13218results about "Speed controller" patented technology

A system and method for electric vehicle operational optimization is disclosed. The system comprises a memory storing processor-executable instructions and a processor, communicably coupled with the memory. The system obtains input data and predict health and performance parameters. The system generates computer simulated instances which emulate a behavior and a performance of the electric vehicle. The system, further, validates the health and the performance parameters by simulating the computer simulated instances in a virtual environment. The system determines a behavior status, a performance status and a health status of the electric vehicle. Thereafter, the system determines abnormality associated with the electric vehicle, followed by determining action for rectifying the abnormality. Consequently, the system controls an operation by performing the determined action at the electric vehicle.

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.

The invention relates to the technical field of electric vehicle control, and provides an intelligent new energy locomotive power distribution and energy recovery control system and method. The system comprises a multi-source information sensing layer, an intelligent decision control layer and an execution feedback layer. The multi-source information sensing layer collects the state of a vehicle and external environment data. The intelligent decision control layer generates a power distribution and energy recovery instruction through data fusion and preprocessing, fuzzy logic assistance, reinforcement learning decision and cooperative work of a multi-objective optimization module; and the execution feedback layer executes the instruction, monitors feedback in real time, and optimizes related actions. According to the method, advanced technologies such as reinforcement learning and fuzzy logic are fused, complex and changeable driving scenes are accurately dealt with, self-adaptive optimization of power distribution and energy recovery strategies is achieved, the energy recovery efficiency is improved, power distribution is accurate and efficient, dynamic adjustment can be achieved according to driving intentions and working conditions, the driving safety and comfort are guaranteed, and meanwhile the driving efficiency is improved. And efficient utilization of energy is realized.

A Level IV fuel-saving robot system for ACE HDTs of the present disclosure focuses on the minimization of actual fuel consumption (L / 100 km) for long-haul freight at first based on an electrical power split device (ePSD) and a mixed hybrid powertrain architecture. A basic model Level I fuel-saving robot realizes a longitudinal L1 automatic driving function through a predictive adaptive cruise (PACC) technology within an Operational Design Domain (ODD) for highways and reduces the actual fuel consumption of an ACE HDT by more than 20% compared with modern diesel HDTs, and the energy-saving and emission-reducing effect of the basic model Level I fuel-saving robot is decoupled from both the technical level of a vehicle engine and the driving level of a driver; an advanced Level IV fuel-saving robot has a L4 automatic driving function within the ODD for highways, operates in a “shadow mode” or “detached mode”, automatically generates a discrepancy report or detachment report, completes the “3R” batch validation for an L4 system on a billion mile scale quickly with high cost effectiveness on the premise of ensuring the traffic safety of existing road users and reduces the total validation expense by more than 65% compared with the modern HDT with internal combustion engine equipped with the L4 system, promoting the early commercialization of the Level IV fuel-saving robot.

The invention relates to a vehicle control method, a medium and a vehicle, and belongs to the technical field of vehicle control, and the method comprises the steps: determining the tracking deviation between the position of the vehicle and the planned path of the vehicle, and controlling the output torque of four-wheel independent motors of the vehicle according to the tracking deviation, so that the vehicle returns to the planned path. Therefore, when the vehicle tracks the tracking deviation generated when the vehicle runs in the planned path, the vehicle can return to the planned path by controlling the output torque of the four-wheel independent motor, so that a new path does not need to be planned again, and the experience of a driver is improved.

One variation of a system includes a modular kit. The modular kit includes: a driven axle configured to install on a bogie chassis supporting a trailer chassis of a trailer; a motor coupled to the driven axle; a battery assembly; a set of electrical cables; and a cable carrier. The battery assembly includes a set of retention features configured to transiently engage a set of engagement features on the trailer chassis to couple the battery assembly to the trailer chassis and is configured to: supply electrical energy to the motor to drive the driven axle; and receive electrical energy from the motor to regeneratively brake the driven axle. The set of electrical cables are configured to electrically couple the motor and the battery assembly. The cable carrier is configured to: locate between the bogie chassis and the battery assembly; and support the set of electrical cables over a range of longitudinal positions.

There is provided a control system for an electric vehicle comprising a power train, the control system comprising: a first controller configured to automatically select a drive mode for the vehicle based on a whole vehicle operation model; and a second controller coupled to the first controller, the second controller configured to adjust a set of tuning parameters for the vehicle, whereby the second controller is configured to receive a signal from the first controller to adjust the set of tuning parameters such that the vehicle operates in the selected drive mode. Thus enhancing the operation and durability characteristics of the vehicle.

A drive device for an electric vehicle is provided for reducing the input speed of a motor and transmitting the reduced input speed to an output shaft. The device comprises a first planetary gear set comprising three first planetary gear elements, a first element connected to the motor and a second element connected to a first output shaft, a second planetary gear set comprising three second planetary gear elements, a first element connected to a third element of the three first planetary gear elements, a second element connected to a second output shaft, and a third element fixed, and a third planetary gear set comprising three third planetary gear elements, a first and a second element each connected to two elements of the three first planetary gear elements, and a third element connected to a torque vectoring motor.

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 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.

A modular electric power take-off (E-PTO) system for a refuse vehicle includes a modular housing. The modular E-PTO also includes a motor, an inverter, a battery, and a hydraulic pump positioned within the modular housing. The modular housing is removably coupled with a front of a waste receptacle or a hopper of the waste receptacle of a refuse vehicle.

The invention relates to the technical field of driving torque control of vehicles, in particular to a driving torque safety auxiliary control system based on multi-sensor data fusion. Comprising a sensor module, a multi-sensor data fusion and processing module and a self-adaptive driving torque revision module, the sensor module is used for detecting the speed, the position, the motion information, the road surface information, the surrounding environment and the like of the vehicle. The invention provides a driving torque safety auxiliary control system based on multi-sensor data fusion, and aims to optimize driving torque control of an electric vehicle and improve dynamic response precision, safety and stability of the vehicle under various complex road conditions. By integrating data from a plurality of sensors in real time, the driving state, the external environment and the vehicle position information of the vehicle are accurately sensed, and the driving torque output of the vehicle is dynamically and auxiliarily adjusted in combination with the information, so that the optimal power performance and control stability of the vehicle under different working conditions can be ensured.

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.

The invention relates to an electric scooter remote monitoring method, device and equipment based on the Internet of Things and a storage medium, and the method comprises the following steps: if a scooter state feature vector represents that an electric scooter is abnormal, carrying out the multi-modal feature mapping of the electric scooter based on the scooter state feature vector, and carrying out the remote monitoring of the electric scooter based on the multi-modal feature mapping; vehicle abnormal behavior mode characteristics are obtained; performing risk prediction on the electric scooter based on the vehicle abnormal behavior mode characteristics to obtain a risk grade division result; performing distributed processing and priority ranking on the risk grade division result through an edge computing gateway to obtain a control strategy instruction set; the control strategy instruction set is input into the remote motor controller to control the electric scooter, and the technical problems that a traditional management mode mainly depends on manual inspection and regular maintenance, the efficiency is low, the state of the scooter is difficult to monitor in real time, and potential safety hazards are difficult to find in time are solved.

A “one-pedal rocking mode” for extricating an electric vehicle from a stuck condition is implemented by: receiving, via a human-machine interface (HMI) of the electric vehicle, an indication of a user selection to activate the one-pedal rocking mode; operating the electric vehicle in the one-pedal rocking mode responsive to detecting movement of an accelerator pedal of the electric vehicle; controlling at least one electric motor of the electric vehicle to: (i) apply a forward torque to corresponding drive wheels when the accelerator pedal is in a first pedal position that exceeds a forward threshold, (ii) apply a reverse torque to the corresponding drive wheels when the accelerator pedal is in a second pedal position that is less than or equal to the forward threshold; and exiting the one-pedal rocking mode by switching operations of the electric vehicle to a second operating mode responsive to detecting an exit condition.

An electric vehicle battery control system including a first electric motor configured to propel a vehicle in a drive mode in response to a drive current, a second electric motor operative to generate a back electromotive force voltage in a fault mode, a battery, having an open circuit battery discharge voltage, for generating the drive current for coupling to the first electric motor, and a vehicle controller for limiting the drive current in response to a difference between the back electromotive force voltage and the open circuit battery discharge voltage being less than a threshold voltage difference.

A method includes receiving a turning angle indication indicating a turn of front wheels of the vehicle in one of a left direction or a right direction by a turning angle relative to a first direction of travel of the vehicle, and determining that a magnitude of the turning angle satisfies a threshold turn angle. Based on determining that the magnitude of the turning angle satisfies the threshold turn angle, the method also includes operating the vehicle in a drift mode by determining that an inner rear wheel includes one of a left rear wheel or a right rear wheel of the vehicle, determining that an outer rear wheel includes the other one of the left rear wheel or the right rear wheel, and commanding at least one torque machine to provide forward torque to the inner rear wheel without providing any forward torque to the outer rear wheel of the vehicle.

A motor controller for pulse heating of a power battery. The motor controller includes an inverter circuit, the inverter circuit includes three switching transistor bridge arms, two ends of each switching transistor bridge arm are respectively configured to connect to positive and negative electrodes of the power battery through a direct current bus, and bridge arm midpoints of the three switching transistor bridge arms are separately configured to connect to a three-phase winding of a motor. The bridge arm midpoints of the three switching transistor bridge arms are configured to output a three-phase current, the three-phase current is used to generate a pulse alternating current on the direct current bus, and the pulse alternating current is used to heat the power battery. A current of each phase of the three-phase current is a unipolar triangular wave, and a sum of the three-phase current is zero.

A speed limit value adjustment method includes obtaining an accelerator pedal opening and / or an accelerator pedal opening change rate of a vehicle running in a first mode, where a speed limit value of the vehicle in the first mode is a first speed limit value; and adjusting the speed limit value of the vehicle to a second speed limit value when it is detected that the accelerator pedal opening and / or the accelerator pedal opening change rate meet / meets a preset condition. The second speed limit value is greater than the first speed limit value.

The invention discloses a vehicle transverse movement control method and device of a dual-motor vehicle, a storage medium and a computer program product, and relates to the field of vehicles, the method comprises the steps that under the condition that a vehicle transverse movement request is received, the working mode of a wheel steering system of the dual-motor vehicle is switched to a target mode; determining an output motor torque of the dual-motor vehicle when the dual-motor vehicle starts to slip in the target area, and determining a feed-forward item torque according to the output motor torque; pID item closed-loop torques of the multiple wheels are determined according to the real-time rotating speeds of the multiple wheels in the vehicle transverse moving process; and according to the feedforward item torque and the PID item closed-loop torque, real-time demand torque of a vehicle front axle motor and a vehicle rear axle motor in the vehicle transverse moving process is determined, and the vehicle front axle motor, the vehicle rear axle motor and a wheel brake are controlled according to the real-time demand torque. And the dual-motor vehicle is controlled to transversely move in the target area in a driving and braking cooperative control mode.

The invention relates to the technical field of electric vehicle control, in particular to an AI-based electric vehicle intelligent starting and acceleration control method and system. The method comprises the following steps: acquiring a driving log of a vehicle owner, performing intelligent vehicle optimization after starting, and constructing an intelligent starting mode selection strategy; physiological state data of a driver are obtained, state change evolution analysis is carried out, and a personalized driving portrait is constructed; holographic state sensing and vehicle virtual simulation are carried out, and a vehicle synchronous digital simulation model is constructed; radar feedback sensing information and a real-time monitoring image in front of a vehicle are collected, traffic situation prediction and vehicle scene prediction and perception are carried out, and a vehicle scene prediction and perception model is constructed. According to the method, the driving starting mode of the vehicle is intelligently selected, vehicle owner requirements and environment available power constraints are matched, and vehicle acceleration efficiency maximization and safety optimization are achieved.

Disclosed is an adaptive control method and system for train operating condition identification. The method includes acquiring train operating state data under various traction conditions and calculating traction and braking performance data. An adhesion condition characteristic data set is formed, and train adhesion conditions are divided into Nz grades based on this data set, where Nz is a natural number. The current adhesion condition grade Nd is identified using the number of adhesion condition grades and current operating state data, where Nd≤Nz, establishing a mapping relationship between an adhesion coefficient and maximum deceleration for braking under the identified grade. An optimal train protection curve is generated. By clustering and grading adhesion conditions through a clustering algorithm, the method achieves more accurate adhesion condition analysis. Adaptively generating optimal train protection curves based on adhesion condition grades enhances the reliability and safety of train operations.

The invention provides a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system, and belongs to the technical field of artificial intelligence risk prediction. The vehicle is provided with an upper-layer torque decision module, a lower-layer torque cooperation module and a vehicle-mounted sensor; selecting a pivot steering auxiliary strategy according to an actual driving scene, and pressing a function key to enter a working condition; vehicle dynamic information is obtained through a vehicle-mounted sensor, whether a vehicle is in an in-situ steering state or a small-radius steering state is judged by combining vehicle body static data, and when a selected function key is in an in-situ steering state or a small-radius steering state, the vehicle dynamic information is obtained in real time, and fuzzy reasoning and a neural network algorithm are combined. The ground attachment coefficient and the target yaw angle speed are intelligently judged, and therefore accurate control over the steering torque is achieved; or a double-controller structure is adopted, the torque of each wheel can be effectively distributed and adjusted under complex working conditions, and flexible control and stable running of the vehicle in a narrow space are ensured.

System and methods are disclosed for engaging and disengaging a regenerative braking system of a first vehicle coupled to a second vehicle. In an embodiment, a coupler (e.g., a tow bar) connects the front of a first vehicle (e.g., a towed vehicle) to the rear of a second vehicle (e.g., towing vehicle). The regenerative braking systems includes a motor-generator coupled to a wheel set of the first vehicle and a battery for storing electrical power generated by the motor-generator. The motor-generator is used to apply an accelerating force to the driven wheel set when forward acceleration is needed. The motor-generator is also used to generate electrical power by harnessing the vehicles' momentum to turn the rotor of the motor-generator, which causes a decelerating torque to be applied to the driven wheel set.

The embodiment of the invention provides a multi-stage energy recovery control method and device for a running vehicle, and the method comprises the steps: obtaining vehicle running parameters of the running vehicle, and generating a dynamic working condition index; obtaining driving operation data and importing the driving operation data into the behavior prediction model to generate a braking demand probability grade; determining a corresponding energy recovery level according to the dynamic working condition index and the braking demand probability level; and when the energy recovery grade is a middle grade, a dynamic torque change curve is generated to control the vehicle to execute energy recovery. And the balance problem of the energy recovery efficiency and the driving comfort is effectively solved. The generation of the dynamic working condition index ensures the accurate perception of the complex driving environment, and the introduction of the behavior prediction model realizes the pre-judgment of the driving intention. The energy recovery intensity is optimally matched with the real-time working condition through a two-factor linkage hierarchical control strategy, and the pause feeling generated by traditional stepped adjustment is remarkably reduced through application of a dynamic torque curve.

The invention discloses a power domain controller system, which relates to the technical field of vehicle design and research and development and comprises a domain control board, a power module and a high-voltage power distribution part, the domain control board comprises a vehicle control unit, a heat management controller, a battery management system, a gear shifting controller and a power distribution control unit, wherein the heat management controller, the battery management system, the gear shifting controller and the power distribution control unit are electrically connected with the vehicle control unit. The heat management controller is electrically connected with the power distribution control unit. The power module comprises a slow charging motor, a DCDC converter and a relay, the slow charging motor is electrically connected with the battery management system, and the DCDC converter is electrically connected with the gear shifting controller; and the high-voltage power distribution part comprises a copper bar, a relay and a fuse. Through multi-module integration, the number of physical controllers and wiring harness complexity are reduced, the system development and maintenance cost is remarkably reduced, and meanwhile platform expansion is facilitated.

The invention relates to the technical field of electric vehicle control, in particular to an electric vehicle control method and system. The method comprises the following steps: acquiring background control terminal authority of the electric power intelligent inspection electric vehicle, extracting motor operation state data and analyzing current fluctuation change to obtain working condition conversion current fluctuation data, then performing equivalent fitting of motor permanent magnet demagnetization intensity, and analyzing a proportional relation between the motor permanent magnet demagnetization intensity and motor output torque loss; designing a motor field weakening control strategy by combining the demagnetization intensity and the output torque loss data, and developing motor control firmware based on the strategy; and finally, the firmware is sent to the electric vehicle control terminal, and the electric vehicle control method is executed. The electric vehicle control technology is optimized, so that the electric vehicle control technology is more perfect.

A Level IV fuel-saving robot system for heavy-duty trucks (HDT) focuses on the minimization of actual fuel consumption (L / 100 km) for long-haul freight based on an electrical power split device and a mixed hybrid powertrain architecture. The Level IV fuel-saving robot has an L4 autonomous driving function within the Operational Design Domain (ODD) of expressways, operates in a “shadow mode” or “Disengagement Mode,” automatically generates a discrepancy report or detachment report, completes the “3R” (Real Vehicle, Real Road, Real Payload) batch validation for an L4 system on a billion mile scale quickly with high performance to cost ratio under the condition of ensuring the traffic safety of existing road users and reduces the total validation expense by more than 65% compared with the modern HDT with internal combustion engine equipped with the L4 system, promoting the early commercialization of the fuel-saving robot.