System, method and equipment for testing energy recovery function of pure electric heavy truck

Abstract

The invention provides a system, a method and equipment for testing the energy recovery function of a pure electric heavy truck, and belongs to the technical field of new energy vehicles. According to the system, a non-intrusive sensor module is adopted to collect multi-dimensional original data when a vehicle runs, the data collection and transmission module is used for preprocessing and transmission, and a test platform is used for calculating the energy recovery efficiency of the vehicle through a built-in energy recovery efficiency calculation model and a built-in braking stability evaluation model based on preset conditions and soft trigger rules. And carrying out quantitative calculation and performance analysis on the braking energy recovery event. According to the method, on the premise that normal control of the vehicle is not interfered, accurate and objective evaluation of the energy recovery efficiency and the braking smoothness under the complex and real working conditions can be achieved, and direct data support and decision basis are provided for system optimization.

Description

Technical Field

[0001] This invention belongs to the field of new energy vehicle technology, and more specifically relates to a testing system, method and equipment for the energy recovery function of a pure electric heavy truck. Background Technology

[0002] With the promotion of new energy pure electric heavy-duty trucks, the key role of their energy recovery systems in improving range and braking efficiency is becoming increasingly prominent. However, this system faces severe challenges under complex actual operating conditions. First, the adaptability of recovery efficiency to different operating conditions is generally poor. Heavy-duty truck operation is often accompanied by drastic changes in load, speed, and road conditions, but existing systems mostly adopt control strategies based on fixed thresholds, making it difficult to dynamically adjust the recovery intensity. This results in insufficient recovery efficiency under low-speed, unloaded conditions, while at high speeds and fully loaded conditions, excessive recovery torque may cause abrupt braking and even affect vehicle stability, making it impossible to achieve an ideal balance between energy efficiency and safety.

[0003] Secondly, insufficient precision in the coordinated control between energy recovery and mechanical braking leads to a decline in braking quality. Existing solutions lack in-depth analysis and synergistic optimization of the coupling effect between the two systems, easily causing significant fluctuations in deceleration during braking, resulting in "nose-diving" or "jerking" phenomena, severely impacting ride comfort and posing potential risks to driving safety. Achieving a balance between efficient energy recovery and a smooth, linear braking feel has become a prominent technical challenge.

[0004] Furthermore, the testing methods supporting the development and optimization of this system have significant limitations. Current tests are mostly conducted on test benches with overly simplified operating conditions, such as constant speed and constant load, which cannot effectively reproduce the complex scenarios of varying loads, gradients, and braking requests in real-world roads. This kind of testing, detached from actual operating environments, fails to comprehensively reflect the system's performance under boundary and transient conditions, potentially leading to biased optimization directions and hindering further improvements in the overall performance of the energy recovery system.

[0005] In summary, existing technologies have shortcomings in terms of the intelligent adaptability of control strategies, the precision of system coordination, and the adequacy of testing and verification. Therefore, there is an urgent need for a method that can conduct comprehensive and accurate testing and in-depth analysis under complex working conditions, either real or highly simulated, without interfering with the normal control of the vehicle. This would provide reliable data support and clear directions for targeted optimization of energy recovery systems. Summary of the Invention

[0006] To address the above problems, the present invention aims to provide a testing system, method, and equipment for the energy recovery function of pure electric heavy-duty trucks. By non-invasively collecting multi-dimensional operating condition data and using a built-in professional model for quantitative calculation and analysis, the energy recovery efficiency and braking smoothness can be accurately evaluated without interfering with vehicle control, providing direct data basis for system optimization.

[0007] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, embodiments of this application provide a testing system for the energy recovery function of a pure electric heavy truck, comprising: a sensor module, a data acquisition and transmission module, and a testing platform, wherein the sensor module is connected to the testing platform through the data acquisition and transmission module; The sensor module is used to collect raw data of the vehicle in a non-invasive manner during operation; the raw data includes vehicle speed, motor speed, battery current, battery voltage, brake pedal travel, total vehicle mass, axle load signal, and triaxial acceleration. The data acquisition and transmission module is used to preprocess the raw data and transmit the processed data through a high-speed communication bus according to the test platform. The testing platform is used to store raw data, set test conditions and define soft trigger conditions for data segmentation. Based on the raw data, it uses the built-in energy recovery efficiency calculation model and braking stability evaluation model to calculate recovery efficiency, evaluate braking smoothness, determine analysis results and generate structured test reports.

[0008] In one optional implementation, the sensor module includes a vehicle speed sensor, an acceleration sensor, a brake pedal travel sensor, a motor speed sensor, a battery status sensor, an OBD data acquisition unit for acquiring vehicle CAN bus data, and a high-precision inertial measurement unit for measuring triaxial acceleration.

[0009] Secondly, embodiments of this application also provide a test method for the energy recovery function of a pure electric heavy-duty truck, including: Connect and verify the communication link of the test system for the energy recovery function of the pure electric heavy truck; Under the set test conditions, the vehicle is made to run and perform routine operations including braking. Raw data of the vehicle running is collected in real time by the sensor module. The raw data is preprocessed by the data acquisition and transmission module and then transmitted to the test platform. The received data stream is time-aligned using a testing platform, and braking energy recovery event datasets are extracted from the raw data based on preset soft trigger conditions. For each braking energy recovery event dataset, the built-in energy recovery efficiency calculation model is invoked to calculate the energy recovery efficiency. Simultaneously, the main braking phase is defined as the time interval from the first set percentage of the brake pedal travel reaching its maximum value to the end of the time interval when the vehicle speed drops to the set percentage of its initial value. The braking smoothness of the main braking phase is analyzed using a braking stability evaluation model. The influence of the battery's initial SOC and temperature on the recovery effect is correlated and analyzed to generate analysis results. The testing platform automatically generates test analysis reports based on the analysis results.

[0010] In an optional implementation, the preprocessing of the raw data via the data acquisition and transmission module includes: High-frequency noise in the original data is removed by using a sliding window averaging filter, and physical invalid values ​​in the original data are removed by using a range check method.

[0011] In one optional implementation, the preset soft triggering condition is: when the vehicle speed is stable within a preset speed range and the brake pedal travel exceeds a first preset threshold, the brake energy recovery event is marked as starting, until the vehicle speed drops to a second preset threshold, the brake energy recovery event is marked as ending.

[0012] In an optional implementation, the energy recovery efficiency calculation model includes: Through formula Calculate the kinetic energy loss during braking. Where M is the total mass of the vehicle. The vehicle speed at the start of the regenerative braking event. The vehicle speed at the end of the regenerative braking event; Through formula Calculate the recovered electrical energy E; where, The time when the regenerative braking event begins. The time when the regenerative braking event ends. Let be the battery voltage at time t. Let be the battery current at time t; Through formula Calculate the energy recovery efficiency .

[0013] In one optional implementation, the braking stability evaluation model includes: The standard deviation of longitudinal acceleration is calculated using the following formula. and peak value To assess the magnitude of deceleration fluctuations:

[0014]

[0015] Where N is the total number of data points during the main braking phase. This refers to the longitudinal acceleration during the main braking phase. This represents the average longitudinal acceleration during the main braking phase. The Pearson correlation coefficient r between brake pedal travel and longitudinal acceleration is calculated using the following formula:

[0016] in, Brake pedal travel during the main braking phase, This represents the average brake pedal travel during the main braking phase. The linearity of the response was evaluated by plotting hysteresis curves of longitudinal acceleration and brake pedal travel during the main braking phase. Vertical acceleration during the main braking phase In the middle, through the formula Extract the maximum impact peak value az within the set time window t, and extract the rate of change of vertical acceleration to evaluate the vehicle pitch impact.

[0017] In an optional implementation, the correlation analysis of the impact of the battery's initial SOC and temperature on the recycling effect specifically includes: statistically analyzing the variation patterns of average energy recovery efficiency and maximum recovery power under different battery initial SOC ranges and different battery temperature ranges, and evaluating the stability of battery voltage during the recycling process.

[0018] In one optional implementation, the test conditions include: driving speed conditions, load conditions, and road condition conditions; Driving speed conditions include: 40km / h, 60km / h, and 80km / h; Loading conditions include: unloaded, half-loaded, and fully loaded; Road conditions include: straight roads and gentle slopes.

[0019] Thirdly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the test method for the energy recovery function of a pure electric heavy truck as described in any of the above.

[0020] As can be seen from the above technical solutions, the present invention has the following advantages: This application uses non-invasive data acquisition to realistically reflect the working status of the vehicle's energy recovery system under complex actual conditions. Without interfering with vehicle control, it utilizes professional models to conduct in-depth analysis of multi-source data, achieving accurate quantitative evaluation of energy recovery efficiency and braking smoothness. It can also clearly identify the impact of battery status on recovery performance, providing comprehensive and reliable data support and clear improvement directions for targeted optimization of the energy recovery system. This effectively solves the problems of single test scenarios, insufficient evaluation dimensions, and inadequate optimization basis in existing technologies, and is of great significance for improving the energy efficiency and driving quality of pure electric heavy trucks.

[0021] This application adopts a completely non-intrusive data acquisition method, which conducts tests without changing the vehicle's original hardware and software or sending any control commands. This allows for the uninterrupted capture of the energy recovery system's operating status under the vehicle's actual control strategy, ensuring the originality of the acquired data and the reliability of the conclusions. It overcomes the performance distortion problem caused by the intervention of vehicle control in traditional testing methods.

[0022] This application, through systematic test condition presets and synchronous data collection from multi-dimensional sensors, can effectively simulate and cover the variable operating conditions in actual heavy truck operation, including combinations of different speeds, loads, road conditions and braking intensities. It achieves full-scenario testing capabilities from single steady-state to complex transient operating conditions, solving the shortcomings of insufficient coverage of traditional bench or simplified road test scenarios and disconnection from real vehicle applications.

[0023] This application uses an energy recovery efficiency calculation model to accurately separate and calculate the kinetic energy loss and electrical energy recovery during braking, thus achieving precise quantification of efficiency. Through a braking stability evaluation model, subjective comfort is transformed into objective indicators such as deceleration fluctuation, response linearity, and impact, thereby achieving a scientific evaluation of braking smoothness.

[0024] This application, through multi-dimensional data correlation analysis, can clearly reveal the intrinsic relationship between performance shortcomings and specific operating conditions and system states. For example, it can accurately diagnose whether inefficiency occurs at low speed and full load or at high speed and low SOC, and whether braking shock is caused by sluggish pedal response or sudden change in regenerative torque, thereby transforming the vague system "tuning" problem into a clear and locatable engineering optimization goal. Attached Figure Description

[0025] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A schematic diagram of the structure of the test system for the energy recovery function of the pure electric heavy truck provided in this application.

[0027] Figure 2 A flowchart illustrating the test method for the energy recovery function of the pure electric heavy-duty truck provided in this application.

[0028] Figure 3 A schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0029] The various embodiments of this disclosure will be described more fully in the following detailed description of the test system and method for the energy recovery function of pure electric heavy-duty trucks. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0030] In the following, the terms “comprising” or “may include”, which may be used in various embodiments of this disclosure, indicate the presence of the disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in various embodiments of this disclosure, the terms “comprising,” “having,” and their cognates are intended only to indicate a particular feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or the possibility of adding one or more combinations of the foregoing.

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Please see Figure 1 The diagram shows a structural schematic of a test system for the energy recovery function of a pure electric heavy-duty truck in a specific embodiment. The system includes a sensor module, a data acquisition and transmission module, and a test platform. The sensor module is connected to the test platform through the data acquisition and transmission module.

[0033] The sensor module is used to collect raw data of the vehicle in a non-invasive manner during operation; the raw data includes vehicle speed, motor speed, battery current, battery voltage, brake pedal travel, total vehicle mass, axle load signal and triaxial acceleration.

[0034] The sensor module includes a vehicle speed sensor, an acceleration sensor, a brake pedal travel sensor, a motor speed sensor, a battery status sensor, an OBD data acquisition unit for acquiring vehicle CAN bus data, and a high-precision inertial measurement unit for measuring triaxial acceleration.

[0035] For example, the sensor module utilizes sensors in key parts of the vehicle, such as vehicle speed sensor, acceleration sensor, brake pedal travel sensor, motor speed sensor, and battery status sensor, to collect real-time vehicle operating data (vehicle speed, acceleration, brake pedal travel, motor speed, battery voltage / current / charge).

[0036] The data acquisition and transmission module is used to preprocess the raw data and transmit the processed data to the test platform via a high-speed communication bus.

[0037] For example, the data acquisition and transmission module organizes and initially filters the raw data collected by the sensors (removing invalid and interfering data), and transmits the clean data to the test platform through a high-speed communication bus, without sending any control commands to the central controller, motor controller, etc.

[0038] The testing platform is used to store raw data, set test conditions and define soft trigger conditions for data segmentation. Based on the raw data, it uses the built-in energy recovery efficiency calculation model and braking stability evaluation model to calculate recovery efficiency, evaluate braking smoothness, determine analysis results and generate structured test reports.

[0039] For example, the test platform is built on computer technology and professional analysis software, possessing three core functions: first, data storage, securely storing all raw data transmitted by sensors; second, data visualization, visually displaying data trends through charts (line graphs, bar charts); and third, professional analysis, with built-in energy recovery efficiency calculation models and braking stability evaluation models, performing only data analysis and calculations. After analysis, the test platform generates an "Energy Recovery Function Test Analysis Report," clarifying the advantages and disadvantages of the energy recovery system under different operating conditions (such as low recovery efficiency under low-speed, full-load conditions and large deceleration fluctuations during high-speed braking), providing data basis for subsequent optimization of the vehicle's energy recovery system.

[0040] In this embodiment, multi-dimensional operating condition data is collected by non-invasive sensors, processed by an independent data acquisition and transmission module, and transmitted to the test platform. The built-in quantitative model is used for offline analysis and evaluation, thereby achieving objective and accurate testing of energy recovery efficiency and braking smoothness without interfering with the vehicle control logic, providing comprehensive and reliable data for system optimization.

[0041] like Figure 2As shown, the following is an embodiment of the test method for the energy recovery function of pure electric heavy-duty trucks provided in this disclosure. This method and the test system for the energy recovery function of pure electric heavy-duty trucks in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the test method for the energy recovery function of pure electric heavy-duty trucks, please refer to the embodiments of the test system for the energy recovery function of pure electric heavy-duty trucks described above.

[0042] A test method for the energy recovery function of a pure electric heavy-duty truck includes the following steps: S1: The communication link of the test system for connecting and verifying the energy recovery function of the pure electric heavy truck.

[0043] In a specific implementation, the system connectivity is first checked, and the communication between the sensor module, the data acquisition and transmission module, and the test platform is tested to ensure that the data transmission is without delay or loss.

[0044] Then, test conditions are set. Based on the actual operating scenarios of heavy trucks, multi-dimensional test conditions are preset without changing the original driving and braking control logic of the vehicle. Test conditions include driving speed conditions, load conditions, and road condition conditions.

[0045] Specifically, the driving speed conditions include: 40km / h, 60km / h, and 80km / h; the load conditions include: empty, half-loaded, and fully loaded; and the road conditions include: straight roads and gentle slopes.

[0046] S2: Under the set test conditions, the vehicle is made to run and perform routine operations including braking. Raw data of the vehicle running is collected in real time by the sensor module. The raw data is preprocessed by the data acquisition and transmission module and then transmitted to the test platform.

[0047] In a specific implementation, high-frequency noise in the original data is filtered out by using a sliding window averaging filter, and physical invalid values ​​in the original data are removed by using a range check method.

[0048] For example, the vehicle is allowed to operate normally under preset conditions. The driver accelerates and brakes according to conventional operations. The sensors collect full data under the corresponding conditions in real time. The data acquisition and transmission module transmits the data to the test platform synchronously. The entire process does not interfere with the vehicle's driving operation or the original working state of the energy recovery system.

[0049] S3: The received data stream is time-aligned using the testing platform, and a braking energy recovery event dataset is extracted from the original data based on preset soft trigger conditions. For each braking energy recovery event dataset, the built-in energy recovery efficiency calculation model is called to calculate the energy recovery efficiency. At the same time, the main braking phase is defined as the time interval from the first set percentage of the brake pedal travel reaching its maximum value to the set percentage of the vehicle speed decreasing to its initial value. The braking stability evaluation model is called to analyze the braking smoothness of the main braking phase. The influence of the battery's initial SOC and temperature on the recovery effect is correlated and analyzed to generate analysis results.

[0050] In a specific implementation, the preset soft triggering condition is as follows: when the vehicle speed is stable within a preset speed range and the brake pedal travel exceeds a first preset threshold, the brake energy recovery event is marked as starting, and the event ends when the vehicle speed drops to a second preset threshold. Using this preset soft triggering condition, a brake energy recovery event dataset can be extracted from the raw data, including the vehicle speed at the start and end of the brake energy recovery event, and the start and end times of the brake energy recovery event.

[0051] Based on the braking energy recovery event dataset, the following calculation process is performed using the energy recovery efficiency calculation model: Through formula Calculate the kinetic energy loss during braking. Where M is the total mass of the vehicle. The vehicle speed at the start of the regenerative braking event. The vehicle speed at the end of the regenerative braking event; Through formula Calculate the recovered electrical energy E; where, The time when the regenerative braking event begins. The time when the regenerative braking event ends. Let be the battery voltage at time t. Let be the battery current at time t; Through formula Calculate the energy recovery efficiency .

[0052] Once the main braking phase is determined, the braking smoothness during the main braking phase is analyzed using the relevant raw data and a braking stability assessment model. The specific process is as follows: The standard deviation of longitudinal acceleration is calculated using the following formula. and peak value To assess the magnitude of deceleration fluctuations:

[0053]

[0054] Where N is the total number of data points during the main braking phase. This refers to the longitudinal acceleration during the main braking phase. This represents the average longitudinal acceleration during the main braking phase. The Pearson correlation coefficient r between brake pedal travel and longitudinal acceleration is calculated using the following formula:

[0055] in, Brake pedal travel during the main braking phase, This represents the average brake pedal travel during the main braking phase. The linearity of the response was evaluated by plotting hysteresis curves of longitudinal acceleration and brake pedal travel during the main braking phase. Vertical acceleration during the main braking phase In the middle, through the formula Extract the maximum impact peak value az within the set time window t, and extract the rate of change of vertical acceleration to evaluate the vehicle pitch impact.

[0056] Finally, the variation patterns of average energy recovery efficiency and maximum recovered power were statistically analyzed under different initial SOC ranges and different battery temperature ranges, and the stability of battery voltage during the recovery process was evaluated.

[0057] Finally, by combining the information from the above calculation, analysis, and evaluation processes, the final analysis results are generated.

[0058] S4: The test platform automatically generates a test analysis report based on the analysis results.

[0059] In a specific implementation, the testing platform generates an "Energy Recovery Function Test Analysis Report" based on the final analysis results, which clarifies the advantages and disadvantages of the energy recovery system under different operating conditions (such as low recovery efficiency under low-speed full-load conditions and large deceleration fluctuations during high-speed braking), providing data basis for subsequent optimization of the vehicle energy recovery system.

[0060] The testing method for the energy recovery function of pure electric heavy-duty trucks provided in this embodiment firstly captures the working state of the energy recovery system under complex actual conditions through non-intrusive data acquisition throughout the entire process, without interfering with the vehicle's original control logic. This solves the problems of traditional testing scenarios being too simplistic and disconnected from real-world applications. Secondly, by using a built-in energy recovery efficiency calculation model and braking stability evaluation model, abstract concepts of efficiency and comfort are transformed into quantitative indicators such as precisely calculable efficiency percentage, deceleration fluctuation, response linearity, and impact, achieving objectivity and scientific rigor in the evaluation process. Finally, this method can systematically diagnose the correlation between performance shortcomings and specific operating conditions and battery status, generating a clearly targeted structured analysis report. This provides direct and reliable data support and engineering basis for optimizing recovery strategies, co-calibrating electromechanical braking, and improving battery management strategies, significantly enhancing the targeting and efficiency of R&D optimization.

[0061] Figure 3 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0062] The testing method for the energy recovery function of pure electric heavy-duty trucks provided in this application embodiment can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiments of this invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, the electronic device includes, but is not limited to, laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0063] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0064] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0065] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0066] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0067] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0068] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0069] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.

[0070] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0071] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0072] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.

[0073] Electronic devices can achieve display functions through GPUs, displays, and application processors.

[0074] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0075] A display screen is used to display images, videos, etc. A display screen includes a display panel.

[0076] The aforementioned electronic device realizes the test method for the energy recovery function of pure electric heavy trucks in this application. It collects multi-dimensional working condition data in a non-intrusive manner and performs quantitative analysis using a built-in professional model. This achieves the beneficial effect of accurately evaluating energy recovery efficiency and braking smoothness without interfering with vehicle control, and providing direct data basis for system optimization.

[0077] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A test system for energy recovery function of a pure electric heavy truck, characterized in that, The system comprises a sensor module, a data acquisition and transmission module, and a test platform, wherein the sensor module is connected to the test platform through the data acquisition and transmission module. The sensor module is used to collect raw data in a non-invasive manner when the vehicle is running, and the raw data includes vehicle speed, motor speed, battery current, battery voltage, brake pedal stroke, vehicle gross mass, axle load signal, and three-axis acceleration. The data acquisition and transmission module is used to preprocess the raw data and transmit the processed data to the test platform through a high-speed communication bus. The test platform is used to store raw data, set test conditions, define soft trigger conditions for data segmentation, calculate recovery efficiency based on raw data using built-in energy recovery efficiency calculation model and brake stability evaluation model, evaluate brake smoothness, determine analysis results, and generate structured test reports. The sensor module includes a vehicle speed sensor, an acceleration sensor, a brake pedal stroke sensor, a motor speed sensor, a battery status sensor, an OBD data collector for acquiring vehicle CAN bus data, and a high-precision inertial measurement unit for measuring three-axis acceleration.

2. The test system for energy recovery function of a pure electric heavy truck according to claim 1, characterized in that, The method uses the test system for energy recovery function of pure electric heavy truck as claimed in any one of claims 1 to 2.

3. A test method for an energy recovery function of a pure electric heavy truck, characterized in that, The method comprises: connecting and verifying the communication link of the test system for energy recovery function of pure electric heavy truck; under the set test conditions, making the vehicle run and performing normal operations including braking, collecting raw data in real time through the sensor module, preprocessing the raw data through the data acquisition and transmission module, and transmitting the raw data to the test platform; aligning the received data stream in time through the test platform, extracting brake energy recovery event data sets from the raw data based on the pre-set soft trigger conditions, calculating the energy recovery efficiency for each brake energy recovery event data set by calling the built-in energy recovery efficiency calculation model, defining the main braking stage as the time interval from the first time the brake pedal stroke reaches the maximum set percentage to the time when the vehicle speed drops to the initial set percentage, analyzing the brake smoothness of the main braking stage by calling the brake stability evaluation model, and correlating the influence of the initial SOC and temperature of the battery on the recovery effect to generate analysis results; generating a test analysis report automatically based on the analysis results through the test platform. The preprocessing of the raw data through the data acquisition and transmission module comprises:

4. The method according to claim 3, wherein, filtering out high-frequency noise in the raw data by using a sliding window average filtering method, and eliminating physically invalid values in the raw data by using a range verification method. The pre-set soft trigger condition is that when the vehicle speed is stable within the pre-set speed range and the brake pedal stroke exceeds the first pre-set threshold, it is marked as the start of the brake energy recovery event, and until the vehicle speed drops to the second pre-set threshold, it is marked as the end of the brake energy recovery event.

5. The method of claim 3, wherein the method further comprises: The energy recovery efficiency calculation model comprises:

6. The method of claim 4, wherein the method further comprises: The brake stability evaluation model comprises: The kinetic energy loss during braking is calculated by the formula ; where M is the total mass of the vehicle, is the vehicle speed at the start of the braking energy recovery event, is the vehicle speed at the end of the braking energy recovery event;​ The recovered electric energy E is calculated by the formula wherein, tstart is the time at which the braking energy recovery event starts, tend is the time at which the braking energy recovery event ends, Vt is the battery voltage at time t, It is the battery current at time t. The energy recovery efficiency is calculated by the formula .​ 7. The method of claim 3, wherein the method further comprises: The Pearson correlation coefficient r between the brake pedal stroke and the longitudinal acceleration is calculated by the following formula: The standard deviation of the longitudinal acceleration is calculated by the following equation and the peak-to-peak value to assess the magnitude of deceleration fluctuations: wherein N is the total number of data points in the main braking phase, is the longitudinal acceleration in the main braking phase, is the average value of the longitudinal acceleration in the main braking phase; ​ wherein, brake pedal travel in the main braking phase, is the average value of the brake pedal travel in the main braking phase; The response linearity is evaluated by drawing a hysteresis curve of longitudinal acceleration and brake pedal stroke in the main braking stage; From the vertical acceleration in the main braking phase In this case, the maximum impact peak value az in the set time window t is extracted by the formula The vertical acceleration rate is extracted to evaluate the body pitch impact. 8.The method of claim 3, wherein, The influence of the initial SOC and temperature of the battery on the recovery effect is analyzed, specifically including: statistically analyzing the variation of the average energy recovery efficiency and the maximum recovery power in different initial SOC intervals and different battery temperature intervals, and evaluating the stability of the battery voltage during the recovery process. 9.The method of claim 3, wherein, The test conditions include: driving speed conditions, load conditions and road conditions; The driving speed conditions include: 40km / h, 60km / h, 80km / h; The load conditions include: empty load, half load, full load; The road conditions include: straight road, gentle slope road.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the steps of the test method of the energy recovery function of the pure electric heavy truck according to the program.

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