwards under the guidance of the stepped air guide plate, and the top air exhaust assembly generates strong upward suction, part of the airflow can be dispersed and mixed in advance through these openings 10 in the horizontal and vertical directions. This design effectively weakens the rigid speed gradient of the main airflow direction, allowing the upward airflow at the bottom and the downward negative pressure airflow at the top to transition smoothly in this area, avoiding the formation of strong airflow shear and vortex in the middle of the cabinet, thereby ensuring that the airflow flowing through the surface of each battery station 4 is more stable and uniform.

[0035] As a preferred solution, the battery to be packaged 11 includes but is not limited to at least one of lithium ion batteries, lead-acid batteries and nickel-hydrogen batteries. In the internal database of the control module 9, there are pre-stored exclusive control parameter sets for each of the above battery types, to realize precise adaptation of the heat dissipation strategy.

[0036] For lithium ion batteries, their characteristic is that the heat generation is concentrated and significant at the end of charging and discharging, especially at high rates. Therefore, the system presets a lower upper limit threshold for the high temperature interval for them, and the prediction step k value in the prediction model is larger (for example, k=7), to ensure that the heat dissipation is enhanced in advance before the temperature rises rapidly.

[0037] For lead-acid batteries, their heat generation process is relatively stable and continuous. The system presets a wider range of medium temperature intervals for them, and the control strategy focuses on smooth linear power regulation within this interval to maintain a stable heat dissipation intensity.

[0038] For nickel-hydrogen batteries, which are sensitive to low temperatures, excessive heat dissipation can inhibit their chemical activity. The system therefore sets a higher lower temperature interval lower threshold (e.g. 15°C), and controls the exhaust assembly to operate at a very low maintenance power near this temperature, avoiding excessively low battery micro-environment temperatures.

[0039] Embodiment 2 This embodiment proposes a dynamic adjustment method based on the hybrid heat dissipation system described in Embodiment 1. The method is executed by the control module 9 in the system, and its core is to achieve advanced adjustment of heat dissipation intensity by predicting temperature prediction values k seconds in advance, in order to overcome the thermal inertia and mechanical lag of the system. The method includes the following specific steps: S1. The control module 9 acquires real-time raw temperature data of the three key points detected by the temperature sensing module at a fixed sampling period (e.g. once every second): the inlet air area temperature T1, the battery station micro-environment temperature T2, and the top exhaust area temperature T3.

[0040] Due to the complexity of the industrial site environment, random noise and transient pulse interference may be contained in the raw temperature data, which will affect the control accuracy if used directly. Therefore, this step first performs a median average filtering algorithm on the acquired raw temperature data, especially the key battery station micro-environment temperature T2.

[0041] The specific filtering sub-steps are as follows: S101. Data sampling: continuously sample each detection point N times (N is an odd number, dynamically configured according to the battery type: e.g. nickel-hydrogen battery N=7, lead-acid battery N=5, lithium-ion battery N=9), forming a sampling array T=[T1,T2,...,T n ].

[0042] S102. Pulse elimination: sort the sampling array, eliminate the maximum value T max and the minimum value T min , and retain the middle N-2 data to form an effective array T valid .

[0043] S103. Smooth calculation: perform arithmetic average operation on the effective array to obtain the filtered temperature value T filtered , the calculation formula is as follows: ; Where, is the effective array formed by i data.

[0044] This algorithm can simultaneously suppress accidental pulse interference and periodic noise, and the filtering effect on temperature, which is a slow-varying signal, is better than that of a single moving average method, and the error of the filtered data can be controlled within ±0.1°C.

[0045] S2. The control module 9 determines the type of battery 11 to be sorted in the current sorting operation. This process can be achieved by manual selection by the operator on a human-machine interface, scanning the battery identity (e.g. a QR code) or automatically detected by the device.

[0046] Subsequently, the control module 9 calls from its internal non-volatile memory, a pre-stored set of control parameters strictly corresponding to the type of battery. This parameter set includes: Hierarchical temperature intervals: defining the low temperature interval, the medium temperature interval , and the high temperature interval for this type of battery.

[0047] Thermal time constant parameters: embodied in the smoothing coefficient a, the trend gain coefficient b and the prediction step k required by the EWMA-AR prediction model.

[0048] By way of example, the present embodiment gives a set of specific pre-stored parameter values: For nickel-hydrogen batteries (large thermal inertia, slow temperature change): a = 0.04, b = 0.25, k = 3.

[0049] For lead-acid batteries (moderate thermal inertia): a = 0.07, b = 0.45, k = 5.

[0050] For lithium-ion batteries (small thermal inertia, fast temperature change): a = 0.125, b = 0.70, k = 7.

[0051] S3. The control module 9 performs an Exponentially Weighted Moving Average-Autoregressive (EWMA-AR) hybrid calculation on the filtered battery station micro-environment temperature T2 data sequence obtained in step S1, based on the thermal time constant parameters (a, b, k) called in step S2, to generate a temperature prediction value k seconds ahead t+k .

[0052] The specific iterative prediction sub-steps are as follows: Calculate the exponentially weighted moving average value at the current time ; Calculate the first-order temperature change rate at the current time ; Based on the moving average value and the first-order temperature change rate, calculate the k-step ahead prediction value ; Where, is the filtered temperature value at the current time, is the filtered temperature value at the last time, is the exponentially weighted moving average value at the last time, a is the smoothing coefficient, b is the trend gain coefficient, and k is the prediction step.

[0053] This model smooths the historical data through a t and the trend gain coefficient bt Capture the trend of change, and then extrapolate it to the future through k, so as to realize the estimation of temperature trend.

[0054] S4. The control module 9 compares the leading prediction temperature value obtained in step S3 with the threshold value of the hierarchical temperature interval called in step S2, and according to the comparison result, executes the hierarchical power consumption dynamic adjustment strategy, which specifically includes: t+k , and according to the comparison result, executes the hierarchical power consumption dynamic adjustment strategy, which specifically includes: When t+k (low temperature interval): determine the target running power as low power (for example, 20% of the rated power), only maintain the necessary basic ventilation, prevent "excessive heat dissipation" to the low-temperature-sensitive battery such as nickel-hydrogen battery.

[0055] When ≤ t+k ≤ (medium temperature interval): combine the predicted value t+k , determine the target running power through the linear mapping relationship, which realizes the matching of heat dissipation intensity and expected heat load.

[0056] The linear mapping is realized by the following formula: ; Wherein, , ] is the medium temperature interval, , The minimum and maximum power limits of the adjustable power extraction assembly running in the medium temperature interval, respectively. (For example = 30%, = 70%). Finally, is constrained in the interval , .

[0057] When t+k > (high temperature interval): immediately determine the target running power as full power (100%), and at the same time trigger the auxiliary air inlet assembly (such as an additional small power fan) in the cabinet 1 to quickly cool down with maximum capacity and ensure the safety of the battery.

[0058] ​Finally, the control module 9 converts the determined target operating power into corresponding control signals (such as PWM waves or analog signals) and outputs to the adjustable power extraction assembly, completing a control cycle. This process is repeated to form a closed-loop dynamic adjustment system based on prediction.

[0059] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solutions. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0060] In addition, each functional module in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0061] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A hybrid heat dissipation system of a multi-chemical system battery equalization equipment, the equalization equipment comprising a cabinet (1) and a plurality of layers of detection stations (4) for carrying batteries (11) to be equalized arranged in the cabinet, characterized in that, The system comprises: An air inlet module (2) arranged at the bottom of the cabinet (1) and comprising an air inlet (13) for guiding natural wind to uniformly diffuse to each detection station (4); An air outlet module (3) arranged at the top of the cabinet (1) and comprising a variable-diameter air duct (5) and an adjustable power air extraction assembly arranged in the variable-diameter air duct (5); A temperature sensing module comprising a first temperature sensor (6) arranged at an air inlet area in the cabinet (1), a second temperature sensor (7) arranged at a battery station, and a third temperature sensor (8) arranged at a top air outlet area; A control module (9) electrically connected with the temperature sensing module and the adjustable power air extraction assembly; The control module (9) is configured to: determine a target battery type corresponding to a current capacity grading operation, and call a thermal time constant parameter and a hierarchical temperature interval corresponding to the target battery type; based on the thermal time constant parameter, perform an exponential weighted moving average-autoregressive hybrid calculation on a temperature data sequence at the battery station to generate a leading prediction value of the battery station micro-environment temperature; compare the leading prediction value with a threshold value of the hierarchical temperature interval, and dynamically determine a target operating power of the adjustable power air extraction assembly according to a comparison result.

2. The hybrid heat sink system for a multi-chemistry battery cell equalization apparatus of claim 1, wherein, In the air inlet module (2), an air inlet direction of the air inlet (13) is inclined towards the detection station (4), a drainage gap (12) is opened below the air inlet (13), and a waterproof and breathable film is attached to the inside of the drainage gap (12).

3. The hybrid heat sink system for a multi-chemistry battery unit equalization apparatus of claim 1, wherein: The air outlet module (3) further comprises a negative pressure upper limit protection device, the negative pressure upper limit protection device comprises a pressure sensor and an electric air supplement valve, when the pressure sensor detects that an absolute value of the cabinet negative pressure exceeds a set threshold value, the electric air supplement valve is controlled to be opened, so that the cabinet negative pressure is maintained in a preset interval.

4. The hybrid heat sink system for a multi-chemistry battery unit equalization apparatus of claim 1, wherein: The air inlet module (2) is provided with a plurality of openings (10) in a direction towards the detection station (4), which are used to relieve the airflow collision between the air inlet module (2) and the air outlet module (3).

5. The hybrid heat sink system for a multi-chemistry battery individual bating apparatus of claim 1, wherein, The battery to be graded (11) comprises at least one of a lithium ion battery, a lead-acid battery and a nickel-hydrogen battery.

6. A dynamic adjustment method based on the hybrid heat dissipation system according to any one of claims 1-5, characterized in that, The method comprises the following steps: S1. Real-time acquisition of the air inlet area temperature T1, the battery station micro-environment temperature T2 and the top air outlet area temperature T3 detected by the temperature sensing module, and filtering; S2. determining the type of the battery to be graded (11) in the current capacity grading operation, and calling a pre-stored hierarchical temperature interval and a thermal time constant parameter corresponding to the type of the battery to be graded (11); S3. based on the thermal time constant parameter, performing an exponential weighted moving average-autoregressive hybrid calculation on the filtered battery station micro-environment temperature T2 data sequence to generate a temperature prediction value k seconds in advance; S4. comparing the temperature prediction value with a threshold value of the hierarchical temperature interval, dynamically determining a target operating power of the adjustable power air extraction assembly according to a comparison result and outputting a control signal.

7. The method of dynamically adjusting of claim 6, wherein, The method of performing an exponential weighted moving average-autoregressive hybrid calculation on the filtered battery station micro-environment temperature T2 data sequence to generate a temperature prediction value k seconds in advance comprises: calculating an exponentially weighted moving average of the current time ; calculating a first order temperature rate of change of the current time ; calculating a k-step ahead prediction value based on the moving average value and a first-order temperature change rate t+k : t+k =S t +k*β*ΔT t ; wherein, is a filtered temperature value at a current time, is a filtered temperature value at a previous time, is an exponential weighted moving average value at the previous time, a is a smoothing coefficient, β is a trend gain coefficient, and k is a prediction step.

8. The method of dynamically adjusting of claim 7, wherein, In step S2, the thermal time constant parameters called include pre-stored values of a smoothing coefficient a, a trend gain coefficient b and a prediction step k corresponding to the battery type; The pre-stored values are: For nickel-hydrogen batteries: a = 0.04, b = 0.25, k = 3; For lead-acid batteries: a = 0.07, b = 0.45, k = 5; For lithium-ion batteries: a = 0.125, b = 0.70, k = 7.

9. The method of dynamically adjusting of claim 7, wherein, Step S4 specifically includes: when the temperature prediction value t+k determining the target operating power to be a low power when the temperature prediction value is lower than a lower temperature interval limit of the temperature interval ; When the temperature prediction value t+k When 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 determining the target operating power as full power when the temperature prediction value and triggering auxiliary air intake in the cabinet (1).

10. The method of dynamically adjusting of claim 9, wherein, The binding temperature prediction value t+k The target operating power of the adjustable power exhaust assembly is determined through a linear mapping relationship 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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