Energy storage battery integrated thermal management method and system

Abstract

The present application relates to the technical field of energy storage battery thermal management, and discloses an energy storage battery integrated thermal management method and system, aiming at the defects of existing passive feedback control response lag, single target and inability to cope with dynamic working conditions, the present application acquires the battery body, the thermal management system, the environment boundary and the future charging and discharging plan parameters in real time, calculates the total heat production power and the actual heat dissipation power to determine the thermal balance state; in combination with the next time zone charging and discharging plan, the heat production average rate is predicted, the correlation model of the cooling flow, the heat dissipation power, the battery temperature and the system energy consumption is established; a weighted multi-objective optimization function including temperature safety, system energy consumption and battery consistency is constructed, and the optimal cooling flow is solved; the present application realizes predictive multi-objective collaborative thermal management, can effectively reduce the thermal management energy consumption, reduce the battery monomer temperature difference, and improve the safety of the energy storage system operation.

Description

Technical Field

[0001] This invention relates to the field of thermal management technology for energy storage batteries, and more specifically, to an integrated thermal management method and system for energy storage batteries. Background Technology

[0002] Currently, existing energy storage battery thermal management systems mainly adopt a passive feedback control architecture. This involves collecting the battery's current average or maximum temperature using temperature sensors and adjusting the cooling pump's speed and flow rate based on PID control algorithms or simple on / off control logic. This traditional control method has the following drawbacks in practical applications: The control response is severely lagging and cannot cope with dynamic operating conditions. Due to the large heat capacity and thermal resistance of the battery pack, there is a significant time delay in the transfer of heat from the inside of the battery to the temperature sensor. When the system detects that the battery temperature exceeds the limit and then starts cooling regulation, the battery temperature has already fluctuated significantly. Especially under dynamic operating conditions such as grid frequency regulation and peak-valley arbitrage, the charging and discharging power of the energy storage battery can change drastically on the order of seconds to minutes. Traditional feedback control cannot predict the heat generation trend in advance, resulting in frequent battery temperature exceeding the limit, which seriously threatens the safety of system operation. Secondly, the control objective is singular and lacks multi-dimensional collaborative optimization. Existing thermal management systems generally take battery temperature control as the only objective. To ensure temperature safety, they often adopt a conservative strategy of "overcooling," resulting in high energy consumption of the cooling system and significantly reducing the overall economic efficiency of the energy storage system. At the same time, existing systems hardly consider the active control of the temperature difference between individual battery cells, which cannot effectively improve the temperature uniformity of the battery pack. Long-term operation will exacerbate the performance differences between individual battery cells and significantly shorten the overall lifespan of the battery pack.

[0003] In summary, existing thermal management technologies for energy storage batteries suffer from core problems such as control lag, single objective, low prediction accuracy, and poor engineering practicality, which can no longer meet the comprehensive requirements of large-scale energy storage systems for safety, economy, and long life. Therefore, an integrated thermal management method and system for energy storage batteries is proposed. Summary of the Invention

[0004] To overcome the above-mentioned deficiencies of the prior art, embodiments of the present invention provide an integrated thermal management method and system for energy storage batteries.

[0005] To achieve the above objectives, the present invention provides the following technical solution: An integrated thermal management method for energy storage batteries, comprising: S1: Real-time acquisition of battery body parameters, thermal management system parameters, environmental boundary parameters, and future charge / discharge plan parameters; S2: Calculate the total heat generation power and actual heat dissipation power of the battery based on the current time zone parameters, and determine the thermal balance state; the thermal balance state includes the heat accumulation state and the heat dissipation state. S3: Based on the determined thermal equilibrium state, combined with the predicted heat generation rate of the next time zone charge and discharge plan, analyze the correlation between cooling flow rate and heat dissipation power, battery temperature and system energy consumption, establish a multi-objective optimization function, and solve for the optimal cooling flow rate in the next time zone.

[0006] Specifically, the battery parameters include: real-time charging and discharging current, single cell voltage, state of charge, and the temperatures of all single cells collected by the NTC temperature sensor array, and the average temperature, maximum temperature, minimum temperature and temperature difference of the single cells are calculated. Thermal management system parameters: cooling medium mass flow rate, coolant inlet temperature, coolant outlet temperature, heat exchanger inlet and outlet temperatures, cooling pump speed and power; Environmental boundary parameters: ambient air temperature; Future charge / discharge plan parameters: Discrete sequence of charge / discharge power for the next pre-divided time zone.

[0007] Specifically, the logic for calculating the total heat generation power of the battery; The total heat generation power of the battery pack at time k ; in This represents the total discharge current of the battery pack at time k in the current time zone; Let k be the state of charge of the battery at time k. Let K be the average temperature of all cells in the battery pack at time k. This represents the DC internal resistance of the battery at time k; It is the heat of electrochemical reaction.

[0008] Specifically, the logic for calculating actual heat dissipation power; Actual heat dissipation power of the battery pack at time k ; Let k be the cooling system heat dissipation power at time k; expressed by the formula Calculated; Where c is the specific heat capacity of the cooling medium; Let k be the actual mass flow rate of the cooling medium at time k. Let k be the temperature at which the coolant flows out of the battery pack. The temperature at which the coolant enters the battery pack at time k; Let K be the natural heat dissipation power at time k; expressed by the formula Calculated; Let k be the ambient air temperature inside the battery compartment. This is the total thermal resistance between the battery pack casing and the environment.

[0009] Specifically, the logic for determining the thermal equilibrium state; After averaging the total heat generation power and actual heat dissipation power of the battery at each time in the current time zone, we obtain the average heat generation rate and the average heat dissipation rate. The difference between the heat production rate and the heat dissipation rate is used to obtain the heat balance determination value; If the thermal balance judgment value is higher than the preset judgment range, the thermal balance state is judged to be a state of heat accumulation; otherwise, the thermal balance state is judged to be a state of heat dissipation.

[0010] Specifically, the logic for obtaining the average heat generation rate predicted by the charging and discharging plan in the next time zone; The charging and discharging power plan for the next time zone is a predetermined discrete sequence. N is / t, t is the preset discrete time step; Charge and discharge current sequence Calculated from the power plan: ;in The charging and discharging power for the i-th time step in the next time zone; Let be the average battery pack voltage at the i-th time step in the next time zone; using the state of charge (SOC) at the end of the current time zone as the initial value, predict the SOC for each time step in the next time zone based on the ampere-hour integration method: ; in This represents the battery's state of charge at the end of the current time zone. The nominal capacity of the battery pack; Based on the formula for calculating the total heat generation power of the battery in step S2, the predicted current, SOC and temperature sequence are substituted to calculate the heat generation power at each time step. The heat production rate is obtained by taking the arithmetic mean of the heat production power at all time steps in the next time zone.

[0011] Specifically, the process of constructing the temperature sequence; A temperature sequence is constructed using the average temperature of all cells in the battery pack at each time in the current time zone. The temperature change trend over time is fitted, and the temperature-time linear equation is fitted using the least squares method. Extrapolate the fitted linear trend to the next time zone to obtain the initial predicted temperature for each time step in the next time zone.

[0012] Specifically, the relationship between cooling flow rate and heat dissipation power, battery temperature, and system energy consumption is analyzed. The convective heat transfer coefficient h between the battery pack and the coolant is established to satisfy a power-law relationship with the mass flow rate m of the cooling medium: ;where u1 and The preset empirical coefficient; The convective heat transfer coefficient is given by the flow rate m. The total heat transfer resistance between the battery pack and the coolant consists of convection thermal resistance and wall conductivity thermal resistance. Where A is the total effective heat exchange area between the battery pack and the coolant; The total thermal resistance between the battery pack wall and the cooling pipes; The total heat transfer resistance is given at the corresponding flow rate m. Based on the thermal resistance network, the heat dissipation power of the cooling system is equal to the average battery temperature. The difference between the temperature at the coolant inlet and the total heat transfer resistance: ;in This is the coolant inlet temperature for the next time zone. For the corresponding flow rate m and average battery temperature The cooling system's heat dissipation power is below; Extract the predicted ambient air temperature at the i-th time step in the next time zone ; Based on the thermal equilibrium steady-state approximation, the explicit function of battery pack average temperature versus flow rate is derived: ; In the formula This represents the average steady-state temperature of all cells in the battery pack in the next time zone when the cooling flow rate is m. The mean heat production rate for the next time zone; The relationship between cooling flow rate and the battery's maximum temperature and temperature difference is established, as follows: ; This represents the temperature difference between the hottest and coldest cells in the battery pack in the next time zone, given a cooling flow rate m. The measured temperature difference of the individual unit ends in the current time zone; Rated charge and discharge power of the battery pack; This is the rated flow rate of the cooling system; ; Given a cooling flow rate m, the temperature of the hottest cell in the battery pack in the next time zone; The set temperature offset correction factor; Establish the relationship between cooling flow rate and system energy consumption, which is reflected in: cooling pump shaft power. ; , , , The preset cooling pump power fitting coefficient; total energy consumption of the cooling system in the next time zone. ;in This represents the time zone length.

[0013] Specifically, construct a constrained multi-objective optimization function; The temperature safety target is to keep the average battery temperature close to the optimal operating temperature. Temperature safety target ; Constraints ; This is the preset optimal operating temperature for the battery; and These are the maximum and minimum allowable operating temperatures, respectively.

[0014] The system energy consumption target is to minimize the energy consumption of the cooling system. ; This represents the total energy consumption in the time zone under maximum flow. The goal of battery consistency is to minimize the temperature difference between individual battery cells. Battery consistency target ; This is the preset maximum allowable temperature difference; The objectives are weighted and fused together using a pre-defined set of weights to output a multi-objective optimization function. ; Extract the cooling flow rate that is lower than the preset function reference value of the multi-objective optimization function, find the cooling flow rate with the minimum function value as the optimal mass flow rate, and convert it into a cooling pump speed command.

[0015] An integrated thermal management system for energy storage batteries, comprising: The data acquisition module is used to collect battery body parameters, thermal management system parameters, environmental boundary parameters, and future charge and discharge plan parameters in real time. The thermal balance determination module is used to calculate the total heat generation power and actual heat dissipation power of the battery based on the collected parameters of the current time zone. The thermal balance state is determined by the difference between the average heat generation rate and the average heat dissipation rate. The thermal balance state includes heat accumulation state, heat loss state and stable state. The multi-objective optimization module is used to analyze the relationship between cooling flow rate and heat dissipation power, battery temperature and system energy consumption based on a determined thermal equilibrium state and the predicted heat generation rate in the next time zone charge and discharge plan. It constructs a constrained multi-objective optimization function to solve for the optimal cooling mass flow rate in the next time zone. The execution control module is used to convert the optimal cooling mass flow rate obtained from the solution into a cooling pump speed command to drive the cooling system to operate.

[0016] The technical effects and advantages of this invention are as follows: This invention realizes predictive multi-objective collaborative thermal management, which completely solves the problems of lag and single objective of traditional passive feedback control. By combining the charging and discharging plan of the next time zone to predict the battery heat generation trend in advance, the cooling flow rate is actively adjusted before the temperature changes. This can effectively avoid the risk of large fluctuations and over-limits in battery temperature under dynamic operating conditions such as grid frequency regulation and peak-valley arbitrage. At the same time, a weighted multi-objective optimization function of temperature safety, system energy consumption and battery consistency is constructed. The weight priority can be dynamically adjusted according to different operating conditions. This abandons the traditional conservative strategy of "over-cooling" and significantly improves the safety and economy of energy storage system operation. This invention significantly improves the accuracy and reliability of temperature prediction. Taking into account the multi-component characteristics of battery heat generation, the nonlinear characteristics of cooling heat transfer, and the influence of environmental boundary conditions, it introduces a temperature offset correction coefficient to correct the asymmetry of battery pack temperature distribution, thereby achieving accurate prediction of battery average temperature, maximum temperature, and individual cell temperature difference, providing a reliable decision-making basis for advance adjustment of cooling flow. Attached Figure Description

[0017] Figure 1 This is a flowchart of an integrated thermal management method for energy storage batteries according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1 like Figure 1 As shown, an integrated thermal management method for energy storage batteries is as follows: S1: Real-time acquisition of battery body parameters, thermal management system parameters, environmental boundary parameters, and future charge / discharge plan parameters, and preprocessing them; Battery parameters: Real-time charging and discharging current, cell voltage, and state of charge are collected; the temperature of all cells is collected by an NTC temperature sensor array arranged in the battery pack (one sensor is arranged for every 2-4 cells), and the average temperature, maximum temperature, minimum temperature, and temperature difference of each cell are calculated. Thermal management system parameters: The current mass flow rate of the cooling medium is collected through a flow sensor; the inlet temperature of the coolant, the outlet temperature of the coolant, and the inlet and outlet temperatures of the heat exchanger are collected through a temperature sensor; the speed and power of the cooling pump are collected through a frequency converter. Environmental boundary parameters: Ambient air temperature is collected through weather stations; Plan parameters: Obtain the pre-determined charge and discharge power plan for the next time zone (the time zone is pre-defined by technicians).

[0020] Preprocessing includes: Outliers are removed using the 3σ principle; high-frequency noise is eliminated using a first-order low-pass filter.

[0021] S2: Calculate the total heat generation power and actual heat dissipation power of the battery based on the current time zone parameters, and determine the thermal balance state; the thermal balance state includes the heat accumulation state and the heat dissipation state. Specifically: The total heat output of the battery is calculated based on both Joule heat and reaction heat. The calculation formula is as follows: The total heat generation power of the battery pack at time k ; in This represents the total discharge current of the battery pack at time k in the current time zone; Let k be the state of charge of the battery at time k. Let K be the average temperature of all cells in the battery pack at time k. This represents the DC internal resistance of the battery at time k; the internal resistance MAP diagrams at different SOCs and temperatures are obtained through offline calibration, and can be obtained by looking up a table during online operation; The heat of electrochemical reaction is determined based on the battery type; For example, for lithium iron phosphate batteries, its value is approximately 10%-15% of the Joule heat, which can be calculated using empirical formulas. Calculated; where The open-circuit voltage is obtained through offline calibrated SOC-OCV curves. The average voltage of a single cell; The actual heat dissipation power is calculated based on the heat dissipation power of the cooling system and the natural heat dissipation power of the battery pack to the environment. The calculation formula is: Actual heat dissipation power of the battery pack at time k ; Let k be the cooling system heat dissipation power at time k; expressed by the formula Calculated; Where c is the specific heat capacity of the cooling medium; Let k be the actual mass flow rate of the cooling medium at time k. Let k be the temperature at which the coolant flows out of the battery pack. The temperature at which the coolant enters the battery pack at time k; Let K be the natural heat dissipation power at time k; expressed by the formula Calculated; in Let k be the ambient air temperature inside the battery compartment. The total thermal resistance between the battery pack casing and the environment; calibrated offline; typical value for containerized energy storage battery packs: 0.03-0.08℃ / W.

[0022] After averaging the total heat generation power and actual heat dissipation power of the battery at each time in the current time zone, we obtain the average heat generation rate and the average heat dissipation rate. The difference between the heat production rate and the heat dissipation rate is used to obtain the heat balance determination value; If the thermal balance judgment value is higher than the preset judgment range, the thermal balance state is judged to be a heat accumulation state, and the battery temperature will rise; otherwise, the thermal balance state is judged to be a heat dissipation state, and the battery temperature will drop; if it is within the judgment range, the thermal balance state is judged to be stable. If the judgment value is negative, the larger the negative value, the higher the probability of it being a state of heat loss.

[0023] S3: Based on the determined thermal equilibrium state, combined with the predicted heat generation rate of the next time zone charge and discharge plan, analyze the correlation between cooling flow rate and heat dissipation power, battery temperature and system energy consumption, establish a multi-objective optimization function, and solve for the optimal cooling flow rate in the next time zone. Specifically: Define the next time zone as the time interval. ;in The length of the time zone is determined in advance by technical personnel. The start time of the next time zone is equal to the end time of the current time zone, i.e. ; The charging and discharging power plan for the next time zone is a predetermined discrete sequence. N is / t, t is the preset discrete time step; Charge and discharge current sequence Calculated from the power plan: ;in The charging and discharging power for the i-th time step in the next time zone; The average battery pack voltage at the i-th time step in the next time zone is obtained by looking up a table using the offline calibrated SOC-OCV curve; Using the state of charge (SOC) at the end of the current time zone as the initial value, predict the SOC for each time step in the next time zone based on the ampere-hour integration method: ; in This represents the battery's state of charge at the end of the current time zone. The nominal capacity of the battery pack is determined by the equipment parameters provided by the battery manufacturer. Based on the formula for calculating the total battery heat generation power in step S2, the predicted current, SOC, and temperature sequences are substituted to calculate the heat generation power at each time step. ; in It is the heat of electrochemical reaction, expressed by the formula Calculated; In the formula The battery DC internal resistance at the i-th time step in the next time zone is obtained by looking up a table from the offline calibrated SOC-temperature-internal resistance MAP. Let the predicted average battery temperature be the temperature at the i-th time step in the next time zone. The initial value is the average temperature at the end of the current time zone. The battery open-circuit voltage at the i-th time step in the next time zone is obtained by looking up a table using the offline calibrated SOC-OCV curve; The predicted average voltage of a single cell at the i-th time step in the next time zone is obtained by dividing the average voltage of the battery pack by the total number of cells connected in series within the battery pack. The process of constructing temperature sequences: A temperature sequence is constructed using the average temperature of all cells in the battery pack at each time point in the current time zone. The temperature variation trend over time is fitted, and the temperature-time linear equation is fitted using the least squares method. Where a is the rate of temperature change, t is the time point, and b is the intercept term. Let t be the temperature at time t; Extrapolate the fitted linear trend to the next time zone to obtain the initial predicted temperature for each time step in the next time zone.

[0024] The heat production rate is obtained by taking the arithmetic mean of the heat production power at all time steps in the next time zone.

[0025] The convective heat transfer coefficient h between the battery pack and the coolant is established to satisfy a power-law relationship with the mass flow rate m of the cooling medium: ;where u1 and These are preset empirical coefficients; calibrated through offline heat exchange experiments of the battery pack liquid cooling system. is the convective heat transfer coefficient at the corresponding flow rate m.

[0026] The total heat transfer resistance between the battery pack and the coolant consists of convection thermal resistance and wall conductivity thermal resistance. ; Where A is the total effective heat exchange area between the battery pack and the coolant, which is determined by the battery pack structural design parameters; The total thermal resistance of the battery pack wall and cooling pipes is obtained through offline thermal resistance testing and calibration. The total heat transfer resistance is given at the corresponding flow rate m. Based on the thermal resistance network, the heat dissipation power of the cooling system is equal to the average battery temperature. The difference between the temperature at the coolant inlet and the total heat transfer resistance: ; in The coolant inlet temperature for the next time zone is the average value of the coolant inlet temperature for the current time zone. For the corresponding flow rate m and average battery temperature The cooling system's heat dissipation power.

[0027] Extract the predicted ambient air temperature at the i-th time step in the next time zone ; Obtained through short-term forecasts from weather stations; if no forecast data is available, the average ambient temperature of the current time zone is used.

[0028] Based on the steady-state approximation of thermal equilibrium (average heat generation rate = average total heat dissipation rate), the explicit function of battery pack average temperature versus flow rate is derived: ; The average temperature of a battery is determined by the heat generation capacity, the heat dissipation capacity of the cooling system, and the heat dissipation capacity of the environment.

[0029] In the formula This represents the average steady-state temperature of all cells in the battery pack in the next time zone when the cooling flow rate is m. This represents the average heat production rate for the next time zone.

[0030] The relationship between cooling flow rate and the battery's maximum temperature and temperature difference is established, as follows: ; It represents the temperature difference between the highest-temperature and lowest-temperature cells in the battery pack in the next time zone when the cooling flow rate is m, directly reflecting the battery consistency and cooling uniformity. The measured temperature difference of a single cell at the end of the current time zone; the actual temperature difference of a single cell in the battery pack at the end of the previous control cycle is the predicted baseline value, reflecting the degree of unevenness in the current temperature distribution. Rated charge and discharge power of the battery pack; the maximum power that the battery pack can continuously output / input under standard operating conditions, which is the rated reference parameter of the battery system; The rated flow rate of the cooling system; the maximum mass flow rate that the cooling pump can provide at its rated speed is the rated reference parameter of the cooling system. To clarify, 0.4 and 0.3 are the preset heat generation power influence index and cooling flow rate influence index, respectively. Both are empirically fitted values, reflecting the degree of influence of heat generation power on temperature difference and the degree of improvement of temperature difference by flow rate.

[0031] ; Given a cooling flow rate m, the temperature of the hottest cell in the battery pack in the next time zone is the first core indicator for battery thermal safety control, directly determining whether over-temperature protection is triggered. The set temperature offset correction factor; Additional explanation: Temperature deviation correction factor: reflects the asymmetry of temperature distribution within the battery pack; if the temperature is perfectly symmetrical (the highest / lowest temperature is equidistant from the average temperature), the value is 1. If the offset on the high-temperature side is greater (e.g., the upper part of the battery pack is generally hotter), the value is greater than 1; if the offset on the low-temperature side is greater, the value is less than 1. ;in and The highest and lowest measured temperatures at the end of the current time zone are respectively compared to the actual temperature of the highest-temperature cell in the battery pack at the end of the previous control cycle, and the average actual temperature of all cells at the end of the previous control cycle.

[0032] Establish the relationship between cooling flow rate and system energy consumption, which is reflected in: cooling pump shaft power. ; For the cooling pump shaft power, given a mass flow rate m, the mechanical power output by the centrifugal pump shaft is a monotonically increasing function of the flow rate m. , , , The preset cooling pump power fitting coefficient is determined by the performance curve provided by the manufacturer or by offline experiments. Total energy consumption of cooling system in the next time zone ;in Time zone length; Construct a constrained multi-objective optimization function; The temperature safety target is to keep the average battery temperature close to the optimal operating temperature while avoiding exceeding the maximum temperature limit. Temperature safety target ; Constraints ; The preset optimal operating temperature for the battery, based on the battery type; and These are the maximum and minimum allowable operating temperatures, respectively.

[0033] The system energy consumption target is to minimize the energy consumption of the cooling system. ; This represents the total energy consumption in the time zone under maximum flow. ; The goal of battery consistency is to minimize the temperature difference between individual battery cells. Battery consistency target ; This is the preset maximum allowable temperature difference; The objectives are weighted and fused together using a pre-defined set of weights to output a multi-objective optimization function. ; Right now ; , as well as These are the preset weighting coefficients.

[0034] Additionally, all candidate flows must satisfy the following constraints; otherwise, they will be directly deemed invalid solutions: Flow physical constraints: ; and These are the minimum allowable flow rate and the maximum allowable flow rate of the cooling pump, respectively. Pump power constraints: ; The rated power of the cooling pump is provided by the manufacturer. Temperature hard constraint: ; This is the absolute minimum safe temperature.

[0035] Extract the cooling flow rate that is lower than the preset function reference value in the multi-objective optimization function, find the cooling flow rate with the minimum function value as the optimal mass flow rate, and convert it into a cooling pump speed command; Example 2 Based on the integrated thermal management method for energy storage batteries provided in Embodiment 1 of this application, Embodiment 2 of this application proposes an integrated thermal management system for energy storage batteries. Embodiment 2 is merely a preferred embodiment of Embodiment 1, and its implementation will not affect the individual implementation of Embodiment 1.

[0036] Specifically, Embodiment 2 of this application provides an integrated thermal management system for energy storage batteries, comprising: The data acquisition module is used to collect battery body parameters, thermal management system parameters, environmental boundary parameters, and future charge and discharge plan parameters in real time, and to perform outlier removal and noise filtering preprocessing on the collected data. The thermal balance determination module is used to calculate the total heat generation power and actual heat dissipation power of the battery based on the collected parameters of the current time zone. The thermal balance state is determined by the difference between the average heat generation rate and the average heat dissipation rate. The thermal balance state includes heat accumulation state, heat loss state and stable state. The multi-objective optimization module is used to analyze the relationship between cooling flow rate and heat dissipation power, battery temperature and system energy consumption based on a determined thermal equilibrium state and the predicted heat generation rate in the next time zone charge and discharge plan. It constructs a constrained multi-objective optimization function to solve for the optimal cooling mass flow rate in the next time zone. The execution control module is used to convert the optimal cooling mass flow rate obtained from the solution into a cooling pump speed command to drive the cooling system to operate.

[0037] The above formulas are all dimensionless calculations. Dimensionless calculations can be performed using various methods such as standardization, which will not be elaborated here. The formulas are derived from software simulations based on a large amount of collected data, and the preset parameters in the formulas can be set by those skilled in the art according to the actual situation.

[0038] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0039] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0040] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0041] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0042] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0043] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0044] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable ATA hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0045] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An integrated thermal management method for energy storage batteries, characterized in that, include: S1: Real-time acquisition of battery body parameters, thermal management system parameters, environmental boundary parameters, and future charge / discharge plan parameters; S2: Calculate the total heat generation power and actual heat dissipation power of the battery based on the current time zone parameters, and determine the thermal balance state; the thermal balance state includes the heat accumulation state and the heat dissipation state. S3: Based on the determined thermal equilibrium state, combined with the predicted heat generation rate of the next time zone charge and discharge plan, analyze the correlation between cooling flow rate and heat dissipation power, battery temperature and system energy consumption, establish a multi-objective optimization function, and solve for the optimal cooling flow rate in the next time zone.

2. The integrated thermal management method for energy storage batteries according to claim 1, characterized in that: Battery parameters: real-time charge and discharge current, cell voltage, state of charge, and all cell temperatures collected by the NTC temperature sensor array, and the average cell temperature, maximum cell temperature, minimum cell temperature and temperature difference are calculated. Thermal management system parameters: cooling medium mass flow rate, coolant inlet temperature, coolant outlet temperature, heat exchanger inlet and outlet temperatures, cooling pump speed and power; Environmental boundary parameters: ambient air temperature; Future charge / discharge plan parameters: Discrete sequence of charge / discharge power for the next pre-divided time zone.

3. The integrated thermal management method for energy storage batteries according to claim 2, characterized in that: Logic for calculating total battery heat generation power; The total heat generation power of the battery pack at time k ； in This represents the total discharge current of the battery pack at time k in the current time zone; Let k be the state of charge of the battery at time k. Let K be the average temperature of all cells in the battery pack at time k. This represents the DC internal resistance of the battery at time k; It is the heat of electrochemical reaction.

4. The integrated thermal management method for energy storage batteries according to claim 3, characterized in that: Logic for calculating actual heat dissipation power; Actual heat dissipation power of the battery pack at time k ; Let k be the cooling system heat dissipation power at time k; expressed by the formula ； Calculated; Where c is the specific heat capacity of the cooling medium; Let k be the actual mass flow rate of the cooling medium at time k. Let k be the temperature at which the coolant flows out of the battery pack. The temperature at which the coolant enters the battery pack at time k; Let K be the natural heat dissipation power at time k; expressed by the formula Calculated; Let k be the ambient air temperature inside the battery compartment. This is the total thermal resistance between the battery pack casing and the environment.

5. The integrated thermal management method for energy storage batteries according to claim 4, characterized in that: Logic for determining thermal equilibrium state; After averaging the total heat generation power and actual heat dissipation power of the battery at each time in the current time zone, we obtain the average heat generation rate and the average heat dissipation rate. The difference between the heat production rate and the heat dissipation rate is used to obtain the heat balance determination value; If the thermal balance judgment value is higher than the preset judgment range, the thermal balance state is judged to be a state of heat accumulation; otherwise, the thermal balance state is judged to be a state of heat dissipation.

6. The integrated thermal management method for energy storage batteries according to claim 5, characterized in that: Logic for obtaining the average heat production rate predicted by the charging and discharging plan in the next time zone; The charging and discharging power plan for the next time zone is a predetermined discrete sequence. N is / t, t is the preset discrete time step; Charge and discharge current sequence Calculated from the power plan: ;in The charging and discharging power for the i-th time step in the next time zone; Let be the average battery pack voltage at the i-th time step in the next time zone; using the state of charge (SOC) at the end of the current time zone as the initial value, predict the SOC for each time step in the next time zone based on the ampere-hour integration method: ； in This represents the battery's state of charge at the end of the current time zone. The nominal capacity of the battery pack; Based on the formula for calculating the total heat generation power of the battery in step S2, the predicted current, SOC and temperature sequence are substituted to calculate the heat generation power at each time step. The heat production rate is obtained by taking the arithmetic mean of the heat production power at all time steps in the next time zone.

7. The integrated thermal management method for energy storage batteries according to claim 6, characterized in that: The process of constructing temperature sequences; A temperature sequence is constructed using the average temperature of all cells in the battery pack at each time in the current time zone. The temperature change trend over time is fitted, and the temperature-time linear equation is fitted using the least squares method. Extrapolate the fitted linear trend to the next time zone to obtain the initial predicted temperature for each time step in the next time zone.

8. The integrated thermal management method for energy storage batteries according to claim 7, characterized in that: Analyze the relationship between cooling flow rate and heat dissipation power, battery temperature, and system energy consumption; The convective heat transfer coefficient h between the battery pack and the coolant is established to satisfy a power-law relationship with the mass flow rate m of the cooling medium: ;where u1 and The preset empirical coefficient; The convective heat transfer coefficient is given by the flow rate m. The total heat transfer resistance between the battery pack and the coolant consists of convection thermal resistance and wall conductivity thermal resistance. Where A is the total effective heat exchange area between the battery pack and the coolant; The total thermal resistance between the battery pack wall and the cooling pipes; The total heat transfer resistance is given at the corresponding flow rate m. Based on the thermal resistance network, the heat dissipation power of the cooling system is equal to the average battery temperature. The difference between the temperature at the coolant inlet and the total heat transfer resistance: ;in This is the coolant inlet temperature for the next time zone. For the corresponding flow rate m and average battery temperature The cooling system's heat dissipation power is below; Extract the predicted ambient air temperature at the i-th time step in the next time zone ; Based on the thermal equilibrium steady-state approximation, the explicit function of battery pack average temperature versus flow rate is derived: ； In the formula This represents the average steady-state temperature of all cells in the battery pack in the next time zone when the cooling flow rate is m. The mean heat production rate for the next time zone; The relationship between cooling flow rate and the battery's maximum temperature and temperature difference is established, as follows: ； This represents the temperature difference between the hottest and coldest cells in the battery pack in the next time zone, given a cooling flow rate m. The measured temperature difference of the individual unit ends in the current time zone; Rated charge and discharge power of the battery pack; This is the rated flow rate of the cooling system; ; Given a cooling flow rate m, the temperature of the hottest cell in the battery pack in the next time zone; The set temperature offset correction factor; Establish the relationship between cooling flow rate and system energy consumption, which is reflected in: cooling pump shaft power. ； , , , The preset cooling pump power fitting coefficient; total energy consumption of the cooling system in the next time zone. ;in This represents the time zone length.

9. The integrated thermal management method for energy storage batteries according to claim 7, characterized in that: Construct a constrained multi-objective optimization function; The temperature safety target is to keep the average battery temperature close to the optimal operating temperature. Temperature safety target ； Constraints ； This is the preset optimal operating temperature for the battery; and These are the maximum and minimum allowable operating temperatures, respectively. The system energy consumption target is to minimize the energy consumption of the cooling system. ; This represents the total energy consumption in the time zone under maximum flow conditions. The goal of battery consistency is to minimize the temperature difference between individual battery cells. Battery consistency target ； This is the preset maximum allowable temperature difference; The objectives are weighted and fused together using a pre-defined set of weights to output a multi-objective optimization function. ; Extract the cooling flow rate that is lower than the preset function reference value of the multi-objective optimization function, find the cooling flow rate with the minimum function value as the optimal mass flow rate, and convert it into a cooling pump speed command.

10. An integrated thermal management system for energy storage batteries, applied to the integrated thermal management method for energy storage batteries according to any one of claims 1-9, characterized in that, include: The data acquisition module is used to collect battery body parameters, thermal management system parameters, environmental boundary parameters, and future charge and discharge plan parameters in real time. The thermal balance determination module is used to calculate the total heat generation power and actual heat dissipation power of the battery based on the collected parameters of the current time zone. The thermal balance state is determined by the difference between the average heat generation rate and the average heat dissipation rate. The thermal balance state includes heat accumulation state, heat loss state and stable state. The multi-objective optimization module is used to analyze the relationship between cooling flow rate and heat dissipation power, battery temperature and system energy consumption based on a determined thermal equilibrium state and the predicted heat generation rate in the next time zone charge and discharge plan. It constructs a constrained multi-objective optimization function to solve for the optimal cooling mass flow rate in the next time zone. The execution control module is used to convert the optimal cooling mass flow rate obtained from the solution into a cooling pump speed command to drive the cooling system to operate.

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