Energy consumption feedback type thermal management method for charge and discharge process of energy storage system

Abstract

The energy consumption feedback type thermal management method of the energy storage system charging and discharging process is disclosed, real-time acquisition of the energy storage system charging and discharging working condition, temperature and temperature control device operation data, correction of the battery dynamic internal resistance based on the cumulative cycle charging and discharging energy consumption, calculation of the real-time heat generation power combined with the corrected Bernardi model, prediction of the future temperature rising trend through the rolling time domain algorithm; with the temperature control precision as the constraint, the minimum energy consumption of the energy storage system as the target, the temperature control device feedforward optimization control instruction is generated. The present application solves the technical problems of temperature control response lag, insufficient heat generation calculation precision, non-implementation of charging and discharging energy consumption quantitative pre-control and non-optimization of temperature control itself energy consumption in the prior art, realizes the dynamic adaptation of the thermal management strategy and the charging and discharging energy consumption, and significantly improves the temperature control precision, operation energy efficiency, cycle life and safety performance of the energy storage system.

Description

Technical Field

[0001] This invention relates to an energy consumption feedback-based thermal management method for the charging and discharging process of an energy storage system. Background Technology

[0002] Existing thermal management controls for energy storage systems often rely solely on the measured temperature of individual battery cells / modules / clusters as the primary or core control feedback, employing a hysteretic on / off control logic of "temperature exceeding threshold - activation of temperature control device." For example, current energy storage battery thermal management methods adjust the power of the heat dissipation device based solely on the difference between the battery's highest temperature and a preset threshold, failing to predict heat generation trends during charging and discharging. Under conditions of high-rate charging and discharging and abrupt power changes, this easily leads to temperature overshoot and hysteretic temperature control response, resulting in reduced battery cycle life and increased risk of thermal runaway.

[0003] Some solutions incorporate battery SOC and charge / discharge rate as auxiliary control parameters, but they lack a quantitative mapping model between real-time charge / discharge power, cumulative energy consumption, battery heat generation rate, and temperature rise trend, making it impossible to achieve energy consumption-based front-end pre-control. Furthermore, existing technologies generally ignore the dynamic decay characteristics of battery internal resistance with cumulative charge / discharge energy consumption and cycle count, leading to large deviations in heat generation calculations and insufficient temperature control accuracy. In addition, existing technologies lack full lifecycle adaptability; the parameters of thermal management models are mostly calibrated once at the factory, without establishing a parameter iteration mechanism based on actual operating data. This fails to adapt to the aging characteristics of the battery throughout its entire lifecycle, resulting in a continuous deterioration of control performance in the later stages of system operation. Summary of the Invention

[0004] To address the technical problems in existing energy storage system thermal management, such as lag in temperature control response, poor strategy adaptability, and lack of quantitative feedback optimization of charging and discharging energy consumption, this invention provides an energy consumption feedback-based thermal management method for the charging and discharging process of an energy storage system. This method achieves dynamic adaptation between thermal management strategies and charging and discharging energy consumption, thereby improving the overall energy efficiency and cycle life of the system.

[0005] A method for energy consumption feedback-based thermal management during the charging and discharging process of an energy storage system includes the following steps:

[0006] S1. Data Acquisition: The initial calibration of the battery body parameters and thermal management system parameters of the energy storage system is completed in advance. Through the BMS (Battery Management System) and thermal management controller of the energy storage system, the charging and discharging condition data, battery temperature data, ambient temperature data and operating status data of the temperature control device of the energy storage system are collected in real time.

[0007] S2. Battery dynamic internal resistance correction: Based on the cumulative charge and discharge data collected in step S1, the cumulative cycle charge and discharge energy consumption of the battery throughout its entire life cycle is statistically analyzed. The battery internal resistance attenuation coefficient is calculated based on the cumulative cycle charge and discharge energy consumption. Combined with the battery nominal internal resistance calibrated in the initial step, the real-time dynamic internal resistance of the battery is calculated.

[0008] S3. Real-time heat generation power calculation: Based on the modified Bernardi battery heat generation model, combined with the charge and discharge condition data collected in real time in step S1 and the real-time dynamic internal resistance of the battery obtained in step S2, the real-time total heat generation power of the energy storage system is quantitatively calculated.

[0009] S4. Temperature rise prediction: Based on the real-time total heat generation power obtained in step S3, combined with the initialized and calibrated heat conduction model of the energy storage system, within the preset prediction time domain, the predicted battery temperature at each future time is calculated in a rolling manner, with the measured battery temperature at the current moment as the initial value.

[0010] S5. Control Decision: The predicted battery temperature values ​​at each future time obtained in step S4 are compared with the preset target battery temperature value and multi-level temperature control threshold. Based on the threshold range into which the predicted temperature value falls and the amount of temperature deviation, a temperature control strategy adapted to the charging and discharging conditions is generated and sent to the temperature control device for execution.

[0011] Furthermore, in step S1, the battery body parameters initialized and calibrated include: the battery nominal internal resistance under reference operating conditions, the battery open-circuit voltage temperature coefficient, the total mass of the battery system, the battery average constant-pressure specific heat capacity, and the total number of battery cells in the energy storage system.

[0012] The initial calibration parameters of the thermal management system include: the total heat exchange area of ​​the battery system, the mapping relationship between the convective heat transfer coefficient and the operating parameters of the temperature control device, and the rated power parameters of the temperature control device;

[0013] The data collected in real time specifically includes: charging and discharging condition data, including real-time charging and discharging power, charging and discharging current, real-time total system voltage, battery SOC, and cumulative cycle charging and discharging capacity; battery temperature data, including real-time temperature of individual cells / modules / clusters and average temperature of the battery system; ambient temperature data, including real-time ambient temperature of the energy storage compartment / container; and temperature control device operating status data, including fan speed, water pump flow rate, and real-time operating power.

[0014] Furthermore, in step S2, the formula for calculating the real-time dynamic internal resistance of the battery is:

[0015]

[0016] In the formula: The nominal internal resistance of the battery under reference operating conditions, in mΩ, is defined as SOC=50%, ambient temperature 25℃, and the battery is in brand new condition, as determined by the battery's factory parameters. The cumulative cycle energy consumption decay coefficient is used to characterize the internal resistance increase characteristics caused by battery cycle aging. This represents the cumulative charge-discharge energy consumption over the entire battery lifecycle up to the current moment, expressed in kWh, and is calculated by the BMS based on the cumulative integration of charge and discharge capacity.

[0017] Furthermore, the cumulative cycle energy consumption attenuation coefficient The polynomial model calibrated using the battery life cycle aging test is as follows:

[0018]

[0019] In the formula: a and b are battery aging calibration coefficients, which are obtained by fitting the battery's full life cycle charge and discharge test, and a>0 and b>0.

[0020] Furthermore, in step S3, the formula for calculating the real-time total heat production power of the energy storage system is:

[0021]

[0022] In the formula: This refers to the total number of individual battery cells in the energy storage system. The current is the real-time charge / discharge current, in amperes (A), and is derived from the real-time charge / discharge power. The conversion is calculated using the following formula: ,in This is the real-time total voltage of the energy storage system, in V, which is acquired in real time by the BMS. The real-time dynamic internal resistance of the battery is calculated in step S2. The measured average temperature of the battery system at the current moment, in °C; Temperature coefficient of battery open circuit voltage, in units of V / ℃, is calibrated by the battery manufacturer.

[0023] Furthermore, in step S4, the preset prediction time domain The value ranges from 5s to 30s. The rolling calculation formula for the predicted battery temperature is as follows:

[0024]

[0025] In the formula: , is used to predict the time step size in the time domain; , which is the measured average temperature of the battery system at the current moment, and is used as the initial value for rolling calculation; The system control step size, in seconds (s). The real-time total heat production power calculated in step S3; The system's heat dissipation power at the predicted time, in watts (W). The total mass of the battery system is expressed in kg. This represents the average constant-pressure specific heat capacity of the battery system, expressed in J / (kg·℃).

[0026] Furthermore, the formula for calculating the system heat dissipation power at the predicted time is as follows:

[0027]

[0028] In the formula: The convective heat transfer coefficient, in units of W / (m²·℃), is positively correlated with the fan speed / pump flow rate of the temperature control device and is obtained from calibration tests. The total heat exchange area of ​​the battery system is expressed in m². The current measured ambient temperature is expressed in °C.

[0029] Furthermore, in step S5, the preset multi-level temperature control threshold is divided into three control intervals, specifically:

[0030] Optimal operating range: battery temperature 20℃~35℃, corresponding to the basic operating strategy of the temperature control device;

[0031] Warning range: battery temperature 35℃~45℃, corresponding to the power boost feedforward strategy of the temperature control device;

[0032] Exceeding limits: Battery temperature ≥ 45℃, corresponding to the temperature control device operating at full power + charge / discharge power limiting protection strategy.

[0033] Furthermore, in step S5, the logic for generating the temperature control strategy is specifically as follows:

[0034] Extract the predicted maximum battery temperature within the prediction time domain and determine which threshold range it falls into;

[0035] When the highest temperature prediction value falls into the optimal operating range, the operating power of the temperature control device is linearly adjusted based on the deviation between the temperature prediction value and the temperature target value to maintain the battery temperature stable in the optimal range.

[0036] When the predicted maximum temperature falls into the warning range, a feedforward boosting command is generated to increase the operating power of the temperature control device in advance, so as to prevent the temperature from rising further.

[0037] When the predicted maximum temperature falls into the over-limit range, a full-power operation command is immediately generated, and a charging and discharging power limit signal is simultaneously sent to the BMS to trigger the safety protection strategy.

[0038] In step S5, when generating the temperature control strategy, a multi-objective optimization function is constructed to optimize and solve the temperature control strategy, with the constraint of achieving the battery temperature control accuracy and the optimization objective of minimizing the overall energy consumption of the energy storage system. The multi-objective optimization function is as follows:

[0039]

[0040] The constraints are: ,

[0041] In the formula: To optimize the target value; This is a predicted value for the highest battery temperature in the time domain; This is the preset target value for battery temperature; This refers to the operating power of the temperature control device; , These are the temperature deviation weighting coefficient and the temperature control energy consumption weighting coefficient, respectively, which can be dynamically adjusted according to the charging and discharging conditions; , These are the minimum and maximum allowable operating temperatures of the battery, respectively. This is the rated maximum power of the temperature control device.

[0042] Simultaneously, after the temperature control strategy is issued in step S5, a verification step is also included: real-time collection of battery temperature data and energy consumption data after the temperature control device is executed, comparison of the deviation between the measured battery temperature and the predicted temperature, and the deviation between the actual energy consumption of the temperature control device and the optimized target energy consumption; when the temperature deviation exceeds the preset deviation threshold, the model parameter correction is triggered; when the energy consumption deviation exceeds the preset threshold, the optimization solution is re-executed and the temperature control strategy is updated.

[0043] Beneficial effects:

[0044] 1. Feedforward thermal management control is realized. This invention uses charging and discharging energy consumption as the core feedback quantity and establishes a full-link quantitative mapping model of "energy consumption-internal resistance-heat generation-temperature rise". By using a rolling time domain algorithm to predict the future temperature rise trend in advance, feedforward control commands are generated based on the prediction results. Compared with the traditional "overthreshold-start" lag control, the temperature control response speed is improved by more than 80%, and the temperature overshoot under high-rate charging and discharging conditions is reduced by more than 90%, which greatly reduces the risk of thermal runaway.

[0045] 2. Significantly improves the accuracy of heat generation calculation. This invention introduces the cumulative cycle charge and discharge energy consumption to dynamically correct the battery internal resistance, accurately characterizing the internal resistance growth characteristics caused by battery cycle aging. Compared with the heat generation calculation model with fixed internal resistance, the heat generation calculation deviation is controlled within 5% throughout the entire life cycle, significantly improving the accuracy of temperature control strategy. Attached Figure Description

[0046] Figure 1 This is a schematic diagram showing the connections of each operating module in Implementation Example 1;

[0047] Figure 2 This is a comparison table of the temperature control effects and performance of the two embodiments;

[0048] Figure 3 These are the real-time temperature control curves of Examples 1 and 2 and the comparative example within an 18-hour operating cycle. Detailed Implementation

[0049] To enhance understanding of the present invention, the present invention will be further described in detail below with reference to embodiments and accompanying drawings. These embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0050] Example 1: The basic configuration of the energy storage system used in this example is as follows:

[0051] Battery cell: Commercial 280Ah square lithium iron phosphate cell, nominal voltage 3.2V, nominal internal resistance under reference operating conditions (SOC=50%, 25℃, brand new condition). Open-circuit voltage temperature coefficient The weight of a single unit is 5.2 kg.

[0052] System architecture: 1P240 series, total number of battery cells The system has a nominal voltage of 768V, a rated charge / discharge power of 200kW, and a total battery system mass of [missing information]. Average constant-voltage specific heat capacity of battery ;

[0053] Thermal management system: Forced air cooling system, total heat exchange area of ​​battery pack Calibrate the mapping relationship between convective heat transfer coefficient and fan speed (30% of rated speed corresponds to) 50% of rated speed corresponds to 100% of rated speed corresponds to The temperature-controlled fan has a rated total power of 2.5kW and a maximum heat dissipation capacity of 8kW.

[0054] Control parameters: System control step size Predicting the time domain Target temperature of the battery Three-level temperature control thresholds: optimal operating range 20℃-35℃, warning range 35℃-45℃, and over-limit range ≥45℃.

[0055] This embodiment describes the 1C rated power (200kW) constant current discharge condition under daily peak shaving and valley filling scenarios of an energy storage system. The process strictly follows steps S1-S5 of claim 1, as detailed below:

[0056] S1, Data Acquisition

[0057] After completing the system parameter initialization and calibration, the operating data is collected in real time through the BMS and thermal management controller:

[0058] Charge / discharge status data: Currently in 1C constant current discharge mode, real-time charge / discharge power. Real-time total voltage of the system Battery SOC=58%, total energy consumption over the entire life cycle (cumulative charge / discharge cycles) ;

[0059] Temperature data: Actual measured average temperature of the battery system Energy storage compartment ambient temperature ;

[0060] Temperature control device data: The current fan operating speed is 30% of the rated speed, and the real-time operating power is 0.75kW.

[0061] S2, Battery Dynamic Internal Resistance Correction

[0062] The polynomial parameters of the internal resistance decay coefficient were obtained through full life cycle aging tests of the battery. , First, calculate the internal resistance attenuation coefficient corresponding to the cumulative cycle energy consumption: Calculate the battery's real-time dynamic internal resistance based on the nominal internal resistance: The total internal resistance of the system is This aligns with the actual internal resistance change pattern after battery cycle aging.

[0063] S3, Real-time heat generation power calculation

[0064] Calculate the charging and discharging current based on the real-time charging and discharging power: Based on the modified Bernardi battery heat generation model, the real-time total heat generation power of the energy storage system is calculated: Substitute the numerical values ​​into the calculation:

[0065] Joule fever (irreversible heat):

[0066] Reversible heat:

[0067] Real-time total heat production capacity: The heat generation value is consistent with the 95%~96% charge and discharge efficiency of lithium iron phosphate energy storage systems, and is completely matched with the actual operation data of the project.

[0068] S4, Temperature Rise Forecast

[0069] Based on the convective heat transfer coefficient corresponding to the current fan speed Calculate the current heat dissipation power: Using the current measured average temperature of 30℃ as the initial value, the predicted battery temperature is calculated over a 10-second prediction time domain. The core calculation formula is as follows: Taking k=1 (after 1 second) as an example, the calculation yields: By sequentially performing rolling calculations within the 10-second time domain, the predicted value of the battery's highest temperature within the prediction time domain is finally obtained. It is still in the optimal working range.

[0070] S5, Control Decision

[0071] Comparing the predicted temperature with the preset target temperature and control threshold, the predicted temperature of 30.021℃ is within the optimal operating range, with a deviation of 5.021℃ from the target temperature of 25℃. Taking "achieving the required temperature control accuracy as a constraint and minimizing the overall system energy consumption" as the objectives, a multi-objective optimization function is constructed, and a temperature deviation weighting coefficient is set. Temperature control energy consumption weighting coefficient The constraints are battery temperature ≤35℃ and fan power ≤2.5kW. The optimal temperature control strategy is obtained through quadratic programming: maintain the fan at 30% of its rated speed, corresponding to an operating power of 0.75kW. The control command is sent to the temperature control device for execution. Monitoring data for 10 consecutive minutes shows that the actual battery operating temperature is stable between 29.8℃ and 30.3℃, with a deviation from the predicted value of ≤0.3℃, always within the optimal operating range. The energy consumption of the temperature control system is stable at 0.75kW, achieving a balance between temperature control and energy consumption optimization, and fully meeting the daily operation requirements.

[0072] Example 2: Power Step Frequency Modulation Condition

[0073] This embodiment illustrates a primary frequency regulation scenario for the power grid, where the energy storage system's charging and discharging power jumps from 0.5C (100kW) to 2C (400kW). The specific steps are as follows:

[0074] S1, Data Acquisition

[0075] Real-time acquisition of operating condition data after a step jump:

[0076] Charge and discharge operating data: Charge and discharge power jumps from 100kW to 400kW, real-time power Real-time total voltage of the system Cumulative cycle charge and discharge energy consumption ;

[0077] Temperature data: Measured average temperature of the battery before the step change Energy storage compartment ambient temperature ;

[0078] Temperature control device data: The operating speed of the fan before the step is 30% of the rated speed, and the real-time operating power is 0.75kW.

[0079] S2, Battery Dynamic Internal Resistance Correction

[0080] Similar to Example 1, the real-time dynamic internal resistance of the battery was calculated. .

[0081] S3, Real-time heat generation power calculation

[0082] Calculate the real-time charge and discharge current after the step change: Real-time total heat production power was calculated based on the modified Bernardi model:

[0083] Joule fever:

[0084] Reversible heat:

[0085] Real-time total heat production capacity: This value conforms to the actual heat generation law under 2C high-rate charge and discharge, and matches the battery charge and discharge efficiency characteristics.

[0086] S4, Temperature Rise Forecast

[0087] Set the prediction time domain Based on the heat dissipation power corresponding to 30% of the current fan speed The predicted temperature is calculated over a 10-second period, and the highest predicted battery temperature within the predicted time domain is finally obtained. It is still in the optimal operating range, but continued operation will quickly climb to the warning range.

[0088] S5, Control Decision

[0089] Comparing the predicted temperature with the preset threshold, although the current predicted temperature is within the optimal range, the heat generated under high-rate continuous discharge has far exceeded the current heat dissipation capacity. To prevent the temperature from rising, a feedforward temperature control strategy is generated:

[0090] Construct an optimization function and dynamically adjust the weight coefficients. , Prioritize ensuring temperature control stability;

[0091] The optimal control command obtained is: the fan speed is increased to 80% of the rated speed, corresponding to an operating power of 2kW, at which point the heat dissipation power is increased to... This can effectively suppress temperature rise;

[0092] After the command was issued and executed, monitoring data for 18 consecutive hours showed that the highest battery temperature did not exceed 34.8℃, never entered the warning range, there was no temperature overshoot problem, and the temperature control response was lag-free under high-rate conditions, effectively ensuring the battery cycle life and operational safety.

[0093] Comparative Example: The hardware configuration, battery parameters, thermal management system rated parameters, preset three-level temperature control thresholds, temperature control device performance parameters, and operating environment conditions of the energy storage system used in this comparative example are completely consistent with those of Example 2.

[0094] The temperature control system adopts a conventional control scheme commonly used in the energy storage field: it uses only the measured average temperature of the battery module collected in real time by the BMS as the sole control feedback quantity, and employs the control logic of "measured temperature exceeds threshold - adjust the power of the temperature control device". It directly adjusts the operating power of the cooling fan linearly based on the comparison between the real-time measured temperature and the preset threshold, without any heat generation trend prediction or front-end pre-control. The rest is the same as in Example 2.

[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An energy consumption feedback-based thermal management method for the charging and discharging process of an energy storage system, characterized in that, Includes the following steps: S1. Data Acquisition: Complete the initialization and calibration of the battery body parameters and thermal management system parameters of the energy storage system, and collect the charging and discharging condition data, battery temperature data, ambient temperature data and operating status data of the temperature control device in real time. S2. Battery dynamic internal resistance correction: The internal resistance attenuation coefficient is calculated based on the cumulative cycle charge and discharge energy consumption of the battery throughout its entire life cycle, and the real-time dynamic internal resistance is obtained by combining it with the battery's nominal internal resistance. S3. Real-time heat generation power calculation: Based on the Bernardi battery heat generation model, combined with real-time charge and discharge condition data and dynamic internal resistance, the real-time heat generation power of the energy storage system is calculated. S4. Temperature rise prediction: Based on real-time heat generation power and combined with the heat conduction model of the energy storage system, the predicted battery temperature value is calculated in a rolling manner at each future moment within the preset prediction time domain. S5. Control Decision: The predicted battery temperature values ​​at each future time obtained in step S4 are compared with the preset target battery temperature value and multi-level temperature control threshold. Based on the threshold range into which the predicted temperature value falls and the amount of temperature deviation, a temperature control strategy adapted to the charging and discharging conditions is generated and sent to the temperature control device for execution.

2. The energy consumption feedback thermal management method for the charging and discharging process of an energy storage system according to claim 1, characterized in that, In step S1, the battery body parameters initialized and calibrated include: the battery nominal internal resistance under reference operating conditions, the battery open-circuit voltage temperature coefficient, the total mass of the battery system, the battery average constant-pressure specific heat capacity, and the total number of battery cells in the energy storage system. The initial calibration parameters of the thermal management system include: the total heat exchange area of ​​the battery system, the mapping relationship between the convective heat transfer coefficient and the operating parameters of the temperature control device, and the rated power parameters of the temperature control device; The data collected in real time specifically includes: charging and discharging condition data, including real-time charging and discharging power, charging and discharging current, real-time total system voltage, battery SOC, and cumulative cycle charging and discharging capacity; battery temperature data, including real-time temperature of individual cells / modules / clusters and average temperature of the battery system; ambient temperature data, including real-time ambient temperature of the energy storage compartment / container; and temperature control device operating status data, including fan speed, water pump flow rate, and real-time operating power.

3. The energy consumption feedback thermal management method for the charging and discharging process of an energy storage system according to claim 1, characterized in that, In step S2, the formula for calculating the real-time dynamic internal resistance of the battery is: In the formula: The nominal internal resistance of the battery under reference operating conditions, in mΩ, is defined as SOC=50% and ambient temperature 25℃, as determined by the battery's factory parameters. The cumulative cycle energy consumption decay coefficient is used to characterize the internal resistance increase characteristics caused by battery cycle aging. This represents the cumulative charge-discharge energy consumption over the entire battery lifecycle up to the current moment, expressed in kWh, and is calculated by the BMS based on the cumulative integration of charge and discharge capacity.

4. The energy consumption feedback thermal management method for the charging and discharging process of an energy storage system according to claim 3, characterized in that, The cumulative cycle energy consumption attenuation coefficient The polynomial model calibrated using the battery life cycle aging test is as follows: In the formula: a and b are battery aging calibration coefficients, which are obtained by fitting the battery's full life cycle charge and discharge test, and a>0 and b>0.

5. The energy consumption feedback thermal management method for the charging and discharging process of an energy storage system according to claim 1, characterized in that, In step S3, the formula for calculating the real-time total heat production power of the energy storage system is: In the formula: This refers to the total number of individual battery cells in the energy storage system. The current is the real-time charge / discharge current, in amperes (A), and is derived from the real-time charge / discharge power. The conversion is calculated using the following formula: ; in This is the real-time total voltage of the energy storage system, in V, which is acquired in real time by the BMS. The real-time dynamic internal resistance of the battery is calculated in step S2. The measured average temperature of the battery system at the current moment, in °C; Temperature coefficient of battery open-circuit voltage, in V / ℃.

6. The energy consumption feedback thermal management method for the charging and discharging process of an energy storage system according to claim 1, characterized in that, In step S4, the preset prediction time domain The value ranges from 5s to 30s. The rolling calculation formula for the predicted battery temperature is as follows: In the formula: , is used to predict the time step size in the time domain; , which is the measured average temperature of the battery system at the current moment, and is used as the initial value for rolling calculation; The system control step size, in seconds (s). The real-time total heat production power calculated in step S3; The system's heat dissipation power at the predicted time, in watts (W). The total mass of the battery system is expressed in kg. This represents the average constant-pressure specific heat capacity of the battery system, expressed in J / (kg·℃).

7. The energy consumption feedback thermal management method for the charging and discharging process of an energy storage system according to claim 6, characterized in that, The formula for calculating the system heat dissipation power at the predicted time is: In the formula: The convective heat transfer coefficient, in units of W / (m²·℃), is positively correlated with the fan speed / pump flow rate of the temperature control device and is obtained from calibration tests. The total heat exchange area of ​​the battery system is expressed in m². The current measured ambient temperature is expressed in °C.

8. The energy consumption feedback thermal management method for the charging and discharging process of an energy storage system according to claim 1, characterized in that, In step S5, the preset multi-level temperature control threshold is divided into three control intervals, specifically: Optimal operating range: battery temperature 20℃~35℃, corresponding to the basic operating strategy of the temperature control device; Warning range: battery temperature 35℃~45℃, corresponding to the power boost feedforward strategy of the temperature control device; Exceeding limits: Battery temperature ≥ 45℃, corresponding to the temperature control device operating at full power + charge / discharge power limiting protection strategy.

9. The energy consumption feedback thermal management method for the charging and discharging process of an energy storage system according to claim 8, characterized in that, In step S5, the specific logic for generating the temperature control strategy is as follows: Extract the predicted maximum battery temperature within the prediction time domain and determine which threshold range it falls into; When the highest temperature prediction value falls into the optimal operating range, the operating power of the temperature control device is linearly adjusted based on the deviation between the temperature prediction value and the temperature target value to maintain the battery temperature stable in the optimal range. When the predicted maximum temperature falls into the warning range, a feedforward boosting command is generated to increase the operating power of the temperature control device in advance, so as to prevent the temperature from rising further. When the predicted maximum temperature falls into the over-limit range, a full-power operation command is immediately generated, and a charging and discharging power limit signal is simultaneously sent to the BMS to trigger the safety protection strategy.

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