An energy management method for hybrid energy storage fast charging stations based on retired batteries

Abstract

The present invention relates to the field of hybrid energy storage management technology, specifically a hybrid energy storage fast charging station energy management method based on retired batteries, comprising the following steps: based on the load demand, the remaining capacity of the retired batteries and the health status parameters, comparing the load demand with the battery capacity, dividing the load interval by time, calculating the load change rate in each time period, and performing battery health status evaluation and power response selection to generate a time interval battery pack allocation result. In the present invention, the retired batteries are clustered and divided by a fuzzy control algorithm, and a comprehensive calculation is performed in combination with the battery health status and power allocation to optimize the power output inside the battery pack, extend the service life of the retired batteries, avoid battery overload or unreasonable use, and reduce the energy storage cost. Through the optimization of the real-time power regulation scheme, the battery pack can be adjusted in time according to the grid load fluctuation to ensure stability during high load periods.

Description

Technical Field

[0001] The present invention relates to the technical field of hybrid energy storage management, and in particular to an energy management method for a hybrid energy storage fast charging station based on retired batteries. Background Art

[0002] The field of hybrid energy storage management technology aims to maximize the overall efficiency, stability and service life of the system by combining energy storage devices and conducting collaborative management and control within a system, and optimize the energy storage, release and distribution process by utilizing the respective characteristics of different energy storage units.

[0003] The energy management method for hybrid energy storage fast charging stations based on retired batteries aims to extend the service life of retired batteries, reduce energy storage costs, improve the charging efficiency of fast charging stations through rapid response energy management, utilize the remaining available capacity of retired batteries, and work in conjunction with high-efficiency energy storage devices to optimize the distribution and utilization efficiency of system energy while meeting the fast charging needs of electric vehicles. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and propose an energy management method for a hybrid energy storage fast charging station based on retired batteries.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a hybrid energy storage fast charging station energy management method based on retired batteries, comprising the following steps:

[0006] Step 1: Based on the load demand, remaining capacity, and health status parameters of the energy storage fast charging station, the load demand is compared with the battery capacity. The load interval is divided by time, and the load change rate within each time period is calculated. The battery health status is evaluated and the power response is selected to generate the battery pack allocation results for the time interval.

[0007] Step 2: Based on the battery pack allocation results for the time interval, the battery pack charge and discharge power is calculated. By matching the battery health status with the current load, the upper and lower power limits are determined to determine whether they meet the actual load requirements. Unsuitable battery packs are then eliminated to generate a battery pack power output allocation plan.

[0008] Step 3: Based on the battery pack power output allocation plan, a fuzzy control algorithm is used to cluster the retired batteries in the battery pack. Through comprehensive calculation of battery health status and power allocation, the optimal charge and discharge sequence is determined, and power output overload reduction and balancing are performed to generate battery power output optimization results.

[0009] Step 4: Based on the battery power output optimization results, the charge and discharge status of the retired batteries is monitored in real time, the load demand is compared with the current power output of the batteries, and the output power is redistributed by adjusting and eliminating batteries with low remaining capacity to generate a real-time battery power adjustment plan;

[0010] Step 5: Based on the real-time battery power regulation scheme, perform a superposition analysis of the energy storage battery power output and grid load fluctuations, divide the load demand by time period, adjust the battery pack power output to match the grid load, select a suitable battery pack for power redistribution, and generate a battery pack power matching scheme;

[0011] Step 6: Based on the battery pack power matching solution, the overall power scheduling and monitoring of the energy storage fast charging station is carried out. The battery pack output is adjusted during high-load periods based on the real-time battery pack output and the grid frequency fluctuations. By balancing the battery health status and power output, a frequency compensation and power adjustment solution is generated.

[0012] Step 7: Based on the frequency compensation and power adjustment scheme, the energy storage system's historical operating data and battery health status are evaluated to determine whether the batteries meet the standards for continued operation. The remaining batteries with poor health status are retired, and the battery operating status is updated to generate a battery health status update result.

[0013] As a further solution of the present invention, the time interval battery group allocation result includes load time division, battery group responsiveness, and battery remaining capacity evaluation; the battery group power output allocation plan includes upper and lower limits of charge and discharge power, battery health status allocation, and a battery elimination list; the battery power output optimization result includes charge and discharge priority, overload battery reduction list, and power allocation adjustment; the battery real-time power regulation plan includes adjustment of batteries with low remaining capacity, real-time power output curve, and elimination battery list; the battery group power matching plan includes load demand period, battery power adjustment, and battery group reallocation list; the frequency compensation and power adjustment plan includes high-load battery output adjustment, frequency compensation amplitude, and battery group balance adjustment value; the battery health status update result includes a battery health status list, a list of batteries to be retired, and an updated value of the operating battery status.

[0014] As a further solution of the present invention, the specific steps of generating the time interval battery group allocation result are:

[0015] Based on the load demand of the energy storage fast charging station and the remaining capacity and health status parameters of the retired batteries, the load demand is compared with the battery capacity. The load demand is classified by time period using a time period division method. Combined with the battery health status parameters, the load demand in each time period is matched to generate a load interval and battery health status matching result;

[0016] Based on the matching results between the load interval and the battery health status, the load demand in each time interval is refined, and the power response upper and lower limits are determined by using a power response calculation method combined with the health status parameters of the battery pack to generate a time interval battery pack power response analysis result;

[0017] Based on the battery pack power response analysis results of the time interval, the health status of the battery pack in each time interval is evaluated one by one, and the power response is selected in combination with the load demand, and unsuitable battery packs are eliminated to generate the time interval battery pack allocation results.

[0018] As a further solution of the present invention, the specific steps of generating the battery pack power output distribution plan are:

[0019] Based on the battery pack allocation results of the time interval, the battery pack charge and discharge power in each time interval is calculated, and the power upper and lower limits are used as judgment criteria, combined with the actual load demand, to generate the battery pack power calculation results;

[0020] Based on the battery pack power calculation result, check whether the battery pack power meets the load demand one by one, eliminate unsuitable battery packs, and adjust the power distribution method of the remaining battery packs to generate a battery pack power distribution adjustment result;

[0021] Based on the battery pack power allocation adjustment result, power output of qualified battery packs is allocated according to time intervals, and load demand is used as the allocation basis to ensure that the battery pack output power matches the demand, and a battery pack power output allocation plan is generated.

[0022] As a further solution of the present invention, the specific steps of generating the battery power output optimization result are:

[0023] Based on the battery pack power output allocation scheme, data on the health status of retired batteries is collected. By reading parameters such as the remaining capacity, voltage, and internal resistance of the batteries, the battery status is standardized and clustered to generate a clustering result for the batteries within the battery pack.

[0024] Based on the battery clustering results within the battery pack, by analyzing the battery health status and power allocation parameters, the charge and discharge sequence of each battery is calculated, and the battery capacity and power demand are combined to prioritize and generate a charge and discharge sequence result;

[0025] Based on the charge and discharge sequence results, a fuzzy control algorithm is used to detect the real-time status of power output, determine whether there is a power overload, and perform a power reduction operation. By adjusting the load distribution to balance the power output, the battery power output optimization result is generated.

[0026] As a further solution of the present invention, the fuzzy control algorithm is according to the formula:

[0027]

[0028] Where: ΔP(t) is the power adjustment at the current moment, α is the power second-order change response coefficient, is the second-order derivative of power, β is the environmental power influence coefficient, P env is the impact of the external environment on the power output, e(t) is the error between the current power output and the set target, K p is the proportionality coefficient, K i is the integral coefficient, K d is the differential coefficient, is the cumulative sum of errors, is the derivative of the error.

[0029] As a further solution of the present invention, the specific steps of generating the battery real-time power adjustment solution are:

[0030] Based on the battery power output optimization results, the charge and discharge status of the retired battery is monitored in real time, and the battery current, voltage and temperature parameters are used to compare and analyze the battery with the load demand, and the charge and discharge data are obtained to generate a real-time load and battery status comparison result;

[0031] Based on the comparison result of the real-time load and battery status, the remaining capacity values ​​of the batteries are determined by screening the batteries with low remaining capacity one by one, and the batteries are eliminated in combination with the power requirements to generate a battery elimination adjustment result;

[0032] Based on the battery elimination adjustment result, the output power of the remaining batteries is redistributed, the power distribution value of each battery in the battery pack is adjusted one by one, and the final distribution is performed in combination with the load demand to generate a battery real-time power adjustment plan.

[0033] As a further solution of the present invention, the specific steps of generating the battery pack power matching solution are:

[0034] Based on the battery real-time power regulation scheme, the power output data of the energy storage battery is collected. By obtaining the real-time output data of the battery pack and the grid load fluctuation data, the load demand is divided into intervals using a time period division method, and power superposition analysis is performed to generate the superposition analysis results of the energy storage battery and grid load;

[0035] Based on the analysis results of the energy storage battery and grid load superposition, real-time adjustment operations are performed on the load period and the battery pack power output. By matching the battery output power with the grid load demand in each period, the battery pack power is redistributed to generate a battery pack power adjustment matching result;

[0036] Based on the battery pack power adjustment and matching results, load demand analysis is performed, and a battery pack power matching solution is generated by selecting and matching appropriate battery packs in each time period and reallocating battery pack power output according to different load time periods.

[0037] As a further solution of the present invention, the specific steps of generating the frequency compensation and power adjustment solution are:

[0038] Based on the battery pack power matching solution, the overall power dispatch monitoring of the energy storage fast charging station is carried out, and the real-time output data of the battery pack and the grid frequency fluctuation data are collected. The relationship between the battery output and the grid frequency is analyzed by data comparison, and the comparison results of the battery pack and the grid frequency fluctuation are generated;

[0039] Based on the comparison result of the frequency fluctuation of the battery pack and the power grid, the power output of the battery pack during the high-load period is adjusted, and the power adjustment result of the battery pack during the high-load period is generated by analyzing the load in different time periods one by one and adjusting the output of the battery pack during the high-load period one by one;

[0040] Based on the battery pack power adjustment results during the high-load period, by analyzing the battery health status and power output, the battery output power is balanced, the relationship between the battery pack output and the grid frequency is adjusted, and a frequency compensation and power adjustment plan is generated.

[0041] As a further solution of the present invention, the specific steps of generating the battery health status update result are:

[0042] Based on the frequency compensation and power adjustment scheme, historical data of energy storage system operation is collected, key battery operation parameters are extracted from the historical data, the battery operation data is classified and processed, and health status analysis is performed to generate battery health status assessment results;

[0043] Based on the battery health status assessment results, the health status of each battery is analyzed one by one, and health indicators such as the remaining capacity, internal resistance, and depth of discharge of the battery are compared to determine whether the battery meets the continued operation standard, and the battery that needs to be retired is marked to generate a battery continued operation determination result;

[0044] Based on the battery continued operation judgment result, by confirming the batteries marked as having poor health status, the battery operation status is updated one by one, and the battery operation information is updated by using the retirement processing and health status data update method to generate a battery health status update result.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are:

[0046] In the present invention, retired batteries are clustered and divided through a fuzzy control algorithm, and a comprehensive calculation is performed in combination with the battery health status and power allocation to optimize the power output within the battery pack, extend the service life of retired batteries, avoid battery overload or unreasonable use, and reduce energy storage costs. Through the optimization of the real-time power regulation scheme, the battery pack can be adjusted in time according to grid load fluctuations to ensure stability during high-load periods. The frequency compensation scheme is also used to enhance the adaptability of the energy storage system to frequent load fluctuations, thereby improving overall efficiency and sustainable operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the main steps of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0049] Example 1

[0050] See also Figure 1 The present invention provides a technical solution: an energy management method for a hybrid energy storage fast charging station based on retired batteries, comprising the following steps:

[0051] Step 1: Based on the load demand, remaining capacity, and health status parameters of the energy storage fast charging station, the load demand is compared with the battery capacity. The load interval is divided by time, and the load change rate within each time period is calculated. The battery health status is evaluated and the power response is selected to generate the battery pack allocation results for the time interval.

[0052] Step 2: Based on the battery pack allocation results for each time interval, the battery pack charge and discharge power is calculated. By matching the battery health status with the current load, the upper and lower power limits are determined to determine whether they meet the actual load requirements. Unsuitable battery packs are then eliminated to generate a battery pack power output allocation plan.

[0053] Step 3: Based on the battery pack power output allocation plan, a fuzzy control algorithm is used to cluster retired batteries within the battery pack. Through comprehensive calculation of battery health status and power allocation, the optimal charge and discharge sequence is determined. Power output overload reduction and balancing are also performed to generate battery power output optimization results.

[0054] Step 4: Based on the battery power output optimization results, the charge and discharge status of retired batteries is monitored in real time. The load demand is compared with the current power output of the batteries. By adjusting and eliminating batteries with low remaining capacity, the output power is redistributed to generate a real-time battery power adjustment plan.

[0055] Step 5: Based on the real-time battery power regulation solution, perform a superposition analysis of the energy storage battery power output and grid load fluctuations. Divide the load demand by time period, adjust the battery pack power output to match the grid load, select the appropriate battery pack for power redistribution, and generate a battery pack power matching solution.

[0056] Step 6: Based on the battery pack power matching plan, the overall power scheduling and monitoring of the energy storage fast charging station is carried out. The battery pack output is adjusted during high-load periods based on the real-time battery pack output and grid frequency fluctuations. By balancing the battery health status and power output, a frequency compensation and power adjustment plan is generated.

[0057] Step 7: Based on the frequency compensation and power adjustment scheme, the energy storage system's historical operating data and battery health status are evaluated to determine whether the batteries meet the standards for continued operation. By retiring the remaining batteries with poor health, the battery operating status is updated to generate a battery health status update result.

[0058] The time interval battery group allocation results include load time division, battery group responsiveness, and battery remaining capacity assessment. The battery group power output allocation plan includes upper and lower limits of charge and discharge power, battery health status allocation, and battery elimination list. The battery power output optimization results include charge and discharge priority, overload battery reduction list, and power allocation adjustment. The battery real-time power adjustment plan includes adjustment of batteries with low remaining capacity, real-time power output curve, and elimination battery list. The battery group power matching plan includes load demand period, battery power adjustment, and battery group reallocation list. The frequency compensation and power adjustment plan includes high-load battery output adjustment, frequency compensation amplitude, and battery group balance adjustment value. The battery health status update results include battery health status list, list of batteries to be retired, and updated value of running battery status.

[0059] The specific steps for generating the time interval battery group allocation results are:

[0060] Based on the load demand of the energy storage fast charging station and the remaining capacity and health status parameters of the retired batteries, the load demand is compared with the battery capacity. The load demand is classified by time period using a time period division method. Combined with the battery health status parameters, the load demand in each time period is matched to generate a load interval and battery health status matching result;

[0061] Based on the matching results between the load interval and the battery health status, the load demand within each time interval is refined. The power response calculation method is used, combined with the health status parameters of the battery pack, to determine the upper and lower limits of the power response, and generate the battery pack power response analysis results for the time interval;

[0062] Based on the time interval battery pack power response analysis results, the health status of the battery pack in each time interval is evaluated one by one, and the power response is selected based on the load demand, unsuitable battery packs are eliminated, and the time interval battery pack allocation results are generated;

[0063] Based on the load demand of energy storage fast charging stations and the remaining capacity and health status parameters of retired batteries, a dynamic matching algorithm is used to compare load demand with battery capacity. By calculating the time series distribution of battery remaining capacity and load demand, linear interpolation is used to interpolate the load demand for each period. The remaining capacity parameters are used to calculate the available capacity value of the battery. The battery capacity and load demand are compared using a dichotomy method. The time period is divided into time periods and a time series segmentation algorithm is used to classify the load demand by time period. Combined with the battery health status parameters, a Kalman filter algorithm is used to update the battery health status parameters in real time based on the state transition matrix to generate a matching result between the load interval and the battery health status.

[0064] Based on the matching results between the load interval and the battery health status, a power response calculation method is used to refine the load demand within each time interval. The battery pack health status parameters are used to build a battery pack power response model based on the autoregressive integral moving average model. The upper limit of the power response is set to the battery pack rated power, and the lower limit of the power response is set to the battery pack's minimum available power. The power response model is used to perform detailed calculations on the load demand in each time period. Combined with the health status parameters, a linear regression model is used to fit and estimate the battery pack's health status, generating the battery pack power response analysis results within the time interval;

[0065] Based on the power response analysis results of battery packs in time intervals, the support vector machine algorithm is used to evaluate the health status of the battery packs in each time interval one by one. The support vector machine is used to classify the discharge curve and state of charge of the battery. Combined with the power response requirements of the load demand, the power response is selected and unsuitable battery packs are eliminated. Based on the priority sorting algorithm, combined with the power response capability and health status parameters of each battery pack, it is sorted according to the response priority to generate the allocation results of the battery packs in the time interval.

[0066] The specific steps to generate a battery pack power output distribution plan are:

[0067] Based on the battery pack allocation results for each time interval, the battery pack charge and discharge power within each time interval is calculated. The upper and lower power limits are used as judgment criteria, and combined with the actual load demand, the battery pack power calculation results are generated.

[0068] Based on the battery pack power calculation results, the battery pack power is checked one by one to see if it meets the load requirements. Inappropriate battery packs are eliminated and the power distribution method of the remaining battery packs is adjusted to generate the battery pack power distribution adjustment result.

[0069] Based on the battery pack power allocation adjustment results, power output is allocated to qualified battery packs according to time intervals. Load demand is used as the allocation basis to ensure that the battery pack output power matches the demand and generate a battery pack power output allocation plan;

[0070] Based on the battery pack allocation results within the time interval, a power upper and lower limit judgment algorithm is used to calculate the battery pack charge and discharge power within each time interval. The upper and lower power limits of the battery pack are used as judgment criteria, and a power boundary calculation function is used to judge the charge and discharge power of each battery pack. Combined with the actual load demand, a weighted allocation algorithm is used to weight the load demand according to the health status parameters of the battery pack. The battery pack power is iteratively solved using a numerical iteration method to generate the battery pack power calculation result;

[0071] Based on the battery pack power calculation results, a successive inspection method is used to check whether the power of each battery pack meets the load requirements. The power of each battery pack is checked item by item using the successive inspection algorithm, and battery packs that do not meet the requirements are eliminated. The power of the remaining battery packs is redistributed using the dynamic adjustment algorithm. The power distribution method of the remaining battery packs is recalculated using a linear regression model. In combination with the power distribution boundary, the battery pack power distribution ratio is used to generate the battery pack power distribution adjustment result;

[0072] Based on the battery pack power distribution adjustment results, a load demand matching algorithm is used to distribute power output of qualified battery packs according to time intervals. Load demand is used as the basis for allocating battery pack power output. The battery pack output power is adjusted using the successive approximation method. The load demand data of each time interval is used as a constraint condition, and the power output of each battery pack is sorted using a priority sorting algorithm. Combined with the power demand, a battery pack power output distribution plan is generated.

[0073] The specific steps to generate battery power output optimization results are:

[0074] Based on the battery pack power output allocation plan, data on the health status of retired batteries is collected. By reading parameters such as remaining capacity, voltage, and internal resistance, the battery status is standardized and clustered to generate the battery clustering results within the battery pack.

[0075] Based on the battery clustering results within the battery pack, the battery health status and power allocation parameters are analyzed to calculate the charge and discharge sequence of each battery. The battery capacity and power requirements are combined to prioritize and generate the charge and discharge sequence results.

[0076] Based on the charge and discharge sequence results, a fuzzy control algorithm is used to detect the real-time status of power output to determine whether there is a power overload and perform power reduction operations. By adjusting the load distribution to balance the power output, the battery power output optimization result is generated;

[0077] Based on the battery pack power output allocation plan, a data acquisition algorithm is used to collect data on the health status of retired batteries. Sensors in the battery management system are used to read battery parameters such as remaining capacity, voltage, and internal resistance. The Modbus protocol is used to read data, and the acquisition frequency is set to once per second. The parameters are standardized and each parameter is normalized to the range of [0, 1] using the maximum and minimum normalization method. The standardized battery parameters are clustered using the K-Means clustering algorithm. The number of cluster centers is set to 3, and the cluster centers are initialized. The distance between each data point and the cluster center is calculated using Euclidean distance. The cluster centers are iteratively updated to generate the battery clustering results within the battery pack.

[0078] Based on the battery clustering results within the battery pack, a priority sorting algorithm is used to analyze the battery health status and power allocation parameters. The charge and discharge order of each battery is calculated one by one. The battery's remaining capacity, voltage, and internal resistance are used as sorting parameters. A weighted priority sorting algorithm is used, with the remaining capacity weight set to 0.5, the voltage weight set to 0.3, and the internal resistance weight set to 0.2. A priority score is calculated based on the weight of each battery, and the scores are arranged in descending order. Combined with the battery's power demand and capacity, the batteries are sorted by priority to generate the charge and discharge order result.

[0079] Based on the charge and discharge sequence results, a fuzzy control algorithm is used to detect the power output status of the battery pack in real time. The upper and lower limits of the power output are set to 90% and 110% of the power capacity. The detected power output value is fuzzy processed using the fuzzy control rule base, and the membership function of the fuzzy variable is set. The triangular membership function is used to perform fuzzy judgment on the power status. Combined with the fuzzy control decision table, it is determined whether the power output is overloaded. If overload exists, the peak shaving and valley filling algorithm is used to reduce power, adjust the load distribution after reduction, recalculate the power output of the battery pack, and generate the battery power output optimization result.

[0080] Fuzzy control algorithm, according to the formula:

[0081]

[0082] Where: ΔP(t) is the power adjustment at the current moment, α is the power second-order change response coefficient, is the second-order derivative of power, β is the environmental power influence coefficient, P envis the impact of the external environment on the power output, e(t) is the error between the current power output and the set target, K p is the proportionality coefficient, K i is the integral coefficient, K d is the differential coefficient, is the cumulative sum of errors, is the derivative of the error;

[0083] Execution process: First, the calculation process of the power adjustment ΔP(t) starts from the error value e(t). The error value represents the deviation between the current power output and the set target. p Generate preliminary adjustment signal and pass integral term Calculate the cumulative sum of historical errors, correct the deviation caused by long-term errors, and then, the differential term By reflecting the rate of error change, the trend of error change is corrected, rapid changes in power output are predicted in advance, and the second-order derivative of power is added. And add the compensation term β·P of environmental factors env , used to compensate for the impact of the external environment on power output, β is the weight coefficient of environmental impact, and is fitted and adjusted according to the correlation between environmental monitoring data and power output to ensure that power output remains stable when the external environment changes.

[0084] The specific steps to generate a real-time battery power regulation solution are:

[0085] Based on the battery power output optimization results, the charging and discharging status of retired batteries is monitored in real time. Battery current, voltage, and temperature parameters are used to compare and analyze the battery with the load demand. The charging and discharging data is then acquired to generate real-time load and battery status comparison results.

[0086] Based on the comparison results of real-time load and battery status, the remaining capacity of batteries with low remaining capacity is screened one by one to determine the remaining capacity of the batteries. The batteries are then removed based on the power requirements to generate the battery removal adjustment results.

[0087] Based on the battery removal adjustment results, the output power of the remaining batteries is redistributed, the power allocation value of each battery in the battery pack is adjusted one by one, and the final allocation is made based on the load demand to generate a real-time battery power adjustment plan;

[0088] Based on the battery power output optimization results, a real-time monitoring algorithm is used to monitor the charge and discharge status of retired batteries in real time. The battery management system reads the battery's current, voltage, and temperature parameters. The current reading frequency is set to once per second, and the voltage and temperature parameter reading frequency is set to once every 10 seconds. Load demand data is used for comparison and analysis. A differential algorithm is used to calculate the difference between the current battery status and the load demand. Real-time battery charge and discharge data is obtained, and interpolation is used to fill in missing data. The collected data is smoothed to generate real-time load and battery status comparison results.

[0089] Based on the comparison results of real-time load and battery status, a capacity screening algorithm is used to screen batteries with low remaining capacity one by one. The battery management system reads the remaining capacity value of the battery, sets the remaining capacity threshold to 20%, and uses a segmented inspection method to test the remaining capacity of all batteries. A recursive algorithm is used to screen batteries with insufficient capacity, and the remaining capacity value of each battery is determined one by one. Based on the current power demand, batteries that do not meet the requirements are eliminated. The dynamic array is used to update the remaining battery group to generate the battery elimination adjustment results;

[0090] Based on the battery elimination adjustment results, a power allocation algorithm is used to redistribute the output power of the remaining batteries. The power allocation value of each battery in the battery pack is adjusted one by one. The linear programming algorithm is used to set the upper and lower power limits of each battery. The target power allocation value of each battery is calculated based on the load demand. The power allocation value is adjusted multiple times using an iterative method to ensure that the power allocation value is between the upper and lower power limits. The allocation result is then verified again based on the load demand and battery health status parameters to generate a real-time power adjustment plan for the battery.

[0091] The specific steps to generate a battery pack power matching solution are:

[0092] Based on the battery real-time power regulation solution, the power output data of the energy storage battery is collected. By obtaining the real-time output data of the battery pack and the grid load fluctuation data, the load demand is divided into intervals using the time division method, and power superposition analysis is performed to generate the superposition analysis results of the energy storage battery and grid load;

[0093] Based on the analysis results of the energy storage battery and grid load superposition, real-time adjustment operations are performed on the load period and battery pack power output. By matching the battery output power with the grid load demand in each period, the battery pack power is redistributed and the battery pack power adjustment matching results are generated;

[0094] Based on the battery pack power adjustment and matching results, load demand analysis is performed. By selecting and matching appropriate battery packs in each time period, the battery pack power output is redistributed according to different load periods to generate a battery pack power matching solution.

[0095] Based on the battery real-time power regulation scheme, a data acquisition algorithm is used to collect the power output data of the energy storage battery. The battery management system is used to obtain the real-time output power data of the battery pack. The data acquisition frequency is set to once per second. The power grid load fluctuation data is obtained through power sensors. The collection frequency of load fluctuation data is set to once every 10 seconds. The time period division method is adopted. The sliding window technology is used to divide the load demand into time periods. The window size is set to 1 hour. The load demand is divided into time intervals. The load fluctuation data within the time period is superimposed and the cumulative function is used to perform superimposed analysis on the load fluctuation in each time period to generate the superimposed analysis results of the energy storage battery and power grid load.

[0096] Based on the analysis results of the superposition of energy storage batteries and grid loads, a time-period matching algorithm is used to make real-time adjustments to the load period and battery pack power output. The battery output power in each time period is compared with the grid load demand one by one using the time-period matching method. The difference in power demand for each time period is calculated using the difference calculation method. Combined with the remaining capacity and health status of the battery, the linear programming algorithm is used to reallocate the battery pack power, set the upper and lower limits of the power distribution, adjust the power output by time period, and generate the battery pack power adjustment matching results.

[0097] Based on the battery pack power adjustment and matching results, the load demand is analyzed using a load demand analysis algorithm. Appropriate battery packs are selected and matched time period by time period. The battery power is allocated within each time period using a dynamic programming algorithm. The time period is set to 1 hour, and the upper and lower limits of the battery power output are set. Combined with the load demand and battery status parameters of each time period, the battery pack power is reallocated by time period to generate a battery pack power matching plan.

[0098] The specific steps to generate frequency compensation and power adjustment scheme are:

[0099] Based on the battery pack power matching solution, the overall power dispatch monitoring of the energy storage fast charging station is carried out. The real-time output data of the battery pack and the grid frequency fluctuation data are collected. By comparing and analyzing the relationship between battery output and grid frequency, the comparison results of battery pack and grid frequency fluctuation are generated.

[0100] Based on the comparison results of the battery pack and the grid frequency fluctuation, the battery pack power output is adjusted during the high-load period. By analyzing the load in different time periods one by one, the battery pack output during the high-load period is adjusted one by one to generate the battery pack power adjustment results during the high-load period;

[0101] Based on the battery pack power adjustment results during high-load periods, the battery health status and power output are analyzed to balance the battery output power, adjust the relationship between the battery pack output and the grid frequency, and generate frequency compensation and power adjustment plans;

[0102] Based on the battery pack power matching solution, a real-time data acquisition algorithm is used to monitor the overall power scheduling of the energy storage fast charging station. The battery management system collects real-time output power data of the battery pack, with a data acquisition frequency set to once per second. Power sensors are used to collect grid frequency fluctuation data, with a frequency set to once every five seconds. A differential algorithm is used to compare and analyze the battery pack output power and grid frequency fluctuation data. By calculating the difference between the output power and the frequency fluctuation, a comparison result of the battery pack and grid frequency fluctuation is generated.

[0103] Based on the comparison results of the battery pack and the grid frequency fluctuations, a load period analysis algorithm is used to adjust the battery pack power output during high-load periods. By analyzing the load conditions in different time periods one by one, the sliding window technology is used to divide the time periods. The window size is set to 30 minutes. The output power of the battery pack during high-load periods is adjusted one by one. The battery pack output during high-load periods is fitted and calculated using a linear regression algorithm. Adjustments are made based on the fitting results to generate the battery pack power adjustment results for high-load periods.

[0104] Based on the power adjustment results of the battery pack during high-load periods, a power balancing algorithm is adopted. By analyzing the battery health status and power output data, the battery health status parameters are updated in real time using the Kalman filter algorithm. Combined with the output power data, the output power of the battery pack is balanced using a dynamic balance model, and the relationship between the battery pack output power and the grid frequency is adjusted. A frequency compensation algorithm is used to generate a frequency compensation and power adjustment plan by real-time detection of the matching between the grid frequency fluctuation and the battery pack power.

[0105] The specific steps to generate the battery health status update result are:

[0106] Based on the frequency compensation and power adjustment scheme, the system collects historical operating data of the energy storage system. Key battery operating parameters are extracted from the historical data, and the battery operating data is classified and processed. The health status analysis is then performed to generate a battery health status assessment result.

[0107] Based on the battery health assessment results, the system analyzes the health status of each battery one by one, comparing health indicators such as remaining capacity, internal resistance, and depth of discharge to determine whether the battery meets the continued operation standards. It then marks batteries that need to be retired and generates a battery continued operation judgment result.

[0108] Based on the battery's continued operation judgment result, the battery's operating status is updated one by one by confirming the battery marked as having a poor health status. The battery's operating information is updated by using a retirement process and health status data update method to generate a battery health status update result.

[0109] Based on the frequency compensation and power adjustment scheme, a historical data collection algorithm is used to collect historical data on the energy storage system's operation. Database query commands are used to extract key battery operating parameters from the historical data set, including the battery's remaining capacity, voltage, temperature, and internal resistance. The query frequency is set to once per minute. A data classification algorithm is used to classify the battery operating data, with the key classification parameters set as remaining capacity, temperature, and internal resistance. The battery data is classified according to operating status, and a classification model is used to predict the battery's health status. The battery operating data is analyzed one by one to generate a battery health assessment result.

[0110] Based on the battery health assessment results, a health indicator comparison algorithm is used to analyze the health status of each battery one by one. By extracting key health indicators such as remaining capacity, internal resistance, and depth of discharge, a multivariate regression analysis method is used to compare the health status. The remaining capacity threshold is set at 20%, the internal resistance threshold is set at 50 milliohms, and the depth of discharge threshold is set at 80%. Each battery is judged one by one to see if it meets the continued operation standard. Batteries that need to be retired are marked, and the battery status is updated using a dynamic array to generate a battery continued operation judgment result.

[0111] Based on the judgment result of battery continued operation, a status update algorithm is used to update the battery operation status one by one by confirming the batteries marked as having poor health status. The battery health status data is updated item by item using the database update operation. The decommissioning procedure is used to decommission the batteries that do not meet the continued operation standards. The health status data is updated using the data update command to complete the update of the battery operation information and generate the battery health status update result.

[0112] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An energy management method for a hybrid energy storage fast charging station based on retired batteries, characterized in that: The following steps are involved: Step 1: Based on the load demand, remaining capacity, and health status parameters of the retired batteries, the load demand is compared with the battery capacity, the load interval is divided by time, the load change rate within each time period is calculated, and the battery health status is evaluated and the power response is selected to generate the time interval battery group allocation result; Step 2: Based on the battery pack allocation results for the time interval, the battery pack charge and discharge power is calculated. By matching the battery health status with the current load, the upper and lower power limits are determined to determine whether they meet the actual load requirements. Unsuitable battery packs are then eliminated to generate a battery pack power output allocation plan. Step 3: Based on the battery pack power output allocation plan, a fuzzy control algorithm is used to cluster the retired batteries in the battery pack. Through comprehensive calculation of battery health status and power allocation, the optimal charge and discharge sequence is determined, and power output overload reduction and balancing are performed to generate battery power output optimization results. Step 4: Based on the battery power output optimization results, the charge and discharge status of the retired batteries is monitored in real time, the load demand is compared with the current power output of the batteries, and a real-time battery power adjustment plan is generated by adjusting and eliminating batteries with low remaining capacity; Step 5: Based on the real-time battery power regulation scheme, perform a superposition analysis of the energy storage battery power output and grid load fluctuations, divide the load demand by time period, adjust the battery pack power output to match the grid load, and generate a battery pack power matching scheme; Step 6: Based on the battery pack power matching solution, perform overall power scheduling monitoring of the energy storage fast charging station. Combined with the real-time output of the battery pack and the grid frequency fluctuation, adjust the battery pack output during high-load periods and generate a frequency compensation and power adjustment solution. Step 7: Based on the frequency compensation and power adjustment scheme, evaluate the energy storage system's historical operating data and battery health status, determine whether the battery meets the continued operation standards, and generate a battery health status update result.

2. The energy management method of a hybrid energy storage fast charging station based on retired batteries according to claim 1 is characterized in that: The specific steps of generating the time interval battery group allocation result are: Based on the load demand of the energy storage fast charging station and the remaining capacity and health status parameters of the retired batteries, the load and battery capacity are compared. The load demand is divided by time period, and combined with the battery health status matching, the load interval and battery health status matching results are generated; Based on the matching results of the load interval and the battery health status, the load demand of each time interval is refined, and the upper and lower limits of the power response are determined in combination with the battery pack health status, and the battery pack power response analysis results of the time interval are generated; Based on the battery pack power response analysis results of the time interval, the health status of the battery packs is evaluated one by one, the power response is selected in combination with the load demand, unsuitable battery packs are eliminated, and the battery pack allocation results of the time interval are generated.

3. The energy management method of a hybrid energy storage fast charging station based on retired batteries according to claim 1, characterized in that: The specific steps of generating the battery pack power output distribution plan are: Based on the battery pack allocation results of the time interval, calculating the battery pack charge and discharge power in each time interval, and generating a battery pack power calculation result in combination with the load demand; Based on the battery pack power calculation result, checking whether the battery pack power meets the load demand, eliminating unsuitable battery packs and adjusting the power distribution of the remaining battery packs to generate a battery pack power distribution adjustment result; Based on the battery pack power allocation adjustment result, the power output of the battery pack that meets the conditions is allocated according to the time interval to ensure that the output power matches the load demand, and a battery pack power output allocation plan is generated.

4. The energy management method of a hybrid energy storage fast charging station based on retired batteries according to claim 1, characterized in that: The specific steps of generating the battery power output optimization result are: Based on the battery pack power output allocation scheme, health status data of retired batteries is collected, and parameters such as remaining capacity, voltage, and internal resistance of the batteries are read, and standardized and clustered to generate clustering results of batteries within the battery pack; Based on the battery clustering results within the battery pack, analyzing the battery health status and power allocation parameters, calculating the charge and discharge sequence, and prioritizing the battery capacities and power requirements to generate a charge and discharge sequence result; Based on the charge and discharge sequence results, a fuzzy control algorithm is used to detect the real-time status of power output, determine whether there is overload and perform reduction operations, adjust load distribution, and generate battery power output optimization results.

5. The energy management method of a hybrid energy storage fast charging station based on retired batteries according to claim 1, characterized in that: The fuzzy control algorithm is based on the formula: Where: ΔP ( t ) is the power adjustment amount at the current moment, α is the power second-order change response coefficient, is the second-order derivative of power, β is the environmental power influence coefficient, P env is the impact of the external environment on power output, e ( t ) is the error between the current power output and the set target, K p is the proportionality coefficient, K i is the integral coefficient, K d is the differential coefficient, is the cumulative sum of errors, is the derivative of the error.

6. The energy management method of a hybrid energy storage fast charging station based on retired batteries according to claim 1, characterized in that: The specific steps of generating the battery real-time power regulation scheme are: Based on the battery power output optimization results, the charge and discharge status of the retired battery is monitored in real time, and current, voltage and temperature parameters are used to compare with the load demand, to obtain charge and discharge data and generate real-time load and battery status comparison results; Based on the comparison result of the real-time load and battery status, screening batteries with low remaining capacity, determining the remaining capacity value, eliminating them in combination with power requirements, and generating a battery elimination adjustment result; Based on the battery elimination adjustment result, the output power of the remaining batteries is redistributed, the power distribution value of each battery is adjusted, and the final distribution is performed in combination with the load demand to generate a battery real-time power adjustment plan.

7. The energy management method of a hybrid energy storage fast charging station based on retired batteries according to claim 1, characterized in that: The specific steps for generating the battery pack power matching solution are: Based on the battery real-time power regulation scheme, the power output data of the energy storage battery is collected, the real-time output of the battery pack and the grid load fluctuation data are obtained, the load demand is divided by time period, and power superposition analysis is performed to generate the energy storage battery and grid load superposition analysis results; Based on the analysis results of the energy storage battery and grid load superposition, the load period and battery pack power output are adjusted in real time, the battery output power is matched with the grid load demand in each period, and the battery pack power adjustment matching result is generated; Based on the battery pack power adjustment and matching results, the load demand is analyzed, appropriate battery packs are selected and matched in each time period, the battery pack power output is reallocated, and a battery pack power matching solution is generated.

8. The energy management method of a hybrid energy storage fast charging station based on retired batteries according to claim 1, characterized in that: The specific steps of generating the frequency compensation and power adjustment scheme are: Based on the battery pack power matching solution, the overall power scheduling of the energy storage fast charging station is monitored, the real-time output of the battery pack and the grid frequency fluctuation data are collected, the relationship between the battery output and the grid frequency is analyzed, and the comparison results of the battery pack and the grid frequency fluctuation are generated; Based on the comparison result of the frequency fluctuation of the battery pack and the power grid, the power output of the battery pack during the high load period is adjusted, and the output of the battery pack during the high load period is analyzed and adjusted one by one to generate the power adjustment result of the battery pack during the high load period; Based on the battery pack power adjustment results during the high load period, the battery health status and power output are analyzed, the battery pack output and the grid frequency are balanced, and a frequency compensation and power adjustment plan is generated.

9. The energy management method of a hybrid energy storage fast charging station based on retired batteries according to claim 1, characterized in that: The specific steps of generating the battery health status update result are: Based on the frequency compensation and power adjustment scheme, historical data of the energy storage system operation is collected, key battery operation parameters are extracted, the battery operation data is classified and processed, and the health status is analyzed to generate a battery health status assessment result; Based on the battery health status assessment results, analyze the battery health status, compare the battery health indicators such as remaining capacity, internal resistance, and depth of discharge, determine whether the battery meets the continued operation standard, mark the battery that needs to be retired, and generate a battery continued operation determination result; Based on the battery continued operation judgment result, the batteries marked as having poor health status are identified, the operation status of the batteries is updated one by one, and the battery health status update result is generated through decommissioning processing and data update.

10. The energy management method of a hybrid energy storage fast charging station based on retired batteries according to claim 1, characterized in that: The time interval battery group allocation result includes load time division, battery group responsiveness, and battery remaining capacity assessment; the battery group power output allocation plan includes upper and lower limits of charge and discharge power, battery health status allocation, and a battery elimination list; the battery power output optimization result includes charge and discharge priority, overload battery reduction list, and power allocation adjustment; the battery real-time power regulation plan includes adjustment of batteries with low remaining capacity, real-time power output curve, and elimination battery list; the battery group power matching plan includes load demand period, battery power adjustment, and battery group reallocation list; the frequency compensation and power adjustment plan includes high-load battery output adjustment, frequency compensation amplitude, and battery group balance adjustment value; the battery health status update result includes a battery health status list, a list of batteries to be retired, and an updated value of the operating battery status.

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