A remote safety operation status monitoring method and system for energy storage charging vehicle

Abstract

The present application provides a remote safety operation status monitoring method and system for an energy storage charging vehicle, which relates to the technical field of safety monitoring, and remotely monitors the battery operation status of the energy storage charging vehicle; extracts battery operation temperature data, determines the battery temperature fluctuation characteristics of the energy storage charging vehicle through the battery operation temperature data, and determines the battery safety margin of the energy storage charging vehicle under a temperature rise state according to the battery temperature fluctuation characteristics; extracts battery energy consumption change data of the energy storage charging vehicle under a temperature rise state, and determines the battery load coordination coefficient of the energy storage charging vehicle under a temperature rise state based on the battery energy consumption change data; determines the battery safety monitoring coefficient of the energy storage charging vehicle under a temperature rise state according to the battery safety margin and the battery load coordination coefficient, and sends a safety monitoring report to a remote control center according to the battery safety monitoring coefficient. The present application can effectively combine the battery operation parameters of the energy storage charging vehicle to monitor the operation status, so as to improve the accuracy of safety monitoring of the energy storage charging vehicle.

Description

Technical Field

[0001] The present application relates to the field of safety monitoring technology, and more specifically, to a remote safety operation status monitoring method and system for an energy storage charging vehicle. Background Art

[0002] Remote safety operation status monitoring of energy storage charging vehicles is a highly comprehensive technical application that aims to monitor the vehicle operation status in real time through advanced Internet of Things technology, sensor networks and intelligent algorithms to ensure the safety and efficiency of batteries and systems. The core technologies include battery management system (BMS), cloud data processing and analysis, and multi-parameter linkage monitoring. The monitoring system usually covers the following key parameters: battery operating temperature, current, voltage, state of charge (SOC), state of health (SOH), power input / output level, and environmental variables (such as external temperature and humidity).

[0003] However, in the prior art, the nonlinear coupling relationship between multiple parameters, for example, the complex interaction between battery temperature, load, SOC and power output, makes it difficult to build an accurate safety assessment model. In addition, under the condition of temperature rise, an overly conservative algorithm may lead to excessive system intervention, such as frequent power limiting or alarming, affecting the normal use of the vehicle; while an overly aggressive algorithm may ignore certain potential risks, leading to catastrophic consequences such as thermal runaway. Therefore, how to effectively combine the battery operating parameters of energy storage charging vehicles to monitor the operating status in order to improve the accuracy of safety monitoring of energy storage charging vehicles is a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a remote safety operation status monitoring method and system for an energy storage charging vehicle, which can effectively combine the battery operation parameters of the energy storage charging vehicle to monitor the operation status, so as to improve the accuracy of safety monitoring of the energy storage charging vehicle.

[0005] In a first aspect, the present application provides a remote safety operation status monitoring method for an energy storage charging vehicle, the monitoring method comprising the following steps:

[0006] Remotely monitor the battery operating status of the energy storage charging vehicle to obtain the battery operating status data of the energy storage charging vehicle;

[0007] Extracting battery operating temperature data from the battery operating status data, determining the battery temperature fluctuation characteristics of the energy storage charging vehicle through the battery operating temperature data, and determining the battery safety margin of the energy storage charging vehicle under the temperature rise state according to the battery temperature fluctuation characteristics;

[0008] Extracting the battery energy consumption change data of the energy storage charging vehicle under the temperature rise state from the battery operation state data, and determining the battery load coordination coefficient of the energy storage charging vehicle under the temperature rise state based on the battery energy consumption change data;

[0009] The battery safety monitoring coefficient of the energy storage charging vehicle under the temperature rise state is determined according to the battery safety margin and the battery load coordination coefficient, and a safety monitoring report is sent to a remote control center according to the battery safety monitoring coefficient.

[0010] In this embodiment, the battery operating status of the energy storage charging vehicle is remotely monitored through a sensor network in the energy storage charging vehicle.

[0011] In this embodiment, determining the battery temperature fluctuation characteristics of the energy storage charging vehicle through the battery operating temperature data specifically includes:

[0012] Preprocessing the battery operating temperature data to obtain preprocessed battery operating temperature data;

[0013] Determining the temperature rise rate of each section in the preprocessed battery operating temperature data;

[0014] The battery temperature fluctuation characteristics of the energy storage charging vehicle are determined according to the temperature rise rates of all sections.

[0015] In this embodiment, determining the battery safety margin of the energy storage charging vehicle under the temperature rise state according to the battery temperature fluctuation characteristics specifically includes:

[0016] Predicting the battery temperature of the energy storage charging vehicle based on the battery temperature fluctuation characteristics to obtain a battery predicted temperature;

[0017] Obtaining a temperature threshold for safe operation of the battery of the energy storage charging vehicle;

[0018] The battery safety margin of the energy storage charging vehicle under a temperature rise state is determined by the battery predicted temperature and the temperature threshold.

[0019] In this embodiment, extracting the battery energy consumption change data of the energy storage charging vehicle under the temperature rise state from the battery operation state data specifically includes:

[0020] Obtain all temperature rise stages of the energy storage charging vehicle in a temperature rise state;

[0021] For each temperature rise stage, the output energy consumption change amount and energy consumption change time in the temperature rise stage are extracted from the battery operation status data;

[0022] Performing dynamic data aggregation on the output energy consumption change amount and the energy consumption change time to obtain the energy consumption change data segment of the temperature rise stage, and further obtaining the energy consumption change data segment of each temperature rise stage;

[0023] The energy consumption change data segments in all temperature rise stages are spliced ​​to obtain the battery energy consumption change data of the energy storage charging vehicle under the temperature rise state.

[0024] In this embodiment, determining the battery load coordination coefficient of the energy storage charging vehicle under the temperature rise state based on the battery energy consumption change data specifically includes:

[0025] Obtaining the battery energy consumption loss of the energy storage charging vehicle under the temperature rise state;

[0026] Determining the battery load concentration of the energy storage charging vehicle under the temperature rise state;

[0027] The battery load coordination coefficient of the energy storage charging vehicle under the temperature rise state is determined by the battery energy consumption change data, the battery energy consumption loss amount and the battery load concentration degree.

[0028] In this embodiment, determining the battery load concentration of the energy storage charging vehicle in the temperature rise state is to use the variance of the output power of all batteries of the energy storage charging vehicle in the temperature rise state as the battery load concentration of the energy storage charging vehicle in the temperature rise state.

[0029] In this embodiment, determining the battery safety monitoring coefficient of the energy storage charging vehicle under the temperature rise state according to the battery safety margin and the battery load coordination coefficient specifically includes:

[0030] According to the operation requirements of the energy storage charging vehicle, weights are respectively assigned to the battery safety margin and the battery load coordination coefficient, and then the battery safety margin and the battery load coordination coefficient are weightedly summed, and the weighted sum result is used as the battery safety monitoring coefficient of the energy storage charging vehicle under the temperature rise state.

[0031] In this embodiment, sending the safety monitoring report to the remote control center according to the battery safety monitoring coefficient specifically includes:

[0032] Obtain a pre-set safety monitoring interval;

[0033] When the battery safety monitoring coefficient is lower than the lower limit of the safety monitoring interval, it is determined that the energy storage charging vehicle is in a dangerous operating state;

[0034] When the battery safety monitoring coefficient is within the safety monitoring interval, it is determined that the energy storage charging vehicle is in a warning operation state;

[0035] When the battery safety monitoring coefficient is higher than the upper limit of the safety monitoring interval, it is determined that the energy storage charging vehicle is in a safe operating state;

[0036] Based on the above judgment results, a safety monitoring report of the energy storage charging vehicle is sent to the remote control center.

[0037] In a second aspect, the present application provides a remote safety operation status monitoring system for an energy storage charging vehicle, which is used to perform a remote safety operation status monitoring method for an energy storage charging vehicle, and the monitoring system includes:

[0038] A remote monitoring module, used to remotely monitor the battery operating status of the energy storage charging vehicle and obtain the battery operating status data of the energy storage charging vehicle;

[0039] A safety margin determination module, used to extract battery operating temperature data from the battery operating status data, determine the battery temperature fluctuation characteristics of the energy storage charging vehicle through the battery operating temperature data, and determine the battery safety margin of the energy storage charging vehicle under the temperature rise state according to the battery temperature fluctuation characteristics;

[0040] A load coordination coefficient determination module, used to extract the battery energy consumption change data of the energy storage charging vehicle under the temperature rise state from the battery operation state data, and determine the battery load coordination coefficient of the energy storage charging vehicle under the temperature rise state based on the battery energy consumption change data;

[0041] The safety monitoring module is used to determine the battery safety monitoring coefficient of the energy storage charging vehicle under the temperature rise state according to the battery safety margin and the battery load coordination coefficient, and send a safety monitoring report to the remote control center according to the battery safety monitoring coefficient.

[0042] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0043] The battery operating status data of the energy storage charging vehicle is obtained by remotely monitoring the battery operating status of the energy storage charging vehicle; the battery operating temperature data is extracted from the battery operating status data, the battery temperature fluctuation characteristics of the energy storage charging vehicle are determined through the battery operating temperature data, and the battery safety margin of the energy storage charging vehicle under the temperature rise state is determined according to the battery temperature fluctuation characteristics; the battery energy consumption change data of the energy storage charging vehicle under the temperature rise state is extracted from the battery operating status data, and the battery load coordination coefficient of the energy storage charging vehicle under the temperature rise state is determined based on the battery energy consumption change data; the battery safety monitoring coefficient of the energy storage charging vehicle under the temperature rise state is determined according to the battery safety margin and the battery load coordination coefficient, and a safety monitoring report is sent to the remote control center according to the battery safety monitoring coefficient.

[0044] It can be seen that in this application, firstly, by analyzing the temperature fluctuation characteristics, the behavior pattern of the battery under different loads and working environments can be revealed. Based on the battery safety margin, it is possible to accurately determine whether the battery is close to the safety threshold during operation, so as to ensure that the battery operates at the optimal operating temperature and avoid safety risks; then, the battery energy consumption change data can reveal early signs of problems such as uneven battery load and overheating. The battery load coordination coefficient reflects the energy efficiency performance and temperature regulation ability of the battery under different loads. By extracting the battery energy consumption change data and determining the battery load coordination coefficient based on these data, the monitoring accuracy and safety of the operating status of the energy storage charging vehicle can be improved; finally, based on the prediction of the battery safety monitoring coefficient and the real-time sending of the safety monitoring report, potential risks can be identified in advance and measures can be taken to avoid failures and reduce the risk of equipment damage.

[0045] In summary, the technical solution adopted in this application can effectively combine the battery operating parameters of the energy storage charging vehicle to monitor the operating status, so as to improve the accuracy of safety monitoring of the energy storage charging vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0047] Figure 1 It is a flow chart of a remote safe operation status monitoring method of an energy storage charging vehicle provided in this application;

[0048] Figure 2 is an exemplary flow chart for determining the battery safety margin of an energy storage charging vehicle under a temperature rise state provided by the present application;

[0049] Figure 3 is an exemplary flow chart for determining the battery load coordination coefficient of an energy storage charging vehicle under a temperature rise state according to the present application;

[0050] Figure 4 It is a module structure diagram of the remote safe operation status monitoring system of the energy storage charging vehicle provided in this application. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] The embodiment of the present application provides a remote safety operation status monitoring method and system for an energy storage charging vehicle, the core of which is to remotely monitor the battery operation status of the energy storage charging vehicle and obtain the battery operation status data of the energy storage charging vehicle; extract the battery operation temperature data from the battery operation status data, determine the battery temperature fluctuation characteristics of the energy storage charging vehicle through the battery operation temperature data, and determine the battery safety margin of the energy storage charging vehicle under the temperature rise state according to the battery temperature fluctuation characteristics; extract the battery energy consumption change data of the energy storage charging vehicle under the temperature rise state from the battery operation status data, and determine the battery load coordination coefficient of the energy storage charging vehicle under the temperature rise state based on the battery energy consumption change data; determine the battery safety monitoring coefficient of the energy storage charging vehicle under the temperature rise state according to the battery safety margin and the battery load coordination coefficient, and send a safety monitoring report to the remote control center according to the battery safety monitoring coefficient. The above scheme can effectively combine the battery operation parameters of the energy storage charging vehicle to monitor the operation status, so as to improve the accuracy of safety monitoring of the energy storage charging vehicle.

[0053] Embodiment 1

[0054] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 As shown in the figure, this figure is an exemplary flow chart of a remote safety operation status monitoring method of an energy storage charging vehicle shown in this embodiment of the present application, and the monitoring method includes the following steps:

[0055] In step S1, the battery operating status of the energy storage charging vehicle is remotely monitored to obtain the battery operating status data of the energy storage charging vehicle.

[0056] In this embodiment, the battery operating status of the energy storage charging vehicle is remotely monitored through the sensor network in the energy storage charging vehicle; in specific implementation, first, a distributed sensor network architecture can be used to collect data from each node through a master node (such as a battery management system); then, the battery operating temperature of the energy storage charging vehicle can be monitored and collected through the temperature sensor in the sensor network, thereby obtaining battery operating temperature data. The battery output voltage of the energy storage charging vehicle can be monitored and collected through a voltage sensor to obtain battery output voltage data. In this application, the combination of battery operating temperature data and battery output voltage data can be used as the battery operating status data of the energy storage charging vehicle.

[0057] In step S2, battery operating temperature data is extracted from the battery operating status data, the battery temperature fluctuation characteristics of the energy storage charging vehicle are determined through the battery operating temperature data, and the battery safety margin of the energy storage charging vehicle under the temperature rise state is determined according to the battery temperature fluctuation characteristics.

[0058] In this embodiment, the battery temperature fluctuation characteristics of the energy storage charging vehicle can be determined by the battery operating temperature data in the following manner, namely:

[0059] Preprocessing the battery operating temperature data to obtain preprocessed battery operating temperature data;

[0060] Determining the temperature rise rate of each section in the preprocessed battery operating temperature data;

[0061] The battery temperature fluctuation characteristics of the energy storage charging vehicle are determined according to the temperature rise rates of all sections.

[0062] In specific implementation, first, the battery operating temperature data can be preprocessed. Since the sensor may be disturbed when collecting data, filtering technology needs to be used to remove noise. For example, the battery operating temperature data is processed using a moving average filter or a Kalman filter to make the fluctuation of the battery operating temperature data smoother and reduce the abnormal values ​​of sudden changes, thereby obtaining the preprocessed battery operating temperature data; then, the entire battery operating temperature data is divided into multiple sections according to the time window. For example, the battery operating temperature data can be divided into multiple sections according to a fixed time period (such as every minute), and each section contains a number of battery operating temperature data points, so that the temperature rise rate at each moment in the section can be determined by a differential calculation method, and the average of the temperature rise rates at all moments in the section is used as the temperature rise rate of the section, so that the temperature rise rate of each section in the preprocessed battery operating temperature data can be obtained; finally, the battery temperature fluctuation characteristics of the energy storage charging vehicle can be determined by the temperature rise rates of all sections. The battery temperature fluctuation characteristics indicate the severity of the battery temperature fluctuation of the energy storage charging vehicle, and the standard deviation of the temperature rise rates of all sections can be used as the battery temperature fluctuation characteristics of the energy storage charging vehicle.

[0063] Preferably, in this embodiment, reference Figure 2 As shown, this figure is an exemplary flow chart of determining the battery safety margin of the energy storage charging vehicle under the temperature rise state in an embodiment of the present application. In this embodiment, the battery safety margin of the energy storage charging vehicle under the temperature rise state is determined according to the battery temperature fluctuation characteristics, which can be implemented by the following steps:

[0064] First, in step S21, the battery temperature of the energy storage charging vehicle is predicted based on the battery temperature fluctuation characteristics to obtain a predicted battery temperature;

[0065] Then, in step S22, a temperature threshold for safe operation of the battery of the energy storage charging vehicle is obtained;

[0066] Finally, in step S23, the battery safety margin of the energy storage charging vehicle under the temperature rise state is determined by the battery predicted temperature and the temperature threshold.

[0067] In specific implementation, first, the long short-term memory network (LSTM) in deep learning can be used for temperature prediction. By training the LSTM model, the future battery temperature can be predicted through the collected battery operating temperature data and battery temperature fluctuation characteristics, thereby obtaining the battery predicted temperature; then, the theoretical temperature threshold for safe operation of the battery of the energy storage charging vehicle can be obtained from the battery manual or the battery manufacturer, so that the temperature threshold for safe operation can be dynamically adjusted based on the battery health state (SOH) and historical temperature data. The temperature threshold refers to the current maximum safe operating temperature of the battery of the energy storage charging vehicle. For example, as the battery ages, the temperature threshold may need to be lowered to prevent malfunctions caused by excessive temperature; finally, the battery safety margin of the energy storage charging vehicle in the temperature rise state can be determined by the battery predicted temperature and the temperature threshold, that is, the difference between the temperature threshold and the battery predicted temperature is used as the battery safety margin of the energy storage charging vehicle in the temperature rise state. The battery safety margin represents the temperature safety margin of the battery of the energy storage charging vehicle in the temperature rise state. It should be noted that the temperature of the battery is gradually increasing in the temperature rise state, but does not exceed the temperature threshold, that is, in this application, the battery predicted temperature is less than the temperature threshold.

[0068] It should be noted that by analyzing the temperature fluctuation characteristics, the battery's behavior patterns under different loads and working environments can be revealed. Based on the battery safety margin, it is possible to accurately determine whether the battery is close to the safety threshold during operation. As the temperature of the battery changes during use, the battery safety margin will change dynamically. By continuously evaluating and adjusting the safety margin, the operating strategy of the energy storage charging vehicle can be optimized in real time to ensure that the battery operates at the optimal operating temperature and avoid safety risks.

[0069] In step S3, the battery energy consumption change data of the energy storage charging vehicle under the temperature rise state is extracted from the battery operation state data, and the battery load coordination coefficient of the energy storage charging vehicle under the temperature rise state is determined based on the battery energy consumption change data.

[0070] In this embodiment, the battery energy consumption change data of the energy storage charging vehicle under the temperature rise state can be extracted from the battery operation state data in the following manner, namely:

[0071] Obtain all temperature rise stages of the energy storage charging vehicle in a temperature rise state;

[0072] For each temperature rise stage, the output energy consumption change amount and energy consumption change time in the temperature rise stage are extracted from the battery operation status data;

[0073] Performing dynamic data aggregation on the output energy consumption change amount and the energy consumption change time to obtain the energy consumption change data segment of the temperature rise stage, and further obtaining the energy consumption change data segment of each temperature rise stage;

[0074] The energy consumption change data segments in all temperature rise stages are spliced ​​to obtain the battery energy consumption change data of the energy storage charging vehicle under the temperature rise state.

[0075] In the specific implementation, first, it is necessary to determine the different stages of temperature rise in the battery of the energy storage charging vehicle during operation. Each temperature rise stage represents a specific time period of battery temperature rise, in which the battery temperature continues to rise to a critical point. By real-time monitoring of the battery operating temperature data, the start and end time of the battery temperature rise state can be calibrated. For example, when the battery temperature rises above a predetermined threshold, it is considered to enter a certain temperature rise stage. Based on the change in battery temperature, the battery temperature rise state can be divided into multiple temperature rise stages; then, for each temperature rise stage, the battery output voltage data corresponding to the temperature rise stage is extracted from the battery operating status data, and the battery output voltage data can be used as the output energy consumption data of the battery, so that the output energy consumption change in the temperature rise stage can be calculated based on the output energy consumption data, that is, the difference between the initial output energy consumption data and the final output energy consumption data of the temperature rise stage is used as the output energy consumption change in the temperature rise stage, and the time range corresponding to the temperature rise stage is used as the energy consumption change time.

[0076] In addition, in the specific implementation, a fixed time window can be set first for dynamic data aggregation. For example, every 2 minutes is set as a time window. In each time window, the data of the output energy consumption change is aggregated, and the average value method can be used for aggregation to obtain the aggregated value of the output energy consumption change. Similarly, in each time window, the data of the energy consumption change time is aggregated, and the average value method can be used for aggregation to obtain the aggregated value of the energy consumption change time. The above method can complete the dynamic data aggregation of the output energy consumption change and the energy consumption change time, and obtain the energy consumption change data segment in the temperature rise stage, thereby obtaining the energy consumption change data segment in each temperature rise stage; finally, the energy consumption change data segments in all temperature rise stages can be spliced, that is, spliced ​​in chronological order, so as to obtain the battery energy consumption change data of the energy storage charging vehicle in the temperature rise state.

[0077] Preferably, in this embodiment, reference Figure 3 As shown, this figure is an exemplary flow chart of determining the battery load coordination coefficient of the energy storage charging vehicle under the temperature rise state in an embodiment of the present application. In this embodiment, the battery load coordination coefficient of the energy storage charging vehicle under the temperature rise state is determined based on the battery energy consumption change data, which can be implemented by the following steps:

[0078] First, in step S31, the battery energy consumption loss of the energy storage charging vehicle in the temperature rise state is obtained;

[0079] Then, in step S32, the battery load concentration of the energy storage charging vehicle in the temperature rise state is determined;

[0080] Finally, in step S33, the battery load coordination coefficient of the energy storage charging vehicle under the temperature rise state is determined according to the battery energy consumption change data, the battery energy consumption loss amount and the battery load concentration degree.

[0081] In specific implementation, first, the output voltage under the temperature rise state can be used as the current output quantity, and the average value of the battery output voltage when the energy storage charging vehicle is working normally can be obtained, and the average value can be used as the normal output quantity. Each current output quantity is subtracted from the normal output quantity, and the absolute value is taken and then the average is calculated, and the result is used as the battery energy consumption loss of the energy storage charging vehicle under the temperature rise state; then, the battery load concentration degree of the energy storage charging vehicle under the temperature rise state is determined by taking the variance of the output power of all batteries of the energy storage charging vehicle under the temperature rise state as the battery load concentration degree of the energy storage charging vehicle under the temperature rise state. The battery load concentration degree represents the battery energy consumption loss of the energy storage charging vehicle under the temperature rise state. concentration of battery output voltage changes; finally, the battery load coordination coefficient of the energy storage charging vehicle under the temperature rise state can be determined through the battery energy consumption change data, the battery energy consumption loss and the battery load concentration. The battery load coordination coefficient is an indicator for evaluating the coordination between the operating temperature and the output load of the energy storage charging vehicle under the temperature rise state, and is used to better adjust the output in the temperature rise state to balance stability and performance. The battery load coordination coefficient can be determined according to the following method, namely: battery load coordination coefficient = K1 * energy consumption change rate calculated by battery energy consumption change data + K2 * battery load concentration + K3 * battery energy consumption loss, where K1, K2 and K3 are the weights of the corresponding variables, which can be determined by a multiple linear regression algorithm and will not be repeated here.

[0082] It should be noted that the battery energy consumption change data can reveal early signs of problems such as uneven battery load and overheating. The battery load coordination coefficient reflects the energy efficiency performance and temperature regulation ability of the battery under different loads. By extracting the battery energy consumption change data and determining the battery load coordination coefficient based on this data, the monitoring accuracy and safety of the operating status of the energy storage charging vehicle can be greatly improved.

[0083] In step S4, the battery safety monitoring coefficient of the energy storage charging vehicle under the temperature rise state is determined according to the battery safety margin and the battery load coordination coefficient, and a safety monitoring report is sent to the remote control center according to the battery safety monitoring coefficient.

[0084] In this embodiment, the battery safety monitoring coefficient of the energy storage charging vehicle under the temperature rise state is determined according to the battery safety margin and the battery load coordination coefficient, which can be specifically determined in the following manner, namely:

[0085] According to the operation requirements of the energy storage charging vehicle, weights are respectively assigned to the battery safety margin and the battery load coordination coefficient, and then the battery safety margin and the battery load coordination coefficient are weightedly summed, and the weighted sum result is used as the battery safety monitoring coefficient of the energy storage charging vehicle under the temperature rise state.

[0086] It should be noted that in this application, the battery safety monitoring coefficient is a safety indicator used to evaluate the battery of the energy storage charging vehicle under temperature rise conditions.

[0087] In specific implementation, weights can be assigned to the battery safety margin and the battery load coordination coefficient respectively according to the operating requirements of the energy storage charging vehicle. For example, for energy storage charging vehicles with large load fluctuations, a higher weight may be given to the load coordination coefficient; and for systems with large temperature changes, the weight of the battery safety margin may be higher.

[0088] In this embodiment, the safety monitoring report is sent to the remote control center according to the battery safety monitoring coefficient in the following manner:

[0089] Obtain a pre-set safety monitoring interval;

[0090] When the battery safety monitoring coefficient is lower than the lower limit of the safety monitoring interval, it is determined that the energy storage charging vehicle is in a dangerous operating state;

[0091] When the battery safety monitoring coefficient is within the safety monitoring interval, it is determined that the energy storage charging vehicle is in a warning operation state;

[0092] When the battery safety monitoring coefficient is higher than the upper limit of the safety monitoring interval, it is determined that the energy storage charging vehicle is in a safe operating state;

[0093] Based on the above judgment results, a safety monitoring report of the energy storage charging vehicle is sent to the remote control center.

[0094] In specific implementation, first of all, the safety monitoring interval refers to a reasonable range set for the battery safety monitoring coefficient of the energy storage charging vehicle, which is usually set by the system administrator or technical expert based on the working characteristics, safety standards and experience values ​​of the battery; when the battery safety monitoring coefficient is lower than the lower limit of the safety monitoring interval, it means that the battery's safety margin is insufficient, the load coordination is poor, and there may be risks such as overheating, short circuit, and energy efficiency reduction. Therefore, the system will determine that the energy storage charging vehicle is in a dangerous operating state and immediately activate the alarm mechanism to prompt relevant personnel to take emergency measures, such as reducing the load, switching batteries, and shutting down; when the battery safety monitoring coefficient is within the safety monitoring interval, it means that the battery is within the normal working range, but due to temperature rise, load changes or other factors, there are still certain risks. At this time, the system will determine the energy storage charging vehicle as a warning operating state, requiring further monitoring and taking preventive measures, such as continuous monitoring of battery temperature, energy consumption changes, etc., to prevent the situation from deteriorating; when the battery safety monitoring coefficient is higher than the upper limit of the safety monitoring interval, it means that the battery is in a very good operating state and can work stably under higher load and temperature rise conditions. The system will determine that the energy storage charging vehicle is in a safe operating state and allow it to continue charging or discharging operations according to normal procedures; finally, based on the above judgment results, a safety monitoring report of the energy storage charging vehicle can be sent to the remote control center. The safety monitoring report includes the current battery safety monitoring coefficient, the current determined operating status (dangerous, warning or safe), battery temperature, load and other key operating parameters, detailed records of any abnormal conditions (such as overtemperature, load fluctuations, etc.) and recommended operating measures or improvement suggestions (such as reducing load, adjusting temperature control strategy, etc.).

[0095] It should be noted that based on the prediction of the battery safety monitoring coefficient, the system can identify potential risks in advance and take measures to avoid failures and reduce the risk of equipment damage. By sending safety monitoring reports in real time, the remote control center can quickly obtain the current status of the energy storage charging vehicle and make timely decisions or adjustments.

[0096] It can be seen that in this application, firstly, by analyzing the temperature fluctuation characteristics, the behavior pattern of the battery under different loads and working environments can be revealed. Based on the battery safety margin, it is possible to accurately determine whether the battery is close to the safety threshold during operation, so as to ensure that the battery operates at the optimal operating temperature and avoid safety risks; then, the battery energy consumption change data can reveal early signs of problems such as uneven battery load and overheating. The battery load coordination coefficient reflects the energy efficiency performance and temperature regulation ability of the battery under different loads. By extracting the battery energy consumption change data and determining the battery load coordination coefficient based on these data, the monitoring accuracy and safety of the operating status of the energy storage charging vehicle can be improved; finally, based on the prediction of the battery safety monitoring coefficient and the real-time sending of the safety monitoring report, potential risks can be identified in advance and measures can be taken to avoid failures and reduce the risk of equipment damage.

[0097] In summary, the technical solution adopted in this application can effectively combine the battery operating parameters of the energy storage charging vehicle to monitor the operating status, so as to improve the accuracy of safety monitoring of the energy storage charging vehicle.

[0098] Embodiment 2

[0099] This application provides a remote safety operation status monitoring system for energy storage charging vehicles, referring to Figure 4 As shown, this figure is a schematic diagram of a monitoring system according to this embodiment of the present application, and the monitoring system includes:

[0100] A remote monitoring module 100 is used to remotely monitor the battery operating status of the energy storage charging vehicle and obtain the battery operating status data of the energy storage charging vehicle;

[0101] A safety margin determination module 200 is used to extract battery operating temperature data from the battery operating status data, determine the battery temperature fluctuation characteristics of the energy storage charging vehicle through the battery operating temperature data, and determine the battery safety margin of the energy storage charging vehicle under the temperature rise state according to the battery temperature fluctuation characteristics;

[0102] A load coordination coefficient determination module 300 is used to extract the battery energy consumption change data of the energy storage charging vehicle under the temperature rise state from the battery operation state data, and determine the battery load coordination coefficient of the energy storage charging vehicle under the temperature rise state based on the battery energy consumption change data;

[0103] The safety monitoring module 400 is used to determine the battery safety monitoring coefficient of the energy storage charging vehicle under the temperature rise state according to the battery safety margin and the battery load coordination coefficient, and send a safety monitoring report to the remote control center according to the battery safety monitoring coefficient.

[0104] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0105] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, the storage medium including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically-erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0106] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. A remote safety operation status monitoring method for an energy storage charging vehicle, characterized in that: The monitoring method comprises the following steps: Remotely monitor the battery operating status of the energy storage charging vehicle to obtain the battery operating status data of the energy storage charging vehicle; Extracting battery operating temperature data from the battery operating status data, determining the battery temperature fluctuation characteristics of the energy storage charging vehicle through the battery operating temperature data, and determining the battery safety margin of the energy storage charging vehicle under the temperature rise state according to the battery temperature fluctuation characteristics; Extracting the battery energy consumption change data of the energy storage charging vehicle under the temperature rise state from the battery operation state data, and determining the battery load coordination coefficient of the energy storage charging vehicle under the temperature rise state based on the battery energy consumption change data; Determine the battery safety monitoring coefficient of the energy storage charging vehicle under the temperature rise state according to the battery safety margin and the battery load coordination coefficient, and send a safety monitoring report to the remote control center according to the battery safety monitoring coefficient; Wherein, determining the battery load coordination coefficient of the energy storage charging vehicle under the temperature rise state based on the battery energy consumption change data specifically includes: Obtain the battery energy consumption loss of the energy storage charging vehicle in the temperature rise state, wherein the battery energy consumption loss of the energy storage charging vehicle in the temperature rise state is determined by: subtracting the output voltage of each energy storage charging vehicle in the temperature rise state from the average value of the battery output voltage during normal operation, taking the absolute value and then calculating the average; Determine the battery load concentration of the energy storage charging vehicle under the temperature rise state, wherein determining the battery load concentration of the energy storage charging vehicle under the temperature rise state is to use the variance of the output power of all batteries of the energy storage charging vehicle under the temperature rise state as the battery load concentration of the energy storage charging vehicle under the temperature rise state; Determine the battery load coordination coefficient of the energy storage charging vehicle under the temperature rise state through the battery energy consumption change data, the battery energy consumption loss and the battery load concentration. The battery load coordination coefficient is an indicator for evaluating the coordination between the operating temperature and the output load of the energy storage charging vehicle under the temperature rise state, wherein the battery load coordination coefficient=K1*energy consumption change rate calculated through the battery energy consumption change data+K2*battery load concentration+K3*battery energy consumption loss, wherein K1, K2 and K3 are weights of the corresponding variables; Wherein, determining the battery safety margin of the energy storage charging vehicle under the temperature rise state according to the battery temperature fluctuation characteristics specifically includes: Predicting the battery temperature of the energy storage charging vehicle based on the battery temperature fluctuation characteristics to obtain a battery predicted temperature; Obtaining a temperature threshold for safe operation of the battery of the energy storage charging vehicle; The battery safety margin of the energy storage charging vehicle under a temperature rise state is determined by the battery predicted temperature and the temperature threshold.

2. A remote safety operation status monitoring method for an energy storage charging vehicle as claimed in claim 1, characterized in that: The battery operating status of the energy storage charging vehicle is remotely monitored through the sensor network in the energy storage charging vehicle.

3. A remote safety operation status monitoring method for an energy storage charging vehicle as claimed in claim 1, characterized in that: Determining the battery temperature fluctuation characteristics of the energy storage charging vehicle through the battery operating temperature data specifically includes: Preprocessing the battery operating temperature data to obtain preprocessed battery operating temperature data; Determining the temperature rise rate of each section in the preprocessed battery operating temperature data; The battery temperature fluctuation characteristics of the energy storage charging vehicle are determined according to the temperature rise rates of all sections.

4. A remote safety operation status monitoring method for an energy storage charging vehicle as claimed in claim 1, characterized in that: Extracting the battery energy consumption change data of the energy storage charging vehicle under the temperature rise state from the battery operation status data specifically includes: Obtain all temperature rise stages of the energy storage charging vehicle in a temperature rise state; For each temperature rise stage, the output energy consumption change amount and energy consumption change time in the temperature rise stage are extracted from the battery operation status data; Performing dynamic data aggregation on the output energy consumption change amount and the energy consumption change time to obtain the energy consumption change data segment of the temperature rise stage, and further obtaining the energy consumption change data segment of each temperature rise stage; The energy consumption change data segments in all temperature rise stages are spliced ​​to obtain the battery energy consumption change data of the energy storage charging vehicle under the temperature rise state.

5. A remote safety operation status monitoring method for an energy storage charging vehicle as claimed in claim 1, characterized in that: Determining the battery safety monitoring coefficient of the energy storage charging vehicle under the temperature rise state according to the battery safety margin and the battery load coordination coefficient specifically includes: According to the operation requirements of the energy storage charging vehicle, weights are respectively assigned to the battery safety margin and the battery load coordination coefficient, and then the battery safety margin and the battery load coordination coefficient are weightedly summed, and the weighted sum result is used as the battery safety monitoring coefficient of the energy storage charging vehicle under the temperature rise state.

6. A remote safety operation status monitoring method for an energy storage charging vehicle as claimed in claim 1, characterized in that: Sending a safety monitoring report to the remote control center according to the battery safety monitoring coefficient specifically includes: Obtain a pre-set safety monitoring interval; When the battery safety monitoring coefficient is lower than the lower limit of the safety monitoring interval, it is determined that the energy storage charging vehicle is in a dangerous operating state; When the battery safety monitoring coefficient is within the safety monitoring interval, it is determined that the energy storage charging vehicle is in a warning operation state; When the battery safety monitoring coefficient is higher than the upper limit of the safety monitoring interval, it is determined that the energy storage charging vehicle is in a safe operating state; Based on the above judgment results, a safety monitoring report of the energy storage charging vehicle is sent to the remote control center.

7. A remote safety operation status monitoring system for an energy storage charging vehicle, used to execute a remote safety operation status monitoring method for an energy storage charging vehicle as claimed in any one of claims 1 to 6, characterized in that: The monitoring system comprises: A remote monitoring module, used to remotely monitor the battery operating status of the energy storage charging vehicle and obtain the battery operating status data of the energy storage charging vehicle; A safety margin determination module, used to extract battery operating temperature data from the battery operating status data, determine the battery temperature fluctuation characteristics of the energy storage charging vehicle through the battery operating temperature data, and determine the battery safety margin of the energy storage charging vehicle under the temperature rise state according to the battery temperature fluctuation characteristics; A load coordination coefficient determination module, used to extract the battery energy consumption change data of the energy storage charging vehicle under the temperature rise state from the battery operation state data, and determine the battery load coordination coefficient of the energy storage charging vehicle under the temperature rise state based on the battery energy consumption change data; The safety monitoring module is used to determine the battery safety monitoring coefficient of the energy storage charging vehicle under the temperature rise state according to the battery safety margin and the battery load coordination coefficient, and send a safety monitoring report to the remote control center according to the battery safety monitoring coefficient.

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