Truck lithium battery test method and device based on Bluetooth communication

Abstract

The invention relates to the technical field of battery testing, and discloses a truck lithium battery testing method and device based on Bluetooth communication. The method comprises the following steps: performing intelligent remote matching processing on Bluetooth identification information of a truck lithium battery management system to obtain a remote Bluetooth control key, and performing lithium battery data acquisition protocol test processing to obtain a dual-channel acquisition instruction set; performing dynamic voltage, current and temperature cooperative acquisition processing on the dual-channel acquisition instruction set to obtain a multi-dimensional battery working condition characteristic spectrum; remote parameter linkage calibration processing is carried out on the multi-dimensional battery working condition characteristic spectrum, an intelligent calibration control sequence is obtained, remote charging and discharging management test processing is carried out, and a battery running state monitoring record is obtained; and performing remote health assessment processing on the battery running state monitoring record to obtain a lithium battery performance test report. According to the lithium battery testing scheme, physical connection is not needed, operation is easy and convenient, and the real-time monitoring capability is achieved, so that the testing efficiency and flexibility are improved.

Description

Technical Field

[0001] The present application relates to the field of battery testing, and in particular to a method and device for testing a truck lithium battery based on Bluetooth communication. Background Art

[0002] With the rapid development of the new energy vehicle industry, trucks, as an important means of logistics and transportation, are becoming increasingly electrified. As the core component of electric trucks, the stability and safety of lithium batteries are directly related to the operating efficiency and safety of trucks. Traditional lithium battery system testing methods mainly rely on dedicated test instruments, such as battery testers or multimeters, which are directly connected to the battery through physical connections (such as clips or wiring) to obtain data such as voltage, current and temperature. After the test is completed, the data needs to be manually recorded to form a test record.

[0003] However, the existing lithium battery testing technology has the following disadvantages: the data acquisition method is limited. Due to the limitation of physical connection, users can only perform tests at specific locations, and the flexibility of the test is greatly limited; the operation interface is complex, and data is usually read through a complex instrument interface. The operation process is complicated, and users need to have certain professional knowledge and skills, which may lead to errors in data interpretation; real-time and convenience are poor, and data acquisition and analysis often require manual operation. If multiple batteries need to be monitored, the process will be more cumbersome, reducing the test efficiency; function expansion is difficult, and generally the functions are relatively fixed. If new functions need to be added, it is often necessary to replace or upgrade the hardware, which is costly and time-consuming. Summary of the invention

[0004] The present application provides a truck lithium battery testing method and device based on Bluetooth communication, which is used to provide a lithium battery testing solution that does not require physical connection, is easy to operate, and has real-time monitoring capabilities, so as to improve testing efficiency and flexibility.

[0005] In a first aspect, the present application provides a truck lithium battery testing method based on Bluetooth communication, the truck lithium battery testing method based on Bluetooth communication comprising: performing intelligent long-distance matching processing on the Bluetooth identification information of the truck lithium battery management system to obtain a remote Bluetooth control key; performing lithium battery data acquisition protocol test processing on the remote Bluetooth control key to obtain a dual-channel acquisition instruction set; performing dynamic voltage, current and temperature collaborative acquisition processing on the dual-channel acquisition instruction set to obtain a multi-dimensional battery operating condition characteristic spectrum; performing remote parameter linkage calibration processing on the multi-dimensional battery operating condition characteristic spectrum to obtain an intelligent calibration control sequence;

[0006] The intelligent calibration control sequence is remotely charged and discharged for test processing to obtain the battery operation status monitoring record; the battery operation status monitoring record is remotely health evaluated to obtain the lithium battery performance test report.

[0007] In a second aspect, the present application provides a truck lithium battery testing device based on Bluetooth communication, and the truck lithium battery testing device based on Bluetooth communication includes:

[0008] The matching module is used to perform intelligent long-distance matching processing on the Bluetooth identification information of the truck lithium battery management system to obtain the remote Bluetooth control key;

[0009] The test module is used to perform lithium battery data acquisition protocol test processing on the remote Bluetooth control key to obtain a dual-channel acquisition instruction set;

[0010] An acquisition module is used to perform dynamic voltage, current and temperature collaborative acquisition processing on a dual-channel acquisition instruction set to obtain a multi-dimensional battery operating condition characteristic spectrum;

[0011] A calibration module is used to perform remote parameter linkage calibration processing on the multi-dimensional battery operating condition characteristic spectrum to obtain an intelligent calibration control sequence;

[0012] The test module is used to perform remote charge and discharge management test processing on the intelligent calibration control sequence to obtain the battery operation status monitoring record;

[0013] The evaluation module is used to perform remote health evaluation on the battery operation status monitoring records to obtain a lithium battery performance test report.

[0014] In the technical solution provided by the present application, a safe and reliable Bluetooth communication link is established through intelligent long-distance matching processing, which effectively avoids wireless signal interference and illegal device access, and improves the security and stability of communication. The lithium battery data acquisition protocol test processing adopts a dual-channel structure design to realize the separate transmission of control instructions and data acquisition, and enhances the real-time and reliability of data transmission. The dynamic voltage, current and temperature collaborative acquisition processing realizes the synchronous acquisition and correlation analysis of multiple parameters, accurately reflects the operating status of the battery under different working conditions, and provides comprehensive data support for battery performance evaluation. The remote parameter linkage calibration processing establishes a correlation mechanism between parameters, improves the accuracy of the measurement data through real-time calibration and compensation, and reduces the interference of environmental factors. The remote charge and discharge management test processing realizes the precise control and real-time monitoring of the charge and discharge process, timely discovers abnormal conditions, and effectively prevents safety hazards such as overcharging and over-discharging. The remote health assessment processing accurately evaluates the health status and remaining life of the battery through multi-dimensional data analysis and intelligent algorithms, providing a scientific basis for maintenance decisions. The overall test solution reduces manual intervention, improves test efficiency, and reduces test costs. At the same time, through standardized test processes and data processing methods, the repeatability and comparability of test results are guaranteed. This solution is particularly suitable for battery management of large-scale electric truck fleets, realizing remote monitoring and predictive maintenance of battery performance, and providing technical support for the safe operation of electric trucks. Through efficient data collection and analysis and processing, the solution can timely detect battery performance degradation trends, and warn of potential failures in advance, effectively extending battery life and reducing operation and maintenance costs. Most importantly, the solution realizes intelligent and automated battery testing, greatly reducing manual operation errors and improving test accuracy and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0016] Figure 1 This is a schematic diagram of an embodiment of a truck lithium battery testing method based on Bluetooth communication in an embodiment of the present application;

[0017] Figure 2 This is a schematic diagram of an embodiment of a truck lithium battery testing device based on Bluetooth communication in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The embodiments of the present application provide a method and device for testing truck lithium batteries based on Bluetooth communication. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0019] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of a truck lithium battery testing method based on Bluetooth communication includes:

[0020] Step S101, performing intelligent long-distance matching processing on the Bluetooth identification information of the truck lithium battery management system to obtain a remote Bluetooth control key;

[0021] Step S102, performing lithium battery data acquisition protocol test processing on the remote Bluetooth control key to obtain a dual-channel acquisition instruction set;

[0022] Step S103, performing dynamic voltage, current and temperature collaborative acquisition processing on the dual-channel acquisition instruction set to obtain a multi-dimensional battery operating condition characteristic spectrum;

[0023] Step S104, performing remote parameter linkage calibration processing on the multi-dimensional battery operating condition characteristic spectrum to obtain an intelligent calibration control sequence;

[0024] Step S105, performing remote charge and discharge management test processing on the intelligent calibration control sequence to obtain a battery operation status monitoring record;

[0025] Step S106: remotely perform health assessment on the battery operation status monitoring record to obtain a lithium battery performance test report.

[0026] It is understandable that the execution subject of the present application can be a truck lithium battery testing device based on Bluetooth communication, or a terminal or a server, which is not limited here. The present application embodiment is described by taking the server as the execution subject as an example.

[0027] Specifically, a communication link is established by matching Bluetooth identification information, which contains key data such as device MAC address, signal strength, and device type identification. During intelligent long-distance matching processing, the mobile terminal scans the surrounding Bluetooth devices, screens devices with signal strength higher than -80dBm, and encrypts the MAC address with RSA to generate a remote Bluetooth control key. In the lithium battery data acquisition protocol test, a two-way communication channel is established based on the generated control key, and channel parameter tests are performed. Channel parameters include indicators such as bandwidth, delay, and packet loss rate. The data acquisition protocol defines the data frame format, which includes a frame header, control code, data segment, and checksum. The accuracy of data transmission is ensured by CRC check, and the acquisition instructions are sorted by priority to form a dual-channel acquisition instruction set. In the dynamic voltage, current, and temperature collaborative acquisition stage, multi-parameter synchronous sampling is performed according to the acquisition instruction set. The voltage sampling accuracy is 0.1mV and the sampling frequency is 10Hz; the current sampling accuracy is 0.1A and the sampling frequency is 10Hz; the temperature sampling accuracy is 0.1℃ and the sampling frequency is 1Hz. The collected raw data is de-noised, the spectrum characteristics are analyzed by Fourier transform, the operating condition characteristics are extracted, and a multi-dimensional battery operating condition characteristic spectrum is generated. In the remote parameter linkage calibration process, the parameters in the operating condition characteristic spectrum are calibrated and corrected. The measurement error is corrected by the Kalman filter algorithm, and the error correction coefficient range is 0.95-1.05. Linkage constraints are established for parameters such as voltage, current, and temperature, and a calibration rule base is generated to form an intelligent calibration control sequence.

[0028] In the remote charge and discharge management test, the charge and discharge process is controlled and monitored based on the calibration control sequence. The charging process controls the current within the range of 0.2C-0.5C, monitors the voltage balance state of the single cell, and controls the temperature distribution deviation within 3°C. The discharge process records the capacity decay data, analyzes the consistency of the single cell voltage, and records the BMS operation status data. In the remote health assessment stage, the battery historical operation data is learned through the neural network algorithm to extract health features. The SOC estimation uses a particle filter algorithm to predict the remaining battery life. The battery health level is divided through fuzzy reasoning, the cycle performance and temperature sensitivity are evaluated, and a performance test report is generated.

[0029] For example, a truck equipped with a 100kWh lithium battery pack scans the Bluetooth device of the battery management system through a mobile terminal APP, and the signal strength is -65dBm. The system generates a 16-bit RSA encryption key and establishes a communication link. The data acquisition protocol configures a sampling period of 100ms and a transmission success rate of 99.9%. During the acquisition process, the voltage data shows that the highest voltage of the single cell is 3.65V and the lowest voltage is 3.62V; the current data shows that the charging current is 50A, and the temperature data shows that the highest temperature is 35℃ and the lowest temperature is 32℃. After Kalman filter correction, the measurement error is reduced to within 0.1%. In the charge and discharge test, the battery is charged at a current of 0.3C, and the voltage difference of the single cell is monitored to be less than 20mV, and the temperature difference is kept within 2℃. The health assessment results show that the battery SOC is 85%, the SOH is 92%, and the predicted remaining cycle life is 2000 times. It is recommended to optimize the charging strategy and control the charging rate within 0.3C.

[0030] In the embodiment of the present application, a safe and reliable Bluetooth communication link is established through intelligent long-distance matching processing, which effectively avoids wireless signal interference and illegal device access, and improves the security and stability of communication. The lithium battery data acquisition protocol test processing adopts a dual-channel structure design to realize the separate transmission of control instructions and data acquisition, and enhances the real-time and reliability of data transmission. The dynamic voltage, current and temperature collaborative acquisition processing realizes the synchronous acquisition and correlation analysis of multiple parameters, accurately reflects the operating status of the battery under different working conditions, and provides comprehensive data support for battery performance evaluation. The remote parameter linkage calibration processing establishes a correlation mechanism between parameters, improves the accuracy of the measurement data through real-time calibration and compensation, and reduces the interference of environmental factors. The remote charge and discharge management test processing realizes the precise control and real-time monitoring of the charge and discharge process, timely discovers abnormal conditions, and effectively prevents safety hazards such as overcharging and over-discharging. The remote health assessment processing accurately evaluates the health status and remaining life of the battery through multi-dimensional data analysis and intelligent algorithms, providing a scientific basis for maintenance decisions. The overall test solution reduces manual intervention, improves test efficiency, and reduces test costs. At the same time, through standardized test processes and data processing methods, the repeatability and comparability of test results are guaranteed. This solution is particularly suitable for battery management of large-scale electric truck fleets, realizing remote monitoring and predictive maintenance of battery performance, and providing technical support for the safe operation of electric trucks. Through efficient data collection and analysis and processing, the solution can timely detect battery performance degradation trends, and warn of potential failures in advance, effectively extending battery life and reducing operation and maintenance costs. Most importantly, the solution realizes intelligent and automated battery testing, greatly reducing manual operation errors and improving test accuracy and efficiency.

[0031] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0032] (1) Perform Bluetooth scanning on the device identification code of the truck lithium battery management system to obtain a target device list, and perform Bluetooth signal strength detection on the target device list to obtain a signal strength matrix;

[0033] (2) performing device screening processing on the signal strength matrix to obtain a candidate device sequence, and performing identity authentication processing on the candidate device sequence to obtain a device authentication identifier;

[0034] (3) The device authentication identifier is encrypted using the RSA algorithm to generate a key to obtain a temporary session key, and the temporary session key is bidirectionally transmitted to obtain key confirmation information;

[0035] (4) performing consistency verification processing on the key confirmation information to obtain a verification result sequence, and performing security level assessment processing on the verification result sequence to obtain a security level identifier;

[0036] (5) Establish a communication channel for the security level identifier to obtain a remote Bluetooth control key.

[0037] Specifically, the mobile terminal performs active scanning in the Bluetooth communication spectrum of the 2.4GHz frequency band with a scanning cycle of 1.28 seconds. The scanning process adopts a frequency hopping mechanism to poll and send scanning request packets on 79 channels, and the dwell time of each channel is 0.625 milliseconds. After receiving the scanning request, the Bluetooth module of the truck lithium battery management system returns a broadcast packet containing the device identification code. The device identification code consists of a 48-bit MAC address (such as F8: E1: A2: B3: C4: D5) and a 16-bit device type code (such as 0x1234, indicating BMS equipment). After receiving the broadcast packet, the mobile terminal extracts the device identification code, device name, service UUID and other information to form a target device list. For each device in the target device list, a signal strength detection is performed. The received signal strength indicator (RSSI) reflects the attenuation degree of the Bluetooth signal. The measurement adopts a sampling frequency of 10 times / second and continuously collects 30 samples. Each sample records the RSSI value (in dBm) and the corresponding timestamp to construct a signal strength matrix. The number of rows in the matrix corresponds to the number of devices detected, the number of columns is the number of sampling times, and each matrix element contains the RSSI value and time information.

[0038] The device screening phase first calculates the RSSI average and standard deviation of each device in the signal strength matrix. Set the screening threshold: the RSSI average must be greater than -75dBm (to ensure signal quality), the standard deviation must be less than 3dBm (to ensure signal stability), and the link quality index LQI must be greater than 160 (indicating link reliability). Devices that pass the threshold screening form a candidate device sequence. Authentication is then performed. The authentication process includes: reading the device manufacturer ID (16 bits), product serial number (32 bits), firmware version number (8 bits), and matching with the preset authorized device database. After the authentication is passed, a 32-bit device authentication identifier is generated. The identifier is composed of a timestamp (16 bits) and a random sequence (16 bits). The RSA key generation process uses a 2048-bit key length. First, two prime numbers are generated: p (1024 bits) and q (1024 bits), and the product n is calculated as the modulus. Select the public key exponent e (usually 65537) and calculate the private key exponent d. The device authentication identifier is encrypted with the private key to generate a 256-bit temporary session key. During the two-way transmission process, the mobile terminal and the BMS device exchange their own encrypted session key fragments, each of which is 128 bits long. The complete session key is synthesized through XOR operation to generate key confirmation information.

[0039] The consistency check uses the HMAC (Hash Message Authentication Code) algorithm to calculate the SHA-256 hash value of the key confirmation information. The verification content includes: key length verification, timestamp check (error must be less than 100ms), and key fragment integrity verification. A verification result sequence is generated based on the verification results. The security level assessment is based on multiple indicators: key strength (2048 bits is the highest level), authentication method (two-way authentication is the highest level), and encryption algorithm type (asymmetric encryption is the highest level). The assessment results are quantified into a 3-digit security level identifier, such as "A10" for the highest security level. Finally, an encrypted communication channel is established, the Advanced Encryption Standard (AES-256) encryption algorithm is selected, and the remote Bluetooth control key is used to encrypt and transmit data. The channel establishment process includes: negotiating encryption parameters (key length, encryption mode), setting the data transmission rate (default is 1Mbps), and configuring the retransmission mechanism (the maximum number of retransmissions is 3 times). After completion, the remote Bluetooth control key is formally generated.

[0040] For example, in an actual truck lithium battery test, the mobile terminal starts Bluetooth scanning and scans 4 Bluetooth devices. The MAC address of the first device is "F8:E1:A2:B3:C4:D5", the device type code is "0x1234", and the RSSI sampling values ​​of 30 consecutive times are [-68, -67, -69, -68, -67, -68, -69, -68, -67, -68...] dBm, and the average value is -68.2 dBm, the standard deviation is 1.8 dBm, and the LQI value is 175; the average RSSI of the second device is -82.5 dBm, the standard deviation is 5.6 dBm, and the LQI value is 145; the average RSSI of the third device is -71.3 dBm, the standard deviation is 2.1 dBm, and the LQI value is 168; the average RSSI of the fourth device is -77.8 dBm, the standard deviation is 4.2 dBm, and the LQI value is 152. After threshold screening, the first and third devices enter the candidate sequence. Authentication shows that the first device is the target BMS, the manufacturer ID "0xABCD", the serial number "12345678", the firmware version "0x25" matches the authorized database, and the authentication identifier "8A7B6C5D" is generated. The temporary session key "7F6E5D4C3B2A1908" is generated through the RSA algorithm, the two-way transmission verification is successful, the HMAC check is passed, and the security level is rated as "A10". Finally, an AES-256 encrypted channel is established, and a remote Bluetooth control key is generated for subsequent communication.

[0041] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0042] (1) parsing the status code of the remote Bluetooth control key to obtain a key status sequence, and performing link channel allocation processing on the key status sequence to obtain a bidirectional communication channel;

[0043] (2) performing channel parameter testing on the bidirectional communication channel to obtain channel performance indicators, and performing communication protocol matching on the channel performance indicators to obtain protocol adaptation parameters;

[0044] (3) performing data frame format definition processing on the protocol adaptation parameters to obtain a data frame structure template, and performing redundancy check processing on the data frame structure template through a CRC check algorithm to obtain a check rule set;

[0045] (4) performing data packet segmentation processing on the verification rule set to obtain a data packet sequence, and performing transmission timing planning processing on the data packet sequence to obtain a transmission scheduling scheme;

[0046] (5) performing bandwidth resource allocation processing on the transmission scheduling scheme to obtain a resource allocation matrix, and performing channel coding processing on the resource allocation matrix to obtain a coding instruction sequence;

[0047] (6) performing instruction priority sorting processing on the encoded instruction sequence to obtain a priority queue, and performing channel switching test processing on the priority queue to obtain a channel switching parameter;

[0048] (7) performing data acquisition mode configuration processing on the channel switching parameters to obtain an acquisition configuration scheme, and performing sampling frequency adjustment processing on the acquisition configuration scheme to obtain a frequency control sequence;

[0049] (8) performing acquisition time window division processing on the frequency control sequence to obtain a time window sequence, and performing data cache strategy formulation processing on the time window sequence to obtain a cache control parameter;

[0050] (9) performing channel load balancing processing on the cache control parameters to obtain a load distribution plan, and performing data flow control processing on the load distribution plan to obtain a flow control parameter set;

[0051] (10) The flow control parameter set is processed by instruction set generation to obtain a dual-channel acquisition instruction set.

[0052] Specifically, when the status code of the remote Bluetooth control key is parsed, the status identification bits in the key are first extracted, including the key validity bit (8 bits), the encryption type bit (4 bits), the communication priority bit (4 bits) and the check bit (8 bits), to form a 24-bit status code. The status codes are arranged in time sequence to generate a key status sequence, which is used to determine the working status and authority level of the key. Based on the key status sequence, the Bluetooth master-slave bidirectional communication channel is allocated, the master channel is responsible for the control command transmission, and the slave channel is responsible for data collection and return. After the bidirectional communication channel is established, the channel parameter test is performed, including the channel quality test (sending 100 test packets to calculate the packet loss rate), the transmission delay test (measuring the round-trip delay RTT) and the bandwidth test (testing the maximum throughput). The channel performance indicators cover key parameters such as packet loss rate, average delay, and effective bandwidth. According to the test results, the best communication protocol parameters are matched, including the data packet size (adjustable from 20 to 100 bytes), the transmission rate (optional from 1Mbps / 2Mbps) and the number of retransmissions (settable from 1 to 5 times). The data frame format definition adopts a hierarchical structure. The frame header contains a synchronization word (8 bits), an address code (16 bits), and a control word (8 bits). The data segment length is variable (maximum 255 bytes), and the frame tail is a check code (16 bits). The CRC check algorithm uses the CRC-16-CCITT polynomial to perform redundancy check calculations on the data frame and generate check codes and check rules. The data packet segmentation uses a sliding window mechanism to segment long data streams into fixed-size data packets (such as 64 bytes / packet). Each data packet contains a sequence number, data load, and check bits. The transmission timing planning uses time division multiplexing to allocate transmission time slots for different types of data packets.

[0053] Bandwidth resource allocation is based on data priority and timeliness requirements, and an N×M resource allocation matrix is ​​constructed (N is the number of data types, M is the number of time segments). Channel coding uses differential Manchester coding to improve anti-interference capabilities. Instruction priority is determined according to data type: emergency alarm is the highest (priority 3), control command is the second (priority 2), and ordinary data is the lowest (priority 1). The channel switching test verifies the stability of dual-channel switching by sending a test sequence. The data acquisition mode configuration includes three modes: continuous acquisition, triggered acquisition, and timed acquisition, and the sampling frequency range is adjustable from 1Hz to 1kHz. The time window division adopts a combination of fixed window size (such as 100ms) and sliding window (50% overlap). The data cache strategy adopts a double buffering mechanism with a buffer size of 1KB. When the buffer occupancy reaches 80%, data upload is triggered.

[0054] Channel load balancing uses a dynamic weight distribution method to adjust the load ratio of the master and slave channels according to the real-time data flow. Data flow control is based on the token bucket algorithm to control the data transmission rate, and the token generation rate matches the bandwidth limit. The final dual-channel acquisition instruction set includes three categories: channel configuration instructions, data acquisition instructions, and control instructions.

[0055] For example, in the test of lithium battery packs for electric trucks, the remote Bluetooth control key is parsed to obtain the status code "0xAF1234", indicating that the encryption type is AES and the communication priority is high. The channel parameter test shows that the packet loss rate of the main channel is 0.5%, the average delay is 15ms, and the bandwidth is 945kbps; the packet loss rate of the slave channel is 0.8%, the average delay is 18ms, and the bandwidth is 927kbps. The data frame structure is set to: 8-byte frame header + 64-byte data segment + 2-byte CRC check. When the data packet is segmented, the battery voltage data uses a 20ms sampling window, and the temperature data uses a 100ms sampling window. Bandwidth allocation: 60% of the main channel is allocated for control instruction transmission, and 40% of the slave channel is allocated for data acquisition. Sampling frequency settings: 100Hz for voltage and current, 10Hz for temperature. Cache strategy: 512 bytes for the main channel buffer and 1024 bytes for the slave channel buffer. Through the above configuration, the dual-channel acquisition instruction set is successfully established, and stable monitoring of battery parameters is achieved. In the 1-minute test, a total of 60,000 data points were collected, with a data transmission success rate of 99.7% and an average delay of less than 16ms, fully meeting real-time monitoring requirements.

[0056] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0057] (1) performing channel parameter parsing processing on the dual-channel acquisition instruction set to obtain an acquisition configuration sequence, and performing data sampling timing processing on the acquisition configuration sequence to obtain a sampling trigger identifier;

[0058] (2) performing voltage signal acquisition processing on the sampling trigger identifier to obtain raw voltage data, and performing noise elimination processing on the raw voltage data to obtain a voltage effective value;

[0059] (3) performing segmented quantization processing on the voltage effective value to obtain voltage characteristic data, and performing current signal synchronous acquisition processing on the voltage characteristic data to obtain a current sampling value;

[0060] (4) performing waveform feature extraction processing on the current sampling value to obtain a current feature sequence, and performing temperature signal acquisition processing on the current feature sequence to obtain a temperature detection value;

[0061] (5) Perform multi-point distribution analysis on the temperature detection values ​​to obtain temperature distribution characteristics, and perform time series correlation processing on the temperature distribution characteristics to obtain a parameter correlation matrix;

[0062] (6) performing data alignment processing on the parameter correlation matrix to obtain a synchronized data set, and performing feature space construction processing on the synchronized data set to obtain a feature vector group;

[0063] (7) performing data standardization processing on the feature vector group to obtain standard feature data, and performing spectrum analysis processing on the standard feature data using a Fourier transform algorithm to obtain a spectrum feature set;

[0064] (8) performing working condition pattern recognition processing on the frequency spectrum feature set to obtain a working condition type identifier, and performing feature combination processing on the working condition type identifier to obtain a working condition feature combination;

[0065] (9) Performing data compression encoding processing on the operating condition feature combination to obtain an operating condition code sequence, and performing feature spectrum generation processing on the operating condition code sequence to obtain a multi-dimensional battery operating condition feature spectrum.

[0066] Specifically, the channel parameters of the dual-channel acquisition instruction set are parsed to extract the acquisition parameter configuration information, including sampling frequency (voltage / current 100Hz, temperature 10Hz), sampling accuracy (voltage 0.1mV, current 0.1A, temperature 0.1℃), trigger mode (timing trigger / threshold trigger), etc. The acquisition configuration sequence is generated according to the parameter configuration, and the sampling timer is designed according to the timing to generate a periodic sampling trigger mark. After receiving the sampling trigger mark, the voltage signal acquisition is started. The acquisition process uses 16-bit ADC conversion, the range is 0-5V, and the resolution is 0.076mV. The collected voltage raw data contains high-frequency noise and power frequency interference. The noise is eliminated by a Butterworth low-pass filter. The cutoff frequency is set to 500Hz to filter out high-frequency interference components and obtain the voltage effective value. The voltage effective value is processed by segmented quantization to divide the continuous voltage value into multiple discrete levels. Each level corresponds to a voltage interval of 0.1V, and the voltage characteristic data is extracted, including characteristic quantities such as mean, peak, and valley.

[0067] The current signal acquisition is carried out synchronously with the voltage signal, using a Hall current sensor with a range of ±500A and a resolution of 0.1A. The waveform features of the collected current sampling values ​​are extracted, and the characteristic parameters such as the effective value, peak factor, and waveform distortion rate of the current are calculated to form a current characteristic sequence. The temperature signal is collected using a thermistor array, which is distributed in key positions of the battery pack. The sampling frequency is 10Hz and the measurement range is -20℃ to 80℃. The collected temperature detection values ​​are subjected to multi-point distribution analysis, the temperature gradient and temperature difference of each temperature measurement point are calculated, the temperature distribution curve is drawn, and the temperature distribution characteristics are extracted. Through time series correlation analysis, the correlation relationship between the three parameters of voltage, current, and temperature is established, and a parameter correlation matrix is ​​generated. The matrix elements represent the correlation coefficients between the parameters.

[0068] The voltage, current, and temperature data are aligned by timestamp to eliminate sampling time differences and form a synchronized data set. The feature space is constructed based on the synchronized data, and each sample point contains the eigenvalues ​​of the three dimensions of voltage, current, and temperature. The feature vector group is standardized, and the Z-score standardization method is used to make the data mean 0 and the standard deviation 1. The standardized feature data is converted to the frequency domain through the fast Fourier transform algorithm to analyze the spectral characteristics of the signal. The Fourier transform sets the number of sampling points to 1024 and the frequency resolution to 0.1Hz to obtain the spectral feature set. The operating mode is identified based on the spectral characteristics, and different voltage, current, and temperature combination modes are divided into charging, discharging, static, and balancing operating conditions.

[0069] The identified working condition type identifiers are combined with features, and the multi-dimensional features under the same working condition are combined into a feature vector. Huffman coding is used to compress the working condition feature combination to reduce data storage space and generate a working condition coding sequence. Finally, a multi-dimensional battery working condition characteristic spectrum is generated based on the coding sequence, including the time domain features and frequency domain features of voltage, current, and temperature.

[0070] For example, when testing an electric truck equipped with a 200kWh lithium battery pack, the dual-channel acquisition instruction configures the sampling parameters: voltage sampling frequency 100Hz, accuracy 0.1mV; current sampling frequency 100Hz, accuracy 0.1A; temperature sampling frequency 10Hz, accuracy 0.1℃. Within the 10-second test window, the voltage channel collects 1000 raw data points, and the raw voltage value fluctuates between 3.2V-3.8V. After low-pass filtering, the voltage effective value stabilizes at around 3.65V, and the peak-to-peak fluctuation is reduced to less than 0.05V. The current sampling records the charging process, and the current value gradually increases from 0A to 50A. The extracted features include rise time 2s and steady-state ripple ±0.5A. The temperature data comes from 12 temperature measurement points, with the highest temperature of 38.5℃, the lowest temperature of 36.2℃, and the temperature difference of 2.3℃. Parameter correlation analysis shows that the temperature shows a linear increase trend during the current rise, with a correlation coefficient of 0.92. After the characteristic data was standardized and FFT transformed, an obvious charging characteristic frequency component was found in the 0-10Hz frequency band. The working condition identification results showed that it was currently in the constant current charging stage, and the encoded characteristic spectrum accurately reflected the working state of the battery pack. The data compression ratio of the entire test process reached 5:1, which effectively saved storage space.

[0071] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0072] (1) performing feature quantity analysis processing on the multi-dimensional battery operating condition characteristic spectrum to obtain an operating condition parameter sequence, and performing benchmark value calibration processing on the operating condition parameter sequence to obtain a calibration benchmark set;

[0073] (2) performing range correction processing on the calibration reference set to obtain a correction parameter matrix, and performing error correction processing on the correction parameter matrix through a Kalman filter algorithm to obtain a correction coefficient set;

[0074] (3) performing parameter linkage analysis on the correction coefficient set to obtain a linkage variable group, and performing dynamic range definition on the linkage variable group to obtain parameter constraint conditions;

[0075] (4) performing calibration rule generation processing on the parameter constraint conditions to obtain a calibration rule base, and performing rule verification processing on the calibration rule base to obtain a rule validity index;

[0076] (5) A control sequence is constructed for the rule effectiveness indicators to obtain an intelligent calibration control sequence.

[0077] Specifically, the characteristic quantity of the multi-dimensional battery operating condition characteristic spectrum is analyzed, and the operating condition parameters are extracted, including voltage characteristic values ​​(cell voltage, voltage difference), current characteristic values ​​(charge and discharge current, peak current), temperature characteristic values ​​(cell temperature, temperature gradient), and the change trend of these parameters over time. The parsed parameters form a sequence of operating condition parameters, and each sequence contains a timestamp, a parameter value, and a characteristic identifier. The operating condition parameters are calibrated with reference values ​​through standard test equipment, and the corresponding relationship between the measured value and the true value is established to form a calibration reference set. The calibration reference set includes voltage calibration points (2.5V, 3.0V, 3.5V, 4.0V), current calibration points (-100A, -50A, 0A, 50A, 100A), and temperature calibration points (0℃, 25℃, 45℃). The calibration reference set enters the range calibration stage, and the range correction coefficient of each parameter is calculated according to the full scale range and resolution of the measuring instrument. The voltage range is 0-5V, with a resolution of 0.1mV; the current range is ±500A, with a resolution of 0.1A; the temperature range is -20℃ to 80℃, with a resolution of 0.1℃. After correction, a correction parameter matrix is ​​generated, and the matrix elements contain correction parameters such as zero drift, gain error, and linearity error. The Kalman filter algorithm is used to perform dynamic error correction on the correction parameter matrix. The algorithm process includes: state prediction (predicting the current state based on the state at the previous moment), measurement update (correcting the predicted value in combination with the actual measured value), and gain calculation (calculating the optimal gain based on the predicted error and the measured error). The correction coefficient set is obtained through iterative calculation, which effectively eliminates random errors and systematic errors.

[0078] The correction coefficient set enters the parameter linkage analysis link to analyze the mutual influence relationship between the three parameters of voltage, current and temperature. Establish a mapping relationship between parameters, such as the voltage drop caused by current change, the temperature change law during charging and discharging, etc., to form a linkage variable group. Define the dynamic range of the linkage variable group to determine the reasonable range and limit of parameter changes. The voltage change rate is limited to ±0.1V / s, the current change rate does not exceed ±50A / s, and the temperature change rate is controlled within ±1℃ / min to generate parameter constraints. Based on the parameter constraints, calibration rules are generated, and the rules include: parameter over-limit protection rules (such as voltage exceeding 4.2V triggers overcharge protection), parameter balance rules (such as temperature difference exceeding 5℃ to start temperature balance), and parameter compensation rules (such as temperature compensation coefficient for voltage). After the calibration rules are stored in the rule base, verification tests are carried out. Typical working condition data is used to verify the effectiveness of the rules, and indicators such as rule response time, accuracy, and reliability are calculated to form rule effectiveness indicators.

[0079] Finally, a control sequence is constructed based on the rule effectiveness index. The sequence includes parameter collection instructions, data processing instructions, protection control instructions, etc., which are sorted according to the execution priority to generate an intelligent calibration control sequence. The control sequence adopts a hierarchical structure. The bottom layer performs basic data collection and processing, the middle layer implements parameter calibration and protection, and the top layer is responsible for strategy optimization and control decision-making.

[0080] For example, a test was conducted on a heavy truck loaded with a 300kWh lithium battery pack. The characteristic quantity analysis extracted the single cell voltage data (3.45V, 3.47V, 3.46V...), charging current (80A), and temperature distribution (36.5℃-38.2℃). The calibration process uses a high-precision reference source. The actual value of 3.502V is measured at the 3.5V voltage point, and the voltage correction coefficient is 1.0006; the actual value of 80.3A is measured at the 80A current point, and the current correction coefficient is 1.0037; the actual value of 37.1℃ is measured at the 37℃ temperature point, and the temperature correction coefficient is 1.0027. The state noise covariance of the Kalman filter is set to 0.01, and the measurement noise covariance is 0.1. After 100 iterations, the voltage measurement error is reduced from ±5mV to ±1mV, and the current measurement error is reduced from ±0.5A to ±0.1A. Parameter linkage analysis found that at 80A charging current, the cell voltage decreased by 2mV for every 1°C increase in battery temperature, and the temperature compensation coefficient of -2mV / °C was established accordingly. Calibration rule verification showed that the voltage measurement accuracy after temperature compensation increased by 85%, the response time was less than 100ms, and the rule effectiveness reached 98%. The final generated intelligent calibration control sequence includes: data acquisition cycle 20ms, temperature compensation update cycle 100ms, overvoltage protection threshold 4.25V, overtemperature protection threshold 50°C and other control parameters.

[0081] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0082] (1) performing parameter classification and indexing processing on the intelligent calibration control sequence to obtain a charge and discharge test parameter group, and performing standard working condition matching processing on the charge and discharge test parameter group to obtain a working condition matching strategy;

[0083] (2) performing BMS communication protocol parsing on the working condition matching strategy to obtain communication interface parameters, and performing data encapsulation processing on the communication interface parameters through a Bluetooth data frame encoding algorithm to obtain a test instruction package;

[0084] (3) performing charging process data collection and processing on the test instruction packet to obtain a charging process record, and performing temperature balance judgment processing on the charging process record to obtain a temperature distribution state;

[0085] (4) performing a discharge capacity test on the temperature distribution state to obtain capacity decay data, and performing a voltage consistency analysis on the capacity decay data to obtain a single cell voltage deviation;

[0086] (5) The BMS system status record is processed for the single cell voltage deviation to obtain the battery operation status monitoring record.

[0087] Specifically, the intelligent calibration control sequence is first subjected to parameter classification indexing processing, and the parameters are divided into control parameters (charging current, cut-off voltage, charging time) and protection parameters (overvoltage value, undervoltage value, overtemperature value) according to the functional type. A hash indexing method is used to assign a unique identification code to each type of parameter, such as the charging parameter group identification "CH01" and the discharge parameter group identification "DCH01", to form a charging and discharging test parameter group. The test parameter group is matched with the standard working condition, and the parameters are matched with the preset working condition types by table lookup, including standard charging conditions (0.5C constant current charging to 4.2V), fast charging conditions (1C charging to 4.2V), standard discharge conditions (0.5C discharge to 3.0V), etc., to establish a working condition matching strategy table. The working condition matching strategy is transferred to the BMS communication protocol parsing link to parse the BMS communication frame format, including frame header (2 bytes), command word (1 byte), data length (1 byte), data segment (variable length), and checksum (2 bytes). The communication interface parameters are extracted: baud rate 19200bps, 8 data bits, 1 stop bit, no check bit. The communication data is encapsulated through the Bluetooth data frame encoding algorithm, and differential Manchester encoding is used to convert each original data bit into two jump levels to improve the anti-interference ability. The data packet size is controlled within 20 bytes, and the CRC16 check code is added to generate a test instruction packet. The charging process data acquisition adopts a multiplexing method to monitor the voltage, current and temperature data of 96 single cells at the same time. The sampling frequency is set to: voltage 100Hz, current 100Hz, temperature 10Hz. The data record is marked with a timestamp, accurate to milliseconds. The charging process record contains: single cell voltage curve, charging current curve, temperature change curve, charging time, charging capacity and other information. The temperature balance of the recorded data is judged, the temperature mean and standard deviation of all temperature measurement points are calculated, the temperature interval is divided, and the temperature distribution state diagram is generated.

[0088] The discharge capacity test is performed based on the temperature distribution state, and the constant current discharge method is used to record the discharge time and discharged capacity. Through multiple cycle tests, the capacity change trend is tracked and the capacity decay rate is calculated. The capacity data record content includes: initial capacity, current capacity, number of cycles, decay ratio, etc. The consistency of the single cell voltage during the discharge process is analyzed, the maximum voltage difference and voltage distribution discreteness are calculated, the consistency level of the battery pack is evaluated, and the single cell voltage deviation data is obtained. The single cell voltage deviation data is entered into the BMS status record as an important indicator, and the record content covers the key battery status parameters: SOC (remaining capacity percentage), SOH (health state), battery internal resistance, insulation resistance, etc. All status data are stored in time series to form a complete battery operation status monitoring record.

[0089] For example, in a test of a power battery pack consisting of 96 series and 3 parallel batteries, the parameter index generates charging control parameters: constant current charging current 150A (0.5C), cut-off voltage 4.2V, maximum charging time 4 hours; protection parameters: overvoltage protection 4.25V, undervoltage protection 2.8V, overtemperature protection 55℃. The 0.5C constant current charging mode is selected for standard working condition matching. BMS communication uses a baud rate of 19200bps, and the data frame format is: frame header 0xAA55, command word 0x01 (charging control), data length 0x08, data segment (current value, voltage value, etc.), checksum. During the charging test, real-time data collection shows that the charging current is stable at 150±0.5A, the single cell voltage rises from 3.50V to 4.15V, and the temperature rises from 28℃ to 35℃. The temperature distribution data of 96 temperature measurement points are calculated to obtain: the average temperature is 32.5℃, the standard deviation is 1.2℃, and the maximum temperature difference is 3.8℃. Discharge capacity test results: initial capacity 300Ah, capacity dropped to 285Ah after 100 cycles, capacity attenuation rate 5%. Single cell voltage consistency analysis: maximum voltage difference 35mV, voltage standard deviation 12mV. The final state monitoring record includes: SOC 85%, SOH 95%, internal resistance increased by 8%, insulation resistance greater than 1MΩ and other key indicators, which fully records the performance changes of the battery pack during the charge and discharge process.

[0090] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0091] (1) Extracting and processing historical data from battery operation status monitoring records to obtain state change time series data, and performing health feature learning processing on the state change time series data through a neural network algorithm to obtain a battery health feature vector;

[0092] (2) Perform SOC estimation and analysis on the battery health feature vector to obtain a power estimation value, and then use a particle filter algorithm to predict the SOH decay trend of the power estimation value to obtain a life prediction curve;

[0093] (3) Analyze the internal resistance change of the life prediction curve to obtain a resistance change sequence, and classify the resistance change sequence into health levels using a fuzzy inference algorithm to obtain a battery health level identifier;

[0094] (4) Performing cycle performance evaluation on the battery health level label to obtain capacity retention rate data, and performing charge and discharge efficiency calculation on the capacity retention rate data to obtain energy conversion efficiency;

[0095] (5) Performing temperature sensitivity analysis on the energy conversion efficiency to obtain a temperature-sensitive characteristic curve, and performing safety risk assessment on the temperature-sensitive characteristic curve to obtain a risk warning indicator;

[0096] (6) Conduct consistency evaluation on the risk warning indicators to obtain a battery performance score, and perform life influencing factor analysis on the battery performance score to obtain an influencing factor matrix;

[0097] (7) Perform maintenance suggestion generation processing on the influencing factor matrix to obtain a maintenance strategy list, and formulate a performance improvement plan on the maintenance strategy list to obtain an optimization suggestion plan;

[0098] (8) Extract key parameters of the optimization proposal to obtain a parameter diagnosis report, and perform battery fault diagnosis on the parameter diagnosis report to obtain a fault type determination result;

[0099] (9) Perform performance test report generation processing on the fault type determination result to obtain a lithium battery performance test report.

[0100] Specifically, historical data is extracted from the battery operation status monitoring records, including time series data such as voltage curve, current curve, temperature curve, SOC change curve, etc. The extracted data is sorted by timestamp to form a state change time series data set. The neural network algorithm adopts a three-layer BP network structure. The input layer contains 10 feature nodes such as voltage, current, temperature, SOC, etc., the hidden layer has 20 nodes, and the output layer has 4 nodes corresponding to health feature parameters. Through 5000 iterations of training, the battery health status characteristics are learned to generate a battery health feature vector. The SOC estimation analysis uses the ampere-hour integration method combined with Kalman correction to calculate the remaining capacity percentage. The power estimation value is processed by particle filtering, and 100 particles are set, each particle contains the SOH decay parameter. The particle filter algorithm predicts the SOH decay trend through three steps of prediction-update-resampling, and outputs the life prediction curve. The life prediction curve is analyzed by internal resistance, and the DC internal resistance method is used to calculate the battery internal resistance. The internal resistance values ​​at different SOC points are recorded to form a resistance change sequence. The fuzzy reasoning algorithm fuzzifies indicators such as the internal resistance growth rate and capacity attenuation rate, sets the health level (excellent, good, medium, poor), and outputs the battery health level label.

[0101] Among them, the battery health feature vector includes: voltage characteristics (reflecting the charge and discharge capacity): average voltage: Vmean = (∑Vi) / n, where Vi is the voltage value of the i-th sampling point (V), and n is the total number of sampling points

[0102] Voltage fluctuation amplitude: ΔV = Vmax-Vmin; Vmax is the maximum voltage value (V), Vmin is the minimum voltage value (V);

[0103] Voltage standard deviation: σv = √(∑(Vi-Vmean)2 / n); where Vi is the voltage value of the i-th sampling point (V), Vmean is the average voltage value (V), and n is the total number of sampling points.

[0104] Current characteristics (reflecting load capacity): Average current: Imean = (∑Ii) / n, where Ii is the current value (A) of the i-th sampling point and n is the total number of sampling points.

[0105] Current fluctuation rate: γi=(Imax-Imin) / Imean;

[0106] Where Imax is the maximum current value (A), Imin is the minimum current value (A), and Imean is the average current value (A).

[0107] Temperature characteristics (reflecting thermal characteristics): Average temperature: Tmean = (∑Ti) / n; where Ti is the temperature value of the i-th sampling point (°C), and n is the total number of sampling points.

[0108] Temperature gradient: dT / dt=(T2-T1) / (t2-t1); where T2 and T1 are the temperatures at two moments (°C), and t2-t1 is the time interval (s).

[0109] SOH attenuation trend prediction model: SOH(t)=SOH0exp(-kt)+Aexp(-BS)

[0110] Where: SOH(t) is the percentage of health status at time t (%); SOH0 is the initial health status (usually 100%); t is the number of cycles; k is the basic attenuation coefficient (range 0.0001-0.001); A and B are fitting coefficients (calibrated by experimental data); S is the comprehensive stress factor, and the calculation formula is:

[0111] S=w1(T-Tref) / Tref+w2(I-Iref) / Iref+w3DOD;

[0112] Where: T is the actual working temperature (℃); Tref is the reference temperature (usually 25℃); I is the actual working current (A); Iref is the reference current (usually the current corresponding to 0.5C rate); DOD is the depth of discharge (a decimal between 0-1); w1, w2, w3 are weight coefficients (∑wi=1).

[0113] Resistance change sequence:

[0114] The instantaneous internal resistance calculation formula is: R(t)=ΔV / ΔI=(V2-V1) / (I2-I1); where: R(t) is the internal resistance value at time t (mΩ); V2, V1 are the voltage values ​​of two adjacent sampling points (V); I2, I1 are the current values ​​of two adjacent sampling points (A).

[0115] The change law of internal resistance with the number of cycles: R(n)=R0(1+αn)

[0116] Where: R(n) is the internal resistance value after the nth cycle (mΩ); R0 is the initial internal resistance value (mΩ); n is the number of cycles; α is the internal resistance growth coefficient (range 0.001-0.005).

[0117] The cycle performance evaluation is based on the capacity retention rate data, and the attenuation ratio is calculated by comparing the initial capacity and the current capacity. The charge and discharge efficiency calculation considers the ratio of the charging input energy to the discharge output energy, takes into account the temperature compensation coefficient, and obtains the actual energy conversion efficiency. The temperature sensitivity analysis records the changes in performance parameters at different temperatures and draws the temperature-sensitive characteristic curve. The safety risk assessment sets the risk level according to abnormal temperature, abnormal voltage, abnormal internal resistance, etc., and generates risk warning indicators. The risk warning indicators are evaluated for consistency, and the battery performance score is calculated using the weighted scoring method. The weight distribution is: capacity retention rate 30%, internal resistance growth 20%, temperature balance 20%, and voltage consistency 30%. The analysis of factors affecting life uses the principal component analysis method to extract key factors such as temperature stress, charge and discharge rate, and number of cycles, and construct an influencing factor matrix.

[0118] Among them, the construction of the temperature-sensitive characteristic curve:

[0119] It is built based on the long short-term memory network (LSTM) model. The specific structure is as follows:

[0120] Input layer: 4 feature nodes (temperature T, charge rate C, discharge rate D, number of cycles N);

[0121] LSTM hidden layer 1: 32 neurons, activation function tanh;

[0122] LSTM hidden layer 2: 16 neurons, activation function tanh;

[0123] Fully connected layer: 8 neurons, activation function ReLU;

[0124] Output layer: 3 nodes (capacity retention rate Cr, internal resistance growth rate Rr, voltage response Vr);

[0125] Temperature sensitive characteristic parameter calculation:

[0126] Capacity temperature coefficient: αc = (C2-C1) / (T2-T1);

[0127] Where C2, C1 are the capacity values ​​(Ah) at temperatures T2, T1;

[0128] Internal resistance temperature coefficient: αr = (R2-R1) / (T2-T1);

[0129] Where R2, R1 are the internal resistance values ​​(mΩ) at temperatures T2, T1;

[0130] Voltage temperature coefficient: αv = (V2-V1) / (T2-T1);

[0131] Where V2, V1 are the voltage values ​​(V) at temperatures T2, T1.

[0132] 2. Construction of impact factor matrix:

[0133] The dimension of the matrix M is n×p, where n is the number of sampled data sets and p is the number of influencing factors (in this example, p=6).

[0134] The matrix element Mij represents the jth impact factor value of the i-th group of data:

[0135] The specific expression of M is:

[0136] [T1 C1 D1 N1 S1 E1]

[0137] [T2 C2 D2 N2 S2 E2] [..................]

[0139] [Tn Cn Dn Nn Sn En]

[0140] The columns represent:

[0141] T: Temperature stress = (T-25) / 25, T is the actual temperature (°C);

[0142] C: Charging rate = Icharge / In, Icharge is the charging current, In is the rated current;

[0143] D: Discharge rate = I discharge / In, I discharge is the discharge current, In is the rated current;

[0144] N: Normalized cycle number = n / nmax, n is the current cycle number, nmax is the design life cycle number.

[0145] S: SOC change rate = ΔSOC / Δt, ΔSOC is the SOC change, Δt is the time interval;

[0146] E: Environmental stress index = (H-50) / 50, H is the environmental humidity (%).

[0147] This matrix is ​​processed by principal component analysis (PCA):

[0148] 1. Data standardization: Z = (X-μ) / σ

[0149] 2. Calculate the covariance matrix: C = (1 / n) Z^T·Z

[0150] 3. Eigenvalue decomposition: C = VΛV^T

[0151] 4. Select the eigenvectors corresponding to the largest k eigenvalues ​​to construct the transformation matrix W

[0152] 5. Get the feature matrix after dimensionality reduction: Y = Z·W

[0153] The principal component contribution rate and cumulative contribution rate obtained can be used to analyze the influence of each factor.

[0154] Maintenance recommendations are generated based on the influencing factor matrix, including specific measures such as charging strategy optimization, temperature management improvement, and balanced maintenance. The performance improvement plan provides optimization solutions for different fault types, including parameter adjustment suggestions and improved operating procedures. Key parameter diagnosis covers four dimensions: voltage characteristics, temperature characteristics, capacity characteristics, and internal resistance characteristics. Fault diagnosis uses a decision tree algorithm to determine the fault type based on abnormal parameter combinations.

[0155] For example: In a set of 300kWh truck power battery tests, historical data recorded the operating data of 1000 cycles. The neural network training results show: voltage feature weight 0.3, temperature feature weight 0.25, internal resistance feature weight 0.25, capacity feature weight 0.2. The SOC estimation accuracy reached 97%, and the particle filter prediction showed that the SOH would drop to 80% at the 2000th cycle. Internal resistance test data: the new battery internal resistance is 0.5mΩ, the current internal resistance is 0.65mΩ, and the growth rate is 30%. The capacity test shows: the initial capacity is 300Ah, the current capacity is 276Ah, and the capacity retention rate is 92%. Charge and discharge efficiency test: charging capacity is 295Ah at 25℃, discharge capacity is 285Ah, and energy conversion efficiency is 96.6%. The temperature test shows: the capacity drops by 2% at 40℃ and the capacity drops by 15% at 0℃. Consistency evaluation score: capacity retention rate 27 points (full 30 points), internal resistance 19 points (full 20 points), temperature balance 18 points (full 20 points), voltage consistency 28 points (full 30 points), total score 92 points. Analysis of influencing factors found that: temperature fluctuation contributes 40%, overcharge contributes 30%, high current discharge contributes 20%, and other factors contribute 10%. Maintenance recommendations include: controlling the charging temperature at 15-35°C, the charging rate does not exceed 0.5C, and regular battery balancing. The final diagnostic report pointed out that the overall condition of the battery pack is good, it is recommended to optimize the temperature management strategy, and the remaining service life is expected to be 1,000 cycles.

[0156] The above describes the truck lithium battery testing method based on Bluetooth communication in the embodiment of the present application. The following describes the truck lithium battery testing device based on Bluetooth communication in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of a truck lithium battery testing device based on Bluetooth communication includes:

[0157] The matching module 201 is used to perform intelligent long-distance matching processing on the Bluetooth identification information of the truck lithium battery management system to obtain a remote Bluetooth control key;

[0158] The test module 202 is used to perform a lithium battery data acquisition protocol test process on the remote Bluetooth control key to obtain a dual-channel acquisition instruction set;

[0159] The acquisition module 203 is used to perform dynamic voltage, current and temperature collaborative acquisition processing on the dual-channel acquisition instruction set to obtain a multi-dimensional battery operating condition characteristic spectrum;

[0160] The calibration module 204 is used to perform remote parameter linkage calibration processing on the multi-dimensional battery operating condition characteristic spectrum to obtain an intelligent calibration control sequence;

[0161] The test module 205 is used to perform remote charge and discharge management test processing on the intelligent calibration control sequence to obtain a battery operation status monitoring record;

[0162] The evaluation module 206 is used to perform remote health evaluation processing on the battery operation status monitoring record to obtain a lithium battery performance test report.

[0163] Through the collaboration of the above components, a safe and reliable Bluetooth communication link is established through intelligent long-distance matching processing, which effectively avoids wireless signal interference and illegal device access, and improves the security and stability of communication. The lithium battery data acquisition protocol test processing adopts a dual-channel structure design to realize the separate transmission of control instructions and data acquisition, and enhances the real-time and reliability of data transmission. Dynamic voltage, current and temperature collaborative acquisition processing realizes the synchronous acquisition and correlation analysis of multiple parameters, accurately reflects the operating status of the battery under different working conditions, and provides comprehensive data support for battery performance evaluation. Remote parameter linkage calibration processing establishes a correlation mechanism between parameters, improves the accuracy of measurement data through real-time calibration and compensation, and reduces the interference of environmental factors. Remote charge and discharge management test processing realizes precise control and real-time monitoring of the charge and discharge process, timely detects abnormal conditions, and effectively prevents safety hazards such as overcharging and over-discharging. Remote health assessment processing accurately evaluates the health status and remaining life of the battery through multi-dimensional data analysis and intelligent algorithms, providing a scientific basis for maintenance decisions. The overall test solution reduces manual intervention, improves test efficiency, and reduces test costs. At the same time, through standardized test processes and data processing methods, the repeatability and comparability of test results are guaranteed. This solution is particularly suitable for battery management of large-scale electric truck fleets, realizing remote monitoring and predictive maintenance of battery performance, and providing technical support for the safe operation of electric trucks. Through efficient data collection and analysis and processing, the solution can timely detect battery performance degradation trends, and warn of potential failures in advance, effectively extending battery life and reducing operation and maintenance costs. Most importantly, the solution realizes intelligent and automated battery testing, greatly reducing manual operation errors and improving test accuracy and efficiency.

[0164] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A truck lithium battery testing method based on Bluetooth communication, characterized in that: The truck lithium battery testing method based on Bluetooth communication includes: Perform intelligent long-distance matching processing on the Bluetooth identification information of the truck lithium battery management system to obtain the remote Bluetooth control key; Perform lithium battery data acquisition protocol test on the remote Bluetooth control key to obtain a dual-channel acquisition instruction set; Perform dynamic voltage, current and temperature collaborative acquisition processing on the dual-channel acquisition instruction set to obtain a multi-dimensional battery operating condition characteristic spectrum; Perform remote parameter linkage calibration on the multi-dimensional battery operating condition characteristic spectrum to obtain an intelligent calibration control sequence; Perform remote charge and discharge management test processing on the intelligent calibration control sequence to obtain battery operation status monitoring records; Perform remote health assessment on the battery operation status monitoring records to obtain a lithium battery performance test report.

2. The truck lithium battery testing method based on Bluetooth communication according to claim 1, characterized in that: The method of performing intelligent long-distance matching processing on the Bluetooth identification information of the truck lithium battery management system to obtain a remote Bluetooth control key includes: Performing Bluetooth scanning processing on the device identification code of the truck lithium battery management system to obtain a target device list, and performing Bluetooth signal strength detection processing on the target device list to obtain a signal strength matrix; Performing device screening processing on the signal strength matrix to obtain a candidate device sequence, and performing identity authentication processing on the candidate device sequence to obtain a device authentication identifier; Performing key generation processing on the device authentication identifier through an RSA encryption algorithm to obtain a temporary session key, and performing bidirectional transmission processing on the temporary session key to obtain key confirmation information; Performing consistency verification processing on the key confirmation information to obtain a verification result sequence, and performing security level assessment processing on the verification result sequence to obtain a security level identifier; A communication channel establishment process is performed on the security level identifier to obtain the remote Bluetooth control key.

3. The truck lithium battery testing method based on Bluetooth communication according to claim 1, characterized in that: The remote Bluetooth control key is subjected to a lithium battery data acquisition protocol test process to obtain a dual-channel acquisition instruction set, including: Performing status code parsing processing on the remote Bluetooth control key to obtain a key status sequence, and performing link channel allocation processing on the key status sequence to obtain a bidirectional communication channel; Performing channel parameter testing on the bidirectional communication channel to obtain channel performance indicators, and performing communication protocol matching on the channel performance indicators to obtain protocol adaptation parameters; Performing data frame format definition processing on the protocol adaptation parameters to obtain a data frame structure template, and performing redundancy check processing on the data frame structure template through a CRC check algorithm to obtain a check rule set; Performing data packet segmentation processing on the verification rule set to obtain a data packet sequence, and performing transmission timing planning processing on the data packet sequence to obtain a transmission scheduling scheme; Performing bandwidth resource allocation processing on the transmission scheduling scheme to obtain a resource allocation matrix, and performing channel coding processing on the resource allocation matrix to obtain a coding instruction sequence; Performing instruction priority sorting processing on the encoded instruction sequence to obtain a priority queue, and performing channel switching test processing on the priority queue to obtain a channel switching parameter; Performing data acquisition mode configuration processing on the channel switching parameters to obtain an acquisition configuration scheme, and performing sampling frequency adjustment processing on the acquisition configuration scheme to obtain a frequency control sequence; Performing a collection time window division process on the frequency control sequence to obtain a time window sequence, and performing a data cache strategy formulation process on the time window sequence to obtain a cache control parameter; Performing channel load balancing processing on the cache control parameters to obtain a load distribution scheme, and performing data flow control processing on the load distribution scheme to obtain a flow control parameter set; The flow control parameter set is processed to generate an instruction set to obtain the dual-channel acquisition instruction set.

4. The truck lithium battery testing method based on Bluetooth communication according to claim 1, characterized in that: The dual-channel acquisition instruction set is subjected to dynamic voltage, current and temperature collaborative acquisition processing to obtain a multi-dimensional battery operating condition characteristic spectrum, including: Performing channel parameter parsing processing on the dual-channel acquisition instruction set to obtain an acquisition configuration sequence, and performing data sampling timing processing on the acquisition configuration sequence to obtain a sampling trigger identifier; Performing voltage signal acquisition processing on the sampling trigger identifier to obtain voltage raw data, and performing noise elimination processing on the voltage raw data to obtain a voltage effective value; Performing segmented quantization processing on the voltage effective value to obtain voltage characteristic data, and performing current signal synchronous acquisition processing on the voltage characteristic data to obtain a current sampling value; Performing waveform feature extraction processing on the current sampling value to obtain a current feature sequence, and performing temperature signal acquisition processing on the current feature sequence to obtain a temperature detection value; Performing multi-point distribution analysis processing on the temperature detection values ​​to obtain temperature distribution characteristics, and performing time series correlation processing on the temperature distribution characteristics to obtain a parameter correlation matrix; Performing data alignment processing on the parameter association matrix to obtain a synchronized data set, and performing feature space construction processing on the synchronized data set to obtain a feature vector group; Performing data standardization processing on the feature vector group to obtain standard feature data, and performing spectrum analysis processing on the standard feature data through a Fourier transform algorithm to obtain a spectrum feature set; Performing working condition pattern recognition processing on the frequency spectrum feature set to obtain a working condition type identifier, and performing feature combination processing on the working condition type identifier to obtain a working condition feature combination; The operating condition characteristic combination is subjected to data compression encoding processing to obtain an operating condition coding sequence, and the operating condition coding sequence is subjected to characteristic spectrum generation processing to obtain the multi-dimensional battery operating condition characteristic spectrum.

5. The truck lithium battery testing method based on Bluetooth communication according to claim 1, characterized in that: The remote parameter linkage calibration process is performed on the multi-dimensional battery operating condition characteristic spectrum to obtain an intelligent calibration control sequence, including: Performing feature quantity analysis processing on the multi-dimensional battery operating condition characteristic spectrum to obtain an operating condition parameter sequence, and performing reference value calibration processing on the operating condition parameter sequence to obtain a calibration reference set; Performing range correction processing on the calibration reference set to obtain a correction parameter matrix, and performing error correction processing on the correction parameter matrix through a Kalman filter algorithm to obtain a correction coefficient set; Performing parameter linkage analysis on the correction coefficient set to obtain a linkage variable group, and performing dynamic range definition on the linkage variable group to obtain parameter constraint conditions; Performing calibration rule generation processing on the parameter constraint conditions to obtain a calibration rule base, and performing rule verification processing on the calibration rule base to obtain a rule validity index; A control sequence construction process is performed on the rule effectiveness index to obtain the intelligent calibration control sequence.

6. The truck lithium battery testing method based on Bluetooth communication according to claim 1, characterized in that: The remote charge and discharge management test process is performed on the intelligent calibration control sequence to obtain a battery operation status monitoring record, including: Performing parameter classification indexing processing on the intelligent calibration control sequence to obtain a charge and discharge test parameter group, and performing standard working condition matching processing on the charge and discharge test parameter group to obtain a working condition matching strategy; Performing BMS communication protocol parsing processing on the working condition matching strategy to obtain communication interface parameters, and performing data encapsulation processing on the communication interface parameters through a Bluetooth data frame encoding algorithm to obtain a test instruction packet; Performing charging process data collection processing on the test instruction packet to obtain a charging process record, and performing temperature balance judgment processing on the charging process record to obtain a temperature distribution state; Performing a discharge capacity test on the temperature distribution state to obtain capacity decay data, and performing a voltage consistency analysis on the capacity decay data to obtain a single cell voltage deviation; The single cell voltage deviation is processed by BMS system status record to obtain the battery operation status monitoring record.

7. The truck lithium battery testing method based on Bluetooth communication according to claim 1, characterized in that: The remote health assessment process of the battery operation status monitoring record is performed to obtain a lithium battery performance test report, including: Performing historical data extraction processing on the battery operation status monitoring record to obtain state change time series data, and performing health feature learning processing on the state change time series data through a neural network algorithm to obtain a battery health feature vector; Performing SOC estimation and analysis processing on the battery health feature vector to obtain a power estimation value, and performing SOH decay trend prediction processing on the power estimation value through a particle filter algorithm to obtain a life prediction curve; Performing internal resistance change analysis on the life prediction curve to obtain a resistance change sequence, and performing health grade classification on the resistance change sequence through a fuzzy inference algorithm to obtain a battery health grade identifier; Performing cycle performance evaluation processing on the battery health level identifier to obtain capacity retention rate data, and performing charge and discharge efficiency calculation processing on the capacity retention rate data to obtain energy conversion efficiency; Performing temperature sensitivity analysis on the energy conversion efficiency to obtain a temperature-sensitive characteristic curve, and performing safety risk assessment on the temperature-sensitive characteristic curve to obtain a risk warning indicator; Performing consistency evaluation processing on the risk warning indicators to obtain a battery performance score, and performing life influencing factor analysis processing on the battery performance score to obtain an influencing factor matrix; Performing maintenance suggestion generation processing on the influencing factor matrix to obtain a maintenance strategy list, and performing performance improvement plan formulation processing on the maintenance strategy list to obtain an optimization suggestion plan; Performing key parameter extraction processing on the optimization suggestion scheme to obtain a parameter diagnosis report, and performing battery fault diagnosis processing on the parameter diagnosis report to obtain a fault type determination result; The fault type determination result is processed for performance test report generation to obtain the lithium battery performance test report.

8. A truck lithium battery testing device based on Bluetooth communication, used to implement the truck lithium battery testing method based on Bluetooth communication as described in any one of claims 1 to 7, characterized in that: The truck lithium battery testing device based on Bluetooth communication includes: The matching module is used to perform intelligent long-distance matching processing on the Bluetooth identification information of the truck lithium battery management system to obtain the remote Bluetooth control key; The test module is used to perform lithium battery data acquisition protocol test processing on the remote Bluetooth control key to obtain a dual-channel acquisition instruction set; An acquisition module is used to perform dynamic voltage, current and temperature collaborative acquisition processing on a dual-channel acquisition instruction set to obtain a multi-dimensional battery operating condition characteristic spectrum; A calibration module is used to perform remote parameter linkage calibration processing on the multi-dimensional battery operating condition characteristic spectrum to obtain an intelligent calibration control sequence; The test module is used to perform remote charge and discharge management test processing on the intelligent calibration control sequence to obtain the battery operation status monitoring record; The evaluation module is used to perform remote health evaluation on the battery operation status monitoring records to obtain a lithium battery performance test report.

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