2020results about "Testing electric installations on transport" patented technology

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 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.

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 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 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 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.

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.

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.

The invention discloses a method and system for determining battery electric vehicle endurance reliability, and relates to the field of new energy vehicle endurance evaluation, the method comprises the following steps: respectively carrying out a plurality of driving range tests on each target vehicle, and calculating the experiment energy consumption of each target vehicle under each driving range test; determining a first matrix based on the actual annual comprehensive energy consumption of all the target vehicles, and determining a second matrix based on the experimental energy consumption of all the target vehicles under different driving range tests; determining N driving range tests having the highest correlation with the first matrix as N test indexes; carrying out weight product summation on the driving mileage feedback of each target vehicle under the N test indexes to obtain a driving mileage evaluation value of each target vehicle; and the calibrated endurance of the target vehicle of which the driving range evaluation value is greater than the set threshold value is real and reliable. According to the method, the reliability of the calibrated endurance of the pure electric vehicle can be determined, and an objective basis is provided for a consumer to select a target vehicle.

The invention relates to the technical field of control and regulation, in particular to a new energy automobile battery health state prediction system which comprises a data acquisition module, a quantum feature evolution module, a digital twinning prediction module, a dynamic control module, a federal learning evolution module and a quantum verification module. The data acquisition module generates a spatio-temporal data base. The quantum feature evolution module outputs an ion desolventizing agent energy barrier change gradient, and the digital twinborn prediction module eliminates prediction offset through compensation and correction when the environment changes suddenly. The dynamic control module generates an environmental adaptation degradation suppression strategy. The federal learning evolution module encrypts transmission attenuation data and updates global model parameters, and the quantum verification module verifies the causality of a control instruction and rolls back to a safe steady state when the control instruction fails. The system forms quantum sensing, environment modeling, prediction compensation, risk suppression, model evolution and a safety verification closed loop, the battery life prediction precision in a dynamic environment is improved, and safety maintenance lag caused by environment sudden change is eliminated.

An apparatus for safety testing a battery pack of an electric vehicle includes a low voltage data connector configured to couple to a battery management system controller of the electric vehicle battery pack for data communication with the battery management system controller. A test connector is configured to couple a battery module of the electric vehicle battery pack for use in testing the battery module. Measurement circuitry measures a parameter of the battery module through the test connector. A diagnostic controller couples to the low voltage data connector and the measurement circuitry and is configured to identify an error in data received from the battery management system controller based upon a comparison of the parameter measured by the measurement circuitry and the data received from the battery management system controller.

The invention relates to the field of power supply fault prediction, in particular to a power supply power distribution fault prediction method and system based on BMS data analysis driving. The method comprises the following steps: calling a BMS (Battery Management System) to collect a historical monitoring log of a power supply, carrying out periodic power disturbance change analysis and normalized power perturbation evolution, and constructing a normalized power perturbation state diagram of a plurality of battery cells; state evolution mining is carried out on the battery cells one by one based on a power supply historical monitoring log, multi-dimensional personalized state perception is carried out, and a personalized state portrait of each battery cell is constructed; calculating request power deviation of the battery cells, performing power distribution coordination analysis among the battery cells, performing power distribution game logic mining based on the personalized state portrait, and identifying a power distribution game rule of the battery cells; and performing power distribution prediction and power distribution track deviation comparison according to the power distribution game rule, and extracting a continuous deviation accumulation region. According to the method, the accuracy and the real-time performance of power distribution fault prediction of the power supply are improved.

The invention discloses a motor bearing fault diagnosis method, device and equipment and a medium, and relates to the technical field of motor bearing fault diagnosis, and the method comprises the steps: obtaining a vibration signal and a current signal of a motor bearing, and carrying out the feature extraction of the vibration signal and the current signal, obtaining a first time domain feature, a first frequency domain feature and a first energy feature of the vibration signal and a second time domain feature, a second frequency domain feature and a second energy feature of the current signal; carrying out feature fusion and feature dimension reduction on the first time domain feature, the first frequency domain feature, the first energy feature, the second time domain feature, the second frequency domain feature and the second energy feature to obtain a feature after fusion dimension reduction, and taking the feature after fusion dimension reduction as input; and determining whether the motor bearing has a fault and the fault type when the fault exists by using the fault diagnosis model. According to the invention, the fault diagnosis precision can be improved.

The invention relates to the technical field of radio orientation, in particular to an unmanned aerial vehicle flight state detection method based on a frequency spectrum detection technology, which comprises the following steps: acquiring an initial time domain signal of an initial frequency spectrum signal of an unmanned aerial vehicle in a monitoring period in a flight process; segmenting the initial time domain signal according to the flight state so as to screen out each complex change time period; performing signal decomposition on the time domain sub-signals in each complex change time period to obtain a plurality of components; and determining an interference frequency tendency index of each component, determining an interference characteristic comprehensive index of each component according to the interference frequency tendency index of each component, the oscillation intensity characteristic value and the correlation coefficient, and reconstructing the frequency spectrum sub-signal corresponding to the time domain sub-signal to obtain a target frequency spectrum signal. According to the method, the data reference value of the target spectrum signal obtained through adaptive filtering is larger, and the accuracy of detection of the flight state of the unmanned aerial vehicle is improved.

A fault diagnosis device and a fault diagnosis method for an electric vehicle charging system. The fault diagnosis device may include: a first terminal node receiving a control pilot (CP) signal from a connector of a charger through an inlet and connected to an anode of a diode; a first node connected to a cathode of the diode; a second node selectively connected to the first terminal node by a switch; a third node connected to the second node through a first resistor; a second terminal node connected to the third node through a second resistor; and a processor configured to measure a first voltage of the first node, a second voltage of the second node, and a third voltage of the third node to diagnose a fault on the CP line.

The invention discloses an adaptive weighted physical information neural network-based monomer voltage prediction method. The method comprises the steps of constructing a sample set according to real vehicle battery multi-dimensional time domain data; establishing a physical branch output monomer voltage physical prediction vector based on an equivalent circuit model; constructing data branches based on a graph attention mechanism to extract node time sequence features to obtain a data-driven prediction vector; a residual error is calculated, a compensation module generates a correction amount, and the correction amount is superposed to a physical prediction vector to obtain a final prediction result; and constructing a multi-step loss function of physical loss and data loss, and introducing an adaptive weighting mechanism to dynamically adjust loss weight iteration optimization parameters. According to the method, the precision and stability of single voltage prediction under a complex operation condition are effectively improved, and the adaptability and generalization performance of the model are enhanced.

Various embodiments disclosed by the present invention include an exemplary system for remotely testing the continuity of electrical wiring, an electric vehicle charging system, and a charge coupler for an electric vehicle charging system. In an exemplary embodiment, the system for remotely testing the continuity of electrical wiring includes a signal generator configured to generate a signal having a predetermined frequency. A controlled impedance network is configured to attenuate the signal. The controlled impedance network can be electrically connected towards a first end of the electrical wiring, which is terminated at a terminal at the first end and can be electrically connected to the signal generator at a second end to carry the signal. A signal detector is configured to detect the signal.

A system and method for monitoring power of a vehicle may monitor a state of charge of a battery of the vehicle and may notify a customer of the state and its response. A system for monitoring power of a vehicle may include: a processor; and a storage medium having recorded therein one or more programs configured to be executable by the processor. The processor may execute one or more programs to: store power consumption data for each preset vehicle state; calculating an expected discharge time of a battery of the vehicle based on the power consumption data classified by the preset electrical device and the stored power consumption data; and displaying the power consumption data of the electrical device and the expected discharge time of the battery.

A trailer test module of a vehicle is electrically connected between a trailer tow electrical connector of the vehicle and a wiring harness of the vehicle that carries electrical signals for a trailer such that when the electrical connector of the trailer is attached to the trailer tow electrical connector, the trailer tow electrical connector is between the trailer test module and the electrical connector of the trailer. The trailer test module includes circuitry that replicates an electrical load of the trailer.

The invention relates to the technical field of electric vehicle charging equipment testing, in particular to direct-current charging and discharging gun interoperation portable testing equipment and method, and the equipment comprises a direct-current charging gun plug; the charging and discharging control circuit is arranged in the direct-current charging gun plug and is used for controlling the switching of the connection states of the timing plugging gun, the fixed-frequency plugging gun and the charging gun of the direct-current charging gun; and the controller is connected with the charging and discharging control circuit, controls the charging and discharging control circuit to simulate one or more target test scenes of the direct current charging gun, and generates a test result of the direct current charging gun according to test data in the target test scenes. Therefore, the problems that in the charging process of an electric vehicle, due to the fact that an effective charging gun electronic lock unlocking and locking function testing means is lacked, whether the electronic lock function meets the national standard requirement and the actual use requirement of a user cannot be comprehensively and accurately verified, and then charging potential safety hazards and poor user experience are possibly caused are solved.

The invention discloses an intelligent test system and method for traction current interference, and belongs to the technical field of high-speed railway signal system testing, and the system comprises a signal generation module which is used for generating an interference signal and a track circuit signal; the track circuit transmission module is used for simulating electrical characteristics of a real track circuit and transmitting interference signals and track circuit signals; the mobile vehicle-mounted equipment is used for dynamically simulating the train operation condition, receiving the interference signal and the track circuit signal and generating a vehicle-mounted signal decoding result; the signal acquisition module is used for acquiring current signals in a receiving end steel rail; and the test control analysis platform is used for receiving the output signal and performing data processing to generate a test report. According to the invention, by introducing the electrical environment simulation and mobile vehicle-mounted equipment test module of a real track circuit and fusing the steady state and transient interference simulation technology, the comprehensive and accurate evaluation of the anti-interference performance of the high-speed railway signal system in a complex electromagnetic environment can be realized.

The invention relates to the technical field of chip detection, and discloses a high-reliability power supply chip circuit fault detection system, which comprises a built-in self-test architecture integrated in a low dropout regulator chip, and the built-in self-test architecture is provided with a first test mode and a second test mode which are switched based on a mode switching control circuit. The detection module is used for detecting key performance parameters of an error amplifier sub-circuit in a low dropout linear regulator chip, the detection module is used for measuring the direct current loop gain, the offset voltage, the common mode rejection ratio and the power supply ripple rejection ratio of the amplifier in the first test mode, and the detection module is used for measuring the unity gain bandwidth of the amplifier in the second test mode. According to the invention, through the design of a built-in self-test architecture and two test modes, abnormal states such as differential circuit imbalance and feedback loop fault can be identified in a bare chip stage, chips with poor performance are rejected in advance, and the LDO chip is ensured to reach a ppb-magnitude high reliability standard.

The invention discloses an electric vehicle BMS battery state accurate monitoring system, and relates to the technical field of battery state monitoring, and the method comprises the steps: obtaining a multi-dimensional battery operation parameter set; inputting the multi-dimensional battery operation parameter set into a preset multi-physical field coupling model, and extracting a battery state correlation characteristic quantity according to electrochemical reaction characteristics and a heat conduction rule; calculating to obtain SOC, SOH and SOE initial parameters of the battery; the estimation deviation of battery aging is eliminated, and calibrated core parameters are obtained; identifying a fault type and a corresponding risk level; and synchronizing the calibrated core parameters, fault information and risk early warning signals to the vehicle-mounted terminal and the cloud platform in real time. According to the invention, a multi-physics field coupling modeling and estimation system is constructed, aging and temperature dual calibration of SOC and SOE is realized through SOH derivation characteristics, fault traceability and a double-end synchronization mechanism are linked, and accurate monitoring of the battery state under a complex working condition is achieved through modular collaborative design.

The present invention discloses a method for fault monitoring and early warning of electromechanical equipment, including: collecting the high-voltage power data and low-voltage power data of subway electromechanical equipment, respectively preprocessing and time-synchronizing the high-voltage power data and the low-voltage power data to obtain a high-voltage power data sequence and a low-voltage power data sequence; performing attention cross-fusion on the high-voltage power data sequence and the low-voltage power data sequence and then inputting them into a fault diagnosis model to obtain the fault type and its confidence level; inputting the fault type into a pre-constructed fault propagation knowledge graph to query and obtain the potential fault propagation path; inputting the high-voltage power data sequence, the low-voltage power data sequence, the fault type and its confidence level, and the potential fault propagation path into a Bayesian network to dynamically calculate the fault propagation probability and generate an early warning level.

The present invention obtains a hydraulic pressure control unit capable of accurately evaluating viscosity of a hydraulic fluid.The hydraulic pressure control unit according to the present invention includes a controller including an evaluation section that evaluates the viscosity of the hydraulic fluid. The evaluation section evaluates the viscosity on the basis of a reduced amount of a current value of a current flowing through a wire (314) at a time when the current value is temporarily reduced in a process in which the current value is increased toward a target current value at beginning of application of the current to the wire (314) of an electromagnetic valve (31).

A method for controlling electrical connection of at least two battery packs (202, 203) of an energy storage System (200) of a vehicle (201) to a common load during operation of the vehicle, each one of the battery packs being connectable to the load via at least one respective switching device, the method comprising: —receiving measurement data relating to current operating conditions of the energy storage System, —based on at least the measurement data, estimating at least one battery state of each one of the battery packs, wherein said at least one battery state is at least one of an open circuit voltage and a state of charge, —based on the estimated at least one battery state of each one of the battery packs, controlling electrical connection of each battery pack to the load via the at least one respective switching device.