CN-121983713-A - Heat dissipation optimization control method and system for energy storage battery pack

CN121983713ACN 121983713 ACN121983713 ACN 121983713ACN-121983713-A

Abstract

The invention provides a heat dissipation optimization control method and a heat dissipation optimization control system for an energy storage battery pack, and relates to the field of intelligent heat control of electric power energy storage; and in the sequence of the adaptive heat dissipation regulation and control period, carrying out iterative optimization of a heat dissipation control scheme according to the adaptive heat dissipation control optimization strategy, and determining an optimal heat dissipation control scheme to carry out iterative heat dissipation regulation and control. The heat dissipation control scheme of the traditional energy storage battery pack depends on a fixed threshold value and a reactive strategy, the heat dissipation strategy cannot be accurately matched with a battery operation mode, the technical problems of temperature control response lag, heat dissipation overshoot or deficiency and the like are caused, the fine and self-adaptive regulation and control of a liquid cooling system of the energy storage battery pack can be realized, and the temperature control precision, the safety and the overall energy efficiency of the liquid cooling system under a dynamic working condition are remarkably improved.

Inventors

Shi Chenlan
HAO HONGKE
ZHANG YANCHEN
ZHAO XIAOYI
TANG XINYUE
ZHU YONGQI
PENG XIAOLU
LI ZIQI

Assignees

西北农林科技大学

Dates

Publication Date: 20260505
Application Date: 20260206

Claims (10)

1. The heat dissipation optimization control method of the energy storage battery pack is characterized by comprising the following steps of: Carrying out battery heat production fluctuation analysis according to a predicted discharge power sequence and a predicted environment temperature sequence of the energy storage battery pack in a future preset time zone to obtain predicted battery heat production fluctuation degree; setting an adaptive heat dissipation regulation and control period and an adaptive heat dissipation control optimization strategy based on the predicted battery heat generation fluctuation degree, and dividing the preset time zone according to the adaptive heat dissipation regulation and control period to obtain an adaptive heat dissipation regulation and control period sequence; And in the adaptive heat dissipation regulation and control period sequence, performing iterative optimization of a heat dissipation control scheme according to the adaptive heat dissipation control optimization strategy, and determining an optimal heat dissipation control scheme to perform iterative heat dissipation regulation and control on the energy storage battery pack.
2. The method for optimizing heat dissipation control of an energy storage battery pack according to claim 1, wherein obtaining a predicted discharge power sequence of the energy storage battery pack within a preset time zone comprises: acquiring a predicted physical state index sequence, a predicted power market node electricity price sequence and a predicted frequency modulation auxiliary service market price sequence of a power grid participating in frequency modulation of the energy storage battery pack in a preset time zone; Performing index change state analysis on the predicted physical state index sequence to obtain a physical index variation coefficient; performing index change state analysis on the predicted power market node electricity price sequence to obtain a node electricity price variation coefficient; Performing index change state analysis on the forecast frequency modulation auxiliary service market price sequence to obtain price change coefficients; and determining a predicted complex index based on the physical index variation coefficient, the node electricity price variation coefficient and the price-finding variation coefficient, calling a pre-constructed discharge power prediction plug-in, and predicting and obtaining a predicted discharge power sequence of the energy storage battery pack in a preset time zone according to the predicted physical state index sequence, the predicted electricity price sequence of the electricity market node and the price-finding sequence of the predicted frequency modulation auxiliary service market.
3. The method of claim 2, wherein determining a predictive complexity index based on the physical index variation coefficient, the node price variation coefficient, and the price-for-play variation coefficient evaluation, invoking a pre-built discharge power prediction plug-in, comprising: acquiring a historical maximum physical index variation coefficient, a historical maximum node electricity price variation coefficient and a historical maximum clear price variation coefficient of the power grid in a historical time range; setting the ratio of the physical index variation coefficient to the historical maximum physical index variation coefficient as a first prediction complexity coefficient; setting the ratio of the node electricity price variation coefficient to the historical maximum node electricity price variation coefficient as a second prediction complexity coefficient; setting the ratio of the price change coefficient to the historical maximum price change coefficient as a third prediction complexity coefficient; obtaining a prediction complexity index according to the first prediction complexity coefficient, the second prediction complexity coefficient and the third prediction complexity coefficient through weighted calculation; The method comprises the steps of pre-constructing a discharge power prediction plug-in, wherein the discharge power prediction plug-in comprises D discharge power prediction units, the discharge power prediction units are obtained by taking attribute characteristics of an energy storage battery pack as retrieval constraint, collecting D sample training sets and performing supervision training on a long-short-time memory network, sample input data of the sample training sets are sample physical state index sequences, sample electric power market node electricity price sequences and sample frequency modulation auxiliary service market price clearing sequences, and sample output data are sample discharge power sequences; and rounding the product of the predicted complex index and D to obtain the optimal unit selection quantity F, randomly selecting F predicted units from the D discharge power prediction units, predicting according to the predicted physical state index sequence, the predicted power market node electricity price sequence and the predicted frequency modulation auxiliary service market clear price sequence, and performing mean fitting on F predicted results to obtain a predicted discharge power sequence.
4. The method for optimizing and controlling heat dissipation of an energy storage battery pack according to claim 1, wherein the performing the battery heat generation fluctuation analysis according to the predicted discharge power sequence of the energy storage battery pack in a preset time zone in the future and the predicted ambient temperature sequence to obtain the predicted battery heat generation fluctuation degree comprises: Carrying out discharge power fluctuation calculation according to the predicted discharge power sequence to obtain a first battery heat production fluctuation coefficient; Carrying out temperature fluctuation calculation according to the predicted environmental temperature sequence to obtain a heat generation fluctuation coefficient of the second battery; and calculating according to the first battery heat production fluctuation coefficient and the second battery heat production fluctuation coefficient to obtain the predicted battery heat production fluctuation degree.
5. The method for optimizing heat dissipation control of an energy storage battery according to claim 4, wherein calculating the predicted battery heat generation fluctuation degree according to the first battery heat generation fluctuation coefficient and the second battery heat generation fluctuation coefficient comprises: Carrying out average value calculation on the predicted environmental temperature sequence to obtain a predicted environmental temperature average value; if the predicted ambient temperature average meets the working temperature threshold of the energy storage battery pack, taking an initial weight ratio as a current adaptive weight, wherein the initial weight ratio comprises a first initial weight and a second initial weight, the first initial weight is 0.9, and the second initial weight is 0.1; if the predicted ambient temperature average value exceeds the working temperature threshold value of the energy storage battery pack, calculating to obtain overflow temperature deviation according to the predicted ambient temperature average value, and adding 1 to the ratio of the overflow temperature deviation to a preset temperature difference step length to be used as a second compensation coefficient, wherein the preset temperature difference step length is 10 ℃; taking the product of the second compensation coefficient and the second initial weight as a second adaptation weight, subtracting the second adaptation weight from 1 to obtain a first adaptation weight, and taking the first adaptation weight and the second adaptation weight as current adaptation weights; And calculating according to the first battery heat production fluctuation coefficient and the second battery heat production fluctuation coefficient based on the current adaptive weight to obtain the predicted battery heat production fluctuation degree.
6. The heat dissipation optimization control method of an energy storage battery pack according to claim 1, wherein the setting of the adaptive heat dissipation regulation period based on the predicted battery heat generation fluctuation degree comprises: setting the ratio of the heat generation fluctuation degree of a preset standard battery to the heat generation fluctuation degree of the predicted battery as a heat dissipation regulation compensation coefficient; And setting the product of the heat dissipation regulation compensation coefficient and the initial heat dissipation regulation period of the energy storage battery pack as an adaptive heat dissipation regulation period.
7. The method of claim 6, wherein the setting the adaptive heat dissipation control optimization strategy based on the predicted battery heat generation fluctuation comprises: The method comprises the steps of obtaining a heat dissipation monitoring index set of the energy storage battery pack, wherein the heat dissipation monitoring index set at least comprises total current of the battery pack, charge state of the battery pack, highest temperature of a battery, lowest temperature of the battery, maximum temperature difference of a battery pack, ambient temperature in a battery compartment, cooling medium inlet temperature, cooling medium outlet temperature and cooling medium flow; Based on historical heat dissipation operation logs of similar energy storage battery packs, respectively carrying out correlation analysis on Q heat dissipation monitoring indexes in the heat dissipation monitoring index sets and the battery heat production state of the energy storage battery packs, and sequencing the Q heat dissipation monitoring indexes according to the index correlation degree from large to small to generate a heat dissipation monitoring index sequence, wherein Q is the number of heat dissipation monitoring indexes in the heat dissipation monitoring index sets; obtaining K by rounding the product of the heat dissipation regulation compensation coefficient and Q, and selecting the first K heat dissipation monitoring indexes in the heat dissipation monitoring index sequence as an adaptive heat dissipation monitoring index set, wherein K is more than or equal to 2 and less than or equal to Q; taking the product of the regulating compensation coefficient and the initial optimizing convergence number as the adaptive optimizing convergence number, and taking the product of the reciprocal of the regulating compensation coefficient and the initial optimizing step length as the adaptive optimizing step length; And taking the adaptive heat dissipation monitoring index set, the adaptive optimizing convergence times and the adaptive optimizing step length as an adaptive heat dissipation control optimizing strategy.
8. The method for optimizing and controlling heat dissipation of an energy storage battery pack according to claim 7, wherein in the adaptive heat dissipation control period sequence, performing iterative optimization of a heat dissipation control scheme according to the adaptive heat dissipation control optimization strategy, determining an optimal heat dissipation control scheme to perform iterative heat dissipation control on the energy storage battery pack comprises: Selecting a first adaptive heat dissipation regulation period from the adaptive heat dissipation regulation period sequence as a first regulation period, and setting adjacent regulation periods of the first regulation period as second regulation periods; Monitoring and acquiring a first heat dissipation monitoring data sequence set in the first regulation and control period according to the adaptive heat dissipation monitoring index set; respectively carrying out fluctuation analysis on a plurality of first heat dissipation monitoring data sequences in the first heat dissipation monitoring data sequence set, and carrying out weighted calculation to obtain a first heat dissipation monitoring fluctuation coefficient, wherein an index weight value and an index association degree are positively correlated; Multiplying the ratio of the preset standard heat radiation monitoring fluctuation coefficient to the first heat radiation monitoring fluctuation coefficient by the adaptive optimizing convergence times and rounding to obtain a first adaptive optimizing convergence times; Multiplying the ratio of the first heat radiation monitoring fluctuation coefficient to a preset standard heat radiation monitoring fluctuation coefficient by the adaptive optimizing step length to obtain a first adaptive optimizing step length; Performing iterative optimization of the heat dissipation control scheme based on the first heat dissipation monitoring data sequence set, the first adaptive optimizing convergence times and the first adaptive optimizing step length to obtain a second optimal heat dissipation control scheme; And in the second regulation period, carrying out heat dissipation regulation and control on the energy storage battery pack according to the second optimal heat dissipation control scheme, and carrying out iterative heat dissipation regulation and control according to the sequence of the adaptive heat dissipation regulation and control period.
9. The method for optimizing heat dissipation control of an energy storage battery pack according to claim 8, wherein performing iterative optimization of a heat dissipation control scheme based on the first heat dissipation monitoring data sequence set, the first adaptation optimizing convergence number and the first adaptation optimizing step length, and obtaining a second optimal heat dissipation control scheme comprises: Acquiring a heat dissipation control parameter adjustment threshold of a heat dissipation device of the energy storage battery pack, and randomly selecting a plurality of initial heat dissipation control parameter groups in the heat dissipation control parameter adjustment threshold to serve as a plurality of heat dissipation control schemes, wherein the heat dissipation device is a liquid cooling heat dissipation system, and the heat dissipation control parameters at least comprise the rotation speed of a water pump, the opening degree of a branch valve and the temperature of a cooling liquid outlet; in a heat dissipation control simulation space, respectively combining the first heat dissipation monitoring data sequence set with the plurality of heat dissipation control schemes, carrying out heat dissipation simulation, outputting a plurality of heat dissipation simulation results, and evaluating according to the plurality of heat dissipation simulation results to obtain a plurality of heat dissipation fitness values, wherein the heat dissipation simulation results comprise a simulated battery maximum temperature sequence, a simulated battery maximum temperature difference sequence, a simulated temperature tracking error sequence and a simulated device energy consumption; Setting a heat radiation control scheme as an initial solution, arranging a plurality of initial solutions from large to small according to heat radiation fitness, generating an initial solution sequence, setting the first solution of the initial solution sequence as an optimal solution, and setting the rest initial solutions as inferior solutions to obtain a plurality of inferior solutions; Taking the optimal solution as a direction, and adjusting the plurality of inferior solutions according to the first adaptive optimizing step length to obtain a plurality of once updated inferior solutions; Reordering the optimal solution and the plurality of primary update inferior solutions according to the heat radiation fitness from large to small to generate a primary update solution sequence; and continuing to perform iterative optimization of the heat dissipation control scheme based on the primary updated solution sequence until the first adaptive optimization convergence times are reached, and outputting an optimal solution of the current updated solution sequence to be set as a second optimal heat dissipation control scheme.
10. A heat radiation optimizing control system of energy storage battery pack is characterized in that, a step for implementing a heat dissipation optimization control method of an energy storage battery pack according to any one of claims 1 to 9, comprising: The battery heat production fluctuation analysis module is used for carrying out battery heat production fluctuation analysis according to a predicted discharge power sequence of the energy storage battery pack in a future preset time zone and a predicted environment temperature sequence to obtain predicted battery heat production fluctuation degree; the heat dissipation regulation scheme optimizing module is used for setting an adaptive heat dissipation regulation period and an adaptive heat dissipation control optimizing strategy based on the predicted battery heat generation fluctuation degree, dividing the preset time zone according to the adaptive heat dissipation regulation period and obtaining an adaptive heat dissipation regulation period sequence; And the battery iterative heat dissipation control module is used for carrying out iterative optimization of a heat dissipation control scheme according to the adaptive heat dissipation control optimization strategy in the adaptive heat dissipation control period sequence, and determining an optimal heat dissipation control scheme to carry out iterative heat dissipation control on the energy storage battery pack.

Description

Heat dissipation optimization control method and system for energy storage battery pack Technical Field The invention relates to the field of intelligent thermal control of electric power energy storage, in particular to a heat dissipation optimization control method and system of an energy storage battery pack. Background In energy storage systems, particularly large-scale energy storage power stations on the power grid side, a battery thermal management system is a key link for guaranteeing safe, efficient and long-life operation. However, as the role of energy storage in new power systems evolves from simple energy storage to providing multiple services such as frequency modulation, peak shaving, standby, etc., the operating conditions exhibit unprecedented dynamics and complexity, and in this context, the heat dissipation control scheme of conventional energy storage battery packs has revealed fundamental technical limitations. The current mainstream heat dissipation control technology generally depends on switch control or classical PID feedback control based on a fixed temperature threshold value, the scheme essentially belongs to a passive response strategy, the control logic only reacts to real-time or historical temperature data of a battery, and core driving factors which cause heat generation of the battery, namely future power and heat generation change trend determined by a power grid dispatching instruction, an electric power market signal and an operation mode are completely ignored, the control mode which only treats the symptoms but does not treat the cause, and the actual operation of a heat dissipation system and the battery is severely disjointed, and the scheme is particularly characterized in that when the battery is subjected to severe power impact for responding to a power grid second-level frequency modulation instruction, the traditional temperature control system responds to severe hysteresis due to the unpredictability of the heat generation peak, so that instantaneous heat accumulation risk is caused, and in a power stable stage, heat dissipation overshoot or deficiency is easy to occur due to strategy stiffness, so that energy is wasted and the temperature uniformity of the battery is influenced. In summary, the conventional fixed threshold and reactive strategy cannot meet the requirement of the power grid on the rapid, accurate and efficient adjustment of the energy storage device under the high-proportion new energy access, and further improvement of the overall economy and safety of the energy storage power station is restricted. Disclosure of Invention The invention aims to provide a heat dissipation optimization control method and a heat dissipation optimization control system of an energy storage battery pack, which are used for solving the technical problems that a heat dissipation strategy cannot be accurately matched with a battery operation mode due to the dependence of a fixed threshold value and a reactive strategy on a heat dissipation control scheme of a traditional energy storage battery pack, and temperature control response lag, heat dissipation overshoot or deficiency and the like are caused, and comprise the following steps: The invention provides a heat dissipation optimization control method of an energy storage battery pack, which comprises the steps of carrying out battery heat production fluctuation analysis according to a predicted discharge power sequence and a predicted environment temperature sequence of the energy storage battery pack in a future preset time zone to obtain predicted battery heat production fluctuation degree, setting an adaptive heat dissipation regulation and control period and an adaptive heat dissipation control optimization strategy based on the predicted battery heat production fluctuation degree, dividing the preset time zone according to the adaptive heat dissipation regulation and control period to obtain an adaptive heat dissipation regulation and control period sequence, carrying out iterative optimization of a heat dissipation control scheme according to the adaptive heat dissipation control optimization strategy in the adaptive heat dissipation regulation and control period sequence, and determining an optimal heat dissipation control scheme to carry out iterative heat dissipation regulation and control on the energy storage battery pack. The invention further provides a heat dissipation optimization control system of the energy storage battery pack, which comprises a battery heat generation fluctuation analysis module, a heat dissipation regulation and control scheme optimization module and a battery iteration heat dissipation control module, wherein the battery heat generation fluctuation analysis module is used for carrying out battery heat generation fluctuation analysis according to a predicted discharge power sequence and a predicted environment temperature sequence of the energy storage battery pack in a future p