374 results about "Driving cycle" patented technology

The invention relates to the technical field of driver behavior recognition, in particular to an intelligent behavior monitoring and recognition system for coping with a vehicle driver, which comprises a multi-mode driver data information module used for acquiring first driver multi-dimensional state information in a preset historical driving period, extracting and generating a first dangerous behavior feature set and a first dangerous response feature set according to the multi-dimensional state information of the driver; the environment information acquisition module is used for acquiring first driving environment interaction risk information in a preset historical driving period; a risk assessment model construction module; a real-time data evaluation module; according to the invention, the dangerous behavior of the driver is identified by combining the multi-dimensional state information of the driver with the environmental information, and the parameters of the dangerous behavior risk assessment model are continuously updated through the real-time data, so that the accuracy of monitoring and identifying the behavior of the vehicle driver is improved; meanwhile, high-risk behaviors can be obtained in advance, and the driving risk is prevented.

The invention relates to a load spectrum determination method and device, electronic equipment, a storage medium and a vehicle. The method comprises the steps that motion characteristic parameters of each historical driving cycle in at least one historical driving cycle of each vehicle in multiple vehicles of the same vehicle type are acquired; based on the motion characteristic parameters of each historical driving cycle of each alternative vehicle, determining at least one kinematics fragment belonging to different motion types from a plurality of kinematics fragments to obtain a plurality of typical kinematics fragments, the plurality of kinematics fragments being kinematics fragments corresponding to each historical driving cycle of each alternative vehicle, each kinematic fragment is used for indicating a speed-time curve of a corresponding historical driving cycle; splicing the plurality of typical kinematics fragments to obtain a road spectrum of a target time length; and inputting the road spectrum of the target duration into a whole vehicle dynamics simulation model of the target vehicle model, and performing simulation to obtain a load spectrum of a target rotating part of the target vehicle model.

The invention provides an evaporation system leakage fault diagnosis method and a storage medium, and belongs to the technical field of automobile fuel oil equipment control. The method comprises the steps that firstly, signals are collected, whether fault diagnosis conditions are met or not is judged according to the signals, diagnosis working condition request conditions are entered, and evaporation system leakage fault diagnosis is conducted after the fault diagnosis conditions are met and the diagnosis working condition request conditions are entered; moreover, in the process of diagnosing the leakage fault of the evaporation system, if the condition of quitting the working condition coordination is met, the diagnosis is interrupted. Evaporation system leakage fault diagnosis can be carried out in the running state, the diagnosis completion probability is increased, the driving cycle diagnosis frequency is reduced, and NVH complaints caused by engine starting and running during parking or pre-stopping (low vehicle speed) are reduced. In addition, the number of times of interruption diagnosis is reduced, the one-time diagnosis completion rate is increased, and the IUPR rate is increased.

A method provides information on a distance to empty (DTE) of a vehicle and an information providing apparatus performs the method. The method includes: determining average fuel efficiency based on a travel distance of a vehicle and the cumulative amount of electrical power consumption after start of one driving cycle by vehicle key-on; determining short fuel efficiency by averaging fuel efficiencies calculated at a predetermined travel distance interval after the start of one driving cycle; determining an average fuel efficiency application ratio corresponding to vehicle driving information after the start of one driving cycle using setting information; determining a guide DTE value reflecting the average fuel efficiency and the short fuel efficiency according to the average fuel efficiency application ratio; and controlling operation of a display device to display the guide DTE value.

A vehicle fuel system according to the invention includes a fuel filler flap that blocks an opening of a fuel filler space to be openable and closable, a locking mechanism that regulates opening and closing operation of the fuel filler flap, and a detector that detects the opening and closing operation of the fuel filler flap. A controller is able to determine an operational state of the fuel filler flap multiple times within one driving cycle of a vehicle, which improves a frequency of diagnosing the operational state of the fuel filler flap and a stuck-closed state of the detector.

The invention discloses a plug-in hybrid power bus energy management system and method based on extremum search, and the method comprises the steps: initializing a management system, specifically, initializing a threshold parameter initial value; outer-layer extremum searching is carried out based on current input data to obtain global reference parameters, and the input data comprise current outer-layer reference parameters and complete driving period working condition data; performing inner-layer extremum search on the basis of the global reference parameter in combination with the driving condition of the current road section to obtain an adaptive threshold parameter of each road section; vehicle energy management, including mode switching, torque distribution, and battery management, is performed based on global reference parameters and / or adaptive threshold parameters in combination with real-time vehicle states. According to the method, the limitation of a traditional method based on a fixed parameter rule is overcome, meanwhile, low calculation requirements, high reliability and nearly optimal performance are achieved, and the deployment feasibility in large-scale mass production of vehicles is ensured.

An automotive diagnostic method aimed at focusing diagnostic data retrieval and analysis on a possible fault condition includes receiving vehicle data from a vehicle at a data acquisition and transfer device (DAT) and analyzing the vehicle data to identify a possible fault condition. A drive cycle associated with the identified possible fault condition is identified, and driving conditions are monitored to identify completion of the identified drive cycle. Upon completion of the identified drive cycle, vehicle data is analyzed to identify an abnormal monitor. Vehicle data is also analyzed to identify an abnormal value of In-Use Monitor Performance Ratio (IUMPR) associated with the abnormal monitor. When an abnormal value of IUMPR is identified, a likely diagnostic condition associated with the identified abnormal IUMPR is further identified.

The invention discloses a lithium battery charge state estimation method based on a grey Kalman filtering model. The method specifically comprises the following steps: establishing a recursive relational expression of lithium battery state-of-charge (soc) estimated values at previous and later moments according to a gray GM (1, 1) model; constructing a state space expression according to the recursive relational expression; establishing a grey Kalman filtering model according to the state space expression in combination with the Kalman principle; soc is extracted from a public lithium battery data set of the Wishball University to serve as input of a gray Kalman filtering model, and the state of charge is estimated; and performing precision evaluation on an estimation result. According to the method, the state of charge of the lithium battery is estimated in three different scenes, including different driving cycles, random hybrid driving cycles and different temperatures under the same driving cycle condition; the result shows that the grey Kalman filtering model provided by the invention has a better estimation and prediction effect.

The invention discloses a vehicle emission conformity online evaluation method and device, a storage medium, equipment and an evaluation method, and the method comprises the following steps: obtaining a remote monitoring data flow of a to-be-evaluated vehicle in a preset time period, carrying out the preprocessing of the remote monitoring data flow, and generating a valid data set; identifying and segmenting a plurality of independent driving cycles from the effective data set, and screening out the independent driving cycles with cold start characteristics and engine accumulated work not smaller than a preset cycle work threshold value as effective driving cycles; dividing the data of each effective driving cycle into a plurality of preset working condition partitions according to the operating characteristics; aiming at each preset working condition subarea, adopting a corresponding calculation model, and calculating a pollutant emission result of the target pollutant; and comparing the pollutant discharge result of the target pollutant in each subarea with a corresponding preset discharge limit value for judgment. By using the scheme of the invention, low-cost, large-scale and high-frequency emission supervision is realized.

The invention provides a sample construction method for a control model of a hybrid vehicle, and is applied to the technical field of data processing. The method comprises the steps that under the condition that a driving speed sequence of the hybrid power vehicle in a historical driving cycle is kept unchanged, a target decision sequence corresponding to the minimum weighted value of a plurality of driving cost indexes is determined, and the target decision sequence comprises target engine power corresponding to each driving speed; determining a vehicle driving parameter set corresponding to each driving speed according to the driving speed sequence and the multiple target engine powers; and according to the driving speed, the target engine power corresponding to the driving speed and the vehicle driving parameter set corresponding to the driving speed, a training sample used for training a control model of the hybrid vehicle is constructed. The invention further provides a control method of the hybrid vehicle.

A fastening tool comprises a driver member, a controller configured to control a supply of power from a power source to a motor and to initiate a drive cycle, a piston configured to travel between at least two positions in a compression cylinder, a drive assembly, a drive cylinder, and a valve assembly operatively connected with drive cylinder and the compression cylinder. The driver member is at least partially disposed within the drive cylinder for movement along a drive axis to drive a lead fastener into a workpiece. The piston is configured to engage with and open the valve assembly to release the buildup of air in the compression cylinder, due to the travel of the piston between the at least two positions in the compression cylinder, to the drive cylinder so as to move the driver member along the drive axis to drive the lead fastener into the workpiece.

The invention provides a vehicle speed correction method and device, equipment and a medium, and relates to the technical field of vehicle control, and the method comprises the steps: in a current driving period, obtaining a vehicle moving distance, a real-time vehicle speed and vehicle state data of a target vehicle in a first time period; if the real-time vehicle speed and the vehicle state data meet the configured correction triggering rule, the ratio of the vehicle moving distance to the duration of the first time period is determined as the calculated vehicle speed; according to the real-time vehicle speed and the calculated vehicle speed, the actual wheel rolling radius of the target vehicle in the first time period is calculated; and correcting the preset wheel rolling radius of the target vehicle by using the actual wheel rolling radius so as to correct the real-time vehicle speed of the target vehicle in the next driving period. According to the invention, an accurate real-time vehicle speed can still be obtained and vehicle speed interference to an intelligent driving assistance system can be reduced after a vehicle user replaces a tire with the size different from that of an original tire.

The invention relates to the technical field of Internet of Vehicles, in particular to a vehicle driving condition data acquisition method and device, equipment and a storage medium, and the method comprises the steps: obtaining a plurality of target driving segments included in a target driving period of a vehicle; classifying the target driving segment to obtain a plurality of segment types; based on the driving characteristic parameters of the driving segments included in each segment type, dividing the segment types to obtain a first segment set and a second segment set; extracting a first driving segment from the driving segments of each segment type in the first segment set, and extracting a second driving segment from the driving segments of each segment type in the second segment set; and combining the first driving segment and the second driving segment to obtain driving condition data of the vehicle in the target driving period. The driving condition of the vehicle in the whole target driving period can be accurately represented, and then an accurate simulation data basis is provided for durability evaluation of vehicle parts.

The invention relates to the technical field of vehicle control, in particular to a vehicle energy management control method and device, a vehicle, a storage medium and a product. The method comprises: determining whether a current vehicle activates an energy management mode; if the current vehicle activates the energy management mode, the current driving cycle period health degree of the current vehicle is obtained, and the current battery state is determined according to the current driving cycle period health degree; and determining the backup electric quantity of the power battery and an energy management strategy of the current vehicle according to the current battery state, and performing energy management on the vehicle according to the backup electric quantity and the energy management strategy. Therefore, by adjusting the backup energy storage of the power battery and optimizing the start-stop strategy of the engine according to the total driving mileage of the vehicle or the health state of the power battery, self-adaptive adjustment according to energy management in the life cycle of the whole vehicle is realized, the service life of the power battery is prolonged, and the user experience is improved.

The invention relates to the technical field of electric vehicles, in particular to an extended-range electric vehicle endurance mileage algorithm, which comprises the following steps of: S1, aiming at different batteries, obtaining real-time available residual electric quantity Ebatteriaval of the batteries; the method comprises the following steps: S1, starting a VCU (vehicle control unit), S2, after the VCU is initialized, reading data of a previous driving cycle in an EEPROM (electrically erasable programmable read-only memory), and calculating average power consumption data per hundred kilometers and pure electric endurance mileage, S3, carrying out upper and lower limit value processing on the filtered pure electric endurance mileage, S4, calculating oil-electricity comprehensive endurance mileage, S5, carrying out limit value processing on the oil-electricity comprehensive endurance mileage again, and S5, carrying out limit value processing on the oil-electricity comprehensive endurance mileage. The problem that the driving mileage is calculated inaccurately at the present stage, if a driver does not supplement energy in time when a charging pile is far away from a gas station, the vehicle may lie down due to no oil and no electricity, and normal vehicle use of a user is affected is solved.

The present invention is a piezoelectric actuator drive method, a piezoelectric actuator drive circuit, and a piezoelectric actuator drive system capable of causing a piezoelectric element to vibrate in a maximum amplitude state. The piezoelectric actuator drive circuit includes: an obtainment unit that obtains operation information pertaining to operation of the piezoelectric element in a period that is a part of one cycle of a drive cycle in which the piezoelectric element is driven; and a control unit that performs feedback control of a drive parameter for driving the piezoelectric element based on the operation information.

The embodiment of the invention is suitable for the technical field of vehicles, and provides a control method of a rear oxygen sensor, a vehicle and a product, and the method comprises the steps that in the current driving cycle, starting and stopping information of an engine when a hybrid vehicle runs is obtained; determining start-stop working conditions of the engine based on the start-stop information; when the start-stop working condition is a frequent start-stop working condition, a heating voltage control strategy of the rear oxygen sensor is switched from a first strategy to a second strategy; when the rear oxygen sensor is in the same working scene, the heating voltage determined from the second strategy is greater than or equal to the heating voltage determined from the first strategy; and adjusting the heating voltage of the rear oxygen sensor based on a second strategy. By adopting the method, the failure rate of the oxygen sensor when the engine is frequently started and stopped can be reduced.

The invention discloses a method and device for calculating pressure after air filtration and a gas engine. The accuracy of pressure calculation after air filtration can be improved. The method for calculating the post-air-filtering pressure comprises the steps that at the starting node of a target driving cycle, the first pressure difference between the gas inlet pressure and the post-air-filtering pressure is obtained; in the driving process of the target driving cycle, a second pressure difference between the gas inlet pressure and the air-filtered pressure is obtained; based on the first pressure difference and the second pressure difference, a pressure difference change value is obtained; wherein the pressure difference change value comprises a difference value between the first pressure difference and the second pressure difference; and the pressure difference change value serves as the pressure after air filtering, and the absolute pressure after air filtering is obtained.

The invention relates to the technical field of vehicle control, in particular to an optimization method and system for an energy management strategy of a hybrid vehicle, and aims to determine a calibration working condition of an oil consumption test through vehicle type information of the hybrid vehicle, and perform a driving cycle test and collect vehicle operation data under the working condition. The calibration working condition is divided into a plurality of stages, the motor torque and the gear in each stage are used as control variables, the SOC of the battery is used as a state variable, and the control variables and the state variable are discretized to obtain a state transfer function; and constructing a cost function corresponding to each stage by taking the minimum fuel consumption as a target, taking the sum of the cost functions from the end stage to the starting stage as a target function, taking the minimum value of the target function, reversely calculating an optimal control variable and an accumulated cost function corresponding to each state variable from the end stage, and forming a query table. And a dynamic control switching threshold value is designed through an offline global optimal solution, so that the fuel consumption of the vehicle in each stage is close to a theoretical optimal value.

The invention provides a real-time control method for an extended-range electric vehicle with a battery and a cabin heated in winter. The method comprises the following steps: acquiring driving cycle data; analyzing a state variable based on the driving cycle data, and solving according to a duality principle and a Lagrange multiplier method to obtain an equivalent factor; and determining the optimal output power of the range extender, the battery and the PTC heater according to the optimal equivalent factor value. Compared with a traditional energy management strategy, the thermal management requirement of the whole vehicle in the low-temperature environment is considered, the problem is simplified and optimized, the solving process does not depend on a complex algorithm, the requirement for the algorithm and the computing power of the controller is low, and the method is suitable for an existing vehicle controller.

The invention relates to the technical field of diesel engines, and discloses a diesel engine urea concentration diagnosis method, device and equipment, a medium and a product. Searching whether a normal urea concentration value is detected in the last driving cycle or not, wherein the driving cycle represents the process of the vehicle from power-on to power-off or ignition to flameout; detecting whether to inject urea according to the urea liquid level of the urea box; if the normal urea concentration value is detected in the last driving cycle and urea is not injected, the normal urea concentration value serves as the current urea concentration value to be output. The urea concentration detection accuracy is improved.

Method and control arrangement (17) for controlling the operation of a fan (11) in a cooling system (20) of a vehicle, wherein the operation of the fan (11) is controlled based on the temperature of a coolant in the cooling system (20) in accordance with a setpoint temperature of the coolant. The method comprises monitoring (S1) an engine oil temperature of an engine (14) of the vehicle (1) during a plurality of driving cycles with the vehicle (1); and setting (S3) the setpoint temperature of the coolant based on a measurement indicating a plurality of maximum engine oil temperatures during the plurality of driving cycles.

Procedures for controlling an electric vehicle, which include the following: Changing the operation of an electric vehicle in response to a predicted energy consumption value; characterized in that the method further comprises the following: Adjusting the predicted energy consumption in response to a standard deviation of fluctuations in past energy consumption values ​​(R); and Reducing a predicted remaining range (DTE) in a non-linear manner using the adjusted predicted energy consumption as a battery state of charge (BSOC, CSOC) decreases, where the predicted energy consumption value X is calculated using the following equation (1): X = μ+ z'* σ , where µ is the mean energy consumption value during a driving cycle, σ is the standard deviation of the energy consumption value during the driving cycle, and z' is a weighting factor of σ.

A method for optimising vehicle calibration parameters, including: providing a representative real world drive cycle to a physical unit under test; generating performance data for the physical unit under test as the physical unit under test traverses the drive cycle; uploading the performance data as training data to generate a digital twin of a system, sub-system or vehicle in which the physical unit under test is configured to be used; providing vehicle performance prediction data from the digital twin to a calibration optimiser; and optimising one or more vehicle calibration parameters using the calibration optimiser.

This application provides a stepper motor driving device, method, and system, relating to the field of equipment control technology. The device includes: a first signal generation module for generating a first SPWM signal and a second SPWM signal; wherein the first SPWM signal and the second SPWM signal have the same waveform but are time-staggered by half a carrier cycle; and a first current control module for applying the first SPWM signal and the second SPWM signal to the upper bridge arm switches on both sides of a first H-bridge driving circuit, respectively, to control the magnitude and direction of the first driving current. This application, by staggering the SPWM control signals on both sides of the same H-bridge by half a cycle, naturally divides the H-bridge behavior into two driving segments and two slow decay modes within a driving cycle, effectively avoiding reverse current during the driving process, reducing operating noise, and improving driving efficiency.

The application relates to an adaptive rule energy management method and device of a hybrid logistics vehicle, wherein the method comprises the following steps: constructing a hybrid logistics vehicle model of a target hybrid logistics vehicle; determining a working mode rule and a plurality of adjustable threshold parameters of the target hybrid logistics vehicle based on the hybrid logistics vehicle model; in an outer layer control process, iteratively optimizing global benchmark rule threshold parameters in a whole driving cycle through a finite difference random approximation extreme value search algorithm, and dynamically adapting to periodic working condition changes of a route; and in an inner layer control process, separately fine-tuning parameters for each type of working condition through a pre-trained working condition clustering model, and accurately responding to real-time dynamic differences. Therefore, the problems in the prior art, such as the difficulty of fixed parameter rules in adapting to the periodic changes of the driving characteristics of a plug-in hybrid logistics vehicle, and the incapability of single route global parameter optimization in accurately responding to dynamic road conditions and environmental condition changes, are solved.

Predictive control techniques for an electrified powertrain of a hybrid electric vehicle (HEV) include maintaining a database of past measured operational parameters of an electrified powertrain during a plurality of past drive cycles of the HEV, monitoring and comparing measured operational parameters of the electrified powertrain during a current drive cycle of the HEV to the past measured operational parameters of the electrified powertrain stored in the database, predicting or estimating operational parameters of the electrified powertrain for a remainder of the current drive cycle of the HEV based on the comparison, and controlling the electrified powertrain during the remainder of the current drive cycle of the HEV based on the predicted / estimated operational parameters of the electrified powertrain including at least controlling recharging of a high voltage (HV) battery system to increase a fuel economy or operational efficiency of the electrified powertrain.

The invention discloses an unmanned express delivery vehicle control system and method based on operation state perception, and relates to the field of unmanned express delivery vehicle control systems. The method comprises the following steps: acquiring vehicle-mounted data in a driving period through a vehicle-mounted sensor array, performing time alignment and validity marking on the vehicle-mounted data to obtain a data quality label, constructing an evidence item set based on the data quality label, generating a sub-portrait label set according to the evidence item set, and displaying the sub-portrait label set in the driving period. And generating a portrait parameter set based on the sub-portrait label set through a preset priority rule and a persistence judgment strategy. Although the scheme has excellent performance in the aspects of improving system adaptability, safety, dynamic decision-making capability, reviewable performance and resource utilization efficiency, the problems of sensor faults, data loss, real-time data processing bottleneck, label interpretation errors and the like may influence the real-time response and decision-making precision of the system in a complex or extreme environment. Therefore, the overall performance and stability are reduced, especially under the condition of high load or low signal.

The application relates to the technical field of driving behavior analysis, and particularly provides a multi-resolution driving working condition identification and classification method. A plurality of time length sliding windows are set to perform multi-scale division on a multi-channel driving time sequence data stream, and a multi-resolution analysis system is constructed; longitudinal driving working conditions are identified and classified according to a yaw angular velocity energy index; a dynamic time warping algorithm is used to match data segments with transverse working condition templates, so that the identification and classification of the transverse driving working conditions are realized; and key driving events are calibrated, thereby providing fine data support for subsequent driving style analysis. The application combines the multi-scale sliding window and time sequence similarity measurement technology, effectively improves the driving working condition identification precision, and significantly reduces the misjudgment rate in complex scenes.

The invention relates to a method for pattern recognition in a drive cycle. The method herein includes (a) selecting an indefinite number of samples from a plurality of sensor datasets, wherein each sensor dataset represents a drive cycle. (b) performing unsupervised pattern recognition within the indefinite number of samples to identify a pattern is then performed. (c) performing supervised pattern recognition in the remaining sensor datasets of the plurality of sensor datasets to re-recognize a pattern and (d) determining, for each sensor dataset of the plurality of sensor datasets, a correspondence of the sensor dataset to the pattern. In this case, steps (a) to (d) are repeated until each correspondence exceeds a correspondence threshold.