New energy vehicle battery remote monitoring method and system based on cloud computing

Abstract

The invention relates to the technical field of remote monitoring, in particular to a new energy vehicle battery remote monitoring method and system based on cloud computing, and the method comprises the following steps: extracting the temperature data of a plurality of monitoring points through a vehicle-mounted temperature sensor based on the data of a new energy vehicle battery, recording the current and voltage information of a BMS (Battery Management System), and arranging the data according to a time sequence and storing the data in a cloud database to obtain battery operation monitoring data. According to the invention, by accurately collecting the operation data of the new energy vehicle battery and performing time sequence storage, data management with finer granularity is realized, the battery state is ensured to be comprehensively monitored, the abnormal temperature detection precision is improved, the quantum key generation is utilized to generate and distribute keys, the data transmission security is enhanced, and the data transmission efficiency is improved through quantum encryption and remote identity authentication. Data integrity and privacy are guaranteed, cooling and charging strategies are optimized, temperature control accuracy is improved, the service life of the battery is prolonged, energy consumption is reduced, and new energy vehicle battery management is more efficient, safer and more intelligent.

Description

Technical Field

[0001] The present invention relates to the technical field of remote monitoring, in particular to a method and system for remotely monitoring a new energy vehicle battery based on cloud computing. Background Art

[0002] The technical field of remote monitoring includes non-contact measurement, data acquisition, information transmission, and remote analysis and processing of the state parameters of a target object. The core content of this technical field includes using sensing devices to obtain the physical or chemical properties of the monitored object, transmitting data through wired or wireless communication methods, and relying on data analysis and storage technologies to perform calculations and processing on the monitored data. Remote monitoring technology is widely used in many fields such as industrial manufacturing, intelligent transportation, environmental monitoring, and medical device management. Among them, the remote monitoring of new energy vehicle batteries is an important application direction in this technical field, involving multiple links such as the acquisition of battery state data, remote transmission, and cloud computing processing.

[0003] Among them, the method for remotely monitoring a new energy vehicle battery refers to a technical solution for non-contact monitoring and management of a new energy vehicle battery using a cloud computing platform. The technical matters of this method cover the acquisition of battery state data, remote transmission of data, and data calculation and processing in the cloud. Data acquisition relies on voltage sensors, current sensors, and temperature sensors to obtain operating data such as the voltage, current, and temperature of the battery respectively, and uses a wireless network interface or a wired network interface to complete the remote transmission of data, perform storage management of the data, and complete the parsing and calculation of the battery operating data through a data analysis application program to achieve remote monitoring and management of the operating state of the new energy vehicle battery.

[0004] The existing technologies have multiple deficiencies in the remote monitoring of new energy vehicle batteries. The acquisition of state data depends on the data upload at a fixed frequency, which is difficult to cope with the drastic temperature fluctuations in a short period, resulting in insufficient timeliness of temperature anomaly identification and an easy risk of high-temperature out-of-control. The security of data transmission depends on traditional encryption mechanisms. Facing complex network environments and potential attack methods, there are hidden dangers of data leakage and tampering, affecting the reliability of battery management. The verification method for remote transmission is relatively single, lacking end-to-end high-strength encryption and verification, and it is difficult to effectively prevent man-in-the-middle attacks. Thermal management regulation depends on preset threshold control, lacking intelligent matching based on the real-time operating state, resulting in problems such as response lag or high energy consumption of the cooling fan or liquid cooling system, reducing the adaptability of battery thermal management. Data storage and calculation mainly rely on traditional cloud models, failing to fully combine encryption management and dynamic adjustment mechanisms. When facing large-scale data streams, both the calculation efficiency and security are difficult to meet high-standard requirements. Summary of the Invention

[0005] To address the multiple deficiencies in the existing technology for remote monitoring of new energy vehicle batteries, the collection of status data relies on fixed-frequency data uploads, making it difficult to cope with drastic temperature fluctuations within a short period, resulting in insufficient timeliness for temperature anomaly identification and an increased risk of high-temperature runaway. The security of data transmission depends on traditional encryption mechanisms, which pose risks of data leakage and tampering in the face of complex network environments and potential attack methods, affecting the reliability of battery management. The verification method for remote transmission is relatively simple, lacking end-to-end high-strength encryption and verification, making it difficult to effectively prevent man-in-the-middle attacks. Thermal management regulation relies on preset threshold control and lacks intelligent matching based on real-time operating conditions, leading to problems such as delayed response or excessive energy consumption in the cooling fan or liquid cooling system, reducing the adaptability of battery thermal management. Data storage and calculation mainly rely on traditional cloud models, failing to fully integrate encryption management and dynamic adjustment mechanisms. In the face of large-scale data streams, both computational efficiency and security are difficult to meet high-standard requirements. The embodiments of the present invention provide a method and system for remote monitoring of new energy vehicle batteries based on cloud computing. The technical solutions are as follows:

[0006] On the one hand, a method for remote monitoring of new energy vehicle batteries based on cloud computing is provided. The method includes:

[0007] S1: Based on the new energy vehicle battery data, extract the temperature data of multiple monitoring points through in-vehicle temperature sensors, record the current and voltage information of the BMS, arrange the data in chronological order, and store it in the cloud database to obtain battery operation monitoring data;

[0008] S2: Based on the battery operation monitoring data, screen the temperature fluctuation data within a short period, compare it with the battery cell temperature threshold and the module temperature change rate, determine whether there is a situation exceeding the fluctuation range, and match it through the abnormal temperature monitoring threshold to obtain the battery temperature anomaly determination result;

[0009] S3: Combine the battery temperature anomaly determination result and the battery operation monitoring data, obtain a random key through the quantum key distribution mechanism, distribute the key according to the data transmission node, and store the key in the encryption management configuration to obtain the new energy vehicle battery secure transmission key;

[0010] S4: Based on the new energy vehicle battery secure transmission key, encrypt the data, perform key exchange according to the quantum key distribution mechanism, verify the encrypted data, and decrypt it through the receiving-end quantum key distribution to output the battery remote encrypted transmission monitoring data.

[0011] As a further solution of the present invention, the battery operation monitoring data includes battery cell temperature data, module voltage data, charge and discharge current data, ambient temperature data, and time series records. The battery temperature anomaly determination result includes temperature overlimit status, temperature fluctuation anomaly status, and abnormal temperature distribution characteristics. The new energy vehicle battery security transmission key includes a random key, node key mapping, and encryption configuration parameters. The battery remote encrypted transmission monitoring data includes encrypted data packets, key verification information, and remote authentication results.

[0012] As a further solution of the present invention, the steps of the battery operation monitoring data are specifically as follows:

[0013] S101: Based on the new energy vehicle battery data, call the in-vehicle temperature sensor to monitor the temperature parameters at multiple positions, record the temperature values of each monitoring point, detect the current and voltage data of the BMS, arrange each item of data in chronological order, and synchronize the ambient temperature information to obtain the basic battery operation data;

[0014] S102: Based on the basic battery operation data, screen the temperature, current, and voltage data at each moment, extract the charge and discharge states within different time periods, correlate the ambient temperature with the temperature values of the monitoring points in time series, and calculate the temperature deviation value and the change rates of current and voltage to obtain the battery operation state parameters;

[0015] S103: Call the battery operation state parameters, match the operation parameters in different temperature ranges according to the current and voltage fluctuations under the charge and discharge states, and organize the battery operation conditions at time points to obtain the battery operation monitoring data.

[0016] As a further solution of the present invention, the steps of the battery temperature anomaly determination result are specifically as follows:

[0017] S201: Based on the battery operation monitoring data, screen the temperature change data within a short time period, extract the temperature change values and corresponding time sequences of the monitoring points at continuous time nodes, and identify the continuous temperature difference and adjacent time intervals to obtain the temperature fluctuation interval value;

[0018] S202: Call the temperature fluctuation interval value, compare the ratio of the continuous temperature difference to the adjacent time interval according to the battery cell temperature threshold and the module temperature change rate, and determine whether there are data segments higher than the battery cell temperature threshold and higher than the module temperature change rate to obtain the temperature fluctuation deviation degree;

[0019] S203: According to the temperature fluctuation deviation degree, match the abnormal temperature monitoring threshold, screen the temperature fluctuation data segments higher than the threshold, and organize the corresponding time nodes and temperature change values to obtain the battery temperature anomaly determination result.

[0020] As a further solution of the present invention, the steps for securely transmitting the key of the new energy vehicle battery are specifically as follows:

[0021] S301: Based on the battery temperature anomaly determination result and battery operation monitoring data, extract data transmission node information, screen the data flow between nodes, calculate the data interaction frequency of nodes, and obtain the data transmission node distribution;

[0022] The data interaction frequency of the node is calculated using the formula:

[0023]

[0024] where F node represents the data interaction frequency of the node, d i,j represents the data transmission volume between node i and node j, represents the average data transmission volume of node j, n represents the total number of nodes, and T i represents the transmission duration of node i;

[0025] S302: Invoke the data transmission node distribution, randomly generate a key sequence according to the quantum key distribution mechanism, extract the distribution characteristics of the data transmission nodes, and allocate the keys corresponding to the data interaction relationship between nodes to obtain node-allocated keys;

[0026] S303: Invoke the node-allocated keys, combine with the encryption management configuration, screen the key data that meets the storage requirements, sort out the key storage path and access permissions, and store the keys in the encryption management configuration to obtain the secure transmission key of the new energy vehicle battery.

[0027] As a further solution of the present invention, the steps for remotely encrypting and transmitting the monitoring data of the battery are specifically as follows:

[0028] S401: Based on the secure transmission key of the new energy vehicle battery, extract the battery operation data, process the data according to the key encryption rule, allocate encryption parameters to the data blocks, and adjust the encryption key sequence to obtain the encrypted data stream;

[0029] S402: Invoke the encrypted data stream, exchange the key information during the data transmission process according to the quantum key distribution mechanism, match the remote cloud identity authentication rule, perform permission verification, and screen the encrypted data that meets the transmission security standard to obtain the verified encrypted data;

[0030] S403: Invoke the verified encrypted data, match the corresponding key according to the quantum key distribution at the receiving end, parse the encryption parameters, perform decryption operations, calculate the decryption value of the battery operation data, sort out the decrypted data blocks, and output the remotely encrypted and transmitted monitoring data of the battery.

[0031] As a further solution of the present invention, the decryption value of the battery operation data adopts the formula:

[0032]

[0033] where D represents the decryption value of the battery operation data, C k represents the k-th block of the encrypted transmission data, represents the k-th block of data after decryption at the receiving end, α k represents the weight coefficient of the k-th block of data, and M represents the total number of encrypted data blocks.

[0034] As a further solution of the present invention, the method further includes step S5:

[0035] S5: Based on the remotely encrypted transmission data of the battery, compare the cooling fan speed, liquid cooling flow rate, and charging current adjustment parameters according to the current operating state of the battery, determine the matching hot and cold mode, and perform dynamic thermal management adjustment of the new energy vehicle battery;

[0036] The dynamic thermal management adjustment of the new energy vehicle battery includes fan speed adjustment, liquid cooling flow control, and charging current adjustment.

[0037] As a further solution of the present invention, the steps of the dynamic thermal management adjustment of the new energy vehicle battery are specifically as follows:

[0038] S501: Based on the remotely encrypted transmission monitoring data of the battery, extract the current operating state of the battery, call the cooling fan speed, liquid cooling flow rate, and charging current adjustment parameters, identify the change trend of the parameters in the time series, and obtain the battery operating state parameters;

[0039] S502: Call the battery operating state parameters, compare the hot and cold modes under different operating states, screen the modes with high matching degrees, compare the temperature control thresholds under the modes, screen the hot and cold modes that match the current operating state, and obtain the hot and cold mode matching results;

[0040] S503: Call the hot and cold mode matching results, adjust the cooling fan speed, liquid cooling flow rate, and charging current adjustment parameters, match the target temperature control range, adjust the battery thermal management parameters, perform dynamic adjustment, and output the dynamic thermal management adjustment of the new energy vehicle battery.

[0041] On the other hand, the electric vehicle status monitoring system is used to execute the above-mentioned electric vehicle status monitoring method, and the system includes:

[0042] The data monitoring module extracts the temperature data of the on-vehicle temperature sensor monitoring points based on the new energy vehicle battery data, records the BMS current and voltage information, stores them in the cloud database in chronological order, and obtains the battery operation monitoring data;

[0043] The temperature anomaly detection module filters the temperature fluctuation data based on the battery operation monitoring data, compares the single battery temperature threshold and the module temperature change rate, matches the abnormal temperature monitoring threshold, and obtains the battery temperature anomaly determination result;

[0044] The security key distribution module performs remote battery monitoring based on the battery temperature anomaly determination result, extracts in-vehicle communication network nodes, distributes remote transmission keys for new energy vehicles, compares the data transmission node information, extracts the key management configuration and stores it, and obtains the battery security transmission key for new energy vehicles;

[0045] The data encryption transmission module performs key exchange on the quantum key distribution mechanism based on the battery security transmission key for new energy vehicles, encrypts the battery operation monitoring data, performs permission verification through remote cloud identity authentication, and decrypts the remotely encrypted transmission monitoring data of the battery;

[0046] The thermal management adjustment module extracts the battery cooling control unit based on the remotely encrypted transmission monitoring data of the battery, compares the cooling fan speed, liquid cooling flow rate, and charging current adjustment parameters, filters and matches the mode, and performs dynamic thermal management adjustment of the new energy vehicle battery.

[0047] The beneficial effects brought by the technical solution provided by the embodiments of the present invention at least include:

[0048] Through the precise collection and time-series storage of the battery operation data of new energy vehicles, more fine-grained data management is realized, ensuring the comprehensive monitoring of the battery state. Based on temperature fluctuation screening and dynamic comparison, efficient detection of abnormal temperature changes is achieved, improving the recognition accuracy of temperature anomalies. Combining the temperature anomaly determination result with the operation data, the quantum key distribution mechanism is used for key generation and distribution, enabling data secure transmission to have higher anti-attack capabilities. Through the encryption and verification of quantum keys, a highly secure data transmission method is constructed based on remote identity authentication, ensuring data integrity and privacy. Dynamically adjusting the thermal management system according to the current operation state of the battery improves the intelligence of the cooling and charging strategies, making the battery temperature control more accurate, effectively extending the battery service life and optimizing the energy consumption distribution. This method based on full-process data security protection and intelligent thermal management breaks through the limitations of traditional solutions in terms of temperature monitoring accuracy, security guarantee, and energy consumption optimization, making the battery monitoring and management of new energy vehicles more efficient, secure, and intelligent. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the working process of the present invention;

[0050] Figure 2 It is a detailed flowchart of S1 of the present invention;

[0051] Figure 3 It is the detailed flowchart of S2 of the present invention;

[0052] Figure 4 It is the detailed flowchart of S3 of the present invention;

[0053] Figure 5 It is the detailed flowchart of S4 of the present invention;

[0054] Figure 6 It is the detailed flowchart of S5 of the present invention;

[0055] Figure 7 It is the system flowchart of the present invention. Specific Embodiments

[0056] Next, the technical solutions in the present invention will be described with reference to the accompanying drawings.

[0057] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or more advantageous than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either of the two can be selected.

[0058] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0059] In the embodiments of the present invention, sometimes subscripts such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0060] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0061] Please refer to Figure 1 , the embodiments of the present invention provide a method for remotely monitoring the battery of a new energy vehicle based on cloud computing. The processing flow of this method can include the following steps:

[0062] S1: Based on new energy vehicle battery data, including temperature, voltage, current, charge and discharge status, and ambient temperature data, extract temperature data of multiple monitoring points through in-vehicle temperature sensors, record the current and voltage information of the BMS, arrange the collected data in chronological order and store it in the cloud database to obtain battery operation monitoring data;

[0063] S2: Based on the battery operation monitoring data, screen the temperature fluctuation data within a short period of time, compare it with the battery cell temperature threshold and the module temperature change rate, judge whether there is a situation exceeding the fluctuation range, and match it through the abnormal temperature monitoring threshold to obtain the battery temperature anomaly determination result;

[0064] S3: Combine the battery temperature anomaly determination result and the battery operation monitoring data, obtain a random key through the quantum key distribution mechanism, distribute the key according to the data transmission node, and store the key in the encryption management configuration to obtain the new energy vehicle battery secure transmission key;

[0065] S4: Based on the new energy vehicle battery secure transmission key, encrypt the data, perform key exchange according to the quantum key distribution mechanism, verify the encrypted data, perform permission verification through remote cloud identity authentication, and decrypt through the receiving end quantum key distribution to output the battery remote encrypted transmission monitoring data;

[0066] S5: Based on the battery remote encrypted transmission data, compare the cooling fan speed, liquid cooling flow rate, and charging current adjustment parameters according to the current operating state of the battery, judge the matching cooling and heating mode, and perform dynamic thermal management adjustment of the new energy vehicle battery.

[0067] The battery operation monitoring data includes battery cell temperature data, module voltage data, charge and discharge current data, ambient temperature data, and time series records. The battery temperature anomaly determination result includes temperature overlimit status, temperature fluctuation anomaly status, and abnormal temperature distribution characteristics. The new energy vehicle battery secure transmission key includes random key, node key mapping, and encryption configuration parameters. The battery remote encrypted transmission monitoring data includes encrypted data packets, key verification information, and remote authentication results. The dynamic thermal management adjustment of the new energy vehicle battery includes fan speed adjustment, liquid cooling flow control, and charging current adjustment.

[0068] Specifically, as Figure 2 shown, the steps of the battery operation monitoring data are specifically as follows:

[0069] S101: Based on the new energy vehicle battery data, call the in-vehicle temperature sensor to monitor the temperature parameters at multiple locations, record the temperature values of each monitoring point, detect the current and voltage data of the BMS, arrange each item of data in chronological order, and synchronize the ambient temperature information to obtain the battery basic operation data;

[0070] In the battery management system (BMS) of new energy vehicles, the on-vehicle temperature sensors play a crucial role. Their purpose is to monitor the temperature changes at different positions of the battery in real time. Each sensor is arranged at multiple key positions of the battery. By monitoring the temperatures of different areas of the battery, the sensors transmit data in a timely manner. The temperature values of each sensor are recorded in chronological order and are correlated with the ambient temperature synchronously. The temperature data, together with the battery current and voltage data recorded by the BMS, function together. The on-vehicle temperature sensors are located on the front, back, and sides of the battery module respectively. The recorded temperature data can be, for example: 30°C at the front of the module, 28°C at the back, and 32°C on the side. The BMS will synchronously record the battery voltage (such as 36V) and current (such as 50A) through its internal current and voltage sensors. The data will be organized in chronological order to form the basic battery operation data, ensuring the synchronous acquisition of factors such as temperature changes, current fluctuations, and voltage changes, thus providing a complete basic dataset for subsequent analysis and being used for subsequent state evaluation and performance analysis.

[0071] S102: Based on the basic battery operation data, screen the temperature, current, and voltage data at each moment, extract the charge and discharge states within the differential time period, correlate the ambient temperature and the temperature values at the monitoring points in time series, calculate the temperature deviation value and the change rates of current and voltage, and obtain the battery operation state parameters;

[0072] First, it is necessary to screen the temperature, current, and voltage data at each time point in chronological order according to the basic battery operation data to ensure the validity of the data. For the charge and discharge states in different time periods, appropriate thresholds are set to determine whether it is a charging state or a discharging state. For example, the charge and discharge states can be distinguished according to the positive and negative values of the current. When the current is positive, it is judged as the charging state, and when the current is negative, it is the discharging state. The screened current and voltage data will be correlated with the ambient temperature and the temperature data at the monitoring points. For example, assume that during a certain charging time period, the temperature at the battery monitoring point is 32°C and the ambient temperature is 25°C. By calculating its temperature deviation value, that is, 32°C - 25°C = 7°C, the temperature deviation for this period is obtained. In addition, to evaluate the state of the battery, it is also necessary to calculate the change rates of current and voltage. For example, if the current increases from 50A to 55A, the current change rate is 5A / second. If the voltage rises from 36V to 37V, the voltage change rate is 1V / second. Combining the data with the temperature deviation to form the battery operation state parameters can effectively extract the battery performance parameters in different charge and discharge stages.

[0073] S103: Invoke the battery operation state parameters, match the operation parameters in the differential temperature range according to the current and voltage fluctuations in the charge and discharge states, organize the battery operation conditions at the time points, and obtain the battery operation monitoring data;

[0074] Based on the fluctuations of current and voltage under charge and discharge conditions, the operation of the battery is further sorted out. For different charge and discharge states, the temperature, power consumption, and current and voltage characteristics inside the battery will be different. Suppose the battery is in a discharge state during a certain period, and the current fluctuates greatly. For example, the current fluctuation increases from -50A to -40A, while the voltage fluctuation decreases from 36V to 34V. According to the changes in current and voltage, match the corresponding temperature change range. For example, when the current is large, the temperature of the battery will rise significantly. Therefore, it is necessary to select a suitable range in the temperature range (for example: 32°C to 36°C) to compare the temperature data of the battery. The matching process will help to further analyze the operation state of the battery at this time point. And so on, match the fluctuations of temperature, current, and voltage at each time point and organize them into a detailed battery operation monitoring data. The data provides an accurate basis for the health assessment of the battery and can provide data support for future optimization and adjustment.

[0075] Specifically, as Figure 3 shown, the steps for determining the abnormal result of the battery temperature are specifically as follows:

[0076] S201: Based on the battery operation monitoring data, screen the temperature change data in a short time period, extract the temperature change values and corresponding time sequences at the monitoring points under continuous time nodes, identify the continuous temperature differences and adjacent time intervals, and obtain the temperature fluctuation interval values;

[0077] First, determine the length of the time window. For example, select every 5 minutes as a time point and extract the temperature values at the corresponding time nodes from the battery operation monitoring data. For example, during a certain period, the temperature changes from 30°C to 32°C, and then at the next time point, the temperature changes from 32°C to 34°C. For the changes in each time period, calculate the temperature change values. For example, the change from 30°C to 32°C is 2°C, and the change from 32°C to 34°C is 2°C. The temperature change values in each continuous time period can be extracted, and the adjacent time intervals can be calculated. For example, the time taken for the temperature to change from 30°C to 32°C is 10 minutes, and the time taken for the temperature to change from 32°C to 34°C is 8 minutes. By screening and extracting all the temperature change values and the corresponding time sequences, the temperature fluctuations in some continuous time periods can be finally identified, and the temperature fluctuation interval values can be obtained. The temperature changes by 2°C in 10 minutes and 2°C in 8 minutes. The data constitutes the basic data for monitoring the temperature fluctuation of the battery and provides a basis for further temperature analysis.

[0078] S202: Call the temperature fluctuation interval values, and according to the battery cell temperature threshold and the module temperature change rate, compare the ratio of the continuous temperature difference to the adjacent time interval to determine whether there are data segments higher than the battery cell temperature threshold and higher than the module temperature change rate, and obtain the temperature fluctuation deviation degree;

[0079] Compare the temperature fluctuation range value with the temperature threshold of the battery cell and the temperature change rate of the module. The temperature threshold of the battery cell will be set to a standard value, such as 40 °C, and the temperature change rate of the module will be set to change by 0.5 °C per minute. Suppose that within a certain period of time, the temperature monitored by the battery rises from 30 °C to 32 °C, and the change occurs within 10 minutes, that is, the temperature change is 2 °C, and the change rate is 0.2 °C / minute. For the current temperature change rate, it is lower than the set threshold of 0.5 °C / minute, so further abnormal temperature monitoring will not be triggered. If within another period of time, the battery temperature changes from 30 °C to 35 °C in 5 minutes, at this time the change rate is 1 °C / minute, exceeding the threshold of 0.5 °C / minute, and the temperature fluctuation difference is 5 °C. At this time, it will be judged that the temperature fluctuation deviation is relatively large and enters the abnormal fluctuation state. By calculating the ratio of the continuous temperature difference to the adjacent time interval and comparing whether the temperature change exceeds the set threshold, data segments with large temperature fluctuations can be effectively identified, and then whether there is a temperature abnormality can be judged.

[0080] S203: According to the temperature fluctuation deviation, match the abnormal temperature monitoring threshold, screen the temperature fluctuation data segments higher than the threshold, sort out the corresponding time nodes and temperature change values, and obtain the battery temperature abnormality determination result;

[0081] Through the temperature fluctuation deviation, compare with the preset abnormal temperature monitoring threshold for screening. Suppose the abnormal temperature fluctuation threshold is 4 °C. When the temperature change within a certain period of time exceeds 4 °C, for example, within a certain period of time, the battery temperature rises from 30 °C to 36 °C, and the temperature change is 6 °C. At this time, the abnormal temperature fluctuation determination is triggered, and all data segments higher than this threshold are further screened out, and then the corresponding time nodes and temperature change values are sorted out. For example, suppose the temperature changes from 30 °C to 36 °C in 6 minutes, changing 1 °C per minute, the data will be marked as an abnormal temperature data segment, and the time node is the interval from T1 to T6. The fluctuation data segments higher than the threshold can help technicians accurately diagnose whether the battery has abnormal conditions such as overheating or overcooling, so as to carry out further prevention and treatment.

[0082] Specifically, as Figure 4 shown, the steps of the new energy vehicle battery security transmission key are specifically as follows:

[0083] S301: Based on the battery temperature abnormality determination result and the battery operation monitoring data, extract the data transmission node information, screen the data flow situation between nodes, calculate the data interaction frequency of the nodes, and obtain the data transmission node distribution;

[0084] The data interaction frequency of the nodes adopts the formula:

[0085]

[0086] Among them, F node represents the data interaction frequency of the node, d i,j represents the data transmission volume between node i and node j, represents the average data transmission volume of node j, n represents the total number of nodes, T i represents the transmission duration of node i;

[0087] Parameter quantization and numerical acquisition process:

[0088] d i,j (Data transmission volume between nodes): The data transmission volume is obtained by monitoring the communication traffic between nodes. The data traffic transmission records are monitored in real time by network devices (such as routers, switches, etc.), and the data transmission volume of each node per unit time is calculated. For example, the transmission volume between a certain node i and node j is 500MB;

[0089] (Average data transmission volume of the node): By monitoring all data transmission records of node j for a period of time, the average transmission volume of node j is calculated. Assuming that node j has transmitted a total of 1000MB of data in the past 30 minutes, its average transmission volume is:

[0090] n (Total number of nodes participating in data exchange): Obtained through the network topology structure diagram or data monitoring tool. For example, if there are 10 nodes in the system for data transmission, then n = 10;

[0091] T i (Node transmission duration): The transmission duration of each node is obtained through real-time monitoring. During the transmission process, the network device records the time of each node from the start of sending data to the end of receiving. For example, the transmission duration of node i is 45 seconds;

[0092] Assume that during the data transmission process, the actual transmission volume between node i and node j is 500MB, the average transmission volume of node j is 33.33MB / minute, the total number of nodes is 10, and the transmission duration of node i is 45 seconds. Now, calculate the data transmission node distribution according to the formula;

[0093] Calculate the square of the transmission volume deviation between each node:

[0094] Calculate the square of the deviation:

[0095] Calculate the weighted square root of the transmission duration:

[0096] Calculate the weighted square root:

[0097] Substitute the calculation result into the formula:

[0098] The calculated F node = 3266.16 represents the data interaction frequency of the node. This value can be used to analyze the data flow frequency and volume between nodes. A higher value indicates frequent data interaction between nodes and active data flow, while a lower value indicates sparse data interaction between nodes.

[0099] S302: Invoke the distribution of data transmission nodes. According to the quantum key distribution mechanism, randomly generate a key sequence, extract the distribution characteristics of the data transmission nodes, and allocate the keys corresponding to the data interaction relationship between nodes to obtain the node-allocated keys;

[0100] First, according to the distribution information of the data transmission nodes, randomly generate a set of key sequences according to the quantum key distribution (QKD) mechanism. For example, if there are three data transmission nodes A, B, and C, and a secure channel has been established through the quantum key distribution mechanism for the communication link, the keys can be allocated according to the data interaction relationship between the nodes. Assume that there is a transmission link between node A and node B, and there is also a link between node B and node C, and there is no direct link between node A and node C. The generated keys will be allocated according to the relationships between the nodes for different links to ensure that there is a corresponding encryption key for data transmission on each link. For example, the generated key sequence is K1, K2, K3, where K1 is allocated to the link between A and B, and K2 is allocated to the link between B and C. The key allocation for each link ensures the security of the transmitted data, avoids potential man-in-the-middle attacks, and through the allocation mechanism, secure data transmission between nodes can be achieved, and it is ensured that each node can only access its corresponding key to obtain the node-allocated keys for subsequent encrypted data transmission.

[0101] S303: Invoke the node-allocated keys, combine with the encryption management configuration, screen the key data that meets the storage requirements, sort out the key storage path and access permissions, and store the keys in the encryption management configuration to obtain the secure transmission key for the new energy vehicle battery;

[0102] The keys allocated according to the nodes are combined with the encryption management configuration to filter the key data that meets the storage requirements. The encryption management configuration defines the storage method, path, and access rights of the keys. For example, check whether each key meets the storage requirements (such as length, complexity, etc.), and store the qualified key data in a preset path. Suppose the generated key K1 is applicable to the link between nodes A and B, then this key will be stored under an encryption management path configured specifically for this link, and access rights are set for the storage of the key to ensure that only authorized users or nodes can access or modify the key. For example, the administrator of node A has the right to access K1, while the administrator of node C cannot access it. The qualified key data will be organized and sorted, and finally, the keys will be stored in the encryption management configuration system according to the set storage path and access rights, which can ensure the secure storage and management of the keys, ensure the secure data transmission of new energy vehicle batteries, avoid the risk of key leakage or unauthorized access, and obtain the secure transmission keys of new energy vehicle batteries.

[0103] Specifically, as Figure 5 shown, the steps of remotely encrypting and transmitting the monitoring data of the battery are specifically as follows:

[0104] S401: Based on the secure transmission key of the new energy vehicle battery, extract the battery operation data, process the data according to the key encryption rules, allocate encryption parameters to the data blocks, adjust the encryption key sequence, and obtain the encrypted data stream;

[0105] First, extract the operation data of the new energy vehicle battery, including data such as battery voltage, current, and temperature. The data is stored in the vehicle's battery management system (BMS). Suppose the current battery voltage is 36V, the current is 50A, and the temperature is 32°C. The data will be extracted as part of the battery operation data. According to the preset key encryption rules, encrypt the extracted battery operation data. The encryption rules can include the allocation of data blocks. The data blocks are grouped according to the type and size of the data. Suppose a data block contains the battery voltage and current data. Each data block will be assigned an encryption parameter, such as an encryption algorithm (such as AES), an encryption key, etc. According to the actual security requirements and encryption policies, adjust the encryption key sequence. For example, according to the difference in each data transmission, use different keys to encrypt the data to prevent security risks caused by key leakage. The operation data of the battery is encrypted into an encrypted data stream, where each data block contains the corresponding key and encryption parameter to ensure the security during data transmission.

[0106] S402: Call the encrypted data stream, exchange the key information during the data transmission according to the quantum key distribution mechanism, match the remote cloud identity authentication rules, perform permission verification, filter the encrypted data that meets the transmission security standards, and obtain the verified encrypted data;

[0107] Exchange the key information in the encrypted data stream through the Quantum Key Distribution (QKD) mechanism. Quantum key distribution utilizes the properties of quantum physics to ensure the security of the key during transmission. For example, using quantum entanglement technology, the encryption key can be securely exchanged. Even if the data is intercepted during transmission, the key information cannot be cracked. Verify the identities of both parties in the data transmission through the identity authentication rules of the remote cloud, ensuring that only authenticated devices or users can access the encrypted data. The methods of identity verification can include digital certificates, fingerprint recognition, or biometric methods to ensure that all operations are authorized. Perform permission verification to ensure that each entity requesting access to the data can only access the data within its permission scope. For example, the cloud only allows administrators or devices with specific permissions to read the operation data of the battery. By screening the encrypted data that meets the transmission security standards, determine which data has not been tampered with or threatened during transmission. After all verifications and screenings, finally obtain the verified encrypted data. The data undergoes complete identity authentication and permission verification to ensure its security.

[0108] S403: Invoke the verified encrypted data, match the corresponding key according to the quantum key distribution at the receiving end, parse the encryption parameters, perform the decryption operation, calculate the decryption value of the battery operation data, integrate the parsed decrypted data blocks, and output the monitored data of the battery's remote encrypted transmission;

[0109] The decryption value of the battery operation data uses the formula:

[0110]

[0111] where D represents the decryption value of the battery operation data, C k represents the k-th block of the encrypted transmission data, represents the k-th block of data after decryption at the receiving end, α k represents the weight coefficient of the k-th block of data, and M represents the total number of encrypted data blocks;

[0112] In this formula, D represents the decryption value of the battery operation data, which is calculated based on the difference between the encrypted transmission data and the decrypted data, and weighted average processing is performed on it. The weight coefficient α k of each data block is related to the characteristics of the data block, such as the importance of the data, the reliability of the receiving end, etc.;

[0113] C k : The k-th block of the encrypted transmission data, which is generated by encryption through quantum key distribution. It is obtained and processed through the quantum communication network during data collection;

[0114] The k-th block of data after decryption at the receiving end. After the receiving end decrypts it using the quantum key, this data is obtained through quantum communication decoding;

[0115] α k : The weight coefficient of the k-th data block, which is calculated based on the quality assessment criteria of the received data. The weight coefficient depends on factors such as data integrity, accuracy, and communication signal strength. Assume that when the signal strength of the data block is relatively high, the weight coefficient α k takes a higher value, indicating that the data block has a greater impact on the decryption result;

[0116] Assume the number of data blocks M = 5;

[0117] C1 = 100, C2 = 105, C3 = 98, C4 = 101, C5 = 99 represent the values of five encrypted data blocks;

[0118] represents the value of the data block after decryption;

[0119] Assume the weight coefficients α1 = 0.8, α2 = 1.0, α3 = 0.9, α4 = 0.7, α5 = 1.2, representing the quality assessment values of different data blocks;

[0120] Calculate the absolute value of the difference of each data block multiplied by the weight coefficient:

[0121]

[0122]

[0123] Calculate the square of each difference: 1.6 2 = 2.56, 1.0 2 = 1.00, 0.9 2 = 0.81, 0.7 2 = 0.49, 1.2 2 = 1.44;

[0124] Calculate the sum of the weighted squared differences:

[0125] Calculate the sum of the weight coefficients:

[0126] Divide the sum of the weighted squared differences by the sum of the weight coefficients: 5.30 / 4.6 = 1.15;

[0127] Finally, take the square root to obtain the decryption value D:

[0128] This result indicates that the decryption value D of the battery operation data is 1.07, representing the error metric after the encrypted data is restored. By comparing with the encrypted transmission data blocks of the received data, this value helps to quantify the accuracy of the decryption process and the quality of data restoration.

[0129] Specifically, as Figure 6 shown, the steps for adjusting the dynamic thermal management of new energy vehicle batteries are specifically as follows:

[0130] S501: Based on the battery remote encrypted transmission monitoring data, extract the current battery operating status, call the cooling fan speed, liquid cooling flow rate, and charging current adjustment parameters, identify the change trend of the parameters in the time series, and obtain the battery operating status parameters;

[0131] Extract parameters such as the current temperature, voltage, current, and health status of the battery through the real-time monitoring data of encrypted transmission. These parameters will serve as the basic data for the battery operating status. Through the set algorithm, analyze the temperature changes, charging status, and current fluctuations faced by the battery during actual operation. The specific analysis methods include: Based on the battery voltage and current, combined with the temperature change trend, compare with the previous data, and use the time series method to judge whether the overall health status of the battery matches the current operating environment. For example, in some cases, when the battery voltage reaches a certain value, such as 4.2V, the temperature rises rapidly to 70°C, indicating that the battery has an overheating problem during high-load operation. At this time, it is necessary to further identify whether there are abnormalities in the continuous operation of the battery and predict the stable operation mode of the battery by comparing historical data. The extracted battery operation data will also be combined with parameters such as the current cooling fan speed, liquid cooling flow rate, and charging current to form a holographic monitoring model of the battery, and then can provide the real-time battery working status. By analyzing the relationship between the efficiency, flow rate of the cooling system and the battery temperature, further accurately adjust the parameters to optimize the battery status and ensure that the battery operates within a safe temperature range to obtain the battery operating status parameters.

[0132] S502: Call the battery operating status parameters, compare the hot and cold modes under different operating states, screen the modes with high matching degrees, compare the temperature control thresholds under the modes, screen the hot and cold modes that match the current operating state, and obtain the hot and cold mode matching results;

[0133] Compare the currently extracted battery operating state parameters with multiple preset cooling and heating modes. Each mode defines specific adjustment parameters such as temperature control ranges, fan speeds, liquid cooling flow rates, and charging currents. The parameters are divided into multiple cooling modes according to the battery's operating state. For example, when the battery's working environment is in a high-temperature state (such as exceeding 75°C), the cooling mode selection will tend to be a liquid cooling mode with high wind speed and high flow rate. When the battery operating state is relatively stable (such as the temperature is between 50°C and 60°C), a cooling mode with a lower wind speed and lower flow rate will be selected. Based on the current battery temperature, current, and battery load state, select the matching mode. At this time, the temperature control threshold of the current battery will be calculated in real time (for example, when the battery temperature reaches 75°C, the flow rate of the cooling configuration must reach 30 L / min and the wind speed is not lower than 2500 RPM). The parameters are obtained by gradually comparing multiple cooling and heating modes and evaluating their matching degrees, and finally the matching result of the cooling and heating modes is obtained.

[0134] S503: Invoke the matching result of the cooling and heating modes, adjust the cooling fan speed, liquid cooling flow rate, and charging current adjustment parameters, match the target temperature control range, adjust the battery thermal management parameters, perform dynamic adjustment, and output the dynamic thermal management adjustment of the new energy vehicle battery;

[0135] First, accurately calculate the adjustment of the cooling fan speed, liquid cooling flow rate, and charging current according to the target temperature control range required by the matched cooling mode. For example, when the high-load, high-temperature cooling mode is matched, automatically increase the fan speed to above 3000 RPM and the liquid cooling flow rate to 35 L / min to quickly reduce the battery temperature. In the case of low load and low temperature, the fan speed will be adjusted to 1500 RPM and the liquid cooling flow rate will be reduced to 20 L / min to reduce energy consumption. According to the charging current of the battery, corresponding adjustments will also be made according to the cooling mode to ensure that the temperature control range of the battery during charging always remains within the safe threshold, avoiding battery performance degradation or safety hazards caused by overcharging or overheating. Real-time monitor the working state of the battery through sensors and dynamically adjust the above parameters, and finally output the dynamic thermal management adjustment of the new energy vehicle battery suitable for the current working environment.

[0136] As Figure 7 shown, the remote monitoring system for new energy vehicle batteries based on cloud computing, the system includes:

[0137] The data monitoring module extracts the temperature data of the in-vehicle temperature sensor monitoring points based on the new energy vehicle battery data, records the BMS current and voltage information, stores them in the cloud database in chronological order, and obtains the battery operation monitoring data;

[0138] The temperature anomaly detection module filters temperature fluctuation data based on the battery operation monitoring data, compares the single-cell battery temperature threshold and the module temperature change rate, matches the abnormal temperature monitoring threshold, and obtains the battery temperature anomaly determination result;

[0139] Based on the battery temperature anomaly determination result, the security key distribution module conducts remote battery monitoring, extracts in-vehicle communication network nodes, distributes new energy vehicle remote transmission keys, compares the data transmission node information, extracts the key management configuration and stores it, and obtains the new energy vehicle battery secure transmission key;

[0140] Based on the new energy vehicle battery secure transmission key, the data encryption transmission module performs key exchange on the quantum key distribution mechanism, encrypts the battery operation monitoring data, performs permission verification through remote cloud identity authentication, and decrypts the battery remote encrypted transmission monitoring data;

[0141] Based on the battery remote encrypted transmission monitoring data, the thermal management adjustment module extracts the battery cooling control unit, compares the cooling fan speed, liquid cooling flow rate, and charging current adjustment parameters, filters and matches the mode, and performs dynamic thermal management adjustment of the new energy vehicle battery.

[0142] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A remote monitoring method for new energy vehicle batteries based on cloud computing, characterized in that, It includes the following steps: S1: Based on the new energy vehicle battery data, extract the temperature data of multiple monitoring points through the in-vehicle temperature sensor, record the current and voltage information of the BMS, arrange the data in chronological order and store it in the cloud database to obtain the battery operation monitoring data; S2: Based on the battery operation monitoring data, screen the temperature fluctuation data within a short period of time, compare it according to the battery cell temperature threshold and the module temperature change rate, judge whether there is a situation exceeding the fluctuation range, pass the abnormal temperature monitoring threshold and perform matching to obtain the battery temperature anomaly determination result; S3: Combine the battery temperature anomaly determination result and the battery operation monitoring data, obtain a random key through the quantum key distribution mechanism, allocate the key according to the data transmission node, store the key in the encryption management configuration, and obtain the new energy vehicle battery secure transmission key; S4: Based on the new energy vehicle battery secure transmission key, encrypt the data, perform key exchange according to the quantum key distribution mechanism, verify the encrypted data, and decrypt it through the receiving end quantum key distribution to output the battery remote encrypted transmission monitoring data.

2. The method for remotely monitoring a new energy vehicle battery based on cloud computing according to claim 1, wherein The battery operation monitoring data includes battery cell temperature data, module voltage data, charge and discharge current data, ambient temperature data, and time series records. The battery temperature anomaly determination result includes temperature overlimit status, temperature fluctuation anomaly status, and abnormal temperature distribution characteristics. The new energy vehicle battery secure transmission key includes a random key, node key mapping, and encryption configuration parameters. The battery remote encrypted transmission monitoring data includes encrypted data packets, key verification information, and remote authentication results.

3. The method for remotely monitoring the battery of a new energy vehicle based on cloud computing according to claim 1, wherein The steps of the battery operation monitoring data are specifically as follows: S101: Based on the new energy vehicle battery data, call the in-vehicle temperature sensor to monitor the temperature parameters at multiple positions, record the temperature values of each monitoring point, detect the current and voltage data of the BMS, arrange each item of data in chronological order, and synchronize the ambient temperature information to obtain the battery basic operation data; S102: Based on the battery basic operation data, screen the temperature, current, and voltage data at each moment, extract the charge and discharge states within different time periods, correlate the ambient temperature with the temperature values of the monitoring points in time series, and calculate the temperature deviation value and the change rates of current and voltage to obtain the battery operation state parameters; S103: Call the battery operation state parameters, match the operation parameters in different temperature ranges according to the current and voltage fluctuations under the charge and discharge states, and organize the battery operation conditions at the time points to obtain the battery operation monitoring data.

4. The remote monitoring method for the new energy vehicle battery based on cloud computing according to claim 1, characterized in that The steps of the battery temperature anomaly determination result are specifically as follows: S201: Based on the battery operation monitoring data, screen the temperature change data within a short period of time, extract the temperature change values and corresponding time sequences of the monitoring points at continuous time nodes, identify the continuous temperature difference and the adjacent time intervals to obtain the temperature fluctuation interval value; S202: Call the temperature fluctuation range value, compare the ratio of the continuous temperature difference to the adjacent time interval according to the battery cell temperature threshold and the module temperature change rate, determine whether there is a data segment higher than the battery cell temperature threshold and higher than the module temperature change rate, and obtain the temperature fluctuation deviation degree; S203: Based on the temperature fluctuation deviation degree, match the abnormal temperature monitoring threshold, screen the temperature fluctuation data segments higher than the threshold, sort out the corresponding time nodes and temperature change values, and obtain the battery temperature anomaly determination result.

5. The remote monitoring method for the new energy vehicle battery based on cloud computing according to claim 1, characterized in that, The steps of the new energy vehicle battery security transmission key are specifically as follows: S301: Based on the battery temperature anomaly determination result and the battery operation monitoring data, extract the data transmission node information, screen the data flow between nodes, calculate the data interaction frequency of the nodes, and obtain the data transmission node distribution; The data interaction frequency of the nodes adopts the formula: Among them, F node represents the data interaction frequency of the node, d i,j represents the data transmission volume between node i and node j, represents the average data transmission volume of node j, n represents the total number of nodes, T i represents the transmission duration of node i; S302: Call the data transmission node distribution, randomly generate a key sequence according to the quantum key distribution mechanism, extract the distribution characteristics of the data transmission nodes, and allocate the keys according to the data interaction relationship between the nodes to obtain the node allocated keys; S303: Call the node allocated keys, combine the encryption management configuration, screen the key data that meets the storage requirements, sort out the key storage path and access permissions, and store the keys in the encryption management configuration to obtain the new energy vehicle battery security transmission key.

6. The remote monitoring method for new energy vehicle batteries based on cloud computing according to claim 1, wherein, The steps of the battery remote encrypted transmission monitoring data are specifically as follows: S401: Based on the new energy vehicle battery security transmission key, extract the battery operation data, process the data according to the key encryption rule, allocate the encryption parameters to the data blocks, and adjust the encryption key sequence to obtain the encrypted data stream; S402: Call the encrypted data stream, exchange the key information during the data transmission process according to the quantum key distribution mechanism, match the remote cloud identity authentication rule, perform the permission verification, and screen the encrypted data that meets the transmission security standard to obtain the verified encrypted data; S403: Call the verified encrypted data, match the corresponding key according to the quantum key distribution at the receiving end, analyze the encryption parameters, perform the decryption operation, calculate the decryption value of the battery operation data, sort out the decrypted data blocks, and output the battery remote encrypted transmission monitoring data.

7. The method for remotely monitoring a new energy vehicle battery based on cloud computing according to claim 6, wherein, The decryption value of the battery operation data adopts the formula: Among them, D represents the decryption value of battery operation data, and C k represents the k-th block of encrypted transmission data, represents the k-th block of data after decryption at the receiving end, and α k represents the weight coefficient of the k-th block of data, and M represents the total number of encrypted data blocks.

8. The method for remotely monitoring a new energy vehicle battery based on cloud computing according to claim 1, wherein The method further includes step S5: S5: Based on the battery remote encrypted transmission data, compare the cooling fan speed, liquid cooling flow rate, and charging current adjustment parameters according to the current operation state of the battery, determine the matching cooling and heating mode, and perform the new energy vehicle battery dynamic thermal management adjustment; The new energy vehicle battery dynamic thermal management adjustment includes fan speed adjustment, liquid cooling flow control, and charging current adjustment.

9. The method for remotely monitoring a new energy vehicle battery based on cloud computing according to claim 8, wherein, The steps of the new energy vehicle battery dynamic thermal management adjustment are specifically as follows: S501: Based on the battery remote encrypted transmission monitoring data, extract the current battery operation state, call the cooling fan speed, liquid cooling flow rate, and charging current adjustment parameters, identify the change trend of the parameters in the time series, and obtain the battery operation state parameters; S502: Invoke the battery operating state parameters, compare the hot and cold modes under different operating states, screen the modes with high matching degrees, compare the temperature control thresholds under the modes, screen the hot and cold modes that match the current operating state, and obtain the hot and cold mode matching results; S503: Invoke the hot and cold mode matching results, adjust the cooling fan speed, liquid cooling flow rate, and charging current adjustment parameters, match the target temperature control range, adjust the battery thermal management parameters, perform dynamic adjustment, and output the dynamic thermal management adjustment of the new energy vehicle battery.

10. A remote monitoring system for new energy vehicle batteries based on cloud computing, characterized in that, The remote monitoring method for the new energy vehicle battery based on cloud computing according to any one of claims 1-9, the system includes: The data monitoring module extracts the temperature data of the in-vehicle temperature sensor monitoring points based on the new energy vehicle battery data, records the BMS current and voltage information, stores them in the cloud database in chronological order, and obtains the battery operation monitoring data; The temperature anomaly detection module filters the temperature fluctuation data based on the battery operation monitoring data, compares the battery cell temperature threshold and the module temperature change rate, matches the abnormal temperature monitoring threshold, and obtains the battery temperature anomaly determination result; The security key distribution module performs remote battery monitoring based on the battery temperature anomaly determination result, extracts the in-vehicle communication network nodes, distributes the remote transmission key for the new energy vehicle, compares the data transmission node information, extracts the key management configuration and stores it, and obtains the secure transmission key for the new energy vehicle battery; The data encryption transmission module performs key exchange on the quantum key distribution mechanism based on the secure transmission key for the new energy vehicle battery, encrypts the battery operation monitoring data, performs permission verification through remote cloud identity authentication, and decrypts the remotely encrypted transmission monitoring data of the battery; The thermal management adjustment module extracts the battery cooling control unit based on the remotely encrypted transmission monitoring data of the battery, compares the cooling fan speed, liquid cooling flow rate, and charging current adjustment parameters, filters the matching mode, and performs the dynamic thermal management adjustment of the new energy vehicle battery.

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