A truck lithium battery testing method and device based on Bluetooth communication

Abstract

The present application relates to the field of battery testing technology, and discloses a truck lithium battery testing method and device based on Bluetooth communication. The method includes: 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 and perform lithium battery data acquisition protocol test processing 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 and perform remote charge and discharge management test processing to obtain a battery operating status monitoring record; performing remote health assessment processing on the battery operating status monitoring record to obtain a lithium battery performance test report. The present application provides a lithium battery testing solution that does not require physical connection, is easy to operate, and has real-time monitoring capabilities to improve testing efficiency and flexibility.

Description

Technical Field

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

[0002] With the rapid development of the new energy vehicle industry, trucks, as a key means of logistics and transportation, are increasingly becoming electric. Lithium batteries, as core components of electric trucks, have a performance stability and safety that is directly related to the truck's operating efficiency and safety. Traditional lithium battery system testing methods rely primarily on specialized test instruments, such as battery testers or multimeters. These devices connect directly to the battery through physical connections (such as clips or wires) to obtain data such as voltage, current, and temperature. After the test is completed, the data must be manually recorded to form a test record.

[0003] However, existing lithium battery testing technology has the following shortcomings: limited data acquisition methods. Due to physical connection limitations, users can only perform tests at specific locations, and the flexibility of the test is greatly limited; the operating interface is complex, and data usually needs to be 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 performance 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 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 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 tested for charge and discharge management to obtain a battery operation status monitoring record; the battery operation status monitoring record is remotely evaluated for health to obtain a lithium battery performance test report.

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

[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] The acquisition module 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;

[0011] The calibration module is used to perform remote parameter linkage calibration 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 battery operation status monitoring records;

[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] The technical solution provided in this application establishes a secure and reliable Bluetooth communication link through intelligent remote matching, effectively preventing wireless signal interference and unauthorized device access, and improving communication security and stability. The lithium battery data acquisition protocol test process utilizes a dual-channel design, enabling separate transmission of control commands and data acquisition, enhancing the real-time and reliability of data transmission. Dynamic voltage, current, and temperature collaborative acquisition and processing enable the simultaneous acquisition and correlation analysis of multiple parameters, accurately reflecting the battery's operating status under different operating conditions and providing comprehensive data support for battery performance evaluation. Remote parameter linkage calibration establishes a correlation mechanism between parameters, improving measurement data accuracy and reducing environmental interference through real-time calibration and compensation. Remote charge and discharge management test processing enables precise control and real-time monitoring of the charge and discharge process, promptly detecting abnormalities and effectively preventing safety hazards such as overcharging and over-discharging. Remote health assessment processing accurately assesses the battery's health status and remaining life through multi-dimensional data analysis and intelligent algorithms, providing a scientific basis for maintenance decisions. This integrated testing solution reduces manual intervention, improves testing efficiency, and reduces testing costs. Furthermore, standardized testing procedures and data processing methods ensure the repeatability and comparability of test results. This solution is particularly well-suited for battery management in large-scale electric truck fleets, enabling remote monitoring of battery performance and predictive maintenance, providing technical support for safe electric truck operations. Through efficient data collection and analysis, the solution promptly identifies battery degradation trends and provides early warning of potential failures, effectively extending battery life and reducing operational costs. Most importantly, the solution implements intelligent and automated battery testing, significantly reducing manual 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 following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without 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 this 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 that are 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: Perform intelligent remote matching processing on the Bluetooth identification information of the truck lithium battery management system to obtain a remote Bluetooth control key;

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

[0022] Step S103: 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;

[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: perform remote 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 this 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 embodiment of this application 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 includes key data such as the device's MAC address, signal strength, and device type. During intelligent remote matching, the mobile terminal scans for nearby Bluetooth devices and screens those with signal strengths above -80dBm. The MAC address is then encrypted with RSA to generate a remote Bluetooth control key. During lithium battery data acquisition protocol testing, a bidirectional communication channel is established based on the generated control key, and channel parameters are tested. Channel parameters include bandwidth, latency, and packet loss rate. The data acquisition protocol defines a data frame format consisting of a header, control code, data segment, and checksum. CRC checksums ensure data transmission accuracy, and acquisition instructions are prioritized to form a dual-channel acquisition instruction set. During the dynamic voltage, current, and temperature collaborative acquisition phase, multi-parameter sampling is performed synchronously according to the acquisition instruction set. Voltage sampling accuracy is 0.1mV at a sampling frequency of 10Hz; current sampling accuracy is 0.1A at a sampling frequency of 10Hz; and temperature sampling accuracy is 0.1°C at a sampling frequency of 1Hz. The collected raw data is de-noised, and the spectral characteristics are analyzed through Fourier transform to extract operating condition characteristics and generate a multi-dimensional battery operating condition characteristic spectrum. During remote parameter linkage calibration, the parameters in the operating condition characteristic spectrum are calibrated and corrected. Measurement errors are corrected using a Kalman filter algorithm, with an error correction coefficient ranging from 0.95 to 1.05. Linkage constraints are established for parameters such as voltage, current, and temperature, generating a calibration rule base and forming an intelligent calibration control sequence.

[0028] During remote charge and discharge management testing, the charge and discharge process is controlled and monitored based on a calibrated control sequence. During the charging process, the current is controlled within the 0.2C-0.5C range, the cell voltage equilibrium is monitored, and the temperature distribution deviation is controlled within 3°C. During the discharge process, capacity decay data is recorded, cell voltage consistency is analyzed, and BMS operating status data is recorded. During the remote health assessment phase, a neural network algorithm is used to learn historical battery operating data and extract health characteristics. A particle filter algorithm is used to estimate the state of charge (SOC) and predict the remaining battery life. Fuzzy inference is used to classify battery health levels, evaluate cycle performance and temperature sensitivity, and generate performance test reports.

[0029] For example, a truck equipped with a 100kWh lithium-ion battery pack scanned the battery management system's Bluetooth device via a mobile app, with a signal strength of -65dBm. The system generated a 16-bit RSA encryption key and established a communication link. The data acquisition protocol was configured with a sampling period of 100ms, achieving a 99.9% transmission success rate. During the acquisition process, voltage data showed a maximum cell voltage of 3.65V and a minimum voltage of 3.62V; current data showed a charging current of 50A; and temperature data showed a maximum temperature of 35°C and a minimum temperature of 32°C. After Kalman filtering, the measurement error was reduced to within 0.1%. During charge and discharge tests, charging at a current of 0.3C, the cell voltage difference was monitored to be less than 20mV, and the temperature difference remained within 2°C. The health assessment results showed a battery SOC of 85% and a SOH of 92%, with a predicted remaining cycle life of 2000 cycles. It was recommended to optimize the charging strategy and control the charge rate to within 0.3C.

[0030] In the embodiments of this application, intelligent remote matching processing establishes a secure and reliable Bluetooth communication link, effectively preventing wireless signal interference and unauthorized device access, and improving communication security and stability. The lithium battery data acquisition protocol test process utilizes a dual-channel design, enabling separate transmission of control commands and data acquisition, enhancing the real-time and reliability of data transmission. Dynamic voltage, current, and temperature collaborative acquisition processing enables simultaneous acquisition and correlation analysis of multiple parameters, accurately reflecting the battery's operating status under different operating conditions and providing comprehensive data support for battery performance evaluation. Remote parameter linkage calibration processing establishes a correlation mechanism between parameters, improving measurement data accuracy and reducing the impact of environmental interference through real-time calibration and compensation. Remote charge and discharge management testing and processing enable precise control and real-time monitoring of the charge and discharge process, promptly detecting abnormalities and effectively preventing safety hazards such as overcharging and over-discharging. Remote health assessment processing accurately assesses the battery's health status and remaining life through multi-dimensional data analysis and intelligent algorithms, providing a scientific basis for maintenance decisions. This integrated testing solution reduces manual intervention, improves testing efficiency, and reduces testing costs. Furthermore, standardized testing procedures and data processing methods ensure the repeatability and comparability of test results. This solution is particularly well-suited for battery management in large-scale electric truck fleets, enabling remote monitoring of battery performance and predictive maintenance, providing technical support for safe electric truck operations. Through efficient data collection and analysis, the solution promptly identifies battery degradation trends and provides early warning of potential failures, effectively extending battery life and reducing operational costs. Most importantly, the solution implements intelligent and automated battery testing, significantly reducing manual 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) Performing key generation processing on the device authentication identifier using the RSA encryption algorithm to obtain a temporary session key, and performing bidirectional transmission processing on the temporary session key to obtain key confirmation information;

[0035] (4) performing consistency verification on the key confirmation information to obtain a verification result sequence, and performing security level assessment 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 2.4 GHz Bluetooth communication spectrum with a scan cycle of 1.28 seconds. This scanning process uses a frequency hopping mechanism, polling and sending scan request packets on 79 channels, with a dwell time of 0.625 milliseconds on each channel. Upon receiving the scan request, the Bluetooth module of the truck's lithium battery management system returns an advertising packet containing the device identification code. The device identification code consists of a 48-bit MAC address (e.g., F8:E1:A2:B3:C4:D5) and a 16-bit device type code (e.g., 0x1234, indicating a BMS device). After receiving the advertising packet, the mobile terminal extracts the device identification code, device name, service UUID, and other information to form a target device list. Signal strength testing is performed for each device in the target device list. The received signal strength indicator (RSSI) reflects the degree of Bluetooth signal attenuation. Measurements are performed using a sampling frequency of 10 times / second, continuously collecting 30 samples. The RSSI value (in dBm) and the corresponding timestamp are recorded for each sample to construct a signal strength matrix. The number of rows in the matrix corresponds to the number of detected devices, the number of columns is the number of sampling times, and each matrix element contains the RSSI value and time information.

[0038] During the device screening phase, the RSSI average and standard deviation are calculated for each device in the signal strength matrix. Screening thresholds are set: 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 these thresholds are considered candidate devices. Identity verification then proceeds. This process involves reading the device's manufacturer ID (16 bits), product serial number (32 bits), and firmware version number (8 bits), and matching these numbers against a pre-configured database of authorized devices. Upon successful verification, a 32-bit device authentication identifier is generated, consisting of a 16-bit timestamp and a 16-bit random sequence. RSA key generation uses a 2048-bit key length. First, two prime numbers, p (1024 bits) and q (1024 bits), are generated. The product, n, is calculated as the modulus. A public key exponent, e (typically 65537), is selected, and a private key exponent, d, is calculated. 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, calculating a SHA-256 hash value for the key confirmation information. This check 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 is assessed based on multiple indicators: key strength (2048 bits is the highest level), authentication method (bidirectional authentication is the highest level), and encryption algorithm type (asymmetric encryption is the highest level). The assessment result is quantified into a three-digit security level identifier, such as "A10" for the highest security level. Finally, an encrypted communication channel is established, selecting the Advanced Encryption Standard (AES-256) encryption algorithm and using the remote Bluetooth control key for encrypted data transmission. The channel establishment process includes negotiating encryption parameters (key length and encryption mode), setting the data transmission rate (default is 1Mbps), and configuring the retransmission mechanism (maximum 3 retransmissions). Upon completion, the remote Bluetooth control key is officially generated.

[0040] For example, during an actual truck lithium battery test, a mobile terminal initiated a Bluetooth scan and detected four Bluetooth devices. The first device had a MAC address of "F8:E1:A2:B3:C4:D5" and a device type code of "0x1234." Its 30 consecutive RSSI samples were [-68, -67, -69, -68, -67, -68, -69, -68, -67, -68...] dBm, resulting in an average of -68.2 dBm, a standard deviation of 1.8 dBm, and an LQI of 175. The second device had an average RSSI of -82.5 dBm, a standard deviation of 5.6 dBm, and an LQI of 145. The third device had an average RSSI of -71.3 dBm, a standard deviation of 2.1 dBm, and an LQI of 168. The fourth device had an average RSSI of -77.8 dBm, a standard deviation of 4.2 dBm, and an LQI of 152. After threshold screening, the first and third devices were selected as candidates. Identity verification revealed that the first device was the target BMS. Its manufacturer ID "0xABCD," serial number "12345678," and firmware version "0x25" matched the authorized database, generating an authentication identifier "8A7B6C5D." The RSA algorithm generated a temporary session key "7F6E5D4C3B2A1908." Bidirectional transmission was successfully verified, and the HMAC check passed, resulting in a security level of "A10." Finally, an AES-256 encrypted channel was established, generating a remote Bluetooth control key for subsequent communications.

[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) Perform channel parameter testing on the bidirectional communication channel to obtain channel performance indicators, and perform 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 using 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 on the coded instruction sequence to obtain a priority queue, and performing channel switching test processing on the priority queue to obtain channel switching parameters;

[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 cache control parameters;

[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 generating an instruction set to obtain a dual-channel acquisition instruction set.

[0052] Specifically, when parsing the status code of a remote Bluetooth control key, the key's status identification bits are first extracted, including the key validity bit (8 bits), encryption type bit (4 bits), communication priority bit (4 bits), and check bit (8 bits), forming a 24-bit status code. The status codes are arranged in chronological order to generate a key status sequence, which is used to determine the key's operating status and permission level. Based on the key status sequence, a Bluetooth master-slave bidirectional communication channel is allocated, with the master channel responsible for control command transmission and the slave channel responsible for data collection and return. After the bidirectional communication channel is established, channel parameter tests are performed, including channel quality testing (sending 100 test packets to calculate packet loss rate), transmission delay testing (measuring round-trip time (RTT)), and bandwidth testing (testing maximum throughput). Channel performance indicators cover key parameters such as packet loss rate, average delay, and effective bandwidth. Based on the test results, optimal communication protocol parameters are matched, including packet size (adjustable from 20 to 100 bytes), transmission rate (selectable from 1 Mbps or 2 Mbps), and number of retransmissions (configurable from 1 to 5). The data frame format definition uses 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 (up to 255 bytes), and the frame trailer contains a checksum (16 bits). The CRC checksum algorithm uses the CRC-16-CCITT polynomial to perform redundancy check calculations on the data frame and generate the checksum and verification rules. Data packet segmentation uses a sliding window mechanism to divide long data streams into fixed-size data packets (e.g., 64 bytes / packet). Each packet contains a sequence number, data payload, and check bits. 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, constructing an N×M resource allocation matrix (N is the number of data types, M is the number of time slices). Channel coding uses differential Manchester coding to improve anti-interference capabilities. Command priority is determined by data type: emergency alarms have the highest priority (priority 3), control commands are next (priority 2), and general data is the lowest (priority 1). Channel switching testing verifies the stability of dual-channel switching by sending test sequences. Data acquisition modes include continuous, triggered, and timed acquisition, with an adjustable sampling frequency range of 1Hz-1kHz. Time windowing uses a combination of fixed window sizes (e.g., 100ms) and sliding windows (50% overlap). Data caching uses a double buffering mechanism with a buffer size of 1KB. Data upload is triggered when the buffer occupancy reaches 80%.

[0054] Channel load balancing uses a dynamic weighting method to adjust the load ratio between the master and slave channels based on real-time data traffic. Data flow control is based on a token bucket algorithm, controlling the data transmission rate and matching the token generation rate with bandwidth constraints. The resulting dual-channel acquisition instruction set consists of three categories: channel configuration instructions, data acquisition instructions, and control instructions.

[0055] For example, during testing of lithium-ion battery packs for electric trucks, the remote Bluetooth control key parsing yielded the status code "0xAF1234," indicating AES encryption and high communication priority. Channel parameter testing revealed a 0.5% packet loss rate, 15ms average latency, and 945kbps bandwidth for the primary channel; a 0.8% packet loss rate, 18ms average latency, and 927kbps bandwidth for the secondary channel. The data frame structure was configured as an 8-byte header, a 64-byte data segment, and a 2-byte CRC checksum. During packet segmentation, a 20ms sampling window was used for battery voltage data, and a 100ms window for temperature data. Bandwidth allocation: 60% for control command transmission on the primary channel and 40% for data acquisition on the secondary channel. The sampling frequency was set to 100Hz for voltage and current, and 10Hz for temperature. The cache strategy employed a 512-byte buffer for the primary channel and a 1024-byte buffer for the secondary channel. With this configuration, a dual-channel acquisition instruction set was successfully established, enabling stable monitoring of battery parameters. 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 the real-time monitoring requirements.

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

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

[0058] (2) collecting and processing the voltage signal of the sampling trigger mark to obtain the original voltage data, and performing noise elimination processing on the original voltage data to obtain the effective value of the voltage;

[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 current sampling values;

[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 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 coding processing on the operating condition feature combination to obtain an operating condition coding sequence, and performing feature spectrum generation processing on the operating condition coding 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 acquisition parameter configuration information, including sampling frequency (100Hz for voltage / current, 10Hz for temperature), sampling accuracy (0.1mV for voltage, 0.1A for current, and 0.1°C for temperature), and trigger mode (timed trigger / threshold trigger). Based on the parameter configuration, an acquisition configuration sequence is generated, and a sampling timer is designed according to the timing sequence to generate a periodic sampling trigger flag. Upon receiving the sampling trigger flag, voltage signal acquisition is initiated. The acquisition process uses a 16-bit ADC with a range of 0-5V and a resolution of 0.076mV. The collected raw voltage data contains high-frequency noise and power frequency interference. This noise is removed using a Butterworth low-pass filter with a cutoff frequency set to 500Hz to filter out high-frequency interference and obtain the voltage RMS value. The voltage RMS value is then segmented and quantized, dividing the continuous voltage value into multiple discrete levels, each corresponding to a 0.1V voltage interval. Voltage characteristic data, including mean, peak, and valley values, is then extracted.

[0067] Current signal acquisition is performed synchronously with voltage signal acquisition, using a Hall current sensor with a range of ±500A and a resolution of 0.1A. Waveform feature extraction is performed on the collected current sampling values, and characteristic parameters such as the effective value of the current, peak factor, and waveform distortion rate are calculated to form a current feature sequence. Temperature signal acquisition uses a thermistor array distributed at key locations in the battery pack, with a sampling frequency of 10Hz and a measurement range of -20°C to 80°C. Multi-point distribution analysis is performed on the collected temperature detection values, and the temperature gradient and temperature difference at each temperature measurement point are calculated. The temperature distribution curve is plotted 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. A feature space is constructed based on the synchronized data, with each sample point containing eigenvalues for the three dimensions of voltage, current, and temperature. The feature vector group is standardized using the Z-score standardization method to achieve a mean of 0 and a standard deviation of 1. The standardized feature data is converted to the frequency domain using 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 a spectral feature set. Based on the spectral characteristics, operating mode recognition is performed, and different voltage, current, and temperature combinations are classified into operating conditions such as charging, discharging, static, and balancing.

[0069] The identified operating condition type identifiers are combined to create a feature vector from multidimensional features within the same operating condition. Huffman coding is used to compress the combined operating condition features, reducing data storage space and generating a condition code sequence. Finally, a multidimensional battery operating condition characteristic spectrum is generated based on the code sequence, including both time-domain and frequency-domain features of voltage, current, and temperature.

[0070] For example, during testing of an electric truck equipped with a 200kWh lithium-ion battery pack, dual-channel acquisition instructions configured the following sampling parameters: voltage sampling frequency of 100Hz, accuracy of 0.1mV; current sampling frequency of 100Hz, accuracy of 0.1A; and temperature sampling frequency of 10Hz, accuracy of 0.1°C. Within a 10-second test window, the voltage channel collected 1000 raw data points. The raw voltage values fluctuated between 3.2V and 3.8V. After low-pass filtering, the RMS voltage stabilized at around 3.65V, with peak-to-peak fluctuations reduced to less than 0.05V. Current sampling recorded the charging process, with current values gradually increasing from 0A to 50A. Extracted features included a rise time of 2s and a steady-state ripple of ±0.5A. Temperature data was collected from 12 measurement points, with a maximum temperature of 38.5°C and a minimum temperature of 36.2°C, resulting in a temperature difference of 2.3°C. Parameter correlation analysis showed that temperature increased linearly during the current increase, with a correlation coefficient of 0.92. After standardization and FFT transformation of the characteristic data, a clear charging characteristic frequency component was detected in the 0-10Hz frequency band. The operating condition identification results indicate that the battery is currently in the constant current charging phase. The encoded characteristic spectrum accurately reflects the operating status of the battery pack. The data compression ratio during the entire test process reached 5:1, effectively saving 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 on the multi-dimensional battery operating condition characteristic spectrum to obtain an operating condition parameter sequence, and performing benchmark value calibration on the operating condition parameter sequence to obtain a calibration benchmark set;

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

[0074] (3) Perform parameter linkage analysis on the correction coefficient set to obtain a linkage variable group, and perform 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) The control sequence is constructed and processed for the rule effectiveness indicators to obtain an intelligent calibration control sequence.

[0077] Specifically, the multidimensional battery operating condition characteristic spectrum is analyzed for characteristic quantities, extracting operating condition parameters including voltage characteristic values (cell voltage, voltage difference), current characteristic values (charge / discharge current, peak current), and temperature characteristic values (cell temperature, temperature gradient), as well as their temporal trends. The analyzed parameters are then formed into an operating condition parameter sequence, each containing a timestamp, parameter value, and characteristic identifier. The operating condition parameters are calibrated to baseline values using standard test equipment, establishing a correspondence between measured values and true values to form a calibration benchmark set. This calibration benchmark 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°C, 25°C, 45°C). The calibration benchmark set then enters the range calibration phase, calculating the range correction factor for each parameter based on 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; and the temperature range is -20°C to 80°C with a resolution of 0.1°C. After calibration, a correction parameter matrix is generated. The matrix elements contain correction parameters such as zero drift, gain error, and linearity error. A Kalman filter algorithm is used to dynamically correct errors in the correction parameter matrix. The algorithm process includes: state prediction (predicting the current state based on the previous state), measurement update (correcting the predicted value based on the actual measurement value), and gain calculation (calculating the optimal gain based on the predicted error and measurement error). Through iterative calculation, a set of correction coefficients is obtained, effectively eliminating random and systematic errors.

[0078] The correction coefficient set enters the parameter linkage analysis phase, analyzing the interplay between voltage, current, and temperature. Mapping relationships between these parameters are established, such as the voltage drop caused by current changes and the temperature variation patterns during charging and discharging, to form a linkage variable group. Dynamic range definition is performed on the linkage variable group to determine the appropriate range and limits for 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°C / min, generating parameter constraints. Calibration rules are generated based on these parameter constraints. These rules include: parameter over-limit protection rules (e.g., voltage exceeding 4.2V triggers overcharge protection), parameter balancing rules (e.g., temperature balancing is activated when a temperature difference exceeds 5°C), and parameter compensation rules (e.g., the temperature-to-voltage compensation coefficient). After the calibration rules are stored in the rule base, they are validated and tested using typical operating data to verify their effectiveness. Metrics 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 indicator. This sequence includes parameter collection instructions, data processing instructions, and protection control instructions. These instructions are sorted by 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-duty truck equipped with a 300kWh lithium-ion battery pack. Feature analysis extracted cell voltage data (3.45V, 3.47V, 3.46V, etc.), charging current (80A), and temperature distribution (36.5°C-38.2°C). Using a high-precision reference source, the calibration process measured an actual value of 3.502V at 3.5V, resulting in a voltage correction factor of 1.0006. At 80A, the actual value was 80.3A, resulting in a current correction factor of 1.0037. At 37°C, the actual value was 37.1°C, resulting in a temperature correction factor of 1.0027. The Kalman filter's state noise covariance was set to 0.01, and its measurement noise covariance to 0.1. After 100 iterations, the voltage measurement error decreased from ±5mV to ±1mV, and the current measurement error decreased from ±0.5A to ±0.1A. Parameter linkage analysis revealed that at an 80A charging current, the cell voltage decreases by 2mV for every 1°C increase in battery temperature. Based on this, a temperature compensation coefficient of -2mV / °C was established. Calibration rule verification showed that temperature compensation improved voltage measurement accuracy by 85%, with a response time of less than 100ms and a rule effectiveness of 98%. The resulting intelligent calibration control sequence includes control parameters such as a 20ms data acquisition period, a 100ms temperature compensation update period, an overvoltage protection threshold of 4.25V, and an overtemperature protection threshold of 50°C.

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

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

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

[0084] (3) performing charging process data acquisition 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 and indexing, categorizing the parameters by functional type into control parameters (charging current, cutoff voltage, and charging time) and protection parameters (overvoltage, undervoltage, and overtemperature). Using a hash indexing method, a unique identification code is assigned to each parameter type, such as "CH01" for the charging parameter group and "DCH01" for the discharging parameter group, forming a charge and discharge test parameter group. Standard operating condition matching is performed on the test parameter group. A table lookup is used to match the parameters to preset operating condition types, including standard charging conditions (0.5C constant current charging to 4.2V), fast charging conditions (1C charging to 4.2V), and standard discharging conditions (0.5C discharging to 3.0V), thereby establishing an operating condition matching strategy table. The operating condition matching strategy is then transferred to the BMS communication protocol parsing phase, parsing the BMS communication frame format, which includes a frame header (2 bytes), a command word (1 byte), a data length (1 byte), a data segment (variable length), and a checksum (2 bytes). Communication interface parameters were extracted: baud rate 19200 bps, 8 data bits, 1 stop bit, and no parity. Communication data was encapsulated using the Bluetooth data frame encoding algorithm, employing differential Manchester encoding to convert each raw data bit into two transition levels to improve anti-interference capabilities. Data packet size was kept within 20 bytes, a CRC16 checksum was added, and a test instruction packet was generated. Charging process data was collected using a multiplexing method, simultaneously monitoring the voltage, current, and temperature of 96 battery cells. The sampling frequency was set to 100 Hz for voltage, 100 Hz for current, and 10 Hz for temperature. Data was recorded using timestamps with millisecond accuracy. The charging process records included information such as cell voltage curves, charging current curves, temperature change curves, charging duration, and charged capacity. The recorded data was then evaluated for temperature uniformity. The mean and standard deviation of the temperatures at all measurement points were calculated, and temperature intervals were divided to generate a temperature distribution diagram.

[0088] The discharge capacity test is performed based on the temperature distribution state, using a constant current discharge method 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 dispersion 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 cells in series and 3 cells in parallel, the parameter index generates charging control parameters: constant current charging current 150A (0.5C), cutoff voltage 4.2V, maximum charging time 4 hours; protection parameters: overvoltage protection 4.25V, undervoltage protection 2.8V, and overtemperature protection 55°C. The 0.5C constant current charging mode is selected for standard operating conditions. 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, voltage, etc.), and checksum. During the charging test, real-time data collected shows that the charging current remains stable at 150±0.5A, the cell voltage increases from 3.50V to 4.15V, and the temperature rises from 28°C to 35°C. The temperature distribution data at 96 measurement points is calculated to yield an average temperature of 32.5°C, a standard deviation of 1.2°C, and a maximum temperature difference of 3.8°C. Discharge capacity test results: Initial capacity of 300Ah dropped to 285Ah after 100 cycles, with a capacity decay rate of 5%. Cell voltage consistency analysis showed a maximum voltage difference of 35mV and a voltage standard deviation of 12mV. The resulting status monitoring record includes key indicators such as SOC 85%, SOH 95%, an 8% increase in internal resistance, and an insulation resistance greater than 1MΩ, fully documenting 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) Extract and process historical data from battery operation status monitoring records to obtain state change time series data, and perform 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) Performing SOC estimation and analysis on the battery health feature vector to obtain a power estimation value, and then performing SOH attenuation trend prediction on the power estimation value through a particle filter algorithm 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 then 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 identification 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 conduct life-influencing factor analysis on the battery performance score to obtain an influencing factor matrix;

[0097] (7) Perform maintenance suggestion generation processing on the impact 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) The fault type determination result is processed to generate a performance test report to obtain a lithium battery performance test report.

[0100] Specifically, historical data is extracted from battery operating status monitoring records, including time-series data such as voltage, current, temperature, and SOC curves. The extracted data is sorted by timestamp to form a state change time-series dataset. The neural network algorithm utilizes a three-layer BP network structure. The input layer contains 10 feature nodes, including voltage, current, temperature, and SOC; the hidden layer contains 20 nodes; and the output layer contains 4 nodes corresponding to health characteristic parameters. Through 5000 training iterations, the battery health characteristics are learned and a battery health feature vector is generated. SOC estimation analysis uses the ampere-hour integration method combined with Kalman correction to calculate the remaining capacity percentage. The estimated charge value is processed using a particle filter with 100 particles, each containing a SOH decay parameter. The particle filter algorithm uses a three-step process: prediction, update, and resampling, to predict the SOH decay trend and output a life prediction curve. Internal resistance analysis is performed on the life prediction curve. The battery internal resistance is calculated using the DC internal resistance method. 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; where Vmax is the maximum voltage value (V) and 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) at 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 healthy state at time t (%); SOH0 is the initial healthy state (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, calculated as follows:

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

[0112] Where: T is the actual operating temperature (°C); Tref is the reference temperature (usually 25°C); I is the actual operating current (A); Iref is the reference current (usually the current corresponding to a 0.5C rate); DOD is the depth of discharge (a decimal between 0 and 1); w1, w2, and 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 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] Cycling performance evaluation is based on capacity retention data, calculating the attenuation ratio by comparing the initial capacity with the current capacity. Charge and discharge efficiency calculations consider the ratio of charging input energy to discharging output energy, incorporating a temperature compensation factor to determine the actual energy conversion efficiency. Temperature sensitivity analysis records changes in performance parameters at different temperatures and plots temperature-sensitive characteristic curves. Safety risk assessments set risk levels based on abnormal temperature, voltage, and internal resistance, generating risk warning indicators. Risk warning indicators undergo consistency evaluation, and a weighted scoring method is used to calculate battery performance scores. The weights are: 30% for capacity retention, 20% for internal resistance growth, 20% for temperature uniformity, and 30% for voltage consistency. Lifespan influencing factors analysis uses principal component analysis 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] Calculation of temperature-sensitive characteristic parameters:

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

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

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

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

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

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

[0132] 2. Construction of impact factor matrix:

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

[0134] The matrix element Mij represents the j-th 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, where n is the current cycle number and 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, where H is the environmental humidity (%).

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

[0148] 1. Data normalization: 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 obtained principal component contribution rate and cumulative contribution rate can be used to analyze the influence of each factor.

[0154] Maintenance recommendations are generated based on an influencing factor matrix and include specific measures such as charging strategy optimization, temperature management improvements, and balanced maintenance. Performance improvement plans offer 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 utilizes a decision tree algorithm to determine the fault type based on abnormal parameter combinations.

[0155] For example, in a test of a 300kWh truck power battery, historical data recorded 1000 cycles of operation. Neural network training results showed a voltage feature weight of 0.3, a temperature feature weight of 0.25, an internal resistance feature weight of 0.25, and a capacity feature weight of 0.2. State-of-charge (SOC) estimation accuracy reached 97%, and particle filter prediction indicated that the SOH would drop to 80% by the 2000th cycle. Internal resistance test data showed a new battery internal resistance of 0.5mΩ and a current internal resistance of 0.65mΩ, a 30% increase. Capacity tests revealed an initial capacity of 300Ah, a current capacity of 276Ah, and a capacity retention rate of 92%. Charge and discharge efficiency tests showed a charge capacity of 295Ah and a discharge capacity of 285Ah at 25°C, with an energy conversion efficiency of 96.6%. Temperature tests revealed a capacity drop of 2% at 40°C and a 15% drop at 0°C. Consistency evaluation scores: Capacity retention rate 27 points (out of 30 points), internal resistance 19 points (out of 20 points), temperature balance 18 points (out of 20 points), voltage consistency 28 points (out of 30 points), total score 92 points. Analysis of influencing factors found that: temperature fluctuations contributed 40%, overcharge contributed 30%, high current discharge contributed 20%, and other factors contributed 10%. Maintenance recommendations include: controlling the charging temperature at 15-35°C, the charge rate not exceeding 0.5C, and regular battery balancing. The final diagnostic report pointed out that the overall condition of the battery pack is good, and it is recommended to optimize the temperature management strategy. 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] Matching module 201, used to perform intelligent remote 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 lithium battery data acquisition protocol test processing 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 on the battery operation status monitoring record to obtain a lithium battery performance test report.

[0163] Through the collaborative efforts of these components, intelligent remote matching establishes a secure and reliable Bluetooth communication link, effectively preventing wireless signal interference and unauthorized device access, and improving communication security and stability. The lithium battery data acquisition protocol test processing utilizes a dual-channel design, enabling separate transmission of control commands and data acquisition, enhancing the real-time and reliability of data transmission. Dynamic voltage, current, and temperature collaborative acquisition processing enables simultaneous acquisition and correlation analysis of multiple parameters, accurately reflecting the battery's operating status under different operating conditions and providing comprehensive data support for battery performance evaluation. Remote parameter linkage calibration processing establishes a correlation mechanism between parameters, improving measurement data accuracy and reducing environmental interference through real-time calibration and compensation. Remote charge and discharge management test processing enables precise control and real-time monitoring of the charge and discharge process, promptly detecting anomalies and effectively preventing safety hazards such as overcharging and over-discharging. Remote health assessment processing uses multi-dimensional data analysis and intelligent algorithms to accurately assess battery health and remaining life, providing a scientific basis for maintenance decisions. This integrated testing solution reduces manual intervention, improves testing efficiency, and reduces testing costs. Furthermore, standardized testing procedures and data processing methods ensure the repeatability and comparability of test results. This solution is particularly well-suited for battery management in large-scale electric truck fleets, enabling remote monitoring of battery performance and predictive maintenance, providing technical support for safe electric truck operations. Through efficient data collection and analysis, the solution promptly identifies battery degradation trends and provides early warning of potential failures, effectively extending battery life and reducing operational costs. Most importantly, the solution implements intelligent and automated battery testing, significantly reducing manual 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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above 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 remote matching on the Bluetooth identification information of the truck's lithium battery management system to obtain the remote Bluetooth control key; Perform lithium battery data acquisition protocol testing on remote Bluetooth control keys to obtain dual-channel acquisition instruction sets; 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; The remote parameter linkage calibration process of the multi-dimensional battery operating condition characteristic spectrum is performed 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 benchmark value calibration processing on the operating condition parameter sequence to obtain a calibration benchmark set; performing range correction processing on the calibration benchmark 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 processing on the correction coefficient set to obtain a linkage variable group, and performing dynamic range definition processing 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; performing control sequence construction processing on the rule validity index to obtain the 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 intelligent long-distance matching process of the Bluetooth identification information of the truck lithium battery management system to obtain the 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 using 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 on the key confirmation information to obtain a verification result sequence, and performing security level assessment 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 state sequence, and performing link channel allocation processing on the key state 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 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 cache control parameters; 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 subjected to instruction set generation processing 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 raw voltage data, and performing noise elimination processing on the raw voltage 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 current sampling values; 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 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 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; 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; Performing an operating condition pattern recognition process on the frequency spectrum feature set to obtain an operating condition type identifier, and performing a feature combination process on the operating condition type identifier to obtain an operating condition feature combination; The operating condition characteristic combination is subjected to data compression coding 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 charge and discharge management test processing of the intelligent calibration control sequence is performed 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 using 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 cell voltage deviation; The cell voltage deviation is processed by BMS system status record to obtain the battery operation status monitoring record.

6. The truck lithium battery testing method based on Bluetooth communication according to claim 1, characterized in that: The battery operation status monitoring record is remotely evaluated 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 characteristic vector to obtain a power estimation value, and performing SOH attenuation 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 using a fuzzy inference algorithm to obtain a battery health grade identifier; Performing a cycle performance evaluation process on the battery health level identifier to obtain capacity retention rate data, and performing a charge and discharge efficiency calculation process 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 on the risk warning indicators to obtain a battery performance score, and performing life influencing factor analysis on the battery performance score to obtain an influencing factor matrix; Performing maintenance suggestion generation processing on the impact 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 solution 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 to generate a performance test report to obtain the lithium battery performance test report.

7. A truck lithium battery testing device based on Bluetooth communication, used to implement the truck lithium battery testing method based on Bluetooth communication according to any one of claims 1 to 6, 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; The acquisition module 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; The calibration module is used to perform remote parameter linkage calibration on the multi-dimensional battery operating condition characteristic spectrum to obtain an intelligent calibration control sequence; The calibration module is specifically configured to: perform characteristic quantity analysis processing on the multi-dimensional battery operating condition characteristic spectrum to obtain an operating condition parameter sequence, perform benchmark value calibration processing on the operating condition parameter sequence to obtain a calibration benchmark set; perform range correction processing on the calibration benchmark set to obtain a correction parameter matrix, perform error correction processing on the correction parameter matrix through a Kalman filter algorithm to obtain a correction coefficient set; perform parameter linkage analysis processing on the correction coefficient set to obtain a linkage variable group, perform dynamic range definition processing on the linkage variable group to obtain parameter constraint conditions; perform calibration rule generation processing on the parameter constraint conditions to obtain a calibration rule base, perform rule verification processing on the calibration rule base to obtain a rule validity index; perform control sequence construction processing on the rule validity index to obtain the 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 battery operation status monitoring records; 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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