1800results 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 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.

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.

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 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.

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.

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.

Systems and methods are provided for operation of a vehicle to prevent the vehicle from entering unsafe driving states. In examples, a current operational state of a vehicle is determined based on sensor data collected by sensors on the vehicle. Based on the current operational state, a safety-critical threshold value for a vehicle dynamic parameter at which the vehicle is predicted to breach a limit-of-handling is determined. A command to operate the vehicle in a requested operational state is received, which includes driving inputs for operating the vehicle in the requested operational state. The command is filtered by constraining the requested operational state according to a control barrier function based on the safety-critical threshold value and a requested value of the vehicle dynamic parameter determined from the requested operational state. A control signal is generated that controls the vehicle by modifying the driving inputs based on the filtered command.

To solve the problem that large commercial electric vehicles with range extenders require a large-capacity battery and a motor generator with high output power, which limits the number of passengers and loaded cargo, and generates large amounts of exhaust gas from the internal combustion engine of the power plant, a planned power generation and energy storage control technique is applied, in which, as a method for estimating the amount of power generation required during travel and the pre-travel power generation distance in a route plan of an electric vehicle with range extenders, assuming that travel from the starting point to the destination is carried out using exactly the energy stored in the battery, an absolute value in which a minimum storage requirement (SCL) of the battery has been deducted from a negative energy storage quantity (SOC) of the battery at the destination,as the amount of energy generation required during the journey, whereby a reduction in the installed secondary battery and the installed generator is preferably achieved by correcting the route plan during the journey.

The invention belongs to the technical field of industrial vehicle active safety control, and particularly discloses an industrial vehicle safety anti-collision early warning method and device, and the method comprises the steps: building a unified space-time perception reference through fusing ultrasonic wave, inertial navigation, laser radar and camera data; extended Kalman filtering is fused with inertial navigation prediction and laser radar poses, and high-precision positioning in the tunnel is achieved. Constructing a high-dimensional time sequence feature vector, and predicting a future collision risk in combination with a Bi-LSTM model; the early warning threshold value is dynamically adjusted according to the load, the gradient and the sensor state; the method has the following advantages that the early warning accuracy and response speed under the complex working condition are remarkably improved, and the operation safety of the industrial vehicle is guaranteed.

Instantaneous Tire Traction Modulating Valves [ITTMV] is a wheels internal and external highly time-sensitive application specific fail-safe electro-pneumatic valve design, valve's current-profile and regulator technique that proactively sense and instantaneously modulate the tire pressure between tire's upper and lower cut-off pressure valves from present / recommended values particularly during imminent / inevitable critical driving situations to mitigating-aquaplaning / hydroplaning, loss of stability / control, over / under steering, loss-of-traction, minimise-emergency / high speed braking distance, roll-over and loss of control due to puncture; by real-time sensing, perform context-aware computing and directing Tyre Pressure Modulating Units [TPMU] to actively modulate the tyre pressure in right time with right pressure on right tyres thereby instantly optimizing footprint / sidewall deformation rate to enhance tire traction simultaneously sustaining stability / steer-ability and restore / optimize to pre-set tyre pressure value immediately after overcoming critical situation for further safe and comfortable driving. Wheel hub housing integrated with rotary-link comprising electrically-insulated conductive-fluidic containers to transfer ITTMV's operational power and control-signal / data to wheels.

An AI-connected-electrified (ACE) heavy truck system equipped with an intelligent multi-mode hybrid (iMMH) powertrain system and a vehicle supervision control strategy based on machine learning (ML) or reinforcement learning (RL) paradigm are presented. This system ensures industry-leading power and braking performance of the ACE heavy truck while automatically optimizes both energy saving and emission reduction based on the vehicle's dynamic driving data and 3D electronic map information of roads for any transport event. In this application, the conventional analog electronic control (AEC) method is replaced by a novel digital pulse control (DPC) method on the instantaneous power function of the engine. The DPC method converts the complex surface working conditions of the AEC engine of the hybrid vehicles into simpler pre-defined working condition lines of the DPC engine. Consequently, the multi-variable nonlinear technical problem of simultaneously optimizing real driving environment (RDE) fuel consumption and pollutant emissions of the ACE heavy truck is simplified into two decoupled quasi-liner optimization problems, and ensures the reduction of on-vehicle computing resources for real-time AI inference computation and the improvement of the optimal performance, the convergent rate, and the robustness of the corresponding fuel-saving algorithm for the trained ML model or the learned RL model. Ultimately, the ACE truck achieves in the engineering sense the global minimum RDE fuel consumption and pollutant emissions meeting the standard consistently at high performance to cost ratio for any transport event and the RDE fuel consumption is decoupled from the vehicle configuration parameters and the human driver.

A computer system has processing circuitry to selectively operate the powertrain system in at least a freewheeling mode, in which an output shaft of the engine is non-rotating, and the engine is disconnected from the one or more drive wheels. The processing circuitry is further configured to: determine an opportunity for the freewheeling mode by predicting a feasibility of restarting the engine along an intended route by using a controllable clutch. Predicting the feasibility of restarting the engine by using the controllable clutch includes determining fulfillment of any one of a first restarting condition and a second restarting condition. Determining fulfillment of the first restarting condition includes comparing current total weight of the vehicle with a threshold value indicative of a minimum total weight for avoiding excessive braking of the vehicle during restarting at a high friction road condition with a given total gear ratio. Determining fulfillment of the second restarting condition includes comparing a current drive axle load with a corresponding threshold value indicative of a minimum drive axle load on a drive axle for avoiding excessive wheel slip during restarting at a low friction road condition with a given total gear ratio. The powertrain system is controlled into the freewheeling mode upon the fulfillment of any one of the first restarting condition and the second restarting condition.

The invention provides a vehicle stability control method, device and system and electronic equipment, relates to the technical field of vehicle active safety control, and aims to construct a control framework based on a two-degree-of-freedom dynamical model and realize accurate control of the yaw velocity and the side slip angle of a vehicle. By introducing the road adhesion coefficient and combining the vertical load of each wheel for independent torque distribution, the tire adhesion utilization rate and the environmental adaptability and safety of the vehicle under different road conditions (such as ice and snow, wet and slippery conditions and dry conditions) are improved. According to the scheme, calculation is easy and convenient, response is rapid, good engineering practicability is achieved, and the robustness and adaptability of the system under the complex working condition are enhanced while the vehicle stability and the path tracking capacity are improved.

A computer system and computer-implemented method for determining a control input for a vehicle are provided, the method comprising: acquiring, by processing circuitry of a computer system, operational information relating to a manoeuvre of the vehicle; determining, by the processing circuitry, a cost associated with energy efficiency of one or more motion support devices of the vehicle performing the manoeuvre; determining, by the processing circuitry, a cost associated with tyre wear of the vehicle performing the manoeuvre; and determining, by the processing circuitry, a control input such that a cumulative cost of energy efficiency and tyre wear is below a threshold.

The embodiment of the invention discloses a double-motor electric drive axle vehicle control method and system. The double-motor electric drive axle vehicle control method comprises the steps that accelerator pedal opening degree information, vehicle speed information, acceleration information, battery electric quantity information and load weight information are collected in real time; according to the accelerator pedal opening degree information, the vehicle speed information, the acceleration information, the battery electric quantity information and the load weight information and a pre-established multi-parameter complex working condition judgment model, the current working mode of the vehicle is determined; and according to the current working mode of the vehicle, the working state of at least one of the engine and the two motors is adjusted in real time. According to the control method, various working condition factors can be comprehensively considered, efficient operation of the double-motor electric drive axle vehicle in various modes is accurately controlled, the double-motor electric drive axle vehicle can be kept in the optimal working state in the whole scene, the power performance and the cruising ability of the vehicle are improved, energy consumption is reduced, and the service life of the vehicle is prolonged. And the requirements of the heavy-duty commercial vehicle in diversified application scenes such as logistics transportation and engineering operation are met.

The invention relates to the technical field of vehicle safety, in particular to a vehicle autonomous emergency braking-oriented road surface attachment sensing method, which comprises the following steps of: acquiring all initial point cloud data in a preset sensing area in front of a vehicle by using a laser radar at the top of the vehicle, and segmenting the initial point cloud data to obtain a road surface point cloud data set in the preset sensing area; acquiring a reflected light intensity value of each piece of road surface point cloud data in a preset sensing area, and taking the road surface point cloud data of which the reflection intensity value is not in a high reflection intensity threshold range but is in a road surface reflection intensity threshold range as target point cloud data; based on the reflection intensity values of all the target point cloud data, generating a grayscale image, inputting the grayscale image into a classification model, outputting a pavement type prediction result, determining a pavement adhesion coefficient, and calculating brake pressure; and if the distance between the current vehicle and the front vehicle is smaller than the safe distance, the vehicle performs autonomous emergency braking based on the braking pressure. The safety and comfort of emergency braking are improved.

Systems, methods, and articulated vehicles which use sensor rails attached to trailers to provide improved control for autonomous articulated vehicles. An example vehicle can receive, at a computer system of a tractor operating in an autonomous mode, trailer sensor data from a plurality of trailer sensors located on at least one trailer, the at least one trailer being coupled to the tractor. The computer system can also receive road condition data from at least one sensor, the at least one sensor being located on at least one of the tractor and the at least one trailer. The computer system can then identify, based on the trailer sensor data and the road condition data, a physical condition of the at least one trailer to be modified, and modify, via an electrical signal transmitted from the computer system, the physical condition.

A system for controlling an overtake maneuver of a control vehicle includes a controller. The controller is structured to: receive at least one overtake parameter associated with a control vehicle for overtaking a front vehicle in a vehicle lane; receive an indication of a direction of traffic in an overtake lane that is adjacent to the vehicle lane; receive an indication that the at least one overtake parameter satisfies at least one condition according to the direction of the traffic in the overtake lane; and in response to the at least one overtake parameter satisfying the at least one condition according to the direction of traffic in the overtake lane, execute an overtake maneuver by adjusting at least one parameter of the control vehicle or providing a command to an operator of the control vehicle to execute the overtake maneuver.

A monitoring system and method determine a consumption metric representative of one or more of an amount of fuel consumed or an amount of energy consumed by a vehicle during travel over a route. The consumption metric is independent of one or more of vehicle load or elevation change over the route. The system and method optionally can determine a route condition metric representative of a condition of a route traveled upon by a vehicle. The route condition metric is based on a comparison between an actual grade of the route at one or more locations along the route and an estimated grade of the route at the one or more locations.

A method for controlling a vehicle train, having a towing vehicle and at least one trailer vehicle. The towing vehicle includes a continuous deceleration device for performing a continuous deceleration. The method includes the steps: determining a trailer mass of the trailer vehicle in the prevailing vehicle configuration of the vehicle train; determining a towing vehicle trailer mass of the towing vehicle in the prevailing vehicle configuration; determining a mass ratio of the prevailing vehicle configuration based on the trailer mass and the towing vehicle mass; and limiting a maximum permissible continuous deceleration power of the continuous deceleration device based on the mass ratio. A driver assistance system is configured to perform the method. A commercial vehicle includes the driver assistance system. A computer program product is configured to perform the method.