CN114497784A - Energy storage battery management optimization method and air volume adjusting method based on trend analysis - Google Patents
Energy storage battery management optimization method and air volume adjusting method based on trend analysis Download PDFInfo
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- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/60—Heating or cooling; Temperature control
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Abstract
The application relates to the technical field of battery heat dissipation, and discloses a trend analysis energy storage battery management optimization method and an air volume adjusting method, wherein the optimization method comprises the following steps: setting working mode states of the battery module, wherein the working mode states comprise a thermal balance mode and a thermal runaway risk mode; acquiring real-time performance parameter information of the battery module; if the real-time performance parameter information meets the preset thermal runaway risk state to thermal balance state transition condition, switching the thermal balance mode; if the real-time performance parameter information meets the preset thermal balance state to thermal runaway risk state transfer condition, the thermal runaway risk mode is switched.
Description
Technical Field
The invention relates to the technical field of battery heat dissipation, in particular to an energy storage battery management optimization method and an air volume adjusting method based on trend analysis.
Background
An electric cabinet unit of an energy storage power station generally comprises a plurality of battery modules (short for electric boxes), and is provided with a cooling fan for air cooling, heat dissipation and cooling aiming at an air-cooled electric box. The lithium ion battery can generate chemical reaction heat, polarization heat, joule heat, side reaction heat and the like in the charging and discharging processes. Because thermal accumulation, if not timely give off the heat, can make lithium ion battery surpass normal operating temperature scope to reduce the life of lithium cell, more serious can lead to the work that the battery module stopped to charge and discharge.
The conventional thermal management strategies currently employed are: setting upper and lower threshold values of the working temperature range of the battery module, and starting a heat dissipation fan to operate at full power to dissipate heat and cool when the temperature reaches the upper limit of the threshold values; and when the temperature is reduced to the lower threshold, the fan stops running.
Since the number of start-stops of a wind turbine during its life cycle is limited, this thermal management strategy may lead to frequent start-stops of the wind turbine, which may reduce the life of the wind turbine even if the risk of damage to the wind turbine is increased. Once the fan is damaged or the performance is reduced, heat can not be dissipated timely, so that the performance of the battery module is reduced, the service life of the battery module is shortened, and even the battery module stops working. And the fan needs to be replaced due to damage or due to the expiration of the service life, the operation of the whole electric cabinet needs to be stopped, so that certain economic loss is caused to the energy storage power station (the capacity of the energy storage power station is reduced in a short period). In order to avoid frequent start and stop of the fan or reduce the false alarm probability of the fan failure, the setting range of the upper and lower threshold values of the battery module temperature is often wider than the most appropriate temperature range.
In view of the above-mentioned related technologies, the inventor believes that such a thermal management strategy will cause the temperature of the electrical box to fluctuate up and down within a large range, and it is inconvenient to select a proper operating mode according to the real-time performance parameters of the battery, which will cause a certain thermal shock to the electrical box, increase the possibility of battery failure, and reduce the service life of the battery.
Disclosure of Invention
In order to select a proper working mode according to real-time performance parameters of a battery and enable the battery to have better performance and longer service life, the application provides an energy storage battery management optimization method and an air volume adjusting method based on trend analysis.
In a first aspect, the energy storage battery management optimization method based on trend analysis provided by the application adopts the following technical scheme:
a method for optimizing energy storage battery management based on trend analysis comprises the following steps:
setting working mode states of the battery module, wherein the working mode states comprise a thermal balance mode and a thermal runaway risk mode;
acquiring real-time performance parameter information of the battery module;
if the real-time performance parameter information meets the preset thermal runaway risk state to thermal balance state transition condition, switching the thermal balance mode;
and if the real-time performance parameter information meets the preset thermal balance state to thermal runaway risk state transition condition, switching the thermal runaway risk mode.
Through adopting above-mentioned technical scheme, at the in-process of battery module power supply, the real-time performance parameter information of real-time detection battery module, real-time performance parameter information includes current temperature measurement value, historical temperature data's rate of change and the temperature prediction value of any moment in the future, change according to these parameter information, judge which kind of transfer condition that battery module accords with, come to switch battery module to thermal balance mode or thermal runaway risk mode, consequently can select suitable mode according to the real-time performance parameter of battery, make the battery possess better performance and longer life-span.
Optionally, the thermal equilibrium mode comprises:
and the preset cooling fan stops running, the preset battery monitoring module continuously acquires the current temperature measured value of the battery module, and calculates the change rate of the historical temperature data of the battery module and the predicted temperature value at any time in the future.
Through adopting above-mentioned technical scheme, at radiator fan operation's in-process, the heat balance mode does not need radiator fan operation for current battery module is in the normal state of heat dissipation, but at this in-process still needs the real-time performance parameter information of real-time supervision battery module, is convenient for in time according to the real-time performance parameter information who monitors, switches operating mode.
Optionally, the thermal runaway risk pattern comprises:
the cooling fan operates according to a preset optimization operation strategy, the battery monitoring module continuously obtains a preset current temperature measured value of the battery module, and calculates the change rate of historical temperature data of the battery module and a predicted temperature value at any time in the future.
Through adopting above-mentioned technical scheme, at radiator fan operation's in-process, thermal runaway risk mode is that current battery module is in the unusual state of heat dissipation, need pass through radiator fan, according to optimizing operation strategy to battery module, but still need the real-time performance parameter information of real-time supervision battery module at this in-process, is convenient for in time according to the real-time performance parameter information who monitors, switches operating mode.
Optionally, the condition for transferring the thermal equilibrium state to the thermal runaway risk state includes:
the change rate of the historical temperature data in the t1 time period is larger than a preset first temperature change rate or smaller than a preset second temperature change rate;
or the like, or a combination thereof,
the predicted value of the temperature time series data at the moment t2 is greater than a preset first temperature value or less than a preset second temperature value;
or the like, or, alternatively,
the current temperature measurement over time period t3 is greater than the first temperature value or less than the second temperature value.
By adopting the technical scheme, when the arbitrary conditions are met, the thermal equilibrium mode needs to be switched to the thermal runaway risk mode.
Optionally, the thermal runaway risk state to thermal equilibrium state transition condition includes:
a rate of change of the historical temperature data over a time period t1 is less than the first rate of temperature change and greater than the second rate of temperature change;
and the number of the first and second electrodes,
a predicted value of the temperature time series data at time t4 is smaller than the first temperature value and larger than the second temperature value;
and the number of the first and second electrodes,
the current temperature measurement over time period t5 is less than the first temperature value and greater than the second temperature value.
By adopting the technical scheme, when the arbitrary conditions are met, the thermal runaway risk mode needs to be switched to the thermal balance mode.
Optionally, the change rate of the historical temperature data and the predicted value of the temperature time series data are calculated by a preset linear fitting algorithm and other time series data trend identification algorithms.
By adopting the technical scheme, the change rate of the historical temperature data and the predicted value of the temperature time sequence data can be accurately calculated through linear fitting and other time sequence data trend identification algorithms.
Optionally, the step of operating the cooling fan according to a preset optimized operation strategy includes:
the cooling fan is adjusted to be operated in the state of the optimal air quantity, and the temperature fluctuation range of the battery module is reduced;
the state of the optimal air volume is the maximum value of the air volume calculated in the following two ways;
the first method is as follows: q1= k1 r, Q1 is a first air volume, r is the change rate of the historical temperature data, and k1 is a preset proportional constant;
the second method comprises the following steps: q2= k2/min (dT1, dT2), Q1 is the second air volume, dT1 is the absolute value of the difference between the current temperature measurement value and the first temperature value, dT2 is the absolute value of the difference between the current temperature measurement value and the second temperature value, and k2 is a preset direct proportionality constant.
By adopting the technical scheme, because the optimal heat dissipation effect needs to be obtained and the air volume needs to be maximized, two modes are set to calculate the optimal air volume, the maximum air volume is selected as the optimal air volume through the air volume calculated by the two modes, so that the calculation result has selectivity, and the optimal operation strategy of the heat dissipation fan is convenient to determine.
In a second aspect, the present application provides an air volume adjusting method for energy storage battery management based on trend analysis, which adopts the following technical scheme:
an air volume adjusting method for energy storage battery management based on trend analysis is applied to the energy storage battery management optimization method based on trend analysis, and comprises the following steps:
analyzing the type of the cooling fan;
if the cooling fan is a variable frequency control fan, adjusting the air quantity Q by adjusting the rotating speed of the cooling fan, namely: q = c1 v, v being the adjustable rotational speed of the radiator fan;
if the cooling fan is a fixed-frequency fan, adjusting the air quantity Q by adjusting the running time of the cooling fan, namely: q = c2 vf*dt，vfIs the fixed rotation speed of the cooling fan, dtThe operation time of the heat radiation fan can be adjusted.
By adopting the technical scheme, the air volume adjusting modes are different for different types of cooling fans, the air volume is adjusted according to different adjusting modes respectively according to different internal structures of the variable frequency control fan and the fixed frequency fan, the adjustable rotating speed of the cooling fan needs to be adjusted for the variable frequency control fan, and the adjustable running time of the cooling fan needs to be adjusted for the fixed frequency fan; therefore, the device has a targeted adjusting function.
To sum up, the application comprises the following beneficial technical effects:
1. in the process of supplying power to the battery module, real-time performance parameter information of the battery module is detected in real time, wherein the real-time performance parameter information comprises a current temperature measurement value, a change rate of historical temperature data and a temperature predicted value at any time in the future, and according to the change of the parameter information, the battery module is judged to meet which transfer condition to switch the battery module to a thermal balance mode or a thermal runaway risk mode, so that a proper working mode can be selected according to the real-time performance parameters of the battery, and the battery has better performance and longer service life;
2. the method comprises the following steps that for different types of cooling fans, air volume adjusting modes are different, the air volume is adjusted according to different adjusting modes according to different internal structures of a variable frequency control fan and a fixed frequency fan, for the variable frequency control fan, the adjustable rotating speed of the cooling fan needs to be adjusted, and for the fixed frequency fan, the adjustable operation duration of the cooling fan needs to be adjusted; therefore, the device has a targeted adjusting function.
Drawings
Fig. 1 is a flowchart of an energy storage battery management optimization method based on trend analysis according to an embodiment of the present disclosure.
Fig. 2 is a graph of temperature versus time of the battery module.
Fig. 3 is a flow chart of an air volume adjusting method for energy storage battery management based on trend analysis.
Detailed Description
The present application is described in further detail below with reference to figures 1-3.
Example 1
Referring to fig. 1, the embodiment of the application discloses a method for optimizing energy storage battery management based on trend analysis, which includes steps S100 to S300.
Step S100: and setting the working mode state of the battery module, wherein the working mode state comprises a thermal balance mode and a thermal runaway risk mode.
Step S200: and acquiring real-time performance parameter information of the battery module. The real-time performance parameter information comprises a current temperature measurement value, a change rate of historical temperature data and a predicted temperature value at any time in the future.
Step S300: if the real-time performance parameter information meets the preset thermal runaway risk state to thermal balance state transition condition, switching the thermal balance mode;
and if the real-time performance parameter information meets the preset thermal balance state to thermal runaway risk state transition condition, switching the thermal runaway risk mode.
The thermal equilibrium mode includes: the preset cooling fan stops running, the preset battery monitoring module continuously obtains a current temperature measured value of the battery module, and the change rate of historical temperature data of the battery module and a predicted temperature value at any time in the future are calculated.
The thermal runaway risk patterns include: the cooling fan operates according to a preset optimization operation strategy, the battery monitoring module continuously obtains a preset current temperature measured value of the battery module, and the change rate of historical temperature data of the battery module and a predicted temperature value at any time in the future are calculated.
The rotating speed of the cooling fan is dynamically and moderately controlled, performance reduction, damage or service life shortening of the cooling fan due to frequent starting and stopping are prevented, the risk that a battery module stops running due to premature damage of the cooling fan is prevented, and running benefits of a power station are guaranteed. The replacement period of the cooling fan is prolonged, and the maintenance cost of the power station is reduced. The electric box can operate within the optimum temperature range (for example, 10-40 ℃), so that the battery module has better performance and longer service life.
The thermal equilibrium state to thermal runaway risk state transition condition includes:
the change rate of the historical temperature data in the time period t1 is greater than a preset first temperature change rate or less than a preset second temperature change rate;
or the like, or, alternatively,
the predicted value of the temperature time series data at the time t2 is greater than a preset first temperature value or less than a preset second temperature value;
or the like, or, alternatively,
the current temperature measurement during the time period t3 is either greater than the first temperature value or less than the second temperature value. The first temperature change rate is 1 degree centigrade per hour, and the second temperature change rate is-1 degree centigrade per hour.
Exemplary data are as follows:
the time period t1 is 1 hour, the time t2 indicates the current monitoring time +1 hour, and the time period t3 is 5 seconds.
The thermal runaway risk state to thermal equilibrium state transition condition includes:
the rate of change of the historical temperature data over the time period t1 is less than the first rate of temperature change and greater than the second rate of temperature change;
and the number of the first and second electrodes,
the predicted value of the temperature time series data at the time t4 is smaller than the first temperature value and larger than the second temperature value;
and the number of the first and second electrodes,
the current temperature measurement over the time period t5 is less than the first temperature value and greater than the second temperature value.
Example data are as follows:
the time t2 indicates the current monitoring time +1 hour, the time t4 indicates the current monitoring time +1 hour, the time period t3 is 5 seconds, the first temperature value is 25 degrees, and the second temperature value is 0 degree.
The change rate of the historical temperature data and the predicted value of the temperature time sequence data are calculated through preset linear fitting and other time sequence data trend identification algorithms.
The method for operating the cooling fan according to the preset optimized operation strategy comprises the following steps:
the cooling fan is adjusted to operate in the state of the optimal air quantity, and the temperature fluctuation range of the battery module is reduced;
the state of the optimal air volume is the maximum value of the air volume calculated in the following two ways;
the first method is as follows: q1= k1 r, Q1 is the first air volume, r is the change rate of the historical temperature data, and k1 is a preset proportional constant;
the second method comprises the following steps: q2= k2/min (dT1, dT2), Q1 is the second air volume, dT1 is the absolute value of the difference between the current temperature measurement value and the first temperature value, dT2 is the absolute value of the difference between the current temperature measurement value and the second temperature value, and k2 is a preset direct proportional constant.
The variable r is a calculated quantity, and may be obtained, for example, by the formula abs (T2-T1)/dT, where abs is an absolute value, T1 and T2 are temperature measurements at times tn and tm, respectively, and dT is the time difference tm-tn.
The coefficients k1 and k2 are determined based on information such as the model of the fan, its rated air volume, the temperature change rate range, and the unit of use, and are generally obtained by field debugging.
Referring to fig. 2, a graph of a relationship between a temperature of the battery module and time is shown, a relatively smooth curve is a fluctuation range of the battery temperature after the operation using the optimized operation strategy, and a relatively steep curve is a fluctuation range of the battery temperature before the operation using the optimized operation strategy.
The implementation principle of the energy storage battery management optimization method based on trend analysis in the embodiment of the application is as follows: in the process of supplying power to the battery module, the real-time performance parameter information of the battery module is detected in real time, the real-time performance parameter information comprises a current temperature measurement value, the change rate of historical temperature data and a temperature prediction value at any moment in the future, and according to the change of the parameter information, the battery module is judged to accord with which transfer condition, so that the working mode of the battery module is switched to a thermal balance mode or a thermal runaway risk mode, therefore, a proper working mode can be selected according to the real-time performance parameter of the battery, and the battery has better performance and longer service life.
Example 2
Referring to FIG. 3, the embodiment of the application discloses an air volume adjusting method for energy storage battery management based on trend analysis, which comprises steps SA 00-SB 00.
The type of the cooling fan is analyzed.
If the cooling fan is a variable frequency control fan, the air quantity Q is adjusted by adjusting the rotating speed of the cooling fan, namely: q = c1 v, v being the adjustable rotational speed of the radiator fan;
if the cooling fan is a fixed-frequency fan, the air quantity Q is adjusted by adjusting the running time of the cooling fan, namely: q = c2 vf*dt，vfFor a fixed rotational speed of the radiator fan, dtThe operation time of the cooling fan can be adjusted.
The coefficients c1 and c2 are determined according to information such as the model of the fan, the rated air volume thereof, and the unit of use, and are generally obtained by field debugging. v. offAn exemplary value may be 1000RPM (revolutions per minute), and v and dt are automatically controlled adjustable amounts, automatically adjusted according to the desired optimal air volume.
The implementation principle of the air volume adjusting method for energy storage battery management based on trend analysis in the embodiment of the application is as follows: the method comprises the following steps that for different types of cooling fans, air volume adjusting modes are different, the air volume is adjusted according to different adjusting modes according to different internal structures of a variable frequency control fan and a fixed frequency fan, for the variable frequency control fan, the adjustable rotating speed of the cooling fan needs to be adjusted, and for the fixed frequency fan, the adjustable operation duration of the cooling fan needs to be adjusted; therefore, the device has a targeted adjusting function.
The above embodiments are preferred embodiments of the present application, and the protection scope of the present application is not limited by the above embodiments, so: all equivalent changes made according to the structure, shape and principle of the present application shall be covered by the protection scope of the present application.
Claims (8)
1. A method for managing and optimizing an energy storage battery based on trend analysis is characterized by comprising the following steps: the method comprises the following steps:
setting working mode states of the battery module, wherein the working mode states comprise a thermal balance mode and a thermal runaway risk mode;
acquiring real-time performance parameter information of the battery module;
if the real-time performance parameter information meets the preset thermal runaway risk state to thermal balance state transition condition, switching the thermal balance mode;
and if the real-time performance parameter information meets the preset thermal balance state to thermal runaway risk state transition condition, switching the thermal runaway risk mode.
2. The method for optimizing energy storage battery management based on trend analysis as claimed in claim 1, wherein: the thermal equilibrium mode includes:
and the preset cooling fan stops running, the preset battery monitoring module continuously acquires the current temperature measured value of the battery module, and calculates the change rate of the historical temperature data of the battery module and the predicted temperature value at any time in the future.
3. The method for optimizing energy storage battery management based on trend analysis as claimed in claim 2, wherein: the thermal runaway risk pattern comprises:
the cooling fan operates according to a preset optimization operation strategy, the battery monitoring module continuously obtains a preset current temperature measured value of the battery module, and calculates the change rate of historical temperature data of the battery module and a predicted temperature value at any time in the future.
4. The method according to claim 3, wherein the method comprises the following steps: the thermal equilibrium state to thermal runaway risk state transition condition includes:
the change rate of the historical temperature data in the t1 time period is larger than a preset first temperature change rate or smaller than a preset second temperature change rate;
or the like, or, alternatively,
the predicted value of the temperature time series data at the moment t2 is greater than a preset first temperature value or less than a preset second temperature value;
or the like, or, alternatively,
the current temperature measurement over time period t3 is greater than the first temperature value or less than the second temperature value.
5. The method for optimizing energy storage battery management based on trend analysis as claimed in claim 4, wherein: the thermal runaway risk state to thermal equilibrium state transition condition includes:
a rate of change of the historical temperature data over a time period t1 is less than the first rate of temperature change and greater than the second rate of temperature change;
and the number of the first and second electrodes,
a predicted value of the temperature time series data at time t4 is smaller than the first temperature value and larger than the second temperature value;
and the number of the first and second electrodes,
the current temperature measurement over time period t5 is less than the first temperature value and greater than the second temperature value.
6. The method for optimizing energy storage battery management based on trend analysis according to any one of claims 4-5, wherein:
and the change rate of the historical temperature data and the predicted value of the temperature time sequence data are calculated through preset linear fitting and other time sequence data trend identification algorithms.
7. The method for optimizing energy storage battery management based on trend analysis as claimed in claim 4, wherein: the step of operating the cooling fan according to a preset optimized operation strategy comprises the following steps:
the cooling fan is adjusted to be operated in the state of the optimal air quantity, and the temperature fluctuation range of the battery module is reduced;
the state of the optimal air volume is the maximum value of the air volume calculated in the following two ways;
the first method is as follows: q1= k1 r, Q1 is a first air volume, r is the change rate of the historical temperature data, and k1 is a preset proportional constant;
the second method comprises the following steps: q2= k2/min (dT1, dT2), Q1 is the second air volume, dT1 is the absolute value of the difference between the current temperature measurement value and the first temperature value, dT2 is the absolute value of the difference between the current temperature measurement value and the second temperature value, and k2 is a preset direct proportionality constant.
8. A wind volume adjusting method for energy storage battery management based on trend analysis is characterized in that: the method for optimizing the management of the energy storage battery based on the trend analysis, which is applied to any one of the claims 1 to 7, comprises the following steps:
analyzing the type of the cooling fan;
if the cooling fan is a variable frequency control fan, adjusting the air quantity Q by adjusting the rotating speed of the cooling fan, namely: q = c1 v, v being the adjustable rotational speed of the radiator fan;
if the cooling fan is a fixed-frequency fan, adjusting the air quantity Q by adjusting the running time of the cooling fan, namely: q = c2 vf*dt，vfIs the fixed rotation speed of the cooling fan, dtThe operation time of the heat radiation fan can be adjusted.
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| CN207098007U (en) * | 2017-07-03 | 2018-03-13 | 深圳市沃特玛电池有限公司 | A kind of battery bag cooling system |
| CN107579308A (en) * | 2017-08-31 | 2018-01-12 | 江苏大学 | A kind of batteries of electric automobile bag heat management and temperature equalization control method |
| CN113506924A (en) * | 2021-06-17 | 2021-10-15 | 重庆金康动力新能源有限公司 | Thermal runaway early warning method and system for battery pack |
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| CN115954989A (en) * | 2023-03-09 | 2023-04-11 | 中能建储能科技(武汉)有限公司 | Energy storage power station operation monitoring management system |
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