A smart monitoring system for engineering vehicle batteries

Abstract

This application relates to the technical field of intelligent battery monitoring, and in particular to an intelligent monitoring system for engineering vehicle batteries. The system includes an electrical parameter sensor, and further comprises: a data acquisition unit for acquiring the real-time vibration frequency and real-time driving speed of the engineering vehicle; an adaptive vibration suppression unit for dynamically adjusting the equivalent stiffness of the mounting structure of the electrical parameter sensor based on the real-time vibration frequency, causing the natural frequency of the mounting structure to deviate from the real-time vibration frequency; and a collaborative noise reduction unit for: determining the center frequency of signal processing based on the real-time vibration frequency, and simultaneously adjusting the signal processing bandwidth associated with the center frequency based on the real-time driving speed, and using the determined center frequency and the adjusted signal processing bandwidth to perform noise reduction processing on the signal output by the electrical parameter sensor. This application can solve the problem of inaccurate battery signal acquisition in road rollers under vibration and speed-changing coupled operating conditions, achieving high-precision monitoring.

Description

Technical Field

[0001] This application relates to the technical field of intelligent battery monitoring, and in particular to an intelligent monitoring system for engineering vehicle batteries. Background Technology

[0002] As a key energy component of engineering vehicles, the real-time and accurate monitoring of the battery's operating status is crucial for ensuring vehicle uptime, preventing sudden malfunctions, and optimizing energy management. In complex operating scenarios such as road construction and mining, engineering vehicles often face harsh conditions including high-intensity vibration, frequent starts and stops, and drastic load changes, which places extremely high demands on the reliability and accuracy of battery monitoring systems.

[0003] Currently, common intelligent battery monitoring systems typically include voltage sensors, current sensors, temperature sensors, and a core processing unit. They collect basic electrical parameters of the battery and estimate its state of charge, health status, and energy consumption based on algorithms. Related technical solutions mostly focus on algorithm-level optimization, such as using improved ampere-hour integration methods or fusion filtering algorithms to improve estimation accuracy, providing some reference value when vehicle operating conditions are relatively stable. However, these systems still have significant limitations when practically applied to engineering vehicles with special operating modes, failing to fundamentally solve the problem of measurement signals being interfered with by complex operating conditions at the data acquisition source.

[0004] Specifically, when typical engineering vehicles like asphalt pavement rollers are operating, the vibrator generates high-frequency vibrations of at least 30Hz for compaction. This vibration is transmitted through the vehicle body to the battery compartment, causing the sensitive elements inside the current and voltage sensors mounted thereto generate micro-amplitude mechanical vibrations at the same frequency. This mechanical vibration is not the measured electrical signal, but it couples with the actual battery signal, injecting significant periodic noise into the sensor output. This results in distortion and glitches in the acquired voltage and current waveforms, causing substantial deviations in the original electrical parameter measurements. Simultaneously, the roller needs to dynamically adjust its compaction speed according to the asphalt temperature, typically varying between 2 km / h and 5 km / h. Changes in speed directly affect the power demand of the drive motor, leading to a wide dynamic range in the amplitude, peak value, and duty cycle of the battery discharge current. Existing monitoring systems often use static or quasi-static energy consumption statistical models, failing to fully consider the correlation between these dynamic current waveform characteristics and real-time speed. They merely average and integrate the distorted current sample values, introducing significant calculation errors and causing distortion in energy consumption statistics. More seriously, the periodic noise caused by vibration overlaps with the dynamic characteristics of the real signal due to speed changes in the frequency domain, forming coupled interference. If the system uses a filter with fixed parameters, it is difficult to simultaneously filter out vibration noise and retain the necessary high-frequency dynamic components in the current signal, resulting in a dilemma of either noise residue or signal distortion. These source signal distortions caused by the coupled operation of high-frequency vibration and variable-speed loads mean that all subsequent advanced calculations, including state of charge estimation, health status assessment, and energy consumption statistical analysis, are based on data with large errors. The errors are amplified step by step, ultimately leading to unreliable monitoring results and failing to provide an effective basis for precise energy management and preventive maintenance of engineering vehicles. Therefore, existing technologies lack a battery intelligent monitoring solution that can suppress vibration transmission from the sensor installation structure and dynamically adjust signal processing parameters based on real-time operating conditions, thereby achieving coordinated anti-interference from the source to the processing link. Summary of the Invention

[0005] To address the issue of inaccurate battery signal acquisition in road rollers under vibration and speed-changing coupled operating conditions and to achieve high-precision monitoring, this application provides an intelligent monitoring system for engineering vehicle batteries.

[0006] This application provides an intelligent monitoring system for engineering vehicle batteries, employing the following technical solution: An intelligent monitoring system for engineering vehicle batteries includes an electrical parameter sensor for collecting battery electrical parameters, and further includes:

[0007] The data acquisition unit is used to obtain the real-time vibration frequency and real-time driving speed of the engineering vehicle;

[0008] An adaptive vibration damping unit, connected to the data acquisition unit, is used to dynamically adjust the equivalent stiffness of the electrical parameter sensor mounting structure based on the real-time vibration frequency, so that the natural frequency of the mounting structure deviates from the real-time vibration frequency.

[0009] A collaborative noise reduction unit is connected to the data acquisition unit and the electrical parameter sensor; the collaborative noise reduction unit is used to: determine the center frequency of signal processing based on the real-time vibration frequency, and adjust the signal processing bandwidth associated with the center frequency based on the real-time driving speed, and use the determined center frequency and the adjusted signal processing bandwidth to perform noise reduction processing on the signal output by the electrical parameter sensor.

[0010] Optionally, the adaptive vibration damping unit includes a controllable variable stiffness element and a frequency management controller;

[0011] The controllable variable stiffness element constitutes the support part of the mounting structure, and its equivalent stiffness is adjusted in real time by the control signal.

[0012] The frequency management controller connects the data acquisition unit and the controllable variable stiffness element, and is used to generate the control signal according to the real-time vibration frequency, and dynamically adjust the equivalent stiffness of the controllable variable stiffness element so that the natural frequency of the installation structure is continuously lower than the dynamic threshold determined by the real-time vibration frequency.

[0013] Optionally, the collaborative noise reduction unit includes:

[0014] The frequency locking submodule is used to lock the center frequency of the signal processing to the real-time vibration frequency acquired by the data acquisition unit in real time.

[0015] The bandwidth adaptive submodule is used to adjust the signal processing bandwidth around the center frequency based on the real-time driving speed obtained by the data acquisition unit. Its adjustment logic is as follows: when the real-time driving speed is lower than a preset speed threshold, a first processing bandwidth is selected; when the real-time driving speed reaches or exceeds the speed threshold, a second processing bandwidth wider than the first processing bandwidth is selected.

[0016] Optionally, the logic for adjusting the signal processing bandwidth by the bandwidth adaptive submodule further includes: based on the difference between the real-time driving speed and the speed threshold, continuously or progressively adjusting the width of the signal processing bandwidth on the basis of the first processing bandwidth or the second processing bandwidth, so that the width of the signal processing bandwidth is positively correlated with the difference.

[0017] Optionally, it also includes an energy consumption calculation and compensation unit, wherein the energy consumption calculation and compensation unit includes:

[0018] An energy consumption calculation unit is used to receive the signal output by the collaborative noise reduction unit and calculate the basic energy consumption value of the battery by performing integration on the signal.

[0019] The operating condition compensation unit is connected to the data acquisition unit and the energy consumption calculation unit. It is used to perform joint compensation calculation on the basic energy consumption value based on the real-time vibration frequency and the real-time driving speed, and output the compensated energy consumption data.

[0020] Optionally, the operating condition compensation unit may perform joint compensation calculations on the base energy consumption value in the following ways:

[0021] Based on the different speed ranges in which the real-time driving speed is located, the corresponding first compensation factor is invoked;

[0022] Based on whether the real-time vibration frequency exceeds the preset vibration intensity threshold, the corresponding second compensation factor is invoked;

[0023] The base energy consumption value is multiplied by both the first compensation factor and the second compensation factor to generate the compensated energy consumption data.

[0024] Optionally, a compensation factor optimization unit may also be included to perform the following optimization steps:

[0025] Regularly obtain baseline energy consumption measurements;

[0026] The baseline energy consumption measurement value is compared with the compensated energy consumption data;

[0027] When the difference exceeds the preset deviation threshold, the real-time driving speed and real-time vibration frequency corresponding to the compensated energy consumption data are retrieved as the associated operating condition.

[0028] Based on the associated operating conditions, the first compensation factor and / or the second compensation factor are adjusted in a targeted manner.

[0029] Optionally, a state evaluation unit may also be included, which is connected to the collaborative noise reduction unit, for receiving the processed signal and performing the following evaluation steps:

[0030] The signal is fused using the ampere-hour integration method and the open-circuit voltage method to estimate the real-time state of charge of the battery.

[0031] Using the estimated results of the real-time state of charge and the signal, the changes in the battery's internal resistance and the trend of capacity decay are analyzed to comprehensively assess the battery's health status.

[0032] Optionally, it also includes a closed-loop optimization unit for performing the following closed-loop optimization process:

[0033] Regularly obtain the calibrated state of charge and calibrated state of health values ​​of the battery as baseline data;

[0034] The baseline data is continuously compared with the real-time state of charge and health status output by the state assessment unit.

[0035] When the deviation of the comparison result exceeds the preset error threshold, the calibrable parameters in the fusion algorithm used by the state evaluation unit are adaptively adjusted according to the direction and magnitude of the deviation.

[0036] Optionally, the controllable variable stiffness element is a magnetorheological elastomer element;

[0037] The frequency management controller is configured to generate the control signal based on the comparison result between the real-time vibration frequency and the preset frequency threshold, so as to perform closed-loop adjustment of the equivalent stiffness of the magnetorheological elastomer element, so that the natural frequency of the installation structure stably deviates from the real-time vibration frequency.

[0038] The electrical parameter sensor includes a microelectromechanical system current sensor and a microelectromechanical system voltage sensor, which are mounted on the mounting structure composed of the magnetorheological elastomer element.

[0039] The signal output terminal of the electrical parameter sensor is connected to the collaborative noise reduction unit so that the electrical signal after vibration suppression processing is input to the collaborative noise reduction unit for frequency domain noise reduction processing.

[0040] In summary, this application includes the following beneficial technical effects:

[0041] This application, through the collaborative operation of an adaptive vibration suppression unit and a cooperative noise reduction unit, constructs a dual anti-interference mechanism from the physical structure to the signal link, directly overcoming the core technical problem of inaccurate battery signal acquisition in engineering vehicles such as road rollers under vibration and speed-changing coupling conditions. The system dynamically adjusts the stiffness of the sensor mounting structure based on the real-time vibration frequency to avoid resonance points, suppressing mechanical vibration coupling at its source. Simultaneously, the signal processing center frequency tracks the vibration frequency in real time and adaptively adjusts the processing bandwidth in conjunction with the driving speed, thereby accurately filtering out vibration noise while fully preserving the dynamic characteristics of the current caused by speed changes. This synergy between physical vibration suppression and frequency domain filtering achieves full-process interference suppression from the source of interference to the signal processing link, fundamentally ensuring the fidelity of the acquired electrical signal and providing a high-quality data foundation for all subsequent advanced calculations.

[0042] This application achieves adaptive matching of signal processing parameters to complex dynamic operating conditions, resolving the contradiction between noise filtering and fidelity preservation that traditional fixed-parameter filters struggle to balance under vibration and speed-coupled interference. The system incorporates real-time driving speed into the processing bandwidth adjustment logic. When the speed is low and the current waveform is smooth, a narrow bandwidth is used to thoroughly filter out noise; when the speed increases and the current contains rich high-frequency dynamic components, the bandwidth is automatically widened to retain useful signals. This dynamic parameter adjustment based on real-time operating condition perception enables the system to intelligently distinguish between noise and true signal characteristics, ensuring the output of clean and distortion-free electrical parameter signals under different operating modes, significantly improving the robustness and accuracy of the monitoring system in varying operating conditions.

[0043] This application constructs a high-precision monitoring system with self-learning and self-calibration capabilities by introducing dynamic energy consumption compensation and closed-loop optimization of state assessment, effectively overcoming the problems of calculation distortion and error accumulation in traditional static models under dynamic loads. The system jointly compensates for the basic energy consumption value based on real-time speed and vibration frequency, correcting for additional losses related to operating conditions. Simultaneously, by periodically calibrating the benchmark value, the system can automatically diagnose and adjust key parameters in the state estimation algorithm, continuously converging assessment errors. This not only makes energy consumption statistics more aligned with actual operational needs but also ensures the long-term reliability of state of charge and health state estimation results, providing continuous and reliable data support for precise energy management, preventative maintenance, and battery life prediction of engineering vehicles. Attached Figure Description

[0044] Figure 1 This is a flowchart of the logic for vibration suppression through the synergy of physics and signals.

[0045] Figure 2 This is a flowchart of the dynamic energy consumption compensation and optimization logic.

[0046] Figure 3 This is a flowchart of the state assessment and closed-loop self-optimization logic. Detailed Implementation

[0047] The following combination Figures 1-3 This application will be described in further detail.

[0048] This application discloses an intelligent monitoring system for engineering vehicle batteries. The system includes an electrical parameter sensor, a data acquisition unit, an adaptive vibration suppression unit, a collaborative noise reduction unit, an energy consumption calculation and compensation unit, a compensation factor optimization unit, a state assessment unit, and a closed-loop optimization unit. These units cooperate to solve the technical problems of inaccurate battery electrical parameter acquisition, distorted energy consumption statistics, and biased state assessment in asphalt pavement rollers operating under high-frequency vibration and variable-speed driving conditions. Specifically, this application includes the following steps:

[0049] S1 System Core Component Deployment and Parameter Calibration

[0050] This step focuses on the typical working conditions of high-frequency vibration and variable speed operation of asphalt pavement rollers, and completes the selection, installation and parameter setting of data acquisition unit, adaptive vibration suppression unit and collaborative noise reduction unit, so that the performance of each component is adapted to the working environment and the parameter accuracy is sufficient to support subsequent signal processing and status assessment.

[0051] S11 Data Acquisition Unit Deployment

[0052] The data acquisition unit is responsible for capturing real-time operating data of the engineering vehicle, and it includes a vibration frequency acquisition module and a driving speed acquisition module.

[0053] S111 vibration frequency acquisition module deployment

[0054] The vibration frequency acquisition module uses the PZT-5H piezoelectric ceramic vibration sensor. This sensor is a mature application in the field of industrial vibration monitoring, with fast response speed and wide measurement range, which fully meets the vibration monitoring needs of road rollers. Technicians rigidly fix the sensor to the road roller chassis near the battery compartment. This position minimizes signal transmission loss and accurately captures the vibration signals transmitted from the vehicle body to the battery compartment during operation.

[0055] The industry technical standard for asphalt pavement compaction stipulates that the optimal operating frequency of the roller vibrator should not be lower than 30Hz. This frequency ensures the compaction density of the pavement while avoiding excessive damage to the vehicle structure from high-frequency vibration. Based on this standard, we set the sensor measurement range to 10Hz to 100Hz, fully covering all possible vibration frequencies during roller operation. According to the vibration testing requirements for vehicle-mounted equipment (Class 1B) in GB / T21563-2018 "Shock and Vibration Tests for Rail Transit Locomotives and Rolling Stock Equipment," the vibration frequency range monitored by the system fully covers the typical vibration spectrum of engineering vehicles, ensuring that the collected data meets the vibration environment adaptability standards of the rail transit industry. Field measurements show that within the core operating frequency range of 30Hz to 50Hz, the sensor measurement error is stable within ±0.5Hz, sufficient to provide high-precision vibration frequency data for the adaptive vibration damping unit.

[0056] S112 driving speed acquisition module deployment

[0057] The driving speed acquisition module uses bus communication to acquire data. Technicians connect it to the engineering vehicle's driving control system via a CAN bus to directly read the real-time driving speed within the system. This connection method eliminates the hassle of installing additional speed measurement hardware, effectively reducing the difficulty of system integration.

[0058] General road construction specifications require that the roller's travel speed be dynamically adjusted according to the asphalt temperature. When the initial asphalt temperature is between 120℃ and 150℃, the speed should be controlled at 2 km / h to prevent road surface shoving; as the temperature drops to 80℃ to 120℃, the speed can be gradually increased to 5 km / h to balance work quality and efficiency. Based on this specification, we set the unit measurement range to 0 km / h to 10 km / h, fully covering the actual operating speed range. This range covers the maximum operating speed of the engineering vehicle in battery-powered mode and provides sufficient measurement margin for the typical speed range (2 km / h-5 km / h) during compaction operations. The measurement error is guaranteed by the original accuracy of the vehicle control system, and after testing, it is stable within ±0.1 km / h, meeting usage requirements without additional calibration.

[0059] S12 Adaptive Vibration Damping Unit Deployment

[0060] The adaptive vibration damping unit suppresses vibration interference from a physical level by dynamically adjusting the equivalent stiffness of the electrical parameter sensor mounting structure. It includes a controllable variable stiffness element and a frequency management controller.

[0061] S121 Controllable Variable Stiffness Element Deployment

[0062] The controllable variable stiffness element uses a magnetorheological elastomer, which can quickly adjust the equivalent stiffness by changing the magnetic field, making it very suitable for the dynamic working conditions of engineering vehicles. The element uses silicone rubber as a base and is filled with 20 vol% carbonyl iron particles with a thickness of 5 mm.

[0063] These parameters were determined through multiple sets of comparative tests. The test results show that, under this parameter combination, the equivalent stiffness of the component can be continuously adjusted within the range of 10MPa to 100MPa, with a response time of no more than 10ms, enabling it to quickly follow changes in the vibration frequency of the road roller. The magnetorheological elastomer element serves as the supporting part of the electrical parameter sensor mounting structure, on which microelectromechanical system (MEMS) current and voltage sensors, models ADIAD22151 and LTC6804 respectively, are fixedly mounted. Both sensors are 10mm × 10mm × 5mm in size, with a vibration resistance of no less than 1000g, making them suitable for the harsh operating environment of engineering vehicles. This vibration resistance level meets and exceeds the stringent requirements of GB / T21563-2018 for the severity of mechanical shock and vibration of rail transit equipment, ensuring that the sensors can still operate reliably under high-intensity vibrations transmitted by the vehicle body.

[0064] S122 Frequency Management Controller Deployment

[0065] The frequency management controller uses an STM32H7 series microcontroller. This controller has a main frequency of up to 480MHz, multi-channel data acquisition and PWM signal output capabilities, and can process the vibration frequency data transmitted by the data acquisition unit in real time and quickly generate stiffness adjustment signals. Technicians connected the controller's signal input terminal to the vibration frequency output terminal of the data acquisition unit, and the signal output terminal to the drive coil of the magnetorheological elastomer element, establishing a complete stiffness adjustment control link to ensure that the controller can directly regulate the equivalent stiffness of the controllable variable stiffness element.

[0066] S13 Collaborative Noise Reduction Unit Deployment

[0067] The collaborative noise reduction unit is responsible for targeted noise reduction of the raw signal output by the electrical parameter sensor. It includes a frequency locking submodule and a bandwidth adaptive submodule. The signal processing functions of both are implemented in hardware through the FPGA chip.

[0068] We selected the Xilinx Artix-7 FPGA chip, which boasts high data processing speed, controlling signal processing latency to within 1ms, fully meeting real-time noise reduction requirements. Its operating temperature range matches the BMS management system and battery operating environment temperature requirements in the "Lead-Acid Power Battery System Technical Specifications," ensuring stable operation across the entire operating temperature range of the engineering vehicle. The frequency locking submodule is responsible for locking the center frequency of signal processing, while the bandwidth adaptive submodule dynamically adjusts the signal processing bandwidth. Technicians connected the signal input terminals of the two subunits to the data acquisition unit and the electrical parameter sensor, respectively, allowing the subunits to simultaneously receive operating condition data and raw electrical signals, preparing for subsequent targeted noise reduction.

[0069] S2 Adaptive Vibration Damping Unit Working Process

[0070] like Figure 1 As shown, the adaptive vibration damping unit dynamically adjusts the equivalent stiffness of the electrical parameter sensor mounting structure based on the real-time vibration frequency output by the data acquisition unit, thereby physically cutting off the path of vibration interference to the sensor and laying a solid foundation for the accurate acquisition of subsequent electrical parameter signals.

[0071] S21 Vibration Frequency Acquisition and Dynamic Threshold Determination

[0072] S211 Real-time Vibration Frequency Receiver

[0073] After the frequency management controller starts, it immediately establishes a stable communication link with the data acquisition unit deployed in step S1, and continuously receives real-time vibration frequency data transmitted by the vibration frequency acquisition module. We record this frequency data as... Step S1 has been verified; within the core operating range of 30Hz to 50Hz, the measurement error of this acquisition unit can be controlled within ±0.5Hz, a level of accuracy fully capable of supporting the accuracy of subsequent stiffness adjustments. The controller's built-in data verification program automatically filters out transient abnormal data, ensuring the accuracy of the received data. It accurately reflects the vibration status of the road roller during operation.

[0074] S212 Dynamic Threshold Determination

[0075] The frequency management controller determines a dynamic threshold based on preset logic. This threshold is the core reference for regulating the natural frequency of the installation structure. Through multiple engineering tests, we found that when the natural frequency of the installation structure is consistently below one-third of the real-time vibration frequency, the vibration transmission rate to the sensor can be reduced by more than 90%, effectively preventing vibration from causing micro-vibrations in the sensor's sensitive elements. Based on this experimental conclusion, the controller sets the dynamic threshold to the real-time vibration frequency. One-third of the time, this threshold ensures that the natural frequency of the installation structure deviates sufficiently from the real-time vibration frequency, thus avoiding the risk of resonance at its source.

[0076] S22 Stiffness Adjustment Control Execution

[0077] S221 Control Signal Generation

[0078] The frequency management controller incorporates a built-in PID algorithm, which can quickly respond to changes in input parameters and output a stable control signal. The controller then uses the real-time vibration frequency... As input, the "natural frequency of the installation structure ≤ dynamic threshold" is taken as the control target, and a DC control signal is generated after calculation by the PID algorithm. The current range of this signal is limited to 0A-2A. This range is set in combination with the performance parameters of the magnetorheological elastomer element determined in step S1. Under the action of the magnetic field corresponding to this current, the element has the fastest stiffness adjustment response and the adjustment range is most suitable for the operation requirements.

[0079] S222 Magnetic Field Effect and Stiffness Change

[0080] The control signal is directly input to the drive coil of the magnetorheological elastomer element deployed in step S1. This coil has 1000 turns, and the parameters are pre-calibrated according to the element size and magnetic field requirements. When current passes through the coil, a magnetic field is generated. The magnetic field strength changes linearly with the current magnitude, corresponding to a magnetic field strength of 0mT-500mT within a current range of 0A to 2A. The carbonyl iron particles inside the magnetorheological elastomer element will arrange themselves in an orderly manner under the influence of the magnetic field, thereby changing the equivalent stiffness of the element. The larger the current and the stronger the magnetic field, the denser the particle arrangement and the higher the equivalent stiffness of the element, ultimately achieving continuous adjustment of the equivalent stiffness within the range of 10MPa-100MPa.

[0081] S223 achieves vibration damping effect.

[0082] Changes in the equivalent stiffness of a magnetorheological elastomer element directly alter the natural frequency of the mounting structure it supports. The frequency management controller adjusts the frequency through real-time feedback to ensure the natural frequency of the mounting structure remains below a dynamic threshold. Taking a common operating scenario for a road roller as an example, when the real-time vibration frequency... When the frequency is 30Hz, the dynamic threshold is 10Hz. The controller will adjust the regulating current to a suitable value to stabilize the natural frequency of the installation structure below 10Hz. At this time, the acceleration amplitude transmitted to the electrical parameter sensor by vibration is attenuated by more than 90%, and the micro-displacement amplitude of the sensor's sensitive element is controlled within 0.02mm. This significantly reduces the coupling interference between mechanical vibration and electrical signals, laying a solid physical foundation for the subsequent output of accurate electrical parameter signals by the sensor.

[0083] S3 Collaborative Noise Reduction Unit Signal Processing Process

[0084] like Figure 1 As shown, the collaborative noise reduction unit receives real-time operating condition data output by the data acquisition unit and adjusts the signal processing parameters accordingly. This retains the useful dynamic components in the electrical signal while avoiding signal distortion caused by over-filtering, providing high-quality data input for subsequent energy consumption calculations and condition assessments.

[0085] S31 center frequency lock

[0086] Center frequency locking is the core prerequisite for collaborative noise reduction. It is accomplished by the frequency locking submodule. The goal is to accurately align the filter stopband with the dominant frequency of vibration noise. This process needs to be synchronized in real time with the vibration frequency data of the data acquisition unit.

[0087] S311 Real-time Vibration Frequency Receiver

[0088] After the frequency locking submodule is activated, it immediately establishes communication with the data acquisition unit deployed in step S1, continuously receiving real-time vibration frequency data transmitted by the vibration frequency acquisition module. Step S2 has verified that the frequency data error is controlled within ±0.5Hz in the core operating range, and this accuracy fully supports the precision of subsequent frequency locking. The submodule's built-in signal synchronization unit performs millisecond-level verification on the received frequency data, eliminating instantaneous jumps caused by electromagnetic interference, ensuring that the input vibration frequency truly reflects the current operating status.

[0089] S312 Center Frequency Generation and Locking

[0090] The frequency locking submodule employs digital frequency synthesis technology to generate a corresponding center frequency signal based on the received real-time vibration frequency. This center frequency signal is denoted as... ,and The center frequency signal is completely consistent with the real-time vibration frequency value. Subsequently, the subunit directly inputs this center frequency signal into the core parameter configuration port of the notch filter to complete the dynamic calibration of the filter's stopband center.

[0091] Simulation tests show that the response time of this locking mechanism is controlled within 5ms, and even if the real-time vibration frequency fluctuates within ±5Hz, the center frequency tracking error can be stabilized within ±0.1Hz. This performance indicator enables the system to effectively cope with the broadband random vibration and impact excitation covered by the GB / T21563-2018 standard, achieving precise tracking and suppression of vibration noise frequencies. This precise locking ensures that the filter's stopband always coincides with the dominant frequency of vibration noise, providing parameter assurance for targeted filtering of noise in this frequency band and solving the problem that existing fixed-frequency filters cannot adapt to dynamic changes in vibration frequency.

[0092] S32 signal processing bandwidth adjustment

[0093] The signal processing bandwidth adjustment is performed by the bandwidth adaptive submodule. Its core function is to dynamically adjust the stopband width of the filter according to the real-time driving speed, balancing the noise filtering effect with the retention of useful signals.

[0094] S321 speed threshold determination

[0095] The bandwidth adaptive submodule first calls a preset speed threshold, which is a current waveform characteristic boundary point determined through bench testing. This threshold can be calibrated according to different vehicle models or actual operating conditions; in this embodiment, it is set to 3 km / h. During the test, monitoring the battery discharge current waveform at different driving speeds using a high-precision oscilloscope revealed that when the driving speed is below 3 km / h, the power demand of the driving motor is stable, the peak current fluctuation amplitude does not exceed 20A, the waveform is smooth, and the noise frequency band is concentrated. When the driving speed reaches or exceeds 3 km / h, the motor power dynamically changes with the road surface compaction demand, the peak current fluctuation amplitude increases to over 50A, and the waveform contains rich high-frequency dynamic components. Based on this experimental conclusion, the speed threshold is set to 3 km / h to provide a basis for subsequent bandwidth selection.

[0096] S322 Base Bandwidth Selection

[0097] The bandwidth adaptive submodule receives the real-time driving speed output from the data acquisition unit, compares it with a speed threshold, and selects the corresponding basic processing bandwidth. When the real-time driving speed is below 3 km / h, the submodule selects the first processing bandwidth, with a value of 2Hz, corresponding to a filter stopband range of [missing value]. ±1Hz, this narrow bandwidth design can completely filter out concentrated vibration noise while avoiding interference with smooth current signals; when the real-time driving speed reaches or exceeds 3km / h, the subunit selects the second processing bandwidth, with a value of 4Hz, corresponding to a filter stopband range of... ±2Hz, a relatively wide bandwidth, can filter out noise while fully preserving the high-frequency dynamic components related to speed in the current signal, avoiding excessive attenuation of useful signals.

[0098] S323 bandwidth fine adjustment

[0099] To further improve bandwidth adaptation accuracy, the subunit will make fine adjustments based on the difference between the real-time driving speed and the speed threshold. This difference will be denoted as... The calculation method is the absolute value of the real-time driving speed and the speed threshold, while setting an adjustment coefficient. The bandwidth after fine adjustment is 0.5 Hz / km·h. According to "basic bandwidth + × The formula is used to calculate the bandwidth to ensure that the bandwidth is positively correlated with the difference magnitude.

[0100] For example, when the real-time driving speed is 4 km / h At a speed of 1 km / h, the second processing bandwidth, after fine adjustment, becomes 4Hz + 0.5Hz = 4.5Hz; at a real-time driving speed of 2 km / h, For the same speed of 1 km / h, the first processing bandwidth is adjusted to 2Hz + 0.5Hz = 2.5Hz. This adjustment method allows the bandwidth to accurately adapt to the differences in signal characteristics at different speeds, further improving the targeting of filtering.

[0101] S33 signal noise reduction output

[0102] S331 Signal Input and Processing

[0103] The raw voltage and current signals output by the electrical parameter sensors, after vibration suppression processing in step S2, still retain some coupling noise related to the vibration frequency. These signals are directly input to the collaborative noise reduction unit. The signal processing link built into the FPGA chip performs notch filtering on the raw signals according to the locked center frequency and adjusted bandwidth parameters. This processing is hardware-based parallel computation, with a total latency controlled within 1ms, fully meeting the requirements of real-time monitoring.

[0104] S332 Processing Performance and Signal Output

[0105] After the synergistic processing of "center frequency locking + bandwidth adaptive adjustment," vibration coupling noise is significantly suppressed in the output noise-reduced voltage and current signals. Field measurement data shows that the signal-to-noise ratio of the processed signal increases from the original 20dB to over 60dB. Simultaneously, the dynamic peak components related to driving speed in the current signal are fully preserved without significant distortion, providing a core guarantee for the accuracy of subsequent energy consumption calculations and the reliability of condition assessment. This, combined with the physical vibration suppression in step S2, forms a highly efficient synergistic anti-interference effect, achieving a significant improvement over existing fixed-parameter filtering schemes.

[0106] S4 Energy Consumption Calculation and Compensation Unit Implementation

[0107] like Figure 2 As shown, the energy consumption calculation and compensation unit solves the problem that traditional static energy consumption models are difficult to adapt to the vibration and speed-changing coupling conditions of road rollers by combining basic energy consumption integrals and dynamic compensation under operating conditions, making the energy consumption statistics results more in line with actual operation needs.

[0108] S41 Basic Energy Consumption Calculation

[0109] S411 Signal Reception and Sampling

[0110] Upon startup, the energy consumption calculation unit immediately establishes a data link with the collaborative noise reduction unit, continuously receiving the noise-reduced voltage and current signals, denoted as follows: and To ensure the accuracy of the integration calculation, the unit uses a frequency of 1kHz to synchronously sample the two signals. This sampling frequency is much higher than the highest dynamic frequency of the roller current signal, which can fully capture the detailed features of the current waveform and retain sufficient data for subsequent integration calculations.

[0111] S412 Basic Energy Consumption Integral Calculation

[0112] The energy consumption calculation unit obtains the basic energy consumption value of the battery through integral calculation, denoted as . The core logic of the integral operation is based on the physical definition of electrical energy—electrical energy equals the product of voltage, current, and the time of electricity consumption; therefore, the integral formula is set accordingly. . in the formula and These represent the start and end times for energy consumption statistics. The unit is V. The unit is A. The unit is h, and the three operations are performed. The unit is Wh.

[0113] Through multiple tests, the calculation error of this integral algorithm can be stably controlled within ±0.5%. This level of accuracy supports the system's highly reliable statistics on battery energy consumption, providing a high-quality data foundation for subsequent SOC estimation, and meeting the potential requirements of engineering vehicle battery management systems for cumulative energy consumption accuracy.

[0114] S42 Working Condition Joint Compensation

[0115] S421 Compensation Factor Calibration

[0116] The core of the operating condition compensation unit is to correct the base energy consumption value through compensation factors. These factors are all calibrated through bench tests to ensure they match actual operating conditions. In the test, technicians simulated the typical operating speed range of a road roller from 2km / h to 5km / h and the vibration frequency range from 30Hz to 50Hz, monitoring the deviation between the actual energy consumption of the battery and the base energy consumption value under different operating conditions, and finally determined two sets of compensation factors.

[0117] The first compensation factor is related to driving speed and is denoted as... When the speed is between 2 km / h and 3 km / h, the motor power fluctuation is small, and the energy consumption deviation is only 0% of the baseline value. Take 1.00; when the speed increases to 3km / h to 4km / h, the power fluctuation range increases, and the energy consumption deviation is about 1%. The corresponding value is 1.01; the power dynamic range is largest when the speed reaches 4km / h to 5km / h, with an energy consumption deviation of approximately 3%. Then we take 1.03. The second compensation factor is related to the vibration frequency and is denoted as... Experiments revealed that when the vibration frequency exceeds 35Hz, the impact of mechanical losses on battery energy consumption increases significantly. Therefore, 35Hz was set as the vibration intensity threshold; when the frequency is lower than or equal to this value... Take 1.00; if the value exceeds this, Take 1.01.

[0118] S422 Joint Compensation Calculation

[0119] The operating condition compensation unit receives the real-time driving speed and real-time vibration frequency output from the data acquisition unit in real time, and automatically calls the corresponding [function name] based on these two parameters. and The compensation calculation uses a multiplicative superposition method, multiplying the base energy consumption value by both factors simultaneously to obtain the compensated energy consumption data, denoted as . The calculation expression is: .

[0120] This combined compensation method can simultaneously correct energy consumption deviations caused by speed and vibration. Actual measurements have verified that the calculated error after compensation can be controlled within ±0.3%, making the final output energy consumption data more closely reflect the actual operating energy consumption of the road roller. This improved accuracy helps enhance the overall vehicle's energy management efficiency and provides more reliable data support for battery life prediction and maintenance decisions.

[0121] S43 Compensation Factor Optimization

[0122] S431 Baseline Energy Consumption Acquisition

[0123] The compensation factor optimization unit ensures factor accuracy through periodic calibration. The unit is set to a 30-day calibration cycle. This cycle avoids excessively frequent calibrations that could disrupt system operation, while also promptly correcting deviations in the factor caused by equipment aging. Within each cycle, technicians use a Chroma17021 portable high-precision battery tester to obtain baseline energy consumption measurements, which are recorded as follows: .

[0124] The test uses a static capacity discharge method to directly measure the actual discharge of the battery. The measurement accuracy of the tester reaches ±0.5%, which is far higher than the system's energy consumption calculation error requirement, providing a reliable benchmark for compensation factor optimization.

[0125] S432 Factor Optimization Execution

[0126] The unit takes the reference energy consumption measurement value Energy consumption data after compensation Compare the two and calculate the difference. Specifically Based on the energy consumption accuracy requirements of engineering applications, a preset deviation threshold is set. =3%, when When the threshold is exceeded, it indicates that the current compensation factor is no longer suitable for the actual working conditions.

[0127] At this time, the unit will automatically retrieve the relevant information. The corresponding real-time driving speed and real-time vibration frequency are used as associated operating conditions to analyze the source of deviation—if the deviation is caused by a specific speed range, the corresponding range is adjusted accordingly. If the problem is caused by vibration frequency, then adjust... The adjustment range is ±0.01. The adjusted factors will be updated to the system database to ensure the long-term stability of the accuracy of subsequent energy consumption calculations.

[0128] S5 Status Assessment Unit Implementation

[0129] like Figure 3As shown, the condition assessment unit adopts the technical approach of "dual algorithm fusion estimation + multi-parameter trend analysis", which not only ensures the real-time performance and accuracy of the state of charge, but also captures the changing patterns of the health status through long-term data accumulation, providing a reliable basis for preventive maintenance and energy dispatch of engineering vehicle batteries.

[0130] S51 State of Charge Estimation

[0131] State of charge (SOC) refers to the percentage of a battery's current remaining capacity relative to its rated capacity, and is a core indicator for determining whether a battery needs charging. The cell employs a fusion estimation method combining the ampere-hour integral method and the open-circuit voltage method. The former ensures real-time response, while the latter corrects for long-term drift; the two work together to improve estimation accuracy.

[0132] S511 Initial Parameter Calibration

[0133] Before estimation, two core parameters need to be calibrated to provide a benchmark for subsequent calculations. The first is the battery's rated capacity. This parameter was determined through three consecutive charge-discharge cycle tests: under a standard environment of 25℃, the battery was fully charged and discharged at a constant current of 0.2 times the rated capacity, and the average value of the three discharge capacities was taken as the result. This system is compatible with engineering vehicle batteries. The calibration value is 200Ah, and the test deviation is controlled within ±1Ah. This calibration method and accuracy requirements refer to the capacity testing regulations in relevant battery testing standards such as "YD / T1360-2005 Communication Backup Sealed Gel Batteries," ensuring the accuracy of the rated capacity value. The second item is the open-circuit voltage-SOC mapping table, calibrated through static testing: the battery is left to stand at different SOC nodes for 24 hours, and the open-circuit voltage value is recorded after the voltage stabilizes. The node interval is set to 5%, ultimately forming a mapping table with a voltage range of 12.0V to 13.8V and a corresponding SOC of 0% to 100%, achieving a voltage resolution of ±0.01V to ensure lookup accuracy.

[0134] S512 Dual Algorithm Independent Estimation

[0135] S5121 Ah-hour integral method estimation

[0136] The core principle of the ampere-hour integration method is that the change in the state of charge is directly related to the integral of the charging and discharging current. The unit continuously receives the real-time current signal output from the collaborative noise reduction unit. Starting from the power-on time The initial state of charge at this time The open-circuit voltage-SOC mapping table is consulted. Before powering on, the battery needs to be left to rest for 30 minutes. After measuring its open-circuit voltage, the mapping table is used to obtain the value. The error is controlled within ±1%.

[0137] The estimation formula is derived based on the principle that current integral corresponds to capacity change, specifically as follows: . in the formula For integration variables (unit: hours, h). The current is the real-time current (unit: ampere, A), and the integral result is ampere-hours (Ah), compared with the rated capacity. The ratio (unit: Ah) is a dimensionless percentage, therefore This represents the percentage of the current state of charge. The algorithm can track current changes in real time and respond to fluctuations in operating conditions within 10 seconds, but when used alone, it will experience long-term drift due to accumulated current errors.

[0138] S5122 Open-circuit voltage method estimation

[0139] The open-circuit voltage method utilizes the stable correlation between the battery's open-circuit voltage and its state of charge to estimate the voltage, effectively correcting the long-term drift of the ampere-hour integral method. The unit is set to trigger an open-circuit voltage measurement every two hours. Upon triggering, the system determines whether the vehicle is stopped or idling. If so, it controls the battery to pause high-current discharge for 10 seconds to ensure voltage stability, and then collects the open-circuit voltage value through electrical parameter sensors. If the vehicle is in motion, the execution will be delayed until the next idle window.

[0140] The unit calls the preset open-circuit voltage-SOC mapping table to... The corresponding state of charge value is retrieved from the index and used as an auxiliary estimation result. Experiments have shown that within the core range of 20% to 80% SOC, the estimation error of this method is ≤ ±1.5%, but the response speed is slow and it cannot track changes in operating conditions in real time. It needs to be used in conjunction with the ampere-hour integration method.

[0141] S513 fusion result calculation

[0142] The unit employs a weighted fusion algorithm to obtain the final SOC value. The weight allocation was determined through multiple sets of comparative experiments: the ampere-hour integration method has strong real-time performance but is prone to drift, and is assigned a weight of 0.7; the open-circuit voltage method has high accuracy but slow response, and is assigned a weight of 0.3. The fusion formula is as follows: .

[0143] This weighted approach combines the advantages of two algorithms. Field tests verified that, under coupled operating conditions of a road roller with speed variations from 2km / h to 5km / h and vibrations from 30Hz to 50Hz, the final SOC estimation error was consistently controlled within ±2%. This estimation performance is significantly better than a single algorithm, accurately reflecting the real-time remaining battery capacity. This estimation accuracy meets the technical requirement of ≤5% SOC estimation error for BMS systems in the "Technical Specifications for Lead-Acid Power Battery Systems," and achieves higher estimation reliability under harsh operating conditions.

[0144] S52 Health Status Assessment

[0145] State of Health (SOH) refers to the percentage of the battery's current actual capacity to its rated capacity, reflecting the battery's aging degree and performance degradation trend. The State Assessment Unit conducts a comprehensive assessment using two core indicators: internal resistance change and capacity decay.

[0146] S521 Real-time Internal Resistance Calculation

[0147] Internal resistance is a sensitive indicator of battery health; increased internal resistance is usually accompanied by capacity decay. The unit uses a DC discharge method to calculate the real-time internal resistance. This method is a conventional technique for measuring the internal resistance of batteries. The principle is that "the ratio of the instantaneous voltage change to the instantaneous current change is the internal resistance".

[0148] During the calculation, the state evaluation unit selects the moment of current abrupt change and records the voltage before the abrupt change. Current With the voltage after the sudden change Current Through formula Calculate the internal resistance. To eliminate the interference of polarization effect, the selected time of current change should avoid the instant of charge-discharge transition, and the change amplitude should be controlled above 5A to ensure that the voltage change is significant enough.

[0149] For stable current conditions, the state assessment unit estimates the polarization current using a first-order RC equivalent circuit model. The corrected internal resistance formula is: .in The model is derived through iterative calculations based on historical voltage and current data. The model parameters are calibrated through charge-discharge tests before shipment to ensure estimation accuracy. Verification shows that the internal resistance error calculated using both methods is ≤±5mΩ, effectively capturing subtle changes in internal resistance.

[0150] S522 Capacity Decay Trend Analysis

[0151] The state assessment unit combines the compensated energy consumption data output from step S4. By comparing the SOC estimate with the actual battery capacity, we can deduce the actual battery capacity. The specific logic is as follows: when the SOC... Change to When, the corresponding change in energy consumption The relationship between (unit: Wh) and capacity change (unit: Ah) is as follows: ,in The average voltage of the battery during the statistical period (unit: V), therefore Unit: Ah. To improve accuracy, calculations were performed on complete intervals with SOC variations ≥20%, and the results for each interval were averaged with historical data to reduce random errors.

[0152] Capacity attenuation coefficient Calibration was achieved through long-term cycle testing, in which the same type of battery was subjected to 500 charge-discharge cycles, and the actual capacity after each cycle was recorded to ultimately determine the capacity. =0.002 / cycle, meaning that the capacity decreases by 0.002 times the rated capacity after each charge-discharge cycle. A capacity decay model is established based on this. ,in The number of charge-discharge cycles that the battery has completed is automatically counted by the system.

[0153] S523 Comprehensive Health Status Assessment

[0154] Unit by Calculate the health status value, and combine it with the internal resistance growth rate to assist in the judgment. Internal resistance growth rate. The formula is calculated based on three consecutive internal resistance measurements. The unit is Ω / month.

[0155] Engineering practice has verified that when SOH ≥ 80% and When the SOH level is ≤0.01Ω / month, the battery can meet the continuous operation requirements of the engineering vehicle and is considered to be in good condition; when 60%≤SOH<80% or 0.01Ω / month < When the SOH is ≤0.02Ω / month, the battery capacity shows significant degradation, requiring a shorter monitoring cycle; this condition is considered average. When the resistance rate exceeds 0.02Ω / month, the battery is prone to insufficient power supply, which is considered a poor condition, and the system will output a maintenance reminder. This multi-indicator comprehensive judgment method avoids the limitations of a single parameter, and the evaluation results are more in line with actual use scenarios. The SOH threshold in this judgment logic refers to the common experience standard of the battery industry for the usable capacity of power batteries, while the setting of the internal resistance growth rate combines the aging characteristics of lead-acid batteries with the actual operation and maintenance needs of engineering vehicles, forming a health status assessment system that fits the application scenario.

[0156] S6 Closed-Loop Optimization Unit Implementation

[0157] like Figure 3 As shown, the closed-loop optimization unit continuously optimizes the algorithm parameters of the state evaluation unit through the closed-loop logic of "benchmark data calibration - deviation comparison - parameter adjustment".

[0158] S61 Baseline Data Acquisition

[0159] S611 Calibration Cycle Setting

[0160] The closed-loop optimization unit first defined the calibration period, which was determined by tracking the performance degradation patterns of batteries from 10 engineering vehicles of the same model. Experiments revealed that core battery parameters, such as polarization resistance and capacity degradation coefficient, drifted by no more than 1% within 60 days. A period shorter than 60 days would increase maintenance costs; a period longer than 60 days could lead to excessive evaluation deviations due to parameter drift. Based on this pattern, the unit set the calibration period to 60 days, ensuring both timely optimization and practical engineering applicability.

[0161] S612 Benchmark Test Implementation

[0162] During each calibration cycle, technicians conduct benchmark tests using the Arbin BT2000 high-precision battery testing system. This system is a mature piece of equipment in the field of battery performance testing, with voltage measurement accuracy of ±0.01V, current measurement accuracy of ±0.01A, and capacity testing accuracy of ±0.5%. The measurement accuracy far exceeds the system's own monitoring accuracy requirements, providing a reliable benchmark.

[0163] The test environment was selected under stable operating conditions with no vibration and a driving speed of 2 km / h. This environment completely eliminates the vibration and speed change interference suppressed in steps S2 and S3, ensuring that the test data reflects only the battery's own performance. The benchmark test environment was designed to simulate standard battery test conditions to obtain benchmark performance data unaffected by operating conditions, providing a clean reference for algorithm parameter optimization. During the test, technicians first fully charged the battery to 100% SOC, and then discharged it completely with a constant current of 0.2 times the rated capacity, recording the changes in voltage, current, and capacity throughout the process.

[0164] S613 reference value calibration

[0165] The unit calibrates two sets of reference values ​​based on test data. The first set is the calibration state of charge value. The remaining capacity during discharge is compared with the rated capacity. The ratio is calculated using the following formula: .in The first set represents the real-time remaining capacity, in Ah, and the results are presented as a percentage. The second set represents the calibrated health status values. We take the average actual capacity from three consecutive charge-discharge cycles. ,pass Calculations yielded this result. Experiments verified that... and The calibration errors are all ≤ ±0.5%, providing an accurate benchmark for deviation comparison.

[0166] S62 Deviation Comparison and Threshold Setting

[0167] S621 Real-time Data Reception

[0168] The closed-loop optimization unit establishes real-time communication with the S5 state evaluation unit, continuously receiving the real-time SOC and real-time SOH evaluation outputs. To avoid the influence of instantaneous fluctuations on the judgment, the unit performs a 5-minute moving average processing on the received data to eliminate random errors and obtain a smoothed evaluation value. and This ensures the stability of the comparison objects.

[0169] S622 Deviation Calculation

[0170] The unit calculates the deviation by the absolute value difference, and obtains the SOC deviation respectively. and SOH deviation The calculation method is as follows The two deviation values ​​can intuitively reflect the degree of deviation between the evaluation results and the benchmark data, which facilitates subsequent threshold determination.

[0171] S623 Threshold Determination

[0172] The preset error threshold is set based on the actual needs of monitoring engineering vehicle batteries, while also referencing the industry standard "Technical Requirements for Battery Management Systems of Engineering Machinery". In step S5, the SOC estimation error of the state assessment unit is stabilized within ±2%. Exceeding this value will affect the accuracy of charging decisions, therefore we have preset an error threshold for SOC. The threshold is set at 2%. The SOH assessment is linked to the long-term maintenance plan, allowing for a slightly wider deviation range. Considering the actual impact of capacity degradation, we have preset an error threshold for SOH. The threshold is set at 3%. This threshold setting takes into account the fault tolerance requirements of SOH assessment results in engineering applications as well as the slow degradation characteristics of battery performance. It aims to balance monitoring sensitivity and system stability, and avoid unnecessary parameter adjustments caused by normal fluctuations.

[0173] S63 algorithm parameters adaptively adjusted

[0174] S631SOC Deviation Adjustment Strategy

[0175] when Exceed At this time, the closed-loop optimization unit first analyzes the source of the deviation, and then adjusts the fusion algorithm parameters of the state evaluation unit in S5 accordingly. If the deviation is caused by long-term drift of the ampere-hour integration method, the unit will adjust the weight coefficient of the algorithm. The original weight was 0.7, and the positive deviation is... Greater than When this happens, the weight decreases by 0.01 to 0.05; the reverse deviation is... Less than At that time, the weight increases by 0.01 to 0.05. The adjustment range varies. Increase accordingly to ensure the adjustment is targeted.

[0176] If the deviation is related to the charge / discharge efficiency, the unit corrects the coulomb efficiency parameter in the ampere-hour integration method by ±0.01. The ranges of these adjustment parameters are all experimentally calibrated to ensure that the adjustment does not cause significant fluctuations in the estimated value, allowing the estimated value to converge smoothly to near the reference value.

[0177] S632SOH Deviation Adjustment Strategy

[0178] when Exceed At that time, the deviation mainly stemmed from parameter drift in the capacity decay model, and the unit specifically adjusted the capacity decay coefficient used in the health status assessment in S5. .Original The value is 0.002 / cycle, if Greater than The assessment value is too high, indicating that... The value is too small and needs to be increased by 0.0001 to 0.0002; if Less than If the assessed value is too low, then reduce it. The values ​​are of the same magnitude.

[0179] Adjusted The value will be updated to the capacity decay model. In the process, the unit recalculates the SOH. This adjustment method can accurately match the actual degradation rate of the battery. Testing shows that the deviation of the adjusted SOH can converge to within three monitoring cycles. Within.

[0180] S633 Adjustment Effect Verification

[0181] After the parameters are adjusted, the unit will not immediately end the optimization process, but will continue to monitor the adjusted parameters. Deviation from the benchmark value. If the deviation is consistently less than the corresponding preset error threshold for 24 consecutive hours, the adjustment is considered effective, and the unit will save the new parameters to the system database; if the deviation still does not converge, the unit will repeat the deviation analysis and adjustment process until the accuracy requirements are met.

[0182] This closed-loop adjustment mechanism allows the system to adapt to the effects of factors such as equipment aging and changes in ambient temperature, ensuring that the S5's condition assessment accuracy remains at a high level over the long term. Compared to an unoptimized system, after six months of continuous operation, the assessment errors for SOC and SOH decreased by 40% and 35%, respectively, providing continuous and reliable support for the energy management of engineering vehicles.

[0183] The implementation principle of the intelligent monitoring system for engineering vehicle batteries in this application embodiment is as follows: This application effectively solves the problem of inaccurate battery signal acquisition under vibration and speed-changing coupled conditions by adopting a multi-level collaborative anti-interference design from physical structure to signal processing. First, the system uses an adaptive vibration suppression unit to dynamically adjust the equivalent stiffness of the sensor mounting structure according to the real-time vibration frequency, so that the natural frequency of the structure deviates from the vibration frequency, significantly attenuating the mechanical vibration transmitted to the sensor from the source and suppressing the injection of vibration noise. Second, the collaborative noise reduction unit locks the center frequency of signal processing based on the same vibration frequency and adaptively adjusts the processing bandwidth according to the real-time driving speed, thereby filtering out vibration noise while retaining the dynamic components of the current signal generated by speed changes, achieving a balance between vibration suppression and signal fidelity. On this basis, the system further utilizes the clean electrical signal after vibration suppression and noise reduction processing to estimate the state of charge and health of the battery in real time through a fusion algorithm, and dynamically compensates for energy consumption calculations in combination with real-time operating conditions. Finally, the algorithm parameters are periodically calibrated through a closed-loop optimization unit to ensure long-term monitoring accuracy. Therefore, this application not only blocks vibration interference at the physical installation level, but also achieves precise noise reduction with working condition adaptation at the signal processing level, so that subsequent energy consumption calculation and condition assessment are based on high-quality data. Thus, in high-vibration operating environments such as road rollers, high-precision and high-reliability monitoring of battery electrical parameters is achieved, providing a reliable basis for energy management and preventive maintenance of engineering vehicles.

[0184] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. An intelligent monitoring system for engineering vehicle batteries, comprising electrical parameter sensors for collecting battery electrical parameters, characterized in that, Also includes: The data acquisition unit is used to obtain the real-time vibration frequency and real-time driving speed of the engineering vehicle; An adaptive vibration damping unit, connected to the data acquisition unit, is used to dynamically adjust the equivalent stiffness of the electrical parameter sensor mounting structure based on the real-time vibration frequency, so that the natural frequency of the mounting structure deviates from the real-time vibration frequency. A collaborative noise reduction unit is connected to the data acquisition unit and the electrical parameter sensor; the collaborative noise reduction unit is used to: determine the center frequency of signal processing based on the real-time vibration frequency, and adjust the signal processing bandwidth associated with the center frequency based on the real-time driving speed, and use the determined center frequency and the adjusted signal processing bandwidth to perform noise reduction processing on the signal output by the electrical parameter sensor. The adaptive vibration damping unit includes a controllable variable stiffness element and a frequency management controller; The controllable variable stiffness element constitutes the support part of the mounting structure, and its equivalent stiffness is adjusted in real time by the control signal. The frequency management controller connects the data acquisition unit and the controllable variable stiffness element, and is used to generate the control signal according to the real-time vibration frequency, and dynamically control the equivalent stiffness of the controllable variable stiffness element so that the natural frequency of the installation structure is continuously lower than the dynamic threshold determined by the real-time vibration frequency. The collaborative noise reduction unit includes: The frequency locking submodule is used to lock the center frequency of the signal processing to the real-time vibration frequency acquired by the data acquisition unit in real time. The bandwidth adaptive submodule is used to adjust the signal processing bandwidth around the center frequency based on the real-time driving speed obtained by the data acquisition unit. Its adjustment logic is as follows: when the real-time driving speed is lower than a preset speed threshold, a first processing bandwidth is selected; when the real-time driving speed reaches or exceeds the speed threshold, a second processing bandwidth wider than the first processing bandwidth is selected.

2. The system according to claim 1, characterized in that, The logic for adjusting the signal processing bandwidth by the bandwidth adaptive submodule further includes: based on the difference between the real-time driving speed and the speed threshold, continuously or progressively adjusting the width of the signal processing bandwidth on the basis of the first processing bandwidth or the second processing bandwidth, so that the width of the signal processing bandwidth is positively correlated with the difference.

3. The system according to claim 1, characterized in that, It also includes an energy consumption calculation and compensation unit, which includes: An energy consumption calculation unit is used to receive the signal output by the collaborative noise reduction unit and calculate the basic energy consumption value of the battery by performing integration on the signal. The operating condition compensation unit is connected to the data acquisition unit and the energy consumption calculation unit. It is used to perform joint compensation calculation on the basic energy consumption value based on the real-time vibration frequency and the real-time driving speed, and output the compensated energy consumption data.

4. The system according to claim 3, characterized in that, The operating condition compensation unit performs joint compensation calculations on the basic energy consumption value in the following ways: Based on the different speed ranges in which the real-time driving speed is located, the corresponding first compensation factor is invoked; Based on whether the real-time vibration frequency exceeds the preset vibration intensity threshold, the corresponding second compensation factor is invoked; The base energy consumption value is multiplied by both the first compensation factor and the second compensation factor to generate the compensated energy consumption data.

5. The system according to claim 4, characterized in that, It also includes a compensation factor optimization unit, used to perform the following optimization steps: Regularly obtain baseline energy consumption measurements; The baseline energy consumption measurement value is compared with the compensated energy consumption data; When the difference exceeds the preset deviation threshold, the real-time driving speed and real-time vibration frequency corresponding to the compensated energy consumption data are retrieved as the associated operating condition. Based on the associated operating conditions, the first compensation factor and / or the second compensation factor are adjusted in a targeted manner.

6. The system according to claim 1, characterized in that, It also includes a state evaluation unit, which is connected to the collaborative noise reduction unit, for receiving the processed signal and performing the following evaluation steps: The signal is fused using the ampere-hour integration method and the open-circuit voltage method to estimate the real-time state of charge of the battery. Using the estimated results of the real-time state of charge and the signal, the changes in the battery's internal resistance and the trend of capacity decay are analyzed to comprehensively assess the battery's health status.

7. The system according to claim 6, characterized in that, It also includes a closed-loop optimization unit for performing the following closed-loop optimization process: Regularly obtain the calibrated state of charge and calibrated state of health values ​​of the battery as baseline data; The baseline data is continuously compared with the real-time state of charge and health status output by the state assessment unit. When the deviation of the comparison result exceeds the preset error threshold, the calibrable parameters in the fusion algorithm used by the state evaluation unit are adaptively adjusted according to the direction and magnitude of the deviation.

8. The system according to claim 1, characterized in that: The controllable variable stiffness element is a magnetorheological elastomer element; The frequency management controller is configured to generate the control signal based on the comparison result between the real-time vibration frequency and the preset frequency threshold, so as to perform closed-loop adjustment of the equivalent stiffness of the magnetorheological elastomer element, so that the natural frequency of the installation structure is stably deviated from the real-time vibration frequency. The electrical parameter sensor includes a microelectromechanical system current sensor and a microelectromechanical system voltage sensor, which are mounted on the mounting structure composed of the magnetorheological elastomer element; the signal output terminal of the electrical parameter sensor is connected to the collaborative noise reduction unit so that the electrical signal after vibration suppression processing is input to the collaborative noise reduction unit for frequency domain noise reduction processing.

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