Hybrid energy storage fast charging station energy management method based on retired battery

Abstract

The invention relates to the technical field of hybrid energy storage management, in particular to a hybrid energy storage fast charging station energy management method based on a decommissioned battery, which comprises the following steps of: comparing a load demand with a battery capacity based on the load demand, the residual capacity of the decommissioned battery and a health state parameter, dividing a load interval according to time, and determining the load interval of the decommissioned battery; and calculating the load change rate in each time period, performing battery health state evaluation and power response selection, and generating a time interval battery pack distribution result. According to the method, the fuzzy control algorithm is used for clustering and dividing the retired batteries, comprehensive calculation is carried out by combining the health states of the batteries and power distribution, the power output in the battery pack is optimized, the service life of the retired batteries is prolonged, overload or unreasonable use of the batteries is avoided, the energy storage cost is reduced, and the real-time power regulation scheme is optimized. The battery pack can be adjusted in time according to power grid load fluctuation, and the stability in a high-load period is ensured.

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 to optimize the energy storage, release and distribution process by utilizing the respective characteristics of different energy storage units.

[0003] The energy management method of hybrid energy storage fast charging stations based on retired batteries aims to extend the service life of retired batteries and reduce energy storage costs. It improves the charging efficiency of fast charging stations through rapid response energy management, utilizes the remaining available capacity of retired batteries, and works 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 existing in the prior art and to propose an energy management method for a hybrid energy storage fast charging station based on retired batteries.

[0005] In order to achieve the above-mentioned purpose, 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 of the energy storage fast charging station, the remaining capacity of the retired battery and the health status parameters, the load demand is compared with the battery capacity, the load interval is divided by time, the load change rate in each time period is calculated, and the battery health status is evaluated and the power response is selected to generate the battery group allocation result for the time interval;

[0007] Step 2: Based on the battery group allocation result in the time interval, the battery group charge and discharge power is calculated, and by matching the battery health status with the current load, it is determined whether the upper and lower power limits meet the actual load requirements, and unsuitable battery groups are eliminated to generate a battery group power output allocation plan;

[0008] Step 3: Based on the battery pack power output allocation scheme, a fuzzy control algorithm is used to cluster retired batteries in the battery pack, and the optimal charging and discharging sequence is determined through comprehensive calculation of battery health status and power allocation, and power output overload reduction and balancing processing is performed to generate battery power output optimization results;

[0009] Step 4: Based on the battery power output optimization result, the charging and discharging status of the retired battery is monitored in real time, the load demand is compared with the current power output of the battery, and the output power is redistributed by adjusting and eliminating the battery with lower remaining capacity, so as to generate a real-time power adjustment plan for the battery;

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

[0011] Step 6: Based on the battery pack power matching scheme, the overall power dispatch monitoring of the energy storage fast charging station is carried out. The battery pack output during the high load period is adjusted in combination with the real-time output of the battery pack and the grid frequency fluctuation. The frequency compensation and power adjustment scheme is generated by balancing the battery health status and power output.

[0012] Step 7: Based on the frequency compensation and power adjustment scheme, the energy storage system operation history data and battery health status are evaluated to determine whether the battery meets the standards for continued operation. The battery operation status is updated by retiring the remaining batteries with poor health status 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 scheme includes upper and lower limits of charging and discharging power, battery health status allocation, and a battery elimination list; the battery power output optimization result includes charging and discharging priority, overload battery reduction list, and power allocation adjustment; the battery real-time power regulation scheme includes adjustment of batteries with low remaining capacity, real-time power output curve, and a battery elimination list; the battery group power matching scheme includes load demand period, battery power adjustment, and a battery group reallocation list; the frequency compensation and power adjustment scheme 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 operating battery status update value.

[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 the time period division method. Combined with the battery health status parameters, the load demand of each time period is matched to generate the load interval and battery health status matching results;

[0016] Based on the matching results of 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 the power response calculation method in combination with the health status parameters of the battery pack, and the power response analysis results of the battery pack in the time interval are generated;

[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, the power response is selected in combination with the load demand, unsuitable battery packs are eliminated, and the time interval battery pack allocation results are generated.

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

[0019] Based on the battery group allocation result of the time interval, the battery group 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 group power calculation result;

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

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

[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, and the battery status is standardized by reading parameters such as the remaining capacity, voltage, and internal resistance of the batteries, and clustering is performed to generate clustering results of the batteries in the battery pack;

[0024] Based on the battery clustering results in the battery pack, by analyzing the battery health status and power allocation parameters, the charging and discharging sequence of the batteries is calculated one by one, and the battery capacities and power requirements are combined for priority sorting to generate a charging and discharging 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 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 integration 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 result, the charging and discharging status of the retired battery is monitored in real time, and the battery current, voltage and temperature parameters are used to compare and analyze with the load demand, and the charging and discharging 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 value of the battery is determined by screening the batteries with lower remaining capacity one by one, and the battery is removed in combination with the power demand to generate a battery removal adjustment result;

[0032] Based on the battery elimination adjustment result, 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 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, power output data of the energy storage battery is collected. By acquiring the real-time output data of the battery group and the grid load fluctuation data, the load demand is divided into intervals using the time period division method, and power superposition analysis is performed to generate the superposition analysis results of the energy storage battery and the grid load;

[0035] Based on the analysis results of the energy storage battery and the grid load, a real-time adjustment operation is performed on the load period and the battery pack power output, and the battery pack power is redistributed by matching the battery output power with the grid load demand in each period 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 period and reallocating battery pack power output according to different load 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 scheme, 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, and the relationship between the battery output and the grid frequency is analyzed by data comparison to generate the comparison result of the battery pack and the grid frequency fluctuation;

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

[0040] Based on the battery pack power adjustment results during the high load period, the battery output power is balanced by analyzing the battery health status and power output conditions, 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, the historical operation data of the energy storage system 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 result, the health status of the batteries is analyzed one by one, and the battery is judged whether it meets the continued operation standard by comparing the health indicators such as the remaining capacity, internal resistance and discharge depth of the battery, and the battery that needs to be retired is marked to generate the battery continued operation judgment 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 fuzzy control algorithm, and comprehensive calculation is performed in combination with battery health status and power allocation to optimize the power output inside 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 real-time power regulation schemes, the battery pack can be adjusted in time according to grid load fluctuations to ensure stability during high-load periods, and the frequency compensation scheme is 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 solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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] Embodiment 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 of the energy storage fast charging station, the remaining capacity of the retired battery and the health status parameters, the load demand is compared with the battery capacity, the load interval is divided by time, the load change rate in each time period is calculated, and the battery health status is evaluated and the power response is selected to generate the battery group allocation result for the time interval;

[0052] Step 2: Based on the battery group allocation results in the time interval, the battery group charging and discharging power is calculated. By matching the battery health status with the current load, it is determined whether the upper and lower power limits meet the actual load requirements, and unsuitable battery groups are eliminated to generate a battery group power output allocation plan;

[0053] 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. The optimal charging and discharging sequence is determined through comprehensive calculation of the battery health status and power allocation, and power output overload reduction and balancing are performed to generate battery power output optimization results.

[0054] Step 4: Based on the battery power output optimization results, monitor the charge and discharge status of retired batteries in real time, compare the load demand with the current power output of the battery, adjust and eliminate batteries with low remaining capacity, redistribute the output power, and generate a real-time power adjustment plan for the battery;

[0055] Step 5: Based on the real-time power regulation scheme of the battery, perform a superposition analysis of the power output of the energy storage battery and the grid load fluctuation, divide the load demand by time period, adjust the power output of the battery group to match the grid load, select a suitable battery group for power redistribution, and generate a battery group power matching scheme;

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

[0057] Step 7: Based on the frequency compensation and power adjustment scheme, evaluate the energy storage system operation history data and battery health status to determine whether the battery meets the standards for continued operation. By retiring the remaining batteries with poor health status, update the battery operation status and 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 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 results include battery health status list, list of batteries to be retired, and running battery status update value.

[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 the time period division method. Combined with the battery health status parameters, the load demand of each time period is matched to generate the load interval and battery health status matching results;

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

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

[0063] Based on the load demand of the energy storage fast charging station and the remaining capacity and health status parameters of the retired battery, a dynamic matching algorithm is used to compare the load demand with the battery capacity. By calculating the time series distribution of the battery remaining capacity and the load demand, the linear interpolation method is used to interpolate the load demand in each period, and the remaining capacity parameters are used to calculate the available capacity value of the battery. The binary method is used to compare the battery capacity and the load demand. The time period division method is used, and the time series segmentation algorithm is used to classify the load demand by time period. Combined with the battery health status parameters, the Kalman filter algorithm is used to update the battery health status parameters in real time based on the state transfer matrix to generate the load interval and battery health status matching results.

[0064] Based on the matching results of the load interval and the battery health status, the power response calculation method is used to refine the load demand in 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 power response upper limit is set to the rated power of the battery pack, and the power response lower limit is set to the minimum available power of the battery pack. Combined with the power response model, the load demand in each time period is refined and calculated. Combined with the health status parameters, the linear regression model is used to fit and estimate the health status of the battery pack, and the power response analysis results of the battery pack in the time interval are generated;

[0065] Based on the power response analysis results of the 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, and the discharge curve and charge state of the battery are classified using the support vector machine. The power response requirements of the load demand are combined to select the power response and eliminate the unsuitable battery packs. Based on the priority sorting algorithm, the power response capability and health status parameters of each battery pack are combined, and they are 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 allocation plan are:

[0067] Based on the battery group allocation results in the time interval, the battery group charging and discharging 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 group power calculation results;

[0068] Based on the battery pack power calculation results, check whether the battery pack power meets the load requirements one by one, remove unsuitable battery packs, and adjust the power allocation method of the remaining battery packs to generate the battery pack power allocation adjustment result;

[0069] Based on the battery group power allocation adjustment results, the qualified battery groups are allocated power output according to the time interval, and the load demand is used as the allocation basis to ensure that the battery group output power matches the demand, and the battery group power output allocation plan is generated;

[0070] Based on the battery group allocation results in the time interval, the power upper and lower limit judgment algorithm is used to calculate the battery group charge and discharge power in each time interval. The upper and lower limits of the battery group power are used as the judgment criteria, and the power boundary calculation function is used to judge the charge and discharge power of each battery group. 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 group. The battery group power is iteratively solved using the numerical iteration method to generate the battery group power calculation result;

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

[0072] Based on the battery pack power allocation adjustment results, a load demand matching algorithm is used to allocate power output to qualified battery packs according to time intervals. The load demand is used as the basis for allocating the battery pack power output. The battery pack output power is adjusted using the stepwise 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 allocation plan is generated.

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

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

[0075] Based on the clustering results of the batteries in the battery pack, the battery health status and power allocation parameters are analyzed to calculate the charging and discharging order of each battery. The battery capacity and power demand are combined to prioritize and generate the charging and discharging order results.

[0076] Based on the results of the charge and discharge sequence, 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 reduction operation. 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 scheme, a data acquisition algorithm is used to collect data on the health status of retired batteries. The sensors in the battery management system are used to read the remaining capacity, voltage, internal resistance and other parameters of the battery. The Modbus protocol is used to read the data, and the acquisition frequency is set to once per second. The parameters are standardized, and the maximum and minimum normalization method is used to normalize each parameter to the range of [0, 1]. The K-Means clustering algorithm is used to cluster the standardized battery parameters, and the number of cluster centers is set to 3. The cluster centers are initialized, and the Euclidean distance is used to calculate the distance between each data point and the cluster center. The cluster center is iteratively updated to generate the battery clustering results in the battery pack.

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

[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, and the upper and lower limits of the power output are set to 90% and 110% of the power capacity. The fuzzy control rule base is used to fuzzify the detected power output value, set the membership function of the fuzzy variable, and use the triangular membership function to make a fuzzy judgment on the power status. The fuzzy control decision table is combined to determine whether the power output is overloaded. If there is an overload, the peak shaving and valley filling algorithm is used to reduce the power, adjust the load distribution after the 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 integration 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 a preliminary adjustment signal and pass the 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 of environmental factors β·P env , which is used to compensate for the impact of the external environment on power output. β is the weight coefficient of environmental impact. It is fitted and adjusted according to the correlation between environmental monitoring data and power output to ensure that the power output can remain 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. The battery current, voltage and temperature parameters are used to compare and analyze with the load demand, and the charging and discharging data are obtained 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 the batteries is determined by screening the batteries with low remaining capacity one by one, and the batteries are removed according to the power demand to generate the battery removal adjustment results;

[0087] Based on the battery elimination 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 in combination with 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 is used to read 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. The load demand data is used for comparison and analysis. The difference algorithm is used to calculate the difference between the current battery status and the load demand, and the real-time charge and discharge data of the battery is obtained. The missing data is filled by interpolation, and 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 remaining capacity value of the battery is read by the battery management system, and the remaining capacity threshold is set to 20%. The remaining capacity of all batteries is tested by the segmented inspection method. The batteries with insufficient capacity are screened by a recursive algorithm, and the remaining capacity value of each battery is determined one by one. Combined with the current power demand, the batteries that do not meet the requirements are eliminated, and the remaining battery groups are updated using a dynamic array to generate battery elimination adjustment results;

[0090] Based on the battery elimination adjustment results, a power allocation algorithm is used to reallocate the output power of the remaining batteries, adjust the power allocation value of each battery in the battery pack one by one, use a linear programming algorithm to set the upper and lower power limits of each battery, and calculate the target power allocation value of each battery in combination with 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 verified twice in combination with 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 real-time power regulation scheme of the battery, the power output data of the energy storage battery is collected. By obtaining the real-time output data of the battery group and the grid load fluctuation data, the load demand is divided into intervals using the time period division method, and power superposition analysis is performed to generate the superposition analysis results of the energy storage battery and the 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 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 the battery pack power adjustment matching results;

[0094] Based on the battery power adjustment and matching results, load demand analysis is performed, and the battery power matching solution is generated by selecting and matching appropriate battery packs in each period and reallocating the battery power output according to different load periods;

[0095] Based on the real-time power regulation scheme of the battery, 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 the power sensor. The collection frequency of the 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, and the load demand is divided according to the time interval. The load fluctuation data within the time period is used for superposition operation. The cumulative function is used to perform superposition analysis on the load fluctuation of each time period to generate the superposition analysis results of the energy storage battery and the 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 adjust the load period and battery pack power output in real time. The battery output power in each time period is compared with the load demand of the grid one by one using the time-period matching method. The power demand difference of 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 allocation, adjust the power output time by time, and generate the battery pack power adjustment matching results.

[0097] Based on the power adjustment and matching results of the battery pack, the load demand is analyzed using the load demand analysis algorithm, and the appropriate battery pack is selected and matched in each time period. The battery power in each time period is allocated using the 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, and the real-time output data of the battery pack and the grid frequency fluctuation data are collected. Through data comparison and analysis, the relationship between battery output and grid frequency is analyzed to generate the comparison results of battery pack and grid frequency fluctuation;

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

[0101] Based on the power adjustment results of the battery pack during the high-load period, the battery output power is balanced by analyzing the battery health status and power output, adjusting the relationship between the battery pack output and the grid frequency, and generating 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 dispatch of the energy storage fast charging station. The battery management system is used to collect the real-time output power data of the battery pack, and the data acquisition frequency is set to once per second. The power sensor is used to collect the grid frequency fluctuation data, and the collection frequency of the frequency fluctuation data is set to once every 5 seconds. The differential algorithm is used to compare and analyze the battery pack output power and the grid frequency fluctuation data. By calculating the difference between the output power and the frequency fluctuation, the comparison result of the battery pack and the grid frequency fluctuation is generated;

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

[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's health status and power output data, the Kalman filter algorithm is used to update the battery health status parameters in real time. Combined with the output power data, the dynamic balance model is used to balance the output power of the battery pack, adjust the relationship between the battery pack output power and the grid frequency, and use the frequency compensation algorithm to generate frequency compensation and power adjustment plans 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 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;

[0107] Based on the battery health status assessment results, the health status of each battery is analyzed one by one, and the battery health indicators such as remaining capacity, internal resistance and discharge depth are compared to determine whether the battery meets the continued operation standard, mark the battery that needs to be retired, and generate the battery continued operation judgment result;

[0108] Based on the result of the battery continued operation judgment, the battery operation status is updated one by one by confirming the battery marked as having a poor health status, 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;

[0109] Based on the frequency compensation and power adjustment scheme, a historical data collection algorithm is used to collect the historical data of the energy storage system operation. The key battery operation parameters are extracted from the historical data set using database query commands. The parameters include the remaining capacity, voltage, temperature and internal resistance of the battery. The query frequency is set to once per minute. The data classification algorithm is used to classify the battery operation data. The key parameters for classification are set to remaining capacity, temperature and internal resistance. The battery data is classified according to the operating status. The classification model is used to predict the health status of the battery. The battery operation data is analyzed one by one to generate the battery health status assessment results.

[0110] Based on the battery health status assessment results, a health indicator comparison algorithm is used to analyze the health status of the batteries one by one. By extracting key health indicators such as the remaining capacity, internal resistance and discharge depth of the battery, a multivariate regression analysis method is used to compare the health status. The remaining capacity threshold is set to 20%, the internal resistance threshold is set to 50 milliohms, and the discharge depth threshold is set to 80%. Each battery is judged one by one to see if it meets the continued operation standard, and the batteries that need to be retired are marked. 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 batteries that do not meet the continued operation standards are retired using the retirement processing procedure. 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 only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A hybrid energy storage fast charging station energy management method based on retired batteries, characterized in that: The following steps are involved: Step 1: Based on the load demand, the remaining capacity and health status parameters of the retired battery, the load demand is compared with the battery capacity, the load interval is divided by time, the load change rate in 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 group allocation result in the time interval, the battery group charge and discharge power is calculated, and by matching the battery health status with the current load, it is determined whether the upper and lower power limits meet the actual load requirements, and unsuitable battery groups are eliminated to generate a battery group power output allocation plan; Step 3: Based on the battery pack power output allocation scheme, a fuzzy control algorithm is used to cluster retired batteries in the battery pack, and the optimal charging and discharging sequence is determined through comprehensive calculation of battery health status and power allocation, and power output overload reduction and balancing processing are performed to generate battery power output optimization results; Step 4: Based on the battery power output optimization result, the charging and discharging status of the retired battery is monitored in real time, the load demand is compared with the current power output of the battery, and a real-time power adjustment plan for the battery is generated by adjusting and eliminating the battery with lower remaining capacity; Step 5: Based on the real-time power regulation scheme of the battery, perform a superposition analysis of the power output of the energy storage battery and the grid load fluctuation, divide the load demand by time period, adjust the power output of the battery group to match the grid load, and generate a battery group power matching scheme; Step 6: Based on the battery pack power matching scheme, the overall power dispatch monitoring of the energy storage fast charging station is carried out, and the battery pack output during the high load period is adjusted in combination with the real-time output of the battery pack and the grid frequency fluctuation, and a frequency compensation and power adjustment scheme is generated; Step 7: Based on the frequency compensation and power adjustment scheme, evaluate the energy storage system operation history 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 result 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 power response analysis result of the battery pack in the time interval is generated; Based on the battery group power response analysis results of the time interval, the health status of the battery groups is evaluated one by one, the power response is selected in combination with the load demand, unsuitable battery groups are eliminated, and the battery group 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 is characterized in that: The specific steps of generating the battery pack power output allocation scheme are: Based on the battery group allocation result of the time interval, calculate the charging and discharging power of the battery group in each time interval, and generate the battery group power calculation result in combination with the load demand; Based on the battery pack power calculation result, check whether the battery pack power meets the load demand, remove unsuitable battery packs and adjust the power distribution of 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 generate a battery pack power output allocation plan.

4. 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 battery power output optimization result are: Based on the battery pack power output allocation scheme, health status data of retired batteries is collected, remaining capacity, voltage, internal resistance and other parameters of the batteries are read, standardized and clustered, and a clustering result of the batteries in the battery pack is generated; Based on the battery clustering results in the battery pack, the battery health status and power allocation parameters are analyzed, the charging and discharging sequence is calculated, and the battery capacity and power demand are combined for priority sorting to generate the charging and discharging 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 an overload and perform a reduction operation, adjust the load distribution, and generate a battery power output optimization result.

5. 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 fuzzy control algorithm is based on the formula: 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 influence 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 integration 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 result, the charging and discharging status of the retired battery is monitored in real time, and current, voltage and temperature parameters are used to compare with the load demand, so as to obtain the charging and discharging data and generate the real-time load and battery status comparison result; Based on the comparison result of the real-time load and battery status, screening batteries with low remaining capacity, determining the remaining capacity value, removing them in combination with the power demand, and generating a battery removal 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 of 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 group 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 the grid load, the load period and the 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, the appropriate battery pack is 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 dispatch 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 result between the battery pack and the grid frequency fluctuation is generated; Based on the comparison result between the battery group and the power grid frequency fluctuation, the power output of the battery group during the high load period is adjusted, and the output of the battery group during the high load period is analyzed and adjusted one by one to generate the power adjustment result of the battery group 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, the historical operation data of the energy storage system is collected, the key operation parameters of the battery 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 result, analyze the battery health status, compare the battery remaining capacity, internal resistance, discharge depth and other health indicators, determine whether the battery meets the continued operation standard, mark the battery to be retired, and generate the battery continued operation judgment result; Based on the battery continued operation judgment result, the batteries marked as having poor health status are confirmed, the operation status of the batteries is updated one by one, and the battery health status update result is generated through retirement 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 charging and discharging power, battery health status allocation, and a battery elimination list; the battery power output optimization result includes charging and discharging 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 a battery elimination list; the battery group power matching plan includes load demand period, battery power adjustment, and a 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 operating battery status update value.

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