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CN118244812A - A control method for constant temperature chemical composition equipment - Google Patents

A control method for constant temperature chemical composition equipment Download PDF

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Publication number
CN118244812A
CN118244812A CN202410329239.7A CN202410329239A CN118244812A CN 118244812 A CN118244812 A CN 118244812A CN 202410329239 A CN202410329239 A CN 202410329239A CN 118244812 A CN118244812 A CN 118244812A
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temperature
battery
data
discharge
heat dissipation
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黄福泽
温涛
赵德生
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Guangzhou Shengchuang Electronic Equipment Co ltd
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Guangzhou Shengchuang Electronic Equipment Co ltd
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    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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    • G—PHYSICS
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    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D23/00—Control of temperature
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    • H—ELECTRICITY
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    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/44—Methods for charging or discharging
    • H01M10/446—Initial charging measures
    • H—ELECTRICITY
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    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/60—Heating or cooling; Temperature control
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    • H01M10/613—Cooling or keeping cold
    • H—ELECTRICITY
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    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
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    • H01M10/63—Control systems
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Abstract

本发明公开了一种恒温化成分容设备的控制方法，包括首先，使用温度传感器和电池工作状态传感器采集数据作为基础输入，其次，利用数据处理单元分析电池状态和历史操作行为与温度变化的关联性，然后，应用预测算法预测电池温度和电池容量变化趋势，并根据预测结果调整散热系统，动态调整冷却风扇转速工作频率，平衡散热效率和能量消耗，调整后再次监测电池温度并评估散热效果，若温度未达到要求，优化稳定性参数以提高散热系统的精度，最后，收集调整后的温度数据，评估恒温化设备的效果，并决定是否进一步调整冷却系统的工作参数，通过这一方案，有效优化恒温化成分容设备中的电池的温度控制以及设备散热效果，提升电池性能和寿命。

The present invention discloses a control method for a constant temperature capacity distribution device, comprising: first, using a temperature sensor and a battery working state sensor to collect data as basic input; second, using a data processing unit to analyze the correlation between the battery state and historical operating behavior and temperature changes; then, applying a prediction algorithm to predict the battery temperature and battery capacity change trends, and adjusting the heat dissipation system according to the prediction results, dynamically adjusting the cooling fan speed and operating frequency, balancing the heat dissipation efficiency and energy consumption, and monitoring the battery temperature again after adjustment and evaluating the heat dissipation effect. If the temperature does not meet the requirements, optimizing the stability parameters to improve the accuracy of the heat dissipation system; finally, collecting the adjusted temperature data, evaluating the effect of the constant temperature device, and deciding whether to further adjust the working parameters of the cooling system. Through this solution, the temperature control of the battery in the constant temperature capacity distribution device and the heat dissipation effect of the device are effectively optimized, and the battery performance and life are improved.

Description

Control method of constant-temperature-forming and volume-dividing equipment
Technical Field
The invention relates to the technical field of battery temperature control, in particular to a control method of a constant-temperature component forming device.
Background
In new energy applications and power systems, the capacity of a battery type such as a lithium battery directly affects the energy storage capacity and the energy release rate, which is one of the core performances of the battery, however, the battery is accompanied by a rapid temperature rise during discharge, especially under high load or rapid discharge.
The constant temperature formation component equipment aims at controlling the temperature of the working environment of the battery and ensuring that the battery operates in a safe and stable temperature range, so that the technical problem is how to more accurately adjust temperature control measures to adapt to the change of the battery capacitance under different load states.
The specific technical problems are as follows: how the constant temperature forming and dividing equipment monitors the change condition of the battery capacitance in real time, and dynamically adjusts the heat dissipation mechanism according to the changes, so that the cooling requirements under different capacitance states are met, supercooling and overheating are avoided, the battery is kept to work in an optimal temperature interval, the constant temperature equipment is required to sense the temperature of the battery, the temperature change and the capacitance change trend in the discharging process of the battery are predicted, and corresponding cooling or heating measures are further implemented.
One of the immediate technical difficulties is the design of a heat dissipation mechanism that must be able to respond quickly and have sufficient heat dissipation capacity to cope with the high temperature conditions that may occur in batteries when large capacitors are discharged, while at the same time, such a heat dissipation mechanism does not interfere with the normal operation and efficiency of the battery and should be optimized as much as possible to reduce additional energy consumption.
To sum up, we are faced with the technical problems of: how to realize an intelligent constant temperature control system capable of sensing and predicting the change of the battery capacitance, and cooperate with a heat dissipation scheme with high efficiency, quick response and minimized energy consumption, so as to ensure that the battery can be maintained in an ideal temperature range under various working conditions.
Disclosure of Invention
The present invention is to solve the above-mentioned problems and to provide a control method for a constant temperature component device.
The technical scheme of the invention is realized as follows: a method of controlling a thermostatted component-containing apparatus, the method comprising:
s1, acquiring temperature data and discharge characteristics of a battery pack in real time by adopting a temperature sensor and a battery working state sensor, and taking the temperature data and the discharge characteristics as basic input for subsequent data processing and analysis;
Transmitting the acquired battery temperature data and discharge characteristic data to a data processing unit, analyzing the current state of the battery by an analysis technology, and exploring the relevance of historical operation behaviors and temperature changes;
S2, according to the analysis result of the processing unit, a prediction algorithm, such as a time sequence analysis method, is applied to predict the variation trend of the battery temperature and the capacitance in the next discharging period, and the variation trend is used as a basis for adjusting a heat dissipation system;
S3, adjusting a heat dissipation system according to the predicted battery temperature trend and the predicted discharge characteristic, and dynamically adjusting the rotation speed of a cooling fan and the working frequency of a cooling fin by adopting a cooling device controlled by frequency conversion to ensure that the heat dissipation efficiency and the energy consumption are optimally balanced;
S4, after the heat dissipation system is adjusted, the temperature change condition of the battery is monitored in real time through the temperature sensor and the battery state sensor, and the actual cooling effect of the heat dissipation system is evaluated;
s5, if the monitoring data show that the battery temperature cannot reach the preset cooling effect, adopting temperature control stability parameters to optimize cooling measures, and improving the reaction precision of the heat dissipation system;
and S6, collecting temperature data of the battery after the heat dissipation system is adjusted, evaluating the control effect of the thermostatic component equipment, and determining whether to further adjust the working parameters of the cooling system according to the control effect, so as to ensure that the temperature of the battery is always maintained in an ideal state.
Further, the method for acquiring the temperature data and the discharge characteristics of the battery pack in real time by using the temperature sensor and the battery working state sensor as basic inputs for subsequent data processing and analysis comprises the following steps:
acquiring the temperature of each battery unit in real time by adopting a temperature sensor, and acquiring the temperature information of each battery unit by a data acquisition system;
If the battery cell temperature data shows abnormal fluctuation, judging conditions: if the temperature of the battery monomer abnormally fluctuates, namely the temperature change exceeds a preset range (such as +/-2 ℃), performing the next operation;
The operation of the cooling system is regulated according to the temperature control algorithm to maintain the battery cells within the safe operating temperature range:
The minimum rotating speed is preset to be 1000RPM, the maximum rotating speed is 2000RPM, the minimum discharging current is 35A, and the maximum discharging current is 50A;
calculating a rotating speed set value according to a preset calculation formula: rotational speed set value = minimum rotational speed + (discharge current-minimum discharge current) × (maximum rotational speed-minimum rotational speed)/(maximum discharge current-minimum discharge current);
The preset current discharge current is 40A, and the set value of the rotating speed is: rotational speed set point=1000+ (40-35) × (2000-1000)/(50-35) =1286 RPM;
Judging conditions: judging according to a preset threshold judgment numerical range, and if the temperature of the battery monomer exceeds the upper limit of the safe working temperature or is lower than the lower limit of the safe working temperature (such as 25-30 ℃), regulating a cooling system;
the discharge voltage and current of the battery pack are monitored in real time through the battery working state sensor, so that real-time discharge characteristic data are obtained:
According to the real-time discharge voltage and current data, calculating to obtain the state of charge (SOC) and deep discharge (DOD) of the battery, and providing data support for estimating the residual capacity of the battery;
the maximum discharge capacity is preset to be 100Ah, the current discharge current is 40A, and the state of charge can be calculated according to the formula: soc= (discharged capacity/total capacity) ×100% discharged capacity=discharge current×discharge time
The preset discharge time is 1 hour, and the discharge capacity is 40 A.1h=40Ah
The state of charge is: soc= (40 Ah/100 Ah) ×100% = 40%;
Judging the internal resistance of the battery units in the battery pack, if abnormal rise of the internal resistance is detected, judging reasons such as battery aging or battery damage through data analysis, presetting the current internal resistance of the battery to be 0.01Ω, presetting a preset threshold to be 0.008 Ω, and if the abnormal rise of the internal resistance exceeds a preset range, judging and processing faults;
If the temperature sensor monitors that the battery pack has overheat or supercooling, analyzing whether the battery monomer has nonuniform heat release according to the monitoring data of the temperature gradient, further adjusting the space layout or cooling mode of the battery pack, and if the preset temperature gradient exceeds a preset threshold (such as 2 ℃/m), performing analysis and adjustment operation;
Acquiring sensor calibration data, ensuring that all sensors are calibrated, keeping the accuracy of data acquisition, presetting a preset threshold value of preset calibration deviation to be +/-0.5 ℃, if the calibration deviation exceeds a preset range, performing fault judgment and processing, and performing fault judgment and processing on the sensors with the calibration deviation exceeding the preset range;
the triggering record of the short circuit, overcharge and overdischarge protection states is recorded through a safety monitoring system,
If the system detects these safety events, the battery operating parameters that caused these events are analyzed, and the control strategy of the Battery Management System (BMS) is adjusted to prevent the re-occurrence of similar events.
Further, the transmitting the collected battery temperature data and discharge characteristic data to the data processing unit, the analysis technology analyzing the current state of the battery and exploring the correlation between the historical operation behavior and the temperature change, includes:
Continuously monitoring the temperature of the battery through a sensor, and sending temperature data of each time point to a data collector in real time, wherein the data collector records information including a temperature reading value, a change rate and a peak value; meanwhile, the discharge characteristic data sensor monitors discharge related parameters such as discharge rate, discharge depth, discharge time and the like, and synchronously transmits the data to the data collector for recording and integration; the data collector packages the collected battery temperature data and discharge characteristic data, and transmits the data to the data processing unit through a network, and the data processing unit receives and stores the raw data; in the data processing unit, a time sequence analysis method is adopted to establish a time corresponding relation between the battery temperature and the discharge parameter;
According to the historical charging data, the data processing unit executes regression analysis, identifies the specific influence of the charging behavior on the battery temperature, and compares the analysis result with the analysis result of the discharge characteristic data; the effect of the preset charging behavior on the battery temperature is described using a linear regression model, the formula:
Temperature change = β0+β1 charge rate
Wherein β0 and β1 are regression coefficients, and the charging rate refers to the rate of charging
The data processing unit acquires environmental temperature data and evaluates the influence of the environmental temperature on the battery temperature and the discharge characteristic through a statistical analysis method;
the effect of the preset ambient temperature on the battery temperature is described by a linear relationship, the formula is:
Battery temperature = α0+ α1 ambient temperature
Wherein α0 and α1 are regression coefficients obtained by statistical analysis
Analyzing complex relations among battery cycle times, historical maintenance and operation records, battery temperature and discharge characteristics in a data processing unit to obtain a more accurate correlation model of behaviors and temperature changes;
presetting a decision tree algorithm for machine learning modeling, wherein the model is expressed as:
Battery temperature=f (cycle number, history maintenance, operation record)
Where f () is the decision tree model
The data processing unit trains the model by using fault and anomaly records, and optimizes the accuracy of the algorithm so as to more accurately predict the temperature response of the battery under specific operation behaviors.
Further, according to the analysis result of the processing unit, a prediction algorithm, such as a time series analysis method, is applied to predict the variation trend of the battery temperature and the capacitance in the following discharging period, and the prediction algorithm is used as a basis for adjusting the heat dissipation system and comprises the following steps:
according to the historical temperature data, a time sequence analysis method is adopted to obtain a basic mode and a trend of battery temperature change, and a reference standard is provided for subsequent prediction;
acquiring historical capacitance/electric quantity data, analyzing attenuation rules and periodic characteristics of battery capacity, and determining a capacitance change trend in a battery discharging period;
Predicting a change rate of a battery temperature and a capacitor in a next discharge period through discharge rate information recorded by a Battery Management System (BMS), wherein the increase of a preset discharge rate can lead to the increase of a rate of temperature and capacitor reduction;
Collecting environmental temperature data, analyzing the influence of the environmental temperature data on the temperature change of the battery, judging that the temperature of the battery is subjected to additional heating pressure if the environmental temperature is increased, and presetting that the temperature change of the battery is influenced when the environmental temperature exceeds 30 ℃;
acquiring chemical type and health condition information of the battery, and adjusting parameters in a prediction model to reflect the differences by comparing discharge characteristics of different types of batteries and health conditions;
extracting charge and discharge cycle number data from the BMS, if the cycle number is more, predicting that the attenuation of the battery capacity is accelerated, and presetting that the attenuation speed of the battery capacity is accelerated when the charge and discharge cycle number exceeds 1000 times;
predicting the working state of the battery in an upcoming discharging period by analyzing current, voltage and internal resistance parameters recorded in the BMS, and judging that the heat generation of the battery is increased if the internal resistance is increased, wherein the heat dissipation needs to be enhanced;
And acquiring performance data of the heat radiation system, combining the predicted battery temperature trend, judging that the working efficiency of the heat radiation system needs to be improved if the predicted temperature rises, and presetting that the working efficiency of the heat radiation system needs to be enhanced when the predicted battery temperature exceeds 35 ℃.
Further, according to the predicted battery temperature trend and discharge characteristic, the cooling system is adjusted, a variable-frequency controlled cooling device is adopted, the rotation speed of the cooling fan and the working frequency of the cooling fin are dynamically adjusted, and the optimal balance between the heat dissipation efficiency and the energy consumption is ensured, and the method comprises the following steps:
Acquiring battery temperature prediction data: by using a machine learning algorithm, the model predicts the future temperature trend of the battery according to historical temperature data and real-time monitoring data;
The battery discharge characteristics are monitored by a sensor: the sensors monitor the discharge rate, depth, current and voltage of the battery in real time and transmit the data to a Battery Management System (BMS);
and adjusting the variable frequency control system according to the discharge characteristic of the battery and the predicted temperature trend: the BMS analyzes the data and calculates optimal cooling device operating parameters such as the rotation speed of the cooling fan and the operating frequency of the cooling fins according to the information;
Acquiring environmental factor data: environmental sensors monitor the temperature, humidity and air flow of the surrounding environment, which data can be used to refine the control strategy of the heat dissipation system;
Judging the performance of the cooling device through the heat dissipation efficiency data: if the heat exchange efficiency of the heat dissipation system is lower than expected, the BMS adjusts the working parameters of the cooling device, such as increasing the rotating speed of the cooling fan or adjusting the flow rate of the cooling liquid;
Determining an optimal operating state by comparing energy consumption data: analyzing the power consumption of the cooling system under different working parameters and the influence of the parameters on the heat dissipation efficiency, so as to find an optimal balance point between the energy consumption and the heat dissipation efficiency;
adjusting the cooling system response strategy to improve system efficiency: if the system response time is too long, the time from the detection of the temperature change to the adjustment of the working state of the heat dissipation system is reduced through algorithm optimization;
adjust the balance between user demand and device security through BMS: the BMS monitors the operation state of the heat dissipation system and dynamically adjusts the operation parameters of the heat dissipation system in consideration of the use requirements of the user and the safe operation temperature range of the device.
Further, after the adjustment of the heat dissipation system is implemented, the temperature and the battery state sensor monitor the temperature change condition of the battery in real time again, and evaluate the actual cooling effect of the heat dissipation system, including:
Step 1: acquiring initial temperature data of a battery through a temperature sensor, and recording a battery temperature baseline before starting a heat dissipation system;
Initial temperature = temperature sensor measurement
Step 2: after the heat dissipation system is started, a temperature sensor is adopted to continuously monitor the temperature of the battery, so that the temperature change rate is obtained;
Δt: time interval, which means the time difference between observing or measuring two temperature values, in seconds(s), minutes (min);
Δt: a temperature change amount representing a difference between two temperature values, expressed in absolute values, and positive and negative of the temperature change amount depending on an increase or decrease in temperature;
Step 3: if the temperature change rate is lower than the set threshold value, adjusting parameters of the heat radiation system, such as increasing the flow rate of the cooling medium or increasing the rotating speed of the fan, so as to increase the cooling rate;
step 4: continuously monitoring the temperature of the battery within a certain period of time after the heat dissipation system is adjusted, and judging whether the temperature reaches a preset steady-state temperature range or not;
step 5: acquiring temperature distribution data of the interior and the surface of the battery by adopting a thermal imaging camera or a multi-point temperature sensor, and calculating a temperature gradient;
step 6: the method comprises the steps of connecting a battery state sensor, acquiring battery charge and discharge state information, and analyzing the influence of charge and discharge behaviors on temperature change;
Step 7: synchronously monitoring battery capacity and voltage data, performing correlation analysis with temperature change data, and judging potential influence of adjustment of a heat dissipation system on battery performance;
step 8: monitoring the flow rate of the cooling medium of the liquid cooling system through a flow sensor to ensure that sufficient cooling medium passes through the battery;
step 9: analyzing radiator efficiency, such as monitoring the inlet and outlet temperature differences of the cooling medium by a sensor, thereby evaluating heat exchange efficiency;
Further, if the monitoring data shows that the battery temperature fails to reach the predetermined cooling effect, the temperature control stability parameter is adopted to optimize the cooling measure, so as to improve the reaction precision of the heat dissipation system, including:
Step 1: acquiring real-time temperature data of the battery pack by adopting a temperature sensor;
Real-time temperature = temperature sensor measurement
Step 2: judging whether a local high-temperature area exists in the battery pack or not through temperature data analysis, and identifying the hot spot position;
Hotspot location = location of analysis high temperature area
Step 3: according to the hot spot position information, the flow and the flow direction of the cooling medium are adjusted;
Cooling medium flow and direction = f (hot spot location)
Step 4: adopting a PID control strategy, and adjusting working parameters of a cooling system according to real-time temperature feedback;
error = set temperature-real time temperature
Kp: proportional gain, used to adjust the weight of the proportional term, ki: integral gain for adjusting the weight of the integral term, kd: differential gain, for adjusting the weight of the differential term, dt: a sampling time interval representing the time difference between two consecutive measurements;
Step 5: if the PID control strategy fails to realize the expected temperature control effect, a Model Predictive Control (MPC) strategy is adopted for temperature management;
MPC output = f (real-time temperature, set temperature)
Step 6: predicting the thermal behaviors of the battery under different working loads through a software algorithm, and implementing dynamic cooling strategy adjustment;
Dynamic cooling strategy adjustment = f (battery workload)
Step 7: acquiring external environment temperature and humidity data, and adjusting parameters of a cooling system to adapt to external environment changes;
external environmental data = environmental sensor measurement value
Step 8: monitoring the operating state of cooling system components such as pumps, fans, valves, and adjusting maintenance schedules based on equipment performance data;
device status monitoring = monitoring cooling system component operating status
Step 9: and (3) establishing safety protection logic of the cooling system, and immediately starting emergency cooling measures when abnormal temperature is monitored.
Further, the collecting the temperature data of the battery after the adjustment of the heat dissipation system is used for evaluating the control effect of the thermostatic composition equipment, determining whether to further adjust the working parameters of the cooling system according to the control effect, and ensuring that the temperature of the battery is always maintained in an ideal state, and comprises the following steps:
step 1: monitoring the temperature of a battery monomer, the temperature of a module/package, the surface temperature and the internal temperature in real time by adopting a sensor network, and recording the temperature gradient to obtain detailed data of the thermal state of the battery;
monomer temperature = sensor measurement
Step 2: acquiring a sampling time point through a temperature monitoring system, calculating to obtain a temperature change rate, and judging the thermal dynamic characteristics of the battery;
Δt: time interval, which means the time difference between observing or measuring two temperature values, in seconds(s), minutes (min);
Δt: a temperature change amount representing a difference between two temperature values, expressed in absolute values, and positive and negative of the temperature change amount depending on an increase or decrease in temperature;
Step 3: collecting environmental temperature data through environmental monitoring equipment, and analyzing the influence degree of the external environment on the battery temperature by combining heat source information;
external ambient temperature = ambient monitoring device measurement
Step 4: adjusting parameters of a heat radiation system, such as the flow rate and the temperature of a cooling medium, and monitoring the working state of a radiator and the energy consumption of the heat radiation system;
Cooling medium flow rate = adjusted value
Step 5: collecting battery charge-discharge current, voltage, SOC and SOH operation state data in conjunction with a Battery Management System (BMS);
Battery operation state data = { charge-discharge current, voltage, SOC, SOH }
Step 6: recording a heat radiation system adjustment measure comprising specific adjustment parameters and adjustment amplitude, wherein the adjustment parameter and the adjustment amplitude are used for tracking the control effect of the heat radiation system;
Step 7: analyzing the reaction time of the heat radiation system, and judging whether the response speed of the heat radiation system meets the requirement from the time delay from the adjustment parameter to the battery temperature response;
step 8: setting a battery temperature safety threshold value, and combining protective measures such as power supply disconnection or enhanced cooling to automatically process temperature conditions exceeding a safety range;
Step 9: and according to the data and the analysis result, determining whether the working parameters of the cooling system need to be further adjusted so as to continuously optimize the battery temperature control effect.
Advantageous effects
The invention can collect the temperature data and discharge characteristics of the battery pack in real time and transmit the data to the data processing unit, in the data processing unit, the analysis technology analyzes the current state of the battery and explores the relevance of the historical operation behavior and the temperature change, and the analysis results can help the system to better understand the working state and the thermal management requirement of the battery;
The battery temperature and capacitance change trend in the next discharging period can be predicted by applying a time sequence analysis prediction algorithm, the prediction results are used as the basis for adjusting a heat dissipation system, the heat dissipation system is automatically adjusted according to the predicted battery temperature trend and discharging characteristics, a cooling device with variable frequency control is adopted, and the rotating speed of a cooling fan and the working frequency of a cooling fin are dynamically adjusted so as to ensure that the optimal balance between the heat dissipation efficiency and the energy consumption is obtained;
After the heat dissipation system is regulated, the system continuously monitors the temperature change condition of the battery in real time through the temperature and battery state sensors, evaluates the actual cooling effect of the heat dissipation system, and if the monitoring data show that the battery temperature cannot reach the preset cooling effect, the system adopts temperature control stability parameters to optimize cooling measures, so that the reaction precision and the control efficiency of the heat dissipation system are improved;
And collecting temperature data of the battery after the heat dissipation system is adjusted, wherein the data are used for evaluating the control effect of the thermostatic composition equipment, and determining whether to further adjust the working parameters of the cooling system according to the control effect so as to ensure that the temperature of the battery is always maintained in an ideal state, greatly improve the thermal stability and the service life of the battery, and optimize the energy utilization efficiency.
Drawings
FIG. 1 is a block diagram illustrating steps of a method for controlling a thermostat composition device in accordance with an embodiment of the present invention;
fig. 2 is a block diagram showing steps of a control method of a constant temperature component preparation apparatus according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1-2, a control method of a constant temperature component preparation apparatus includes:
s1, acquiring temperature data and discharge characteristics of a battery pack in real time by adopting a temperature sensor and a battery working state sensor, and taking the temperature data and the discharge characteristics as basic input for subsequent data processing and analysis;
Transmitting the acquired battery temperature data and discharge characteristic data to a data processing unit, analyzing the current state of the battery by an analysis technology, and exploring the relevance of historical operation behaviors and temperature changes;
S2, according to the analysis result of the processing unit, a prediction algorithm, such as a time sequence analysis method, is applied to predict the variation trend of the battery temperature and the battery capacity in the next discharging period, and the variation trend is used as a basis for adjusting a heat dissipation system;
S3, adjusting a heat dissipation system according to the predicted battery temperature trend and the predicted discharge characteristic, and dynamically adjusting the working frequency of the rotating speed of a cooling fan by adopting a cooling device controlled by frequency conversion to ensure that the heat dissipation efficiency and the energy consumption are optimally balanced;
S4, after the heat dissipation system is adjusted, the temperature change condition of the battery is monitored in real time through the temperature sensor and the battery state sensor, and the actual cooling effect of the heat dissipation system is evaluated;
s5, if the monitoring data show that the battery temperature cannot reach the preset cooling effect, adopting temperature control stability parameters to optimize cooling measures, and improving the reaction precision of the heat dissipation system;
and S6, collecting temperature data of the battery after the heat dissipation system is adjusted, evaluating the control effect of the thermostatic component equipment, and determining whether to further adjust the working parameters of the cooling system according to the control effect, so as to ensure that the temperature of the battery is always maintained in an ideal state.
Specifically, step 101, the step of collecting temperature data and discharge characteristics of the battery pack in real time by using a temperature sensor and a battery working state sensor, which are used as basic inputs for subsequent data processing and analysis, includes:
acquiring the temperature of each battery unit in real time by adopting a temperature sensor, and acquiring the temperature information of each battery unit by a data acquisition system;
If the battery cell temperature data shows abnormal fluctuation, judging conditions: if the temperature of the battery monomer abnormally fluctuates, namely the temperature change exceeds a preset range (such as +/-2 ℃), performing the next operation;
The operation of the cooling system is regulated according to the temperature control algorithm to maintain the battery cells within the safe operating temperature range:
The minimum rotating speed is preset to be 1000RPM, the maximum rotating speed is 2000RPM, the minimum discharging current is 35A, and the maximum discharging current is 50A;
calculating a rotating speed set value according to a preset calculation formula: rotational speed set value = minimum rotational speed + (discharge current-minimum discharge current) × (maximum rotational speed-minimum rotational speed)/(maximum discharge current-minimum discharge current);
The preset current discharge current is 40A, and the set value of the rotating speed is: rotational speed set point=1000+ (40-35) × (2000-1000)/(50-35) =1286 RPM;
Judging conditions: judging according to a preset threshold judgment numerical range, and if the temperature of the battery monomer exceeds the upper limit of the safe working temperature or is lower than the lower limit of the safe working temperature (such as 25-30 ℃), regulating a cooling system;
the discharge voltage and current of the battery pack are monitored in real time through the battery working state sensor, so that real-time discharge characteristic data are obtained:
According to the real-time discharge voltage and current data, calculating to obtain the state of charge (SOC) and deep discharge (DOD) of the battery, and providing data support for estimating the residual capacity of the battery;
the maximum discharge capacity is preset to be 100Ah, the current discharge current is 40A, and the state of charge can be calculated according to the formula: soc= (discharged capacity/total capacity) ×100% discharged capacity=discharge current×discharge time
The preset discharge time is 1 hour, and the discharge capacity is 40 A.1h=40Ah
The state of charge is: soc= (40 Ah/100 Ah) ×100% = 40%;
Judging the internal resistance of the battery units in the battery pack, judging possible reasons such as battery aging or battery damage through data analysis if abnormal rise of the internal resistance is detected, presetting the current internal resistance of the battery to be 0.01Ω and presetting the threshold to be 0.008 Ω, and judging and processing faults if the abnormal rise of the internal resistance exceeds a preset range;
If the temperature sensor monitors that the battery pack has overheat or supercooling, analyzing whether the battery monomer has nonuniform heat release according to the monitoring data of the temperature gradient, further adjusting the space layout or cooling mode of the battery pack, and if the preset temperature gradient exceeds a preset threshold (such as 2 ℃/m), performing analysis and adjustment operation;
Acquiring sensor calibration data, ensuring that all sensors are calibrated, keeping the accuracy of data acquisition, presetting a preset threshold value of preset calibration deviation to be +/-0.5 ℃, if the calibration deviation exceeds a preset range, performing fault judgment and processing, and performing fault judgment and processing on the sensors with the calibration deviation exceeding the preset range;
the triggering record of the short circuit, overcharge and overdischarge protection states is recorded through a safety monitoring system,
If the system detects these safety events, the battery operating parameters that caused these events are analyzed, and the control strategy of the Battery Management System (BMS) is adjusted to prevent the re-occurrence of similar events.
Specifically, step 102, the collected battery temperature data and discharge characteristic data are transmitted to a data processing unit, and an analysis technique analyzes the current state of the battery and explores the correlation between the historical operation behavior and the temperature change, including:
Continuously monitoring the temperature of the battery through a sensor, and sending temperature data of each time point to a data collector in real time, wherein the data collector records information including a temperature reading value, a change rate and a peak value; meanwhile, the discharge characteristic data sensor monitors discharge related parameters such as discharge rate, discharge depth, discharge time and the like, and synchronously transmits the data to the data collector for recording and integration; the data collector packages the collected battery temperature data and discharge characteristic data, and transmits the data to the data processing unit through a network, and the data processing unit receives and stores the raw data; in the data processing unit, a time sequence analysis method is adopted to establish a time corresponding relation between the battery temperature and the discharge parameter;
According to the historical charging data, the data processing unit executes regression analysis, identifies the specific influence of the charging behavior on the battery temperature, and compares the analysis result with the analysis result of the discharge characteristic data; the effect of the preset charging behavior on the battery temperature is described using a linear regression model, the formula:
Temperature change = β0+β1 charge rate
Wherein β0 and β1 are regression coefficients, and the charging rate refers to the rate of charging
The data processing unit acquires environmental temperature data and evaluates the influence of the environmental temperature on the battery temperature and the discharge characteristic through a statistical analysis method;
the effect of the preset ambient temperature on the battery temperature is described by a linear relationship, the formula is:
Battery temperature = α0+ α1 ambient temperature
Wherein α0 and α1 are regression coefficients obtained by statistical analysis
Analyzing complex relations among battery cycle times, historical maintenance and operation records, battery temperature and discharge characteristics in a data processing unit to obtain a more accurate correlation model of behaviors and temperature changes;
presetting a decision tree algorithm for machine learning modeling, wherein the model is expressed as:
Battery temperature=f (cycle number, history maintenance, operation record)
Where f () is the decision tree model
The data processing unit trains the model by using fault and anomaly records, and optimizes the accuracy of the algorithm so as to more accurately predict the temperature response of the battery under specific operation behaviors.
Specifically, step 103, the step of applying a prediction algorithm, such as a time series analysis method, to predict the battery temperature and the capacitance change trend in the following discharging period according to the analysis result of the processing unit, as a basis for adjusting the heat dissipation system, includes:
according to the historical temperature data, a time sequence analysis method is adopted to obtain a basic mode and a trend of battery temperature change, and a reference standard is provided for subsequent prediction;
acquiring historical capacitance/electric quantity data, analyzing attenuation rules and periodic characteristics of battery capacity, and determining a capacitance change trend in a battery discharging period;
Predicting a change rate of a battery temperature and a capacitor in a next discharge period through discharge rate information recorded by a Battery Management System (BMS), wherein the increase of a preset discharge rate can lead to the increase of a rate of temperature and capacitor reduction;
Collecting environmental temperature data, analyzing the influence of the environmental temperature data on the temperature change of the battery, judging that the temperature of the battery is subjected to additional heating pressure if the environmental temperature is increased, and presetting that the temperature change of the battery is influenced when the environmental temperature exceeds 30 ℃;
acquiring chemical type and health condition information of the battery, and adjusting parameters in a prediction model to reflect the differences by comparing discharge characteristics of different types of batteries and health conditions;
extracting charge and discharge cycle number data from the BMS, if the cycle number is more, predicting that the attenuation of the battery capacity is accelerated, and presetting that the attenuation speed of the battery capacity is accelerated when the charge and discharge cycle number exceeds 1000 times;
predicting the working state of the battery in an upcoming discharging period by analyzing current, voltage and internal resistance parameters recorded in the BMS, and judging that the heat generation of the battery is increased if the internal resistance is increased, wherein the heat dissipation needs to be enhanced;
And acquiring performance data of the heat radiation system, combining the predicted battery temperature trend, judging that the working efficiency of the heat radiation system needs to be improved if the predicted temperature rises, and presetting that the working efficiency of the heat radiation system needs to be enhanced when the predicted battery temperature exceeds 35 ℃.
Specifically, in step 104, according to the predicted battery temperature trend and the predicted discharge characteristic, the cooling system is adjusted, and the variable-frequency controlled cooling device is adopted to dynamically adjust the rotation speed of the cooling fan and the working frequency of the cooling fin, so as to ensure that the optimal balance between the heat dissipation efficiency and the energy consumption is obtained, including:
Acquiring battery temperature prediction data: by using a machine learning algorithm, the model predicts the future temperature trend of the battery according to historical temperature data and real-time monitoring data;
The battery discharge characteristics are monitored by a sensor: the sensors monitor the discharge rate, depth, current and voltage of the battery in real time and transmit the data to a Battery Management System (BMS);
and adjusting the variable frequency control system according to the discharge characteristic of the battery and the predicted temperature trend: the BMS analyzes the data and calculates optimal cooling device operating parameters such as the rotation speed of the cooling fan and the operating frequency of the cooling fins according to the information;
Acquiring environmental factor data: environmental sensors monitor the temperature, humidity and air flow of the surrounding environment, which data can be used to refine the control strategy of the heat dissipation system;
Judging the performance of the cooling device through the heat dissipation efficiency data: if the heat exchange efficiency of the heat dissipation system is lower than expected, the BMS adjusts the working parameters of the cooling device, such as increasing the rotating speed of the cooling fan or adjusting the flow rate of the cooling liquid;
Determining an optimal operating state by comparing energy consumption data: analyzing the power consumption of the cooling system under different working parameters and the influence of the parameters on the heat dissipation efficiency, so as to find an optimal balance point between the energy consumption and the heat dissipation efficiency;
adjusting the cooling system response strategy to improve system efficiency: if the system response time is too long, the time from the detection of the temperature change to the adjustment of the working state of the heat dissipation system is reduced through algorithm optimization;
adjust the balance between user demand and device security through BMS: the BMS monitors the operation state of the heat dissipation system and dynamically adjusts the operation parameters of the heat dissipation system in consideration of the use requirements of the user and the safe operation temperature range of the device.
Specifically, step 105, after the adjustment of the heat dissipation system is performed, the temperature and the battery state sensor monitor the temperature change condition of the battery in real time again, and evaluate the actual cooling effect of the heat dissipation system, which includes:
After the heat dissipation system is adjusted, the temperature change condition of the battery is monitored again in real time through the temperature and battery state sensor, and the actual cooling effect of the heat dissipation system is evaluated, and the method comprises the following steps:
Step 1: acquiring initial temperature data of a battery through a temperature sensor, and recording a battery temperature baseline before starting a heat dissipation system;
Initial temperature = temperature sensor measurement
Step 2: after the heat dissipation system is started, continuously monitoring the temperature of the battery by adopting a high-precision temperature sensor to obtain the temperature change rate;
Δt: time interval, which represents the time difference elapsed between observing or measuring two temperature values, may be in seconds(s), minutes (min);
Δt: a temperature change amount representing a difference between two temperature values, expressed in absolute values, and positive and negative of the temperature change amount depending on an increase or decrease in temperature;
Step 3: if the temperature change rate is lower than the set threshold value, adjusting parameters of the heat radiation system, such as increasing the flow rate of the cooling medium or increasing the rotating speed of the fan, so as to increase the cooling rate;
step 4: continuously monitoring the temperature of the battery within a certain period of time after the heat dissipation system is adjusted, and judging whether the temperature reaches a preset steady-state temperature range or not;
step 5: acquiring temperature distribution data of the interior and the surface of the battery by adopting a thermal imaging camera or a multi-point temperature sensor, and calculating a temperature gradient;
step 6: the method comprises the steps of connecting a battery state sensor, acquiring battery charge and discharge state information, and analyzing the influence of charge and discharge behaviors on temperature change;
Step 7: synchronously monitoring battery capacity and voltage data, performing correlation analysis with temperature change data, and judging potential influence of adjustment of a heat dissipation system on battery performance;
step 8: monitoring the flow rate of the cooling medium of the liquid cooling system through a flow sensor to ensure that sufficient cooling medium passes through the battery;
step 9: analyzing radiator efficiency, such as monitoring the inlet and outlet temperature differences of the cooling medium by a sensor, thereby evaluating heat exchange efficiency;
Specifically, if the monitored data indicate that the battery temperature fails to reach the predetermined cooling effect, the step 106 adopts the temperature control stability parameter to optimize the cooling measure, thereby improving the reaction accuracy of the heat dissipation system, including:
step 1: acquiring real-time temperature data of the battery pack by adopting a high-precision temperature sensor;
Real-time temperature = temperature sensor measurement
Step 2: judging whether a local high-temperature area exists in the battery pack or not through temperature data analysis, and identifying the hot spot position;
Hotspot location = location of analysis high temperature area
Step 3: according to the hot spot position information, the flow and the flow direction of the cooling medium are adjusted so as to improve the heat dissipation efficiency;
Cooling medium flow and direction = f (hot spot location)
Step 4: adopting a PID control strategy, and adjusting working parameters of a cooling system according to real-time temperature feedback;
error = set temperature-real time temperature
Kp: proportional gain, used to adjust the weight of the proportional term, ki: integral gain for adjusting the weight of the integral term, kd: differential gain, for adjusting the weight of the differential term, dt: a sampling time interval representing the time difference between two consecutive measurements;
step 5: if the PID control strategy fails to realize the expected temperature control effect, adopting a Model Predictive Control (MPC) strategy to carry out finer temperature management;
MPC output = f (real-time temperature, set temperature)
Step 6: predicting the thermal behaviors of the battery under different working loads through a software algorithm, and implementing dynamic cooling strategy adjustment;
Dynamic cooling strategy adjustment = f (battery workload)
Step 7: acquiring data such as external environment temperature, humidity and the like, and adjusting parameters of a cooling system to adapt to external environment changes;
external environmental data = environmental sensor measurement value
Step 8: monitoring the operating state of cooling system components such as pumps, fans, valves, and adjusting maintenance schedules based on equipment performance data;
device status monitoring = monitoring cooling system component operating status
Step 9: and (3) establishing safety protection logic of the cooling system, and immediately starting emergency cooling measures when abnormal temperature is monitored.
Specifically, step 107, the step of collecting temperature data of the battery after the adjustment of the heat dissipation system is used for evaluating the control effect of the thermostatic component device, and determining whether to further adjust the working parameters of the cooling system accordingly, so as to ensure that the temperature of the battery is always maintained in an ideal state, and includes:
step 1: monitoring the temperature of a battery monomer, the temperature of a module/package, the surface temperature and the internal temperature in real time by adopting a sensor network, and recording the temperature gradient to obtain detailed data of the thermal state of the battery;
monomer temperature = sensor measurement
Step 2: acquiring a sampling time point through a temperature monitoring system, calculating to obtain a temperature change rate, and judging the thermal dynamic characteristics of the battery;
Δt: time interval, which represents the time difference elapsed between observing or measuring two temperature values, may be in seconds(s), minutes (min);
Δt: a temperature change amount representing a difference between two temperature values, expressed in absolute values, and positive and negative of the temperature change amount depending on an increase or decrease in temperature;
Step 3: collecting environmental temperature data through environmental monitoring equipment, and analyzing the influence degree of the external environment on the battery temperature by combining heat source information;
external ambient temperature = ambient monitoring device measurement
Step 4: parameters of a heat radiation system, such as the flow rate and the temperature of a cooling medium, are adjusted, the working state of the radiator and the energy consumption of the heat radiation system are monitored, and the heat radiation effect is ensured to accord with expectations;
Cooling medium flow rate = adjusted value
Step 5: the method comprises the steps of combining a Battery Management System (BMS), collecting running state data such as battery charge and discharge current, voltage, SOC, SOH and the like, and evaluating the influence of battery running on a temperature control system;
Battery operation state data = { charge-discharge current, voltage, SOC, SOH }
Step 6: recording adjustment measures of the heat radiation system, including specifically adjusting parameters and adjustment amplitude, for tracking the control effect of the heat radiation system;
Step 7: analyzing the reaction time of the heat radiation system, and judging whether the response speed of the heat radiation system meets the requirement from the time delay from the adjustment parameter to the battery temperature response;
step 8: setting a battery temperature safety threshold value, and combining protective measures such as power supply disconnection or enhanced cooling to automatically process temperature conditions exceeding a safety range;
Step 9: and according to the data and the analysis result, determining whether the working parameters of the cooling system need to be further adjusted so as to continuously optimize the battery temperature control effect.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and rules of the present invention should be included in the protection scope of the present invention.

Claims (5)

1.一种恒温化成分容设备的控制方法，其特征在于：1. A control method for a constant temperature chemical composition equipment, characterized in that: S1、采用温度传感器和电池工作状态传感器实时采集电池组的温度数据和放电特性，作为后续数据处理和分析的基础输入；S1. Use temperature sensors and battery working status sensors to collect temperature data and discharge characteristics of the battery pack in real time as the basic input for subsequent data processing and analysis; 将采集到的电池温度数据和放电特性数据传输至数据处理单元，分析技术分析电池当前状态，用于记录历史操作行为与温度变化的关联性；The collected battery temperature data and discharge characteristic data are transmitted to the data processing unit, and the analysis technology analyzes the current state of the battery to record the correlation between historical operating behavior and temperature changes; S2、根据处理单元分析结果，应用预测算法时间序列分析法，预测接下来的放电周期内电池温度和电池容量变化趋势，作为散热系统调整的依据；S2. Based on the analysis results of the processing unit, the prediction algorithm time series analysis method is applied to predict the battery temperature and battery capacity change trends in the next discharge cycle as a basis for adjusting the cooling system; S3、根据预测的电池温度趋势和放电特性，调整散热系统，采用变频控制的冷却装置，动态调整冷却风扇转速工作频率；S3. According to the predicted battery temperature trend and discharge characteristics, adjust the heat dissipation system, use a variable frequency controlled cooling device, and dynamically adjust the speed and operating frequency of the cooling fan; S4、实施散热系统调整后，再次通过温度传感器和电池状态传感器实时监控电池温度变化情况，对散热系统的实际冷却效果进行评估；S4. After the cooling system is adjusted, the temperature sensor and the battery status sensor are used to monitor the battery temperature change in real time again to evaluate the actual cooling effect of the cooling system. S5、若监控数据显示电池温度未能达到预定的降温效果，采用温度控制稳定性参数优化冷却措施；S5. If the monitoring data shows that the battery temperature fails to achieve the predetermined cooling effect, the temperature control stability parameter is used to optimize the cooling measures; S6、对散热系统调整后电池的温度数据进行收集，用于评估恒温化成分容设备的控制效果，并据此决定是否进一步调整冷却系统的工作参数。S6. Collect the temperature data of the battery after the heat dissipation system is adjusted, so as to evaluate the control effect of the constant temperature and capacity composition equipment, and decide whether to further adjust the working parameters of the cooling system accordingly. 2.根据权利要求1所述的一种恒温化成分容设备的控制方法，其特征在于：所述采用温度传感器和电池工作状态传感器实时采集电池组的温度数据和放电特性，以此作为后续数据处理和分析的基础输入，包括：2. The control method of a constant temperature chemical component equipment according to claim 1 is characterized in that: the temperature data and discharge characteristics of the battery pack are collected in real time by using a temperature sensor and a battery working status sensor, which are used as the basic input for subsequent data processing and analysis, including: 采用温度传感器实时采集电池单体温度，通过数据采集系统获取各电池单元的温度信息；Use temperature sensors to collect battery cell temperature in real time, and obtain temperature information of each battery cell through the data acquisition system; 若电池单体温度数据显示异常波动，判断条件：若电池单体温度异常波动，即温度变化超过预设范围(±2℃)，则进行下一步操作；If the battery cell temperature data shows abnormal fluctuations, judgment conditions: If the battery cell temperature fluctuates abnormally, that is, the temperature change exceeds the preset range (±2°C), proceed to the next step; 则根据温度控制算法调节冷却系统的工作，维持电池单元在安全工作温度范围内：The cooling system is adjusted according to the temperature control algorithm to maintain the battery cells within a safe operating temperature range: 预设最小转速为1000RPM，最大转速为2000RPM，最小放电电流为35A，最大放电电流为50A；The preset minimum speed is 1000RPM, the maximum speed is 2000RPM, the minimum discharge current is 35A, and the maximum discharge current is 50A; 根据预设的计算公式进行转速设定值的计算：转速设定值＝最小转速+(放电电流-最小放电电流)*(最大转速-最小转速)/(最大放电电流-最小放电电流)；The speed setting value is calculated according to the preset calculation formula: speed setting value = minimum speed + (discharge current - minimum discharge current) * (maximum speed - minimum speed) / (maximum discharge current - minimum discharge current); 预设当前放电电流为40A，则转速设定值为：转速设定值＝1000+(40-35)*(2000-1000)/(50-35)＝1286RPM；The preset current discharge current is 40A, then the speed setting value is: speed setting value = 1000 + (40-35) * (2000-1000) / (50-35) = 1286RPM; 判断条件：根据预设阈值判断数值范围进行判断，若电池单体温度超过安全工作温度上限或低于安全工作温度下限(25℃-30℃)，则进行冷却系统调节；Judgment conditions: The judgment is made based on the preset threshold value range. If the battery cell temperature exceeds the upper limit of the safe operating temperature or is lower than the lower limit of the safe operating temperature (25°C-30°C), the cooling system is adjusted; 通过电池工作状态传感器实时监测电池组的放电电压和电流，从而得到实时的放电特性数据：The battery working status sensor monitors the discharge voltage and current of the battery pack in real time to obtain real-time discharge characteristic data: 根据实时的放电电压和电流数据，计算得到电池的荷电状态(SOC)和深度放电(DOD)；Calculate the battery state of charge (SOC) and depth of discharge (DOD) based on real-time discharge voltage and current data; 预设最大放电容量为100Ah，当前放电电流为40A，则根据公式计算荷电状态：SOC＝(已放电容量/总容量)*100％已放电容量＝放电电流*放电时间；The preset maximum discharge capacity is 100Ah, and the current discharge current is 40A. The state of charge is calculated according to the formula: SOC = (discharged capacity/total capacity) * 100% Discharged capacity = discharge current * discharge time; 预设已放电时间为1小时，则已放电容量为40A*1h＝40AhThe preset discharge time is 1 hour, so the discharged capacity is 40A*1h＝40Ah 则荷电状态为：SOC＝(40Ah/100Ah)*100％＝40％；Then the state of charge is: SOC = (40Ah/100Ah) * 100% = 40%; 判断电池组内电池单元的内阻，若检测到内阻异常升高，通过数据分析判断原因，如电池老化或电池损伤，预设当前电池内阻为0.01Ω，预设阈值为0.008Ω，若内阻异常升高超出预设范围，则进行故障判断和处理；Determine the internal resistance of the battery cells in the battery pack. If an abnormal increase in internal resistance is detected, determine the cause through data analysis, such as battery aging or battery damage. The current battery internal resistance is preset to 0.01Ω and the preset threshold is 0.008Ω. If the internal resistance increases abnormally beyond the preset range, perform fault judgment and processing; 若温度传感器监测到电池组有过热或过冷现象，通过温度梯度的监测数据，分析是否存在电池单体不均匀放热，进而调整电池组的空间布局或冷却方式，预设温度梯度超过预设阈值(2℃/m)，则进行分析和调整操作；If the temperature sensor detects that the battery pack is overheated or overcooled, the temperature gradient monitoring data is used to analyze whether there is uneven heat release of the battery cells, and then adjust the spatial layout or cooling method of the battery pack. If the preset temperature gradient exceeds the preset threshold (2°C/m), analysis and adjustment operations are performed; 获取传感器标定数据，确保所有传感器均经过校准，保持数据采集的准确性，预设标定偏差的预设阈值为±0.5℃，若标定偏差超出预设范围，则进行故障判断和处理，对于标定偏差超出预定范围的传感器，进行故障判断和处理；Obtain sensor calibration data to ensure that all sensors are calibrated and maintain the accuracy of data collection. The preset threshold of the calibration deviation is ±0.5°C. If the calibration deviation exceeds the preset range, fault judgment and processing are performed. For sensors whose calibration deviation exceeds the preset range, fault judgment and processing are performed; 通过安全监测系统，记录短路、过充、过放保护状态的触发记录，Through the safety monitoring system, the triggering records of short circuit, overcharge and over discharge protection status are recorded. 若系统检测到这些安全事件，分析导致这些事件的电池工作参数，调整电池管理系统(BMS)的控制策略，防止类似事件的再次发生。If the system detects these safety events, it analyzes the battery operating parameters that caused these events and adjusts the control strategy of the battery management system (BMS) to prevent similar events from happening again. 3.根据权利要求1所述的一种恒温化成分容设备的控制方法，其特征在于：所述将采集到的电池温度数据和放电特性数据传输至数据处理单元，分析技术分析电池当前状态，并探究历史操作行为与温度变化的关联性，包括：3. The control method of a constant temperature capacity conversion device according to claim 1 is characterized in that: the collected battery temperature data and discharge characteristic data are transmitted to the data processing unit, the analysis technology analyzes the current state of the battery, and explores the correlation between historical operation behavior and temperature change, including: 通过传感器连续监测电池温度，并将各时间点的温度数据实时发送至数据收集器，数据收集器记录包括温度读值、变化速率和峰值信息；同时，放电特性数据传感器监测放电参数，如放电率、放电深度、放电时间，并将这些数据同步传输至数据收集器进行记录和整合；数据收集器将收集到的电池温度数据和放电特性数据打包，通过网络传输至数据处理单元，数据处理单元接收并存储这些原始数据；在数据处理单元中，采用时间序列分析方法，建立电池温度与放电参数间的时间对应关系；The battery temperature is continuously monitored by sensors, and the temperature data at each time point is sent to the data collector in real time. The data collector records the temperature reading, change rate and peak value information. At the same time, the discharge characteristic data sensor monitors the discharge parameters, such as discharge rate, discharge depth and discharge time, and transmits these data to the data collector for recording and integration. The data collector packages the collected battery temperature data and discharge characteristic data and transmits them to the data processing unit through the network. The data processing unit receives and stores these raw data. In the data processing unit, the time series analysis method is used to establish the time correspondence between the battery temperature and the discharge parameters. 根据历史充电数据，数据处理单元执行回归分析，识别充电行为对电池温度的具体影响，并将分析结果与放电特性数据的分析结果；预设充电行为对电池温度的影响使用线性回归模型来描述，公式为：Based on the historical charging data, the data processing unit performs regression analysis to identify the specific impact of charging behavior on battery temperature, and compares the analysis results with the analysis results of the discharge characteristic data; the impact of the preset charging behavior on the battery temperature is described using a linear regression model, the formula is: 温度变化＝β0+β1*充电率Temperature change = β0 + β1 * charging rate 其中，β0和β1是回归系数，充电率是指充电的速率Among them, β0 and β1 are regression coefficients, and the charging rate refers to the charging rate. 数据处理单元获取环境温度数据，并通过统计分析方法，评估环境温度对电池温度及放电特性的影响；The data processing unit obtains the ambient temperature data and evaluates the influence of the ambient temperature on the battery temperature and discharge characteristics through statistical analysis methods; 预设环境温度对电池温度的影响用线性关系来描述，公式为：The effect of the preset ambient temperature on the battery temperature is described by a linear relationship, the formula is: 电池温度＝α0+α1*环境温度Battery temperature = α0 + α1 * ambient temperature 其中，α0和α1是统计分析得到的回归系数Among them, α0 and α1 are the regression coefficients obtained by statistical analysis. 在数据处理单元中分析电池循环次数、历史维护和操作记录与电池温度及放电特性间的复杂关系；Analyze the complex relationship between battery cycle count, historical maintenance and operation records, and battery temperature and discharge characteristics in the data processing unit; 预设使用决策树算法进行机器学习建模，模型表示为：The default decision tree algorithm is used for machine learning modeling. The model is expressed as: 电池温度＝f(循环次数,历史维护,操作记录)Battery temperature = f(cycle number, historical maintenance, operation record) 其中，f()是决策树模型Among them, f() is the decision tree model 数据处理单元利用故障和异常记录对模型进行训练。The data processing unit uses fault and anomaly records to train the model. 4.根据权利要求2所述的一种恒温化成分容设备的控制方法，其特征在于：所述BMS中提取充放电循环次数数据，若循环次数较多，则预测电池容量的衰减将加速，预设当充放电循环次数超过1000次时，电池容量的衰减速度会加快；4. The control method of a constant temperature capacity conversion device according to claim 2, characterized in that: the BMS extracts the charge and discharge cycle data, and if the cycle number is large, it is predicted that the battery capacity decay will accelerate, and it is preset that when the charge and discharge cycle number exceeds 1000 times, the battery capacity decay speed will accelerate; 通过分析BMS中记录的电流、电压和内阻参数，预测电池在即将到来的放电周期内的工作状态，若内阻增大，判断电池的热生成将增加，需要加强散热。By analyzing the current, voltage and internal resistance parameters recorded in the BMS, the working state of the battery in the upcoming discharge cycle is predicted. If the internal resistance increases, it is judged that the heat generation of the battery will increase and the heat dissipation needs to be strengthened. 5.根据权利要求1所述的一种恒温化成分容设备的控制方法，其特征在于：所述实施散热系统调整后，再次通过温度和电池状态传感器实时监控电池温度变化情况，对散热系统的实际冷却效果进行评估，包括：启动散热系统后，采用温度传感器连续监测电池温度，得到温度变化率；5. The control method of a constant temperature chemical component equipment according to claim 1, characterized in that: after the heat dissipation system is adjusted, the temperature change of the battery is monitored in real time by the temperature and battery status sensor again, and the actual cooling effect of the heat dissipation system is evaluated, including: after starting the heat dissipation system, the temperature sensor is used to continuously monitor the battery temperature to obtain the temperature change rate; Δt：时间间隔，表示观察或测量两个温度值之间经过的时间差，单位为秒(s)、分钟(min)；Δt: time interval, which indicates the time difference between observing or measuring two temperature values, in seconds (s) or minutes (min); ΔT：温度变化量，表示两个温度值之间的差异，用绝对值表示，并且温度变化量的正负取决于温度的增加或减少。ΔT: Temperature change, which represents the difference between two temperature values, expressed as an absolute value, and the positive or negative value of the temperature change depends on whether the temperature increases or decreases.
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