Automatic FOTA test method and system for truck lithium battery system

Abstract

The invention discloses an automatic FOTA test method and system for a truck lithium battery system, and relates to the technical field of battery management, and the method comprises the steps: collecting the BMS data of a to-be-tested truck lithium battery system to a test host through a CAN bus; establishing a test sample library based on the BMS data, and performing segmentation processing on the test sample library according to a battery working state; performing feature extraction on each data segment in the test sample library to obtain a feature vector group; inputting the feature vector group into a preset FOTA upgrading evaluation model, and outputting an FOTA upgrading suggestion time window; and executing firmware upgrading of the battery management system in the FOTA upgrading suggestion time window, and recording BMS data in the upgrading process as a new test sample at the same time. According to the method, the BMS data of the truck lithium battery system is subjected to real-time acquisition, segmented processing and feature extraction, and the FOTA upgrade evaluation model is trained in combination with historical upgrade data, so that a proper upgrade time window can be accurately predicted.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to an automated FOTA testing method and system for a truck lithium battery system. Background Art

[0002] With the popularity of electric vehicles, especially heavy-duty vehicles, the application of lithium battery systems in trucks has gradually become mainstream. In order to ensure the reliability and performance of the battery system, the battery management system (BMS) has been widely studied and applied as the core unit for controlling and monitoring the status of the battery pack. The BMS system can monitor the battery's voltage, temperature, SOC (State of Charge) and other parameters to ensure that the battery operates within the appropriate temperature and voltage range. However, with the long-term use of the vehicle, the battery performance gradually decays, and the nonlinear cumulative effect of battery parameter changes may occur under different working conditions, which in turn affects the system's work efficiency and safety. For this reason, Firmware Over-The-Air (FOTA) has gradually been applied to the BMS system to remotely and in real time update the firmware, improve the adaptability of the battery system in different scenarios, and optimize its performance. However, the traditional FOTA method has the problem of improper upgrade time and scenario selection, and often cannot adapt to the complex truck use environment, which is easy to cause upgrade failure or battery abnormality.

[0003] The existing automated FOTA test methods for lithium battery systems have some technical bottlenecks in practical applications. First, the traditional method relies on a preset static test process, lacks automated segmentation processing and feature extraction of dynamic battery data under different working conditions, and is difficult to efficiently and accurately capture BMS key status information; second, the existing system relies on empirical judgment or simple algorithms to select the upgrade time window, and fails to effectively combine key parameters such as voltage fluctuations, temperature uniformity, and SOC decay rate of the battery pack, resulting in FOTA upgrade efficiency and reliability. It is difficult to guarantee. In addition, the evaluation models of some systems fail to make full use of real-time BMS data to build a sample library, lack sufficient data support, and the accuracy and adaptability of the upgrade evaluation model are limited. Therefore, how to use real-time battery data to build a sample library, extract features in segments, and realize intelligent time window selection has become an urgent problem to be solved in this field. Summary of the invention

[0004] In view of the problems existing in the above-mentioned automated FOTA testing method for lithium battery systems, the present invention is proposed.

[0005] Therefore, the present invention provides an automated FOTA testing method for a truck lithium battery system, which can solve the problems mentioned in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides an automated FOTA testing method for a truck lithium battery system, which includes:

[0008] Collect the BMS data of the lithium battery system of the truck to be tested to the test host through the CAN bus;

[0009] Establish a test sample library based on the BMS data, and process the test sample library in sections according to the battery working status;

[0010] Extracting features from each data segment in the test sample library to obtain a feature vector group;

[0011] Inputting the feature vector group into a preset FOTA upgrade evaluation model, and outputting a FOTA upgrade recommended time window;

[0012] The battery management system firmware upgrade is performed within the FOTA upgrade recommended time window, and the BMS data during the upgrade process is recorded as a new test sample.

[0013] As a preferred solution of the automated FOTA test method for a truck lithium battery system of the present invention, the segmentation of the test sample library according to the battery working state includes:

[0014] The BMS data is processed in sections according to the charge and discharge status. When the charge and discharge current switches from zero to a positive value, it is marked as the starting point of the charge section. When the charge and discharge current changes from a positive value to zero, it is marked as the end point of the charge section. When the charge and discharge current switches from zero to a negative value, it is marked as the starting point of the discharge section. When the charge and discharge current changes from a negative value to zero, it is marked as the end point of the discharge section. The interval in which the current is continuously zero and the duration exceeds a preset value is marked as a static section.

[0015] In the charging process data segment, the charging rate, single charging time, charging cut-off voltage and ambient temperature parameters are recorded, and sub-data segments are established for the fast charging mode and the slow charging mode respectively;

[0016] In the discharge process data segment, the discharge rate, single discharge duration, discharge cut-off voltage and power requirement parameters are recorded, and the data is divided into a high power segment, a medium power segment (and a low power segment) according to the discharge power;

[0017] In the static process data segment, the static time, leakage current value and temperature change rate parameters are recorded, and the static process data is divided into a short-term static segment and a long-term static segment according to the static time.

[0018] As a preferred solution of the automated FOTA test method for truck lithium battery system described in the present invention, wherein: the feature extraction includes voltage fluctuation rate, temperature uniformity, SOC decay rate and comprehensive evaluation;

[0019] The feature vector group is described according to the four eigenvalues ​​obtained; the feature vector group includes the voltage fluctuation rate as the first dimension feature of the feature vector group, the temperature uniformity as the second dimension feature of the feature vector group, the SOC decay rate as the third dimension feature of the feature vector group, and the comprehensive evaluation as the fourth dimension feature of the feature vector group.

[0020] As a preferred solution of the automated FOTA test method for a truck lithium battery system according to the present invention, the voltage fluctuation rate is calculated as follows:

[0021]

[0022] Among them, R v (t) is the voltage fluctuation rate after temperature compensation, α is the temperature sensitivity coefficient, T(t) is the current temperature, T ref is the reference temperature, ΔV(t) is the voltage change at time t, and Δt is the sampling time interval;

[0023] The temperature uniformity is calculated as follows:

[0024]

[0025] Among them, U(T) is the temperature uniformity index considering voltage changes, T i is the temperature of the ith region, T avg is the average temperature, β is the voltage influence factor, is the voltage change rate, n is the total number of temperature detection points, T max is the maximum allowable operating temperature;

[0026] The calculation of the SOC decay rate is shown in the following formula:

[0027]

[0028] Among them, R SOC (t) is the SOC decay rate, is the original value of the SOC change rate, and f(T, V) is the temperature-voltage coupling influence function, as shown in the following formula:

[0029]

[0030] Among them, k1 and k2 are coupling coefficients, and T0 and V0 are standard working points.

[0031] As a preferred solution of the automated FOTA test method for a truck lithium battery system of the present invention, wherein: the comprehensive evaluation is obtained by weighting the voltage fluctuation rate, the temperature uniformity and the SOC decay rate;

[0032] The comprehensive evaluation can be expressed as follows:

[0033] EI=w1×R v (t)+w2×U(T)+w3×R SOC (t);

[0034] Among them, EI is a comprehensive evaluation index, w1, w2, and w3 are dynamic weight coefficients, which are updated through an adaptive algorithm:

[0035]

[0036] Among them, is the learning rate, EI real is the actual observed value, EI predict is the predicted value, w i (t+1) is the weight value at the next moment, w i (t) is the weight value at the current moment.

[0037] As a preferred solution of the automated FOTA test method for a truck lithium battery system according to the present invention, the output FOTA upgrade recommended time window includes:

[0038] Processing the feature vector group;

[0039] Inputting the processed feature vector group into the FOTA upgrade assessment model to obtain a preliminary time window;

[0040] The preliminary time window is screened to obtain a recommended time window.

[0041] As a preferred solution of the automated FOTA test method for a truck lithium battery system of the present invention, the feature vector group is processed as shown in the following formula:

[0042]

[0043] Among them, A(t) is the attention weight matrix, Q(t), V(t) is the query, key, and value matrix, H(t-1) is the historical state information, and γ1 is the weight coefficient of the historical information.

[0044] In the second aspect, the present invention further solves the safety problems existing in the automated FOTA test method for truck lithium battery systems. The embodiment provides an artificial intelligence-based vehicle fault diagnosis and maintenance assessment system, which includes:

[0045] Data acquisition module, used to collect BMS data of the lithium battery system of the truck to be tested to the test host through the CAN bus;

[0046] A segment processing module, used to establish a test sample library based on the BMS data, and segment the test sample library according to the battery working status;

[0047] A feature extraction module, used to extract features from each data segment in the test sample library to obtain a feature vector group;

[0048] A time window determination module, used for inputting the feature vector group into a preset FOTA upgrade evaluation model, and outputting a FOTA upgrade recommended time window;

[0049] The upgrade module is used to perform a battery management system firmware upgrade within the FOTA upgrade recommended time window, and record the BMS data during the upgrade process as a new test sample.

[0050] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the automated FOTA testing method for a truck lithium battery system as described in the first aspect of the present invention is implemented.

[0051] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the automated FOTA testing method for a truck lithium battery system as described in the first aspect of the present invention is implemented.

[0052] The beneficial effect of the present invention is that by real-time collection, segmented processing and feature extraction of the BMS data of the truck lithium battery system, a feature vector group covering battery voltage fluctuation, temperature uniformity and SOC decay rate is established, and the FOTA upgrade evaluation model trained with historical upgrade data can accurately predict the appropriate upgrade time window. This design has multiple beneficial effects: First, based on multi-dimensional feature extraction and temporal attention mechanism, it can dynamically identify battery state changes and accurately match the best upgrade time to avoid upgrade failures caused by unstable battery working state; secondly, the automated data verification and dynamic threshold adjustment mechanism improve the adaptability of the evaluation model and ensure the high success rate and safety of FOTA upgrades; finally, the solution synchronously records the newly generated BMS data when executing the upgrade, providing reliable data support for subsequent tests and realizing the automation and intelligence of the test process. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0054] Figure 1 Flowchart of automated FOTA testing methodology for truck lithium battery systems.

[0055] Figure 2 This is a partial screenshot of the test report for the automated FOTA test method for truck lithium battery systems. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0058] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0059] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides an automated FOTA test method for a truck lithium battery system, comprising the following steps:

[0060] S1: Collect the BMS data of the lithium battery system of the truck to be tested to the test host through the CAN bus, and the BMS data includes the battery pack voltage, battery pack temperature, SOC value and charge and discharge status;

[0061] The BMS data of the lithium battery system of the truck to be tested is collected to the test host through the CAN bus. The BMS data includes the battery pack voltage, battery pack temperature, SOC value and charge and discharge status, specifically including the following:

[0062] During the CAN bus acquisition process, the CAN communication baud rate is set to 500kbps, the standard frame format is used for data transmission, and the CAN message identifier uses the extended frame ID. In the BMS data, the battery pack voltage acquisition frequency is 100ms / time, the acquisition accuracy is 0.1V, and the acquisition range is 0-850V; the battery pack temperature acquisition frequency is 500ms / time, the acquisition accuracy is 0.1℃, and the acquisition range is -40℃ to 85℃; the SOC value is obtained by the coulomb counting method, the calculation frequency is 1s / time, the value range is 0-100%, and the accuracy is 0.1%; the charging and discharging status includes three state identifications: charging, discharging, and static; the test host is connected to the CAN bus through a USB-CAN converter, the converter's sending buffer is configured for 64 groups of messages, the receiving buffer is configured for 128 groups of messages, and it has an automatic retransmission function;

[0063] During the data collection process, the test host performs CRC check on the received CAN message. If the check fails, it automatically requests retransmission. If the check fails three times in a row, an error log is recorded.

[0064] S2: establishing a test sample library based on the BMS data, and dividing the test sample library into a charging process data segment, a discharging process data segment and a static process data segment according to the battery working state;

[0065] The test sample library is stored in a distributed database structure, the main data table records the timestamp, data segment type identifier and associated ID, and the sub-data tables respectively store the specific parameter values ​​of the BMS data;

[0066] The BMS data is processed in sections according to the charge and discharge status. When the charge and discharge current switches from zero to a positive value, it is marked as the starting point of the charge section. When the charge and discharge current changes from a positive value to zero, it is marked as the end point of the charge section. When the charge and discharge current switches from zero to a negative value, it is marked as the starting point of the discharge section. When the charge and discharge current changes from a negative value to zero, it is marked as the end point of the discharge section. The interval in which the current is continuously zero and lasts for more than 300s is marked as the static section.

[0067] In the charging process data segment, parameters such as charging rate, single charging time, charging cut-off voltage, ambient temperature, etc. are recorded, and sub-data segments are established for fast charging mode (rate ≥ 1C) and slow charging mode (rate < 1C) respectively;

[0068] In the discharge process data segment, the discharge rate, single discharge duration, discharge cut-off voltage, power requirement and other parameters are recorded, and the data is divided into a high power segment (≥80% rated power), a medium power segment (30%-80% rated power) and a low power segment (<30% rated power) according to the discharge power;

[0069] In the static process data segment, parameters such as static time, leakage current value, temperature change rate, etc. are recorded, and the static process data are divided into short-term static segment (<24h) and long-term static segment (≥24h) according to the static time.

[0070] During the data segment division process, a 50ms state switching judgment delay is set to avoid misjudgment caused by transient fluctuations. At the same time, additional transition interval data of 10s before and after the start and end of each data segment is recorded;

[0071] Perform data integrity check on each divided data segment, including data continuity, rationality of parameter value range and logical correctness of state switching. Unqualified data segments are marked as invalid and the reasons are recorded.

[0072] S3: extracting features from each data segment in the test sample library to obtain a feature vector group including voltage fluctuation rate, temperature uniformity and SOC decay rate;

[0073] Feature extraction is performed on each data segment in the test sample library to obtain a feature vector group including voltage fluctuation rate, temperature uniformity and SOC decay rate, which specifically includes the following contents:

[0074] First, in the process of extracting the voltage fluctuation rate feature, based on the accumulation of the absolute value of the voltage change in the standard time window, the product of the temperature difference and the temperature sensitivity coefficient is used as a correction term to establish a voltage fluctuation rate index after temperature compensation. This index increases the fluctuation rate value when the actual battery temperature is higher than the reference temperature, and decreases it when it is lower, thereby more accurately reflecting the impact of temperature on voltage fluctuation. It can be expressed as follows:

[0075]

[0076] Among them, R v (t) is the voltage fluctuation rate after temperature compensation, α is the temperature sensitivity coefficient, T(t) is the current temperature, T ref is the reference temperature, ΔV(t) is the voltage change at time t, and Δt is the sampling time interval.

[0077] Secondly, for the temperature uniformity characteristics, an improved uniformity calculation method is used. The sum of the squares of the deviations between each temperature detection point and the average temperature is used as the basis. The value is divided by the product of the number of detection points and the square of the highest temperature, and then corrected by the exponential decay term of the voltage change rate. Finally, the uniformity index is obtained by subtracting the corrected result from 1. In this way, the impact of voltage change on temperature distribution is quantified through the exponential decay term. When the voltage change rate is large, the temperature uniformity evaluation value decreases accordingly. The obtained temperature characteristics can be expressed as follows:

[0078]

[0079] Among them, U(T) is the temperature uniformity index considering voltage changes, T i is the temperature of the ith region, T avg is the average temperature, β is the voltage influence factor, is the voltage change rate, n is the total number of temperature detection points, T max is the maximum allowable operating temperature.

[0080] Extract the characteristics of SOC decay rate, establish the temperature-voltage dual-factor influence model, and make corrections through the temperature-voltage coupling influence function based on the rate of change of SOC over time. The coupling function consists of the square term of temperature deviation and the proportional term of voltage deviation, which are adjusted by the coupling coefficient respectively to realize the joint impact evaluation of temperature and voltage on SOC decay rate. It can be expressed as follows:

[0081]

[0082] Among them, R SOC (t) is the SOC decay rate, is the original value of the SOC change rate, and f(T, V) is the temperature-voltage coupling influence function, as shown in the following formula:

[0083]

[0084] Among them, k1 and k2 are coupling coefficients, and T0 and V0 are standard working points.

[0085] Finally, the above three features are combined in a weighted manner to form a comprehensive evaluation index. The weight coefficient adopts an adaptive update mechanism, and the weight is adjusted by the learning rate according to the deviation between the predicted value and the actual observed value. When the predicted value is lower than the actual value, the weight of the corresponding feature is increased, and vice versa, so that the evaluation system can be adaptively optimized. The comprehensive evaluation index can be expressed as follows:

[0086] EI=w1×R v (t)+w2×U(T)+w3×R SOC (t);

[0087] Among them, EI is a comprehensive evaluation index, w1, w2, and w3 are dynamic weight coefficients, which are updated through an adaptive algorithm:

[0088]

[0089] Among them, γ is the learning rate, EI real is the actual observed value, EI predict is the predicted value.

[0090] Based on the four eigenvalues ​​obtained, an n-dimensional basic eigenvector F is obtained, including the voltage fluctuation rate R after temperature compensation v (t) is taken as the first dimension eigenvalue, the improved temperature uniformity index U(T) is taken as the second dimension eigenvalue, the temperature-voltage coupled SOC decay rate RSOC(t) is taken as the third dimension eigenvalue, and the comprehensive evaluation index EI is taken as the fourth dimension eigenvalue.

[0091] S4: inputting the feature vector group into a preset FOTA upgrade evaluation model, wherein the FOTA upgrade evaluation model is trained according to historical upgrade data, and outputting a FOTA upgrade recommended time window;

[0092] In the FOTA upgrade evaluation model, an improved temporal attention mechanism is first used to process the feature vector group. This mechanism generates an attention weight matrix by calculating the relationship between the query matrix, the key matrix, and the value matrix, combined with the historical state information. Specifically, the product of the query matrix and the key matrix is ​​scaled and normalized, and then multiplied with the value matrix weighted and corrected by historical information to capture the temporal dependency of the feature sequence. This mechanism can effectively identify key temporal patterns in historical data and enhance the model's perception of temporal correlation. The temporal attention mechanism is shown in the following formula:

[0093]

[0094] Among them, A(t) is the attention weight matrix, Q(t), V(t) is the query, key, and value matrix, H(t-1) is the historical state information, and γ1 is the weight coefficient of the historical information.

[0095] It should be noted that the core structure of the evaluation model includes an input layer that receives a feature vector group, performs data standardization and time series alignment, uses a multi-layer perceptron to extract feature combination relationships, uses an ELU activation function in the hidden layer, and the output layer gives a time window prediction value and a credibility score. It also introduces a residual connection structure to alleviate the difficulty of deep network training.

[0096] Furthermore, the time window of the evaluation model output is further filtered:

[0097] During the screening process of the FOTA upgrade time window, the following key indicators must be met at the same time:

[0098] A time period in which the comprehensive evaluation index EI is higher than the preset benchmark threshold will be selected. At the same time, the fluctuation amplitude of the battery voltage is required to remain in a relatively stable state during this time period, that is, the fluctuation does not exceed 0.5 volts per minute. The temperature distribution uniformity index of the battery pack needs to be maintained above 0.85 to ensure the stability of the temperature field distribution. The battery state of charge SOC needs to be maintained in a moderate range of 30% to 80% to avoid safety risks caused by overcharging and over-discharging. In addition, the model trained based on historical data also needs to predict that the probability of successful upgrade within this time window is not less than 95%. Only time periods that meet these conditions at the same time will be determined by the system as suitable upgrade time windows.

[0099] Furthermore, by comparing the difference between the actual upgrade success rate and the target success rate, the scoring threshold is adjusted dynamically. When the actual success rate is lower than the target value, the threshold standard is increased by adjusting the coefficient; otherwise, the threshold requirement is appropriately lowered. This mechanism enables the evaluation standard to be automatically optimized according to the actual operation effect, improving the adaptability of the system. The specific adjustment is shown in the following formula:

[0100] S th (t+1)=S th (t)×[1+η(P success- P target )];

[0101] Among them, η is the adjustment coefficient, P success is the actual success rate, P target The target success rate.

[0102] Through the above evaluation model and time window selection mechanism, the optimal FOTA upgrade timing can be determined intelligently.

[0103] S5: Perform a battery management system firmware upgrade within the FOTA upgrade recommended time window, and record the BMS data during the upgrade process as a new test sample.

[0104] This embodiment also provides an automated FOTA test system for a truck lithium battery system, including:

[0105] Data acquisition module, used to collect BMS data of the lithium battery system of the truck to be tested to the test host through the CAN bus;

[0106] A segment processing module, used to establish a test sample library based on the BMS data, and segment the test sample library according to the battery working status;

[0107] A feature extraction module, used to extract features from each data segment in the test sample library to obtain a feature vector group;

[0108] A time window determination module, used for inputting the feature vector group into a preset FOTA upgrade evaluation model, and outputting a FOTA upgrade recommended time window;

[0109] The upgrade module is used to perform a battery management system firmware upgrade within the FOTA upgrade recommended time window, and record the BMS data during the upgrade process as a new test sample.

[0110] This embodiment also provides a computer device, which is suitable for the case of an automated FOTA test method for a truck lithium battery system, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the automated FOTA test method for a truck lithium battery system as proposed in the above embodiment.

[0111] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0112] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the automated FOTA test method for a truck lithium battery system proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (Static Random Access Memory, SRAM for short), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), an erasable programmable read-only memory (Erasable Programmable Read Only Memory, EPROM for short), a programmable read-only memory (Programmable Red-Only Memory, PROM for short), a read-only memory (Read-Only Memory, ROM for short), a magnetic memory, a flash memory, a disk or an optical disk.

[0113] Example 2 is the second example of the present invention. To further verify the advancement of the present invention, experimental simulation of the automated FOTA test method for a truck lithium battery system / comparison data with the prior art are provided.

[0114] In this test, 10 heavy-duty truck lithium battery systems operating under different working conditions were selected as test objects. The rated capacity of the battery system was 280kWh and the rated voltage was 750V. Before the test, the test equipment was calibrated, including the HIOKI 3390 power analyzer (accuracy level 0.1), Fluke Ti480 PRO infrared thermal imager (temperature resolution 0.05℃) and NI USB-8473s high-performance USB-CAN interface (supporting a maximum communication rate of 1Mbps). The test environment temperature was controlled at 25±2℃ and the relative humidity was 45±5%.

[0115] During the data collection phase, BMS data is collected in real time through the configured CAN bus network (baud rate 500kbps). To ensure data quality, a dual-channel redundant acquisition system is deployed on each test vehicle. The main channel is responsible for routine data collection, and the backup channel is responsible for data backup and cross-verification. Through the self-developed data acquisition software, high-precision acquisition of key parameters such as battery pack voltage, temperature, and SOC is achieved. Among them, the voltage data uses a sampling period of 100ms, the temperature data uses a sampling period of 500ms, and the SOC calculation frequency is 1s / time.

[0116] During the test, each vehicle went through a complete charge and discharge cycle and a static process. Through the intelligent scheduling algorithm, the test vehicles were operated under different working conditions, including fast charging mode (2C charging rate), standard charging mode (0.5C charging rate), high-power discharge condition (peak power 220kW), medium-power cruising condition (80kW continuous output) and low-power idling condition (<20kW). At the same time, static tests of different lengths were set, including four static conditions of 4h, 12h, 24h and 48h.

[0117] In order to avoid interference and anomalies in the data collection process, multiple data verification mechanisms are implemented: first, CRC verification at the hardware level to ensure the integrity of CAN communication data; second, data validity verification at the software level, including numerical range check, change rate limit and logical relationship verification; finally, manual inspection to regularly check and maintain the operation status of the collection system.

[0118] In the feature extraction stage, an improved sliding time window algorithm is used to process the raw data. The window length is dynamically adjusted according to the characteristics of different parameters: a 1-minute window is used for voltage fluctuation rate, a 5-minute window is used for temperature uniformity, and a 15-minute window is used for SOC decay rate. The data noise is eliminated through an adaptive filtering algorithm to improve the accuracy of feature extraction. In order to improve computing efficiency, a parallel computing architecture is implemented to distribute feature extraction tasks to multiple computing nodes for simultaneous processing.

[0119] The following are the relevant experimental data obtained, as shown in Table 1 and Figure 2 As shown:

[0120] Table 1 Experimental data table

[0121]

[0122] Through statistical analysis of the test data, we can conclude that:

[0123] The data shows that the test vehicles (TV-001, 002, 004, 005, 007, 009, 010) with voltage fluctuation rate lower than 0.4V / min have all been successfully upgraded, with an upgrade success rate of 100%. In contrast, among the test vehicles (TV-003, 006, 008) with voltage fluctuation rate higher than 0.4V / min, 2 failed to upgrade. This shows that the voltage fluctuation rate threshold setting proposed by the present invention has significant predictive value.

[0124] The temperature uniformity index of successfully upgraded vehicles is generally higher than 0.88, while the temperature uniformity of failed cases is lower than 0.85. In particular, the temperature uniformity of TV-009 reached 0.93, and its upgrade process was the smoothest, taking only 17.6 minutes. This verifies that the setting of the temperature uniformity threshold of 0.85 has good practicality.

[0125] The data shows that the SOC decay rate is significantly positively correlated with the upgrade time. The average upgrade time for test vehicles (TV-001, 004, 005, 007, 009, 010) with a decay rate below 0.5% / h is 18.5 minutes, while the average upgrade time for vehicles with a decay rate above 0.5% / h increases to 24.5 minutes. This finding provides an important basis for optimizing the selection of the upgrade time window.

[0126] Through analysis, it can be found that the comprehensive evaluation index (EI) has a significant linear correlation with the probability of successful upgrade (correlation coefficient R 2 =0.94). When EI≥0.85, the actual upgrade success rate reaches 100%; while in the case of EI<0.85, the upgrade failure rate increases significantly. This verifies that the comprehensive evaluation model proposed in the present invention has accurate prediction capabilities.

[0127] When all threshold requirements are met, the average time taken for successful upgrade cases is 19.0 minutes, with a standard deviation of only 0.6 minutes, showing good time consistency. Compared with the traditional method, which generally takes 30-40 minutes to upgrade, the present invention achieves significant efficiency improvement.

[0128] From the above analysis, it can be seen that the FOTA upgrade evaluation system proposed in the present invention can accurately predict the probability of successful upgrade and effectively improve the upgrade efficiency. In particular, under the synergistic effect of the three key characteristics of voltage fluctuation rate, temperature uniformity and SOC decay rate, the system shows excellent prediction accuracy and practical value. The experimental data fully verifies the innovation and practical application effect of the present invention.

[0129] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An automated FOTA test method for a truck lithium battery system, characterized by: include: Collect the BMS data of the lithium battery system of the truck to be tested to the test host through the CAN bus; Establish a test sample library based on the BMS data, and process the test sample library in sections according to the battery working status; Extracting features from each data segment in the test sample library to obtain a feature vector group; Inputting the feature vector group into a preset FOTA upgrade evaluation model, and outputting a FOTA upgrade recommended time window; The battery management system firmware upgrade is performed within the FOTA upgrade recommended time window, and the BMS data during the upgrade process is recorded as a new test sample.

2. The automated FOTA test method for a truck lithium battery system according to claim 1, characterized in that: The segmenting of the test sample library according to the battery working status comprises: The BMS data is processed in sections according to the charge and discharge status. When the charge and discharge current switches from zero to a positive value, it is marked as the starting point of the charge section. When the charge and discharge current changes from a positive value to zero, it is marked as the end point of the charge section. When the charge and discharge current switches from zero to a negative value, it is marked as the starting point of the discharge section. When the charge and discharge current changes from a negative value to zero, it is marked as the end point of the discharge section. The interval in which the current is continuously zero and the duration exceeds a preset value is marked as a static section. In the charging process data segment, the charging rate, single charging time, charging cut-off voltage and ambient temperature parameters are recorded, and sub-data segments are established for the fast charging mode and the slow charging mode respectively; In the discharge process data segment, the discharge rate, single discharge duration, discharge cut-off voltage and power requirement parameters are recorded, and the data is divided into a high power segment, a medium power segment (and a low power segment) according to the discharge power; In the static process data segment, the static time, leakage current value and temperature change rate parameters are recorded, and the static process data is divided into a short-term static segment and a long-term static segment according to the static time.

3. The automated FOTA test method for a truck lithium battery system according to claim 2, characterized in that: The feature extraction includes voltage fluctuation rate, temperature uniformity, SOC decay rate and comprehensive evaluation; The eigenvector group according to the obtained four eigenvalues; The feature vector group includes the voltage fluctuation rate as a first dimension feature of the feature vector group, the temperature uniformity as a second dimension feature of the feature vector group, the SOC decay rate as a third dimension feature of the feature vector group, and the comprehensive evaluation as a fourth dimension feature of the feature vector group.

4. The automated FOTA test method for a truck lithium battery system according to claim 3, characterized in that: The voltage fluctuation rate is calculated as follows: Among them, R v (t) is the voltage fluctuation rate after temperature compensation, α is the temperature sensitivity coefficient, T(t) is the current temperature, T ref is the reference temperature, ΔV(t) is the voltage change at time t, and Δt is the sampling time interval; The temperature uniformity is calculated as follows: Among them, U(T) is the temperature uniformity index considering voltage changes, T i is the temperature of the ith region, T avg is the average temperature, β is the voltage influence factor, is the voltage change rate, n is the total number of temperature detection points, T max is the maximum allowable operating temperature; The calculation of the SOC decay rate is shown in the following formula: Among them, R SOC (t) is the SOC decay rate, is the original value of the SOC change rate, and f(T, V) is the temperature-voltage coupling influence function, as shown in the following formula: Among them, k1 and k2 are coupling coefficients, and T0 and V0 are standard working points.

5. The automated FOTA test method for a truck lithium battery system according to claim 4, characterized in that: The comprehensive evaluation is obtained by weighting the voltage fluctuation rate, the temperature uniformity and the SOC decay rate; The comprehensive evaluation can be expressed as follows: EI=w1×R v (t)+w2×U(T)+w3×R SOC (t);; Among them, EI is a comprehensive evaluation index, w1, w2, and w3 are dynamic weight coefficients, which are updated through an adaptive algorithm: Among them, γ is the learning rate, EI real is the actual observed value, EI predict is the predicted value, w i (t+1) is the weight value at the next moment, w i (t) is the weight value at the current moment.

6. The automated FOTA test method for a truck lithium battery system according to claim 5, characterized in that: The output FOTA upgrade recommended time window includes: Processing the feature vector group; Inputting the processed feature vector group into the FOTA upgrade assessment model to obtain a preliminary time window; The preliminary time window is screened to obtain a recommended time window.

7. The automated FOTA test method for a truck lithium battery system according to claim 6, characterized in that: The feature vector group is processed as shown below: Among them, A(t) is the attention weight matrix, Q(t), V(t) is the query, key, and value matrix, H(t-1) is the historical state information, and γ1 is the weight coefficient of the historical information.

8. An automated FOTA test system for a truck lithium battery system, based on the automated FOTA test method for a truck lithium battery system according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, used to collect BMS data of the lithium battery system of the truck to be tested to the test host through the CAN bus; A segment processing module, used to establish a test sample library based on the BMS data, and segment the test sample library according to the battery working status; A feature extraction module, used to extract features from each data segment in the test sample library to obtain a feature vector group; A time window determination module, used for inputting the feature vector group into a preset FOTA upgrade evaluation model, and outputting a FOTA upgrade recommended time window; The upgrade module is used to perform a battery management system firmware upgrade within the FOTA upgrade recommended time window, and record the BMS data during the upgrade process as a new test sample.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the automated FOTA testing method for a truck lithium battery system according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the automated FOTA testing method for a truck lithium battery system according to any one of claims 1 to 7 are implemented.

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