2215results about "Vehicle condition input parameters" 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.

A dual-motor mixed-hybrid powertrain system, by performing pulse modulation control, i.e., series-hybrid intelligent start-stop control or parallel-hybrid intelligent power switching control, on the instantaneous power time-varying functions of an engine and a battery pack, can convert the surface working condition of an analog electric control engine into a simpler line working condition of a digital pulse control (DPC) engine, either a pre-determined high-state line working condition in the high-efficiency combustion area or a pre-determined non-combustion low-state line working condition with zero fuel consumption and zero pollutant emissions multiplexed in time. The traditional fixed one-to-one bidirectional mapping between an engine working condition and a vehicle working condition is converted into a dynamically adjustable many-to-many bidirectional mapping to achieve the full coverage of any overall vehicle working condition, achieving decoupling between a DPC engine working condition and the overall vehicle working condition and decoupling between software and hardware of a hybrid powertrain.

A management server system may obtain data associated with a vehicle. The management server system may obtain the data from a plurality of data sources associated with the vehicle. The data may include a plurality of subsets of data that each correspond to correspond to a particular driver assignment category. Each subset of data may identify a particular persona as a driver of a vehicle. The management server system may determine a driver of the vehicle based on each subset of data and a hierarchical plurality of driver assignment categories. Based on the determination of the driver of the vehicle, the management server system can dynamically map the driver to a particular event. The management server system may generate a user interface identifying the driver and the event.

This application discloses a method, system, and related equipment for intelligent energy management of new energy vehicles. Based on the vehicle's starting and ending points, at least one candidate energy-saving path is determined. The predicted energy consumption of the vehicle along at least one candidate energy-saving path is lower than that of other paths. The total energy consumption is predicted based on road condition information and energy consumption impact information for each path. In response to the selection of at least one candidate energy-saving path, a preset travel route is determined. The preset travel route includes multiple road segments, and the total energy consumption includes the energy consumption of each road segment. With the goal of minimizing fuel consumption along the preset travel route, the engine's operating state is controlled based on the initial state of charge (SOC) of the power battery in each road segment, the energy consumption of the road segment, and the vehicle's actual overall demand, ensuring the engine operates within its high-efficiency range. Using this application can reduce fuel consumption for users and improve the driving experience.

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 vehicle control device of the present invention acquires characteristics of a road condition in front of a traveling vehicle based on external information; acquires vehicle behavior control variables for controlling the behavior of the vehicle based on estimated state variables of the vehicle that are obtained based on the characteristics, and control variables concerning speed of the vehicle based on the external information; acquires trajectory tracking control variables for causing the vehicle to track the target trajectory based on the target trajectory on which the vehicle travels that are obtained based on the characteristics and the estimated state variables; and outputs the control commands for controlling the suspension device, steering device, and braking and driving device based on the vehicle behavior control variables and the trajectory tracking control variables. This improves travel stability of the vehicle on a road surface on which an irregularity such as ruts exists.

The invention discloses a chassis coordination control method and system based on tire side deviation state evaluation, and relates to the technical field of four-wheel steering four-wheel drive vehicle dynamics control. The method comprises the steps that an expected front wheel rotation angle is determined based on an expected steering wheel rotation angle input by a driver, and a front wheel rotation angle is determined; further determining a feed-forward front wheel steering angle, a feed-forward rear wheel steering angle and an additional yaw moment based on feed-forward control and feedback control, determining a first tire slip angle and a second tire slip angle based on a road adhesion coefficient, and further determining a wheel torque control weight, a feedback front wheel steering angle control weight and a feedback rear wheel steering angle control weight; and further determining a driving yaw moment, a feedback front wheel steering angle and a feedback rear wheel steering angle, determining a total longitudinal force based on an expected longitudinal acceleration input by a driver, and performing four-wheel torque distribution based on the total longitudinal force and the driving yaw moment to obtain a four-wheel driving torque. The driving stability of the vehicle under the limiting working condition can be improved.

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.

According to the method of estimating a maximum road friction coefficient, artificial braking or driving-related control is conducted and a maximum road friction coefficient is estimated based on a difference in wheel speeds between front and rear wheels, compensated for slip of tires.

The invention discloses a tire burst control method and device and a vehicle, and the method comprises the steps: obtaining the state information of the vehicle when a tire burst signal is received; the state information comprises an actual vehicle speed and an actual yaw velocity; according to the actual vehicle speed, actual basic correction torque is calculated; calculating an actual feedback correction torque according to the actual yaw velocity; and according to the actual basic correction torque and the actual feedback correction torque, performing weighted calculation on a target correction torque, and according to the target correction torque, controlling the vehicle. The phenomenon that deviation of the vehicle cannot be completely compensated or compensation is excessive can be avoided, so that deviation of the vehicle after tire burst is prevented, and the stability of the vehicle after tire burst is guaranteed.

A new energy vehicle energy intelligent management method and system, and a related device. The method comprises: determining at least one candidate energy-saving path on the basis of a starting point and an ending point of a vehicle, wherein path-based vehicle energy consumption predicted on the basis of the at least one candidate energy-saving path is lower than path-based vehicle energy consumption predicted on the basis of other paths, and the path-based vehicle energy consumption is predicted on the basis of road condition information of each path and energy consumption impact information; in response to a selection operation on the at least one candidate energy-saving path, determining a preset travel path, wherein the preset travel path comprises a plurality of road sections, and the path-based vehicle energy consumption comprises road section-based vehicle energy consumption of the plurality of road sections; and by taking the minimization of fuel consumption along the preset travel path as an objective, controlling an operation state of an engine on the basis of an initial SOC of a power battery and the road section-based vehicle energy consumption at each road section and an actual vehicle demand of the vehicle, so that the engine is in an efficient operation range during operation.

A method is for improving a vehicle-dynamics-related stability of a utility vehicle, wherein the utility vehicle has a lift axle. The method includes: determining a current ground speed of the utility vehicle; determining a stability-critical speed of the utility vehicle; comparing the current ground speed with the stability-critical speed; determining a lift status of the lift axle of the utility vehicle; and lowering the lift axle of the utility vehicle if the lift status represents a raised lift axle and the current ground speed is greater than or equal to the stability-critical speed. A device is for improving a vehicle-dynamics-related stability of a utility vehicle.

Control method and system (99) of a vehicle (1) moving on a road surface, wherein the system comprises a detection module (2) for detecting in real time a respective hydroplaning intensity (HI) for each tyre (3) of the vehicle (1), an actuation device (9) connected to each wheel (7) of the vehicle (1) and a command and control unit (8) programmed and configured for performing the control method comprising:—comparing each respective hydroplaning intensity (HI) with a first threshold;—upon the occurrence of a starting condition such that the respective hydroplaning intensity (HI) of at least one tyre (3) reaches the first threshold, then:—calculating in real time a reference value (YRr) of a parameter representative of a lateral motion of the vehicle (1) as a function of a longitudinal speed (Vxc) and of a steering angle (STA) of the vehicle;—calculating in real time a corrective moment (Mz) as a function of the reference value (YRr) and of a current value (YRc) of the parameter detected in real time;—applying in real time to each wheel (7) a respective torque (Tr) calculated in real time as a function of the corrective moment (Mz) and of the hydroplaning intensity (HI) of the respective tyre (3).

Techniques for dividing a lane into a first virtual lane and a second virtual lane are described. A narrow track vehicle may determine that the narrow track vehicle is traversing a lane that is at least 10 feet wide. The narrow track vehicle may divide the lane into a first virtual lane and a second virtual lane and traverse one of the first virtual lane or the second virtual lane based at least in part on location (e.g., when traversing a highway lane, residential lane, etc.) and / or speed of traffic (e.g., when traffic is traveling at or below a threshold speed). In examples, the narrow track vehicle may traverse the environment in between a first lane and a second lane upon determining that a distance between a first object in the first lane and a second object in the second lane meets a threshold distance.

A system is operable for controlling a force applied on a trailer being towed by a vehicle is described to a desired force Fd. The trailer has brakes and an optional motor for providing a propelling force to the trailer. A force sensor senses fore-aft forces between the vehicle and trailer at all times while the trailer is being towed. A controller receives signals from the force sensor and is operatively connected to the trailer brakes and the motor. The system controls the force between a towing vehicle and a trailer to the desired force Fd, by any one of: (a) operating the brakes to apply a braking force to the trailer; (b) activating the motor to apply a propelling force to the trailer; and (c) operating the brakes and activating the motor to apply both a braking and a propelling force to the trailer.

Disclosed herein are an adhesion coefficient determination method and apparatus, and a vehicle, a medium and a program product. The method comprises: acquiring sensor data collected by at least one sensor of a vehicle; on the basis of the sensor data, determining travelling information of the vehicle, wherein the travelling information comprises at least one of the following: a slip rate, longitudinal stiffness, a tire longitudinal force, a tire lateral force, a tire vertical load, a front-axle lateral force and a rack force; on the basis of the travelling information, or the sensor data and the travelling information, determining at least one reference adhesion coefficient and a confidence level of each reference adhesion coefficient, wherein the at least one reference adhesion coefficient comprises at least one of the following: a steady-state adhesion coefficient, a dynamic adhesion coefficient, a longitudinal adhesion coefficient and a lateral adhesion coefficient; and on the basis of the at least one reference adhesion coefficient and the confidence level of each reference adhesion coefficient, determining a target adhesion coefficient of the vehicle and a confidence level of the target adhesion coefficient. The method in the embodiments of the present application improves the accuracy of adhesion coefficient determination.

A method includes identifying suspension stiffness data and suspension deflection data associated with corresponding distal ends of one or more axles of an autonomous vehicle (AV). The method further includes determining, based on the suspension deflection data and the suspension stiffness data, driving constraint data for traveling at least a portion of a route. The method further includes causing, based on the driving constraint data, performance of a corrective action associated with the AV during the traveling of the at least a portion of a route.

An electrified fire fighting vehicle includes a chassis, a cab, a body, a front axle, a rear axle, an engine, a battery pack, a pump system including a water pump, an electromagnetic device electrically coupled to the battery pack and mechanically coupled to at least one of the front axle or the rear axle, and a power divider coupled to the pump system, the engine, and the electromagnetic device.

A method for automated guidance of a vehicle along a specified setpoint trajectory at an actual speed, wherein the setpoint trajectory includes geometric trajectory variables, the method including ascertaining an actual deviation of the vehicle from the setpoint trajectory and outputting the ascertained actual deviation and generating manipulated variables such that the vehicle, in the case of automated control of a drive system and / or a braking system and / or a steering system of the vehicle, moves closer to the setpoint trajectory. The method also includes ascertaining whether an undesirable driving state is present when the vehicle moves closer to the setpoint trajectory, wherein the undesirable driving state is ascertained from the specified setpoint trajectory based on the geometric trajectory variable, and, if the presence of the undesirable driving state is identified, automatically controlling the vehicle based on generated stability variables and / or adapting the setpoint trajectory.

An apparatus for estimating a vehicle weight includes a memory storing computer-executable instructions and at least one processor that accesses the memory and executes the instructions. The at least one processor applies a real velocity and real acceleration to a trained real-to-virtual transformation model to obtain a virtual output set at a target time point, which includes a virtual gradient of a vehicle, a virtual velocity of the vehicle, and virtual longitudinal acceleration of the vehicle, and applies the virtual output set at the target time point and a real wheel torque at the target time point to a trained mass estimation model to obtain weight information of the vehicle. The real-to-virtual transformation model includes a first sub-model for obtaining an initial virtual velocity, a second sub-model for obtaining the virtual longitudinal acceleration, and a third sub-model for obtaining the virtual gradient.

The invention relates to a vehicle trajectory tracking control method considering dynamic motion requirements. Comprising the following steps: 1, establishing a nonlinear vehicle dynamics model by comprehensively considering longitudinal motion, lateral motion and yawing motion of a vehicle; 2, searching saddle node bifurcation points with qualitative change of the stability state of the vehicle through a particle swarm optimization algorithm and a Lyapunov criterion, and constructing a dynamic stability domain boundary of the vehicle under the change of the longitudinal vehicle speed and the road adhesion coefficient; and 3, designing a vehicle multi-target trajectory tracking controller based on a nonlinear model predictive control theory, quantitatively evaluating the stability state of the vehicle by adopting a stability index, and meanwhile, designing a controller built-in parameter adjustment strategy to match the dynamic motion requirements of the distributed driving electric vehicle in different stability states. According to the method, the driving safety and the movement comfort of the distributed driving electric automobile are effectively ensured, and a set of solution is provided for realizing the optimal cooperative control of the chassis subsystem of the automatic driving automobile.

An electrified fire fighting vehicle includes a chassis, a cab, a body, a front axle, a rear axle, an engine, a battery pack, a pump system including a water pump, an electromagnetic device, and a power divider coupling the engine to the pump system and the electromagnetic device. The electromagnetic device is electrically coupled to the battery pack. The electromagnetic device has (a) an output coupled to at least one of the front axle or the rear axle and (b) a power take-off coupled to the pump system.

An agricultural vehicle includes a chassis, a plurality of tractive elements, a steering input device configured to steer the agricultural vehicle to perform a turn, and a steering control system configured to operate the steering input device. The chassis includes a first chassis portion and a second chassis portion pivotably coupled to the first chassis portion. The steering control system includes processing circuitry configured to obtain steering condition data corresponding to steering conditions of the steering input device, obtain partial curvature data corresponding to curvatures of the first chassis portion, determine, based on the partial curvature data, curvature data corresponding to curvatures of the agricultural vehicle, generate, based on the steering condition data and the curvature data, a primary curvature model that determines steering condition data given commanded curvature data, and operate the steering input device using (1) the primary curvature model and (2) a given command curvature.

A method is for predicting a transverse dynamic stabilization behavior of a present vehicle configuration of a vehicle. The method includes ascertaining two or more geometric characteristics of the present vehicle configuration; ascertaining two or more load characteristics of the present vehicle configuration; generating an individualized vehicle model of the present vehicle configuration from a vehicle base model of the vehicle using the geometric characteristics and the load characteristics; predicting dynamic properties of the present vehicle configuration using the individualized vehicle model; and defining at least one drive dynamic threshold for the vehicle on the basis of the dynamic properties of the present vehicle configuration. A drive assistance system and / or a computer program is configured to perform the method. A vehicle includes the driving assistance system.

The invention is suitable for the technical field of automatic driving vehicle control, and provides a full-state control method for dynamic drifting of a distributed driving vehicle, which comprises the following steps of: tracking a reference path to obtain an expected state quantity; establishing a dynamic error of a full state to obtain expected state derivatives of the velocity, the side slip angle and the yaw velocity; calculating a rear tire force weight, and implementing rear tire force constraint of the distributed driving vehicle; inverting the vehicle dynamics model to obtain expected longitudinal force and lateral force of each wheel; performing tire model inversion to obtain an expected front wheel slip angle and wheel speed of each wheel; and according to the expected front wheel slip angle and the wheel speed of each wheel, an actuator is adjusted, and control instructions of the steering wheel angle and the torque of each wheel are generated. The state of the vehicle can be accurately controlled, the maneuverability of the vehicle is good during conventional turning and limit drifting, and the vehicle can flexibly cope with complex working conditions; the stability and safety of the vehicle are enhanced, and the full-state dynamic drift control of the distributed driving vehicle is realized.

A computer-implemented method performed in a vehicle control unit for controlling motion of a heavy-duty vehicle. The method includes obtaining a vehicle motion request, wherein the vehicle motion request is indicative of a target curvature and a target acceleration, determining a motion support device, MSD, control allocation based on the vehicle motion request, determining a dynamic wheel slip angle limit based on the vehicle motion request, where dynamic wheel slip angle limit increases with a decreasing target acceleration, and controlling the motion of the heavy-duty vehicle based on the MSD control allocation constrained by the dynamic wheel slip angle limit.

A computer implemented method for controlling at least one driven and / or braked wheel of a heavy-duty vehicle includes configuring a default inverse tire model and a boost inverse tire model, where each inverse tire model represents a respective relationship between longitudinal wheel slip and longitudinal wheel force at the wheel, where the boost inverse tire model is associated with a higher maximum obtainable wheel slip value for the wheel compared to the default inverse tire model, obtaining a motion request indicative of a desired longitudinal force to be generated by the wheel, selecting the boost inverse tire model as active inverse tire model in response to detecting a boost signal and selecting the default inverse tire model as active inverse tire model otherwise, and controlling the at least one driven and / or braked wheel in dependence of the motion request and based on the active inverse tire model.

The invention discloses an automatic emergency braking control method and device based on a black spot technology and a vehicle. When the vehicle enters a black spot area, the vehicle obtains an optimal active braking parameter to correct the active braking parameter of the vehicle, and automatic emergency braking is executed based on the corrected active braking parameter; the method for obtaining the optimal active braking parameters comprises the following steps: dynamically delimiting a black spot area according to multi-dimensional data when emergency braking of each vehicle is triggered; obtaining the multi-dimensional data in the black spot area; and obtaining the optimal active braking parameters based on the vehicle speed, deceleration fluctuation, braking distance, vehicle weight and environment information during emergency braking. And the optimal active braking parameter when emergency braking is triggered in the black spot area is obtained. According to the method, the black spot technology is combined, the black spot area is established, ADAS program upgrading is conducted on the vehicle entering the black spot area, the vehicle can better adapt to the vehicle condition and the road condition in the area, the active braking curve is optimized, the comfort degree is improved, the situation that braking is not stopped after the automatic emergency braking function is triggered is reduced, and safety is improved.

A method includes determining a maximum first parameter value and a maximum second parameter value corresponding to an autonomous vehicle (AV) for traveling a route. The method further includes determining, based on a first load value at a first distal end of a first axle of the AV and a second load value at a second distal end of the first axle, an updated first parameter value and an updated second parameter value corresponding to the AV for traveling the route. The method further includes causing the AV to travel the route based on the updated first parameter value and the updated second parameter value.