16248results about "Electric devices" patented technology

An electric motor based system includes an electric motor; an electric motor power controller for controlling the electric motor; a first control unit, adapted to provide first control signals for the electric motor power controller; an electric power storage device adapted to power the electric motor power controller; a first sensor adapted to determine information related to power exchange between the electric power storage device and the electric motor power controller; and a second control unit, wherein the second control unit is adapted to pass the first control signals, received from the first control unit, to the electric motor power controller in a first mode and adapted to provide second control signals for the electric motor power controller in a second mode; and the second control unit inputs and exploits the information.

To enable detection of temperature rise of electronic equipment disposed in a battery pack due to radiation heat in an exhaust passage.SOLUTION: A battery pack 50 disposed on the underfloor of a hybrid vehicle 1 includes: a battery stuck 55 where battery cells are laminated; a battery ECU51 and a monitoring unit 52. A temperature sensor 53 fitted to a flue gas passage 94 is disposed at a position between the battery ECU51 and the monitoring unit 52, and an exhaust pipe 21. The temperature sensor 53 is disposed between the battery ECU51 and the monitoring unit 52, and the exhaust pipe 21, and therefore favorably detects the temperature rise of the battery ECU 51 and the monitoring unit 52 due to radiation heat from the exhaust pipe 21.SELECTED DRAWING: Figure 2

An electrified fire fighting vehicle includes a chassis, a front axle coupled to the chassis, a rear axle coupled to the chassis, a water pump, an energy storage system, an engine, an electric motor coupled to the engine and the energy storage system, and a power divider positioned between the electric motor, the water pump, and the rear axle. The power divider facilitates selectively driving the water pump and the rear axle with the engine and the electric motor.

A method for manufacturing an electric vehicle battery case includes: preparing a frame configured to define a through space inside and a flat plate made of resin; superposing and disposing the flat plate on the frame; and applying pressure to the flat plate from an opposite side from the frame to press the flat plate against the frame to cause the flat plate to swell in the through space, thereby deforming the flat plate into a tray having a bathtub shape including a bottom wall and a peripheral wall provided at a peripheral edge of the bottom wall and configured to define an opening portion, and joining by press-fitting the tray to the frame.

A vehicle includes a chassis including a frame rail extending longitudinally, a tractive element coupled to the chassis, a cab coupled to the chassis and configured to support an operator, an electric motor configured to drive the tractive element to propel the vehicle, a battery configured to supply electrical energy to the electric motor, a heat exchanger, a cable electrically coupling the electric motor and the battery, and a conduit fluidly coupling the battery and the heat exchanger. The cable and the conduit extend laterally from the battery to the frame rail, and the cable and the conduit extend longitudinally along the frame rail.

The invention discloses a system for monitoring line loss rate abnormity of a power distribution network station area, and relates to the technical field of power distribution network monitoring. The line loss rate abnormity monitoring system for the power distribution network station area comprises a data acquisition module, a data processing module, a line loss rate calculation module, an abnormity identification and alarm module and an on-site troubleshooting and diagnosis module. Through integrating functions of intelligent electric meter data acquisition, power grid data collection and area division, data processing, line loss rate calculation, abnormity identification and alarm, on-site troubleshooting and diagnosis and the like, comprehensive and real-time monitoring of the line loss rate of the power distribution network station area is realized. And a regional line loss calculation model is established according to the characteristics of the power distribution network of each region, and the line loss rate can be monitored in real time, dynamically calculated and predicted, so that the efficiency and accuracy of line loss management are greatly improved. And the abnormity identification and alarm module can timely discover and give an alarm of line loss abnormity, provides probability analysis of abnormity reasons, and is helpful for operation and maintenance personnel to quickly respond and process.

The present disclosure relates generally to flight control of electric aircraft and other powered aerial vehicles. In one embodiment, a method is disclosed, comprising: receiving a descent rate command from a pilot input device, determining a proximity of each propeller of at least two propellers to a vortex ring state; and controlling the aircraft's descent rate to be less than the commanded descent rate when at least one of the at least two propellers is within a first threshold proximity to the vortex ring state.

An apparatus and method relate to analyzing metrics relating to EV charging points and facilitating selection of an EV charge point based on metrics associated with the EV charge points. Methods may include: determining locations of one or more electric vehicle charging stations; determining a total power available at the one or more electric vehicle charging stations; determining, for the one or more electric vehicle charging stations, if dynamic load balancing is used; determining occupancy of charge points at each of the one or more electric vehicle charging stations; calculating an available power at a charge point of the one or more electric vehicle charging stations based on the total power available and the occupancy of charge points for the one or more electric vehicle charging stations; and calculating an estimated time to charge a vehicle at the one or more electric vehicle charging stations based on the available power.

The invention discloses an intelligent diagnosis system of a power distribution network fault self-judgment switch device, and relates to the technical field of power system automation and intelligent power grids, and the intelligent diagnosis system comprises an intelligent diagnosis system interconnection hub which is in communication connection with a power parameter monitoring module, an intelligent fault detection module and a visual decision support module; the electric power parameter monitoring module monitors the state of a power distribution network in real time through a mutual inductor, voltage, current, power factors and harmonic data are collected through an intelligent electric meter, the intelligent fault detection module achieves fault recognition, trend prediction and anomaly detection through an LSTM neural network, and the decision support module provides a graphical interface and automatically generates a fault response strategy. According to the method, autonomous judgment and quick response of the power distribution network fault are achieved, the accuracy and efficiency of fault detection are remarkably improved, the false alarm rate and the missing report rate are reduced, meanwhile, the decision support module of the graphical interface simplifies the fault processing flow, and the stability and safety of power grid operation are improved.

The invention discloses an inverter intelligent control method based on a self-adaptive algorithm, and relates to the technical field of inverter intelligent control, and the method comprises the steps: multi-source temperature collection and environment recognition, machine learning-based electric-thermal coupling prediction, self-adaptive inverter parameter optimization, and cooperative thermal management and fault diagnosis self-repairing. According to the high-temperature or low-temperature failure risk of the motor and the inverter under the extreme climate, temperature distribution and load information are obtained in real time, the temperature rise trend is predicted in advance, and the current, voltage and modulation strategy of the inverter are actively adjusted, so that safe derating or torque compensation is achieved; multi-sensor cross validation and observer fusion are carried out when sensors drift or devices are aged, stable operation and efficient energy utilization of the system are kept, and the drivability and the whole vehicle reliability in an extreme environment are remarkably improved; in the process, fault diagnosis and self-repairing can be further improved through online model updating, and the durability and economical efficiency of the electric drive system are improved.

The invention relates to the technical field of battery power supply fault detection, and particularly discloses a power supply fault detection method and system for an electric vehicle battery, and the method comprises the steps: collecting the multi-modal data of the electric vehicle battery, carrying out the preprocessing and feature extraction of the multi-modal data of the electric vehicle battery, and constructing an intelligent diagnosis model. And carrying out potential fault propagation path prediction, predicting the diffusion path and speed of the fault in a battery system, carrying out electric vehicle battery fault positioning and early warning, and outputting a three-dimensional power supply fault thermodynamic diagram and a hierarchical autonomous disposal instruction of the electric vehicle battery. According to the invention, the problems of poor detection effect, emphasis on single fault diagnosis, large diagnosis difficulty, influenced accuracy and the like caused by incapability of accurately adapting to a fault detection method based on a model in a complex and changeable actual working condition in the prior art are solved.

The invention discloses a vehicle stopping electrical safety monitoring system, and relates to the technical field of new energy vehicle control, the vehicle stopping electrical safety monitoring system comprises an intelligent sensing layer, a digital twin decision-making layer, a hierarchical execution layer and a safety escape layer, and the intelligent sensing layer comprises a multi-mode sensor array and is used for collecting physical parameters, chemical parameters and environmental parameters of a vehicle electrical system in real time. According to the method, a high-precision three-dimensional model, an embedded electrolyte leakage model and a wire harness aging model are constructed, the high-precision three-dimensional model comprises a battery compartment geometric structure and a wire harness layout, the former simulates a diffusion path and predicts a short-circuit risk based on fluid dynamics, and the latter realizes fault evolution prediction based on a physical mechanism by combining vibration fatigue and insulation material aging analysis. The hysteresis of traditional threshold value alarm is broken through, a dynamic threshold value table is generated in combination with environment temperature, the charge state and historical fault data, the problem that a traditional fixed threshold value cannot adapt to complex working conditions is solved, and the false alarm rate and the missing alarm rate are reduced.

A vehicle-body front structure includes a partition wall portion which demarcates a space inside a vehicle cabin and a pair of left and right suspension tower portions which are formed to bulge inward in the vehicle width direction and support upper portions of front suspension apparatuses. A lower-side portion of the partition wall portion is formed to be positioned further to vehicle rear toward a lower position. A traveling motor is installed below the lower-side portion of the partition wall portion. An upper-side portion of the partition wall portion extends from an intermediate portion of the left suspension tower portion in a vehicle front-rear direction to an intermediate portion of the right suspension tower portion in the vehicle front-rear direction.

The invention provides an electric power and magnetic power dynamic joint detection method and system based on multivariable fusion, and relates to the technical field of power electronic equipment detection. According to the method, voltage, current and magnetic field sensors are arranged in a to-be-tested electromagnetic system, original physical quantity data are collected, and a multivariable time sequence data set is constructed; through preprocessing and feature extraction, a dynamic feature matrix related to electric power and magnetic power is generated; constructing a joint estimation model based on the matrix, and comparing the joint estimation model with a reference value to obtain a detection residual error; generating a state label according to the residual size and trend, and evaluating the estimation credibility in combination with a confidence interval; and a feedback mechanism is further constructed, model parameters and feature structures are optimized by adopting an adaptive algorithm, and online iteration and self-learning are realized. The method is high in detection precision, high in state recognition capability, good in adaptability and robustness, and suitable for energy flow monitoring and intelligent diagnosis of a complex electromagnetic system.

The invention discloses a battery thermal management method, device and system and a vehicle, and relates to the technical field of battery management. A specific embodiment of the method comprises the following steps: acquiring battery data of a battery, acquiring a maximum battery cell temperature from the battery data, and extracting battery characteristics; determining the thermal runaway probability of the battery according to the battery data and the battery characteristics; selecting a target early warning strategy matched with the maximum cell temperature and the thermal runaway probability from a preset multi-level early warning strategy and a preset hidden state early warning strategy; according to the environment information of the environment where the battery is located and the battery characteristics, rule parameters in the target early warning strategy are adjusted, and regulation and heat dissipation are conducted on the battery through the adjusted target early warning strategy. According to the embodiment, the rule parameters of the early warning strategy are adaptively adjusted according to the actual state of the battery, a fixed threshold value judgment mode is replaced, advanced recognition of potential hazards and dynamic optimization of the early warning strategy are achieved, and therefore early warning accuracy and flexibility are greatly improved.

The invention discloses a charging and discharging monitoring method based on a new energy automobile battery, relates to the technical field of energy, and solves the technical problems that in the prior art, charging and discharging processes of the new energy automobile battery are lack of monitoring, and the charging and discharging states of the new energy automobile battery cannot be dynamically monitored in real time. According to the invention, a digital twinborn model is constructed, a monitoring calculation terminal adopting a GD32ZET6 embedded chip processor is deployed in a battery management system, an AC impedance spectroscopy method is utilized to collect data, an improved Kalman filtering algorithm model is combined to process the data, and an improved self-adaptive charging strategy is adopted at the same time. The charging current and the discharging current are optimized according to the real-time state of the battery and the environment temperature; the battery of the new energy automobile can stably, efficiently and safely run under various conditions; according to the invention, the charging and discharging monitoring capability of the new energy automobile battery is greatly improved.

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 electric forklifts and energy storage, and discloses an intelligent battery management system and prediction method based on optical fiber sensing, the system comprises a distributed optical fiber sensing network, a data acquisition and processing module, an intelligent analysis decision module, a battery management control module and a cloud platform interaction module; the method comprises the following steps: collecting a battery signal through a snakelike-spiral optical fiber network, reconstructing a temperature / strain field through noise reduction and filtering, extracting multi-modal features, inputting the multi-modal features into an LSTM model to predict thermal runaway, generating a risk level in combination with an improved equation, dynamically adjusting charging, discharging and heat dissipation strategies, and realizing three-dimensional visual monitoring through a cloud platform. According to the invention, high-precision distributed monitoring of the temperature / strain field of the battery pack is realized through the BOTDR optical fiber sensing network, the LSTM prediction model is constructed by fusing multi-physical field characteristics, and thermal runaway is warned in advance. The improved algorithm reduces the false alarm rate, charging and discharging parameters are dynamically adjusted to prolong the service life of the battery, and a three-dimensional visual platform improves the operation and maintenance efficiency.

The invention relates to the technical field of electrical performance testing, in particular to a water supply equipment electrical performance detection system based on artificial intelligence, which comprises an electrical parameter acquisition module, an energy efficiency analysis module, an insulation performance evaluation module and a comprehensive diagnosis module. According to the invention, a three-phase voltage and current root-mean-square value is dynamically calculated through a broadband current transformer and a sliding window integration method, a 50Hz fundamental component amplitude is extracted to separate power frequency and high frequency harmonic waves, a continuous period energy efficiency ratio standard deviation triggers an insulation detection threshold value, and a parameter fluctuation and insulation degradation response mechanism is established. Harmonic interference correction synchronous processing energy efficiency ratio and leakage current integration, the detection error being lower than 5%, temperature rise rate linear regression and bearing current threshold value cooperative determination, winding aging cross validation, fault misjudgment rate reduction to 26%, closed-loop detection fusion transient capture, dynamic threshold value triggering and multi-parameter diagnosis, early warning success rate reaching 92%, and positioning response being reduced to 3 seconds.

A method of controlling drift of a vehicle in which the vehicle more easily enters, maintains, releases, and transitions drift behavior, includes determining, by a controller, whether a current driving situation of the vehicle is a drift entry situation or a release situation based on vehicle driving information, and generating a drift index configured to indicate a current degree of drift from a determination result, determining, by the controller, a target yaw moment for left and right speed control depending on a driving situation or a braking situation of the vehicle and target yaw moments for drift assistance control depending on a driver input and a vehicle state in the drift entry situation, based on the vehicle driving information, and generating and outputting, by the controller, a target torque of a motor configured to drive each wheel of the vehicle based on the determined target yaw moments.

The invention discloses a power battery health state on-line monitoring and early warning method and system based on multi-data fusion. The method comprises the following steps: synchronously acquiring multi-dimensional heterogeneous data in a battery operation process through a multi-sensor data acquisition module; carrying out standardization processing and preprocessing on the multi-dimensional heterogeneous data, extracting a comprehensive feature vector and carrying out multi-sensor data fusion; a multi-sensor data fusion algorithm is adopted to carry out weight distribution on the fused comprehensive feature vector, a dynamic weight coefficient is calculated according to the sensitivity and reliability of each parameter to the health state of the battery, and a fused battery state feature descriptor is obtained; and performing mode recognition and state classification on the fused battery state feature descriptors by using a pre-established evaluation model to realize quantitative evaluation of the health degree of the battery. According to the invention, real-time monitoring, evaluation and early warning of the health state of the battery in the whole life cycle are realized, and a reliable basis is provided for safe operation and maintenance decision of a battery system.

Disclosed herein are systems and methods for energy management. A system, such as a vehicle, includes a plurality of energy storage units that include a supercapacitor and an electrochemical battery. The system includes an energy controller that tracks historical power draw from the plurality of energy storage units over time in power tracking data, and that identifies a power draw based on the power tracking data. The energy controller switches between a first configuration and a second configuration based on the identified power draw. The first configuration is configured for drawing power from the electrochemical battery and disconnecting from the supercapacitor, while wherein the second configuration is configured for drawing power from the supercapacitor and disconnecting from the electrochemical battery.

The invention discloses a battery thermal runaway and safety threshold dynamic adjustment system, which comprises a data acquisition module used for acquiring battery state parameters, environmental parameters and vehicle use conditions; the data preprocessing module is used for preprocessing the collected data; the initial safety threshold setting module is used for calculating an initial risk threshold of the thermal runaway of the battery by utilizing a statistical rule in combination with a large amount of collected historical sample data, and taking the initial risk threshold as a judgment standard of an initial deployment stage; the dynamic threshold adjustment module is used for performing dynamic personalized adjustment on the initial risk threshold according to a Bayesian optimization algorithm; and the thermal runaway prediction model module is used for predicting the battery risk level in the future 24 hours and the battery temperature in the future 1 hour in combination with a traditional machine learning and deep learning model. The invention also discloses a battery thermal runaway and safety threshold dynamic adjustment method. According to the invention, an early warning mechanism with high accuracy and high recall rate is realized.

The invention relates to the field of power battery energy management, in particular to a power battery pack charging and discharging efficiency dynamic optimization system and method, and the system comprises a central control unit, a multi-dimensional sensing module, a dynamic charging and discharging control module, a working condition-heat dissipation cooperation module, a parallel cell equalization module and an aging adaptation optimization module. The multi-dimensional sensing module is used for collecting basic parameters, polarization characteristics, parallel state, aging degree and environmental parameters of a battery pack, the dynamic charging and discharging control module is used for collecting data, the working condition-heat dissipation coordination module adopts a three-stage heat dissipation framework, and the parallel battery cell balancing module is used for balancing the battery pack through a dynamic current distribution and voltage active balancing double-coordination mechanism. The aging adaptation optimization module is used for grading and differentially optimizing charge and discharge parameters and heat dissipation strategies based on the aging degree of the battery cell, so that the problems of low charge and discharge efficiency, poor wide temperature range adaptability, weak parallel consistency, fast aging attenuation and multi-factor coupling deterioration of the power battery pack are solved.

The invention relates to the technical field of battery management, and discloses a forklift battery charging method and device, and the method comprises the steps: collecting the real-time multi-dimensional physical state data, including vibration data, of a forklift battery; dynamically reconstructing a multi-physical field coupling model matched with the current working condition in a preset model library according to the data; performing predictive multi-target optimization based on the model to determine an optimal charging equalization strategy considering charging time, energy loss, health attenuation and thermal risk; charging is executed according to the strategy, the deviation between physical response and model prediction is compared in real time, and online self-correction is conducted on model parameters. The system comprises a state acquisition module, a model reconstruction module, a strategy optimization module and a charging control and correction module. According to the method, the digital twin model capable of self-evolution is constructed and closed-loop feedback correction is carried out, so that the charging strategy can actively adapt to working condition changes caused by factors such as vibration and temperature, and safer and more efficient charging management of the forklift battery is realized.

A refuse vehicle includes a cab, a refuse compartment, a chassis, a high voltage power distribution unit (HVPDU) including a battery, a plurality of cables and a manual service disconnect. The manual service disconnect is accessed via a through hole of a wheel well of the refuse vehicle. The refuse vehicle further includes a platform removably coupled to the chassis and the HVPDU and configured to enclose the high voltage power distribution unit.

The invention provides a vehicle anti-skid control method, device and system and a vehicle. According to at least one driving wheel of the vehicle, the chassis wheel speed, the actual wheel speed, the wheel end actual torque, the adhesion coefficient, the total wheel load, the target slip rate, the wheel radius and the vehicle speed are obtained; a feedforward control torque is determined according to an attachment coefficient, a total wheel load and a wheel radius, a wheel speed deviation is determined according to a target slip rate, a chassis wheel speed and an actual wheel speed, a torque deviation is adjusted through a vehicle speed, the attachment coefficient and the wheel speed deviation to obtain a feedback control torque, and the feedback control torque and the attachment coefficient are superposed to serve as an output torque for vehicle anti-skid control. Dynamic wheel load changes and wheel load changes caused by the vehicle on the ramp road surface are considered, the total wheel load is estimated in real time, feed-forward control is introduced to obtain the feed-forward control torque, feedback control is compensated, and the stability and accuracy of anti-skid control are enhanced when the ramp road surface and the loads change.

The invention relates to the technical field of charging control, in particular to a new energy automobile charging safety regulation and control method and system, and the method comprises the steps: collecting a multi-dimensional feature parameter set; acquiring battery health state and charge state information, and collecting multi-point temperature information on the surface of the battery; constructing the multi-dimensional feature parameter set into a multi-dimensional time sequence, performing statistical analysis on the multi-dimensional time sequence, and extracting dynamic feature indexes; performing structured fusion on the dynamic characteristic indexes, thermal field gradient parameters calculated according to multi-point temperature information and a multi-dimensional characteristic parameter set, and projecting the dynamic characteristic indexes, thermal field gradient parameters and the multi-dimensional characteristic parameter set to a key characteristic subspace through principal component analysis to obtain a charging response vector; generating a multi-dimensional security hyperplane through the dynamic security boundary model; continuously comparing the charging response vector with the multi-dimensional safety hyperplane, and calculating a safety margin scalar in real time; and inputting the safety margin scalar into the self-adaptive regulation and control model, and adjusting the charging current output. According to the invention, active predictive charging safety regulation and control are realized through multi-dimensional perception.

The invention relates to the technical field of electric vehicle control, and discloses a stability control method and system for an electric vehicle, and the method comprises the steps: collecting vehicle driving data in real time, and dynamically estimating the state parameters of the vehicle through an unscented Kalman filtering algorithm; according to the state parameters of the vehicle, combined with the kinematic model and the foresight trajectory, the expected yawing moment in a short time in the future is calculated, and the longitudinal traction requirement of the vehicle is obtained; and according to the state parameters of the vehicle and the longitudinal traction requirement of the vehicle, the yawing moment and the traction force are decoupled and distributed to the four electric driving wheel ends, and stable control over the electric vehicle is achieved. Compared with a traditional stable control system only based on closed-loop feedback, the stable control system has the advantages that the control response is faster, the yawing intervention is more accurate, and better control performance and vehicle safety are shown under the low-adhesion road surface and high-load working conditions.

A powered balancing mobility device that can provide the user the ability to safely navigate expected environments of daily living including the ability to maneuver in confined spaces and to climb curbs, stairs, and other obstacles, and to travel safely and comfortably in vehicles. The mobility device can provide elevated, balanced travel.