Remote monitoring operation and maintenance management method and system for energy storage lithium battery

Abstract

The invention relates to the technical field of energy storage batteries, in particular to a remote monitoring operation and maintenance management method and system for an energy storage lithium battery. The remote monitoring operation and maintenance management method provided by the invention comprises the following steps: acquiring the terminal voltage of each lithium battery monomer in a series lithium battery pack, and marking the lithium battery monomer of which the terminal voltage does not meet a terminal voltage threshold range as an abnormal lithium battery monomer; acquiring operation state data of the abnormal lithium battery cell, and evaluating the health state of the abnormal lithium battery cell according to the operation state data; and adjusting the energy state upper limit and the energy state lower limit of the series lithium battery pack according to the health state of the abnormal single lithium battery. According to the method, the abnormal lithium battery monomers in the series lithium battery pack can be identified and monitored, the health state of the abnormal lithium battery monomers can be evaluated, and the upper and lower limits of the energy state of the series lithium battery pack can be dynamically adjusted according to the health state evaluation result of the abnormal lithium battery monomers, so that the batteries can run in the optimal state.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage batteries, and in particular to a remote monitoring, operation and maintenance management method and system for energy storage lithium batteries. Background Art

[0002] Against the backdrop of the growing global demand for renewable energy, energy storage technology has become a core element to ensure efficient energy utilization and stable supply. Among them, energy storage lithium batteries have been widely used in power systems due to their excellent high energy density, long life and fast charging and discharging capabilities. However, with the continuous expansion of the scale and complexity of energy storage battery stations, how to effectively manage and maintain them has become a major challenge that needs to be solved urgently.

[0003] Energy storage battery stations usually contain multiple battery energy storage units, each of which is composed of multiple battery groups connected in series. Among these units, the energy state (SoE) of the battery string is a key parameter for its normal operation. Only within a reasonable SoE range can the battery string ensure a safe and efficient working state. Once this range is exceeded, the battery may be damaged, which in turn affects its service life and overall performance. At present, the SoE control strategy for the battery string mainly relies on static parameters set in the non-operating state. This method cannot effectively cope with various real-time changes during operation. Therefore, it is urgent to explore a control method and system that can realize remote monitoring and dynamically adjust SoE according to the actual operating conditions, so as to achieve the goal of optimizing battery life and improving the overall performance of the energy storage system. Summary of the invention

[0004] In response to the needs of practical applications, the present invention provides a remote monitoring, operation and maintenance management method and system for energy storage lithium batteries, which aims to monitor the operating status of abnormal lithium battery cells in a series lithium battery group, and dynamically adjust the upper and lower limits of the energy state of the battery cells according to the health status of the abnormal lithium battery cells, to ensure that the battery operates in the best state, extend its service life, and prevent overcharging and over-discharging.

[0005] In a first aspect, a remote monitoring, operation and maintenance management method for an energy storage lithium battery provided by the present invention comprises the following steps: obtaining the terminal voltage of each lithium battery cell in a series-connected lithium battery group, and marking the lithium battery cells whose terminal voltage does not meet the terminal voltage threshold range as abnormal lithium battery cells; obtaining the operating status data of the abnormal lithium battery cells, and evaluating the health status of the abnormal lithium battery cells based on the operating status data; and adjusting the energy state upper limit and the energy state lower limit of the series-connected lithium battery group based on the health status of the abnormal lithium battery cells.

[0006] The remote monitoring, operation and maintenance management method of energy storage lithium batteries provided by the present invention has the following benefits: the present invention effectively solves the problem that traditional static parameter settings cannot cope with real-time changes during battery operation, and can realize real-time monitoring of each battery cell in the battery pack, timely discover and handle abnormal battery cells, and can dynamically adjust the upper and lower limits of the energy state of the battery pack to avoid imbalance in the battery pack during operation, thereby extending the service life of the battery pack and improving the overall performance and safety of the energy storage system. In addition, the present invention is combined with existing remote data communication technology to achieve rapid response and processing of abnormal situations, which not only helps to improve the intelligent management level of lithium battery packs, but also helps to meet the needs of modern power systems for efficient, safe and intelligent energy storage solutions.

[0007] Optionally, the method of obtaining the terminal voltage of each lithium battery cell in the series lithium battery group and marking the lithium battery cell whose terminal voltage does not meet the terminal voltage threshold range as an abnormal lithium battery cell includes the following steps: setting a sampling frequency, and based on the sampling frequency, using a voltage sensor to respectively collect the terminal voltage of each lithium battery cell in the series lithium battery group during the charging stage; based on the terminal voltage of each lithium battery cell at each sampling moment in the charging stage and / or the discharging stage, respectively obtaining the terminal voltage threshold range corresponding to the lithium battery cell; marking the lithium battery cell with at least three consecutive marking moments in the charging stage and / or the discharging stage as an abnormal lithium battery cell, wherein the marking moment is the sampling moment when the terminal voltage does not meet the corresponding terminal voltage threshold range. The present invention ensures that the operating status of each battery cell is monitored and evaluated in a timely manner by setting the sampling frequency and using a voltage sensor to collect the terminal voltage of the lithium battery cell; and marking the lithium battery cell with at least three consecutive marking moments in the charging stage and / or the discharging stage as an abnormal lithium battery cell, so as to reduce the false alarm of the abnormal lithium battery cell, which helps to improve the accuracy of abnormal detection.

[0008] Further optionally, the terminal voltage threshold range of any lithium battery cell satisfies the calculation model.

[0009] Optionally, the step of obtaining the operating status data of the abnormal lithium battery cell and evaluating the health status of the abnormal lithium battery cell according to the operating status data comprises the following steps: obtaining the operating status data of the abnormal lithium battery cell in the charging stage and / or the discharging stage respectively; and evaluating the health status of the abnormal lithium battery cell through a lithium battery health status evaluation model based on the operating status data, wherein the lithium battery health status evaluation model is a neural network model that predicts the health status of the lithium battery using at least one operating status data. The present invention is based on a data-driven neural network model, which can achieve accurate and efficient health status evaluation of abnormal lithium battery cells.

[0010] Further optionally, the operating status data is a combination of one or more of voltage change data, current change data and temperature change data.

[0011] Optionally, the lithium battery health status assessment model includes: an input module, the input module includes at least one data input channel, the data input channel is used to input any type of data among the voltage change data, the current change data, and the temperature change data; an assessment module, the assessment module includes at least three parallel assessment sub-models, the neural network structure and / or neural network parameters of any two assessment sub-models are different, the health status assessment rules of the training samples and the verification samples of each assessment sub-model in the assessment module are the same, and the assessment sub-model is used to obtain a preliminary assessment result of the health status of the corresponding lithium battery cell according to the input data of the corresponding data input channel; an integration module, the integration module is connected to the output of each assessment sub-model in the assessment module, the integration module is used to obtain a comprehensive assessment result of the corresponding abnormal lithium battery cell according to the distribution of the preliminary assessment results of each assessment sub-model within the health status range; an output module, the output module includes a unique data output channel, and the data output channel is used to output the comprehensive assessment result of the health status of the abnormal lithium battery cell. The present invention uses multiple neural network models with different neural network structures and / or parameters to analyze one or more operating status data of abnormal lithium battery cells from multiple perspectives, and preliminarily predict the health status of abnormal lithium battery cells, and then comprehensively consider these preliminary evaluation results through the integration module to obtain a more accurate lithium battery health status evaluation result. This multi-level and multi-angle evaluation mechanism helps to improve the accuracy and reliability of battery health status evaluation.

[0012] Further optionally, the comprehensive evaluation result of the corresponding abnormal lithium battery cell is obtained according to the distribution of the preliminary evaluation results of each evaluation sub-model within the health status range, including the following steps: selecting K cluster centers within the health status range; assigning each preliminary evaluation result to the nearest cluster center; calculating the mean of the preliminary evaluation results corresponding to each cluster center respectively; updating the mean of the preliminary evaluation results as the cluster center; repeating the above steps of calculating and updating the cluster center until the cluster center remains unchanged; setting the cluster center containing the most preliminary evaluation results as the comprehensive evaluation result. The present invention is based on the K-value clustering method, which effectively integrates the health status evaluation results of multiple neural network models for the same abnormal lithium battery cell, eliminates the influence of individual outliers, and provides a more reliable data basis for the subsequent precise optimization of the upper and lower limits of the energy state.

[0013] Optionally, the energy state upper limit and energy state lower limit of the series-connected lithium battery group are adjusted according to the health state of the abnormal lithium battery cell, including the following steps: obtaining the initial upper limit and initial lower limit of the energy state of the abnormal lithium battery cell; adjusting the initial upper limit and initial lower limit of the energy state of the corresponding abnormal lithium battery cell according to the comprehensive evaluation result of the abnormal lithium battery cell; setting the lowest energy state upper limit and the highest energy state lower limit corresponding to the abnormal lithium battery cell in the series-connected lithium battery group as the energy state upper limit and energy state lower limit of the series-connected lithium battery group, respectively. The present invention can dynamically optimize and adjust the upper and lower limits of the energy state of the battery group based on the abnormal conditions of the lithium battery cells in the battery group, which helps the battery group to maintain balance during operation, avoid overcharging and over-discharging, and help improve the service life and performance of the battery group.

[0014] Further optionally, the adjusted energy state upper limit and energy state lower limit satisfy the calculation model.

[0015] In a second aspect, a remote monitoring, operation and maintenance management system for an energy storage lithium battery provided by the present invention includes a storage medium for storing a computer program, wherein the computer program includes program instructions, and a processor is configured to call the program instructions to execute the remote monitoring, operation and maintenance management method for the energy storage lithium battery provided by the first aspect.

[0016] The remote monitoring, operation and maintenance management system of the energy storage lithium battery provided by the present invention has the following benefits: the remote monitoring, operation and maintenance management system of the energy storage lithium battery provided by the present invention can realize intelligent and automated monitoring and management of the lithium battery pack based on the remote monitoring, operation and maintenance management method of the energy storage lithium battery; further, the remote monitoring, operation and maintenance management system of the energy storage lithium battery provided by the present invention can be combined with the existing remote data communication technology to realize remote real-time monitoring and data transmission, ensure rapid response and processing of abnormal situations, significantly improve the management efficiency and intelligence level of the lithium battery pack, and meet the needs of modern power systems for efficient, safe and intelligent energy storage solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of a remote monitoring, operation and maintenance management method for an energy storage lithium battery provided by an embodiment of the present invention;

[0018] Figure 2 A flowchart of abnormal lithium battery cell marking provided by an embodiment of the present invention;

[0019] Figure 3 A lithium battery cell health status assessment process provided by an embodiment of the present invention;

[0020] Figure 4 A schematic diagram of the structure of a lithium battery health status assessment model provided by an embodiment of the present invention;

[0021] Figure 5 A flow chart of adjusting the upper and lower limits of the energy state of a series-connected lithium battery pack provided in an embodiment of the present invention;

[0022] Figure 6 This is a schematic diagram of the structure of a remote monitoring, operation and maintenance management system for energy storage lithium batteries provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0024] In one embodiment, see Figure 1 , Figure 1 This is a flow chart of the remote monitoring, operation and maintenance management method of the energy storage lithium battery provided by the embodiment of the present invention. Figure 1 As shown, the remote monitoring, operation and maintenance management method of the energy storage lithium battery provided in the embodiment of the present invention includes the following steps:

[0025] S01. Obtain the terminal voltage of each lithium battery cell in the series-connected lithium battery pack, and mark the lithium battery cells whose terminal voltages do not meet the terminal voltage threshold range as abnormal lithium battery cells.

[0026] It is understood that a series lithium battery pack refers to a battery pack formed by connecting multiple lithium battery cells in series. Generally, in order to ensure good performance of the series lithium battery pack and extend its service life, the parameters of each lithium battery cell selected in the series lithium battery pack are consistent.

[0027] Furthermore, due to slight differences in production processes and materials, lithium battery cells with the same parameters will also show inconsistencies when used in series, such as different charging and discharging rates and terminal voltage change speeds. These inconsistencies will lead to imbalances in the charging and discharging process, affecting the performance and life of the battery pack.

[0028] See also Figure 2 , Figure 2 This is a flow chart of abnormal lithium battery cell marking provided by an embodiment of the present invention. Figure 2 As shown, the step S01 of obtaining the terminal voltage of each lithium battery cell in the series lithium battery group and marking the lithium battery cell whose terminal voltage does not meet the terminal voltage threshold range as an abnormal lithium battery cell includes the following steps:

[0029] S011. Setting a sampling frequency, and based on the sampling frequency, using a voltage sensor to collect the terminal voltage of each lithium battery cell in the series-connected lithium battery pack during the charging phase.

[0030] Furthermore, during the charging stage, the terminal voltage of each lithium battery cell will gradually rise, and when the battery is close to full, the terminal voltage rises faster. Therefore, in order to ensure that the terminal voltage change characteristics during the charging stage can be captured, the sampling frequency can be adjusted according to the battery charging characteristics and detection accuracy.

[0031] Similarly, during the discharge phase, the terminal voltage of each lithium battery cell will gradually decrease, and when the battery is close to being discharged, the terminal voltage decreases faster. Therefore, in order to ensure that the terminal voltage change characteristics during the discharge phase can be captured, the sampling frequency can also be adjusted according to the battery discharge characteristics and detection accuracy.

[0032] S012: based on the terminal voltage of each lithium battery cell at each sampling moment in the charging stage and / or the discharging stage, respectively obtain the terminal voltage threshold range corresponding to each lithium battery cell.

[0033] Furthermore, at a sampling time, the terminal voltage threshold range of any lithium battery cell satisfies the following calculation model:

[0034] Where i and j are both positive integers, i and j are the numbers of the lithium battery cells in the series lithium battery pack, u i,min Indicates the upper limit of the terminal voltage of the ith lithium battery cell in the series lithium battery pack, u i,max represents the lower limit of the terminal voltage of the ith lithium battery cell in the series lithium battery pack, n represents the number of lithium battery cells in the series lithium battery pack, u j It represents the jth lithium battery cell in the series lithium battery pack, and Δμ represents the terminal voltage balancing margin.

[0035] Furthermore, the terminal voltage equalization margin Δμ is used to describe the allowable fluctuation range of the terminal voltage of each lithium battery cell in the series lithium battery pack. It is understandable that the setting of the equalization margin can help identify inconsistent battery cells in the battery pack, so as to take appropriate measures for equalization and protection.

[0036] In this embodiment, the terminal voltage equalization margin Δμ is 30mV; further, at a sampling time, the terminal voltage threshold range of any lithium battery cell is

[0037] S013. Mark a lithium battery cell with at least three consecutive marking moments in the charging stage and / or the discharging stage as an abnormal lithium battery cell, where the marking moment is a sampling moment when the terminal voltage does not meet the corresponding terminal voltage threshold range.

[0038] It is understandable that a single voltage anomaly may be caused by a temporary fluctuation or interference, but the actual anomaly of a battery cell usually lasts for a period of time.

[0039] In this embodiment, step S013 requires that at least three consecutive sampling moments do not meet the voltage threshold range to mark an abnormal lithium battery cell, which can reduce false alarms and improve the accuracy of abnormality detection.

[0040] In one or more other embodiments, the number of consecutive marking moments in step S013 can be adjusted and optimized according to actual conditions, such as increasing the number of consecutive marking moments when the sampling frequency is high, and reducing the number of consecutive marking moments when the sampling frequency is low.

[0041] S02: Acquire the operating status data of the abnormal lithium battery cell, and evaluate the health status of the abnormal lithium battery cell according to the operating status data.

[0042] It is understandable that the health state is used to reflect the performance of the current state of the battery relative to its new state. Generally, the health state can be represented by specific quantitative parameters such as capacity retention rate, internal resistance increase rate and cycle life. In this embodiment, the health state is represented by the capacity retention rate of the lithium battery cell. Specifically, the health state is the percentage of the current capacity of the lithium battery cell to the rated capacity.

[0043] See also Figure 3 , Figure 3 This is a flow chart of the health status assessment of a lithium battery cell provided by an embodiment of the present invention. Figure 3 As shown, the step S02 of obtaining the operating status data of the abnormal lithium battery cell and evaluating the health status of the abnormal lithium battery cell according to the operating status data includes the following steps:

[0044] S021. Obtain operating status data of abnormal lithium battery cells in the charging stage and / or the discharging stage respectively.

[0045] It can be understood that the operating status data obtained in step S021 is data that is relevant to the health status of the lithium battery and is easy to monitor, such as the charging and discharging current and voltage, and the temperature during charging and discharging.

[0046] In this embodiment, the operating status data includes but is not limited to one or more of the voltage change data in the charging stage, the current change data in the charging stage, the temperature change data in the charging stage, the voltage change data in the discharging stage, the current change data in the discharging stage, and the temperature change data in the discharging stage.

[0047] S022. Based on the operating status data, evaluate the health status of the abnormal lithium battery cell using a lithium battery health status evaluation model.

[0048] Furthermore, the lithium battery health status assessment model is a neural network model that uses at least one operating status data to predict the health status of the lithium battery.

[0049] It can be known that in the prior art, neural network models based on different neural network structures (such as BP, RBF, LSTM, etc.) have been proposed for the task of lithium battery health status assessment. These neural network models can predict the corresponding lithium battery health status by learning and analyzing the operating status data.

[0050] Furthermore, when different neural network models process the same data, the differences in structure and training mechanism will lead to different prediction results; however, for the same specific lithium battery, its health status should have the same final evaluation result under an ideal evaluation mechanism.

[0051] In this embodiment, a hybrid lithium battery health status assessment model based on multiple neural network models is proposed, aiming to combine the advantages of different neural network models and improve the accuracy and robustness of lithium battery health status assessment by integrating the prediction results of multiple models.

[0052] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of the lithium battery health status assessment model provided by an embodiment of the present invention. Figure 4 As shown, the lithium battery health status assessment model includes an input module 1, an assessment module 2, an integration module 3 and an output module 4.

[0053] Furthermore, the input module 1 includes at least one data input channel, which is used to input a type of operating status data, such as any one of the voltage change data in the charging stage, the current change data in the charging stage, the temperature change data in the charging stage, the voltage change data in the discharging stage, the current change data in the discharging stage, and the temperature change data in the discharging stage.

[0054] Furthermore, the evaluation module 2 includes at least three parallel evaluation sub-models, and the neural network structures and / or neural network parameters of any two evaluation sub-models are different.

[0055] To ensure that the multiple preliminary health status evaluation results obtained in the evaluation module 2 can be directly integrated, the health status evaluation rules of the training samples and the verification samples of each evaluation sub-model in the evaluation module are the same. In this embodiment, the health status of the training samples and the verification samples of any evaluation sub-model is represented by the capacity retention rate of the lithium battery cell and is obtained by the direct detection method.

[0056] Furthermore, if Figure 4 As shown, any evaluation sub-model of the evaluation module 2 corresponds to at least one data input channel, and the evaluation sub-model is used to obtain a preliminary evaluation result of the health status of the corresponding lithium battery cell according to the input data of the corresponding data input channel.

[0057] Furthermore, the integration module 3 is connected to the output of each evaluation sub-model in the evaluation module 2, and the integration module 3 is used to obtain a comprehensive evaluation result of the corresponding lithium battery cell according to the distribution of the preliminary evaluation results of each evaluation sub-model within the health status range.

[0058] In this embodiment, the integration module obtains the comprehensive evaluation result of the corresponding lithium battery cell according to the distribution of the preliminary evaluation results of each evaluation sub-model within the health state range, including the following steps:

[0059] S0221. Select K cluster centers within the health status range.

[0060] In this embodiment, the health status of the lithium battery cell ranges from [0 to 1]. Further, K cluster centers are selected randomly or by a custom method within this health status range, where K represents the number of cluster centers.

[0061] S0222. Assign each preliminary evaluation result to the nearest cluster center.

[0062] Furthermore, based on each preliminary evaluation result, the difference between the preliminary evaluation result and each cluster center is calculated respectively, and the preliminary evaluation result is matched to the cluster center with the smallest difference.

[0063] S0223. Calculate the mean of the preliminary evaluation results corresponding to each cluster center respectively.

[0064] S0224. Update the mean of the preliminary evaluation results as the cluster center.

[0065] S0225. Repeat steps S0222 to S0225 until the cluster center remains unchanged.

[0066] S0226. Set the cluster center containing the most preliminary evaluation results as the comprehensive evaluation result.

[0067] Furthermore, if Figure 4 As shown, the output module 4 includes a unique data output channel, which is connected to the output end of the integration module 3, and is used to output a comprehensive evaluation result of the health status of the lithium battery cell.

[0068] S03. Adjusting the energy state upper limit and the energy state lower limit of the series-connected lithium battery pack according to the health state of the abnormal lithium battery cell.

[0069] See also Figure 5 , Figure 5 This is a flow chart for adjusting the upper and lower limits of the energy state of a series lithium battery pack provided by an embodiment of the present invention. Figure 5 As shown, the step S03 of adjusting the energy state upper limit and energy state lower limit of the abnormal lithium battery cell according to the health state of the abnormal lithium battery cell includes the following steps:

[0070] S031. Obtain an initial upper limit and an initial lower limit of the energy state of the abnormal lithium battery cell.

[0071] It is understood that the energy state described in the present invention refers to the ratio of the remaining energy of the abnormal lithium battery cell to the maximum available energy. In this embodiment, the initial upper and lower limits of the energy state refer to the upper and lower limits of the energy state of the abnormal lithium battery cell before adjustment, which can be the rated index of the lithium battery cell when it is just put into use (usually 20% to 80%), or the upper and lower limits of the energy state after the most recent adjustment.

[0072] S032. According to the comprehensive evaluation result of the abnormal lithium battery cell, adjust the initial upper limit and the initial lower limit of the energy state of the corresponding abnormal lithium battery cell.

[0073] Furthermore, in order to prevent the abnormal lithium battery cells from being overcharged, the upper limit of the energy state adjusted according to the comprehensive evaluation result of the abnormal lithium battery cells in step S032 satisfies the following calculation formula: SoE' max =SoH·SoE max , among which SoE' max Indicates the upper limit of the adjusted energy state, SoH indicates the comprehensive evaluation result, SoE max Indicates the initial upper limit of the energy state.

[0074] Furthermore, in order to prevent the abnormal lithium battery cells from over-discharging, the lower limit of the energy state adjusted according to the comprehensive evaluation result of the abnormal lithium battery cells in step S032 satisfies the following calculation formula: SoE' min =SoH·(SoE min ) -1 , among which SoE' minrepresents the lower limit of the adjusted energy state, SoH represents the comprehensive evaluation result, and SoE min Indicates the initial lower limit of the energy state.

[0075] S033, respectively setting the lowest energy state upper limit and the highest energy state lower limit corresponding to the abnormal lithium battery cells in the series-connected lithium battery group as the energy state upper limit and the energy state lower limit of the series-connected lithium battery group.

[0076] In one embodiment, see Figure 6 , Figure 6 This is a schematic diagram of the structure of the remote monitoring and operation management system for energy storage lithium batteries provided by an embodiment of the present invention. Figure 6 As shown, the remote monitoring and operation and maintenance management system of the energy storage lithium battery includes a battery data acquisition module, a battery data analysis module and a battery parameter control module. The battery data acquisition module is signal-connected to the battery data analysis module, and the battery parameter control module is signal-connected to the battery data analysis module.

[0077] Furthermore, the battery data acquisition module includes multiple types of data sensors and a first data transceiver device. The data sensor is arranged in the structure of a series-connected lithium battery pack and is used to collect operating status data of each lithium battery cell in the series-connected lithium battery pack. The first data transceiver device is arranged around the data sensor and is connected to each data sensor by wired or wireless signals. The first data transceiver device is signal-connected to the battery data analysis module. The first data transceiver device is used to receive data acquisition signals and to transmit operating status data to the battery data analysis module.

[0078] Furthermore, the battery data analysis module includes a processor and a storage medium, the storage medium is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the remote monitoring, operation and maintenance management method of the energy storage lithium battery provided by the present invention to obtain the upper and lower limits of the energy state after adjustment of the series lithium battery group.

[0079] In this embodiment, the battery data analysis module also includes a second data transceiver device, which is signal-connected to the first data transceiver device, and is signal-connected to the battery parameter control module. The second data transceiver device is used to transmit a data acquisition signal to the first data transceiver device, and to receive operating status data from the first data transceiver device, and is also used to transmit the adjusted upper and lower limits of the energy state of the series lithium battery group to the battery parameter control module.

[0080] Furthermore, the battery parameter control module is connected to the series lithium battery group signal, and the battery parameter control module is used to control the charging process and discharging process of the series lithium battery group according to the adjusted upper and lower limits of the energy state of the series lithium battery group, such as when the energy state of the battery rises to the upper limit of the adjusted energy state, charging is stopped, and when the energy state of the battery drops to the lower limit of the adjusted energy state, discharging is stopped.

[0081] In this embodiment, the battery parameter control module also includes a third data transceiver device, which is signal-connected to the second data transceiver device and is used to receive energy state upper and lower limits from the second data transceiver device.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents.

Claims

1. A remote monitoring, operation and maintenance management method for energy storage lithium batteries, characterized in that: The steps include: Obtain the terminal voltage of each lithium battery cell in the series lithium battery group, and mark the lithium battery cells whose terminal voltage does not meet the terminal voltage threshold range as abnormal lithium battery cells; Acquire the operating status data of the abnormal lithium battery cell, and evaluate the health status of the abnormal lithium battery cell according to the operating status data; According to the health status of the abnormal lithium battery cell, the energy state upper limit and the energy state lower limit of the series-connected lithium battery group are adjusted.

2. The remote monitoring, operation and maintenance management method of energy storage lithium battery according to claim 1 is characterized in that: The method of obtaining the terminal voltage of each lithium battery cell in the series-connected lithium battery pack and marking the lithium battery cells whose terminal voltages do not meet the terminal voltage threshold range as abnormal lithium battery cells comprises the following steps: Setting a sampling frequency, and based on the sampling frequency, using a voltage sensor to collect the terminal voltage of each lithium battery cell in the series-connected lithium battery pack during the charging phase; Based on the terminal voltage of each lithium battery cell at each sampling moment in the charging stage and / or the discharging stage, respectively obtain the terminal voltage threshold range corresponding to the lithium battery cell; A lithium battery cell having at least three consecutive marking moments in the charging stage and / or the discharging stage is marked as an abnormal lithium battery cell, wherein the marking moment is a sampling moment when the terminal voltage does not meet the corresponding terminal voltage threshold range.

3. The remote monitoring, operation and maintenance management method of the energy storage lithium battery according to claim 2 is characterized in that: The terminal voltage threshold range of any lithium battery cell satisfies the calculation model.

4. The remote monitoring, operation and maintenance management method of energy storage lithium battery according to claim 1, characterized in that: The step of obtaining the operating status data of the abnormal lithium battery cell and evaluating the health status of the abnormal lithium battery cell according to the operating status data comprises the following steps: Respectively obtain the operating status data of the abnormal lithium battery cells in the charging stage and / or the discharging stage; Based on the operating status data, the health status of the abnormal lithium battery cell is evaluated through a lithium battery health status evaluation model, and the lithium battery health status evaluation model is a neural network model that uses at least one operating status data to predict the health status of the lithium battery.

5. The remote monitoring, operation and maintenance management method of the energy storage lithium battery according to claim 4 is characterized in that: The operating status data is one or more data combinations of voltage change data, current change data and temperature change data.

6. The remote monitoring, operation and maintenance management method of the energy storage lithium battery according to claim 5 is characterized in that: The lithium battery health status assessment model includes: An input module, the input module comprising at least one data input channel, the data input channel being used to input any type of data among the voltage change data, the current change data, and the temperature change data; An evaluation module, wherein the evaluation module includes at least three parallel evaluation sub-models, wherein the neural network structures and / or neural network parameters of any two evaluation sub-models are different, and the health status evaluation rules of the training samples and the verification samples of each evaluation sub-model in the evaluation module are the same, and the evaluation sub-model is used to obtain a preliminary evaluation result of the health status of the corresponding lithium battery cell according to the input data of the corresponding data input channel; An integration module, the integration module is connected to the output of each evaluation sub-model in the evaluation module, and the integration module is used to obtain a comprehensive evaluation result of the corresponding abnormal lithium battery cell according to the distribution of the preliminary evaluation results of each evaluation sub-model within the healthy state range; An output module includes a unique data output channel, and the data output channel is used to output a comprehensive evaluation result of the health status of abnormal lithium battery cells.

7. The remote monitoring, operation and maintenance management method of energy storage lithium battery according to claim 6, characterized in that: The method of obtaining the comprehensive evaluation result of the abnormal lithium battery cell according to the distribution of the preliminary evaluation results of each evaluation sub-model within the healthy state range comprises the following steps: Within the range of health status, select K cluster centers; Assign each preliminary evaluation result to the nearest cluster center; Calculate the mean of the preliminary evaluation results corresponding to each cluster center respectively; Updating the mean of the preliminary evaluation results as the cluster center; Repeat the above steps of calculating and updating the cluster center until the cluster center remains unchanged; The cluster center containing the most preliminary evaluation results is set as the comprehensive evaluation result.

8. The remote monitoring, operation and maintenance management method of energy storage lithium battery according to claim 1, characterized in that: The step of adjusting the upper and lower limits of the energy state of the series-connected lithium battery pack according to the health state of the abnormal lithium battery cell comprises the following steps: Obtaining the initial upper limit and the initial lower limit of the energy state of the abnormal lithium battery cell; According to the comprehensive evaluation results of the abnormal lithium battery cells, the initial upper limit and the initial lower limit of the energy state of the corresponding abnormal lithium battery cells are adjusted; The lowest energy state upper limit and the highest energy state lower limit corresponding to the abnormal lithium battery cells in the series-connected lithium battery group are respectively set as the energy state upper limit and the energy state lower limit of the series-connected lithium battery group.

9. The remote monitoring, operation and maintenance management method of the energy storage lithium battery according to claim 8, characterized in that: The adjusted energy state upper limit and energy state lower limit satisfy the calculation model respectively.

10. A remote monitoring and operation management system for energy storage lithium batteries, characterized in that: It includes a storage medium for storing a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the remote monitoring, operation and maintenance management method of the energy storage lithium battery according to any one of claims 1-9.

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