775 results about "Vehicle acceleration" patented technology

The invention relates to the field of vehicle braking fault detection, in particular to a real-time fault detection method for a vehicle electronic braking system, which comprises the following steps of: acquiring vehicle driving state data through a vehicle-mounted sensor network and an electronic control unit, calculating filtered residual data of a target wheel speed sensor, and calculating the filtered residual data of the target wheel speed sensor; establishing calibration reference parameters of the health state; performing conjoint analysis on the acceleration data and the residual fluctuation data of the vehicle to obtain a dynamic response adaptive adjustment factor of the vehicle; obtaining a tiny drift confirmation factor of the vehicle through the candidate residual error in the low dynamic interference state of the vehicle; continuous fault judgment is performed by combining the dynamic response adjustment factor and the tiny drift confirmation factor of the vehicle, and the fault judgment result of the wheel speed sensor is obtained, so that the accuracy and sensitivity of fault detection when tiny systematic deviation occurs to the wheel speed sensor are improved.

The invention discloses a hybrid electric vehicle predictive energy management system based on multi-layer cooperative control, and aims to improve the vehicle performance and energy efficiency. According to the method, a hierarchical cooperative control architecture is adopted, and the reinforcement learning, vehicle speed multi-step prediction and model prediction control technologies are combined, so that joint optimization control over vehicle acceleration and power distribution is achieved. The system obtains the vehicle running state and the surrounding environment information in real time through the state sensing module, and determines the expected acceleration by using a strategy network trained by a PPO algorithm. In addition, the future vehicle speed is predicted in combination with an XGBoost multi-step regression model, the optimization module is designed through an MPC strategy, oil consumption, the battery state and driving smoothness are comprehensively considered, and therefore efficient management of the power system is achieved. According to the scheme, the obvious energy-saving advantage is achieved, the driving smoothness and comfort are improved, the good self-adaptive capacity and a real-time feedback mechanism are achieved, and the method is suitable for the complex urban road traffic environment.

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.

A vehicle acceleration control method, includes: obtaining a change rate of an accelerator pedal opening degree of an accelerator pedal and a remaining energy value of a vehicle in a racetrack mode; determining a target torque compensation value based on the change rate of the accelerator pedal opening degree and the remaining energy value of the vehicle; and controlling the vehicle to accelerate based on a sum of the target torque compensation value and a maximum output torque value.

The embodiment of the invention provides a trapped vehicle escape state recognition control method, device and system, and the method comprises the steps: obtaining the wheel speed, wheel acceleration, vehicle speed and vehicle acceleration of a target vehicle; determining a wheel slip rate based on the wheel speed and the vehicle speed; determining the wheel slip number based on the wheel slip rate; based on the vehicle speed, the wheel slip rate, the wheel slip number, the vehicle acceleration and the wheel acceleration, the current state of the target vehicle is determined; the target vehicle speed of the target vehicle is corrected based on the current state, and the target vehicle speed is used for guiding the target vehicle to run. The technical problems that in the prior art, when the vehicle speed is low, the vehicle trapping state of the vehicle cannot be accurately judged, and the vehicle cannot be controlled to be out of trap according to the accurate vehicle trapping state are solved.

A control system for a vehicle is provided, the control system comprising one or more controllers, the control system configured to receive an acceleration or deceleration demand signal from an automatic cruise control system, and request modification of one or more parameters of the vehicle suspension system in response to the received acceleration or deceleration demand signal. In this way, the vehicle suspension system may be modified in preparation for an acceleration or deceleration of the vehicle, to inhibit or at least reduce the occupants of the vehicle experiencing an undesirable pitching movement of the vehicle during the course of the change in speed of the vehicle.

The invention relates to a vehicle control method and device, a vehicle and a medium, and relates to the technical field of control. The control method comprises the steps that according to the first pedal opening degree of an accelerator pedal of the vehicle, the acceleration intention of a driver of the vehicle is determined; according to the acceleration intention and the running speed of the vehicle corresponding to the first pedal opening degree, a correction coefficient corresponding to the first pedal opening degree is determined; determining a second pedal opening degree according to the correction coefficient and the first pedal opening degree; and performing acceleration control on the vehicle according to the second pedal opening degree. According to the technical scheme, the energy utilization rate of the vehicle can be increased.

The invention discloses an entrance ramp fleet confluence method based on reinforcement learning, and the method comprises the steps: carrying out the scene modeling according to an actual scene, and designing a plurality of fleet scales and vehicle speeds; vehicle acceleration and deceleration are designed as action selection, and the running speeds of main road vehicles and ramp vehicles are changed, so that the ramp vehicles select proper confluence opportunities; the change of the total travel time reflects the motorcade afflux efficiency, a reward function is established according to the change of the vehicle speed, the change of the time headway and the queuing waiting time of a ramp vehicle on an acceleration lane, an action mask is designed to improve the training efficiency, an intelligent agent is trained by using a PPO algorithm, and a trained deep reinforcement model is taken as a vehicle afflux model. The method gives consideration to the better passing efficiency of the main line and the shorter queuing waiting time of the vehicles on the ramp, enables the passing of the main line to be smoother, reduces the waiting time of the vehicles on the ramp as much as possible, and reduces the interference on the main line caused by the convergence of the vehicles on the ramp.

The invention provides an in-vehicle noise comfort test method based on a multi-dimensional index, and belongs to the technical field of acoustic test and evaluation, the method is suitable for manual-shift and automatic-shift vehicles, and under the vehicle acceleration operation condition, when a preset acceleration condition is met, the in-vehicle noise comfort test is carried out. The maximum value of the A-weighted sound pressure level, the loudness value and the sharpness value of the preset noise measuring point position can be measured and recorded, multiple times of measurement are completed before the rotating speed of the engine reaches 90% of the rated rotating speed, and the arithmetic mean value is calculated to serve as an intermediate result. For a constant-speed operation condition, the A weighting sound pressure level and the loudness value are measured and recorded for multiple times, and an arithmetic mean value is calculated as an intermediate result; according to the method, the two psychoacoustic parameters including the loudness and the sharpness are introduced to serve as indexes for evaluating the comfort performance of the noise in the vehicle, and the actual perception and subjective experience of human ears on the noise can be reflected more accurately.

The invention relates to the technical field of automatic driving, and particularly discloses a low-delay moving target detection method and system for navigation, and the method comprises the steps: collecting an event flow through a vehicle-mounted event camera; adjusting the time window length of the event flow in real time according to the vehicle speed, the vehicle acceleration and the event density gradient to obtain an event image; inputting the event image into a pulse neural network to obtain an output feature map; detecting the feature map through a feature pyramid network and a detection head to obtain a motion trail flow of the target; and performing nonlinear model predictive control according to the motion trail flow to obtain an optimized motion trail flow of the target.

The invention discloses an automobile driving state blind area monitoring method and a rearview mirror, and relates to the technical field of blind area monitoring. Comprising the following steps of information collection, specifically, sensors and cameras are installed on wheels of an automobile, a steering wheel steering column, an automobile chassis and a steering system respectively, driving state data of the automobile speed, the steering wheel angle, the automobile acceleration and the steering angle and image data around the automobile are collected in real time, and real-time automobile information data are obtained; according to the system, driving state data such as the vehicle speed, the steering wheel angle, the vehicle acceleration and the steering angle are combined, the blind area around the vehicle is monitored in real time through the sensor and the camera, and when the vehicle steers, the blind area monitoring range is adjusted in real time to adapt to the driving direction change; the rearview mirror can display blind area images in real time, the blind area alertness of a driver can be greatly improved, and the traffic accident risk caused by the blind area is effectively reduced.

Aspects of the disclosure relate to thermal management aspects of a battery pack. A casted front member of a battery pack enclosure may facilitate cooling of a high voltage distribution box (HVDB) within a cavity of the casted front member. Cooling of other electrical components, such as a busbar can also be provided. Cooling of the HVDB and / or other electrical components external to the battery modules and / or battery cells in a battery pack can facilitate fast charging, vehicle acceleration, component reliability, and / or other advantages.

A method includes receiving an indication regarding a deceleration event for a vehicle, remapping a response of a prime mover of the vehicle from following a first response curve to following a deceleration response curve in response to (i) the deceleration event and (ii) a speed of the vehicle being greater than a speed threshold, and activating an output of the prime mover to accelerate the vehicle at a relatively lesser amount of depression of an accelerator from a non-depressed state of the accelerator than prior to the deceleration event in response to the accelerator being engaged by an operator following the deceleration event.

The invention provides an intelligent vehicle lane change decision-making method based on a master-slave game in a mixed driving scene, which comprehensively considers the human driver style and the dynamic trajectory influence of surrounding vehicles in various typical lane change scenes, and establishes a more comprehensive multi-vehicle game framework based on the master-slave game. Comprehensive game cost and revenue functions are respectively established for intelligent vehicles and human-driven vehicles on adjacent lanes, and an optimal intelligent vehicle acceleration sequence and lane changing time are iteratively calculated through a double-layer optimization model structure. The accurate planning of the lane changing track of the intelligent vehicle can be realized on the premise of considering multiple aspects such as safety, comfort and traffic efficiency.

The present invention relates to the technical field of autonomous driving, and disclosed are a vehicle acceleration anomaly processing method and apparatus, a storage medium, and a vehicle. The method comprises: acquiring a real-time acceleration command signal and an actual acceleration signal of a vehicle, wherein the acceleration command signal is used for representing a calculated desired acceleration to be achieved of the vehicle, and the actual acceleration signal is used for representing a measured actual achieved acceleration of the vehicle; on the basis of the acceleration command signal and the actual acceleration signal, performing acceleration anomaly detection on the vehicle to obtain a detection result; and in response to the detection result that an acceleration anomaly is present in the vehicle, adjusting an autonomous driving acceleration behavior of the vehicle on the basis of a target processing strategy, wherein the target processing strategy is determined on the basis of an anomaly category of the acceleration anomaly. The present invention solves the technical problem of low autonomous driving safety caused by high difficulty and low precision of the detection of acceleration anomalies in existing autonomous driving functions.

The invention relates to a method for determining a friction potential between a vehicle and a roadway on a roadway section formed in front of the vehicle with respect to a direction of travel of the vehicle, and for determining a vehicle performance calculated from the friction potential, wherein the method comprises at least the following steps: a) Capturing optical properties of the vehicle's surroundings, especially during a journey, using at least one LiDAR sensor device; b) Determining a roadway section from the surroundings using the optical properties and generating a data set depending on the determined roadway section and the optical properties of the respective roadway section; c) Determining a road surface, road condition and / or road wetness level for the road section based on the data set; d) Determining the traction potential based on the road surface, the road condition and / or the degree of road wetness; and e) Calculating vehicle performance using the determined friction potential, in particular a maximum possible positive and a maximum possible negative vehicle acceleration. Furthermore, the invention relates to a corresponding system for determining a friction potential between a vehicle and a road surface.

The invention relates to the technical field of AMT gearbox control, in particular to an AMT gearbox control method, system and equipment in a braking state and a medium, and the method comprises the steps that when a vehicle is in a braking deceleration state, the stepping duration of a brake pedal, the actual vehicle speed, the actual acceleration of the vehicle and the rotating speed of an engine are obtained; whether the braking intention of the driver is emergency braking or stable braking is judged based on the stepping duration of the brake pedal and the actual acceleration of the vehicle; and based on the braking intention, the gear of the AMT gearbox and the separation state of a clutch are controlled till braking is finished. Wherein the current gear is maintained during emergency braking, and a clutch is controlled to be separated according to an engine rotating speed threshold value; during stable braking, after the rotating speed of the engine meets the condition, whether downshift is conducted or not is judged according to the actual vehicle speed, and a clutch is controlled to be separated. The brake intention of a driver can be accurately recognized, differential control is executed, and therefore the brake efficiency is effectively improved, engine flameout is prevented, and the driving smoothness is improved.

A vehicle motion management, VMM, system for a heavy-duty vehicle has at least one wheel speed sensor to output a wheel speed signal indicative of a rotation speed of a respective wheel on the vehicle, at least one inertial measurement unit, IMU, to output an IMU signal indicative of an acceleration of the vehicle, a motion estimation function to estimate a vehicle motion state comprising vehicle speed over ground, based at least in part on the wheel speed signal and at least in part on the IMU signal, and a motion support device, MSD, coordination function to coordinate actuation of a plurality of MSDs of the heavy-duty vehicle in dependence of a vehicle motion request and the vehicle motion state. The motion estimation function models an error in the estimated vehicle motion state, and to output a free-rolling request to the MSD coordination function in case the modelled error fails to meet an acceptance criterion. The MSD coordination function reduces a wheel slip set-point of one or more wheels of the heavy-duty vehicle in response to receiving the free-rolling request from the motion estimation function.

The invention discloses an abrupt slope slow descent system based on energy recovery, and particularly relates to the technical field of vehicle control, and the system comprises a slope estimation module which receives a vehicle speed signal and a vehicle acceleration signal, and estimates the current slope of a vehicle through the vehicle speed signal and the vehicle acceleration signal; the abrupt slope slow descent activation judgment module is used for receiving a slope signal, a vehicle gear signal, a brake pedal signal, an accelerator pedal signal, a driving direction signal and a driving mode signal; when the vehicle is on the slope, a brake pedal and an accelerator pedal are not stepped on, and the vehicle speed is within a vehicle speed threshold interval, the abrupt slope slow descent function is activated, and otherwise, the abrupt slope slow descent function exits; the target vehicle speed calculation module identifies the intention of the driver and gives a target vehicle speed; and the electromechanical braking force distribution module introduces feedforward control through a gradient signal firstly, then gives target braking force by superposing closed-loop control of a target vehicle speed, and then distributes the braking force to electric braking and mechanical braking to realize energy recovery and caliper braking respectively.

The invention discloses an energy management system and method based on distributed braking and a vehicle, and belongs to the technical field of electric vehicles. The sensor array unit is used for collecting the vehicle acceleration, the wheel rotating speed, the wheel vertical load and the road friction coefficient; a wheel vertical load and a road friction coefficient are obtained through a wheel control unit, and the real-time maximum braking force is calculated; the vehicle acceleration, the real-time maximum braking force and the wheel rotating speed are obtained through a central control unit, the vehicle required braking force is calculated according to the vehicle acceleration and the vehicle mass, and according to the real-time maximum braking force, the distribution coefficient is dynamically adjusted, and the vehicle required braking force is distributed to all wheels; according to the wheel rotating speed, the energy recovery efficiency and the regenerative braking torque are combined, and the energy recovery power is determined. Under emergency braking or complex road conditions, the stability of the vehicle can be better maintained, the braking distance can be better shortened, and the problem that an existing electric vehicle cannot deal with road emergency situations to conduct stable braking and energy recovery is solved.

A multi-vehicle control system (MVCS) for control of a multi-vehicle system having independently powered and steered lead and trailer vehicles connected by a slidable length sensing tow bar system. The MVCS includes a controller configured to receive throttle, brake, and steering signals from the lead vehicle, receive signals from the tow bar system indicating an extension length of the tow bar system, a front angle deviation of the lead vehicle, and a rear angle deviation of the trailer vehicle. The controller is further configured to determine, based on the received signals, a trailer vehicle acceleration / deceleration output and a trailer vehicle steering output, determine a driving scenario of the multi-vehicle system, and operate the multi-vehicle system based on the determined driving scenario and the determined trailer vehicle acceleration / deceleration output and trailer vehicle steering output.

The present invention discloses a pedal jamming monitoring method for identifying braking intention based on multi-source data fusion, belonging to the field of automotive braking. S1: Synchronously collect the pressure applied to the brake pedal, the displacement of the brake pedal under the applied pressure, the displacement of the accelerator pedal, and the vehicle acceleration, and obtain the current driving environment information; S2: Calculate the pedal position change rate, the pedal force change rate, the throttle opening change rate, and the relative distance ratio between the current vehicle and the vehicle in front; S3: Use a random forest to generate a preliminary identification intention; S4: Generate a final identification intention; S5: Determine whether there is jamming. By adopting the above-mentioned pedal jamming monitoring method for identifying braking intention based on multi-source data fusion, through fusing multi-source data such as brake pedal displacement, pressure, throttle state, and environmental parameters, and combining mechanisms such as dual-model decision-making, dynamic threshold adjustment, and multi-rule weighted judgment, the accuracy, real-time performance, and environmental adaptability of brake pedal jamming monitoring are significantly improved.

Embodiments of this application provide example torque adjustment methods, apparatuses, and vehicles. One example method includes obtaining a requested torque value. A first intervention torque value is determined based on a wheel acceleration of a vehicle and a vehicle acceleration of the vehicle. Based on the requested torque value and the first intervention torque value, the vehicle is controlled to perform energy regeneration.

The invention belongs to the field of intelligent traffic, and particularly relates to a construction area traffic flow guiding and speed cooperative control system which realizes accurate traffic flow control through cooperative operation of an area sensing module, a distributed guiding module, a communication module and an emergency response module. The area sensing module is based on three-dimensional modeling and digital twinning technologies, fuses real-time traffic flow, vehicle states and environment data, and constructs a three-dimensional simulation space of a target area; the distributed guide module establishes a drainage control model by using a distributed reinforcement learning algorithm, dynamically optimizes a sub-region congestion control strategy in combination with a vehicle-road-cloud three-level communication node, and adjusts the acceleration and speed of a vehicle in real time; and the emergency response module rapidly generates an emergency instruction through an anomaly analysis algorithm and a strategy library and feeds back the emergency instruction to the guide model to solve an emergency abnormal event, and the application integrates multi-source data real-time sensing, distributed decision optimization and a multi-stage communication architecture, and realizes dynamic traffic flow distribution and intelligent traffic control in a construction area.

The invention provides a heating method and device of a vehicle power battery and a storage medium, and belongs to the technical field of battery management. The method comprises the steps that the preset discharge power of a power battery is inquired in a discharge mapping table of the power battery based on the temperature and the remaining capacity of the power battery of the vehicle, and the discharge mapping table is used for recording the preset discharge power of the power battery under different temperatures and remaining capacities; the actual discharge power of the power battery and the target discharge power of the vehicle are obtained, wherein the target discharge power is at least the discharge power to be reached when the vehicle accelerates; and heating the power battery under the condition that the magnitude relationship among the preset discharge power, the actual discharge power and the target discharge power meets the heating condition. According to the technical scheme, a battery heating strategy on the power dimension is provided, the power requirement of the vehicle can be met, meanwhile, heating energy consumption is reduced, and the endurance of the vehicle in the low-temperature environment is improved.

Method for improving the gradient calculation of motor vehicles (1) when cornering, characterized in that the influence of the measured transverse acceleration (AY) on the measured longitudinal acceleration is mathematically determined as a correction value from the known vehicle speed (v) and the mechanical installation position of the longitudinal acceleration sensor (3) of the motor vehicle (1), and the measured longitudinal acceleration is corrected accordingly, wherein the road gradient is determined from the difference between the measured longitudinal acceleration minus the correction value and the vehicle acceleration determined from the wheel or output speeds of the motor vehicle (1), characterized in that the correction value (AX) for the longitudinal acceleration value is determined from the measured transverse acceleration (AY), the vehicle speed (v), and the distance (d) of the longitudinal acceleration sensor (3) to the pivot point,at which the tangential acceleration hits the vehicle's centerline at a right angle, is determined by the following formula for a small ratio of distance d to curve radius r: AX= AY, 2 * d / v 2 .

The invention discloses a vehicle speed estimation method and system based on multi-sensor fusion. The method comprises the steps that wheel speed information of wheels, steering wheel rotation angle information and vehicle acceleration information are collected; based on the steering wheel angle information, mapping the wheel speed information to a preset vehicle mass center to obtain a mass center wheel speed corresponding to the wheel speed information; screening the centroid wheel speeds according to a preset wheel speed diagnosis condition, and integrating screening results to obtain an effective vehicle speed; the effective vehicle speed and the vehicle acceleration information are fused through a Kalman filtering method, and the current estimated vehicle speed is obtained. The accuracy of vehicle speed estimation is improved.

The invention provides a torque control method and device, a vehicle and a readable storage medium, the method is applied to the technical field of vehicles, and the method comprises the steps that the slip rate of the vehicle is determined; under the condition that the slip rate of the vehicle is larger than the slip rate threshold value, responding to the situation that the vehicle is in the variable-speed driving state, and obtaining the rotating speed limit value of a driving motor of the vehicle, the current rotating speed of the driving motor and the current output torque of the driving motor; according to the slip rate, the rotating speed limit value and the current rotating speed of the vehicle and the current output torque of the driving motor, the target output torque of the driving motor is determined, and when the vehicle is in the accelerated driving state, the determined target output torque is smaller than the current output torque; when the vehicle is in the deceleration driving state, the determined target output torque is larger than the current output torque; and adjusting the current output torque of the driving motor to the target output torque so as to adjust the rotating speed of the driving motor. The method can prevent the vehicle from shaking and abnormal sound when the vehicle passes through the deceleration strip during acceleration / deceleration.

The invention provides an in-vehicle acoustic enhancement method and device fusing determinacy-randomness hybrid modeling, and the method comprises the steps: collecting a fitted vehicle acceleration sound sample, extracting audio data, carrying out the analysis, constructing a determinacy component synthesis algorithm, and associating with a vehicle working condition; the method comprises the following steps: constructing a multi-physics-field coupled random noise generation module, wherein the multi-physics-field coupled random noise generation module comprises an aerodynamic noise module for simulating air inlet turbulence, a low-frequency pulse module for representing mechanical shock, a detonation module for simulating combustion uncertainty and a nonlinear distortion module for enhancing exhaust roughness and generating high-order harmonics; each random noise generation module is matched with the deterministic component, and mixing of the deterministic component and the random component is realized through weight adjustment; details are enhanced through dynamic compression, and target sound waves are generated; through collaborative optimization of determinacy-randomness components, the naturalness and brand identification degree of synthetic sound waves are improved, the vehicle working condition and driving information can be converted into perceptible sound effect characteristics, and the problem of information perception simplification in the electric vehicle driving process is solved.

A system for detecting and correcting wheelspin in a vehicle powered by a motor includes drive wheels driven by the motor and a motor controller in communication with the motor. In one embodiment, a vehicle acceleration from an inertial measurement unit (IMU) is compared to an estimated vehicle acceleration determined from an acceleration of the motor's rotor. If the estimated vehicle acceleration is greater than the IMU acceleration the motor controller adjusts the motor to correct the wheelspin. In a second embodiment, the vehicle acceleration is determined from a GPS receiver. In further embodiments, the vehicle acceleration is determined from other motor parameters, including voltage, current, and torque. In various embodiments, the drive motor is an electric motor, an internal combustion engine, or other prime mover device or mechanism.