Energy storage system cell constant temperature control method

Abstract

The application discloses a kind of energy storage system battery constant temperature control method, and specific implementation scheme is: using charge-discharge equipment to the battery cluster of battery is different rate charge-discharge cycle, and adjusting liquid cooling unit liquid temperature, so that battery temperature is kept at different temperature target value, obtains MAP CTT Figure;MAP CTT Figure is input to neural network model, generates liquid cooling unit temperature control curve, utilizes battery cluster charge-discharge efficiency array MAP η And liquid cooling unit power consumption array MAP S Optimize liquid cooling unit temperature control curve, generate energy storage battery constant temperature control model;Optimize the control parameter of energy storage battery constant temperature control model, and the energy storage battery constant temperature control model is optimized, obtains optimal constant temperature control curveThe application can meet the constant temperature control demand of energy storage system battery in the process of charge-discharge under different charge-discharge rate working conditions, reach battery operating temperature range stable, regulation and control accurate, while guaranteeing battery maximum operating efficiency and realizing the purpose of more cycle times of battery cluster.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage, in particular to a constant temperature control method for energy storage system battery cells. BACKGROUND

[0002] In an energy storage system, battery cell temperature control is a key factor to ensure battery performance, safety and life. During charging and discharging, battery cells generate heat, and if the temperature is too high or uneven, it may lead to battery performance degradation, shortened life and even safety accidents.

[0003] Traditional battery cell temperature control methods usually rely on simple PID control or fixed temperature control strategies. These methods often fail to achieve accurate temperature control when faced with complex working condition changes, resulting in battery performance degradation, shortened life and increased safety hazards. SUMMARY

[0004] To solve the problem of traditional battery cell temperature control methods failing to achieve accurate temperature control, the present application provides a constant temperature control method for energy storage system battery cells.

[0005] The present application provides a constant temperature control method for energy storage system battery cells, comprising:

[0006] Using a charging and discharging device to perform charging and discharging cycles at different rates on a battery cluster of the battery cell, adjusting the liquid cooling unit outlet temperature to keep the battery cell temperature at different temperature target values, and obtaining a MAP CTT figure.

[0007] Inputting the MAP CTT figure into a neural network model to generate a liquid cooling unit temperature control curve, using a battery cluster charging and discharging efficiency array MAP η and a liquid cooling unit power consumption array MAP S to optimize the liquid cooling unit temperature control curve to generate an energy storage battery cell constant temperature control model.

[0008] Optimizing the control parameters of the energy storage battery cell constant temperature control model and optimizing the energy storage battery cell constant temperature control model to obtain an optimal constant temperature control curve.

[0009] Before performing charging and discharging cycles at different rates on the battery cluster of the battery cell, the battery cell, battery pack, liquid cooling pipeline and liquid cooling unit are selected, and the battery cell different charging and discharging rate intervals and battery cell temperature target value intervals are determined.

[0010] The battery cluster charging and discharging efficiency array MAP η and the liquid cooling unit power consumption array MAP S include,

[0011] The battery cluster charging and discharging efficiency array MAP is obtained by monitoring the battery cluster charging and discharging efficiency and the power consumption of the liquid cooling unit η And the liquid cooling unit power consumption array MAP S .

[0012] The generation of the liquid cooling unit temperature control curve includes,

[0013] The refrigeration and heating capacity of the liquid cooling unit is taken as the temperature control curve slope of the variable temperature control, the temperature control curve is generated, the temperature control curve is optimized by the neural network model, and the liquid cooling unit temperature control curve is obtained.

[0014] The operation of optimizing the energy storage cell constant temperature control model includes,

[0015] Through the data training of charging and discharging cycles with different charging and discharging rates and different cell control target values, the model control parameters are continuously optimized, so that the energy storage system cell constant temperature control model can make the cell operating temperature in the target value ±1.5℃ range;

[0016] The charging and discharging rate C and the cell temperature target value T are substituted into the liquid cooling unit temperature control curve to obtain the real-time target value T of the liquid cooling unit outlet liquid temperature x ′ y ;

[0017] The cell temperature target value T and the actual cell control temperature T ′ are compared, and when |T-T ′ |>1.5℃, the corresponding liquid cooling unit outlet liquid temperature target value T x ′ y does not meet the control requirements;

[0018] The energy storage cell constant temperature control model returns the liquid cooling unit outlet liquid temperature target value T x ′ y and the corresponding cell temperature target value T and the charging and discharging rate C value to the MAP CTT figure, while supplementing the MAP CTT figure data, the liquid cooling unit outlet liquid temperature target value control curve function T xy =f(C,T) is continuously optimized by using a neural network model, repeated 3-5 times, the liquid cooling unit temperature control curve is optimized, and the cell operating temperature control requirements are met.

[0019] The calculation of the liquid cooling unit outlet liquid temperature target value control curve function T xy includes,

[0020] T xy =F(C,R i ,k,Cp S, v, p, T 回 ) = C 2* R i / (k*C p *S*v* p) + T 回 ;

[0021] C = P / U / C 额 ;

[0022] Wherein, T xy represents the liquid cooling unit outlet temperature target value, P represents the direct current side charge and discharge power of the energy storage system, U represents the total voltage of the battery cluster, C 额 represents the current value corresponding to the rated charge and discharge rate of the battery cell, C represents the system charge and discharge rate under the power P, R i represents the equivalent direct current internal resistance of the battery cluster, k represents the thermal conductivity of the energy storage system PACK, C p represents the specific heat capacity of the cooling liquid, S represents the flow passage cross-sectional area of the liquid cooling plate, v represents the cooling liquid flow rate, p represents the cooling liquid density, T 回 represents the real-time value of the liquid cooling unit return liquid temperature.

[0023] The acquisition of the optimal constant temperature control curve includes,

[0024] While optimizing the liquid cooling unit outlet temperature target value control curve function T xy = f(C, T), the battery cluster charge and discharge efficiency array MAP η and the liquid cooling unit power consumption array MAP S are updated and supplemented, and after the liquid cooling unit outlet temperature target value control curve function T xy = f(C, T) meets the battery cell operating temperature control requirements, the battery cluster charge and discharge efficiency array MAP η and the liquid cooling unit power consumption array MAP S are added as the basic correction options, and the intersection point of the charge and discharge rate C and the battery cell temperature target value T of the battery cluster charge and discharge efficiency array MAP η and the liquid cooling unit power consumption array MAP S is taken as the optimal operating condition result of the energy storage battery cell constant temperature control model output and output to the EMS, and finally the optimal constant temperature control curve is achieved.

[0025] The MAP CTT graph is an array of charge and discharge rate-battery cell temperature-liquid cooling unit outlet temperature.

[0026] Beneficial effects: the constant temperature control model combined with the neural network model of multiple arrays of charge and discharge rate, liquid cooling unit outlet water temperature, battery cell temperature, liquid cooling unit power consumption and direct current side system efficiency generates the battery cell temperature control curve, meets the constant temperature control demand of the energy storage system battery cell in the charge and discharge process under different charge and discharge rate working conditions, achieves the purposes of stable battery cell operating temperature range, accurate regulation and control, and guaranteeing the highest operating efficiency of the battery and realizing more cycle times of the battery cluster.

[0027] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0028] The accompanying drawings are used to better understand the present scheme and do not constitute a limitation on the present application. Among them:

[0029] Figure 1 is a flowchart provided according to the present application;

[0030] Figure 2 is a liquid cooling unit outlet liquid temperature target value generation flowchart according to the present application;

[0031] Figure 3 is a neural network model topology graph according to the present application;

[0032] Figure 4 is a constant temperature control system communication topology graph according to the present application;

[0033] Figure 5 is the battery cell temperature change curve under the indirect temperature control method;

[0034] Figure 6 is the battery cell temperature change curve under the constant temperature control method according to the present application. DETAILED DESCRIPTION

[0035] Exemplary embodiments of the present application are described below with reference to the accompanying drawings, which include various details of the embodiments of the present application to help understanding, and should be considered as merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Also, in order to be clear and concise, the description below omits the description of well-known functions and structures.

[0036] As Figure 1 shown, the present application provides a constant temperature control method for energy storage system battery cells, comprising:

[0037] S1: use the charge and discharge equipment to perform charge and discharge cycles of different rates on the battery cluster of the battery cell, and adjust the liquid cooling unit outlet liquid temperature to keep the battery cell temperature at different temperature target values, and obtain a MAP CTT Figure. It needs to be explained that:

[0038] The battery cell, battery pack, liquid cooling pipeline and liquid cooling unit are selected and determined before the battery cluster of the battery cell is subjected to charge and discharge cycles of different rates, and the intervals of different charge and discharge rates of the battery cell and the intervals of the battery cell temperature target values are determined.

[0039] The battery cell, battery pack, liquid cooling pipeline and liquid cooling unit are selected and determined, thereby guaranteeing the certainty of the parameters of the battery cell constant temperature control method of the energy storage system, such as the battery cell internal resistance, the heat exchange capacity of the cooling system and the battery cell, and the refrigeration capacity of the liquid cooling unit.

[0040] The battery pack is subjected to heat preservation treatment, and the equipment or material used can be equivalent to the heat preservation capacity of the energy storage outdoor cabinet, thereby reducing the influence of the ambient temperature on the battery cell temperature.

[0041] The application selects 10% of the 0.5C of the battery cell as the rate interval point, and selects 5℃ as the temperature interval point, thereby taking into account the data quantity demand of the control method and the control of the time cost.

[0042] The MAP CTT Figure is an array of charge and discharge rate-battery cell temperature-liquid cooling unit outlet liquid temperature.

[0043] MAP CTT The drawing includes,

[0044] The charge and discharge rate is adjusted to C1, and the liquid cooling unit outlet liquid temperature is adjusted to T 11 , T 12 , T 13 , …, T 1n , respectively, so that the battery cell temperature is controlled at T1, T2, T3, …, T n ;

[0045] The charge and discharge rate is adjusted to C2, and the liquid cooling unit outlet liquid temperature is adjusted to T 21 , T 22 , T 23 , …, T 2n , respectively, so that the battery cell temperature is controlled at T1, T2, T3, …, T n ;

[0046] The charge and discharge rate is adjusted to C3, and the liquid cooling unit outlet liquid temperature is adjusted to T 31 , T 32 , T 33 , …, T 3n , respectively, so that the battery cell temperature is controlled at T1, T2, T3, …, Tn ;

[0047] Adjust the charge-discharge rate to C n , adjust the liquid cooling unit outlet temperature to T n1 , T n2 , T n3 , …, T nn , control the cell temperature to T1, T2, T3, …, T n ;

[0048] At the same time, record the direct current side system efficiency η and the liquid cooling unit power consumption S during the 30min stable period of temperature control.

[0049] Draw the MAP CTT figure by the above method.

[0050] S2: input the MAP CTT figure into the neural network model, generate the liquid cooling unit temperature control curve, optimize the liquid cooling unit temperature control curve using the battery cluster charge-discharge efficiency array MAP η and the liquid cooling unit power consumption array MAP S , and generate the energy storage cell constant temperature control model. It should be noted that:

[0051] The battery cluster charge-discharge efficiency array MAP η and the liquid cooling unit power consumption array MAP S include,

[0052] The battery cluster charge-discharge efficiency array MAP η and the liquid cooling unit power consumption array MAP S are obtained by monitoring the battery cluster charge-discharge efficiency and the power consumption of the liquid cooling unit.

[0053] The generation of the liquid cooling unit temperature control curve includes,

[0054] The refrigeration and heating capacity of the liquid cooling unit is taken as the temperature control curve slope of variable temperature control to generate the temperature control curve, and the temperature control curve is optimized by the neural network model to obtain the liquid cooling unit temperature control curve.

[0055] S3: optimize the control parameters of the energy storage cell constant temperature control model, and optimize the energy storage cell constant temperature control model to obtain the optimal constant temperature control curve. It should be noted that:

[0056] By optimizing the control parameters, the system working conditions of the devices such as cells, PACK, liquid cooling pipes, and liquid cooling units in the cold plate type liquid cooling system can be fitted, so that the energy storage cell constant temperature control model is applicable to different liquid cooling systems, and the corresponding cell temperature control curve is obtained by using the constant cell temperature control method to achieve the purpose of cell temperature control requirements.

[0057] The operation of optimizing the energy storage cell constant temperature control model includes,

[0058] Through the data training of different charge and discharge rates and different cell control target values, the model control parameters are continuously optimized, so that the energy storage system cell constant temperature control model can make the cell operating temperature within the target value ±1.5℃ range;

[0059] The charge and discharge rate C and the cell temperature target value T are substituted into the liquid cooling unit temperature control curve to obtain the real-time target value T of the liquid cooling unit outlet liquid temperature x ′ y ;

[0060] The cell temperature target value T and the actual cell control temperature T ′ are compared, and when |T-T ′ |>1.5℃, the corresponding liquid cooling unit outlet liquid temperature target value T x ′ y does not meet the control requirements;

[0061] The energy storage cell constant temperature control model returns the liquid cooling unit outlet liquid temperature target value T x ′ y that does not meet the control requirements, the corresponding cell temperature target value T, and the charge and discharge rate C value to the MAP CTT figure, while supplementing the MAP CTT figure data, the neural network model is used to continuously optimize the liquid cooling unit outlet liquid temperature target value control curve function T xy =f(C,T), which is repeated 3-5 times, and the liquid cooling unit temperature control curve is optimized to meet the cell operating temperature control requirements. The liquid cooling unit outlet liquid temperature target value generation flow chart is shown in Figure 2 .

[0062] The calculation of the liquid cooling unit outlet liquid temperature target value control curve function T xy includes,

[0063] T xy =F(C,R i ,k,C p ,S,v,ρ,T 回 )=C 2* R i / (k*C p *S*v*ρ)+T 回 ;

[0064] C=P / U / C 额 ;

[0065] wherein, Txy represents the liquid cooling unit outlet liquid temperature target value, P represents the energy storage system DC side charge and discharge power, U represents the total voltage of the battery cluster, C 额 represents the current value corresponding to the rated charge and discharge rate of the battery cell, C represents the system charge and discharge rate under the power P, R i represents the equivalent DC internal resistance of the battery cluster, k represents the thermal conductivity of the energy storage system PACK, C p represents the specific heat capacity of the cooling liquid, S represents the cross-sectional area of the liquid cooling plate flow channel, v represents the cooling liquid flow rate, p represents the cooling liquid density, T 回 represents the real-time value of the liquid cooling unit return liquid temperature.

[0066] T xy and T 回 The difference is within 1℃, to ensure that the battery cluster single PACK cell temperature extremum is minimum.

[0067] The acquisition of the optimal constant temperature control curve includes,

[0068] The liquid cooling unit outlet liquid temperature target value control curve function T xy = f(C, T) is optimized, and the battery cluster charge and discharge efficiency array MAP η and the liquid cooling unit power consumption array MAP S are updated and supplemented, and after the liquid cooling unit outlet liquid temperature target value control curve function T xy = f(C, T) meets the battery cell operating temperature control requirements, the battery cluster charge and discharge efficiency array MAP η and the liquid cooling unit power consumption array MAP S are added as the basic correction options, and the intersection of the battery cluster charge and discharge efficiency array MAP η and the liquid cooling unit power consumption array MAP S is taken as the optimal operating condition result of the energy storage battery cell constant temperature control model output and output to the EMS, and finally the constant temperature control curve is optimized.

[0069] The neural network model topology is shown in Figure 3 The neural network model can adjust the charge and discharge rate C, the battery cell internal resistance R i , the circulating liquid flow rate v, etc., and collect the return liquid temperature T 回 in real time, while generating the liquid cooling unit outlet target value T xy and the temperature control curve of C and T, wherein the timeliness of the actual value of the liquid cooling unit outlet temperature tracking the target value T xy is guaranteed by the variable frequency compressor of the liquid cooling unit PID control. At the same time, according to the EMS hardware bearing capacity, the number of neurons of the neural network model can be increased to speed up the optimization rate of the temperature control curve.

[0070] To realize the constant temperature control method of the energy storage system battery cell, a 100kW / 232kWh tower type liquid cooling outdoor cabinet is selected to carry out the MAP of charge-discharge rate-battery cell temperature-liquid cooling unit outlet liquid temperature CTT The control effect verification of the battery cell constant temperature control algorithm of the energy storage system is carried out through the data collection of the graph.

[0071] The conventional energy storage system DC side scheme is used for 5 1P52S battery PACKs (battery packs) connected in series, a high-voltage box and a BMS (Battery Management System, battery management system) system are configured, and the BMS system provides the single battery cell temperature, the average temperature and the extreme temperature (the constant temperature control system communication topology diagram is shown in Figure 4 The battery PACK is installed in the battery cabin, the battery cabin uses 50mm thick heat preservation fireproof rock wool, and the battery cabin protection level is IP65, so that the influence of the heat exchange between the inside and outside of the battery cabin on the battery cell temperature change is small and can be ignored. The PID algorithm control and frequency conversion tracking technology are used for the outlet liquid temperature of the liquid cooling unit of the energy storage system, when the environmental temperature is 45℃, the outlet water temperature is 18℃, the water flow is 46L / min (to ensure that the inlet and outlet liquid temperature difference is less than 1℃), the refrigerating capacity is 5.088kW, the input power is 2.482kW, and the heating power is 2kW, so that the outlet liquid temperature of the liquid cooling unit and the target value are kept consistent in real time. The multi-stage variable-diameter liquid cooling pipeline is used in the temperature control subsystem, so that the inconsistent cooling liquid flow of the PACK caused by the height difference of the installation position of the PACK is avoided, the battery cluster PACK cell temperature difference is reduced, and the interference of the average temperature difference of the PACK battery cell to the constant temperature control method of the energy storage system battery cell is reduced to be negligible. The constant temperature control model of the energy storage system battery cell runs in the EMS (Energy Management System, energy management system) system, the battery cell temperature data are collected through the communication between the EMS and the BMS, the outlet and return liquid temperatures of the liquid cooling unit are collected through the communication between the EMS and the liquid cooling unit, and the outlet liquid temperature target value is output, so that the millisecond level data flow loop of the constant temperature control method of the energy storage system battery cell is formed, and the timeliness of the temperature control regulation of the energy storage system is ensured through the communication architecture.

[0072] Figure 5 The battery cell temperature change curve under the indirect temperature control method is shown in Figure 6 The battery cell temperature change curve under the constant temperature control method proposed in the application is shown in the figure, wherein different color lines represent the running temperature curves of different battery cells, and the comparison Figure 5 and Figure 6It can be known that the constant temperature control method proposed in the application continuously optimizes model control parameters through data training of charging and discharging cycles of different charging and discharging rates and different battery control target values, so that the battery cell constant temperature control model of the energy storage system reaches the target value ±1.5℃ range of the battery cell operating temperature, meets the constant temperature control demand of the energy storage system battery cell in the charging and discharging process under different charging and discharging rate working conditions, reaches the stable battery cell operating temperature range, accurate regulation and control, and at the same time guarantees the highest operating efficiency of the battery and realizes the purpose of more cycle times of the battery cluster.

[0073] The above is only a specific embodiment of the application, but the protection scope of the application is not limited thereto, any change or replacement within the technical scope disclosed by the application should be covered within the protection scope of the application. Therefore, the protection scope of the application should be subject to the protection scope of the claims.

Claims

1. A method for thermostatic control of an energy storage system cell, comprising: The method comprises the following steps: The battery cluster of the battery cell is subjected to charging and discharging cycles of different rates by using a charging and discharging device, and the liquid cooling unit outlet liquid temperature is adjusted to keep the battery cell temperature at different temperature target values, and a MAP is obtained CTT Figure; The MAP CTT The MAP The MAP S The MAP S The MAP S The MAP S The MAP S The MAP S The MAP S The MAP S The MAP S The MAP S The MAP S The MAP S The MAP S The MAP S The MAP S The MAP S The MAP S The MAP S The MAP S The MAP < Optimizing the control parameters of the energy storage battery constant temperature control model, and optimizing the energy storage battery constant temperature control model to obtain an optimal constant temperature control curve; The operation of optimizing the energy storage battery constant temperature control model comprises, Through data training of charging and discharging cycles with different charging and discharging rates and different battery control target values, the model control parameters are continuously optimized, so that the energy storage system battery constant temperature control model can make the battery operating temperature within the target value ± 1.5℃ range; The charge-discharge rate C and the battery cell temperature target value T are substituted into the liquid cooling unit temperature control curve to obtain a real-time target value of the liquid cooling unit outlet liquid temperature ; The cell temperature target value T and the actual cell control temperature When the difference between the cell temperature target value T and the actual cell control temperature does not meet the control requirement, the corresponding liquid cooling unit outlet liquid temperature target value does not meet the control requirement. The energy storage cell constant temperature control model returns the liquid cooling unit outlet temperature target value that does not meet the control requirement And the corresponding cell temperature target value T, the charge-discharge rate C value returns to MAP CTT Figure, in the supplement MAP CTT At the same time, the data volume of the figure, using neural network model to continuously optimize the liquid cooling unit outlet temperature target value control curve function T xy =f (C, T) is repeated 3-5 times, the liquid cooling unit temperature control curve is optimized, and the cell operating temperature control requirement is reached.

2. The method of claim 1, wherein: Before the battery cluster of the battery is subjected to different rate charging and discharging cycles, the battery, the battery pack, the liquid cooling pipeline and the liquid cooling unit are selected, and the interval of different charging and discharging rates of the battery and the interval of the target value of the battery temperature are determined.

3. The method of claim 1 or 2, wherein: Battery cluster charge-discharge efficiency array And liquid cooling unit power consumption array MAP S The acquisition includes, The battery cluster charge-discharge efficiency array is obtained by monitoring the battery cluster charge-discharge efficiency and the power consumption of the liquid cooling unit and the liquid cooling unit power consumption array MAP S .

4. The method of claim 3, wherein: The generation of the liquid cooling unit temperature control curve comprises, The refrigeration and heating capacity of the liquid cooling unit is taken as the temperature control curve slope of the variable temperature control, the temperature control curve is generated, the temperature control curve is optimized in the forward direction through the neural network model, and the liquid cooling unit temperature control curve is obtained.

5. The method of claim 1, wherein: The liquid cooling unit outlet liquid temperature target value control curve function T xy The calculation includes, T xy = F(C,R i ,k, C p ,S, v, ρ,T 回 )= C 2 * R i / (k * C p * S * v * ρ)+ T 回 ; C = P / U / C 额 ; wherein T xy represents the liquid cooling unit outlet temperature target value, P represents the energy storage system DC side charge and discharge power, U represents the total voltage of the battery cluster, C 额 represents the current value corresponding to the rated charge and discharge rate of the battery cell, C represents the system charge and discharge rate under the power P, R i represents the equivalent DC internal resistance of the battery cluster, k represents the thermal conductivity of the energy storage system PACK, C p represents the specific heat capacity of the cooling liquid, S represents the cross-sectional area of the liquid cooling plate flow channel, v represents the cooling liquid flow rate, p represents the cooling liquid density, T 回 represents the real-time value of the liquid cooling unit return liquid temperature.

6. The method of claim 1, wherein: The optimal constant temperature control curve comprises, In the control curve function T xy =f(C,T)optimization of the liquid cooling unit out of the liquid temperature target value, at the same time, the battery cluster charging and discharging efficiency array And the liquid cooling unit power consumption array MAP S Update supplement, when the liquid cooling unit out of the liquid temperature target value control curve function T xy =f(C,T)meets the requirements of the battery cluster operating temperature control, adds the battery cluster charging and discharging efficiency array And the liquid cooling unit power consumption array MAP S As the basic correction option, the charging and discharging efficiency array of the battery cluster And the liquid cooling unit power consumption array MAP S The intersection of the charging and discharging rate C and the battery cell temperature target value T is taken as the optimal operating condition result of the energy storage battery cell constant temperature control model output and output to the EMS, and finally the optimal constant temperature control curve is achieved.

7. The method of claim 1, wherein: The MAP CTT The figure is the array of charge-discharge rate-battery temperature-liquid cooling unit outlet liquid temperature.

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