Refined management method and system for single battery cluster

Abstract

Disclosed in the present invention are a refined management method and system for a single battery cluster. The method comprises: collecting and monitoring, in real time, cell power supply data of each cell in a battery cluster that is currently supplying power, wherein the cell power supply data at least comprises a plurality of power supply time periods, and a voltage, a current, an SOC, an output power and a temperature that correspond to each power supply time period; using the cell power supply data of each cell in the battery cluster to predict whether the cell can supply power normally within the next power supply time period; and if it is predicted that any one or more cells cannot supply power normally within the next power supply time period, isolating, before the next power supply time period, the cells that cannot supply power normally, and also adjusting, for the next power supply time period, the output power of cells that can supply power normally, thereby realizing balanced output of the battery cluster.

Description

Fine management method and system of single cluster battery TECHNICAL FIELD

[0001] The present application relates to the field of locomotive technology, in particular to a fine management method and system of single cluster battery. BACKGROUND

[0002] Locomotives are divided into steam locomotives, diesel locomotives, gas turbine locomotives, etc. These locomotives carry fuel and water and are self-powered locomotives that can travel independently. These locomotives all have the problem of polluting the environment. After that, electric locomotives emerged, but the existing electric locomotives have large-capacity batteries, and the large-capacity batteries are usually composed of multiple battery clusters, and each battery cluster is composed of multiple single cells in parallel. The existing BMS system lacks analysis of the health status of each single cell in the battery cluster. When managing the output of the cells, there is a lack of stability analysis of the cell output. During the uniform operation and simultaneous work of all batteries in the battery cluster, how to maintain the balanced output of the entire battery cluster when any or multiple cells fail. SUMMARY

[0003] The present application provides a fine management method and system of single cluster battery to solve the technical problem of how to maintain the balanced output of the entire battery cluster when any or multiple cells fail during the uniform operation and simultaneous work of all batteries in the battery cluster.

[0004] The present application provides a fine management method and system of single cluster battery to solve the technical problem of how to maintain the balanced output of the entire battery cluster when any or multiple cells fail during the uniform operation and simultaneous work of all batteries in the battery cluster.

[0005] Real-time collection and monitoring of cell power supply data of each cell in the currently powered battery cluster, the cell power supply data at least including multiple power supply time periods and corresponding voltage, current, SOC, output power and temperature of each power supply time period;

[0006] Using the cell power supply data of each cell in the battery cluster, predicting whether each cell can normally supply power in the next power supply time period;

[0007] If it is predicted that any or multiple cells cannot normally supply power in the next power supply time period, shielding the cells that cannot normally supply power before the next power supply time period, and adjusting the output power of the cells that can normally supply power in the next power supply time period, to realize the balanced output of the battery cluster.

[0008] Preferably, it further comprises:

[0009] Obtaining historical cell power supply data of multiple cells at different output powers and corresponding health degrees;

[0010] Establishing a prediction cell health model and training the prediction cell health model using the historical cell power supply data to obtain a trained prediction cell health model.

[0011] Preferably, predicting whether each battery cell can normally supply power in the next power supply time period using the battery cell power supply data of each battery cell in the battery cluster comprises:

[0012] If the corresponding battery cell power supply data in the battery cell power supply data of any battery cell in the battery cluster jumps during the current power supply time period, it is predicted that the battery cell cannot normally supply power in the next power supply time period;

[0013] If the corresponding battery cell power supply data in the battery cell power supply data of any battery cell in the battery cluster does not jump during the current power supply time period, it is further predicted whether the battery cell can normally supply power in the next power supply time period using the trained prediction battery cell health model.

[0014] Preferably, predicting whether each battery cell can normally supply power in the next power supply time period using the trained prediction battery cell health model comprises:

[0015] By inputting the battery cell power supply data of each battery cell in the battery cluster into the trained prediction battery cell health model, the health degree of each battery cell in the next power supply time period is obtained;

[0016] If the health degree of any battery cell in the next power supply time period is not lower than the health degree threshold, it is predicted that the battery cell can normally supply power in the next power supply time period;

[0017] If the health degree of any battery cell in the next power supply time period is lower than the health degree threshold, it is predicted that the battery cell cannot normally supply power in the next power supply time period.

[0018] Preferably, it further comprises:

[0019] Real-time collection and monitoring of battery cell charging data of each battery cell in the battery cluster, the battery cell charging data at least including a plurality of charging time points and corresponding voltage, current, SOC, output power and temperature at each charging time point;

[0020] Using the battery cell charging data of each battery cell in the battery cluster, it is determined whether each battery cell can normally supply power;

[0021] If it is determined that any battery cell in the battery cluster cannot normally supply power, the battery cell is shielded from power supply before the battery cluster is powered, and the output power of other battery cells is adjusted to achieve balanced output of the battery cluster.

[0022] Preferably, using the battery cell charging data of each battery cell in the battery cluster to determine whether each battery cell can normally supply power comprises:

[0023] If the battery cell charging data of any battery cell in the battery cluster jumps, it is determined that the battery cell cannot supply power normally, and the fault type is determined according to the battery cell charging data that jumps, and a corresponding fault alarm is issued based on the fault type.

[0024] Preferably, adjusting the output power of the battery cell that can supply power normally in the next power supply time period comprises:

[0025] According to the total output power of the battery cluster and the number of battery cells that can supply power normally in the next power supply time period, the second output power of each battery cell that can supply power normally is calculated, and the second output power is taken as the output power of each battery cell that can supply power normally in the next power supply time period.

[0026] Preferably, the second output power of each battery cell that can supply power normally is equal or unequal, and the difference between each second output power value is not greater than a preset difference threshold.

[0027] The embodiment of the application also provides a fine management system for a single-cluster battery, comprising:

[0028] A monitoring module is configured to collect and monitor, in real time, battery cell power supply data of each battery cell in a currently powered battery cluster, wherein the battery cell power supply data at least includes a plurality of power supply time periods and corresponding voltage, current, SOC, output power and temperature of each power supply time period.

[0029] A prediction module is configured to predict, by using the battery cell power supply data of each battery cell in the battery cluster, whether each battery cell can supply power normally in a next power supply time period.

[0030] An equalization management module is configured to, if it is predicted that any or multiple battery cells cannot supply power normally in the next power supply time period, shield the battery cells that cannot supply power normally before the next power supply time period, and adjust the output power of the battery cells that can supply power normally in the next power supply time period, so as to realize equalization output of the battery cluster.

[0031] Preferably, the prediction module is further configured to acquire historical battery cell power supply data and corresponding health degrees of the plurality of battery cells under different output powers, establish a prediction battery cell health model, and train the prediction battery cell health model by using the historical battery cell power supply data, to obtain a trained prediction battery cell health model.

[0032] The beneficial effect of the present application is that the battery cell power supply data of each battery cell in the current power supply battery cluster is collected and monitored in real time, and whether each battery cell can normally supply power in the next power supply time period is predicted; if any or more battery cells cannot normally supply power in the next power supply time period, the battery cells that cannot normally supply power are shielded before the next power supply time period, and the output power of the battery cells that can normally supply power in the next power supply time period is adjusted to realize the balanced output of the battery cluster, thereby reducing the consistency requirement, reducing the attenuation, quickly shielding the faulty battery cells, and realizing the balanced output of the entire battery cluster by adjusting the output power of the normal battery cells. BRIEF DESCRIPTION OF DRAWINGS

[0033] FIG. 1 is a flowchart of a fine management method of a single cluster battery according to an embodiment of the present application;

[0034] FIG. 2 is a schematic diagram of the appearance of a single set of 4.5MWh container battery system according to an embodiment of the present application;

[0035] FIG. 3 is a schematic diagram of the internal layout of a single set of 4.5MWh container battery system according to an embodiment of the present application;

[0036] FIG. 4 is a schematic diagram of the architecture of a single set of 4.5MWh container battery system according to an embodiment of the present application;

[0037] FIG. 5 is a schematic diagram of the front view of a battery cluster in a single set of 4.5MWh container battery system according to an embodiment of the present application;

[0038] FIG. 6 is a schematic diagram of the layout of a thermal management system according to an embodiment of the present application;

[0039] FIG. 7 is a schematic diagram of the functional relationship between the power-on and power-off control environment abnormality evaluation value and the battery abnormal interference index according to an embodiment of the present application;

[0040] FIG. 8 is a schematic diagram of the bottom view of a partial structure of an energy storage system in an electric locomotive according to an embodiment of the present application;

[0041] FIG. 9 is a schematic diagram of a structure in which a hoisting cooperation mechanism is arranged on the bottom frame of an energy storage system in an electric locomotive according to an embodiment of the present application. DETAILED DESCRIPTION

[0042] It should be understood that the specific embodiments described herein merely exemplify the application and should not be used to limit the application. In the following description, the suffixes such as "module", "part", or "unit" used to denote elements are merely for the convenience of the description of the application and do not have any specific meaning. Therefore, "module", "part", or "unit" can be used interchangeably.

[0043] FIG. 1 is a flowchart of a fine management method of a single cluster battery according to an embodiment of the present application, as shown in FIG. 1, comprising:

[0044] Step S101: Real-time collection and monitoring of the cell power supply data of each cell in the current power supply battery cluster, the cell power supply data at least including a plurality of power supply time periods and the voltage, current, SOC, output power and temperature corresponding to each power supply time period;

[0045] Step S102: Prediction of whether each cell can normally supply power in the next power supply time period by using the cell power supply data of each cell in the battery cluster.

[0046] Step S103: If any or more cells are predicted to be unable to normally supply power in the next power supply time period, shielding the cells unable to normally supply power before the next power supply time period, and adjusting the output power of the cells able to normally supply power in the next power supply time period to realize balanced output of the battery cluster.

[0047] In the embodiment of the present application, further comprising: obtaining historical cell power supply data of the plurality of cells under different output powers and the corresponding health degrees thereof; establishing a prediction cell health model and training the prediction cell health model by using the historical cell power supply data to obtain a trained prediction cell health model.

[0048] Further, the prediction of whether each cell can normally supply power in the next power supply time period by using the cell power supply data of each cell in the battery cluster comprises: if the corresponding cell power supply data of any cell in the battery cluster during the current power supply time period in the cell power supply data of the cell jumps, it is predicted that the cell cannot normally supply power in the next power supply time period; if the corresponding cell power supply data of any cell in the battery cluster during the current power supply time period in the cell power supply data of the cell does not jump, it is further predicted whether the cell can normally supply power in the next power supply time period by using the trained prediction cell health model.

[0049] Specifically, the prediction of whether each cell can normally supply power in the next power supply time period by using the trained prediction cell health model comprises: inputting the cell power supply data of each cell in the battery cluster into the trained prediction cell health model to obtain the health degree of each cell in the next power supply time period; if the health degree of any cell in the next power supply time period is not lower than a health degree threshold, it is predicted that the cell can normally supply power in the next power supply time period; if the health degree of any cell in the next power supply time period is lower than the health degree threshold, it is predicted that the cell cannot normally supply power in the next power supply time period.

[0050] In the embodiment of the present application, further comprising: collecting and monitoring the battery cell charging data of each battery cell in the battery cluster in real time, the battery cell charging data at least including a plurality of charging time points and corresponding voltage, current, SOC, output power and temperature of each charging time point; determining whether each battery cell can supply power normally by using the battery cell charging data of each battery cell in the battery cluster; if any battery cell in the battery cluster is determined to be unable to supply power normally, shielding the power supply of the battery cell before the battery cluster supplies power, and adjusting the output power of other battery cells to realize balanced output of the battery cluster.

[0051] Further, determining whether each battery cell can supply power normally by using the battery cell charging data of each battery cell in the battery cluster includes: if the battery cell charging data of any battery cell in the battery cluster jumps, determining that the battery cell cannot supply power normally, determining the fault type according to the battery cell charging data that jumps, and issuing a corresponding fault alarm based on the fault type.

[0052] Specifically, adjusting the output power of the battery cell that can supply power normally in the next power supply time period includes: calculating the second output power of each battery cell that can supply power normally according to the total output power of the battery cluster and the number of battery cells that can supply power normally in the next power supply time period, and taking the second output power as the output power of each battery cell that can supply power normally in the next power supply time period. The second output power of each battery cell that can supply power normally is equal or unequal, and the difference between each second output power value is not greater than a preset difference threshold. For example, if the battery cluster is supplied by four battery cells at the same time, the total output power of the battery cluster is 1000W, and the output power of each battery cell is 250W, if it is predicted that a certain battery cell cannot supply power normally in the next power supply time period, the power supply of the battery cell in the next power supply time period is shielded, and the output power of the other three battery cells is adjusted to (1000 / 3)W or adjusted according to demand. Alternatively, if the output power of a certain battery cell in the current power supply time period is much smaller or much larger than that of the other three battery cells, it is predicted that the battery cell cannot supply power normally in the next power supply time period, the power supply of the battery cell in the next power supply time period is shielded, and the output power of the other three battery cells is adjusted according to demand. It should be noted that the output power of each battery cell in the battery cluster during power supply can be the same or different, and can be adjusted according to the load power supply demand.

[0053] The application provides a fine management method of single-cluster batteries.

[0054] The application also provides a fine management system of single-cluster batteries, which comprises a monitoring module, a prediction module and a balanced management module.

[0055] Further, the prediction module is also used to obtain historical battery cell supply data of the plurality of battery cells under different output powers and corresponding health degrees thereof, establish a prediction battery cell health model, and train the prediction battery cell health model by using the historical battery cell supply data to obtain a trained prediction battery cell health model.

[0056] In the embodiment of the application, the container battery system comprises a battery cluster stack, a DC / DC converter, a current collection module and a power distribution module, and can store and release electric energy inside the container battery system according to the whole vehicle control system. The input and output ports of the container battery system are high-voltage direct current and low-voltage communication.

[0057] The application also provides a control method of the battery swap locomotive during the realization of the balanced output of the whole battery cluster, comprising: obtaining the up and down power control environment data and the high voltage system state data through a sensor network, comprehensively analyzing to obtain the battery abnormal interference degree index of the battery swap locomotive, and processing to obtain the battery cluster abnormality degree evaluation threshold according to the battery abnormal interference degree index of the battery swap locomotive; monitoring the abnormal state data of each battery unit of the battery swap locomotive, comprehensively analyzing to obtain the battery cluster abnormality degree evaluation value of the battery swap locomotive; comparing the battery cluster abnormality degree evaluation value of the battery swap locomotive with the battery cluster abnormality degree evaluation threshold, and if the battery cluster abnormality degree evaluation value of the battery swap locomotive is greater than the battery cluster abnormality degree evaluation threshold, the battery swap locomotive is controlled to be powered off.

[0058] Specifically, the battery abnormal interference degree index of the battery swap locomotive is obtained through comprehensive analysis, and the specific analysis process is: deploying a plurality of environment monitoring points, collecting the environment temperature, environment humidity and environment air pressure of each environment monitoring point, and obtaining the reference suitable environment temperature, reference suitable environment humidity and reference suitable environment air pressure from the battery swap locomotive database, and processing to obtain the up and down power control environment abnormality evaluation value.

[0059] In a specific embodiment, the environment abnormality evaluation influence factor corresponding to the set environment temperature, environment humidity and environment air pressure is obtained from the battery swap locomotive database, and the allowed deviation environment temperature, allowed deviation environment humidity and allowed deviation environment air pressure are extracted from the battery swap locomotive database, and the up and down power control environment abnormality evaluation value is obtained through comprehensive analysis, and the specific numerical expression is:

[0060] In the formula, θH represents the up and down power control environment abnormality evaluation value, Qh j represents the environment temperature of the jth environment monitoring point, Wh j represents the environment humidity of the jth environment monitoring point, Ph j represents the environment air pressure of the jth environment monitoring point, Qh0 represents the reference suitable environment temperature, Wh0 represents the reference suitable environment humidity, Ph0 represents the reference suitable environment air pressure, ΔQh represents the allowed deviation environment temperature, ΔΔWh represents the allowed deviation environment humidity, ΔPh represents the allowed deviation environment air pressure, ω1 represents the environment abnormality evaluation influence factor corresponding to the set environment temperature, ω2 represents the environment abnormality evaluation influence factor corresponding to the set environment humidity, ω3 represents the environment abnormality evaluation influence factor corresponding to the set environment air pressure, j represents the number of each environment monitoring point, j = 1, 2, 3, …, m, and m represents the total number of environment monitoring points.

[0061] It should be understood that the value range of the environmental abnormality evaluation influence factor corresponding to the environmental temperature, the environmental humidity and the environmental air pressure in the embodiment is between 0 and 1. A mapping set of the environmental temperature, the environmental humidity and the environmental air pressure and the corresponding environmental abnormality evaluation influence factor can be established according to historical environmental data. According to the real-time monitored environmental temperature, the environmental humidity and the environmental air pressure, the environmental abnormality evaluation influence factor corresponding to the environmental temperature, the environmental humidity and the environmental air pressure can be obtained through the mapping set.

[0062] In a specific embodiment, the power-on / off control environment abnormality evaluation value is used to quantitatively evaluate the abnormality degree of the power-on / off control environment, and to provide data basis for the power-on / off control. The power-on / off control environment abnormality evaluation value is related to the environmental temperature, the environmental humidity and the environmental air pressure. The greater the deviation of the environmental temperature, the environmental humidity and the environmental air pressure from the corresponding reference suitable index, the greater the power-on / off control environment abnormality evaluation value, indicating that the power-on / off control environment is more abnormal.

[0063] It should be understood that the power-on / off control environment data is monitored by deploying environmental monitoring points in the embodiment. The power-on / off control refers to the process management of turning on (power-on) and turning off (power-off) the high-voltage electrical system of the battery swap locomotive. This process involves the orderly power-on and power-off of multiple high-voltage components of the vehicle, ensuring the safety and efficiency of the operation.

[0064] The power-on process refers to starting the entire electrical system, ensuring that each subsystem is activated in a predetermined order, preparing for vehicle operation, including waking up the BMS (Battery Management System) to monitor the battery status, activating the MCU (Microcontroller Unit) to control the motor, and safely closing the high-voltage contactor through the pre-charge circuit, allowing the high-voltage battery to supply power to the vehicle's high-voltage system.

[0065] The power-off process refers to the control flow of system shutdown, including gradually closing high-voltage loads, disconnecting high-voltage power sources, performing necessary safety checks and energy release, and finally making the entire high-voltage system safely enter a non-working state.

[0066] The up and down power control environment data in this embodiment will have a direct impact on the up and down power control. By monitoring the up and down power control environment data, it is helpful to comprehensively improve the system safety and efficiency, to ensure that all operations are carried out under suitable environmental conditions, and to prevent safety hazards caused by extreme environment. In addition, the environmental monitoring data can also optimize the battery management strategy, adjust the charging and discharging process according to the real-time environment, avoid excessive charging and discharging of the battery in extreme environment, not only can improve the charging and discharging efficiency, reduce energy consumption, but also help to maintain the battery health and prolong the service life of the battery. The up and down power control environment data can be used as part of predictive maintenance, by analyzing the relationship between environmental conditions and battery performance degradation, potential problems can be identified in advance, and maintenance intervention can be carried out in time to reduce failures.

[0067] Several time monitoring points are deployed to collect the actual voltage and actual current of the high-voltage system at each time monitoring point, and the reference standard current of the high-voltage system is obtained from the battery swap locomotive database, and the high-voltage system state abnormality evaluation value is obtained after processing.

[0068] In a specific embodiment, the set voltage and current corresponding high-voltage system state abnormality influence factor is obtained from the battery swap locomotive database, and the high-voltage system allowable deviation current is extracted from the battery swap locomotive database, and the high-voltage system state abnormality evaluation value is obtained by comprehensive analysis, and the specific numerical expression is:

[0069] In the formula, θG represents the high-voltage system state abnormality evaluation value, Ug i represents the actual voltage of the i-th time monitoring point, table Ig i represents the actual current of the i-th time monitoring point, Ig0 represents the high-voltage system reference standard current, ΔIg represents the high-voltage system allowable deviation current, ψ1 represents the high-voltage system state abnormality influence factor corresponding to the set voltage, ψ2 represents the high-voltage system state abnormality influence factor corresponding to the set current, i represents the number of each time monitoring point, i = 1, 2, 3,..., n, n represents the total number of time monitoring points.

[0070] It should be understood that the value range of the voltage and current corresponding high-voltage system state abnormality influence factor in this embodiment is between 0 and 1, and the mapping set of voltage and current and corresponding high-voltage system state abnormality influence factor can be established by historical high-voltage system state data, and the voltage and current corresponding high-voltage system state abnormality influence factor can be obtained by mapping set according to the monitored voltage and current of the high-voltage system.

[0071] Table 1 high-voltage system state abnormality evaluation value data example

[0072] As shown in Table 1, in one specific embodiment, let Ig0= 200A, ΔIg= 5A, ψ1= 0.6, ψ2= 0.4, for the sake of simplifying the calculation, it is assumed that five groups of time monitoring point data are collected, and the values of each group of data are the same, and

[0073] It should be understood that the high-voltage system state abnormality evaluation value in the embodiment is used to quantitatively evaluate the abnormality degree of the high-voltage system state of the battery swap locomotive. The high-voltage system state abnormality evaluation value is related to the actual voltage and the actual current of the high-voltage system. When the fluctuation level of the actual voltage is greater, the actual current deviates more from the reference standard value, and the corresponding high-voltage system state abnormality evaluation value is greater, indicating that the high-voltage system state of the battery swap locomotive is more abnormal.

[0074] The formula of the embodiment combines two important parameters of voltage and current, and realizes comprehensive evaluation of the high-voltage system state by calculating the difference between the actual value and the standard value or the average value of each time monitoring point and the weight of these differences in the overall evaluation. By introducing the weight factor, the relative importance of voltage and current in the evaluation can be adjusted according to the actual situation, so that the evaluation result is more in line with the actual demand. The square root and summation operation in the formula helps to highlight the influence of abnormal values, so that when there is a large deviation in the system, the evaluation value can quickly reflect it, thereby achieving the purpose of anomaly detection.

[0075] It should be understood that the high-voltage system of the battery swap locomotive in the embodiment refers to the overall system composed of electrical components involved in high voltage (usually more than 60 volts, commonly hundreds to thousands of volts) and their mutual connections in the vehicle. These components work together to achieve the storage, conversion and transmission of electrical energy, thereby driving the motor to make the vehicle run. In the battery swap locomotive charging and discharging control, analyzing the abnormal state of the high-voltage system is crucial. By monitoring and analyzing the working state of the high-voltage system in real time, faults can be predicted and diagnosed in advance, reducing the downtime of the battery swap locomotive due to faults. Accurate judgment of the abnormality of the high-voltage system can help to implement preventive maintenance and prolong the service life of the battery swap locomotive and the components of the high-voltage system.

[0076] According to the charging and discharging control environment abnormality evaluation value and the high-voltage system state abnormality evaluation value, the battery abnormal interference degree index of the battery swap locomotive is comprehensively analyzed.

[0077] In one specific embodiment, the battery abnormal interference degree influence factor corresponding to the set charging and discharging control environment abnormality evaluation value and the high-voltage system state abnormality evaluation value is obtained from the battery swap locomotive database, and the battery abnormal interference degree index of the battery swap locomotive is comprehensively analyzed, and the specific numerical expression is:

[0078] In the formula, β represents the battery abnormal interference index of the electric locomotive, e represents a natural constant, θH represents the abnormality evaluation value of the power-on / off control environment, θG represents the abnormality evaluation value of the high-voltage system state, φ1 represents the battery abnormal interference influence factor corresponding to the set abnormality evaluation value of the power-on / off control environment, and φ2 represents the battery abnormal interference influence factor corresponding to the set abnormality evaluation value of the high-voltage system state.

[0079] It should be understood that the battery abnormal interference influence factors corresponding to the abnormality evaluation value of the power-on / off control environment and the abnormality evaluation value of the high-voltage system state in the embodiment have a value range of 0 to 1. A mapping set of the abnormality evaluation value of the power-on / off control environment and the abnormality evaluation value of the high-voltage system state and the corresponding battery abnormal interference influence factor can be established through historical data of the battery abnormal interference, and the battery abnormal interference influence factor corresponding to the abnormality evaluation value of the power-on / off control environment and the abnormality evaluation value of the high-voltage system state can be obtained through the mapping set according to the abnormality evaluation value of the power-on / off control environment and the abnormality evaluation value of the high-voltage system state obtained through data processing.

[0080] The battery abnormal interference index of the battery swap locomotive is used to quantify the interference degree of the power-on / off control environment data and the high-voltage system state data on the battery cluster abnormality evaluation. As shown in FIG. 7, in the embodiment, φ1=0.5 and φ2=0.5. When θG=0.042, the functional relationship between the abnormality evaluation value of the power-on / off control environment and the battery abnormal interference index is shown as curve a. When θG=0.284, the functional relationship between the abnormality evaluation value of the power-on / off control environment and the battery abnormal interference index is shown as curve a. When θG=0.404, the functional relationship between the abnormality evaluation value of the power-on / off control environment and the battery abnormal interference index is shown as curve a. The battery abnormal interference index of the battery swap locomotive is determined by the abnormality evaluation value of the power-on / off control environment and the abnormality evaluation value of the high-voltage system state. The greater the abnormality evaluation value of the power-on / off control environment and the abnormality evaluation value of the high-voltage system state, the greater the corresponding battery abnormal interference index of the battery swap locomotive, indicating that the greater the interference degree of the power-on / off control environment data and the high-voltage system state data on the battery cluster abnormality evaluation.

[0081] In the embodiment, the formula uses a Sigmoid function expression form, which can map the input value to an output value between 0 and 1, can help smooth the data fluctuation, and is conducive to the mutual comparison between different types of data.

[0082] It should be understood that the comprehensive analysis of the power-on / off control environment and the high-voltage system state in the embodiment can ensure the operation of the high-voltage system under safe conditions, reduce the occurrence of faults and abnormalities, and help to reduce the interference of the high-voltage system on the battery and improve the performance and safety of the battery. Through optimization of the power-on / off control environment and the high-voltage system state, the synergistic optimization of the two can be achieved. For example, by adjusting the charging and discharging strategy, optimizing the thermal management system, and the like, the charging and discharging performance of the battery and the operation state of the high-voltage system can be improved at the same time, thereby reducing the degree of interference on the battery.

[0083] Specifically, the battery cluster abnormality degree evaluation threshold value is obtained by processing, and the specific process is: comparing the battery abnormality interference index of the battery swap locomotive with the battery cluster abnormality degree evaluation threshold value compensation parameter corresponding to each battery abnormality interference index interval stored in the battery swap locomotive database to obtain the battery cluster abnormality degree evaluation threshold value compensation parameter of the battery swap locomotive.

[0084] It should be understood that the battery cluster abnormality degree evaluation threshold value compensation parameter in the embodiment is used to correct the battery cluster abnormality degree evaluation threshold value, thereby improving the accuracy of subsequent fault threshold value comparison. The set reference battery cluster abnormality degree evaluation threshold value is obtained from the battery swap locomotive database, and the battery cluster abnormality degree evaluation threshold value compensation parameter of the battery swap locomotive is summed with the reference battery cluster abnormality degree evaluation threshold value to obtain the battery cluster abnormality degree evaluation threshold value.

[0085] Specifically, the battery cluster abnormality degree evaluation value of the battery swap locomotive is obtained by comprehensive analysis, and the specific analysis process is: monitoring the voltage of each battery unit in the battery cluster of the battery swap locomotive, and obtaining the voltage-time change curve of each battery unit by processing, which is marked as the voltage-time sequence curve of each battery unit.

[0086] It should be understood that the process of connecting the voltage data points into a curve in the embodiment is a visual processing of voltage time sequence data analysis, which specifically connects adjacent data points with line segments to form a voltage-time change curve by sorting the voltage data in time sequence and using a graphical software or a data analysis tool. The curve graph takes time as the horizontal coordinate and voltage as the vertical coordinate, and can describe the fluctuation trend of the voltage of each battery unit in the time sequence.

[0087] The length of each battery unit voltage time sequence curve is obtained, and each battery unit voltage time sequence curve is compared with each other to extract the overlapping length between each battery unit voltage time sequence curve, and a voltage-time change curve overlapping length matrix D is constructed, and the mathematical expression of the matrix D is:

[0088] In the formula, d rtrepresents the length of overlap between the rth battery cell voltage time series curve and the tth battery cell voltage time series curve, r represents the row number in the matrix, t represents the column number in the matrix, r = 1, 2, 3,..., s, t = 1, 2, 3,..., s, and s represents the total number of battery cells.

[0089] The temperature of each battery cell is collected, and a comprehensive analysis is performed to obtain the battery cluster abnormality degree evaluation value of the electric locomotive.

[0090] It should be understood that the overlap between the battery cell voltage time series curves in the embodiment is disordered, and therefore the matrix D is a disordered matrix, d rt = d tr When r = t, the curve overlap length is equal to the length of the curve itself. To avoid repeated calculation, the upper triangular elements of the matrix D that do not contain diagonal elements can be extracted for subsequent operation.

[0091] In a specific embodiment, the set voltage and temperature corresponding battery cluster abnormality degree evaluation influence factor is obtained from the electric locomotive database, and a comprehensive analysis is performed to obtain the battery cluster abnormality degree evaluation value of the electric locomotive, and the specific numerical expression is:

[0092] In the formula, Δd is the average value of the upper triangular elements of the matrix D, L p represents the length of the pth battery cell voltage time series curve, d r(r+k) represents the length of overlap between the rth battery cell voltage time series curve and the r+kth battery cell voltage time series curve, p represents the number of each battery cell, p = 1, 2, 3,..., s, s represents the total number of battery cells, r+k represents the column number of the upper triangular elements of the matrix D, wherein k = 1, 2, 3,..., s-r, μD represents the battery cluster abnormality degree evaluation value of the electric locomotive, and μQ represents the temperature dispersion degree quantification index of the battery cell, which is used to quantitatively evaluate the temperature deviation degree of each battery cell in the battery cluster of the electric locomotive.

[0093] Qd p represents the temperature of the pth battery cell, τ1 represents the battery cluster abnormality degree evaluation influence factor corresponding to the set voltage, and τ2 represents the battery cluster abnormality degree evaluation influence factor corresponding to the set temperature.

[0094] It should be understood that the value range of the battery cluster abnormality degree evaluation influence factor corresponding to the voltage and temperature in the embodiment is between 0 and 1, and a mapping set of the voltage and temperature and the corresponding battery cluster abnormality degree evaluation influence factor can be constructed through historical data of the battery cluster. According to the collected voltage and temperature of the battery unit, the battery cluster abnormality degree evaluation influence factor corresponding to the voltage and temperature is obtained through the mapping set.

[0095] It should be understood that the value range of the battery cluster abnormality degree evaluation influence factor corresponding to the voltage and temperature in the embodiment is between 0 and 1, and a mapping set of the voltage and temperature and the corresponding battery cluster abnormality degree evaluation influence factor can be constructed through historical data of the battery cluster. According to the collected voltage and temperature of the battery unit, the battery cluster abnormality degree evaluation influence factor corresponding to the voltage and temperature is obtained through the mapping set.

[0096] It should be understood that the battery cluster abnormality degree evaluation value of the battery swap locomotive in the embodiment is used to quantitatively evaluate the abnormality degree of the battery cluster of the battery swap locomotive. By analyzing the voltage time series curve and temperature of each battery unit of the battery cluster, the voltage balance state and temperature balance state of the battery cluster of the battery swap locomotive are evaluated. By monitoring the voltage balance, the overcharging or undercharging problem of a single battery unit can be found and solved in time, which helps to avoid the gradual increase of the capacity difference between the battery units, reduces the overall decay rate of the battery pack, and thus prolongs the battery life. Uneven voltage can cause some battery units to overheat, increasing the risk of battery failure or even thermal runaway. By monitoring the temperature, overheating can be prevented, and cooling measures can be taken in time to avoid safety accidents. Balanced voltage and temperature state helps the battery management system to more accurately estimate the battery state, thereby optimizing the charging and discharging strategy and improving energy utilization efficiency.

[0097] As shown in FIG. 2, the 9MWh battery system provided by the application is mainly composed of two sets of 4.5MWh container battery system units in parallel, and each set of 4.5MWh container system unit is arranged in a 45ft container, the container is carried on a battery special flat car, and the container and the flat car are connected by angle pieces. The end faces of the left and right sides of the container are designed with plug sockets, and the two sets of container systems can be electrically connected through the inter-box connecting wire harness, thereby forming a complete 9MWh battery system. During the use of the system, the user can select any unconnected end of the container as the main output port of the 9MWh battery system according to actual needs, and electrically connect the 9MWh battery system and the locomotive through the connecting wire harness between the locomotive and the container.

[0098] 9MWh battery system is composed of two sets of 4.5MWh container battery system units, which are consistent in internal and external dimensions, layout and function. When describing the internal layout of the unit, any one of the units will be taken as the description object. The internal layout of a single 4.5MWh container battery system is shown in Figure 3. As can be seen from the figure, it is mainly composed of 4 sets of battery cluster stacks, 4 DC / DC cabinets, 1 electrical cabinet and other units. The container is installed on a battery special board car, and the container and the board car are fixed by 14 standard corner fittings; the container battery system is composed of 4 battery cluster stack systems, each battery cluster stack is composed of 4 battery clusters, and each battery cluster is composed of 8 battery packs (also known as battery cells). The core components of the container battery system include battery clusters, electrical cabinets, DC / DC converters and other units.

[0099] As shown in Figure 4, there are 16 battery clusters in the container battery system, and every 4 clusters form a cluster stack, which is connected to the electrical cabinet through 1 DCDC; the electrical cabinet includes power distribution and busbar functions: the power distribution part includes BMS three-level master control, display screen, fire control display, switch, micro-break, liquid cooling machine power supply DCDC, disconnecting switch, fuse, surge protection, cooling fan and other equipment; the busbar function is composed of disconnecting switch and fuse, and the on-off of the disconnecting switch can be controlled by the BMS three-level master control; the container battery system fire protection is distributed, with 1 independent fire protection unit per cluster, the PACK contains a fire detector, and a fire suppression device is provided in the cluster, and every 8 clusters are connected to the fire control display on the electrical cabinet; 8 liquid cooling units are provided in the container, and each liquid cooling unit corresponds to 2 clusters of batteries and is controlled by the BMS two-level master control; the container battery system units are connected through high-voltage cables.

[0100] The battery cluster stack is mainly composed of 4 battery clusters and 1 auxiliary electrical cabinet, and 8 battery boxes are designed in a single battery cluster, which are connected in series. The auxiliary electrical cabinet is designed with 6 layers, of which the top 2 layers are used to arrange the thermal management unit, and the bottom 4 layers are used to arrange the high-voltage box and the fire control host; the layout of the pipes and wire harness units in the entire cluster is shown in Figure 5. As can be seen from the figure, 2 battery cluster units share 1 thermal management unit, the water pipes of the battery boxes in the same layer are in series, and the water pipes between the layers are in parallel; the high-voltage box and the fire control host have a one-to-one correspondence with the battery cluster unit, the battery boxes in each battery cluster are connected in series and then output through the corresponding high-voltage box, and the fire control host is connected to the nozzles of the battery boxes in each battery cluster through the fire control pipeline.

[0101] Further, the thermal management system in the battery cluster is composed of a thermal management unit, a liquid cooling pipeline, an expansion kettle and the like units, as shown in FIG. 6, and is mainly used for adjusting the temperature of the units in the battery box. The thermal management system contains refrigeration and heating units, and the functions of the units can be controlled through a thermal management control unit. The liquid cooling pipeline is used for conducting flow and heat exchange, so that the whole battery cluster is in a heating or refrigeration state, thereby achieving the temperature control effect on the battery cells in the battery pack.

[0102] In the embodiment of the application, the three-level general control is a battery management system level control module (also referred to as a three-level module of the BMS), which can analyze and process the real-time data of the battery collected by the two-level BCMU module and the one-level BMU module, monitor the real-time state of the battery system, and execute control, optimization and protection strategies according to the established scheme. The module can also support data interaction with the locomotive PLC, the ground charging system, the system thermal management unit, the fire extinguishing unit and the like. In addition, in order to facilitate users to learn the battery information in real time, a display screen is configured on the door of the battery distribution cabinet. The display screen communicates with the three-level general control in real time, and users can check the state of the battery system through the data in the display screen.

[0103] The 9MWh battery system placed in the container provided by the application is large in size and not easy to transport. The application further provides a convenient hoisting energy storage system, as shown in FIGS. 8 and 9, which comprises a container 211 and a hoisting cooperation mechanism 219. The container 211 has a cavity, and the cavity is provided with a battery pack, a high-voltage box, a DC-DC converter and a busbar cabinet. The battery pack is electrically connected to the high-voltage box, the high-voltage box is electrically connected to the DC-DC converter, and the busbar cabinet is electrically connected to the DC-DC converter. The hoisting cooperation mechanism 219 comprises a fixed part 2191 and a movable part 2192. The fixed part 2191 is fixedly arranged on the container 211, and the movable part 2192 is movably connected to the fixed part 2191. The hoisting cooperation mechanism 219 has a storage state and a use state. In the use state, the movable part 2192 extends out of the fixed part 2191, and the movable part 2192 is used for connecting a hoisting device. In the storage state, the movable part 2192 is retracted into the fixed part 2191, so as to avoid interference between the movable part 2192 and external structures.

[0104] The convenient hoisting energy storage system of the application is provided with the hoisting cooperation mechanism 219, which can be connected and cooperated with the hoisting device, so as to facilitate the overall disassembly and assembly of the container 211 and improve the assembly efficiency. The hoisting device can have a sleeve ring, which can be sleeved on the extended movable part 2192.

[0105] In some possible embodiments, the container 211 comprises a top frame and a bottom frame 2111. The top frame and the bottom frame 2111 form a cavity therebetween, and the fixed part 2191 of the hoisting cooperation mechanism 219 is arranged on the bottom frame 2111.

[0106] The hoisting cooperation mechanism 219 is arranged on the bottom frame 2111, and the hoisting equipment is directly connected to the hoisting cooperation mechanism 219 on the bottom frame 2111, thereby improving the stability and safety of hoisting operations.

[0107] In some possible embodiments, the moving direction of the moving part 2192 is parallel to the width direction of the bottom frame 2111, and in the use state, the moving part 2192 extends out of the bottom frame 2111 along the width direction of the bottom frame 2111, and in the storage state, the moving part 2192 is retracted to one side of the bottom frame 2111. The moving part 2192 extends out of the bottom frame 2111 along the width direction of the bottom frame 2111, which facilitates connection with the corresponding structure on the hoisting equipment. The bottom frame 2111 can be provided with a plurality of hoisting cooperation mechanisms 219, each of which is arranged on the two sides of the bottom frame 2111 along the width direction, and each of the hoisting cooperation mechanisms 219 on the same side of the bottom frame 2111 is arranged in sequence along the length direction of the bottom frame 2111. By arranging a plurality of hoisting cooperation mechanisms 219, the connection positions of the bottom frame 2111 and the hoisting equipment can be increased, the stability of the connection structure of the hoisting equipment and the bottom frame 2111 is improved, and the arrangement of the plurality of hoisting cooperation mechanisms 219 facilitates uniform stress of the container 211 and stable posture of the container 211, and the container 211 is not prone to tilting.

[0108] In some possible embodiments, the bottom frame 2111 includes a plurality of structural beams 21111, each of which is arranged in sequence along the length direction of the bottom frame 2111, and each of which extends along the width direction of the frame, and the fixed part 2191 is located between the structural beams 21111 and is fixedly connected to the structural beams 21111.

[0109] Each hoisting cooperation mechanism 219 is arranged in the space formed between the two adjacent structural beams 21111, without occupying additional space of the bottom frame 2111. The embodiment of the present application makes full use of the bottom frame 2111 and adaptively designs the assembly structure of the hoisting cooperation mechanism 219 according to the structural characteristics of the bottom frame 2111. The structural beams 21111 have high structural strength, the fixed part 2191 is directly connected to the structural beams 21111, has high structural strength, and the hoisting cooperation mechanism 219 has a long service life.

[0110] In some possible embodiments, the bottom frame 2111 includes a plurality of connecting pieces 21112, the connecting pieces 21112 are located between the two adjacent structural beams 21111, and the connecting pieces 21112 are respectively welded to the two structural beams 21111 and the fixed part 2191 located between the two structural beams 21111. The connecting pieces 21112 can be metal structures, which can be metal plates or metal blocks, and the connecting pieces 21112 are respectively welded to the two structural beams 21111 on the two sides and the fixed part 2191 in the middle, thereby connecting the three into an integrated structure.

[0111] When the fixing part 2191 is cylindrical, the connecting part 21112 can be provided with a circular avoiding hole which can be sleeved on the fixing part 2191, and the inner end surface of the avoiding hole is attached to and welded on the surface of the fixing part 2191.

[0112] In some possible embodiments, the bottom frame 2111 can include two side main beams 21113 which are spaced apart, and each structural beam 21111 is located between the two side main beams 21113 and connected to the two side main beams 21113 at both ends. The side main beam 21113 has a through hole extending in the width direction of the bottom frame 2111, and the fixing part 2191 is arranged through the through hole. The fixing part 2191 can be a cylindrical body, and the fixing part 2191 has a sliding groove extending in a direction perpendicular to the side main beam 21113, and the sliding groove defines the sliding direction of the movable part 2192.

[0113] In some possible embodiments, the fixing part 2191 has a through sliding groove, and the movable part 2192 is arranged through the sliding groove. The movable part 2192 is provided with a flange part 21921 at both ends, and the outer diameter of the flange part 21921 is greater than the inner diameter of the sliding groove. The movable part 2192 is provided with the flange part 21921 at both ends, so as to limit the sliding range of the movable part 2192 and avoid the whole movable part 2192 from sliding out of the fixing part 2191.

[0114] In some possible embodiments, the flange part 21921 of the movable part 2192 located outside the bottom frame 2111 is provided with a connecting hole, and a limiting part is connected to the fixing part 2191 or the bottom frame 2111 of the container 211 at one end and limited on the flange part 21921 at the other end. The limiting part can include a cap body and a screw rod, the screw rod is threadedly connected to a threaded groove provided on the fixing part 2191 or the bottom frame 2111 of the container 211 through the connecting hole, and the cap body is limited on the flange part 21921, so as to fix the position of the movable part 2192 and prevent the movable part 2192 from sliding out accidentally and the external structure from being bumped.

[0115] In summary, the fine management method and system of the single cluster battery provided by the application, by collecting and monitoring the battery cell power supply data of each battery cell in the current power supply battery cluster in real time, predicting whether each battery cell can normally supply power in the next power supply time period; if any or more battery cells cannot normally supply power in the next power supply time period, shielding the battery cells that cannot normally supply power before the next power supply time period, and adjusting the output power of the battery cells that can normally supply power in the next power supply time period, to realize the balanced output of the battery cluster, thereby effectively ensuring the stability of the output of the battery cluster, while maintaining the balanced output of the battery cluster, preventing over-discharge of the battery cluster, this method effectively ensures the rationalization management planning of the output power of each single battery cell in the battery cluster, and shields the battery cells that cannot normally supply power in time, thereby meeting the efficient and stable operation requirements of the battery cluster.

[0116] The preferred embodiments of the application are described above with reference to the accompanying drawings, and are not intended to limit the scope of the application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the application shall be within the scope of the application.

Claims

1. A method of fine management of a single cluster battery, characterized by, The method comprises: real-time acquisition and monitoring of cell power supply data of each cell in the current power supply battery cluster, the cell power supply data at least including a plurality of power supply time periods and corresponding voltage, current, SOC, output power and temperature of each power supply time period; prediction of whether each cell can normally supply power in the next power supply time period by using the cell power supply data of each cell in the battery cluster; if any or more cells are predicted to be unable to normally supply power in the next power supply time period, shielding the cells unable to normally supply power before the next power supply time period, and adjusting the output power of the cells able to normally supply power in the next power supply time period to realize balanced output of the battery cluster; wherein, during realization of balanced output of the entire battery cluster, the sensor network is used to acquire upper and lower power control environment data and high-voltage system state data, and a battery abnormal interference degree index of the battery swap locomotive is obtained through comprehensive analysis, and a battery cluster abnormality degree evaluation threshold is processed according to the battery abnormal interference degree index of the battery swap locomotive; the abnormal state data of each battery unit of the battery swap locomotive is monitored, and a battery cluster abnormality degree evaluation value of the battery swap locomotive is obtained through comprehensive analysis; the battery cluster abnormality degree evaluation value of the battery swap locomotive is compared with the battery cluster abnormality degree evaluation threshold, and if the battery cluster abnormality degree evaluation value of the battery swap locomotive is greater than the battery cluster abnormality degree evaluation threshold, the battery swap locomotive is subjected to power-off control.

2. The method of claim 1, wherein, Further comprising: acquisition of historical cell power supply data of a plurality of cells under different output powers and corresponding health degrees thereof; establishment of a prediction cell health model, and training of the prediction cell health model by using the historical cell power supply data to obtain a trained prediction cell health model.

3. The method of claim 2, wherein, The prediction of whether each cell can normally supply power in the next power supply time period by using the cell power supply data of each cell in the battery cluster comprises: if the corresponding cell power supply data in the current power supply time period in the cell power supply data of any cell in the battery cluster jumps, it is predicted that the cell cannot normally supply power in the next power supply time period; if the corresponding cell power supply data in the current power supply time period in the cell power supply data of any cell in the battery cluster does not jump, it is further predicted whether the cell can normally supply power in the next power supply time period by using the trained prediction cell health model.

4. The method of claim 3, wherein, The prediction of whether the cell can normally supply power in the next power supply time period by using the trained prediction cell health model comprises: inputting the cell power supply data of each cell in the battery cluster into the trained prediction cell health model to obtain the health degree of each cell in the next power supply time period; if the health degree of any cell in the next power supply time period is not lower than a health degree threshold, it is predicted that the cell can normally supply power in the next power supply time period; if the health degree of any cell in the next power supply time period is lower than the health degree threshold, it is predicted that the cell cannot normally supply power in the next power supply time period.

5. The method of claim 1, wherein, Further comprising: real-time acquisition and monitoring of cell charging data of each cell in the battery cluster, the cell charging data at least including a plurality of charging time points and corresponding voltage, current, SOC, output power and temperature of each charging time point; The battery cluster is used to determine whether each battery cell can supply power normally by using the battery cell charging data of each battery cell in the battery cluster. If any battery cell in the battery cluster cannot supply power normally, the battery cluster is shielded from supplying power to the battery cell, and the output power of other battery cells is adjusted to achieve balanced output of the battery cluster.

6. The method of claim 5, wherein, The battery cluster is used to determine whether each battery cell can supply power normally by using the battery cell charging data of each battery cell in the battery cluster. If the battery cell charging data of any battery cell in the battery cluster jumps, it is determined that the battery cell cannot supply power normally, the type of fault is determined according to the battery cell charging data that jumps, and a corresponding fault alarm is issued based on the type of fault.

7. The method of claim 1, wherein, The output power of each battery cell that can supply power normally in the next power supply time period is adjusted, including: The second output power of each battery cell that can supply power normally is calculated according to the total output power of the battery cluster and the number of battery cells that can supply power normally in the next power supply time period, and the second output power is used as the output power of each battery cell that can supply power normally in the next power supply time period.

8. The method of claim 7, wherein, The second output power of each battery cell that can supply power normally is equal or unequal, and the difference between each second output power value is not greater than a preset difference threshold.

9. A fine management system of a single cluster battery, characterized by, It includes: A monitoring module is used to collect and monitor the battery cell supply data of each battery cell in the battery cluster in real time, and the battery cell supply data at least includes multiple power supply time periods and the corresponding voltage, current, SOC, output power and temperature of each power supply time period. A prediction module is used to predict whether each battery cell can supply power normally in the next power supply time period by using the battery cell supply data of each battery cell in the battery cluster. An equalization management module is used to shield the battery cells that cannot supply power normally before the next power supply time period, and adjust the output power of the battery cells that can supply power normally in the next power supply time period to achieve balanced output of the battery cluster. The equalization management module is also used to obtain upper and lower power control environment data and high voltage system state data through a sensor network during the balanced output of the entire battery cluster, to obtain a battery abnormal interference index of the battery swap locomotive by comprehensive analysis, and to obtain a battery cluster abnormality degree evaluation threshold by processing according to the battery abnormal interference index of the battery swap locomotive; to monitor the abnormal state data of each battery unit of the battery swap locomotive, to obtain a battery cluster abnormality degree evaluation value of the battery swap locomotive by comprehensive analysis; to compare the battery cluster abnormality degree evaluation value of the battery swap locomotive with the battery cluster abnormality degree evaluation threshold, and to control the battery swap locomotive to be powered off if the battery cluster abnormality degree evaluation value of the battery swap locomotive is greater than the battery cluster abnormality degree evaluation threshold.

10. The system of claim 9, wherein, The prediction module is also used to obtain historical battery cell supply data and corresponding health degrees of multiple battery cells under different output powers, to establish a prediction battery cell health model, and to train the prediction battery cell health model by using the historical battery cell supply data to obtain a trained prediction battery cell health model.

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