Battery cooling control system and method based on multi-parameter recursive optimization

Abstract

The invention discloses a multi-parameter recursive optimization battery cooling control system and method. The method comprises the following steps: acquiring basic parameters of a battery cooling system at an initial moment of a current period; establishing a battery heat production and exchange model, a compressor refrigeration model and a compressor energy consumption model; according to the cooling system basic parameters, the battery heat production model, the compressor refrigeration model and the compressor energy consumption model, the compressor rotating speed and the water pump rotating speed in the next period are determined; and controlling the compressor and the water pump to work based on the compressor rotating speed and the water pump rotating speed in the next period to control the battery temperature. The method has the advantages that the dynamic adaptability of the battery cooling system is improved, the energy efficiency ratio is optimized, and the service life of the battery is prolonged.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of battery cooling control, and particularly relates to a battery cooling control system and method with multi-parameter recursive optimization. BACKGROUND

[0002] As a core energy component of electric vehicles, the working temperature of a battery directly affects performance, service life and safety. In the charging and discharging process, the heat generation of the battery is affected by dynamic factors such as current, ambient temperature and aging degree. The traditional cooling control method has obvious limitations: the traditional fixed-parameter control model relies too much on model preset parameters. When the internal resistance of the battery changes with the number of uses, the heat generation characteristics also change, and the original model preset parameters cannot adapt to such changes. The parameter correction period of the feedforward compensation control is long, and it is difficult to adapt to the rapidly changing heat generation characteristics. Threshold control lacks dynamic adjustment capability and is prone to cooling lag or excessive cooling.

[0003] The core problem of the prior art is that the optimization process of the control strategy and the dynamic adaptability to real-time working conditions are insufficient. For example, battery aging can change the internal resistance and thus change the heat generation law. The fixed-parameter control model cannot keep up with such changes in real time. Under complex working conditions (such as sudden acceleration of an electric vehicle or sudden load of a energy storage station), the heat generation rate changes suddenly, and the single optimization result of the traditional control cannot be continuously adapted, and frequent re-optimization is required, resulting in control delay.

[0004] In view of the above problems, the prior art needs to be improved. SUMMARY

[0005] The purpose of the present application is to solve the problems in the background art, and to provide a battery cooling control system and method with multi-parameter recursive optimization, which has the advantages of improving the dynamic adaptability of the battery cooling system, optimizing the energy efficiency ratio and prolonging the service life of the battery.

[0006] The technical solution adopted by the present application is: a battery cooling control method with multi-parameter recursive optimization, obtaining the battery cooling system basic parameters at the initial time of the current period; establishing a battery heat generation and exchange model, a compressor refrigeration model and a compressor energy consumption model; determining the compressor speed and the water pump speed of the next period according to the cooling system basic parameters, the battery heat generation model, the compressor refrigeration model and the compressor energy consumption model; controlling the compressor and the water pump to work based on the compressor speed and the water pump speed of the next period, so as to control the battery temperature.

[0007] Further, the battery cooling system basic parameters include battery temperature, SOC, SOH, battery charge and discharge current, battery terminal voltage, battery system water inlet temperature, battery system water outlet temperature, refrigerant high pressure, refrigerant low pressure and ambient temperature.

[0008] Further, the battery heat generation and exchange model is: Q _batt = I _batt ×(U _batt -E _batt )+ I _batt 2 ×R _batt ; Q _pump = h1×A1×(T _batt -T _watter_in ); Wherein, Q _batt is the battery system heat generation rate; I _batt is the battery charge and discharge current; U _batt is the battery terminal voltage; E _batt is the battery electromotive force; R _batt is the battery internal resistance; Q _pump is the heat exchange rate between the battery system and the cooling liquid; h1 is the convective heat transfer coefficient between the battery system and the cooling medium; A1 is the contact area between the battery system and the cooling medium; T _batt is the battery temperature; T _watter_in is the battery system water inlet temperature.

[0009] Further, the compressor refrigeration model is: Q _comp =k1×S _comp_Q ×Δh×η v ×η ad Wherein, Q _comp is the compressor refrigeration rate; k1 is the compressor displacement coefficient; S _comp_Q is the compressor refrigeration model corresponding to the compressor speed; Δh is the enthalpy difference of the refrigerant in the battery cooler; η v is the volumetric efficiency; η ad is the adiabatic efficiency.

[0010] Further, the compressor energy consumption model is: P _ comp =a×S _comp_P 2 +b×S _comp_P +c; Wherein, P _ comp is the compressor energy consumption; a is the first nonlinear coefficient; b is the second nonlinear coefficient; c is the constant term; S_comp_P is the compressor speed corresponding to the compressor energy consumption model of the n+1th period.

[0011] Further, the compressor speed of the next period is determined by the following formula: S _comp (n+1) = ω1×S _comp_Q (n+1) + ω2×S _comp_P (n+1) ; wherein, S _comp (n+1) is the compressor speed of the n+1th period; ω1 is the refrigeration weight coefficient, ω2 is the energy consumption weight coefficient, and ω1 + ω2 = 1; S _comp_Q (n+1) is the compressor speed corresponding to the compressor refrigeration model of the n+1th period; S _comp_P (n+1) is the compressor speed corresponding to the compressor energy consumption model of the n+1th period.

[0012] Further, the water pump speed of the next period is determined by the following formula: S _pump (n+1) = u×m×S _pump (n) ; u = Q _batt (n) / Q _pump (n) ; wherein, S _pump (n+1) is the water pump speed of the n+1th period; u is the dynamic balance coefficient; m is the water pump speed correction constant; S _pump (n) is the water pump speed of the nth period; Q _batt (n) is the heat generation rate of the battery system of the nth period; Q _pump (n) is the heat exchange rate between the battery system and the cooling liquid of the nth period.

[0013] Further, the determined compressor speed is corrected based on the battery inlet water temperature, and the compressor is controlled to work at the corrected compressor speed.

[0014] Further, the corrected compressor speed is determined by the following formula: S _comp_c (n+1) = k s ×T _watter_in / T _watter_standard ×S _comp (n+1) wherein, S _comp_c (n+1) is the corrected compressor speed of the n+1th period; k s is the compressor speed correction coefficient; T _watter_in is the battery system inlet water temperature; T _watter_standard is the ideal preset temperature of the battery system; S_comp (n+1) is the compressor speed of the n+1 period.

[0015] A battery cooling control system with multi-parameter recursive optimization, comprising: A cooling liquid circulation loop for controlling the cooling liquid circulation flow to exchange heat with the refrigerant to dissipate heat from the battery system based on the speed instruction; A refrigerant loop for controlling the refrigerant flow to exchange heat with the cooling liquid based on the speed instruction; A controller for obtaining the basic parameters of the cooling liquid circulation loop and the refrigerant loop in the current period, determining the compressor speed and the water pump speed in the next period according to the basic parameters, the battery heat generation model, the compressor refrigeration model and the compressor energy consumption model, and sending the speed instructions to the compressor and the water pump according to the determined speed.

[0016] The beneficial effects of the present application are: The present application establishes a multi-parameter dynamic model and uses a recursive optimization algorithm to adjust the compressor and water pump speeds in real time, optimizes the energy consumption distribution while ensuring cooling efficiency, solves the technical problem of poor dynamic adaptability of the traditional fixed parameter control strategy, and has the advantages of improving the dynamic response capability of the battery cooling system, optimizing the energy efficiency ratio, and prolonging the service life of the battery. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The schematic diagram of the battery cooling control system of the present application.

[0018] Figure 2 The flowchart of the battery cooling control method of the present application. DETAILED DESCRIPTION

[0019] The specific embodiments of the present application will be further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present application, but does not constitute a limitation on the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as there is no conflict.

[0020] In one embodiment, as Figure 1As shown, the application provides a multi-parameter recursive optimization battery cooling control system, including a cooling liquid circulation loop, a refrigerant circulation loop and a controller (not shown in the figure). The cooling liquid circulation loop is used to control the cooling liquid circulation flow and the heat exchange with the refrigerant to cool the battery system based on the speed instruction; the refrigerant loop is used to control the refrigerant flow and the heat exchange with the cooling liquid based on the speed instruction; the controller is used to obtain the basic parameters of the cooling liquid circulation loop and the refrigerant loop in the current period, to determine the compressor speed and the water pump speed in the next period according to the basic parameters, the battery heat production model, the compressor refrigeration model and the compressor energy consumption model, and to send the speed instruction to the compressor and the water pump according to the determined speed.

[0021] The application combines the double circulation architecture of the cooling liquid circulation loop and the refrigerant loop, and introduces the multi-parameter recursive optimization mechanism of the controller, the whole process constitutes the recursive cycle of "perception → prediction → decision → execution → re-perception", so as to realize the dynamic closed-loop adjustment of the cooling system parameters, and solve the model mismatch problem caused by the battery aging or the working condition change in the traditional fixed parameter control. Specifically, the design realizes the accurate control of the battery temperature while taking into account the energy consumption optimization effect by real-time acquisition of the basic parameters such as battery temperature, SOC, SOH, etc., and combining the battery heat production model, the compressor refrigeration model and the compressor energy consumption model, to form a trade-off mechanism between refrigeration efficiency and energy consumption.

[0022] It can be understood that at the initial moment of each control period, a plurality of basic parameters of the battery cooling system are obtained by sensors or data acquisition devices, including battery temperature, SOC, SOH, battery charge and discharge current, battery terminal voltage, battery system water inlet temperature, battery system water outlet temperature, refrigerant high pressure, refrigerant low pressure and ambient temperature. These parameters comprehensively reflect the current heat production capacity, aging state and external environment influence of the battery, providing necessary input data for subsequent model calculation. On this basis, the battery heat production and exchange model, the compressor refrigeration model and the compressor energy consumption model are established respectively. Among them, the battery heat production and exchange model combines the electrochemical characteristics and thermodynamic characteristics of the battery, and can adjust the heat production calculation result in real time according to the dynamic factors such as SOC, SOH and battery temperature, so as to adapt to the internal resistance change caused by battery aging. The compressor refrigeration model decouples the mechanical characteristics and refrigeration capacity through enthalpy difference calculation and efficiency coefficient correlation, ensuring accurate prediction of refrigeration capacity. The compressor energy consumption model adopts a quadratic function form to represent the nonlinear relationship between speed and energy consumption, providing mathematical support for subsequent optimization.

[0023] Specifically, according to the cooling system basic parameters and the three models established, the compressor speed and the water pump speed in the next cycle are determined. In this process, the required refrigerating capacity in the next cycle is predicted by the battery heat production model, and the corresponding compressor speed is calculated by combining the compressor refrigeration model. At the same time, the energy consumption at different speeds is evaluated by using the compressor energy consumption model, forming a trade-off mechanism between refrigeration efficiency and energy consumption. When the battery temperature is higher than the set value, the refrigeration weight is preferentially improved to meet the cooling demand; while in the case of low remaining battery power, the relationship between refrigeration and energy consumption is balanced to avoid excessive energy consumption. In addition, the determination of the water pump speed is based on the design of the dynamic balance coefficient, which compares the battery system heat production rate and the cooling liquid heat exchange rate in real time, and dynamically adjusts the water pump speed to make the cooling liquid flow and heat production change synchronous matching. This design effectively solves the problem of heat exchange lag in the traditional fixed proportion control under the condition of sudden acceleration and the like.

[0024] Therefore, based on the determined compressor speed and water pump speed in the next cycle, control instructions are sent to the compressor and water pump to adjust their working states, thereby realizing precise control of the battery temperature. The whole technical scheme forms a dynamic cooling control closed loop through multi-model collaborative modeling and recursive optimization mechanism, so that the cooling system parameters can be continuously self-adaptive adjusted with the change of working conditions, solving the problem of model parameter mismatch caused by the change of battery heat production characteristics and aging, and overcoming the defects of response lag and inability to continuously adapt to real-time working conditions of the traditional control strategy.

[0025] The application further proposes a specific structure of the cooling liquid circulation loop, which comprises a water pump, a battery system and a cooling liquid side channel of a battery cooler connected in series through pipelines. The pipeline inlet and outlet of the battery system are respectively provided with a first temperature sensor and a second temperature sensor. The core of the cooling liquid circulation loop is to realize battery thermal management through "water pump driving-heat exchange-cooling battery", to directionally transfer battery heat and maintain the battery system operating in a safe temperature range. The battery system is the object to be cooled, and its heat production characteristics dynamically change with the charging and discharging current, SOH (state of health) and environmental temperature. The battery system transmits its parameters (cell temperature, SOC, SOH, terminal voltage, charging and discharging current) to the controller through a bus as control input parameters.

[0026] The implementation logic of transferring heat is that the cooling liquid flows through the battery system (Battery) to absorb the heat generated by the charging and discharging of the battery (temperature rise). The first temperature sensor (T1) monitors the temperature of the cooling liquid before entering the battery in real time to ensure that the initial temperature meets the heat dissipation requirement; the water pump provides power to drive the circulation of the cooling liquid. The cooling liquid carrying heat enters the cooling liquid side channel of the Chiller (battery cooler) to exchange heat with the refrigerant. The second temperature sensor (T2) monitors the temperature of the cooling liquid after leaving the Chiller to verify the heat dissipation effect. The cooled cooling liquid reflows into the battery system to form a closed loop for continuous heat dissipation. The cooperation of the water pump, T1 / T2 sensor and Chiller realizes the transfer of heat from the battery to the refrigerant (Chiller heat exchange).

[0027] The application further proposes a specific structure of the refrigerant circuit, which comprises a compressor, a condenser, an expansion valve and a refrigerant side channel of the battery cooler connected in series through pipelines. The suction port and the exhaust port of the compressor are respectively provided with a pressure temperature sensor and a pressure sensor. The core function of the refrigerant circuit is to transfer and release heat to discharge the heat transferred by the cooling liquid circuit to the external environment.

[0028] The implementation logic of the refrigerant circuit (based on refrigeration cycle) is that the compressor compresses the low-temperature and low-pressure gaseous refrigerant into high-temperature and high-pressure gas to improve the heat carrying capacity of the refrigerant. The high-pressure side pressure sensor (HP) monitors the refrigerant pressure to ensure system safety. The high-temperature and high-pressure refrigerant enters the condenser (Condenser) to release heat to the ambient air through the forced air cooling of the cooling fan module, and the refrigerant condenses into high-pressure liquid. The high-pressure liquid refrigerant is throttled by the expansion valve to become a low-temperature and low-pressure gas-liquid mixture after pressure reduction and temperature reduction. The expansion valve controls the refrigerant flow to make the entire cooling system operate more accurately and efficiently under different refrigeration requirements. The low-temperature refrigerant flows into the refrigerant side channel of the Chiller to absorb the heat of the cooling liquid (evaporation heat absorption), thereby realizing the cooling of the cooling liquid. The pressure temperature sensor (LPT) monitors the state of the refrigerant to ensure the stability of the evaporation process. The cooperation of the compressor, condenser, expansion valve and Chiller realizes the transfer of heat from the cooling liquid to the external environment (condenser heat dissipation).

[0029] The Chiller of the cooling control system of the application is the heat exchange core of the refrigerant cycle and the cooling liquid cycle: the refrigerant flows in the "refrigerant side" of the Chiller to reduce its temperature through phase change (evaporation heat absorption); the cooling liquid flows in the "cooling liquid side" of the Chiller to carry the heat of the battery system and complete heat exchange with the refrigerant to realize the cooling of the cooling liquid; the cooled cooling liquid reflows into the battery system to continuously dissipate heat for the battery and ensure the stable working temperature of the battery. The Chiller acts as a "bridge" to link the two cycles through heat exchange, and finally achieves the goal of battery thermal management.

[0030] The controller of the application is the core of the whole battery cooling control system, which adopts an advanced recursive optimization algorithm. It can comprehensively analyze the key working condition data reflecting the working state of the battery, such as the refrigerant pressure and temperature, the cooling liquid temperature, the battery temperature, the battery charging and discharging current, and the voltage collected by the sensor, and fully consider various constraint conditions faced by the system in the actual operation process, such as the speed limit of the compressor and the opening limit of the expansion valve. Through complex and accurate calculation, the controller can quickly obtain the optimal control parameters of the refrigeration system under the current working condition, such as the compressor speed and the water pump speed, so as to realize precise control.

[0031] In another embodiment, based on the above-mentioned battery cooling control system, the application provides a multi-parameter recursive optimization battery cooling control method, as shown in Figure 2 The process is: obtaining the battery cooling system basic parameters at the initial time of the current period; the battery cooling system basic parameters include battery temperature, SOC, SOH, battery charging and discharging current, battery terminal voltage, battery system water inlet temperature, battery system water outlet temperature, refrigerant high pressure, refrigerant low pressure, and ambient temperature. Establishing battery heat production and exchange model, compressor refrigeration model, and compressor energy consumption model; determining the compressor speed and water pump speed of the next period according to the cooling system basic parameters, battery heat production model, compressor refrigeration model, and compressor energy consumption model; controlling the compressor and water pump to work based on the compressor speed and water pump speed of the next period, and realizing the control of the battery temperature.

[0032] Among them, obtaining the battery cooling system basic parameters at the initial time of the current period can be understood as reading the running state information of the battery cooling system in real time through sensors or data acquisition devices, for example, using a distributed sensor network to monitor the battery temperature at multiple points, and extracting SOC and SOH data from the vehicle management system through CAN bus protocol. Its main purpose is to realize the comprehensive perception of the dynamic working condition of the battery cooling system. Further, the establishment of the battery heat production and exchange model can obtain relevant parameters through experimental calibration method, for example, using a thermostat to simulate the battery heat production characteristics under different environmental temperatures, or obtaining the heat exchange coefficient between the cooling liquid and the battery through bench testing. The construction of the compressor refrigeration model can adopt the combination of theoretical calculation and experimental verification, for example, through enthalpy difference experiment to measure the performance parameters of the refrigerant under different working conditions. In addition, the establishment of the compressor energy consumption model can be based on historical operation data for fitting analysis, for example, using the least square method to curve fit the energy consumption data under different speeds.

[0033] The application realizes dynamic matching of cooling control parameters through multi-parameter cooperative modeling and recursive optimization mechanism, can automatically adjust model parameters according to the battery aging state and real-time working conditions; through combining the heat production model, the refrigeration model and the energy consumption model to form a multi-objective optimization strategy, both the cooling effect and the energy consumption control are guaranteed, and the problems of response lag and frequent re-optimization of the traditional control strategy are overcome. Therefore, the cooling system can continuously adaptively adjust under different working conditions, and the stability and reliability of the battery temperature control are improved.

[0034] The working principle of the embodiment of the application is as follows: Firstly, the initial parameter setting is initialized, and according to the cooling liquid temperature, the environment temperature, the cell temperature, the battery working state and the like, the initial control parameters (compressor speed, water pump speed) are set.

[0035] Secondly, at the initial moment of each control period, a plurality of basic parameters of the battery cooling system are obtained, including the battery temperature, the SOC, the SOH, the battery charging and discharging current, the battery terminal voltage, the battery system water inlet temperature, the battery system water outlet temperature, the refrigerant high pressure, the refrigerant low pressure and the environment temperature. These parameters comprehensively reflect the current heat production capacity of the battery, the aging state and the influence of the external environment, and provide necessary input data for subsequent model calculation.

[0036] Then, the battery heat production and exchange model, the compressor refrigeration model and the compressor energy consumption model are established respectively. Among them, the battery heat production and exchange model combines the electrochemical characteristics and thermodynamic characteristics of the battery, can adjust the heat production calculation result in real time according to the SOC, the SOH, the battery temperature and the like, so as to adapt to the internal resistance change caused by the battery aging. The compressor refrigeration model is associated through enthalpy difference calculation and efficiency coefficient, decouples the mechanical characteristics and the refrigeration capacity, and ensures the accurate prediction of the refrigeration capacity. The compressor energy consumption model adopts a quadratic function form to represent the nonlinear relationship between the speed and the energy consumption, and provides mathematical support for subsequent optimization.

[0037] Further, according to the cooling system basic parameters and the three established models, the compressor speed and the water pump speed of the next period are determined. In this process, the required refrigeration capacity of the next period is predicted through the battery heat production model, and the corresponding compressor speed is calculated combined with the compressor refrigeration model. At the same time, the energy consumption under different speeds is evaluated by using the compressor energy consumption model, forming a trade-off mechanism between refrigeration efficiency and energy consumption. When the battery temperature is higher than the set value, the refrigeration weight is preferentially improved to meet the cooling demand; and in the case that the remaining battery capacity is low, the relationship between refrigeration and energy consumption is balanced to avoid excessive energy consumption.

[0038] It can be understood that the determination of the water pump rotating speed is based on the design of the dynamic balance coefficient. By comparing the battery system heat generation rate with the cooling liquid heat exchange rate in real time, the water pump rotating speed is dynamically adjusted to make the cooling liquid flow rate match the heat generation change synchronously.

[0039] Therefore, based on the determined next cycle compressor rotating speed and water pump rotating speed, control instructions are sent to the compressor and the water pump to adjust the working state thereof, so as to realize accurate control of the battery temperature. The whole technical scheme forms a dynamic cooling control closed loop through multi-model collaborative modeling and recursive optimization mechanism, so that the cooling system parameters can be continuously self-adaptively adjusted with the working condition change, the model parameter mismatch problem caused by the battery heat generation characteristic change and aging is solved, and the defects of the traditional control strategy, such as response lag and inability to continuously adapt to real-time working conditions, are overcome.

[0040] The application further proposes that the battery heat generation and exchange model is: Q _batt = I _batt ×(U _batt -E _batt )+ I _batt 2 ×R _batt ; Q _pump = h1×A1×(T _batt -T _watter_in ); Wherein, Q _batt is the battery system heat generation rate (W); I _batt is the battery charging and discharging current (A); U _batt is the battery terminal voltage (V); E _batt is the battery electromotive force (V); R _batt is the battery internal resistance, which is obtained by looking up the SOC-SOH-battery temperature-charging and discharging cycle number-battery internal resistance MAP table; Q _pump is the heat exchange rate (W) between the battery system and the cooling liquid; h1 is the convective heat transfer coefficient (W / (m 2 ・℃)) between the battery system and the cooling medium, which is determined by experiment; A1 is the contact area (m 2 ) between the battery system and the cooling medium; T _batt is the battery temperature (℃); and T _watter_in is the battery system water inlet temperature (℃).

[0041] In actual application, the battery heat generation rate Q _batt refers to the heat change rate generated based on the internal chemical reaction and resistance loss of the battery, which can be realized by monitoring the current, voltage and other parameters in the battery charging and discharging process in real time and through mathematical modeling. The battery electromotive force E _battis referred to as the open-circuit voltage characteristic reflecting the battery under specific SOC and temperature conditions, which can be obtained by a curve fitted from experimental data or a table lookup method. The battery internal resistance R _batt is referred to as the dynamic resistance value reflecting the battery aging degree and working state, which can be accurately described by a multi-dimensional parameter mapping table. The heat exchange rate Q _pump between the battery system and the cooling liquid is referred to as a key indicator measuring the cooling capacity of the cooling system, which can be improved by optimizing the flow rate and temperature difference of the cooling medium to improve the heat exchange efficiency.

[0042] Specifically, the scheme realizes the collaborative calculation of heat generation and heat dissipation through a two-dimensional thermodynamic model. In terms of heat generation calculation, a composite function form is used to separate the Joule heat and the polarization heat modeling: where the term I _batt ×(U _batt -E _batt ) reflects the polarization heat effect through the difference between the real-time collected terminal voltage and the dynamic electromotive force, and the term I _batt 2 ×R _batt calculates the Joule heat through the dynamic internal resistance value obtained by the multi-parameter mapping table. This decomposition method enables the model to simultaneously reflect the influence of battery SOC change and aging state on the heat generation characteristics. In terms of heat exchange calculation, the product term of the convection heat transfer coefficient h1 and the contact area A1 is used to decouple the physical properties of the cooling medium and the system structure parameters, so that different cooling medium properties can be adapted under the same heat exchange structure. At the same time, the temperature difference (T _batt -T _watter_in ) realizes the characteristic of dynamic adjustment of cooling capacity with the inlet water temperature. In particular, the dual correlation design of electromotive force E _batt and SOC and temperature enables the model to capture the thermal response differences of the battery under different charge and discharge depths, and the multi-dimensional parameter architecture of the internal resistance mapping table ensures the adaptive ability of the model to the battery aging process. Through the above technical scheme, the problems that the traditional model does not fully consider the dynamic correlation of the battery electromotive force and SOC and temperature, and the characteristics of the battery internal resistance affected by multi-dimensional factors are not effectively modeled are effectively solved, and the precision of the cooling control strategy is significantly improved.

[0043] The present application further proposes a compressor refrigeration model as: Q _comp =k1×S _comp_Q ×Δh×η v ×η ad ; where Q _comp is the compressor refrigeration rate; k1 is the compressor displacement coefficient (unit: m 3 / r), which is related to the compressor cylinder structure and represents the volume of refrigerant transported per revolution; S _comp_Qis the compressor speed corresponding to the compressor refrigeration model; Δh is the enthalpy difference of the refrigerant in the battery cooler (unit: J / kg); η v is the volumetric efficiency; η ad is the adiabatic efficiency.

[0044] In actual application, Q _comp refers to the effective refrigeration capacity of the compressor per unit time, which can be obtained by measurement or calculation, and the purpose is to quantify the refrigeration output of the compressor to guide the speed regulation. k1 can be understood as a parameter reflecting the inherent mechanical characteristics of the compressor, which can be determined by experiment calibration or theoretical derivation, and the purpose is to associate the physical characteristics of the compressor with the refrigeration capacity. S _comp_Q As a speed variable, it can be dynamically adjusted through a control algorithm, and the purpose is to establish a linear relationship between the speed and the refrigeration capacity. Δh refers to the energy absorbed or released by the refrigerant during the phase change process, and its value can be calculated by the thermodynamic state equation, and the purpose is to represent the energy transfer characteristics of the refrigerant. η v and η ad respectively represent the volumetric efficiency and the adiabatic efficiency of the compressor, which can be determined by experimental test or empirical formula, and the purpose is to modify and compensate the theoretical model for actual operating conditions.

[0045] Specifically, this scheme realizes the fine characterization of the refrigeration capacity of the compressor through the modeling method of multi-dimensional dynamic parameters. The compressor displacement coefficient k1 couples the mechanical characteristics with the refrigeration capacity, so that the model can adapt to the inherent characteristics of different specifications of compressors. The speed variable S _comp_Q establishes a direct relationship between the speed regulation and the refrigeration capacity, providing an operable variable for dynamic control. The enthalpy difference parameter Δh explicitly models the energy transfer characteristics of the refrigerant during the phase change process, enabling the refrigeration capacity prediction to respond to changes in the thermodynamic state of the system. Two efficiency factors η v and η ad then incorporate the volumetric loss and thermodynamic irreversible loss inside the compressor into the calculation system, and through efficiency correction, the theoretical model is closer to the actual operating condition. This multi-parameter coupled modeling method not only retains the mechanism characteristics of the physical process, but also realizes empirical compensation through efficiency factors, so that the model can still maintain prediction accuracy under complex working conditions.

[0046] Through the above technical scheme, when the battery system heat generation rate fluctuates dynamically, the actual refrigeration capacity of the compressor can be accurately matched with the system demand according to the real-time working condition, avoiding the problems of energy waste caused by excessive cooling capacity or temperature control failure caused by insufficient cooling, and significantly improving the accuracy and energy efficiency of the battery cooling control.

[0047] The present application further proposes a compressor energy consumption model as: P _comp=L m ×Δh / (η th ×η v ×η ad ×η el ); Among them, L m The refrigerant mass flow rate is proportional to the compressor speed, i.e., L. m =k2×S _comp_P (k2 is a constant coefficient); η th is the thermodynamic efficiency constant.

[0048] Volumetric efficiency η v =1-C1×[(P _high / P _low )^1 / k-1]-C2 / S _comp_P C1 and C2 are constants for experimental fitting.

[0049] Adiabatic efficiency η ad = D1-D2×S _comp_P D1 and D2 are constants.

[0050] Motor efficiency η el =E1-E2×(S _comp_P - S comprated ) 2 E1 and E2 are coefficients, S comprated This refers to the compressor's rated speed.

[0051] The above formula and parameters are combined to obtain P _comp = k2×S _comp_P ×Δh / {1- C1×[(P _high / P _low )^1 / k-1]-C2 / S _comp_P} / (D1-D2×S _comp_P ) / E1-E2×(S _comp_P - S comprated ) 2 .

[0052] The above formula, after simplification for engineering applications, yields the final compressor energy consumption model: P _ comp =a×S _comp_P 2 +b×S _comp_P +c; Where: a and b are the first and second nonlinear coefficients (reflecting the decrease in efficiency with speed); c is a constant term (reflecting the basic energy consumption at low speed).

[0053] Specifically, the compressor energy consumption refers to the energy consumed by the compressor during operation, which can be quantified by establishing a mathematical model. In practical applications, the compressor energy consumption model can be implemented in the form of the above quadratic function, which can more accurately reflect the nonlinear effect of speed change on energy consumption, and achieve a fine description of the compressor energy consumption characteristics. In actual operation, first, the real-time collected compressor speed S _comp_P is substituted into the model to calculate the compressor energy consumption P _comp of the current period. Due to the existence of the quadratic term a x S _comp_P 2 , the model can effectively capture the nonlinear growth rule of energy consumption due to the increase of mechanical friction and the enhancement of air flow disturbance when the speed increases; at the same time, the synergistic effect of the linear term b x S_comp_P and the constant term c makes the model not only adapt to the energy consumption characteristics of the compressor under different working conditions, but also dynamically correct the energy consumption prediction value through real-time speed data. Compared with the traditional linear model, this modeling method significantly improves the accuracy of energy consumption prediction, especially in the scene where the battery system temperature fluctuates frequently, which can better balance the refrigeration efficiency and energy consumption.

[0054] In addition, the above technical solution is organically matched with other models in the cooling control method, for example, by combining the battery heat generation model, the compressor refrigeration model, etc., the optimization adjustment of the compressor speed can be realized. On this basis, through accurate prediction of the compressor energy consumption, the problem of excessive refrigeration or insufficient refrigeration can be effectively avoided, thereby improving the performance and reliability of the overall cooling control system.

[0055] The application further proposes to determine the compressor speed of the next period by the following formula: S _comp (n+1) = ω1 x S _comp_Q (n+1) + ω2 x S _comp_P (n+1) ; Wherein, S _comp (n+1) is the compressor speed of the n+1 period; ω1 is the refrigeration weight coefficient, ω2 is the energy consumption weight coefficient, ω1 + ω2 = 1, ω1 ≥ ω2, in specific cases such as ω1 > ω2 when the battery temperature is greater than the set temperature, and ω1 = ω2 when the battery temperature is less than or equal to the set temperature and the SOC is less than the set threshold; S _comp_Q (n+1) is the compressor speed corresponding to the n+1 period compressor refrigeration model; S _comp_P (n+1) is the compressor speed corresponding to the n+1 period compressor energy consumption model.

[0056] Specifically, S _comp(n+1) is the target speed of the compressor in the next cycle, which is predicted and calculated based on the current cycle state, and can be adjusted by dynamically adjusting the weight coefficient to adapt to different working conditions. In practical application, the calculation process of the target speed depends on two key inputs: S _comp_Q (n+1) and S _comp_P (n+1), which correspond to the output results of the compressor refrigeration model and the energy consumption model, respectively, to ensure that the speed decision takes into account both refrigeration efficiency and energy consumption control, thereby achieving more accurate control.

[0057] where ω1 and ω2 can be understood as dynamically allocated weight coefficients, whose specific values are adjusted according to the real-time monitored battery system temperature and remaining power. For example, when the battery system temperature is high, ω1 can be increased to prioritize refrigeration demand; when the temperature is within the standard but the power is insufficient, ω1 and ω2 can be balanced to avoid excessive refrigeration and energy loss. This dynamic weight mechanism is designed to solve the problem that traditional fixed weight strategies cannot adapt to complex working conditions. For example, when T _batt > 45℃ (high temperature warning) alone: ω1=0.8, prioritize temperature control; when SOC < 20% (low power) alone: ω2=0.5, prioritize energy saving; when T _batt > 45℃ (high temperature warning) and SOC < 20% (low power) simultaneously, prioritize temperature control to prevent thermal runaway.

[0058] In detail, the above scheme realizes dynamic optimization of compressor speed by constructing a double-model-driven speed synthesis mechanism. This process not only embodies the dual attributes of refrigeration capacity and energy consumption control, but also continuously adjusts the weight coefficient by real-time monitoring of the battery system temperature and remaining power, forming a recursive optimization feedback loop. For example, when the battery temperature exceeds the set threshold, the system automatically increases the refrigeration weight coefficient ω1, making the speed synthesis result more biased towards meeting the refrigeration demand; when the temperature is within the standard but the power is insufficient, the weight coefficients are balanced to avoid the decline in endurance caused by excessive refrigeration.

[0059] The present application further proposes a determination process for S _comp_Q (n+1) as follows: Q _batt (n)→ Q _comp (n+1)→S _comp_Q (n+1), that is: Q _batt (n) is used to determine the compressor refrigeration rate Q _comp (n+1) in the n+1 cycle, Q _comp (n+1)=Q _batt (n); according to Q _comp(n+1) and the compressor refrigeration model to determine S _comp_Q (n+1).

[0060] The determination process of S_comp_P(n+1) is as follows: S _comp_P (n)→P _ comp (n)→P _ comp (n+1)→S _comp_P (n+1), that is: The compressor energy consumption prediction curve is obtained by fitting the compressor energy consumption data of the bench test; The compressor speed S _comp_P (n) corresponding to the nth period compressor energy consumption model is determined by the nth period compressor energy consumption P _ comp (n). The first period corresponding to the energy consumption closest to P _ comp (n) in the compressor energy consumption prediction curve is found, and the energy consumption corresponding to the next period of the first period is taken as the compressor energy consumption P _ comp (n+1) of the n+1 period; According to P _ comp (n+1) and the compressor energy consumption model, S _comp_P (n+1) is determined.

[0061] Specifically, this scheme realizes precise adjustment of the compressor speed by establishing a dynamic mapping relationship between the heat generation rate and the refrigeration rate. The battery system heat generation rate Q _batt(n) of the nth period is taken as input and directly set to the compressor refrigeration rate Q _comp(n+1) of the n+1 period, and the required compressor speed S _comp_Q (n+1) is back calculated through the compressor refrigeration model. This direct mapping mechanism based on real-time heat generation data effectively solves the problem that the traditional fixed parameter control cannot adapt to the change in heat generation characteristics caused by battery aging. At the same time, in the determination process of the energy consumption model speed, the energy consumption prediction curve generated by fitting the bench test data is combined with the energy consumption state P _comp (n) of the current period to predict the energy consumption demand P _comp (n+1) of the next period in advance, and the corresponding compressor speed S _comp_P (n+1) is calculated using the compressor energy consumption model. This design idea combining historical data and physical models not only ensures the collaborative optimization of refrigeration efficiency and energy consumption control, but also significantly improves the dynamic response capability of the system.

[0062] In addition, the scheme adopts a hybrid strategy in determining two key rotation speed parameters: the refrigeration model rotation speed relies on theoretical derivation of thermodynamic equations, while the energy consumption model rotation speed is based on trend analysis of measured data. This combination of theory and practice not only enhances the robustness of the system, but also provides an effective technical path for the cooling lag problem caused by sudden changes in heat production rate under complex working conditions. By combining dynamic heat production monitoring with historical energy consumption prediction, the final composite rotation speed calculation method can better adapt to the real-time working condition requirements of the battery system, thereby significantly improving the accuracy and efficiency of cooling control.

[0063] The application further proposes to determine the water pump rotation speed of the next period by the following formula: S _pump (n+1)= u×m×S _pump (n); u=Q _batt (n) / Q _pump (n); Wherein, S _pump (n+1) is the water pump rotation speed of the n+1 period; u is the dynamic balance coefficient; m is the water pump rotation speed correction constant; S _pump (n) is the water pump rotation speed of the n period; Q _batt (n) is the heat production rate of the battery system in the n period; Q _pump (n) is the heat exchange rate between the battery system and the cooling liquid in the n period.

[0064] Specifically, the dynamic balance coefficient u refers to the ratio between the battery system heat production rate and the actual heat exchange rate, which can be calculated by real-time acquisition of temperature change data of the battery system and combination with thermodynamic model. The water pump rotation speed correction constant m can be an empirical parameter obtained based on experimental calibration, used to smooth the rotation speed adjustment process and prevent rotation speed oscillation caused by transient fluctuations. The purpose of introducing the dynamic balance coefficient u is to establish a direct correlation between the water pump rotation speed and the current heat load demand, so as to realize dynamic adaptation of cooling capacity. When u is greater than 1, it indicates that the current cooling liquid flow is insufficient, and the water pump rotation speed needs to be increased; when u is less than 1, it indicates that the current cooling liquid flow is excessive, and the water pump rotation speed needs to be reduced to improve energy consumption; when u is equal to 1, it indicates that the current cooling liquid flow is reasonable, and the water pump rotation speed remains at the current speed. The introduction of the water pump rotation speed correction constant m aims to improve the stability of system regulation and avoid energy waste or control failure caused by excessive response.

[0065] In detail, the scheme realizes self-adaptive adjustment of the cooling liquid circulation system by constructing a water pump rotation speed prediction model with recursive characteristics. In the specific implementation process, first, based on the battery system heat production rate Q _batt (n) and the heat exchange rate Q _pump(n) Calculate the dynamic balance coefficient u, which can intuitively reflect the matching degree of the current cooling capacity and the heat load demand. When the battery heat generation rate suddenly changes due to fluctuations in charging and discharging current or changes in aging degree, the dynamic balance coefficient u will adjust accordingly, thereby driving the water pump speed to change accordingly. On this basis, the water pump speed S _pump (n+1) of the next period is calculated as the iteration reference, combined with the product relationship of the dynamic balance coefficient u and the correction constant m. This real-time thermodynamic feedback-based adjustment mechanism effectively overcomes the dynamic adaptation defects caused by fixed parameters in traditional open-loop control, enabling the cooling system to continuously track changes in battery heat generation characteristics. _pump

[0066] In addition, this scheme cooperates with the compressor speed adjustment mechanism in the aforementioned cooling control method. Through independent and coordinated dynamic adjustment of the compressor and water pump speeds, not only the response speed of the entire cooling system is improved, but also the stability of battery temperature control is significantly improved. Especially in complex working conditions such as sudden acceleration of electric vehicles or sudden load of energy storage power stations, this scheme can quickly adapt to sudden changes in heat generation rate, ensuring that the cooling system is always in the best working state.

[0067] The present application further proposes to further include constraint control: 1) During the operation of the compressor and the water pump, if it is detected that the refrigerant circuit pipeline pressure exceeds the constraint condition, the compressor and the water pump are stopped running; 2) After determining the compressor speed and the water pump speed, it is judged whether the compressor speed and the water pump speed are within the corresponding constraint condition range, if the constraint condition is exceeded, the upper limit value or the lower limit value of the corresponding constraint condition is taken as the final compressor speed and water pump speed.

[0068] The constraint condition is: P _low_min ≤P _low and P _high ≤P _high_max , P _low_min is the lower limit value of the low pressure of the refrigerant; P _low is the low pressure of the refrigerant; P _high is the high pressure of the refrigerant; P _high_max is the upper limit value of the high pressure of the refrigerant; S _comp_min ≤S _comp ≤S _comp_max , S _comp is the compressor speed; S _comp_min is the lower limit value of the compressor speed; S _comp_max is the upper limit value of the compressor speed; S _pump_min ​≤S _pump ≤S _pump_max , S _pump is the water pump speed; S _pump_min is the lower limit value of the water pump speed; S _pump_max is the upper limit value of the water pump speed. In actual control, the water pump speed is constrained in the form of duty ratio, such as 40%≤D≤90%.

[0069] In practical application, the constraint control refers to a technical means for guaranteeing safe operation of the system by setting multiple parameter boundaries, which can be realized by combining real-time monitoring with threshold judgment. The refrigerant circuit pipeline pressure refers to the high and low pressure states generated in the circulation process of the refrigerant, which can be collected in real time by the pressure sensors installed at the suction port and the exhaust port of the compressor. The compressor speed and the water pump speed refer to the mechanical motion rate when the equipment is running, which can be calculated by the algorithm in the controller and verified in combination with the preset safety range. The purpose of introducing the constraint control mechanism is to prevent equipment damage or system failure caused by abnormal parameters, especially to provide immediate response capability in dynamic scenarios.

[0070] Specifically, when the refrigerant circuit pressure parameter is detected to break through the preset threshold (such as the high pressure side exceeding P _high_max or the low pressure side being lower than P _low_min ) during system operation, the controller will immediately trigger a shutdown instruction to block the risk of pressure abnormal accumulation. This immediate response mechanism effectively avoids the continuous operation of mechanical components in dangerous working conditions. At the same time, after calculating the target speeds of the compressor and the water pump based on the multi-parameter model, the system will perform boundary verification on these target values. If the calculated value exceeds the physical limit of the equipment (such as the compressor speed exceeding S _comp_max or being lower than S _comp_min ), the boundary value is replaced, thereby ensuring the feasibility of the control instruction. This hierarchical constraint architecture solves the contradiction between dynamic cooling demand and equipment safety boundary through pressure-speed two-dimensional monitoring. In addition, this scheme can also be combined with the aforementioned battery heat generation model, compressor refrigeration model and other basic technical contents, so that the system can maintain stable battery temperature and prevent mechanical component over-limit damage when dealing with extreme conditions such as sudden acceleration and sudden load, thereby significantly improving the reliability of the overall system.

[0071] Through the above technical solutions, the system can realize fast response and precise control under complex working conditions, effectively avoiding the shortcomings of traditional fixed parameter models in dynamic adaptability, and providing a reliable guarantee for the safe operation of the battery cooling system.

[0072] The application further proposes to correct the determined compressor speed based on the battery inlet water temperature, and control the compressor to work at the corrected compressor speed, which is determined by the following formula: S _comp_c (n+1)= k s ×T _watter_in / T _watter_standard ×S _comp (n+1) wherein, S _comp_c (n+1) is the compressor speed of the n+1 period after correction; k s is the compressor speed correction coefficient; T _watter_in is the battery system water inlet temperature; T _watter_standard is the ideal preset temperature of the battery system; S _comp (n+1) is the compressor speed of the n+1 period.

[0073] Specifically, the compressor speed correction coefficient k s is a proportional factor used to adjust the correction range, which can be calibrated according to the response sensitivity requirements of the system. The battery system water inlet temperature T _watter_in is the actual temperature when the cooling liquid enters the battery system, which can be collected in real time by a temperature sensor. The ideal preset temperature of the battery system T _watter_standard is a standard reference value set according to the optimal working temperature range of the battery, which is generally selected within the range of 15℃ to 25℃. The compressor speed of the n+1 period S _comp (n+1) is the initial speed value calculated based on the basic parameters and the model.

[0074] In detail, this scheme establishes a dynamic speed correction mechanism by introducing the ratio relationship between the water inlet temperature and the standard temperature. When the battery system water inlet temperature deviates from the ideal preset temperature, the control system will automatically calculate the ratio of T _watter_in to T _watter_standard , and adjust the initial calculated compressor speed in combination with the correction coefficient k s . This adjustment mechanism ensures that when the water inlet temperature rises, the compressor speed can be correspondingly increased to enhance the cooling capacity; and when the water inlet temperature decreases, the compressor speed is appropriately reduced to avoid excessive cooling. In this way, not only does it solve the problem of difficult trade-off between cooling capacity and energy consumption in the fixed parameter control mode, but also it realizes precise control of the battery temperature. At the same time, since this correction mechanism directly acts on the determined compressor speed value, it can seamlessly connect with the aforementioned speed determination process based on the heat production model, the refrigeration model and the energy consumption model, forming a complete closed-loop control strategy.

[0075] It should be understood that the particular order or hierarchy of steps in processes disclosed is merely an example. Based upon design preferences, it should be understood that specific order or hierarchy of steps in processes can be re-arranged while remaining within the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not meant to be limited to the specific order or hierarchy presented.

[0076] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. The contents not described in detail in the specification belong to the prior art known by the person skilled in the art.

Claims

1. A multi-parameter recursive optimization method for battery cooling control, characterized in that: Obtain the basic parameters of the battery cooling system at the beginning of the current cycle; Establish battery heat generation and exchange models, compressor refrigeration models, and compressor energy consumption models; The compressor speed and water pump speed for the next cycle are determined based on the basic parameters of the cooling system, the battery heat generation model, the compressor refrigeration model, and the compressor energy consumption model. The compressor and water pump are controlled based on the compressor speed and water pump speed of the next cycle to control the battery temperature.

2. The battery cooling control method with multi-parameter recursive optimization according to claim 1, characterized in that: The basic parameters of the battery cooling system include battery temperature, SOC, SOH, battery charging and discharging current, battery terminal voltage, battery system inlet water temperature, battery system outlet water temperature, refrigerant high pressure, refrigerant low pressure, and ambient temperature.

3. The battery cooling control method with multi-parameter recursive optimization according to claim 1, characterized in that, The battery heat generation and exchange model is as follows: Q _batt = I _batt ×(U _batt -E _batt )+ I _batt 2 ×R _batt ; Q _pump = h1×A1×(T _batt -T _watter_in ); Among them, Q _batt For the heat generation rate of the battery system; I _batt Battery charging and discharging current; U _batt E is the battery terminal voltage. _batt R is the battery electromotive force; _batt Q is the battery's internal resistance. _pump h1 is the heat exchange rate between the battery system and the coolant; h1 is the convective heat transfer coefficient between the battery system and the cooling medium; A1 is the contact area between the battery system and the cooling medium; T _batt Battery temperature; T _watter_in This refers to the water inlet temperature of the battery system.

4. The battery cooling control method with multi-parameter recursive optimization according to claim 1, characterized in that, The compressor refrigeration model is as follows: Q _comp =k1×S _comp_Q ×Δh×η v ×η ad Among them, Q _comp S is the compressor cooling rate; k1 is the compressor displacement coefficient; S _comp_Q η represents the compressor speed corresponding to the compressor refrigeration model; Δh represents the enthalpy difference of the refrigerant in the battery cooler; η represents the compressor speed corresponding to the compressor refrigeration model. v For volumetric efficiency; η ad For adiabatic efficiency.

5. The multi-parameter recursive optimization battery cooling control method according to claim 1, characterized in that, The compressor energy consumption model is as follows: P _ comp =a×S _comp_P 2 +b×S _comp_P +c; Among them, P _ comp For compressor energy consumption; a is the first nonlinear coefficient; b is the second nonlinear coefficient; c is the constant term; S _comp_P This represents the compressor speed corresponding to the compressor energy consumption model.

6. The battery cooling control method with multi-parameter recursive optimization according to claim 1, characterized in that, The compressor speed for the next cycle is determined using the following formula: S _comp (n+1) =ω1×S _comp_Q (n+1)+ω2×S _comp_P (n+1); Among them, S _comp (n+1) represents the compressor speed in the (n+1)th cycle; ω1 is the refrigeration weighting coefficient, ω2 is the energy consumption weighting coefficient, and ω1+ω2=1; S _comp_Q (n+1) represents the compressor speed corresponding to the compressor refrigeration model in the (n+1)th cycle; S _comp_P (n+1) represents the compressor speed corresponding to the compressor energy consumption model in the (n+1)th cycle.

7. The battery cooling control method with multi-parameter recursive optimization according to claim 1, characterized in that, The pump speed for the next cycle is determined using the following formula: S _pump (n+1)= u×m×S _pump (n); u=Q _batt (n) / Q _pump (n); Among them, S _pump (n+1) represents the pump speed in the (n+1)th cycle; u is the dynamic balance coefficient; m is the pump speed correction constant; S _pump (n) represents the pump speed in the nth cycle; Q _batt (n) represents the heat generation rate of the battery system in the nth period; Q _pump (n) represents the heat exchange rate between the battery system and the coolant in the nth cycle.

8. The battery cooling control method with multi-parameter recursive optimization according to claim 1, characterized in that: It also includes correcting the determined compressor speed based on the battery inlet water temperature, and controlling the compressor operation with the corrected compressor speed.

9. The multi-parameter recursive optimization battery cooling control method according to claim 8, characterized in that: The corrected compressor speed is determined using the following formula: S _comp_c (n+1)= k s ×T _watter_in / T _watter_standard ×S _comp (n+1); Among them, S _comp_c (n+1) represents the compressor speed after correction in the (n+1)th cycle; k s T is the compressor speed correction factor; _watter_in T represents the battery system's inlet water temperature. _watter_standard Ideal preset temperature for the battery system; S _comp (n+1) represents the compressor speed in the (n+1)th cycle.

10. A multi-parameter recursive optimization battery cooling control system, characterized in that: include The coolant circulation loop is used to control the circulation flow of coolant and exchange heat with the refrigerant based on the speed command to dissipate heat from the battery system. The refrigerant circuit is used to control the flow of refrigerant and exchange heat with coolant based on speed commands. The controller is used to obtain the basic parameters of the coolant circulation loop and refrigerant loop in the current cycle. Based on the basic parameters, battery heat generation model, compressor refrigeration model, and compressor energy consumption model, it determines the compressor speed and water pump speed in the next cycle and sends speed commands to the compressor and water pump according to the determined speed.

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