Energy storage device constant temperature system for low-voltage management of power distribution network

Abstract

The invention provides an energy storage device constant temperature system for low-voltage management of a power distribution network, and relates to the technical field of micro-grid control. Comprising the steps that a temperature monitoring module collects temperature data of an energy storage battery pack in real time and transmits the temperature data to an intelligent control unit, and a constant-temperature adjusting module serves as an execution unit for temperature control to ensure that a battery is in the optimal temperature; the intelligent control unit is integrated with a fuzzy PID-SOC-voltage cooperative control algorithm and a temperature-performance correlation model and is used for dynamically switching control priorities according to temperature deviation and dynamically coordinating the working state of the constant temperature adjusting module and the charging and discharging behaviors of the energy storage battery pack so as to realize cooperative control of temperature stability, electric quantity safety and voltage treatment. A closed-loop control system is constructed, the battery temperature can be accurately controlled, active resistance to the influence of the environment temperature is achieved, the safety of a power grid is improved, the health of the battery is maintained, it is guaranteed that the battery is in the optimal performance state all the time through constant temperature, and the reliability of the low-voltage treatment function is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid control, in particular to a constant temperature system of energy storage device for low voltage management of power distribution network. BACKGROUND

[0002] Under the background of global energy transformation and power grid structure optimization, the penetration rate of distributed power in power distribution network is continuously increasing. The fluctuation and intermittence of its output can easily cause power flow changes in the grid, leading to low voltage problems at the end of the line, which seriously affects the power quality and power supply reliability. Battery energy storage system has become a key means to address low voltage problems in power distribution network due to its fast active / reactive power response capability, which can dynamically compensate power fluctuations, suppress voltage drop, and form a multi-energy complementary system with distributed power, SVG and other devices to optimize power flow distribution and improve the stability and economy of grid operation.

[0003] However, energy storage devices, especially their core battery packs, exhibit high temperature sensitivity in actual operation. Ambient temperature can significantly affect the electrochemical reaction rate, internal resistance, capacity and cycle life inside the battery. In low temperature environment, the battery activity decreases and the internal resistance increases, resulting in a sharp drop in charge and discharge efficiency and a reduction in available capacity. When low voltage problems occur in power distribution network, energy storage devices affected by low temperature may not be able to provide enough compensation power as expected, not only failing to effectively address low voltage, but also possibly exacerbating system operation risks due to insufficient output capacity.

[0004] Currently, in the research and application of low voltage management in power distribution network, existing technologies mostly focus on improving the charge and discharge control strategy of energy storage devices themselves, optimizing their distribution or capacity configuration, but generally ignore the fundamental impact of ambient temperature changes on the actual operation performance of energy storage devices. This limitation makes it difficult to guarantee the working efficiency and reliability of energy storage devices under extreme temperature conditions, and fails to fully utilize their technical advantages. In addition, even after installing energy storage devices, distributed power sources may still have excess power generation. How to fully utilize this part of power while effectively managing the temperature of energy storage batteries is a difficult problem that has not been systematically solved. The present application innovatively controls the constant temperature system of energy storage devices in power distribution network. SUMMARY

[0005] The main purpose of the present application is to provide a constant temperature system of energy storage device for low voltage management of power distribution network, which solves at least one of the technical problems of the prior art.

[0006] To solve the above technical problems, the technical solution adopted by the present application is: a constant temperature system of energy storage device for low voltage management of power distribution network, comprising: The intelligent thermostat integrates an energy storage battery pack, a constant temperature adjustment module, a temperature monitoring module, and an intelligent control unit, and each module is interconnected through a circuit and a data link to form a closed-loop control system. The temperature monitoring module is composed of a sensing sensor and a signal processing unit, and its output end is connected to the intelligent control unit, and is used for real-time collection of temperature data of the energy storage battery pack. The constant temperature adjustment module has one end connected to the intelligent control unit and the other end acting on the energy storage battery pack, and is used as a temperature control execution unit to heat or cool the energy storage battery pack according to the current temperature and control instructions, so that the temperature is in an optimal interval. The intelligent control unit integrates a fuzzy PID-SOC-voltage cooperative control algorithm, which is used to receive temperature data from the temperature monitoring module, SOC data from the battery management system, and voltage state data from the power distribution network node voltage sensor, dynamically switch the control priority according to the temperature deviation, and dynamically coordinate the working state of the constant temperature adjustment module and the charging and discharging behavior of the energy storage battery pack to realize cooperative control of temperature stability, power safety, and voltage management.

[0007] In a preferred embodiment, the temperature monitoring module collects multiple-point temperature of the battery pack at a preset sampling frequency, and includes sensors and signal processing units arranged uniformly at different positions of the battery pack. The signal processing units filter and amplify the original temperature signals and then transmit the digitized temperature data to the intelligent control unit.

[0008] In a preferred embodiment, the constant temperature adjustment module includes a heating device. When the temperature is lower than the lower limit of the optimal operating temperature interval, the constant temperature adjustment module starts the heater to raise the temperature of the battery pack through resistance heating, and the heater has a self-limiting temperature feature. When the temperature is in the optimal operating temperature interval, the constant temperature adjustment module reduces the heating power. When the temperature is higher than the upper limit of the optimal operating temperature interval, the constant temperature adjustment module is in a standby state. And a heat insulation layer is wrapped around the battery pack to form a sealed structure.

[0009] In a preferred embodiment, the optimal operating temperature interval is 20-30℃, which satisfies the maximum chemical stability of the battery and the optimal energy conversion efficiency, and a temperature-performance correlation model is established based on the current interval as a control reference.

[0010] In the preferred embodiment, the fuzzy PID-SOC-voltage coordinated control algorithm executes the following steps: It collects the temperature deviation e, the rate of change of temperature deviation ec, the SOC value, and the distribution network voltage state U; it dynamically switches the control priority based on the voltage state U and the SOC value: when U=1, low voltage compensation is prioritized, and temperature regulation is constrained by SOC; when U=0, temperature control is prioritized, and fuzzy PID regulation is initiated when the SOC is within a safe range; it outputs heating power commands through fuzzy inference and PID parameter self-tuning mechanisms to control the operation of the constant temperature regulation module, specifically: S101: Determine voltage status: Collect voltage signals at a preset voltage sampling frequency through voltage sensors at distribution network nodes, set voltage threshold U0 = 95% of rated voltage. When voltage < U0, it is a low voltage state: U = 1; otherwise, it is a normal state: U = 0. S102: SOC Constraint Boundary Control: Preset Critical Threshold Second threshold If the current SOC < Suspend unnecessary temperature control and start charging when the current SOC > ω* Afterwards, normal control was restored, among which >ω>1; if ≤Current SOC≤ Temperature is adjusted at full power using a fuzzy PID algorithm; when SOC > When this happens, the charging circuit is shut off; S103: Dynamic priority switching: When U=1: If SOC≥ Perform discharge compensation and limit temperature regulation power to ≤ 10% of discharge power; if SOC < Only freeze protection heating is allowed, and charging should be prioritized until the SOC is ≥ 25%; When U=0: If Temperature is adjusted at full power using a fuzzy PID algorithm; Start charging until ; S104: Fuzzy PID Temperature Control: Collects the average battery pack temperature T, calculates the temperature deviation e=T-T0 and the rate of change of deviation. The formula is: ; in represents the temperature deviation rate, and e' represents the temperature deviation at the previous moment; e and The system is divided into multiple fuzzy subsets and a fuzzy rule matrix is ​​established. The accurate heating power is obtained through fuzzy inference and defuzzification. After dynamically tuning the PID parameters, the power command is output to drive the constant temperature control module.

[0011] In a preferred embodiment, the discharge power in low voltage state is dynamically adjusted according to the formula: ; wherein k is a proportional coefficient, is the voltage drop amplitude.

[0012] In a preferred embodiment, the fuzzy output is converted into an accurate power value by using the area barycenter method to dynamically adjust the PID parameters: ; wherein, is the discrete value of heating power, is the membership degree corresponding to the power value.

[0013] In a preferred embodiment of the fuzzy PID temperature regulation, the PID parameter self-tuning rule is: when the temperature deviation is large, increase the proportional coefficient Kp and reduce the integral coefficient Ki; when the temperature is close to the target, reduce Kp and increase Ki; when the deviation rate is large, increase the differential coefficient Kd.

[0014] In a preferred embodiment, the energy storage device also includes a modular structure design, including: A cabinet including an incoming line switch, a fuse, a surge protector, a bidirectional inverter module, and a main control board; B cabinet, which is a sealed battery box, including the energy storage battery pack, a temperature monitoring module, and a constant temperature regulation module; the A cabinet and the B cabinet are connected through a cable to realize power and signal transmission, and the bidirectional inverter module in the A cabinet is used for AC-DC bidirectional conversion: when charging, the AC power input from the power grid is rectified into DC power and transmitted to the B cabinet to charge the battery; when discharging, the B cabinet receives the instruction from the main control board and converts the DC power output from the battery into AC power to inject into the power distribution network to compensate for low voltage.

[0015] In a preferred embodiment, it also includes a closed-loop feedback correction mechanism for dynamically adjusting control parameters or threshold values according to actual temperature fluctuations, SOC fluctuations, or voltage states.

[0016] The application provides a power distribution network low-voltage management energy storage device constant temperature system, comprising a temperature monitoring module, which is used for collecting temperature data of an energy storage battery pack in real time and transmitting the temperature data to an intelligent control unit, a constant temperature adjustment module serving as a temperature control execution unit, which ensures that the battery is within an optimal temperature range, and when the temperature is higher than the upper limit of the optimal range, the module is in a standby state to reduce energy consumption; the intelligent control unit is integrated with a fuzzy PID-SOC-voltage cooperative control algorithm and a temperature-performance correlation model, which is used for dynamically switching control priorities according to temperature deviations and dynamically coordinating the working state of the constant temperature adjustment module and the charging and discharging behavior of the energy storage battery pack to realize cooperative control of temperature stabilization, power safety and voltage management, and build a closed-loop control system, which can accurately control the battery temperature, actively resist the influence of ambient temperature, improve the safety of the power grid, maintain the health of the battery, ensure that the battery is in the best performance state at all times through constant temperature, and improve the reliability of the low-voltage management function. BRIEF DESCRIPTION OF DRAWINGS

[0017] The application will be further described below in combination with the drawings and embodiments: Figure 1 is the improved algorithm solving flowchart of the application; Figure 2 is the principle diagram of the energy storage device of the application; Figure 3 is the actual effect diagram of the energy storage device of the application; Figure 4 is the PID control flowchart of the application; Figure 5 is the B cabinet battery box simulation diagram of the application. DETAILED DESCRIPTION

[0018] Embodiment 1 As shown in the figure, a power distribution network low-voltage management energy storage device constant temperature system comprises: Figures 1-5 An intelligent constant temperature box integrates an energy storage battery pack, a constant temperature adjustment module, a temperature monitoring module and an intelligent control unit, and the modules are interconnected through a circuit and a data link to form a closed-loop control system; wherein, The temperature monitoring module is composed of a sensing sensor and a signal processing unit, and the output end is connected to the intelligent control unit, which is used for collecting temperature data of the energy storage battery pack in real time, the sensors are uniformly arranged at different positions of the battery pack, the overall and local temperatures of the battery pack are collected, the signal processing unit transmits digital temperature data to the intelligent control unit after filtering and amplifying the original temperature signal, provides real-time basis for constant temperature adjustment, and ensures that the temperature deviation from the optimal range is found in time.

[0019] ​The constant temperature regulation module is connected with the intelligent control unit at one end and acts on the energy storage battery pack at the other end, and serves as an execution unit for temperature control. When the temperature is lower than the lower limit of the optimal interval, the heater is started to quickly raise the temperature of the battery pack through resistance heating, and has a self-limiting temperature feature to avoid local overheating. When the temperature is in the optimal interval, the device reduces the heating power to ensure that the battery is in the optimal temperature. When the temperature is higher than the upper limit of the optimal interval, the module is in standby state to reduce energy consumption.

[0020] The intelligent control unit integrates a fuzzy PID-SOC-voltage cooperative control algorithm and a temperature-performance correlation model, is used for receiving temperature data of the temperature monitoring module, SOC data of the battery management system and voltage state data of the power distribution network node voltage sensor, dynamically switching control priority according to temperature deviation, dynamically coordinating the working state of the constant temperature regulation module and the charging and discharging behavior of the energy storage battery pack, to realize cooperative control of temperature stability, power safety and voltage management, including: discharging compensation is performed in a low voltage state to limit temperature regulation power; temperature stability is performed in a normal voltage state, and charging and discharging are restricted and managed in combination with the SOC value of the energy storage battery; a closed-loop feedback mechanism is adopted to correct control deviation in real time.

[0021] In the embodiment, the temperature monitoring module collects temperature data of the energy storage battery pack in real time and transmits the data to the intelligent control unit. The constant temperature regulation module serves as an execution unit for temperature control, ensures that the battery is in the optimal temperature, and is in standby state to reduce energy consumption when the temperature is higher than the upper limit of the optimal interval. The intelligent control unit integrates a fuzzy PID-SOC-voltage cooperative control algorithm and a temperature-performance correlation model, is used for dynamically switching control priority according to temperature deviation, dynamically coordinating the working state of the constant temperature regulation module and the charging and discharging behavior of the energy storage battery pack, to realize cooperative control of temperature stability, power safety and voltage management, and builds a closed-loop control system, which can accurately control the battery temperature, actively resist the influence of environmental temperature, improve the safety of the power grid, maintain the health of the battery, ensure that the battery is in the best performance state at all times through constant temperature, and improve the reliability of the low voltage management function.

[0022] In the embodiment, PID is proportional-integral-derivative (PID), and SOC is state of charge (SOC).

[0023] The embodiment deeply integrates the three key dimensions of battery state, power grid demand (voltage state) and temperature control, realizes a leap from “single-point control” to “system cooperation”, realizes the cooperation of multiple systems and multiple targets, dynamically adjusts the working priority, and greatly improves the safety of the power grid.

[0024] In a preferred embodiment, the energy storage device further comprises a modular structure design, separating the inverter and the battery box into two independent cabinets, and realizing power and signal transmission through cables. The two independent cabinets include cabinet A and cabinet B. Cabinet A integrates the incoming line switch, the fuse, the surge protector, the bidirectional inverter module, and the main control board. The main control board is the core carrier of the intelligent control unit, and is responsible for power access, protection, conversion, and instruction output.

[0025] Cabinet B is a sealed battery box containing energy storage batteries, a temperature monitoring module, and a constant temperature adjustment module, forming an independent temperature control unit.

[0026] As shown in FIGS. 1, 2, and 3, in the modular split design, the inverter and the battery box are separated into two independent cabinets, and the circuit diagram of the two cabinets is shown in FIG. 4. The bidirectional inverter module in cabinet A is used for AC-DC bidirectional conversion. When charging, the AC power input from the power grid is rectified into DC power and transmitted to cabinet B for battery charging. When discharging, the DC power output from the battery in cabinet B is inverted into AC power by the bidirectional inverter module under the instruction of the main control board, and the AC power is injected into the power distribution network to compensate for low voltage. Figure 2 Figure 3 Figure 2

[0027] Cabinet A is the core unit of power management and control, integrating power access, protection, conversion, and intelligent control functions. The specific analysis is as follows: 1. Incoming line switch X1: controls the on-off of power input and has short circuit protection function. When a short circuit fault occurs in the circuit, the incoming line switch can quickly cut off the path to avoid damage to the rear elements.

[0028] 2. Fuse F1: a protection element connected in series in the incoming line circuit. When the circuit current is overloaded, the fuse wire automatically melts, forcibly cutting off the circuit, and providing overcurrent protection for the elements in the rear.

[0029] 3. Surge protector SP1: mainly suppresses sudden interference signals such as lightning strikes and power grid transient overvoltage, avoids overvoltage impact damage to electronic elements, and ensures stable operation of the control and conversion modules in cabinet A.

[0030] 4. Bidirectional inverter module: responsible for AC-DC bidirectional conversion. When charging, the AC power input from the power grid is rectified into DC power and transmitted to the battery box in cabinet B for battery charging. When discharging, the DC power output from the battery in cabinet B is inverted into AC power under the instruction of the main control board, and the AC power is injected into the power distribution network to compensate for low voltage and quickly raise the voltage.

[0031] ​​​5. The main control board CU1: integrated fuzzy PID temperature control, SOC-voltage coordination algorithm, through voltage sampling, current sampling elements to obtain power grid voltage and current, inverter state, while through the RS485, CV1 interface to receive the temperature of B cabinet, SOC data; output instruction: send charge-discharge power command to the bidirectional inverter, send heating / cooling command to the B cabinet constant temperature adjustment module; cooperative control: trigger CV1 discharge at low voltage, preferentially start charging when SOC<20%, and cut off the circuit and alarm when fault occurs.

[0032] CV1 interface: A cabinet and power grid, B cabinet linkage key signal interface, low voltage state, the main control board CU1 triggers the discharge action through CV1, controls the bidirectional inverter to inject compensation power into the power grid, which is the core execution interface to realize low voltage control. B cabinet uses heat insulation material to form an independent temperature control unit to ensure that the overall temperature fluctuation of the battery pack is ≤±1℃; The modularized split design of A cabinet and B cabinet strengthens the installation convenience while realizing the separation of battery insulation and power conversion functions. B cabinet includes battery and insulation system, and its simulation principle is shown in Figure 5 The simulation principle of the B cabinet is shown in FIG. 1. The simulation principle of the B cabinet is shown in FIG. 1.

[0033] The simulation principle of the B cabinet is shown in FIG. 1. The simulation principle of the B cabinet is shown in FIG. 1.

[0034] The simulation principle of the B cabinet is shown in FIG. 1. The simulation principle of the B cabinet is shown in FIG. 1. The simulation principle of the B cabinet is shown in FIG. 1.

[0035] The simulation principle of the B cabinet is shown in FIG. 1. The simulation principle of the B cabinet is shown in FIG. 1.

[0036] In this embodiment, the intelligent control unit integrates control algorithm and temperature-performance correlation model, is responsible for coordinating the work of each module, receiving real-time data of the temperature monitoring module, comparing with the preset optimal temperature interval, calculating temperature deviation, and generating constant temperature adjustment instructions to control the start, stop and power adjustment of the heating device. At the same time, it is linked with the charge and discharge system of the energy storage battery pack, and gives priority to the battery discharge compensation function in the low voltage management of the power distribution network, and takes into account the temperature control SOC constraint to ensure the cooperation of battery performance and grid stability. As shown in Figure 4 , the integrated fuzzy PID-SOC-voltage cooperative algorithm, the core logic of the overall device is as follows: taking the priority of low voltage management of the power distribution network, temperature stability and SOC cooperation as the goal, through the fusion of fuzzy PID temperature control, SOC constraint judgment and power distribution network voltage state response three logic, the efficient operation of the energy storage device under complex working conditions is realized, and the specific process is as follows: (1) Priority to the voltage state response of the power distribution network: when the power distribution network appears low voltage, the low voltage management priority is taken as the goal, and the discharge compensation function of the energy storage device is preferentially guaranteed, and the temperature regulation only retains the low temperature protection to avoid unnecessary temperature control consumption affecting compensation. When the voltage is normal, temperature stability priority is turned on, and fuzzy PID temperature control dominates the adjustment at this time.

[0037] (2) SOC constraint specifies the temperature regulation boundary: no matter the voltage state, SOC is always a hard constraint for temperature regulation. When SOC is 20%-95%, fuzzy PID can output 0-1500W power to regulate temperature according to demand. When SOC<20%, SOC constraint logic forces temperature regulation, and preferentially starts charging to 25%. When SOC>95%, SOC constraint logic closes the charging to avoid overcharging affecting the safety of the battery, and at this time the temperature regulation is still executed according to the PID logic.

[0038] (3) Fuzzy PID dynamically adapts to the working condition. When the voltage is normal and SOC is safe, fuzzy PID adjusts the parameters in real time through temperature deviation and change rate to output accurate heating power. When the voltage is low and SOC is greater than or equal to 20%, fuzzy PID reduces the adjustment range and only makes fine adjustment to avoid interfering with the discharge compensation. With closed-loop feedback, when the temperature or SOC deviates from the target, dynamically correct the PID parameters or SOC threshold.

[0039] (4) Fuzzy PID can adjust the proportional, integral and differential parameter values of PID through fuzzy algorithm, so that the parameters of PID are always at the best value, and the control effect is more excellent. The control flow chart is shown in Figure 4 .

[0040] As shown in Figure 1 , the algorithm solving flow chart in this embodiment, the specific implementation process is as follows: I. Collecting temperature parameters, through the combination of the temperature monitoring module and the intelligent control unit of the system, the sampling frequency is 1 Hz; calculating the temperature deviation as shown in formula 1: (1); In the formula, e represents the temperature deviation, T represents the average temperature of the battery pack, T 0 represents the midpoint of the optimal temperature interval.

[0041] Then, through the deviation rate as shown in formula 2, the temperature deviation degree and the change trend are quantified.

[0042] (2); In the formula, e c represents the temperature deviation rate, e’ represents the temperature deviation at the last time when the sampling interval is 0.1 seconds.

[0043] In the preferred embodiment, the temperature monitoring module collects multiple-point temperatures of the battery pack at a preset sampling frequency. The temperature monitoring module includes sensors uniformly arranged at different positions of the battery pack and a signal processing unit. After the original temperature signal is filtered and amplified, the digital temperature data is transmitted to the intelligent control unit.

[0044] II. Collecting SOC parameters: The SOC parameters are collected by the battery management system. The battery management system is responsible for real-time monitoring of the state of charge of the energy storage battery, providing data support for subsequent control strategies. The real-time state of charge of the battery is collected by the battery management system, and the sampling frequency is 1 Hz; the SOC critical value S0 is set to 20% to ensure the minimum power of the distribution network voltage stability, and S1 is set to 95% to avoid the upper limit of overcharging.

[0045] 1) When SOC < 20%, the system prioritizes charging the battery, which is dominated by the intelligent control unit, cooperates with the charging module and the constant temperature adjustment module, suspends unnecessary temperature adjustment, prioritizes reverse charging through the power grid, and restores normal control after the SOC is increased to 25%. During the charging process, the intelligent control unit monitors the SOC change in real time to ensure that the charging power is adapted to the capacity of the power grid, avoiding additional load impact on the distribution network.

[0046] 2) When SOC > 95%, the intelligent control unit is linked with the charging circuit, the intelligent control unit sends instructions to close the charging circuit, cutting off the charging channel of the battery from the distributed power or the power grid. At this time, the temperature adjustment is still executed according to the fuzzy PID logic, but the charging function is suspended to avoid safety problems caused by overcharging. When the SOC drops below 95%, the intelligent control unit reactivates the charging circuit and restores the normal charging function.

[0047] III. Collecting voltage state parameters, the voltage sampling of the distribution network node voltage sensor is a module for collecting voltage parameters, and the specific working mode is as follows: the voltage signal of the distribution network is collected at a sampling frequency of 0.5 Hz, the collected voltage value is compared with the threshold rated voltage, the voltage state is determined, and the result is transmitted to the intelligent control unit to provide voltage state data for the "fuzzy PID-SOC-voltage collaborative algorithm" and support the dynamic adjustment of the subsequent control strategy. The voltage signal is collected by the distribution network node voltage sensor, and the sampling frequency is 0.5 Hz; the voltage threshold U0 is set to 95% of the rated voltage, and when U<U0, it is determined to be a low voltage state, at this time, U=1, otherwise, it is a normal state, U=0.

[0048] After obtaining the above data, the algorithm is used to double-determine the voltage state U and the SOC value, and the control priority is dynamically switched to ensure that the core function of low voltage management is not interrupted. The execution module is an intelligent control unit, which realizes dynamic priority switching through the integrated "fuzzy PID-SOC-voltage collaborative algorithm", and the specific steps are as follows: S1: The intelligent control unit receives parameters: voltage state parameters: collected by the distribution network node voltage sensor, to determine whether it is a low voltage state, wherein U=1 indicates low voltage, and U=0 indicates normal. SOC parameters: collected by the battery management system, to obtain the real-time state of charge of the battery, and compared with the critical values =20% and =95%. The intelligent control unit determines the voltage state U and the SOC value through the algorithm, and dynamically switches the priority of control.

[0049] S2: When the low voltage state U=1, the discharge compensation function of the energy storage device is prioritized, and the temperature regulation is subject to the SOC safety constraint: if S≥ : synchronous execution of discharge compensation + temperature regulation, and the temperature regulation power does not affect the discharge output; if S< : trigger "emergency charging priority", suspend unnecessary temperature regulation, only retain basic protection regulation when the temperature is <0℃ to avoid physical damage to the battery, and preferentially charge through the power grid to increase the SOC to 25%, and restore normal control.

[0050] S3: Normal state (U=0): prioritize stabilizing the battery temperature to the optimal interval, and simultaneously manage the SOC in the safe range. If 20%≤SOC≤95%, take temperature control as the core, and adjust the heating device through the fuzzy PID algorithm; if SOC<20%: start charging and use the excess power of the distributed power or the low valley power of the power grid until SOC≥30%; if SOC≥95%: close the charging circuit to avoid overcharging.

[0051] The embodiment sets fuzzy PID temperature control logic, aiming at the hysteresis of the coupling of battery temperature control charging and discharging heat and environmental temperature, and uses fuzzy PID algorithm to realize accurate regulation, the specific process is as follows: A1: Collect the average temperature T of the battery pack through the temperature monitoring module (such as platinum resistance sensor), the sampling frequency is 1Hz; Synchronously obtain the temperature deviation of the last time , calculate the core parameters temperature deviation e and deviation rate of change e c .

[0052] A2: First, fuzzy processing is carried out, the temperature deviation e is divided into 5 fuzzy subsets (negative large, negative small, zero, positive small, positive large), corresponding to the temperature range (-5℃, -2℃, -0.5℃, 0.5℃, 2℃, 5℃); The rate of change of deviation e c is divided into 5 fuzzy subsets (negative large, negative small, zero, positive small, positive large), corresponding to the change rate range (-2℃ / s, -0.5℃ / s, -0.1℃ / s, 0.1℃ / s, 0.5℃ / s, 2℃ / s).

[0053] A3: Design of fuzzy rule base is made, 5x5 fuzzy rule matrix is established, for example: when e =“negative large” (temperature is far below 20℃) and e c =“negative small” (temperature decreases slowly), output heating mode; When e =“zero” (temperature is close to 25℃) and e c =“zero” (temperature is stable), output the current state; When e =“positive large” (temperature is far higher than 30℃) and e c =“positive small” (temperature rises slowly), output stop. The matrix sets different membership degrees according to different conditions. For example, the membership degree of “weak heating” is 0.3, the membership degree of “medium heating” is 0.6, and the membership degree of “strong heating” is 0.8.

[0054] A4: Fuzzy reasoning and defuzzification, as the core link connecting fuzzy logic judgment and actual control action, the purpose is to convert the fuzzy input variables into accurate heating power instructions, the specific process is as follows: According to the above fuzzy temperature deviation e and deviation rate of change e c , match the design of fuzzy rule base made above, get the fuzzy output set, that is, the fuzzy language description of heating power, such as strong heating; Weak heating; Stop, etc. For example, if the inpute = negative large and e c = negative small, according to the rule base matched to "output strong heating", the membership degree of "strong heating" in the fuzzy output set is activated.

[0055] The fuzzy inference obtains a fuzzy set of heating intensity, which needs to be converted into an accurate power value to drive the heater. This step can convert abstract fuzzy rules into specific execution parameters, retaining the adaptability of fuzzy control to complex working conditions and ensuring the accuracy of heating power through precise calculation, providing a reliable basis for subsequent PID parameter tuning and execution output.

[0056] A5: By calculating the "center of gravity" position of the fuzzy set through the area center of gravity method, the most representative accurate value is obtained, and the formula is as follows: (3); In the formula, is the discrete value of the heating power, is the membership degree corresponding to the power value.

[0057] In this embodiment, the PID parameters are self-tuned according to the adjustment amount output by the fuzzy rules, dynamically correcting the proportional coefficient ( K p ), integral coefficient ( K i ), and derivative coefficient ( K d ) of the PID controller: when the temperature deviation is large, increase K p and decrease K i to quickly eliminate the deviation; when the temperature is close to the target, decrease K p and increase K i to avoid overshoot and stabilize the temperature; when the deviation change rate is large, increase K d to suppress the temperature from fluctuating sharply.

[0058] The execution output of this embodiment calculates the power command of the heating device 0W~1500W according to the tuned PID parameters, drives the actuator through the PWM signal, and increases the SOC constraint adaptation: if 20%≤SOC≤25%, limit the heating power ≤500W; if SOC≥25%, release full power regulation; if SOC<20%, only keep the anti-freezing heating, and give priority to charging, wherein: 1) Considering the SOC constraint on temperature regulation, when the SOC is between 20%-25%, the algorithm automatically reduces the temperature regulation power ≤500W to avoid excessive heating consumption, when the SOC ≥25%, the full power temperature regulation is restored, if the SOC <20%: only the anti-freezing heating is retained, and the charging is prioritized.

[0059] 2) Considering voltage response adaptation, in low voltage state U=1, priority is given to discharge compensation, and heating power does not exceed 10% of discharge power.

[0060] The influence of voltage response on charging and discharging, in low voltage state (U=1), the algorithm adjusts the discharge power according to the voltage drop amplitude △U As shown in formula 4: (4).

[0061] Dynamic adjustment of discharge power, as shown in formula 5, to ensure rapid voltage recovery; during discharging, if the temperature deviates from the optimal interval, priority is given to discharge power, and only the temperature is fine-tuned through fuzzy PID to avoid excessive adjustment affecting discharging: (5); In the formula, k is the proportional coefficient, which is not more than 1.2 times the rated power.

[0062] The present embodiment introduces a closed-loop feedback correction mechanism, which is executed by the intelligent control unit to dynamically adjust the control parameters or thresholds according to the actual temperature fluctuation, SOC fluctuation or voltage state, and through the integration of "fuzzy PID-SOC-voltage cooperative algorithm", the real-time data of temperature monitoring module, battery management system and power grid node voltage sensor are received, and the feedback deviation is dynamically corrected through algorithm logic.

[0063] With a period of 0.1s, the intelligent control unit compares the actual output with the target value and performs the following correction: The optimal operating temperature interval used in the present embodiment is 20-30℃, which meets the maximum battery chemical stability and optimal energy conversion efficiency, and a temperature-performance correlation model is established based on the current interval as a control reference.

[0064] Operation when temperature fluctuation is greater than or equal to ±1℃, when the actual temperature collected by the temperature monitoring module deviates from the optimal operation temperature interval by more than ±1℃, the intelligent control unit calls the fuzzy rule base correction function; analyze the deviation type, such as "positive small deviation" means that the temperature is slightly higher than 30℃, "negative small deviation" means that the temperature is slightly lower than 20℃, at this time, the adjustment weight of "positive small / negative small" deviation in the membership function is enlarged, for example, the original "positive small deviation" membership weight is 0.3, and after correction, it is raised to 0.5. The fuzzy PID algorithm recalculates the heating power based on the corrected membership function, and executes through the constant temperature adjustment module until the temperature fluctuation is less than or equal to ±1℃.

[0065] Operation when SOC fluctuates frequently near 20%, when the SOC collected by the battery management system fluctuates frequently near 20%, the intelligent control unit cooperates with the charging module to control, when the intelligent control unit determines that the fluctuation is "frequent switching of charging and discharging", temporarily relaxes the charging start threshold from =20% to 18%, when the SOC is reduced to below 18%, the charging is started, and after the SOC is stabilized above 20% and the fluctuation is reduced, the original threshold of =20% is restored, avoiding long-term relaxation leading to deep discharge of the battery.

[0066] When the distributed power generation capacity is excessive and the SOC is less than 95%, the intelligent control unit automatically starts the charging mode, the charging power is adapted to the size of the excess power, and the constant temperature system adjusts the temperature synchronously during the charging process, to ensure that the charging efficiency is maintained above 92%.

[0067] Through the above algorithm logic, the embodiment realizes the deep cooperation of "temperature stability-power safety-voltage governance", solves the problems of conflict between temperature regulation and core function in traditional control and insufficient adaptability under complex working conditions, provides core technical support for energy storage devices to efficiently participate in low voltage governance of the power distribution network, realizes intelligent priority management, and improves the control precision and dynamic response speed.

[0068] The above embodiments are only preferred technical solutions of the present application, and should not be regarded as limiting the present application, the protection scope of the present application should be based on the technical solutions recited in the claims, including equivalent replacement solutions of the technical features recited in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present application.

Claims

1. An energy storage device thermostatic system for low voltage management of an electrical distribution network, characterized in that, The application relates to a constant-temperature system for a power storage device of a power distribution network low-voltage management system. The constant-temperature system comprises an intelligent constant-temperature box, an energy storage battery pack, a constant-temperature adjusting module, a temperature monitoring module and an intelligent control unit, wherein the modules are interconnected through a circuit and a data link to form a closed-loop control system. The temperature monitoring module is composed of a sensing sensor and a signal processing unit, and the output end of the temperature monitoring module is connected to the intelligent control unit, so that the temperature data of the energy storage battery pack can be collected in real time. The constant-temperature adjusting module is connected to the intelligent control unit at one end and acts on the energy storage battery pack at the other end, and is used as an execution unit for temperature control. The intelligent control unit is integrated with a fuzzy PID-SOC-voltage cooperative control algorithm. The fuzzy PID-SOC-voltage cooperative control algorithm is used for receiving the temperature data of the temperature monitoring module, the SOC data of a battery management system and the voltage state data of a power distribution network node voltage sensor, dynamically switching the control priority according to the temperature deviation, dynamically coordinating the working state of the constant-temperature adjusting module and the charging and discharging behavior of the energy storage battery pack, and realizing the cooperative control of temperature stability, power safety and voltage management.

2. The constant-temperature system for the power storage device of the power distribution network low-voltage management system according to claim 1, wherein the temperature monitoring module collects the multi-point temperature of the battery pack at a preset sampling frequency. The temperature monitoring module comprises sensors and signal processing units which are uniformly arranged at different positions of the battery pack. The signal processing units filter and amplify the original temperature signals and then transmit the digitized temperature data to the intelligent control unit.

3. The constant-temperature system for the power storage device of the power distribution network low-voltage management system according to claim 1, wherein the constant-temperature adjusting module comprises a heating device. When the temperature is lower than the lower limit of the optimal operating temperature interval, the constant-temperature adjusting module starts the heater to increase the temperature of the battery pack through resistance heating, and the heater has a self-limiting temperature characteristic. When the temperature is within the optimal operating temperature interval, the constant-temperature adjusting module reduces the heating power. When the temperature is higher than the upper limit of the optimal operating temperature interval, the constant-temperature adjusting module is in a standby state. The battery pack is wrapped with a heat insulation layer to form a sealed structure.

4. The constant-temperature system for the power storage device of the power distribution network low-voltage management system according to claim 3, wherein the optimal operating temperature interval is 20-30 DEG C. The temperature-performance correlation model based on the current interval is used as a control reference.

5. The constant-temperature system for the power storage device of the power distribution network low-voltage management system according to claim 1, wherein the fuzzy PID-SOC-voltage cooperative control algorithm executes the following steps: collecting the temperature deviation e, the temperature deviation change rate ec, the SOC value and the power distribution network voltage state U, dynamically switching the control priority according to the voltage state U and the SOC value, when U=1, preferentially executing low-voltage compensation, and the temperature adjustment being constrained by the SOC value, when U=0, preferentially executing temperature control, and starting the fuzzy PID adjustment when the SOC value is within a safe range, outputting a heating power instruction through a fuzzy inference and a PID parameter self-tuning mechanism, and controlling the working state of the constant-temperature adjusting module, specifically as follows: S101: Determine the voltage state: Collect voltage signals through power grid node voltage sensors at a preset voltage sampling frequency, set the voltage threshold U0=95% rated voltage, when voltage < U0, it is low voltage state: U=1, otherwise it is normal state: U=0; S102: SOC constraint boundary control: preset critical threshold and the second threshold If the current SOC < ω , suspend the non-essential temperature control and start charging, and when the current SOC > ω , restore normal control, wherein > ω > 1; if ≤ the current SOC ≤ ω , adjust the temperature by full power through a fuzzy PID algorithm; when the SOC > ω , close the charging loop; S103: Dynamic priority switching: U = 1 : if SOC ≥ 25%, perform discharge compensation and limit temperature regulation power to ≤ 10% of discharge power; if SOC < 25%, only allow anti-icing heating and preferentially charge to SOC ≥ 25% ; U = 1 : if SOC ≥ 25%, perform discharge compensation and limit temperature regulation power to ≤ 10% of discharge power; if SOC < 25%, only allow anti-icing heating and preferentially charge to SOC ≥ 25% ; U = 1 : if SOC ≥ 25%, perform discharge compensation and U=0: if , the temperature is regulated by fuzzy PID algorithm with full power; , the charging is started to ; S104: Fuzzy PID temperature regulation: Collect the average temperature T of the battery pack, calculate the temperature deviation e = T - T0 and the deviation change rate , the formula is: ； wherein represents the temperature deviation rate, e' represents the temperature deviation at the previous time point; e and The e is divided into multiple fuzzy subsets and a fuzzy rule matrix is established, the accurate heating power is obtained through fuzzy reasoning and de-fuzzification, the PID parameter is dynamically set, and the power instruction is output to drive the constant temperature regulation module.

6. The electric power grid low voltage management energy storage device thermostatic system of claim 5, wherein, The discharge power in low voltage state is dynamically adjusted according to the formula: ； wherein k is a proportionality factor, is the voltage dip magnitude.

7. The energy storage device constant temperature system for low voltage management of power grid according to claim 5, characterized in that, The fuzzy output is converted into accurate power value by using area barycenter method, and the PID parameters are dynamically adjusted: ； wherein, is a discrete value of heating power, is a membership degree of the corresponding power value.

8. The energy storage device constant temperature system for low voltage management of power grid according to claim 7, characterized in that, In the fuzzy PID temperature regulation, the PID parameter self-tuning rule is: when the temperature deviation is large, increase the proportional coefficient Kp and reduce the integral coefficient Ki; when the temperature is close to the target, reduce Kp and increase Ki; when the deviation rate is large, increase the differential coefficient Kd.

9. The energy storage device constant temperature system for low voltage management of power grid according to claim 1, characterized in that, The energy storage device further comprises a modular structure design, including: A cabinet including incoming line switch, fuse, surge protector, bidirectional inverter module and main control board; B cabinet is a sealed battery box, including the energy storage battery pack, temperature monitoring module and constant temperature regulation module; the A cabinet and the B cabinet are connected through cable to realize power and signal transmission, the bidirectional inverter module in the A cabinet is used for AC / DC conversion: when charging, the AC power input from the power grid is rectified into DC power to charge the battery in the B cabinet; when discharging, the DC power output from the battery in the B cabinet is inverted into AC power to inject into the power grid to compensate for low voltage.

10. The electric power grid low voltage management energy storage device thermostatic system of claim 1, wherein, It further comprises a closed-loop feedback correction mechanism for dynamically adjusting control parameters or threshold values according to actual temperature fluctuations, SOC fluctuations or voltage states.

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