A method for predicting and actively preventing condensation risk of an energy storage cabinet

Abstract

The application discloses a kind of condensation risk prediction and active anti-condensation control methods of energy storage cabinet, suitable for the energy storage cabinet equipped with power conversion system PCS, temperature and humidity sensor and controller.The application estimates the surface temperature of the key cold point of PCS in real time based on first-order lumped heat model by temperature and humidity sensor to collect ambient temperature and humidity, in combination with PCS operating parameters;With the aid of Clausius-Clapeyron equation, the condensation risk factor and its change rate are quantified, and the three-level asymmetric active anti-condensation control of fan speed regulation, PCS loss self-compensation, power derating and auxiliary heating linkage is executed according to the risk level, and the heat model parameters are automatically corrected according to the control effect.The application can accurately estimate the cold point temperature and quantitatively predict the condensation risk, with the advantages of rapid response, high energy efficiency, reliable protection in extreme conditions and algorithm adaptation.

Description

Technical Field

[0001] This invention relates to a method for predicting condensation risk and actively preventing condensation in energy storage cabinets, belonging to the field of thermal management technology for energy storage systems. Background Technology

[0002] With the increasing prevalence of outdoor energy storage cabinets in power systems, the thermal management safety of their internal power conversion systems (PCS) has become a major concern. In high-humidity, high-temperature environments, condensation easily forms on the surface of PCS modules, potentially leading to electrical insulation failure or even short-circuit burnout. To address this issue, existing thermal management solutions often employ passive defense by linking temperature and humidity sensors to liquid-cooled units or electric heating films. For example, the invention patent with patent application number 202311004578.X, entitled "Heat Exchange Type Anti-Condensation Cooling System and Control Method, Energy Storage Cabinet," adjusts the supply liquid temperature by constructing a dual cooling loop and acquiring the ambient dew point temperature. Another example is the invention patent with patent application number 202411929413.8, entitled "Anti-Condensation Control Method, Medium, and System for Energy Storage Cabinets," which modifies liquid-cooling control parameters based on abnormally high supply liquid temperature limits, aiming to reduce the risk of condensation from a macroscopic perspective.

[0003] However, the above-mentioned technical solutions still have significant limitations in actual complex working conditions: While the patent with patent application number 202311004578.X constructs a dual cooling loop, its monitoring still relies on traditional humidity sensors, which are mostly arranged in the air ducts inside the cabinet. The measured macroscopic humidity cannot accurately reflect the surface microenvironment of large thermal inertial components such as the cold plate inside the PCS. Although the patent with patent application number 202411929413.8 considers liquid supply temperature correction, the humidity sensor exhibits physical hysteresis when it approaches saturation in high humidity environments, with response times reaching the minute level, making it difficult to respond quickly in the early stages of microscopic condensation. Furthermore, existing adjustment methods mostly rely on external heat exchange, lacking the ability to actively compensate for the PCS's own power loss and the millisecond-level intervention capability to cope with sudden extreme environments. They also lack quantitative support from underlying physical models, resulting in insufficient environmental adaptability when equipment accumulates dust or its performance degrades. This may even lead to energy conflict between heating and cooling, significantly reducing the system's energy efficiency ratio. Summary of the Invention

[0004] To address the problems of existing technologies, this invention provides a method for predicting condensation risk and actively preventing condensation in energy storage cabinets. This method can predict the condensation risk of critical cold spots in the PCS in advance and actively implement graded anti-condensation control before condensation forms, realizing the transformation from passive and delayed defense to active prediction and intervention, effectively reducing the risk of condensation failure in energy storage cabinets in high humidity environments.

[0005] To solve the above problems, the present invention adopts the following technical solution: A method for predicting condensation risk and actively preventing condensation in energy storage cabinets, the system environment of which includes... The energy storage cabinet, including the power conversion system PCS, temperature and humidity sensors, and controller, comprises the following steps: S1: Data Acquisition and Cold Spot Identification; The ambient temperature (Tamb) and relative humidity monitored by the temperature and humidity sensor are obtained. RH The real-time operating parameters of the PCS are also used to identify the surface areas inside the PCS that are prone to condensation as critical cold spots.

[0006] S2: Cold spot surface temperature estimation; Based on the ambient temperature Tamb, the instantaneous power loss Ploss of the PCS, the equivalent thermal resistance Rth, and the thermal time constant τ, the real-time surface temperature Tsurf of the critical cold point is estimated using a discrete recursive method.

[0007] S3: Quantitative prediction of condensation risk; The corresponding surface saturated vapor pressure e is calculated based on the real-time surface temperature Tsurf. s (Tsurf), calculate the actual water vapor pressure e of the air based on the ambient temperature Tamb and the ambient relative humidity RH. a and the actual water vapor pressure e a With the surface saturated vapor pressure e s The ratio of (Tsurf) is defined as the condensation risk factor Ψ, and the rate of change of the condensation risk factor during the current control period is calculated.

[0008] S4: Three-level asymmetric active anti-condensation control; The condensation risk factor Ψ and its rate of change are matched with preset thresholds, and active anti-condensation control is implemented in a progressive manner from low to high according to the following three-level strategy: the lower risk level prioritizes the use of the heat generated by the operation of the PCS to suppress condensation, and the higher risk level introduces external auxiliary heating; the three-level strategy adopts asymmetric hysteresis control, and the entry threshold of the risk-increasing stage is higher than the exit threshold of the risk-decreasing stage.

[0009] S5: Thermal parameter self-calibration; During the execution of the active anti-condensation control, the rate of decline of the condensation risk factor Ψ within a preset time window is monitored. When the rate of decline is lower than the preset rate threshold, it is determined that there is a deviation between the current thermal parameters and the actual heat dissipation state. The equivalent thermal resistance Rth and thermal time constant τ in step S2 are automatically corrected, and the corrected parameters are used for subsequent surface temperature estimation and condensation risk prediction.

[0010] In step S2, the surface temperature Tsurf of the critical cold spot is calculated using the following recursive formula:

[0011] Where Δt is the control period.

[0012] In step S3, the condensation risk factor Ψ is represented as:

[0013] Wherein, the actual water vapor pressure of the air e a (t) is represented as e a (t) = RH(t). e s (Tamb(t)); Where RH(t) is the ambient relative humidity expressed as a decimal from 0 to 1; e s (Tamb(t)) is the saturated vapor pressure corresponding to the ambient temperature Tamb(t); when Ψ(t) ≥ 1, it is determined that there is a risk of condensation at the critical cold point.

[0014] In step S3, the surface saturated vapor pressure e s (T) is calculated using the Clausius-Clapeyron equation as follows:

[0015] Where L is the latent heat of vaporization of water, Rv is the gas constant of water vapor, T0 is the reference temperature, and e s 0 represents the saturated vapor pressure corresponding to the reference temperature T0, and the temperatures T, Tamb, Tsurf, and T0 in the formula are thermodynamic temperatures. The rate of change Ψ of the condensation risk factor in step S3 is expressed as:

[0016] The first risk level of the three-level strategy in step S4 is: reducing the fan duty cycle of the PCS to weaken forced convection cooling, causing the heat generated by the operation of the PCS to accumulate on the surface of the critical cold point, thereby increasing the surface temperature of the critical cold point.

[0017] The second risk level of the three-level strategy in step S4 is: when the condensation risk factor reaches the second-level trigger threshold, or when the rate of change of the condensation risk factor exceeds the preset rate of change threshold, additional losses are actively introduced for self-heating compensation by adjusting the drive dead time, switching frequency or modulation strategy of the PCS power device.

[0018] The third risk level of the three-level strategy is: limiting the output power of the PCS and simultaneously activating the auxiliary heating device.

[0019] In step S4, when limiting the output power of the PCS, the derating power limit value p lim Represented as:

[0020] Where prated is the rated output power of the PCS, K is the derating factor, and Ψsafe is the safety condensation risk factor threshold.

[0021] The three-level asymmetric active anti-condensation control in step S4 is as follows: during the process of condensation risk increasing, the system enters the corresponding risk level step by step according to the first set of trigger thresholds; during the process of condensation risk decreasing, the system exits the corresponding risk level step by step according to the second set of exit thresholds which are lower than the first set of trigger thresholds, so as to form hysteresis control.

[0022] In step S5, the rate of decline is the rate at which the condensation risk factor Ψ decreases within a preset time window. When the rate of decline is lower than a preset rate threshold, the equivalent thermal resistance Rth and thermal time constant τ in step S2 are corrected to compensate for model offsets caused by equipment dust accumulation, fan performance degradation, or changes in the thermal path.

[0023] The key cold spots in step S1 include at least one of the following: the surface of the power device module housing, the edge area of ​​the heat sink, and the insulating surface near the busbar connection.

[0024] The beneficial effects of this invention are as follows: (1) It can acquire the surface temperature of the key cold spots of PCS in real time, which solves the problem that traditional sensors cannot reflect the micro condensation state of key areas; (2) Condensation risk factors are constructed using the Clausius-Clapeyron equation, which avoids the response lag caused by the physical hysteresis of humidity sensors and can provide early warning before condensation forms. (3) It adopts a three-level asymmetric active anti-condensation control, which requires no additional energy consumption when there is a slight risk, and provides a safety net under extreme conditions, thus taking into account both system energy efficiency and operational safety. (4) It has the ability to self-calibrate parameters, which can compensate for model deviations caused by factors such as dust accumulation and aging of equipment, and maintain control accuracy throughout the entire life cycle. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1: Block diagram of the energy storage cabinet condensation risk prediction and active anti-condensation control system; Figure 2: Condensation risk factor variation curve with critical cold spot surface temperature; Figure 3: Flowchart of three-level asymmetric active anti-condensation control; Figure 4: Flowchart of the steps for predicting condensation risk and actively preventing condensation in energy storage cabinets; Figure 5: Schematic diagram of the first-order lumped thermal model of the PCS critical cold spot; Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. A method for predicting condensation risk and actively preventing condensation in an energy storage cabinet, the system environment of which includes a power conversion system (PCS), a temperature and humidity sensor, and a controller in the energy storage cabinet, the steps of which are as follows: S1: Data Acquisition and Cold Spot Identification Acquire ambient temperature (Tamb) and relative humidity monitored by temperature and humidity sensors. RH (Expressed as a decimal from 0 to 1), and the real-time operating parameters of the power conversion system PCS. Surface areas inside the PCS prone to condensation (including but not limited to: the surface of the power device module housing, the edge area of ​​the heat sink, and the insulating surface near the busbar connection) are identified as critical cold spots.

[0028] S2: Cold Spot Surface Temperature Estimation Based on the ambient temperature Tamb, the instantaneous power loss ploss of the PCS, the equivalent thermal resistance Rth, and the thermal time constant τ, the real-time surface temperature Tsurf of the critical cold point is estimated using a discrete recursive method.

[0029] Where Δt is the control period.

[0030] S3: Quantitative Prediction of Condensation Risk The corresponding surface saturated vapor pressure e is calculated based on the real-time surface temperature Tsurf. s (Tsurf), according to Tamb and RH Calculate the actual water vapor pressure e in the air a : e a (t) = RH(t). e s (Tamb(t)); Where RH(t) is the ambient relative humidity expressed as a decimal from 0 to 1; e s (Tamb(t)) is the saturated vapor pressure corresponding to the ambient temperature Tamb(t); The saturated vapor pressure is calculated using the Clausius-Clapeyron equation:

[0031] Where L is the latent heat of vaporization of water, Rv is the gas constant of water vapor, T0 is the reference temperature, and e s 0 is the saturated vapor pressure corresponding to the reference temperature T0, and the temperatures T, Tamb, Tsurf and T0 in the formula are thermodynamic temperatures.

[0032] The condensation risk factor Ψ is defined as follows:

[0033] When Ψ(t) ≥ 1, it is determined that there is a risk of condensation at the critical cold spot.

[0034] Simultaneously calculate the rate of change of condensation risk factors:

[0035] S4: Three-level asymmetric active anti-condensation control Based on the matching of Ψ and its rate of change Ψ with the preset threshold, a three-level control strategy is executed progressively from low to high.

[0036] Asymmetric hysteresis control is adopted: the entry threshold during the risk-rising phase is higher than the exit threshold during the risk-falling phase.

[0037] Level 1 Risk (Lower Risk): Reducing the fan duty cycle of the PCS weakens forced convection cooling, causing the heat generated by the PCS itself to accumulate on the surface of critical cold spots, thus increasing the surface temperature.

[0038] Second risk level (medium risk): When Ψ reaches the second-level trigger threshold, or when Ψ exceeds the preset rate of change threshold, additional losses are actively introduced for self-heating compensation by adjusting the drive dead time, switching frequency, or modulation strategy of the PCS power device.

[0039] Level 3 Risk (High Risk): Limit the output power of the PCS and simultaneously turn on the auxiliary heating device.

[0040] Power limit values ​​after derating: p lim = p rated . [1 __ K . (Ψ __ Ψ safe )] ; Where, p rated The rated output power of the PCS is K, where K is the derating factor and Ψ is Ψ. safe The threshold for the risk factor of safe condensation.

[0041] S5: Thermal parameter self-calibration During the active anti-condensation control, the rate of Ψ's decline (i.e., the rate of decrease) within a preset time window is monitored. When the decline rate is lower than a preset rate threshold, it is determined that there is a deviation between the current thermal parameters and the actual heat dissipation state (such as dust accumulation on equipment, fan performance degradation, changes in the heat path, etc.). The equivalent thermal resistance Rth and thermal time constant τ in step S2 are automatically corrected, and the corrected parameters are used for subsequent surface temperature estimation and condensation risk prediction.

[0042] Example 1 (First Risk Level: Fan Speed ​​Regulation Self-Heating) This embodiment uses a 144kWh air-cooled DC energy storage cabinet as an example to illustrate the execution process of the method described in this invention. The energy storage cabinet is equipped with a PCS module with a rated power of 50kW. The following values ​​are for illustrative purposes only; in actual applications, the PCS calibration parameters shall prevail.

[0043] S1: Data Acquisition and Cold Spot Identification In this embodiment, the ambient temperature Tamb = 25℃ and the ambient relative humidity were collected. RH =0.90, PCS instantaneous power loss Ploss=200W, the surface of the PCS power module housing is identified as the critical cold spot.

[0044] S2: Cold Spot Surface Temperature Estimation The real-time surface temperature Tsurf of the critical cold spot is estimated using a discrete recursive method, and the recursive formula is as follows:

[0045] Where Δt is the control period, in this embodiment Δt = 1s, Rth = 0.05K / W, τ = 500s. Initially, Tsurf(0) = Tamb = 25℃. After running for 60s, the calculated...

[0046] S3: Quantitative Prediction of Condensation Risk Calculate the saturated vapor pressure at the critical cold spot surface using the Clausius-Clapeyron equation:

[0047] Calculate the ambient saturated vapor pressure based on the ambient temperature: e s Tamb = RH(t). e s (Tamb(t))≈31.6hPa; Further calculation of the actual water vapor pressure in the air: e a =RH × e s Tamb = 0.90×31.6≈28.4hPa; Obtain condensation risk factors: Ψ= 28.4 / 33.8≈0.84; Where L = 2.5 × 10⁶ J / kg, Rv = 461.5 J / (kg·K), T0 = 273.15 K is the reference temperature, and e s 0 = 6.11 hPa is the saturated vapor pressure corresponding to the reference temperature. All temperatures in the formula are thermodynamic temperatures.

[0048] S4: Three-level asymmetric active anti-condensation control P =0.84, exceeding the first risk level entry threshold (0.75) but not reaching the second risk level entry threshold (0.85). The controller determines the current risk level to be first (see...). Figure 2 Implement the lowest energy consumption anti-condensation strategy: The PWM duty cycle of the PCS fan is reduced from 30% to 10%, which weakens the forced convection cooling and causes the heat generated by the PCS operation to accumulate on the surface of critical cold spots.

[0049] After 60 seconds of Level I intervention, the surface temperature of the critical cold spot rose to 27.5°C, the condensation risk factor dropped to 0.77, and the condensation risk was effectively suppressed.

[0050] S5: Thermal parameter self-calibration In this embodiment, Ψ decreased from 0.84 to 0.77 within a 120s window, with a rate of decline of approximately Higher than the preset rate threshold The thermal model parameters remain unchanged.

[0051] Example 2 (Second Risk Level: Power Loss Self-Compensation) This embodiment uses a 144kWh air-cooled DC energy storage cabinet as an example to illustrate the execution process of the method described in this invention. The energy storage cabinet is equipped with a PCS module with a rated power of 50kW. The following values ​​are for illustrative purposes only; in actual applications, the PCS calibration parameters shall prevail.

[0052] S1: Data Acquisition and Cold Spot Identification In this embodiment, the ambient temperature Tamb = 25℃ and the ambient relative humidity were collected. RH =0.95, PCS instantaneous power loss Ploss=200W, the surface of the PCS power module housing is identified as the critical cold spot.

[0053] S2: Cold Spot Surface Temperature Estimation The real-time surface temperature Tsurf of the critical cold spot is estimated using a discrete recursive method, and the recursive formula is as follows:

[0054] Where Δt is the control period, in this embodiment Δt = 1s, Rth = 0.05K / W, τ = 500s. Initially, Tsurf(0) = Tamb = 25℃. After running for 60s, the following is calculated:

[0055] S3: Quantitative Prediction of Condensation Risk Calculate the saturated vapor pressure at the critical cold spot surface using the Clausius-Clapeyron equation:

[0056] Calculate the ambient saturated vapor pressure based on the ambient temperature: e s Tamb = RH(t). e s (Tamb(t))≈31.6hPa; Further calculation of the actual water vapor pressure in the air: e a =RH × e s Tamb = 0.95×31.6≈30.02hPa; Obtain condensation risk factors: Ψ= 30.02 / 33.8≈0.89; Where L = 2.5 × 10⁶ J / kg, Rv = 461.5 J / (kg·K), T0 = 273.15 K is the reference temperature, and e s 0 = 6.11 hPa is the saturated vapor pressure corresponding to the reference temperature. All temperatures in the formula are thermodynamic temperatures.

[0057] S4: Level 3 Asymmetric Active Anti-condensation Control (Second Risk Level) P =0.89>the threshold for the second risk level (0.85), and the rate of change is positive. The controller determines that the risk has been upgraded and triggers the second risk level.

[0058] The controller sends coordinated control commands to the PCS: Flow field adjustment: Maintain the fan PWM duty cycle at a low level of 10% to further reduce forced convection heat transfer; Loss injection: Increase the IGBT switching frequency from 5kHz to 10kHz to increase switching losses, and increase the PCS instantaneous power loss Ploss from 200W to about 450W.

[0059] After performing Level II intervention for 60 seconds, the surface temperature of the critical cold spot is updated based on the first-order thermal model. Substituting Ploss = 450W into the recursive formula, the steady-state temperature rise term is 450 × 0.05 = 22.5℃, and Tsurf(120) ≈ 28.5℃.

[0060] At this point, the saturated vapor pressure at the critical cold spot surface is: e s (28. 5℃) ≈38.9 hPa; Recalculate the condensation risk factor: P =30.0 / 38.9≈0.77; P The value dropped to 0.77, below the exit threshold for the second risk level (0.80), and the system automatically downgraded it to the first risk level, and then... P The further decline gradually subsided, and normal operations resumed.

[0061] This scenario demonstrates that by actively adjusting the switching frequency of the PCS power devices to introduce additional losses, rapid self-heating compensation can be achieved without relying on external heating devices, with a response time in the order of seconds.

[0062] S5: Thermal parameter self-calibration In this embodiment, after implementing the second risk level control, Ψ decreased from 0.89 to 0.77, and the rate of decline was... If the value is above the threshold, the parameter remains unchanged.

[0063] If dust accumulation on the fan leads to decreased heat dissipation, under the same control conditions, Ψ only decreases from 0.89 to 0.86, and the rate of decrease is... If the value is below the threshold, Rth is adjusted from 0.05 to 0.07 K / W, and τ is adjusted from 500 to 600 s for subsequent estimation.

[0064] Example 3 (Third Risk Level: Power Derating and Auxiliary Heating Linkage) This embodiment uses a 144kWh air-cooled DC energy storage cabinet as an example to illustrate the execution process of the method described in this invention. The energy storage cabinet is equipped with a PCS module with a rated power of 50kW. The following values ​​are for illustrative purposes only; in actual applications, the PCS calibration parameters shall prevail.

[0065] S1: Data Acquisition and Cold Spot Identification In this embodiment, the ambient temperature Tamb = 25℃ and the ambient relative humidity were collected. RH =0.98, PCS instantaneous power loss Ploss=200W, the surface of the PCS power module housing is identified as the critical cold spot.

[0066] S2: Cold Spot Surface Temperature Estimation Assume an extreme operating condition: dense fog causes the relative humidity to rise sharply to RH=0.98, while the surface temperature of the critical cold spot drops back to near the ambient temperature (approximately 25.3℃) due to a sudden drop in PCS load.

[0067] S3: Quantitative Prediction of Condensation Risk Calculate the saturated vapor pressure at the critical cold spot surface using the Clausius-Clapeyron equation:

[0068] Calculate the ambient saturated vapor pressure based on the ambient temperature: e s Tamb = RH(t). e s (Tamb(t))≈31.6hPa; Further calculation of the actual water vapor pressure in the air: e a =RH × e s Tamb = 0.98×31.6≈31.0hPa; Obtain condensation risk factors: Ψ= 31.0 / 32.2≈0.96; Where L = 2.5 × 10⁶ J / kg, Rv = 461.5 J / (kg·K), T0 = 273.15 K is the reference temperature, and e s 0 = 6.11 hPa is the saturated vapor pressure corresponding to the reference temperature. All temperatures in the formula are thermodynamic temperatures.

[0069] S4: Three-level asymmetric active anti-condensation control In this embodiment, P =0.96, exceeding the threshold of 0.92 for entering the third risk level, and the current level is determined to be the third risk level.

[0070] This embodiment implements a third-level risk control strategy: limiting the output power of the PCS and simultaneously activating the auxiliary heating device.

[0071] When limiting the output power of the PCS, the dated power limit value p lim Represented as: p lim = p rated . [1 __ K . (Ψ __ Ψ safe )] ; Where, p rated =50kW is the rated output power of the PCS, K=0.5 is the derating factor, Ψ safe =0.85 is the threshold for the safety condensation risk factor. The calculation yields:

[0072] The output power is reduced to minimize internal temperature fluctuations in the PCS and prevent further temperature drops in localized cold spots. Simultaneously, auxiliary heating films positioned in critical cold spot areas are activated to compensate for the heating of the power device module casing and the edges of the heat sink.

[0073] Through the aforementioned fallback measures, the surface temperature of the critical cold spot quickly rebounded. Within the preset assessment window of 120 seconds, the condensation risk factor dropped below the exit threshold of the third risk level (0.88), the system automatically downgraded to the second risk level and gradually released control, the PCS resumed normal power output, and the auxiliary heating device was simultaneously shut down.

[0074] S5: Thermal parameter self-calibration In this embodiment, during the implementation of the third risk level control, Ψ decreased from 0.96 to 0.86 within a 120s window, with a rate of decrease of approximately Higher than the preset rate threshold The control effect met the requirements, and the thermal model parameters remained unchanged (Rth=0.05K / W, τ=500s). If degradation occurs (such as aging of the auxiliary heating film leading to decreased heating efficiency, or changes in the thermal path), under the same control, Ψ only decreases from 0.96 to 0.91, with a decline rate of... If the value is below the threshold, Rth will be automatically corrected from 0.05K / W to 0.065K / W, and τ will be corrected from 500 to 550s for subsequent estimation.

Claims

1. A method for predicting condensation risk and actively preventing condensation in energy storage cabinets, wherein the system environment of the method is... The system includes a power conversion system (PCS), a temperature and humidity sensor, and a controller. The steps are as follows: S1: Data Acquisition and Cold Spot Identification; The ambient temperature (Tamb) and relative humidity monitored by the temperature and humidity sensor are obtained. RH The real-time operating parameters of the PCS are also recorded, and the surface areas inside the PCS prone to condensation are identified as critical cold spots. S2: Cold spot surface temperature estimation; Based on the ambient temperature Tamb and the instantaneous power loss p of the PCS loss The equivalent thermal resistance Rth and the thermal time constant τ are used to estimate the real-time surface temperature Tsurf of the key cold point. S3: Quantitative prediction of condensation risk; The corresponding surface saturated vapor pressure e is calculated based on the real-time surface temperature Tsurf. s (Tsurf), and combined with the actual water vapor pressure of the air e a Define the condensation risk factor Ψ; S4: Three-level asymmetric active anti-condensation control; The condensation risk factor Ψ and its rate of change are matched with a preset threshold, and active anti-condensation control is implemented in a three-level strategy from low to high. S5: Thermal parameter self-calibration; During the execution of the active anti-condensation control, thermal parameter deviations are monitored and the equivalent thermal resistance Rth and thermal time constant in step S2 are automatically corrected. The key feature is that, in step S2, the real-time surface temperature Tsurf is calculated using a discrete recursive method; and in step S3, the condensation risk factor Ψ is defined as the actual air vapor pressure e. a With surface saturated vapor pressure e s ( Tsurf ) The ratio, i.e., Ψ = e a / e s (Tsurf); In step S4, asymmetric hysteresis control is adopted: the entry threshold of the risk-rising stage is higher than the exit threshold of the risk-falling stage; and the lower risk level preferentially utilizes the heat generated by the PCS itself, while the higher risk level introduces external auxiliary heating; In step S5, by monitoring the rate of decline of the condensation risk factor Ψ within a preset time window, when the rate of decline is lower than the preset rate threshold, the equivalent thermal resistance Rth and the thermal time constant τ are automatically corrected.

2. The method for predicting condensation risk and actively preventing condensation in an energy storage cabinet according to claim 1, characterized in that, In step S2, the surface temperature Tsurf of the critical cold spot is calculated using the following recursive formula: Where Δt is the control period.

3. The method for predicting condensation risk and actively preventing condensation in an energy storage cabinet according to claim 1, characterized in that, In step S3, the condensation risk factor Ψ is represented as: Wherein, the actual water vapor pressure of the air e a (t) is represented as e a (t) = RH(t) . e s (T amb (t)); Where RH(t) is the ambient relative humidity expressed as a decimal from 0 to 1; e s (T amb (t) represents the ambient temperature T. amb The saturated vapor pressure corresponding to Ψ(t); when Ψ(t) ≥ 1, it is determined that there is a risk of condensation at the critical cold point.

4. The method for predicting condensation risk and actively preventing condensation in an energy storage cabinet according to claim 1, characterized in that, In step S3, the surface saturated vapor pressure e s ( T ) The calculation is performed using the Clausius-Clapeyron equation as follows: in, L R is the latent heat of water vaporization. v T is the gas constant for water vapor. 0 For reference temperature, e s 0 The reference temperature T 0 The corresponding saturated vapor pressure, and the temperatures T and T in the formula. amb T sur f and T0 are thermodynamic temperatures.

5. The method for predicting condensation risk and actively preventing condensation in an energy storage cabinet according to claim 1, characterized in that, In step S3, the rate of change Ψ of the condensation risk factor is expressed as: 。 6. The method for predicting condensation risk and actively preventing condensation in an energy storage cabinet according to claim 1, characterized in that, In step S4, the first risk level of the three-level strategy is: reduce the PWM duty cycle of the PCS fan to weaken forced convection cooling, so that the heat generated by the operation of the PCS accumulates on the surface of the critical cold point, thereby increasing the surface temperature of the critical cold point.

7. The method for predicting condensation risk and actively preventing condensation in an energy storage cabinet according to claim 1, characterized in that, In step S4, the second risk level of the three-level strategy is: when the condensation risk factor reaches the second-level trigger threshold, or when the rate of change of the condensation risk factor exceeds the preset rate of change threshold, additional losses are actively introduced for self-heating compensation by adjusting the drive dead time, switching frequency or modulation strategy of the PCS power device.

8. The method for predicting condensation risk and actively preventing condensation in an energy storage cabinet according to claim 1, characterized in that, The third risk level of the three-level strategy is: limiting the output power of the PCS and simultaneously activating the auxiliary heating device.

9. The method for predicting condensation risk and actively preventing condensation in an energy storage cabinet according to claim 1 or 8, characterized in that, In step S4, when limiting the output power of the PCS, the derating power limit value p lim Represented as: p lim =p rated . [1 __ K . (Ψ __ Ψ safe )l; Where Prated is the rated output power of the PCS, K is the derating factor, and Ψ safe The threshold for the risk factor of safe condensation.

10. The method for predicting condensation risk and actively preventing condensation in an energy storage cabinet according to claim 1, characterized in that, The three-level asymmetric active anti-condensation control in step S4 is as follows: during the process of condensation risk increasing, the system enters the corresponding risk level step by step according to the first set of trigger thresholds; during the process of condensation risk decreasing, the system exits the corresponding risk level step by step according to the second set of exit thresholds which are lower than the first set of trigger thresholds, so as to form hysteresis control.

11. The method for predicting condensation risk and actively preventing condensation in an energy storage cabinet according to claim 1, characterized in that, In step S5, the rate of decline is the rate at which the condensation risk factor Ψ decreases within a preset time window; when the rate of decline is lower than a preset rate threshold, the equivalent thermal resistance R in step S2 is adjusted. th The thermal time constant τ is corrected to compensate for model offsets caused by dust accumulation on equipment, fan performance degradation, or changes in the thermal path.

12. The method for predicting condensation risk and actively preventing condensation in an energy storage cabinet according to claim 1, characterized in that, In step S1, the critical cold spot includes at least one of the following: the surface of the power device module housing, the edge area of ​​the heat sink, and the insulating surface near the busbar connection.

Images