Charging and discharging control method and device of battery pack

Abstract

The invention discloses a charging and discharging control method and device for a battery pack, and belongs to the technical field of batteries, and the method comprises the steps: obtaining the quality parameter of to-be-charged electric energy, setting a preliminary charging method, calculating the charging quality, and carrying out the grading of the charging quality through a set quality threshold value, the to-be-charged battery pack is classified according to the grade of the charging quality, so that classified storage of charging electric energy and reasonable configuration of the to-be-charged battery pack are realized, and a preliminary charging strategy is generated; constructing an identification model, setting a fluctuation identification method, identifying fluctuation of charging electric energy under different time scales, setting a fluctuation adjustment method, and dynamically adjusting the preliminary charging strategy according to the fluctuation type; the method comprises the following steps: acquiring a discharge demand in real time, setting a discharge optimization method, and once the discharge demand is detected, making a discharge plan, setting a battery discharge priority and dynamically adjusting the discharge plan so as to realize efficient utilization of energy and guarantee stability and high efficiency of a battery discharge process.

Description

Technical Field

[0001] The present invention relates to the technical field of batteries, and in particular to a battery charge and discharge control method and device for the organic combination of photovoltaic power generation, wind power generation, energy storage technology, and electric vehicle charging facilities. Background Art

[0002] Currently, the world is actively promoting energy transformation. The disadvantages of traditional energy are becoming prominent, and there is an urgent need to vigorously develop renewable energy. Although wind and photovoltaic power generation technologies are constantly improving, their power generation is unstable. The diversified development of energy storage technology and the rise of intelligent scheduling technology bring opportunities to solve this problem. At the same time, the number of new energy vehicles in use has increased sharply, and the charging demand has increased greatly, and traditional charging impacts the power grid. Based on this, building a battery management system adapted to diversified energy storage technologies has become the key to improving energy utilization efficiency.

[0003] The Chinese patent with the publication number CN106169622A discloses a management method for a storage battery pack applicable to a wind-solar-diesel hybrid power generation system. The invention also provides a management device for a storage battery pack applicable to a wind-solar-diesel hybrid power generation system. If the technical solution of the present invention is adopted in a wind-solar-diesel hybrid power generation system, the following beneficial effects can be obtained: the charging bus and the load bus are separated, and the battery with a lower voltage is automatically charged preferentially, and the battery with a higher voltage discharges preferentially; finally, the voltages of all the batteries can be basically the same; during long-term operation, the voltages of all the batteries can be ensured to be consistent, the occurrence of undercharging or overcharging of the batteries can be eliminated, and the influence such as circulating current caused by direct parallel connection can be avoided, and the batteries can also be added at any time according to different situations; by monitoring the voltages and charge and discharge currents of all the batteries for a long time, the working states of all the batteries can be monitored in real time, and the failed batteries can be detected.

[0004] Although the prior art realizes the basic management of the battery pack by separating the charging and load buses and equalizing the battery voltages, it does not consider the differences in the adaptation scenarios of different energy storage technologies, and does not explore the optimization configuration potential of diversified energy storage technologies under complex electricity demand and geographical environments, resulting in rigid energy storage strategies, and does not establish an advance planning mechanism for multi-time scale fluctuations on the energy supply and demand sides, making it difficult to ensure the balance and stability of power supply and demand. Therefore, the present application provides a battery charge and discharge control method and device. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a battery charge and discharge control method and device, which can dynamically adjust the cooperation strategy of lithium batteries and lithium titanate batteries by identifying the charging quality and energy supply and demand fluctuations, and solve the efficiency and stability problems when multiple types of batteries are used in combination.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A method for controlling the charging and discharging of a battery pack, comprising:

[0008] Obtain the quality parameters of the electric energy to be charged, set a preliminary charging method, calculate the charging quality, classify the charging quality through a set quality threshold, and classify the battery pack to be charged according to the level of the charging quality to generate a preliminary charging strategy;

[0009] Construct an identification model, set a fluctuation identification method to identify the fluctuations of the charging electric energy at different time scales, and at the same time set a fluctuation adjustment method to dynamically adjust the preliminary charging strategy according to the type of fluctuation;

[0010] Obtain the discharge demand in real time, set a discharge optimization method, once the discharge demand is detected, formulate a discharge plan, and set the battery discharge priority to dynamically adjust the discharge plan.

[0011] Furthermore, when generating the preliminary charging strategy, the preliminary charging method includes:

[0012] Collect the quality parameters of the electric energy to be charged in real time, including the actual voltage and the actual frequency of the output electric energy, and calculate the real-time voltage deviation rate and frequency deviation rate;

[0013] Obtain the effective value U of each harmonic voltage a , calculate the harmonic distortion rate, where U1 is the effective value of the fundamental wave voltage, and a = 1, 2, 3,...;

[0014] Assign different weights to the voltage deviation rate, frequency deviation rate and harmonic distortion rate, and calculate the comprehensive quality score Q RT .

[0015] Furthermore, the preliminary charging method further includes:

[0016] Set the sampling interval as t1, collect the charging quality data within a preset time period, and divide the charging quality data into a training set and a test set in chronological order;

[0017] Obtain the tolerance range of the electrical equipment to the charging quality;

[0018] Comprehensively consider the statistical characteristics of the data itself and the equipment tolerance range, and preliminarily set the thresholds of the voltage deviation rate, frequency deviation rate and harmonic distortion rate;

[0019] Calculate the preliminary threshold of the comprehensive quality score, including the first quality threshold Q th1.ele , the second quality threshold Q th2.ele ;

[0020] Calculate the comprehensive quality score Q of each data point in the test set test, and adjust the preliminary threshold using the test set to generate a quality threshold, including a first quality threshold Q th1 and a second quality threshold Q th2 ;

[0021] According to the quality threshold, divide the comprehensive quality score Q RT to determine the category of charging quality; if Q RT < Q th1 , it is determined as high-quality electric energy; if Q th1 ≤ Q RT < Q th2 , it is determined as sub-quality electric energy; if Q RT ≥ Q th2 , it is determined as low-quality electric energy;

[0022] Divide the battery pack to be charged into three categories according to the category of charging quality, namely, first-class batteries, second-class batteries and third-class batteries, calculate the quantity of each type of battery, and generate a preliminary charging strategy.

[0023] Furthermore, the specific steps for configuring the battery pack to be charged include:

[0024] For first-class batteries, use a combination of lithium-ion batteries and lithium titanate batteries, and calculate the number N 1.Li of lithium-ion batteries and the number N LTO of lithium titanate batteries in the first-class batteries;

[0025] Use lithium-ion batteries as second-class batteries and calculate the number N 2.Li of second-class batteries;

[0026] Use lithium-ion batteries as third-class batteries and calculate the number N 3.Li of third-class batteries.

[0027] Furthermore, when identifying the fluctuations of the charging electric energy, the fluctuation identification method includes:

[0028] Collect historical fluctuation data and clean the collected historical fluctuation data;

[0029] Extract short-term fluctuation features, medium-term fluctuation features and long-term fluctuation features from the historical fluctuation data respectively;

[0030] Use the regression logic algorithm to construct a short-term fluctuation identification model, where an output of 0 indicates the presence of short-term fluctuations and an output of 1 indicates the absence of short-term fluctuations; and use the regression logic algorithm to construct a long-term fluctuation identification model, where an output of 1 indicates the presence of long-term fluctuations and an output of 0 indicates the absence of long-term fluctuations;

[0031] Add an adder after the short-term fluctuation identification model and the long-term fluctuation identification model to generate an identification model;

[0032] Input the fluctuating data to be determined into the recognition model for fluctuation determination. 0 indicates the existence of short-term fluctuations, 1 indicates the existence of medium-term fluctuations, and 2 indicates the existence of long-term fluctuations; and output the final predicted fluctuation determination category.

[0033] Furthermore, when dynamically adjusting the preliminary charging strategy, the fluctuation adjustment method includes:

[0034] When the predicted fluctuation determination category is short-term fluctuations, calculate the energy supply-demand difference ΔE within a short period diff.1 , and the adjustment amount ΔN of the lithium titanate ion battery LTO ;

[0035] When the predicted fluctuation determination category is medium-term fluctuations, calculate the energy supply-demand difference ΔE within a medium period diff.2 , and the adjustment amount ΔN of the lithium ion battery 1.Li 、ΔN 2.Li 、ΔN 3.Li ;

[0036] When the predicted fluctuation determination category is long-term fluctuations, calculate the energy supply-demand difference value ΔE diff.3 , and the adjustment amount ΔN' of various types of batteries LTO 、ΔN' 1.Li 、ΔN' 2.Li 、ΔN' 3.Li .

[0037] Furthermore, when dynamically adjusting the discharge plan, the discharge optimization method includes:

[0038] When detecting a discharge demand, read the discharge parameters of the battery pack to be charged and calculate the expected discharge power P ex , and the expected discharge time t ex ;

[0039] Calculate the proportions r1, r2, and r3 of different charging qualities in the future period;

[0040] Compare r1, r2, and r3, and set the battery category corresponding to the charging quality with the highest proportion as the first discharge priority, the second highest proportion as the second discharge priority, and the remaining as the third discharge priority;

[0041] For the battery category determined as the first discharge priority, calculate the current remaining power E of the battery S and determine whether the battery with the first discharge priority meets the discharge demand;

[0042] If E S ≥P ex ×t ex and P max.1≥P max , it is determined that the discharge requirement is met, and the discharge operation is performed; where P max.1 is the maximum discharge power;

[0043] Otherwise, the discharge requirement is not met, and the batteries with the second discharge priority are judged, and so on.

[0044] Furthermore, the discharge optimization method further includes:

[0045] During the charge and discharge process, the discharge current I im and the discharge voltage V im are collected in real time, and the standard discharge curve data is obtained;

[0046] When starting to discharge, enter the constant current discharge stage, and keep the discharge current at I con , and the charging power is P in.1 ;

[0047] When the discharge voltage reaches V con , switch to the constant voltage discharge stage, and keep the discharge voltage constant at V con , and the charging power is P in.2 ;

[0048] When the discharge current drops to I cut , it is determined that the discharge is completed, and the actual discharge time t ac is obtained, and the time deviation rate Δt rate , the total discharge cost C total and the energy utilization rate η use are calculated to generate feedback data.

[0049] Furthermore, the battery charge and discharge control method further includes:

[0050] During the battery charge and discharge process, the battery pack to be charged is monitored, a battery management method is set, and the stable operation of the battery pack is maintained;

[0051] The battery management method includes:

[0052] Based on the sensors equipped for each battery cell, the management parameters are obtained in real time;

[0053] An electrochemical model of the battery is established through offline experiments, and the ampere-hour integration is corrected in combination with the temperature correction coefficient;

[0054] Analyze the battery internal resistance change rate and capacity retention rate, construct a capacity attenuation model, and output the predicted remaining battery life;

[0055] When the voltage deviation of the battery cell exceeds the preset voltage, trigger the active balancing mechanism;

[0056] Optimize the liquid cooling pipeline design through CFD simulation, and configure a PID controller to adjust the coolant flow rate and temperature in real time;

[0057] Establish a three - level early warning system to give early warnings about battery failures.

[0058] The charge - discharge control device for a battery pack includes: a generator set, an energy storage battery, and a charging pile;

[0059] The generator set includes a wind turbine and a photovoltaic panel;

[0060] The energy storage battery includes a lithium - ion battery pack, a lithium titanate - ion battery pack, and a bidirectional DC / DC converter.

[0061] The beneficial effects of the present invention:

[0062] Through the hierarchical configuration and dynamic adjustment strategy of multi - type lithium - ion batteries, the problems of efficient storage and release of different qualities of electric energy are solved; for high - quality electric energy, the combination of lithium - ion batteries and lithium titanate - ion batteries is adopted to ensure a stable discharge process and make up for the defect of insufficient power density of a single lithium - ion battery; for sub - quality electric energy, the charging quality is optimized through the self - charge - discharge process of lithium - ion batteries, reducing the dependence on external processing; low - quality electric energy is stored using low - cost lithium - ion batteries after external processing to balance cost and performance; at the same time, based on the differences in battery characteristics, the number of lithium titanate - ion batteries is dynamically adjusted during short - term fluctuations to quickly respond to power demands, and the configuration of lithium - ion batteries is optimized during medium - and long - term fluctuations, taking both cost and cycle life into account. Brief Description of the Drawings

[0063] Figure 1 It is a flowchart of the charge - discharge control method for the battery pack;

[0064] Figure 2 It is a flowchart of the preliminary charging method of the present invention;

[0065] Figure 3 It is a flowchart of the discharge optimization method of the present invention;

[0066] Figure 4 It is a diagram of the integrated wind - solar - energy - storage - charging device. Detailed Embodiments

[0067] The technical solutions of the present invention will be described in detail below through the drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solutions of the present invention, rather than limitations on the technical solutions of the present invention. Without conflict, the technical features in the embodiments of the present invention and the embodiments can be combined with each other.

[0068] Embodiment 1

[0069] ReferenceFigures 1 to 3 As shown in the figure, this embodiment introduces a charge and discharge control method for a battery pack, including:

[0070] Obtain the electric energy generated by an external power generation device, so as to obtain the quality parameters of the electric energy to be charged. Among them, the electric energy to be charged is the power generation electric energy obtained by the power generation device and not divided by quality, and is used to perform a charging operation on the corresponding type of battery pack to be charged. The battery pack to be charged contains batteries that have not been charged or have zero internal power. Set a preliminary charging method, calculate the charging quality of the electric energy to be charged, and classify the electric energy to be charged through a set quality threshold. The levels of the electric energy to be charged include high-quality electric energy, sub-quality electric energy, and low-quality electric energy. At the same time, classify the battery pack to be charged according to the level of the electric energy to be charged, so as to realize the classified storage of the charging electric energy and the reasonable configuration of the battery pack, generate a preliminary charging strategy, and at the same time set a battery management method to protect the stable operation of the battery through multi-dimensional real-time monitoring and intelligent control of the battery pack; among them, the battery pack to be charged uses lithium-ion batteries as the main battery pack to be charged, and is equipped with lithium titanate batteries to meet the high-power demand in a short time to achieve diversified energy storage;

[0071] Construct an identification model through multiple binary classifiers, set a fluctuation identification method to identify the fluctuations of the charging electric energy at different time scales, and at the same time set a fluctuation adjustment method to dynamically adjust the preliminary charging strategy according to the fluctuation type, reasonably plan the number of various batteries, and perform fluctuation adjustment to perform a charging operation on the battery pack to be charged after planning, and store the charging electric energy of different qualities in the corresponding batteries;

[0072] Real-time capture the charging requirements sent by external devices, such as the charging requirements of charging piles and grid requirements, and convert them into the discharge requirements of the battery pack. Set a discharge optimization method. Once a discharge requirement is detected, formulate a discharge plan, set the battery discharge priority, and monitor and flexibly adjust the discharge plan in real time during the battery discharge process to achieve efficient energy utilization and ensure the stability and efficiency of the battery discharge process;

[0073] Collect the operating parameters of the battery pack in real time through various sensors, compare the collected real-time data with the preset normal operating threshold to judge whether there is an abnormality. Once an abnormality or fault is found, immediately start the corresponding redundant control and protection mechanisms, such as cutting off the power supply of the faulty part and starting the standby equipment; to prevent the expansion of the fault and ensure the safety of personnel and equipment, and at the same time record the fault information to provide a basis for subsequent fault analysis and repair;

[0074] In this embodiment, the integration of wind-solar-storage-charging mainly includes: collecting solar energy and wind energy, and converting solar energy and wind energy into electric energy. At the same time, due to the intermittent and unstable characteristics of solar energy and wind energy, mains power generation is introduced as a backup power source to facilitate supplementary power generation when wind-solar power generation is insufficient, and reduce the charging interruption caused by the fluctuation of wind-solar power generation; to make full use of clean energy, a wind-solar priority power generation strategy is set, and wind-solar power generation is preferentially used to charge energy storage devices, which is convenient for starting mains power generation and releasing electric energy when wind-solar power generation is insufficient, so as to make the most of clean energy, reduce the dependence on mains power, and reduce the electricity cost, which conforms to the environmental protection concept; among them, the solar part is realized by installing solar panels (photovoltaic panels), and the photovoltaic panels use the photovoltaic effect to convert the photon energy of sunlight into the electric energy of electrons, forming a direct current output. The wind energy part relies on a wind turbine, and the wind drives the wind turbine blades to rotate, and then drives the generator to generate electricity, generating alternating current; at the same time, the operating states of the solar photovoltaic panels and the wind turbines are monitored in real time, and the power generation data of the photovoltaic panels and the wind turbines are collected through sensors; at the same time, a bidirectional full-bridge topology structure is used to realize the conversion between alternating current and direct current, store the alternating current generated by wind energy in a DC energy storage device, and when the direct current in the energy storage device is reversely transmitted to the AC power grid or supplies power to an AC load, through inverter technology, the direct current is inverted into an alternating current that meets the requirements of the power grid, and according to the real-time demand of the power grid, the active power and reactive power output are flexibly adjusted to ensure the stable interaction between the system and the power grid; and store the excess electric energy generated by the power generation module for use when power generation is insufficient or during peak electricity consumption periods. Lithium batteries are used as the main energy storage devices, and supercapacitors are equipped to meet the high-power demand in a short time, forming a diversified energy storage system.

[0075] Furthermore, when generating the preliminary charging strategy, the preliminary charging method includes:

[0076] Real-time collect the quality parameters of the electric energy to be charged, including the actual voltage U RT and the actual frequency f RT of the electric energy to be charged. At the same time, obtain the reference parameters for measuring the actual charging quality, including the rated voltage U0 and the rated frequency f0, and calculate the real-time voltage deviation rate δU RT and the frequency deviation rate δf RT . The voltage deviation rate is used to reflect the degree of deviation of the actual voltage from the rated voltage. The smaller the voltage deviation rate, the higher the voltage stability, which is a reference parameter for measuring the actual charging quality. The frequency deviation rate is used to measure the frequency stability of the electric energy to be charged; the expressions are as follows:

[0077]

[0078] Use a harmonic detection device to obtain the effective value U a, where U1 is the effective value of the fundamental voltage, and a=1,2,3,…, calculate the harmonic distortion rate THD U , the harmonic distortion rate is used to evaluate the harmonic content in the electric energy to be charged; the expression is as follows:

[0079]

[0080] In order to comprehensively evaluate the quality of the electric energy to be charged, a comprehensive quality score is introduced to integrate multiple quality parameters into a quantitative indicator, which is convenient for intuitive and unified evaluation of charging quality; weight coefficients are configured for voltage deviation rate, frequency deviation rate and harmonic distortion rate, which are w1, w2 and w3 respectively, and the comprehensive quality score Q is calculated RT , to achieve the quantification of indicators; the expression is as follows:

[0081] Q RT =w1×δU RT +w2×δf RT +w3×THD U

[0082] In the formula, w1+w2+w3=1, the comprehensive quality score Q RT The lower it is, the higher the charging quality is; in this embodiment, w1=0.4, w2=0.3, w3=0.3.

[0083] The sampling interval is set to t1, and the historical data of various quality parameters within a preset time period (such as at least one year) are collected, including voltage deviation rate, frequency deviation rate and harmonic distortion rate data, and accurate timestamps are marked for each data point to comprehensively cover the charging power situation in different seasons, weather and various working conditions, including multi-scale time information from short-term (such as the impact of instantaneous weather changes on charging power) to long-term (charging power changes caused by seasonal changes), ensuring that the data is widely representative. Among them, the voltage deviation rate data sequence is {δU1,…,δU b}, the frequency deviation rate data sequence is {δf1,…,δf b}, the harmonic distortion rate data sequence is b is the total number of data points, and each data point is also marked with an accurate timestamp to facilitate subsequent time series analysis;

[0084] In order to ensure that the stored and used electric energy meets the normal operation requirements of the equipment and avoid damage to the equipment due to charging quality problems, consult the technical manuals of key power-consuming equipment, such as electric vehicles, to obtain the tolerance range of the power-consuming equipment to the charging quality, including the voltage deviation rate of the equipment within ±δU app Within, the equipment frequency deviation rate is within ±δf app Within, the harmonic distortion rate of the equipment is within THD appWithin, according to GB / T 18487.1-2015 and mainstream electric vehicle technical manuals, such as the Tesla V3 Supercharger technical specifications, the tolerance ranges of key electrical equipment for charging quality are set as follows: the voltage deviation rate is within ±5%, the frequency deviation rate is within ±0.4%, the harmonic distortion rate does not exceed 3%, and at this time, f0 = 50Hz;

[0085] Divide the charging quality data into a training set and a test set in chronological order. Among them, the training set is used to initially set the threshold, and the test set is used to independently verify the accuracy of the threshold;

[0086] Comprehensively considering the statistical characteristics of the data itself and the equipment tolerance range, initially set the thresholds for voltage deviation rate, frequency deviation rate, and harmonic distortion rate, including the first voltage threshold The second voltage threshold The first frequency threshold The second frequency threshold The first harmonic threshold The second harmonic threshold Among them, are the means of the voltage deviation rate, frequency deviation rate, and harmonic distortion rate in the training set respectively, and σ δU 、σ δf 、σ THD are the standard deviations of the voltage deviation rate, frequency deviation rate, and harmonic distortion rate in the training set respectively. For example, the mean of the voltage deviation rate is 1.0%, the standard deviation is 0.8%, the mean of the frequency deviation rate is 0.2%, the standard deviation is 0.1%, and the mean of the harmonic distortion rate is 1.5%, the standard deviation is 0.5%. At this time, the first voltage threshold δU th1 = max{1.0% + 0.8%, -5%} = 1.8%, the second voltage threshold δU th2 = min{1.0% + 2×0.8%, 5%} = 2.6%. Similarly, δf th1 = 0.3%, δf th2 = 0.4%, THD th1 = 2.0%, THD th2 = 2.5%;

[0087] According to the comprehensive quality scoring formula, substitute the above initially set thresholds for voltage deviation rate, frequency deviation rate, and harmonic distortion rate to calculate the initial threshold of the comprehensive quality score, including the first quality threshold Q th1.ele 、the second quality threshold Q th2.ele, realize the conversion from specific parameter thresholds to comprehensive quality score thresholds, which is convenient for directly grading the charging quality according to the comprehensive quality score. Among them, the first quality threshold is used to divide high-quality electric energy and sub-quality electric energy, and the second quality threshold is used to divide sub-quality electric energy and low-quality electric energy. And based on the actual calculated values of the above thresholds, Q can be calculated. th1.ele = 0.4×1.8% + 0.3×0.3% + 0.3×2.0% = 1.41%, Q th2.ele = 0.4×2.6% + 0.3×0.4% + 0.3×2.5% = 1.91%;

[0088] The initially set thresholds may be inaccurate. By evaluating the test set data and adjusting the thresholds, the thresholds can be made more in line with the actual situation and the accuracy of charging quality grading can be improved. Using the initially set thresholds, divide the quality levels of the test set data, and adjust the initially set thresholds according to the feedback results of the level division. For each data point in the test set, calculate the comprehensive quality score Q test , taking the accuracy rate as the evaluation index, set the adjustment amount each time as ΔQ1, ΔQ2, adjust the initially set thresholds, and then recalculate the evaluation index of the test set to observe the adjustment effect until the evaluation index reaches a satisfactory level, and output the final quality thresholds, including the first quality threshold Q th1 and the second quality threshold Q th2 ;

[0089] According to the set quality thresholds, divide the charging quality. A clear charging quality division standard can adopt different processing methods according to different charging qualities. If Q RT < Q th1 , it is determined as high-quality electric energy, and the charging operation can be directly carried out on the battery to be charged to ensure the efficient charge and discharge and long life of the battery. If Q th1 ≤ Q RT < Q th2 , it is determined as sub-quality electric energy, and it is transformed into high-quality electric energy after internal optimization of the battery to be charged (such as adjusting charge and discharge parameters). If Q RT ≥ Q th2 , it is determined as low-quality electric energy, and after being processed by external equipment (such as filtering, voltage stabilization), the charging operation is carried out on the battery to be charged;

[0090] Configure the battery pack to be charged according to the charging quality level. Divide the battery pack to be charged into three categories, namely, category 1 battery, category 2 battery, and category 3 battery, calculate the number of each type of battery, and generate a preliminary charging strategy.

[0091] Furthermore, the specific steps for configuring the battery pack to be charged include:

[0092] High-quality electric energy is directly discharged without treatment to supply power to the equipment and is stored in a type of battery; medium-quality electric energy is discharged after being optimized by the battery itself to supply power to the equipment and is stored in a type of battery; low-quality electric energy is charged into the battery after being processed by an external device and then discharged after being optimized by the battery itself to supply power to the equipment and is stored in a type of battery;

[0093] Since lithium-ion batteries have a high energy density and are suitable for storing a large amount of electric energy for a long time, and lithium titanate batteries have the characteristics of fast charging and discharging and can quickly respond to sudden power demands, making up for the deficiency of low power density of lithium-ion batteries, the type of battery combines lithium-ion batteries and lithium titanate batteries. In some special cases, such as when electric vehicles are charging quickly at the same time or in extreme weather conditions that cause a sudden increase in battery discharge demand, relying solely on lithium-ion batteries cannot quickly meet all discharge demands. The presence of lithium titanate batteries is used to provide additional power support in these sudden situations to ensure the smooth progress of battery discharge; according to the predicted generation amount of high-quality electric energy, discharge demand, and battery monomer capacity, the number of type of batteries is determined, and the expression is as follows:

[0094]

[0095] In the formula, N 1.Li is the number of lithium-ion batteries in the type of battery, N LTO is the number of lithium titanate batteries, E dis is the discharge demand for the whole day, n Li is the number of batteries in the lithium-ion battery cluster, C Li is the capacity of a single lithium-ion battery, E1 is the predicted power of high-quality electric energy, C LTO is the capacity of a single lithium titanate battery, η LTO is the power response efficiency, n LTO is the number of batteries in the lithium titanate battery cluster, k1 is a redundancy coefficient used to cope with sudden charging demands or equipment performance fluctuations, and its value range is [1.2, 1.5]. In this embodiment, k1 = 1.3;

[0096] Since medium-quality electric energy can be optimized to a certain extent in the charging and discharging process of lithium-ion batteries, lithium-ion batteries are used as the type of battery, and the number of type of batteries is calculated. The expression is as follows:

[0097]

[0098] In the formula, N 2.LiLet \(N_2\) be the number of lithium-ion batteries in the secondary-quality batteries, \(k_2\) be the loss coefficient, which takes into account the energy loss caused by internal chemical reactions and resistance during the storage and optimization of secondary-quality electrical energy, and its value range is \([1.1, 1.3]\). In this embodiment, \(k_2 = 1.2\), and \(E_2\) is the predicted electricity quantity of the secondary-quality electrical energy;

[0099] Since the low-quality electrical energy needs to be processed by external equipment, the requirements for the battery are relatively low. Lithium-ion batteries with lower costs are selected as the tertiary-quality batteries, and the number of tertiary-quality batteries is calculated. The expression is as follows:

[0100]

[0101] In the formula, \(N\) 3.Li is the number of lithium-ion batteries in the tertiary-quality batteries, \(k_3\) is the comprehensive coefficient, which comprehensively considers the energy loss caused by equipment conversion efficiency and transmission loss when the low-quality electrical energy is processed by external equipment, as well as the redundancy required to ensure storage stability and cope with emergencies, and its value range is \([1.3, 1.5]\). In this embodiment, \(k_3 = 1.4\), and \(E_3\) is the predicted electricity quantity of the low-quality electrical energy;

[0102] \(E_1\), \(E_2\), and \(E_3\) are all determined by the electrical energy prediction model. The electrical energy prediction model is constructed using a neural network model. The collected meteorological data and historical charging electrical energy data are used as inputs, and after being calculated by the trained model, it is assumed that a day is divided into \(t'\) time periods, and the predicted electricity quantity \(E\) at different time periods of the day is output. pre According to the charging quality classification result, the predicted electricity quantity \(E_1\) of the high-quality electrical energy, the predicted electricity quantity \(E_2\) of the secondary-quality electrical energy, and the predicted electricity quantity \(W_3\) of the low-quality electrical energy are separated from the predicted electricity quantity \(E\). pre In this embodiment, a day is divided into 24 time periods, with one prediction period per hour. Suppose the daily total power generation of a certain wind-solar-storage-charging system is 10,000 kWh, and the charging quality classification result is: high-quality electrical energy \(W_1 = 6000\) kWh, secondary-quality electrical energy \(W_2 = 3000\) kWh, low-quality electrical energy \(W_3 = 1000\) kWh, and the total discharge demand \(E\)

[0103] of the whole day is 4000 kWh, and the sudden power demand is 150 kWh, with a rapid response within 10 minutes. dis

[0104] The lithium-ion battery selects the 300Ah lithium iron phosphate battery cell with the largest production capacity at present, such as the CTP technology of CATL. The voltage is 3.2V, and the energy capacity is 0.96kWh. Limited by the material characteristics, the single-cell capacity of the lithium titanate battery is usually smaller than that of the lithium-ion battery. Take the largest 100Ah model of Toshiba SCiB series, which supports a charge-discharge rate of more than 10C. The voltage is 2.4V, and the energy capacity is 0.24kWh. The power response efficiency is 0.9. Since the monomers are directly connected, the wire length and the number of connection points are huge, and the failure rate is high. Usually, the battery monomers are reduced by forming battery clusters through series / parallel connections, while meeting the system voltage, current, and energy requirements. Common voltages in energy storage systems include 512V and 1000V. Taking the system voltage of 512V as an example, the number of series-connected lithium-ion battery cells is 160, and the number of series-connected lithium titanate battery cells is 213 (here is the integer value obtained by taking the ratio of the system required voltage to the single-cell voltage).

[0105] At this time, calculate the number of single battery cells in each type of battery, N 1.Li = 8125 cells, N LTO = 36111 cells, N 2.Li = 3594 cells, B 3.Li = 1563 cells. After the configuration of the battery clusters, there are 51 lithium-ion battery clusters, 170 lithium titanate battery clusters in one type of battery, 23 clusters in the second type of battery, and 10 clusters in the third type of battery;

[0106] The lithium titanate battery can release 7780kWh within 10 minutes to meet the sudden demand of 1500kWh, and the response time does not exceed 2 seconds. In terms of energy utilization efficiency, the direct discharge efficiency of high-quality electric energy is 98%, the optimized efficiency of medium-quality electric energy is 92%, and the processed efficiency of low-quality electric energy is 80%. At the same time, compared with the single lithium-ion battery solution, the hybrid configuration cost is reduced by 18%. The lithium titanate only accounts for 35% of the total number of batteries, but undertakes the high-frequency charge-discharge tasks and extends the life of the main battery.

[0107] Furthermore, the battery management method includes:

[0108] In order to comprehensively and accurately understand the operating state of the battery, high-precision voltage sensors are deployed at both ends of each battery cell, temperature sensors are installed at the tabs, and Hall current sensors are configured in the battery pack circuit to obtain management parameters in real time, such as single-cell voltage, temperature, charge-discharge current, and the sampling frequency is f EMS ;

[0109] Considering that the battery capacity will change significantly under different working conditions, such as different charge and discharge rates and ambient temperatures, in order to accurately estimate the state of charge (SOC) of the battery, a detailed battery electrochemical model is established through offline experiments to describe the performance of the battery under different working conditions. Combining the temperature correction coefficient, the ampere-hour integration is corrected, and the SOC estimation error is controlled within ±3%. Among them, the temperature correction coefficient is obtained by fitting a large amount of experimental data to dynamically adjust the ampere-hour integration according to the real-time measured temperature value, thereby improving the accuracy of SOC estimation;

[0110] To predict the state of health (SOH) of the battery, analyze the internal resistance change rate and capacity retention rate of the battery. As the battery is used, the internal resistance of the battery will gradually increase and the capacity will gradually decay. By long-term monitoring and analyzing the change trends of these parameters, a capacity decay model is constructed, and the remaining life of the battery is predicted based on the current internal resistance and capacity data, providing a scientific basis for the maintenance and replacement of the battery;

[0111] During the use of the battery pack, due to the manufacturing process differences and different usage environments among battery cells, the cell voltages will deviate. When the cell voltage deviation exceeds ±50 mV, an active equalization mechanism based on the Buck-Boost circuit is triggered. During the active equalization process, the energy of the battery cell with a higher voltage is transferred to the battery cell with a lower voltage through the Buck-Boost circuit, thereby achieving the equalization of the cell voltages within the battery pack;

[0112] To ensure that the battery operates within a suitable temperature range, optimize the liquid cooling pipeline design through CFD simulation, configure a PID controller to adjust the coolant flow rate and temperature in real time. For example, when the battery temperature difference exceeds 5°C, start forced air cooling for auxiliary heat dissipation to ensure that the highest temperature within the battery pack does not exceed 45°C;

[0113] To detect potential faults in the battery in a timely manner and ensure the safe and stable operation of the system, a three-level early warning system is established to conduct fault early warning for the battery. Among them, the first-level early warning (SOC > 90% or < 10%) triggers charging current limiting; the second-level early warning (cell voltage > 4.15 V or < 2.7 V) starts passive equalization; the third-level early warning (temperature difference > 10°C or internal resistance mutation > 15%) automatically cuts off the main contactor and reports a fault code.

[0114] Furthermore, when identifying the fluctuations of charging electric energy, the fluctuation identification method includes:

[0115] Collect historical fluctuation data from the database or relevant record backends. These data contain a long enough time span to ensure that different types of fluctuation situations can be covered. Clean the collected historical fluctuation data to remove outliers, missing values, and incorrect data;

[0116] Extract short-term fluctuation characteristics, medium-term fluctuation characteristics, and long-term fluctuation characteristics from historical fluctuation data respectively, and divide the historical fluctuation data according to the extracted fluctuation characteristics into short-term fluctuations, medium-term fluctuations, and long-term fluctuations. Among them, the short-term fluctuations have a short duration, not exceeding 1 hour, such as the power fluctuations caused by the start and stop of a fan. The characteristics include the maximum change rate, fluctuation amplitude (the difference between the maximum value and the minimum value), and fluctuation frequency (the number of fluctuations per unit time) of voltage, current, and frequency within a short time window. The medium-term fluctuations have a duration exceeding 1 hour but not exceeding 1 day, such as the diurnal light change. The characteristics include the average fluctuation amplitude, fluctuation trend (rising, falling, or stable), and periodic characteristics of the fluctuation within a medium time window. The long-term fluctuations have a duration exceeding 1 day, such as the seasonal power generation change. The characteristics include the average power parameter value, power parameter change trend, and seasonal fluctuation characteristics within a long time window.

[0117] Use the regression logic algorithm to construct a short-term fluctuation identification model, train the short-term fluctuation identification model using the known short-term fluctuation situations in the historical fluctuation data, adjust the model parameters, take the historical fluctuation data as the input feature vector, and the output result is whether there is a short-term fluctuation. 0 indicates the existence of a short-term fluctuation, and 1 indicates the non-existence of a short-term fluctuation.

[0118] Use the regression logic algorithm to construct a long-term fluctuation identification model, train the long-term fluctuation identification model using the known long-term fluctuation situations in the historical data, adjust the model parameters, take the historical fluctuation data as the input feature vector, and the output result is whether there is a long-term fluctuation. 1 indicates the existence of a long-term fluctuation, and 0 indicates the non-existence of a long-term fluctuation, that is, the existence of a medium-term fluctuation.

[0119] Add an adder after the short-term fluctuation identification model and the long-term fluctuation identification model to generate an identification model.

[0120] Input the fluctuation data to be determined into the identification model, obtain the output result for fluctuation determination. 0 indicates the existence of a short-term fluctuation, 1 indicates the existence of a medium-term fluctuation, and 2 indicates the existence of a long-term fluctuation. Since it is difficult to achieve completely no fluctuation in practice, the situation of no fluctuation is not considered here. Among them, the fluctuation data to be determined is the predicted output value of the electric energy prediction model.

[0121] According to the predicted result of the fluctuation determination, output the final predicted fluctuation determination category, including short-term fluctuations, medium-term fluctuations, and long-term fluctuations.

[0122] Furthermore, when dynamically adjusting the preliminary charging strategy, the fluctuation adjustment method includes:

[0123] When the predicted fluctuation determination category is short-term fluctuation, due to the high-quality electrical energy stored in lithium titanate ion batteries in a type of battery, it can more efficiently and stably meet the discharge demand or balance the energy surplus when dealing with short-term fluctuations. In contrast, the energy quality stored in lithium ion batteries is relatively low, and the response speed and stability are insufficient in dealing with short-term fluctuations, making it difficult to quickly and accurately match the changes in short-term discharge demand. Calculate the energy supply-demand difference ΔE under short-term fluctuations diff.1 , and the adjustment amount ΔN of lithium titanate ion batteries LTO . The expressions are as follows:

[0124] ΔE diff.1 =E charge.1 -E power.1

[0125]

[0126] In the formula, E charge.1 is the discharge demand energy within short-term fluctuations, and T' is the number of divided time periods within a day, and E power.1 is the energy that can be supplied by the battery pack within short-term fluctuations, which is determined by the electrical energy prediction model is the ceiling function; when ΔN LTO ≥0, the discharge demand exceeds the available energy, and the increased number of lithium titanate ion batteries is ΔN LTO to meet the discharge demand when future short-term fluctuations occur. When ΔN LTO <0, the available energy is in surplus, and the reduced number of lithium titanate ion batteries is -ΔN LTO to avoid battery life attenuation caused by over-discharge. Since increasing the number of batteries will result in more energy being stored, and at this time the energy is already in surplus, further storage will cause resource waste and thus trigger safety risks. For example, ΔE diff.1 =50 kWh. At this time, the demand is in surplus and the lithium titanate batteries need to be reduced, so ΔN LTO =-2 clusters, and 2 clusters need to be reduced;

[0127] When the predicted fluctuation determination category is medium-term fluctuation, select and adjust the storage quantity of lithium ion batteries based on the adaptability of battery characteristics and application scenarios. Since lithium titanate ion batteries have a high energy density and charge-discharge efficiency, but high cost and relatively short cycle life, they are more suitable for short-term and high-frequency fluctuation scenarios with extremely high requirements for energy response speed, such as coping with instantaneous power peaks. In contrast, lithium ion batteries have more advantages in terms of cost, cycle life and energy storage characteristics, and can better meet the needs of medium-term fluctuation adjustment. Calculate the energy supply-demand difference ΔE under medium-term fluctuations diff.2 , and the adjustment amounts ΔN of lithium ion batteries 1.Li , ΔN 2.Li , ΔN3.Li , the expression is as follows:

[0128] ΔE diff.2 = E charge.2 - E power.2

[0129]

[0130] Wherein, ΔN 1.Li is the adjustment amount of lithium - ion batteries in a certain type of battery, ΔN 2.Li is the adjustment amount of a certain type of battery, ΔN 3.Li is the adjustment amount of a certain type of battery, E power.2 is the energy that the battery pack can supply within the medium - term fluctuation, determined by the electric - energy prediction model, E charge.2 is the discharge - demand energy under the medium - term fluctuation, and T'1 is the number of time periods included in the medium - term fluctuation; if it is predicted that the energy that can be supplied within the medium - term fluctuation gradually decreases while the discharge demand gradually increases, increase the storage quantity of lithium - ion batteries to reserve energy in advance; otherwise, reduce the storage quantity. For example, ΔE diff.2 = - 200 kWh, at this time, since the demand is insufficient and lithium - ion batteries need to be increased, then ΔN 1.Li = ΔN 2.Li = ΔN 3.Li = - 4 clusters, and 4 clusters need to be reduced for each type;

[0131] When the predicted fluctuation is determined to be a long - term fluctuation, according to the prediction of long - term energy supply and discharge demand, combined with seasonal factors, make large - scale adjustments to the overall storage quantity of various types of batteries; calculate the energy supply - demand difference ΔE diff.3 , and the adjustment amount of each type of battery. The expression is as follows:

[0132] ΔE diff.3 = E charge.3 - E power.3

[0133]

[0134] Wherein, ΔN' LTO , ΔN' 1.Li are the adjustment amounts of lithium - titanate - ion batteries and lithium - ion batteries in a certain type of battery, ΔN' 2.Li is the adjustment amount of a certain type of battery, ΔN' 3.Li is the adjustment amount of a certain type of battery, E power.3 is the energy that the battery pack can supply under the long - term fluctuation, determined by the electric - energy prediction model, E charge.3 is the discharge - demand energy under the long - term fluctuation, and T'2 is the number of time periods included in the long - term fluctuation. For example, in winter, the power generation decreases, ΔEdiff.3 = -500 kWh, then ΔN' LTO = -20 clusters, ΔN' 1.Li = ΔN' 2.Li = ΔN' 3.Li = -10 clusters.

[0135] Furthermore, the discharge optimization method includes:

[0136] After the battery pack to be charged is fully charged, monitor the external charging demand. When a charging request is detected from the charging device connected to the battery pack, read the key parameters from the communication device built into the external charging device, and convert the key parameters into the discharge parameters of the battery pack, including the rated discharge capacity C rated , the state of charge SOC of the external device now , the maximum allowable discharge current I max and the rated discharge voltage V rated . Combine with the maximum output power P max of the external device to calculate the predicted discharge power P ex , and the predicted discharge time t ex . The expressions are as follows:

[0137] P ex = min(P max , I max × V rated )

[0138]

[0139] In the formula, η is the discharge efficiency and also the charging efficiency of the external device, such as the charging efficiency of the charging pile itself;

[0140] Invoke the electric energy prediction model to obtain the predicted electricity quantity E ' 1 of high-quality electric energy, the predicted electricity quantity E ' 2 of sub-quality electric energy, and the predicted electricity quantity E ' 3 of low-quality electric energy in a future period of time, and calculate the proportion of different charging qualities in the future period. The expressions are as follows:

[0141]

[0142] In the formula, r1 is the proportion of high-quality electric energy, r2 is the proportion of sub-quality electric energy, and r3 is the proportion of low-quality electric energy;

[0143] Compare these three ratios r1, r2, and r3, and set the battery category corresponding to the highest charging quality ratio as the first discharge priority, the second highest ratio as the second discharge priority, and the remaining as the third discharge priority. For example, in a future period, the charging quality ratios are r1 = 50%, r2 = 30%, and r3 = 20%. At this time, the first priority is the first type of battery, with an expected discharge power of 100 kW and an expected time of 2 hours.

[0144] For the battery category determined as the first discharge priority, obtain the available battery quantity M, the single - cell battery capacity C1, the current state of charge SOC1, and the maximum discharge power P. max.1 Calculate the current remaining power E of the battery. S The expression is as follows:

[0145] E S = M × C1 × SOC1

[0146] In the formula, if the battery category of the first discharge priority is the first type of battery, calculate the remaining power of the lithium titanate ion battery and the lithium - ion battery respectively. At this time, the remaining power of the first type of battery is the sum of the remaining powers of the lithium titanate ion battery and the lithium - ion battery. If the battery category of the first discharge priority is the second type of battery or the third type of battery, the battery that has not completely converted the sub - quality electric energy or low - quality electric energy into high - quality electric energy is an unavailable battery and is not included in the calculation of the remaining power.

[0147] Judge whether the battery of the first discharge priority meets the discharge requirements; if E S ≥ P ex × t ex and P max.1 ≥ P max , then it is determined that the discharge requirements are met, and control the battery pack to discharge to the external device; otherwise, if the discharge requirements are not met, then according to the same judgment method, judge the battery of the second discharge priority, and so on. For example, the remaining power of the first type of battery is 624.896 kWh. When the current state of charge is 80% and the maximum discharge power is 779.9 kW, if the maximum output power of the external device is 100 kW, at this time, E S and P max.1 both meet the above judgment conditions, so the first type of battery is preferentially used for discharge.

[0148] During the discharge process of the battery pack, the discharge current I im and the discharge voltage V im are collected in real - time through the sensors of the battery, and the charging curve of the external device is obtained and converted into the standard discharge curve data of the battery, including the target current value I con in the constant - current discharge stage, the target voltage value V con in the constant - voltage discharge stage, and the cut - off discharge current value Icut ;

[0149] When starting to discharge, it enters the constant-current discharge stage, controlling the output power of the battery pack to maintain the discharge current at I con , and the discharge power is P in.1 , and the expression is as follows:

[0150] P in.1 = I con × V im

[0151] As the discharge progresses, when the discharge voltage reaches V con , it switches to the constant-voltage discharge stage, and at this time, the discharge voltage is kept constant at V con , and the discharge current naturally decreases as the power of the external device gradually saturates. At this time, the discharge power is P in.2 , and the expression is as follows:

[0152] P in.2 = I im × V con

[0153] When the discharge current drops to I cut , it is determined that the discharge is completed, and the actual discharge time t ac is obtained, and the time deviation rate Δt rate is calculated, and the expression is as follows:

[0154]

[0155] The electricity consumption of various charging qualities consumed in this discharge is statistically counted, and combined with the cost of the corresponding charging quality, the total discharge cost C total ;

[0156] The effective electricity quantity E ef actually used for discharge and the total electricity quantity E total provided by the dispatched battery are obtained, and the energy utilization rate η use is calculated, and the expression is as follows:

[0157]

[0158] For example, in the constant-current discharge stage, the current is 300 A, the voltage is 3.2 V, and the power is 960 W. In the constant-voltage discharge stage, the voltage is 3.0 V, the cut-off current is 10 A, and the actual discharge time is 1.9 hours. At this time, the time deviation rate When the cost of high-quality electric energy is 0.5 yuan / kWh, the cost of sub-quality electric energy is 0.3 yuan / kWh, and the cost of low-quality electric energy is 0.1 yuan / kWh, the total discharge cost at this time is 400 yuan, and the energy utilization rate

[0159] Organize these evaluation data into feedback data, including the deviation rate, discharge cost, energy utilization rate, and records of abnormal conditions during the discharge process, and feedback them to the fluctuation identification method. According to these feedback data, adjust its own parameters, such as optimizing the feature weights for identifying fluctuations at different time scales, improving the parameters of the electric energy prediction model, etc., to improve the accuracy and effectiveness of the future discharge optimization method.

[0160] Furthermore, the specific steps to activate the protection mechanism include:

[0161] In the off-grid state, various devices cannot rely on the external power grid for startup, so they need to have independent startup capabilities themselves; when the device is in the off-grid state and needs to be started, activate specific startup battery packs in the energy storage module. These startup battery packs are pre-stored with sufficient energy to supply power to the most critical control devices and some small auxiliary devices in the system;

[0162] Start the starting motor of the wind turbine and the drive circuit of the solar photovoltaic panel in a predetermined order; driven by the starting motor of the wind turbine, the wind turbine blades start to rotate slowly and gradually reach the rotation speed capable of self-sustaining power generation. When the illumination conditions are met, the solar photovoltaic panel also starts to output direct current. When the electric energy output by the wind turbine and the solar photovoltaic panel reaches a certain level and is detected and stabilized by the energy management module, this electric energy is gradually introduced into the energy storage module to charge other energy storage batteries;

[0163] During the startup process, calculate the remaining power of the startup battery pack in real time to ensure that it can continuously supply power to the key devices; assume the initial power of the startup battery pack is E 0.off , the total power of the key devices is P total.off , the startup time is t off , calculate the remaining power E re.off , when E re.off is lower than the set minimum power threshold E th.off , take corresponding measures, such as switching to a backup startup battery pack or adjusting the startup strategy; the expression is as follows:

[0164] E re.off = E 0.off - P total.off

[0165] During the startup process of the wind turbine, according to the characteristic curve of the wind turbine, calculate the time t wind required for the wind turbine to reach the self-sustaining power generation rotation speed and the power P wind required by the starting motor. For the solar photovoltaic panel, calculate the output power P pv according to the illumination intensity and the conversion efficiency of the photovoltaic panel, and judge whether it meets the power demand for system startup;

[0166] Continuously monitor the parameter changes between each power generation device and the load, adopt an islanding detection method combining active and passive methods to determine whether an islanding effect occurs; once the islanding effect is detected, adjust the output power of the power generation module and control the charge and discharge status of the energy storage module to maintain the stable operation of the system, and promptly send an alarm to notify relevant personnel, improving the safety and stability of the system during off-grid operation, avoiding equipment failures and safety accidents caused by the islanding effect, and ensuring that the system can continuously and reliably supply power to the load.

[0167] Embodiment 2

[0168] Please refer to Figure 4 , another embodiment provided by the present invention: a charge and discharge control device for a battery pack, comprising: a generator set, an energy storage battery, and a charging pile;

[0169] The generator set includes a wind turbine and a photovoltaic panel. Among them, the starting wind speed of the wind turbine is < 3 m / s, the photovoltaic panel uses monocrystalline silicon PERC components, the conversion efficiency is ≥ 22%, and the inclination angle is installed according to the local latitude + 5°;

[0170] The energy storage battery includes a lithium-ion battery pack, a lithium titanate ion battery pack, and a bidirectional DC / DC converter. The lithium-ion battery pack is used to balance the energy fluctuations at the 24-hour level, and the lithium titanate ion battery pack is used to buffer the power fluctuations at the 10-second level. The two are connected in parallel through a bidirectional DC / DC converter; among them, the capacity of the lithium-ion battery pack is 500 kWh, using CATL lithium iron phosphate battery cells, the capacity of the lithium titanate ion battery pack is 5 kWh, using Maxwell 3000F monomers, and the efficiency of the bidirectional DC / DC converter is ≥ 98%;

[0171] The charging pile realizes 150 kW fast charging for electric vehicles in an off-grid environment, and the energy storage utilization rate is ≥ 95%.

[0172] In summary, in the embodiments of the present invention, by collecting the quality parameters of the charging electric energy, calculating the voltage deviation rate, frequency deviation rate, and harmonic distortion rate, introducing a comprehensive quality score and setting a quality threshold, the charging quality level is divided. According to the level, three types of batteries are configured. The first type of battery combines lithium-ion and lithium titanate ion batteries, and the second and third types of batteries use lithium-ion batteries to calculate the quantity of each type of battery; the fluctuation type is judged through an identification model, the quantity of lithium titanate ion batteries is adjusted for short-term fluctuations, and the quantity of lithium-ion batteries is adjusted for medium- and long-term fluctuations. At the same time, the operating status of the battery is monitored in real time, the SOC and SOH are estimated, the active equalization mechanism is triggered, the temperature is controlled, and a three-level early warning is established to ensure the stable operation of the battery. When discharging, the priority is set and the discharge plan is adjusted in real time.

[0173] The above is only the preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the concept of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, several improvements and refinements made without departing from the principle of the present invention should also be regarded as within the protection scope of the present invention.

Claims

1. A method for controlling charging and discharging of a battery pack, characterized in that, Including: Obtain the quality parameters of the electric energy to be charged, set a preliminary charging method, calculate the charging quality, classify the charging quality through a set quality threshold, classify the battery pack to be charged according to the level of the charging quality, and generate a preliminary charging strategy; Construct an identification model, set a fluctuation identification method to identify the fluctuations of the charging electric energy at different time scales, and at the same time set a fluctuation adjustment method to dynamically adjust the preliminary charging strategy according to the fluctuation type; Obtain the discharge demand in real time, set a discharge optimization method, once the discharge demand is detected, formulate a discharge plan, and set the battery discharge priority to dynamically adjust the discharge plan.

2. The charging and discharging control method of the battery pack according to claim 1, characterized in that: When generating the preliminary charging strategy, the preliminary charging method includes: Collect the quality parameters of the electric energy to be charged in real time, including the actual voltage and the actual frequency of the output electric energy, and calculate the real-time voltage deviation rate and frequency deviation rate; Obtain the effective value U of each harmonic voltage a , calculate the harmonic distortion rate, where U1 is the effective value of the fundamental voltage, and a = 1, 2, 3, …; Assign different weights to the voltage deviation rate, frequency deviation rate, and harmonic distortion rate, and calculate the comprehensive quality score Q RT .

3. The charge and discharge control method of the battery pack according to claim 2, wherein The preliminary charging method further includes: Set the sampling interval to t1, collect the charging quality data within a preset time period, and divide the charging quality data into a training set and a test set in chronological order; Obtain the tolerance range of the electrical equipment for the charging quality; Comprehensively consider the statistical characteristics of the data itself and the equipment tolerance range, and initially set the thresholds of the voltage deviation rate, frequency deviation rate and harmonic distortion rate; Calculate the preliminary threshold of the comprehensive quality score, including the first quality threshold Q th1.ele , the second quality threshold Q th2.ele ; Calculate the comprehensive quality score Q of each data point in the test set test , and adjust the preliminary threshold using the test set to generate a quality threshold, including a first quality threshold Q th1 and a second quality threshold Q th2 ; According to the quality threshold, the comprehensive quality score Q RT is divided to determine the category of the charging quality; if Q RT < Q th1 , it is determined as high-quality electric energy; if Q th1 ≤ Q RT < Q th2 , it is determined as sub-quality electric energy; if Q RT ≥ Q th2 , it is determined as low-quality electric energy; Divide the battery pack to be charged into three categories according to the category of the charging quality, namely, first-class batteries, second-class batteries and third-class batteries, calculate the number of each type of battery, and generate a preliminary charging strategy.

4. The charging and discharging control method of the battery pack according to claim 3, wherein The specific steps for configuring the battery pack to be charged include: A type of battery adopts a combination of a lithium-ion battery and a lithium titanate battery, and calculates the number N of lithium-ion batteries in the type of battery 1.Li , and the number N of lithium titanate batteries LTO ; Use a lithium-ion battery as the secondary battery and calculate the number N of the secondary batteries 2.Li ; Use a lithium-ion battery as the type-three battery and calculate the number N of type-three batteries 3.Li 。 5. The charging and discharging control method of the battery pack according to claim 4, characterized in that: When identifying the fluctuations of the charging electric energy, the fluctuation identification method includes: Collect historical fluctuation data and clean the collected historical fluctuation data; Extract short-term fluctuation characteristics, medium-term fluctuation characteristics and long-term fluctuation characteristics from the historical fluctuation data respectively; Use the regression logic algorithm to construct a short-term fluctuation identification model, with an output of 0 indicating the existence of short-term fluctuations and 1 indicating the non-existence of short-term fluctuations; and use the regression logic algorithm to construct a long-term fluctuation identification model, with an output of 1 indicating the existence of long-term fluctuations and 0 indicating the non-existence of long-term fluctuations; Add an adder after the short-term fluctuation identification model and the long-term fluctuation identification model to generate an identification model; Input the fluctuation data to be determined into the identification model for fluctuation determination, 0 indicates the existence of short-term fluctuations, 1 indicates the existence of medium-term fluctuations, 2 indicates the existence of long-term fluctuations; and output the final predicted fluctuation determination category.

6. The charging and discharging control method of the battery pack according to claim 5, characterized in that: When dynamically adjusting the preliminary charging strategy, the fluctuation adjustment method includes: When the predicted fluctuation determination category is short-term fluctuation, calculate the energy supply-demand difference ΔE in the short term diff.1 , and the adjustment amount ΔN of the lithium titanate ion battery LTO ; When the predicted fluctuation determination category is medium-term fluctuation, calculate the energy supply-demand difference ΔE within the medium term diff.2 , and the adjustment amount ΔN of the lithium-ion battery 1.Li , ΔN 2.Li , ΔN 3.Li ; When the predicted fluctuation determination category is long-term fluctuation, calculate the energy supply-demand difference ΔE diff.3 , and the adjustment amounts ΔN' of various types of batteries LTO 、ΔN' 1.Li 、ΔN' 2.Li 、ΔN' 3.Li .

7. The charging and discharging control method of the battery pack according to claim 6, characterized in that: When dynamically adjusting the discharge plan, the discharge optimization method includes: When a discharge requirement is detected, read the discharge parameters of the battery pack to be charged and calculate the estimated discharge power P ex , and the estimated discharge time t ex ; Calculate the proportions r1, r2, and r3 of different charging qualities in the future period; Compare r1, r2, and r3, and set the battery category corresponding to the highest charging quality as the first discharge priority, the second highest as the second discharge priority, and the remaining as the third discharge priority; For the battery category determined to have the first discharge priority, calculate the current remaining power E of the battery S And determine whether the battery with the first discharge priority meets the discharge requirement; If E S ≥P ex ×t ex and P max.1 ≥P max , it is determined that the discharge requirement is met and a discharge operation is performed, where P max.1 is the maximum discharge power; Otherwise, if the discharge requirement is not met, judge the battery with the second discharge priority, and so on.

8. The charging and discharging control method of the battery pack according to claim 7, characterized in that The discharge optimization method further includes: During the charging and discharging process, the discharge current I is collected in real time im and the discharge voltage V im , and the standard discharge curve data is obtained; When starting to discharge, it enters the constant-current discharge stage, maintaining the discharge current at I con , and the charging power is P in.1 ; When the discharge voltage reaches V con , switch to the constant-voltage discharge stage and keep the discharge voltage constant at V con , and the charging power is P in.2 ; When the discharge current drops to I cut , it is determined that the discharge is completed, and the actual discharge time t ac is obtained, and the time deviation rate Δt rate , the total discharge cost C total and the energy utilization rate η use are calculated to generate feedback data.

9. The charging and discharging control method of the battery pack according to claim 8, wherein It also includes: During the charging and discharging process of the battery, monitor the battery pack to be charged, set the battery management method, and maintain the stable operation of the battery pack; The battery management method includes: Based on the sensors equipped on each battery cell, obtain management parameters in real time; Establish a battery electrochemical model through offline experiments, and correct the ampere-hour integration in combination with the temperature correction coefficient; Analyze the battery internal resistance change rate and capacity retention rate, construct a capacity attenuation model, and output the predicted remaining battery life; When the voltage deviation of the battery cell exceeds the preset voltage, trigger the active equalization mechanism; Optimize the liquid cooling pipeline design through CFD simulation, and configure a PID controller to adjust the coolant flow rate and temperature in real time; Establish a three-level early warning system to give early warnings about battery failures.

10. A charge and discharge control device for a battery pack, which is used to implement the charge and discharge control method for the battery pack according to any one of claims 1-9, characterized in that, It includes: A generator set, a storage battery, and a charging pile; The generator set includes a wind turbine and a photovoltaic panel; The energy storage battery includes a lithium-ion battery pack, a lithium titanate ion battery pack, and a bidirectional DC / DC converter.

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