3449results about "Testing electric installations on transport" patented technology

The invention relates to the technical field of power battery packs, and discloses a power battery pack state evaluation method and system, and the method comprises the steps: collecting an operation characteristic data set of a power battery pack under various working conditions, and constructing a hybrid evaluation model; performing parameter configuration on the double-extended Kalman filter to obtain an improved filter; acquiring real-time voltage, current and temperature data, and inputting the real-time voltage, current and temperature data into the improved filter for state calculation to obtain a charge state value and an internal parameter value; inputting the charge state value and the internal parameter value to perform multi-branch adversarial neural network analysis to obtain a fault mode type and a fault severity; according to the method, the multi-factor health index is calculated, particle swarm optimization fitting is carried out on the degradation curve of the power battery pack, the residual service life prediction value is obtained, more accurate residual service life prediction is further achieved, the precision in a wide temperature range is remarkably improved, and the difference between monomers in the power battery pack can be effectively processed.

The invention provides an electric vehicle battery health state monitoring and service life estimation method and system, and the method comprises the steps: S1, obtaining a real-time data flow of a battery during the operation of a vehicle, extracting the original records of charging and discharging depth, temperature change, internal resistance parameters and capacity data from the real-time data flow, a dynamic monitoring sample is calculated through voltage, current and temperature values collected by a sensor; s3, calculating a capacity attenuation rule through the time sequence feature set, analyzing the decrease amplitude of the capacity after each charge-discharge cycle by adopting an exponential attenuation model, obtaining a long-term operation trend from historical data, and obtaining a dynamic curve of capacity attenuation; and S8, if the confidence interval of the final estimation probability distribution is greater than a preset threshold value, recollecting data with higher frequency for the dynamic monitoring sample, and performing iterative optimization on the life estimation model through the updated sample to obtain an adjusted life detection result.

The invention discloses a vehicle-mounted boost inductance monitoring simulation test system and a test method thereof. The vehicle-mounted boost inductance monitoring simulation test system comprises a vehicle-mounted power supply simulation module, an accelerated life test module, an environment test box, a fault early warning module and a data analysis and diagnosis module. The actual stress state of the inductor is comprehensively simulated by constructing a power supply simulation model matched with the actual working condition of the electric vehicle and combining a multi-environmental-factor coupled accelerated life test; real-time monitoring and fault early warning of inductive performance degradation are realized by adopting an acoustic emission technology; an inductance fault diagnosis knowledge base and an intelligent reasoning system are constructed by applying big data analysis and machine learning technologies. According to the system and the method provided by the invention, the inductance reliability test efficiency can be remarkably improved, the early warning of inductance degradation is realized, the accuracy and the intelligent level of fault diagnosis are improved, a solid basic guarantee is provided for safe operation of a new energy electric vehicle, and the system and the method have wide application prospects and popularization values.

The invention relates to the technical field of motor train unit battery pack evaluation, and provides a motor train unit battery health state evaluation and life prediction method and system, and the method comprises the steps: building a database through voltage fluctuation and temperature data in a floating charge state, and analyzing a correlation mechanism between target parameters, such as a battery capacity retention ratio and active lithium stock, and internal chemical substance loss; respectively extracting driving behavior characteristics, temperature parameters and electrochemical impedance spectroscopy by adopting second-level, minute-level and hour-level time granularity, and aligning multi-frequency data by utilizing a dynamic time warping algorithm; constructing a graph structure model taking the electrochemical parameters as nodes and cross-level association as edges, and extracting a battery aging characteristic topological relation through a graph neural network; and finally, establishing a prediction model fused with multi-dimensional features, and realizing accurate prediction of the residual life and the capacity recession inflection point of the battery. And the multi-scale characteristics are combined with an electrochemical mechanism, so that the accuracy of battery health state evaluation under a complex working condition is improved.

Examples relate to a battery management system with an enhanced equivalent circuit model (ECM) for electric vehicles, including electric vertical takeoff and landing (eVTOL) aircraft. The system includes a memory to store an equivalent circuit model (ECM) configured to dynamically adjust a series resistance component in real-time based on operational data from a lithium-ion battery. This adjustment models lithium depletion effects under high discharge conditions. At least one processor is configured to continuously refine parameters of the ECM by analyzing discrepancies between predicted and actual battery performance, where the adjustments are based on real-time changes in state of charge, temperature, and current.

The invention relates to the technical field of battery capacity prediction, and discloses a golf cart battery capacity prediction method, device and equipment and a storage medium, and the method comprises the steps: carrying out the collection and preprocessing of historical charging and discharging data of a golf cart battery, and obtaining a time sequence feature data set; generating an incremental capacity curve, inputting the incremental capacity curve into a bidirectional long-short-term memory network, and training to obtain an initial capacity prediction model; taking the initial capacity prediction model as a first-layer model, and taking the metabolism grey model as a second-layer model for stacking and integrating to obtain a mixed prediction model; dynamically adjusting model parameters through a double-layer optimization algorithm to obtain a target capacity prediction model; and inputting real-time battery operation data into the target capacity prediction model to carry out capacity prediction and health state evaluation, and outputting a capacity attenuation trend chart and a residual service life estimation value, so that the precision of predicting the battery capacity of the golf cart is improved.

The invention discloses a battery health state prediction method and device, a detection assembly and a vehicle, and belongs to the technical field of new energy. The method comprises the following steps: acquiring a plurality of time-varying expansive force values and residual electric quantity of a to-be-detected battery in a single charging process; and under the condition that the difference between the second residual electric quantity at the end of charging and the first residual electric quantity at the start of charging is greater than the preset charging depth, determining at least two expansive force extreme values and the residual electric quantity corresponding to the sampling time of each expansive force extreme value. The expansion force extreme value corresponds to a key feature point in a single charging process, and the health degree of the battery can be reflected by combining the synchronous residual electric quantity. According to the method, the at least two expansive force extreme values and the corresponding residual electric quantities are input into the preset prediction model, the correlation between the independent variable (expansive force extreme value) and the dependent variable (health state information) is subjected to regression analysis through the preset prediction model, the health state information is predicted, and the accuracy of the health state of the battery is improved.

An electronic fuse includes a housing, a first housing terminal and a second housing terminal. The electronic fuse further includes a circuit breaker including a first terminal of the circuit breaker and a second terminal of the circuit breaker. The first terminal of the circuit breaker is electrically connected to the first housing terminal of the electronic fuse. The second terminal of the circuit breaker is electrically connected to the second housing terminal of the electronic fuse. A thermal protection is arranged in a current path between the first housing terminal and the second housing terminal in series with the circuit breaker. The thermal protection interrupts the current path when a temperature of the circuit breaker or a temperature within the housing of the electronic fuse and / or a temperature of the housing of the electronic fuse exceeds a switch-off temperature.

The invention provides a battery pack fault diagnosis method and system based on multi-domain characteristics, and relates to the technical field of battery management, and the method comprises the steps: obtaining a median voltage curve of a battery pack and a single voltage curve of a single battery in the battery pack; decomposing the monomer voltage curve by taking the median voltage curve as a decomposition reference; optimizing key parameters of a variational mode decomposition algorithm through a grey wolf optimization algorithm by taking the minimum sum of envelope entropies of decomposition components of the single voltage curve obtained through decomposition as a target; decomposing each monomer voltage curve according to the optimized variational mode decomposition algorithm to obtain a high-frequency fault information component; extracting features of the high-frequency fault information components; performing dimension reduction fusion on the time domain features and the frequency domain features through a principal component analysis algorithm to generate a comprehensive fault coefficient; and according to the time domain feature, the frequency domain feature and the comprehensive fault coefficient, a local gravitation algorithm is combined to identify a fault single battery. According to the invention, the fault diagnosis capability can be improved, and early warning is facilitated.

The invention discloses a rapid calibration method and system for automatic testing equipment of a vehicle machine, and relates to the technical field of automatic calibration, and the method comprises the steps: inputting a preset standard calibration signal to the testing equipment, and collecting the response data of each testing channel of the equipment as calibration reference data; comparing the calibration reference data with a built-in standard reference value of the equipment, and calculating deviation data of each test channel; based on the deviation data, calibration parameters of each channel are calculated, and the calibration parameters comprise a gain compensation coefficient, a frequency compensation coefficient and a phase compensation coefficient; the calibration parameters are loaded to the corresponding channels of the test equipment, a preset standard calibration signal is input for verification test, and when errors of the channels are smaller than a preset residual error threshold value, calibration is completed, and the calibration parameters are stored. By establishing the gain-frequency-phase joint compensation model and combining the parallelization verification and increment optimization algorithm, the high-precision rapid calibration of the automatic test equipment of the vehicle machine is realized.

The invention discloses an insulation detection method for an electric automobile, belongs to the technical field of high-voltage safety monitoring of the electric automobile, and solves the problem that the insulation detection precision is reduced due to electromagnetic interference, impedance fluctuation and capacitive effect coupling in a dynamic load switching process. According to the scheme, a test sequence of discrete frequency points is applied to a high-voltage system; measuring a response current value through a current sensor and calculating a ground distributed capacitance value; connecting a test voltage source between positive and negative electrodes of the high-voltage system through a coupling circuit; applying a first pulse signal during no-load, dynamically setting the width according to the capacitance value, measuring a first response current value, and calculating an initial insulation resistance value; a second pulse signal with the same parameter is applied in the load state window, a second response current value is measured, and differential filtering is carried out in combination with the initial value to eliminate load noise; and comparing the filtered insulation resistance value with a threshold value, and outputting a fault signal when the insulation resistance value is lower than the threshold value. The method is used for monitoring the insulation state of the high-voltage system in real time and accurately early warning faults.

The invention provides an automobile wire harness performance parameter test system, and relates to the technical field of automobile wire harnesses, the automobile wire harness performance parameter test system comprises a test assembly and a control module, the test assembly comprises a simulation test group and a mute test box, and the control module comprises an acquisition module, an analysis module, a judgment model unit and a visual feedback platform; a simulation system is constructed in the simulation test group, and when the automobile wire harness is installed in the simulation system for operation, operation state parameters of an automobile under different road conditions and environment conditions are simulated; the simulation system is constructed to simulate the running state parameters of the automobile under different road conditions and environment conditions, the change relevance between the running state parameters and the current parameters of the automobile wire harness is analyzed, the reference variable is obtained, the first performance of the automobile wire harness is comprehensively judged, the actual running requirements of the automobile wire harness are met, and the reliability of the automobile wire harness is improved. The sound of the automobile wire harness is synchronously recognized and collected through the mute test box, the abnormal type of the wire harness represented by the sound is recognized, the second performance of the automobile wire harness is judged, and the method is more comprehensive.

The invention relates to the technical field of data processing, in particular to a fault detection method for a wire for a new energy automobile, and the method comprises the steps: obtaining a data sequence of detection data; determining the fluctuation time difference weight of the acquisition period based on the change of the autocorrelation strength between the data sequences when the data sequences fluctuate in the acquisition period; setting an amplitude threshold based on the amplitudes of the two kinds of detection data on all frequency components of the frequency domain, determining a fitting straight line based on the screened peak points by using a data fitting mode so as to determine a difference trend factor of the detection data, and determining a fault anomaly weight by combining a fluctuation time difference weight and an energy spectrum density difference so as to realize fault detection. Determining a connection influence coefficient of the detection data by combining the variable quantity of the elements in the data sequence; and obtaining an abnormal data point based on the connection influence coefficient by using a data clustering algorithm, and determining a fault detection result. According to the method, the clustering algorithm is optimized, so that the distinction degree of different fluctuation characteristic data is improved, and a more accurate fault detection result is obtained.

The invention relates to the technical field of power battery testing, in particular to a BMS function testing system and method for a new energy automobile. The method comprises the following steps: constructing battery pack structure model data according to battery pack CAD design drawing data; collecting vehicle working condition charging and discharging sample data; carrying out adaptive current excitation sequence processing according to the vehicle working condition charging and discharging sample data, and generating multi-working condition disturbance pulse excitation sequence data; performing disturbance magnetic field simulation on the battery pack structure model data through the multi-working-condition disturbance pulse excitation sequence data to obtain working condition test magnetic disturbance response data; and performing battery pack BMS monitoring data acquisition based on the working condition test magnetic disturbance response data, performing BMS current distribution monitoring performance evaluation, and generating BMS current monitoring performance evaluation data. By simulating current excitation and magnetic field response under multiple working conditions, comprehensive evaluation of the BMS performance is realized, and the test accuracy and efficiency are improved.

The invention relates to the technical field of automobile electronic testing, and discloses an integrated ECU power-on burning test monitoring system and method. The system comprises a power supply intelligent regulation and control module, a burning verification module, a test diagnosis module, an abnormity monitoring module and a feedback optimization module. The power supply intelligent regulation and control module realizes stable power supply output through a dynamic voltage frequency regulation algorithm and a temperature self-adaptive algorithm; the burning verification module is used for ensuring the burning integrity by using a block redundancy check and increment verification algorithm; the test diagnosis module diagnoses faults based on the multi-dimensional test data and the decision tree model; the abnormity monitoring module monitors abnormity in real time by using real-time streaming data processing and an isolated forest algorithm; and the feedback optimization module optimizes test diagnosis through incremental learning and a Bayesian optimization algorithm. According to the system and the method, comprehensive and accurate monitoring and optimization of the ECU are realized, and the performance and the reliability of the ECU are effectively improved.

The invention discloses a maglev train suspension system electromagnet fault detection algorithm, which comprises the following steps: establishing a suspension system model of a maglev train, and designing a state observer; an electromagnet signal is collected and processed, and a residual value is obtained; the residual value is compared with a threshold value, and fault information of the electromagnet of the maglev train suspension system is obtained; and fault information of the electromagnet is reconstructed. The fault of the electromagnet can be effectively detected and reconstructed by constructing the third-order linear extended state observer (LSEO), system uncertainty, noise and the like can be dealt with through the self-adaption and self-calibration functions of the third-order linear extended state observer, robustness is enhanced, and detection reliability is improved. Fault information of the magnetic levitation system can be quantitatively estimated through LSEO for fault reconstruction, the fault type and position can be accurately identified, and false alarm and missing alarm can be avoided. Meanwhile, fault reconstruction provides an important basis for maintenance, and maintenance personnel can quickly position and take targeted measures, so that the maintenance efficiency is improved, and the cost is reduced.

The invention discloses an electric vehicle battery health assessment method and system based on multi-source data fusion, and relates to the technical field of new energy vehicle battery application, and the method comprises the steps: collecting a Nyquist impedance curve, terminal voltage relaxation time sequence data and equalization current time sequence data based on a constant current stage cut-off event of a battery pack of an electric vehicle; extracting an impedance form embedding vector corresponding to the Nyquist impedance curve based on an auto-encoder; performing wavelet packet decomposition on the terminal voltage relaxation time sequence data to obtain a corresponding multi-scale voltage relaxation energy feature vector; calculating a dynamic equalization imbalance index based on the terminal voltage relaxation time sequence data and the equalization current time sequence data; and inputting the impedance form embedded vector, the multi-scale voltage relaxation energy feature vector and the dynamic equilibrium imbalance index into a battery SOH prediction model to output an SOH estimated value of the battery pack. Therefore, the accuracy of the SOH evaluation result is improved through the joint modeling of the coupling characteristics of the multi-physical process of the battery.

Online electrochemical impedance spectroscopy (EIS) circuitry and methods. For mobile installations, such as in a hybrid or electric vehicle, battery management units or battery management systems having EIS circuits can provide additional data regarding the state, health and potential failure of batteries. New topologies for the EIS circuitry are proposed, allowing voltage controlled and current controlled EIS to be performed.

The invention provides a lithium ion power battery external thermal resistance and internal and external temperature joint estimation method comprising the following steps: building a battery equivalent thermal model according to lithium ion power battery thermal characteristics; identifying the thermal parameters by adopting a self-adaptive particle swarm optimization algorithm based on genetic algorithm optimization; establishing a high-gain observer, and initializing the high-gain observer by taking the external thermal resistance in the identified thermal parameters as an initial value to complete real-time estimation of the external thermal resistance; constructing a self-adaptive strong tracking extended Kalman filter, and inputting the identified thermal parameters and the real-time estimated external thermal resistance into the self-adaptive strong tracking extended Kalman filter; and introducing a multi-strategy improved Tianying optimization algorithm to perform optimization processing on the process noise covariance and the observation noise covariance of the adaptive strong tracking extended Kalman filter to obtain an optimized filter, and performing real-time estimation on the internal and external temperatures of the lithium ion power battery through the optimized filter.

The invention relates to the technical field of electromagnetic environment effect, and discloses a high-intensity radiation field complete machine low-level scanning field extrapolation verification system and verification method, and the verification method comprises the following steps: building a complete machine HIRF high-level scanning field test system under the same test site requirement of a complete machine HIRF-LLSF test, the whole machine HIRF high-level scanning field test system adopts an HPM system to simulate a narrow-band high-level HIRF radiation source, the distance between a transmitting antenna and a test object is kept to be the same as that of a whole machine HIRF-LLSF test, and the placing position of a receiving antenna or a field intensity probe is the same as that of the whole machine HIRF-LLSF test; according to the invention, the technical problems of calibration of the HIRF high-level scanning field of the whole machine and accurate and synchronous measurement of the cabin coupling field and the problem of to-be-verified extrapolation accuracy of the test result of the HIRF-LLSF test of the whole machine can be solved, and a test means is provided for protection design and safety evaluation of the HIRF of the airplane.

The invention discloses a driver controller detection system, which comprises an acquisition module, an acquisition module, a processing module, an identification module, a judgment module, an analysis module and a generation module, the acquisition module captures contact information and electromagnetic interference data of contacts in real time by means of a high-dynamic voltage sensor and a broadband EMC probe; the acquisition module is triggered by a locomotive signal and records dynamic working condition data; the processing module aligns the data, performs denoising and extracts fault parameters; the identification module scans the voltage waveform and judges the transient contact failure; the judgment module monitors EMC data and compares an instruction position, and judges misoperation; the analysis module analyzes fault association, judges a composite type and triggers an alarm; the generation module generates a detection report and a log. According to the system, a multi-dimensional monitoring network is constructed under a dynamic working condition, data reliability is guaranteed, faults are accurately identified, a compound fault source is positioned, and the safety and stability of locomotive operation are improved through risk grading early warning and automatic reporting.

The invention discloses a system and method for measuring the opening and closing state of an electromagnetic valve, and relates to the technical field of electromagnetic valves.The method comprises the steps that after an electromagnetic valve control signal is triggered, dynamic inductance L at each moment t is obtained through real-time backstepping by means of a voltage and current sampling module integrated in a driving loop and an inductance analysis module in a local microcontroller MCU; gaussian filtering processing and derivative calculation are carried out on the dynamic inductance L in the edge calculation module, first-order derivative and second-order derivative characteristics are extracted, and an inductance mutation factor index Cimp is obtained through calculation. According to the method, clamping stagnation, rebound and discontinuous abnormal states occurring in the opening and closing process of the electromagnetic valve are comprehensively reflected, a sudden change threshold value Cthr is set and compared, and a set of dynamic opening and closing behavior diagnosis mechanism with high resolution and high adaptability is constructed. According to the method, high-frequency dynamic response extraction and abnormal trend quantification can be completed within 100 ms after the control signal is triggered, and the method has higher early warning capability and non-intrusive compatibility.

The invention relates to the technical field of battery state-of-charge estimation, and discloses an unmanned aerial vehicle power battery state-of-charge accurate estimation method, which is characterized by comprising the steps of multi-modal dynamic data acquisition and preprocessing, quantum-classical hybrid modeling and feature extraction, meta-learning driven dynamic adaptive optimization, multi-modal dynamic state-of-charge estimation, multi-modal dynamic state-of-charge estimation, multi-modal dynamic state-of-charge estimation and multi-modal dynamic state-of-charge estimation. Uncertainty quantification and security boundary management: multi-modal dynamic data acquisition and preprocessing, quantum-classical hybrid modeling, meta-learning driven dynamic adaptive optimization and uncertainty quantification and security boundary management technologies are adopted; the method solves the problems of insufficient estimation precision and poor adaptability of a traditional method under a dynamic flight working condition due to complex coupling of multi-modal data and strong nonlinearity of a battery polarization effect and an aging characteristic in the estimation of the state of charge of the power battery of the unmanned aerial vehicle.

A battery control component of an electric vehicle may limit charging of a battery on the electric vehicle to a target state of charge (SOC), such as to reserve battery capacity for regenerative braking during downgrades. A computing device associated with the vehicle may calculate an estimated gradient profile and / or an estimated total energy consumption of a predicted upcoming route of the vehicle based on one or more data items, such as GPS data, altitude data, and / or map data. The computing device may determine a target SOC based on the estimated gradient profile. The computing device may determine a confidence factor associated with the estimated gradient profile, where the confidence factor depends on the quantity of data items used to calculate the estimated gradient profile. The computing device may adjust the target SOC based on the confidence factor.

The invention relates to the technical field of automobile batteries, and discloses a battery service life prediction method, which comprises the following steps: S1, collecting use data of a battery; s2, cleaning and preprocessing the collected battery data, removing abnormal data, filling missing values, and performing standardization or normalization processing; s3, a nonlinear dynamic model is constructed, the change relation of the service life of the battery along with time is described, and the model considers multiple influence factors of the battery; and S4, based on battery historical data. According to the method, the prediction strategy is dynamically adjusted by combining Gaussian process regression and reinforcement learning, and the problem that a static model is poor in adaptability to battery state change in the prior art is solved; bayesian optimization is introduced to optimize hyper-parameters, so that prediction precision and stability are improved; and through a real-time data monitoring and dynamic adjustment mechanism, the real-time performance and the accuracy of the prediction model during battery state change are ensured.

The invention relates to the technical field of aircraft lightning protection tests, and discloses an aircraft lightning indirect effect and HIRF conduction sensitivity test device and method, and the device comprises a device under test (EUT), an accompanying monitoring device, an auxiliary device, an HIRF injection probe, a lightning injection probe, a monitoring probe, a lightning signal isolator, and an HIRF conduction test system. The invention discloses a lightning indirect effect test system and a digital oscilloscope. Aiming at the problem of electromagnetic environment potential safety hazards caused by independent evidence obtaining and independent rectification of civil aircraft thunder and lightning and HIRF airworthiness, thunder and lightning indirect effect and HIRF conduction sensitivity tests can be simultaneously carried out on an aircraft electronic and electrical system, the thunder and lightning and HIRF comprehensive protection performance of the aircraft electronic and electrical system is verified, and the potential safety hazards of the electromagnetic environment are effectively reduced. The thunder and lightning indirect effect and HIRF conduction sensitivity test device is simple in construction and easy for engineering realization, and can also be used for thunder and lightning and HIRF protection performance verification tests of other aircrafts.

The invention discloses a new energy automobile rapid charging diagnosis and test system and method. The system comprises an industrial personal computer serving as a control unit, an electrical safety detection module, a charging safety detection module, a man-machine interaction module, a power module, a direct current charging module and a channel switching module. The scheme aims at the intelligent diagnosis technology of the new energy automobile, the function and performance detection of key parts such as power electronic equipment of the new energy automobile can be realized, the performance and the safety condition of the automobile are evaluated, and the safety and the reliability of core components of the automobile are ensured.

The invention relates to the technical field of power battery evaluation, and discloses a power battery health state evaluation and life early warning method. The method comprises the following steps: dividing a target power battery module into a plurality of layers of evaluation areas, and randomly selecting a plurality of measurement position points in each layer of evaluation area; then, collecting battery state original data of each measurement position point of each layer of evaluation area in real time, and comprehensively analyzing the data to obtain a health state evaluation value of the target power battery module; judging and analyzing the health state evaluation value and a preset health state threshold value; if the health state evaluation value is lower than a preset threshold value, triggering a preset life early warning measure; and if the health state evaluation value is higher than a preset threshold value, maintaining the current evaluation period. The method can improve the accuracy of battery health state evaluation, optimizes the time of life early warning, and is suitable for health management of various power battery modules.

Power monitors, power supply circuits, and methods for operating a power supply. The method includes determining a voltage of a power rail provided to a load device from the power supply. The method also includes detecting that the voltage of the power rail is greater than or equal to an over-voltage threshold. The method further includes incrementing an over-voltage counter when the voltage of the power rail is detected as being greater than or equal to the over-voltage threshold. The method also includes detecting that the over-voltage counter is equal to a threshold value. The method further includes generating an interrupt signal when the over-voltage counter is detected as being equal to the threshold value.

The invention relates to the field of vehicle current detection, in particular to an automobile online current detection method and terminal equipment. The method comprises the following steps: acquiring real-time current of a vehicle to obtain a multi-time-sequence sampling synchronous current waveform, performing microsecond offset identification and multi-dimensional sampling feature relevance mining, and constructing a synchronous current response matrix; a vehicle-mounted environment monitoring unit is called to collect real-time environment temperature information of the vehicle, temperature trend change mining and temperature drift factor mining are carried out, and temperature drift influence factors are extracted; current detection reference calibration analysis is carried out based on the temperature drift influence factor and the synchronous current response matrix, temperature deviation compensation intelligent optimization is carried out, and a temperature deviation equivalent regulation and control engine is constructed; the method comprises the following steps: monitoring full-band operation electromagnetic parameters of a plurality of subsystems of a vehicle, carrying out frequency-band electromagnetic fluctuation identification one by one, carrying out multi-scale decomposition, and extracting an electromagnetic interference disturbance spectral line set; according to the invention, real-time and accurate vehicle on-line current detection is realized.