CN109278752B - Energy optimization control method for plug-in hybrid electric vehicle based on cooperative sensing - Google Patents

Energy optimization control method for plug-in hybrid electric vehicle based on cooperative sensing Download PDF

Info

Publication number
CN109278752B
CN109278752B CN201811121379.6A CN201811121379A CN109278752B CN 109278752 B CN109278752 B CN 109278752B CN 201811121379 A CN201811121379 A CN 201811121379A CN 109278752 B CN109278752 B CN 109278752B
Authority
CN
China
Prior art keywords
vehicle
information
automobile
torque
map
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201811121379.6A
Other languages
Chinese (zh)
Other versions
CN109278752A (en
Inventor
肖艳秋
崔光珍
明五一
李晓科
周坤
罗国富
乔东平
文笑雨
焦建强
闫富宏
杨先超
夏琼佩
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhengzhou University of Light Industry
Original Assignee
Zhengzhou University of Light Industry
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhengzhou University of Light Industry filed Critical Zhengzhou University of Light Industry
Priority to CN201811121379.6A priority Critical patent/CN109278752B/en
Publication of CN109278752A publication Critical patent/CN109278752A/en
Application granted granted Critical
Publication of CN109278752B publication Critical patent/CN109278752B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/18—Propelling the vehicle
    • B60W30/182—Selecting between different operative modes, e.g. comfort and performance modes
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0965—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages responding to signals from another vehicle, e.g. emergency vehicle
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
    • Y02T10/84—Data processing systems or methods, management, administration

Abstract

The plug-in hybrid electric vehicle energy optimization control method based on cooperative sensing comprises the following steps: the automobile is accurately positioned by a cooperative sensing system carried by the automobile and obtains surrounding environment information, a local digital grid map representing the environment information is constructed, real-time sharing with an environment grid map sensed by other vehicles is completed through wireless communication, the map is merged to perfect the complete sensing of the road condition of the automobile, the future optimal acceleration of the automobile is predicted by combining automobile CAN bus information, the change condition of the speed in a future period is calculated, the torque and power requirements in the future period are obtained in advance by using the predicted running speed, the occupation ratio of each mode in the running process of the plug-in hybrid electric vehicle is combined by combining the current capacity state of a power battery, and the charge and discharge control is effectively adjusted. The invention realizes the real-time energy optimization control in the operation process of the hybrid electric vehicle, can achieve the aims of improving the fuel economy and reducing the emission, and is easy to popularize and apply on the actual vehicle.

Description

Energy optimization control method for plug-in hybrid electric vehicle based on cooperative sensing
Technical Field
The invention belongs to the field of new energy automobiles, and particularly relates to an energy optimization control method of a plug-in hybrid electric vehicle based on cooperative sensing.
Background
Most of the energy management strategies based on rules are adopted in the current plug-in hybrid electric vehicles and comprise deterministic rules and fuzzy rules, the deterministic rules are formulated according to actual engineering experience to control the switching of the working modes of the hybrid electric vehicle, so that the power of each energy source is reasonably configured, the real-time control system is widely applied, the running condition information of the vehicle is not needed, but the design target of the rules is single, and the effects of energy conservation and emission reduction cannot be exerted to the greatest extent; the fuzzy rule fuzzifies the control rule by adding previous experience, the control rule is described by using the fuzzy value, and the control output of the system is completed by the previous experience and the fuzzy rule, so that the stability and the reliability of the control system can be improved.
The other better method is that an energy management strategy is formulated from the optimization perspective and is divided into instantaneous optimization and global optimization, wherein the instantaneous optimization is to obtain optimal control parameters by carrying out instantaneous optimization on the dynamic system according to the state and the torque demand of the dynamic system at the previous moment in the running process of the automobile by taking the fuel economy and the emission performance as target functions, but the method is easy to fall into local optimization due to poor adaptive capacity to different working conditions; the global optimization is to calculate optimal control parameters by using the constraint conditions of various targets according to the whole known working condition, but the optimization method is difficult to be applied to a real vehicle. The method aims at the problems of self-adaption and real-time control of the working condition of the plug-in hybrid electric vehicle, and the key is how to predict the driving working condition of the vehicle in a future period of time.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides an energy optimization control system of a plug-in hybrid electric vehicle based on cooperative sensing, and the technical scheme of the invention is realized as follows: the plug-in hybrid electric vehicle energy optimization control method based on cooperative sensing comprises the following steps:
step 1: the method comprises the following steps that an automobile starts to run on a road, and GPS-BDS positioning modules installed on the automobile are utilized to accurately position and generate real-time position information of the automobile;
step 2: the method comprises the following steps that a multilayer sensor module arranged on an automobile monitors road information around the automobile in real time, wherein the road information comprises the positions and motion states of other automobiles and pedestrians, road traffic signals and position information of obstacles, the information processing module is used for finishing the processing and fusion of the information, and a local environment grid map of the automobile is constructed;
step 3: the wireless communication module in the cooperation perception system installed on the automobile is used for establishing transmission connection with other vehicles in a communication range, so that the real-time sharing of local environment grid maps generated by all vehicles is completed, and information collected by a CAN bus system installed on the automobile is uploaded;
and 4, step 4: an information processing module in the cooperative sensing system is used for combining an environment grid map shared by other vehicles with an own environment grid map, and finally generating an accurate environment grid map reflecting the surrounding environment of the own vehicle;
and 5: according to the accurate environment grid map generated by combination, the information processing module generates a path optimization function according to the combination result of the accurate environment grid map, and reasonably predicts the acceleration of the automobile by combining the self motion state information acquired by the CAN bus system, so that the driving condition in a future period of time is predicted in real time;
step 6: the speed change is acquired according to the predicted driving condition, the torque and power requirements of the automobile in a period of time in the future can be acquired, and the energy consumption requirements of a pure electric driving mode, an engine direct driving mode, a series hybrid driving charging mode, a parallel hybrid driving charging mode and a braking energy recovery mode are combined, so that the seven modes can be reasonably and timely switched, and the purposes of improving fuel economy, saving energy and reducing emission are achieved.
The method based on cooperative sensing is based on real-time communication of automobiles, an environment grid map around the automobiles is obtained through cooperative sensing among multiple automobiles on a road, information of vehicles, pedestrians, road marking lines and traffic signals in the driving process is represented on the grid map, a path optimization function is generated by utilizing the collected environment information, and finally the driving condition in a period of time in the future is predicted by combining the running state of the automobile.
The information of the driving condition predicted by the cooperative sensing result comprises a braking starting state, an accelerating driving state and a decelerating driving state, and a reasonable operation mode switching scheme can be made by combining the power battery capacity state and the driving torque requirement of the plug-in hybrid electric vehicle according to the speed change condition of the vehicle in a period of time in the future.
The process of obtaining the accurate environment grid map of the surrounding environment of the user through cooperative sensing is completed through a cooperative sensing system installed on an automobile, and the cooperative sensing system comprises: the GPS-BDS positioning module, the multilayer sensor module, the wireless communication module and the information processing module; wherein the content of the first and second substances,
the GPS-BDS positioning module is used for realizing the accurate positioning of the vehicle, acquiring the position information of the vehicle and improving the acquisition precision of the sensor;
the multilayer sensor module comprises a three-dimensional laser radar and a CCD camera sensor, and the CCD camera sensor is divided into three layers: the first layer is used for collecting the position information of surrounding obstacles; the second layer is used for collecting dynamic changes of pedestrians and vehicles; the third layer is used for collecting traffic environment information, including lane line and traffic signal lamp information;
the wireless communication module is used for networking with other vehicles, adopts a 2.5GHZ communication protocol, has a communication range of 50m in radius and a transmission speed of 60MB/s, and shares the acquired environmental information;
the information processing module is used for fusing information detected by the radar and the camera to generate an environment grid map on one hand, and is used for fusing an environment grid map shared by other vehicles around on the other hand.
The CAN bus system installed on the automobile CAN be used for acquiring the motion parameters of the automobile, wherein the motion parameters comprise the speed, the state of a power battery, power, torque and control signals, the motion parameters are connected with the cooperation sensing system to transmit the information of the automobile to the information processing module, and the state information of the automobile is transmitted to other automobiles through the wireless communication module;
the environment grid map information obtained by the cooperation sensing system is combined with the speed information, the power battery state information and the torque information of the automobile obtained by the CAN bus system, and the prediction of the running condition of the automobile in the next period of time is finished through the information processing module;
the power and torque requirements of the automobile can be obtained through calculation in advance by acquiring the speed change state in a period of time in the future, and the energy optimization control system can reasonably regulate and control the driving mode and the charging and discharging states of the energy optimization control system according to the power battery state of the energy optimization control system by combining the power and torque requirements and comparing the power and torque requirements with the high-efficiency running output torque of an engine in a power system of the plug-in hybrid electric vehicle.
In the step 1, the position information of the road vehicle is accurately positioned and obtained by using a method of combining two positioning systems of a GPS and a BDS, and the positioning accuracy of the two positioning systems is improved by fusing data of the two positioning systems.
In the step 2, the perception of the single vehicle to the road and field environment is enriched through the information fusion technology of the bottom layer, and the local environment grid map of the vehicle is constructed by the method for detecting the state of the vehicle, and the process is as follows:
establishing a space coordinate system model according to the installation positions of the three-dimensional laser radar and the CCD camera sensor, and importing information collected by the three-dimensional laser radar and the CCD camera sensor into the coordinate system model;
preprocessing information acquired by a three-dimensional laser radar and a CCD camera sensor, filtering out non-useful information, further extracting data characteristics of the information, and performing normalization processing on the data characteristics;
aiming at the processed data, performing matching calculation by using a Kalman filtering algorithm and combining a real map, and combining the characteristic information of the data;
updating the merged target position information by combining with the grid map constructed last time;
and performing filtering, merging, matching operation and updating processes for target data which is continuously updated for multiple times, and finally constructing a complete environment grid map.
In the step 3, after a single vehicle detects the environment and generates a local environment grid map, a plurality of local incomplete environment grid maps are integrated through wireless communication network sharing to form a relatively complete environment grid map;
in the step 4, the environment grid maps constructed by multiple vehicles are merged, the merged environment grid map solves the problem of blind areas perceived by a single vehicle, the distribution situation of objects around the vehicle is reflected, and the merging process is as follows:
constructing a local map M i And M j And inputting a local map M i And M j ；
Initializing a populationq ij Each individual represents a parameter λ, γ of the transfer function, which is calculated as:
Figure 100002_DEST_PATH_IMAGE001
performing fitness function for each individualf n Calculation, using it as an optimization metric, fitness functionf n The calculation formula of (a) is as follows:
Figure 100002_DEST_PATH_IMAGE002
in the formulaN ij As mapsM i And mapM j The number of grids that are completely fused,αas a function of conversionF s The weight coefficient of (a);
generating new individuals according to selection, crossover and variation;
starting genetic iterative operation, judging whether a termination condition is met, if so, continuing to perform the next step, otherwise, returning to the second step; the setting of the termination condition is to complete iteration within an effective time to realize the correct fusion of the map, and an evaluation function is required
Figure 100002_DEST_PATH_IMAGE003
To determine a mapM i AndM j whether the fusion of (1) failed, the merit function is expressed as follows:
Figure 100002_DEST_PATH_IMAGE004
defining composite computations
Figure 100002_DEST_PATH_IMAGE005
The following were used:
Figure 100002_DEST_PATH_IMAGE006
in the formula (I), the compound is shown in the specification,x、yrespectively, are the coordinate values of the map grid,θis the relative position angle of the two grids,
Figure 100002_DEST_PATH_IMAGE007
and
Figure 100002_DEST_PATH_IMAGE008
respectively representM i AndM j the number of cells with the same median value and different values; when in use
Figure 100002_DEST_PATH_IMAGE009
The overlap area representing the map can be completely matched when =1,
Figure 100002_DEST_PATH_IMAGE010
the smaller the value of (a), the smaller the matching degree of the overlapping region;
outputting an optimal transfer function F s ；
According to the optimal transfer function pairM j Perform translation and rotation, andM i and fusing to obtain a new map.
In the step 5, the real-time prediction of the optimal acceleration of the vehicle is completed by combining the merging results of the accurate environment grid maps and a model prediction control algorithm and formulating a prediction rule, and the optimal acceleration is solved by taking the destination as a constraint condition as fast and safe as possible, so that the running state of the vehicle in a future period of time, namely the real-time working condition, is reasonably predicted; solving the optimal acceleration formula as follows:
obtaining self vehicle from environment digital mapiSurrounding vehiclejPosition ofs i 、s j And obstacle positionz i And the current acceleration is obtained by the CAN busu i Speed of vehiclev i 、v j Andjvehicle relativeiSpeed of the vehiclev ji ；
Calculating the intention track of cooperative perception in the running process of the automobileK i (t)Is of the formula
Figure 100002_DEST_PATH_IMAGE011
；
Calculating vehiclei、jThe distance betweenS ij Whereint h In order to predict the time interval that is set,
Figure 100002_DEST_PATH_IMAGE012
；
the predicted optimal acceleration value is calculated by a model predictive control algorithm,
Figure 100002_DEST_PATH_IMAGE013
wherein the content of the first and second substances,t d t is the total length of the prediction time for a certain moment in the prediction time period,nin order to communicate the number of vehicles,α k (k =1,2,3,4) are weight coefficients,δtin order to iterate the step size,βis the obstacle position error coefficient; after the acceleration is obtained through solving, the speed distribution condition within the time T, namely the running condition, can be obtained through derivation of the acceleration.
In the step 6, the control strategy of the hybrid electric vehicle is formulated, the ratios of seven modes of the vehicle in the running process are reasonably combined by utilizing the predicted working conditions, and the control of charging and discharging of the vehicle is adjusted, wherein the process is as follows:
(1) the system acquires the current SOC state, the current vehicle speed and the predicted vehicle speed, and solves the required torque of the system through the predicted working condition information;
(2) judging whether the current required torque is larger than 0, if so, entering the step (3), otherwise, switching the automobile to a braking energy recovery mode;
(3) if the current SOC value is larger than the SOC1 in the pure electric driving mode and the required torque TqGreater than the upper limit T of the high-efficiency torque of the enginemaxIf the driving mode is the pure electric mode, the driving mode is switched to the pure electric mode; if the current SOC value is larger than the SOC1 in the pure electric driving mode and the required torque TqLess than the high-efficiency torque upper limit T of the enginemaxIf the driving mode is the engine independent driving mode, switching to the engine independent driving mode for driving; if the current SOC value is smaller than the SOC1 in the pure electric driving mode, entering the step (4);
(4) judging whether the current SOC is larger than the SOC2 in the electric quantity consumption mode, if so, entering the step (5), and otherwise, entering the step (8);
(5) judging whether the predicted vehicle speed is greater than the current vehicle speed, if so, determining whether the predicted vehicle speed v is greater than the current vehicle speedfGreater than the current vehicle speed vnEntering the step (6), otherwise entering the step (7);
(6) if the required torque TqGreater than the lower limit T of the torque when the engine operates efficientlyminIf not, switching to a series hybrid driving mode;
(7) at this time, the predicted vehicle speed vfLess than the current vehicle speed vnIf the torque T is requiredqLess than the upper limit T of the torque when the engine operates efficientlymaxIf not, switching to a parallel hybrid driving mode, otherwise, switching to a series hybrid driving mode;
(8) judging whether the predicted vehicle speed is greater than the current vehicle speed, if so, determining whether the predicted vehicle speed v is greater than the current vehicle speedfGreater than the current vehicle speed vnEntering the step (9), otherwise entering the step (10);
(9) if the required torque TqLess than the upper limit T of the torque when the engine operates efficientlymaxIf not, switching to a series hybrid drive charging mode;
(10) at this time, the predicted vehicle speed vfLess than the current vehicle speed vnIf the torque T is requiredqGreater than the upper limit T of the torque when the engine operates efficientlymaxAnd if not, switching to a series hybrid drive charging mode to maintain the battery capacity and prolong the service life of the battery.
Compared with the prior art, the working condition prediction method does not need a historical data processing means in an off-line mode, obtains the current speed and position information of the automobile and a road condition environment map containing other vehicle information by utilizing the cooperative sensing capability of the automobile under the environment of the Internet of vehicles, reasonably predicts the working condition in a future period of time in real time by utilizing the result and combining a prediction control algorithm, realizes real-time dynamic energy control, overcomes the defects of an energy optimization control system based on rules and optimization, ensures better fuel economy of a plug-in hybrid electric automobile, and is easy to use on a real automobile.
Drawings
FIG. 1 is a diagram of a cooperative sensing based energy optimization control system for a plug-in hybrid electric vehicle;
FIG. 2 is a diagram of a CAN bus distribution architecture;
FIG. 3 is a flow chart of cooperative sensing based on multi-vehicle communication;
FIG. 4 is a flow chart of a three-dimensional laser radar for collecting information of vehicles and obstacles;
FIG. 5 is a flow chart of a vision sensor for collecting pedestrian and traffic signals;
FIG. 6 is a flow chart of an environment grid map construction;
FIG. 7 is a flow chart of improved genetic algorithm environment digital map merging;
FIG. 8 is a flow chart of predicted driving conditions;
FIG. 9 is a flow chart of vehicle operating mode switching;
Detailed Description
The present invention will be described in detail with reference to specific examples.
Fig. 1 is a structural diagram of an energy optimization control system based on a cooperative sensing plug-in hybrid electric vehicle according to the present invention, which includes a cooperative sensing system, a CAN bus system, a decision control system and a plug-in hybrid electric vehicle power system. The cooperation perception system comprises a GPS-BDS positioning module, a multilayer sensor module, an information processing module and a wireless communication module.
The GPS-BDS positioning module is connected with the multilayer sensor module, the multilayer sensor module is connected with the information processing module, and the information processing module is connected with the wireless communication module and can transmit information mutually.
The CAN bus system is connected with the wireless communication module and CAN feed back information. The method comprises the steps of transmitting automobile internal information through a CAN bus protocol, completing the prediction of the running condition by combining environment grid map information, and transmitting the predicted running condition information to a decision control system, thereby completing the switching of the running mode of the plug-in hybrid electric vehicle.
The plug-in hybrid electric vehicle power system comprises an engine, a clutch, a power battery, a main motor, an auxiliary motor and driving wheels, and the operation mode of the plug-in hybrid electric vehicle power system is switched according to the separation and combination condition of the clutch and the charging and discharging condition of the power battery.
The invention discloses a plug-in hybrid electric vehicle energy optimization control method based on cooperative sensing, which comprises the following specific implementation steps of:
step 1: in order to accurately acquire the position of an automobile on any road condition and facilitate subsequent acquisition of environmental information, the automobile is accurately positioned by using a GPS-BDS positioning module arranged on the automobile to generate real-time position information of the automobile;
step 2: the method comprises the following steps that a three-dimensional laser radar and a CCD (charge coupled device) camera sensor which are mounted on a vehicle body detect road information around the vehicle in real time, wherein the road information comprises the positions and motion states of other vehicles and pedestrians, road traffic signals and position information of obstacles, the information is processed and fused through a processing module, and an environment grid map of the vehicle is constructed;
step 3: the method comprises the steps that transmission connection is established between a remote communication module in a cooperation sensing system installed on an automobile and other vehicles in a communication range, real-time sharing of an environment grid map generated by each vehicle is completed, and meanwhile speed, power and torque information collected by a CAN bus system installed on the automobile is uploaded;
and 4, step 4: the information processing module is used for combining the environment grid map shared by other vehicles with the self environment grid map to finally generate an accurate environment grid map reflecting the surrounding environment of the self.
And 5: according to the combined accurate environment grid map, the information processing module combines the combined result of the accurate environment grid map and the CAN bus system information, reasonably predicts the acceleration of the automobile by using a model prediction control algorithm, and predicts the running condition in a future period of time in real time;
step 6: the power demand of the automobile in a period of time in the future is obtained according to the predicted working condition result, and the energy consumption demands of the pure electric drive mode, the engine direct drive mode, the series hybrid drive charging mode, the parallel hybrid drive charging mode and the braking energy recovery mode are combined, so that the seven modes can be reasonably and timely switched.
Fig. 2 shows a distribution structure of a CAN bus system, which is connected to an engine control unit, a main motor control unit, an auxiliary motor control unit, a battery management unit, a transmission control unit, a cooperation sensing unit, and a decision control unit. The information communication in the automobile is completed through the CAN bus protocol, and the cooperative sensing information CAN be effectively collected and processed in real time.
Fig. 3 shows a cooperative sensing process based on multi-vehicle communication, in which a vehicle and other vehicle-mounted sensors in a communication range are fused to detect information, including detection and identification of obstacles and traffic signals and identification and tracking of pedestrians and vehicles, and then a digital map with real-scene features is constructed according to an environment map acquired by the vehicle-mounted sensors, and by locking relevant road sections affecting the driving behavior of the vehicle, the digital maps of the vehicles are transmitted by a wireless network and combined according to the digital maps of the same location and the simultaneity by an information processing unit, the output of environment information is finally completed.
Fig. 4 shows a process of collecting information of vehicles and obstacles by a three-dimensional laser radar installed on a vehicle body, which includes the following steps:
step 1: scanning and detecting peripheral information, mainly vehicle information and obstacle information, by a plurality of radars installed on a vehicle body, and displaying the peripheral information on a raster image;
step 2: clustering the collected data of surrounding vehicles and obstacles, and extracting outline features of the data in the raster image;
and step 3: respectively constructing a category list of the targets with different contour characteristics according to the clustering result;
and 4, step 4: screening data information with the same characteristics, namely identifying vehicles and obstacles according to the actual geometric shapes of the data information and the aspect ratio of the images;
and 5: training and learning the homogeneous data by using a radial basis kernel function support vector machine, and estimating the states of the vehicle and the barrier according to the training result;
and 6, continuously updating the positions of the vehicle and the obstacle to fulfill the aims of identification and tracking.
Fig. 5 shows a process of collecting pedestrian and traffic signals by the vehicle-mounted CCD camera sensor, collecting images of pedestrian, lane line and traffic signal lamp by the vehicle-mounted camera, and preprocessing the collected initial image, including image filtering and noise reduction, image histogram equalization, image enhancement and the like. The method is characterized in that the method respectively detects, identifies and tracks pedestrians, lane lines and traffic signal lamps, and comprises the following steps:
lane line identification and tracking: extracting the track contour edge of the lane line aiming at the preprocessed image, calculating the coordinates of a curve through the image, and marking the track in the image; fitting a track curve according to the position coordinates of the lane lines, and calculating a deviation error; and finally, matching the fitted curve model with the actual road image to obtain the real track of the road lane line.
Pedestrian identification and tracking: firstly, extracting the pedestrian edge in the image to obtain a pedestrian target area; extracting HOG characteristic data of an image of a pedestrian region, and training and learning the pedestrian data by using a method of combining a Boltzmann machine and an artificial neural network; and finally, identifying the pedestrian according to the data training set and dynamically updating according to the position change of the pedestrian.
Detecting and identifying traffic signal lamps: setting a threshold value according to the color characteristics of the traffic signal lamp, and performing threshold value segmentation on the traffic signal lamp in an HSV color space to obtain the area where the traffic signal lamp is located; defining the semantics of the signal lamp according to different colors; and finally, matching the detection result with the characteristic template to identify the meaning of the target.
And after the target detection is finished, the information is imported into the information processing module to be fused with the information of other sensors.
Fig. 6 shows a construction process of an environment grid map, which includes the following steps:
step 1: establishing a space coordinate system model according to the installation positions of the three-dimensional laser radar and the CCD camera sensor, and importing information collected by the three-dimensional laser radar and the CCD camera sensor into the model;
step 2: preprocessing information acquired by a three-dimensional laser radar and a CCD camera sensor, filtering out non-useful information, further extracting data characteristics of the information, and performing normalization processing on the data characteristics;
and step 3: aiming at the processed data, performing matching calculation by combining a Kalman filtering algorithm and a real map, and merging the characteristic information of the device;
and 4, step 4: updating the merged target position information by combining the digital environment constructed last time;
and 5: and performing filtering, merging, matching operation and updating processes for target data which is continuously updated for multiple times, and finally constructing a complete environment grid map.
Fig. 7 shows an environment grid map merging process of the improved genetic algorithm, which includes the following steps:
by combining the environment grid maps constructed by multiple vehicles, the combined environment grid map solves the problem of blind areas perceived by a single vehicle, reflects the distribution situation of objects around the vehicle, and the combining process is as follows:
step 1, constructing a local map M i And M j And inputting a local map M i And M j ；
Step 2, initializing a population qij, wherein each individual represents parameters lambda and gamma of a conversion function, and the calculation formula of the conversion function is as follows:
Figure 847927DEST_PATH_IMAGE001
step 3, carrying out fitness function on each individualf n Calculation, using it as an optimization metric, fitness functionf n The calculation formula of (a) is as follows:
Figure 312538DEST_PATH_IMAGE002
in the formulaN ij As mapsM i And mapM j The number of grids that are completely fused,αas a transfer function F s The weight coefficient of (a);
step 4, generating new individuals according to selection, crossing and variation;
step 5, starting to perform genetic iterative operation, judging whether a termination condition is met, if so, continuing to perform the next step, otherwise, returning to the second step; the setting of the termination condition is to complete iteration within an effective time to realize the correct fusion of the map, and an evaluation function is required
Figure DEST_PATH_IMAGE014
To determine a mapM i AndM j whether the fusion of (1) failed, the merit function is expressed as follows:
Figure 889013DEST_PATH_IMAGE004
defining composite computations
Figure 34954DEST_PATH_IMAGE005
The following were used:
Figure 990272DEST_PATH_IMAGE006
in the formula (I), the compound is shown in the specification,x、yrespectively, are the coordinate values of the map grid,θis the relative position angle of the two grids,
Figure 415396DEST_PATH_IMAGE007
and
Figure 761057DEST_PATH_IMAGE008
respectively representM i AndM j the number of cells with the same median value and different values; when in use
Figure 491116DEST_PATH_IMAGE009
The overlap area representing the map can be completely matched when =1,
Figure 973044DEST_PATH_IMAGE010
the smaller the value of (a), the smaller the matching degree of the overlapping region;
step 6, outputting an optimal conversion function Fs；
Step 7, according to the optimal transfer function pairM j Perform translation and rotation, andM i and fusing to obtain a new map.
Fig. 8 shows a flow of predicting the driving condition based on the cooperative sensing, and the driving condition prediction process will be further described with reference to the drawing.
In the step 1, the position of the automobile is accurately positioned by the own vehicle and the surrounding vehicles by using a method of combining GPS and inertial navigation so as to more accurately sense the surrounding environment information;
in the step 2, the automobile acquires running state information of the automobile in real time through a CAN bus installed on the automobile, wherein the running state information comprises speed, power, torque and the like, and is used for judging the relative speed with other vehicles and the state of an energy system;
in the step 3, the automobile collects the information of surrounding pedestrians, vehicles and traffic signals in real time by means of various sensors such as radars, cameras, infrared and the like which are loaded, so as to sense the surrounding road condition environment;
in step 4, the information processing module fuses various information collected by the sensor to complete the unique expression of the road condition and environment information;
in step 5, the information processing module constructs a two-dimensional environment digital map containing vehicle, pedestrian, traffic information and barrier information according to the fused sensor information;
step 6, the communication module simultaneously transmits the digital map of the vehicle, receives the digital maps of other vehicles, inputs the digital maps of other vehicles into the information processing module and merges the maps;
step 7, the communication module transmits information acquired by the CAN bus and receives feedback information of other vehicles in real time;
and 8, fusing and merging the plurality of environment digital maps for multiple times by the information processing module, and fusing the environment maps, so that the environment digital map with the highest integrity is generated, and a better data basis is provided for further predicting the working conditions.
In step 9, the information processing module completes real-time prediction of the optimal acceleration of the vehicle by combining the map merging results and the model prediction control algorithm and formulating a prediction rule, namely, a target is regarded as a mass point with the same proportion of the environmental digital map, and the optimal acceleration is solved by taking the destination which is reached as fast and safely as possible as a constraint condition, so that the running state of the vehicle in a future period, namely the real-time working condition, is reasonably predicted, and the formula is as follows:
obtaining self vehicle from environment digital mapiSurrounding vehiclejPosition ofs i 、s j And obstacle positionz i And the current acceleration is obtained by the CAN busu i Speed of vehiclev i 、v j Andjvehicle relativeiSpeed of the vehiclev ji ;
Calculating the intention track of cooperative perception in the running process of the automobileK i (t) Is of the formula
Figure DEST_PATH_IMAGE015
；
Calculating the distance Sij between the vehicles i and j, wherein th is a predicted set time interval,
Figure 169670DEST_PATH_IMAGE012
；
the predicted optimal acceleration value is calculated by a model predictive control algorithm,
Figure DEST_PATH_IMAGE016
wherein the content of the first and second substances,t d to predict a certain moment in time within the time period,Tin order to predict the total length of time,nto communicate the number of vehicles，α k (k =1,2,3,4) are weight coefficients,δtfor iterative step length, β is the error coefficient of position of obstacle, solving for acceleration and then deriving to obtain timeTAnd (4) inner speed distribution conditions, namely running conditions.
Fig. 9 is a flowchart illustrating a switching process of an operation mode of a plug-in hybrid electric vehicle, in which a control strategy of the hybrid electric vehicle is formulated, and the predicted operating conditions are utilized to reasonably combine the occupation ratios of seven modes, i.e., a pure electric drive mode, an engine direct drive mode, a series hybrid drive charging mode, a parallel hybrid drive charging mode, and a braking energy recovery mode, of the vehicle during the operation process, and adjust the control of charging and discharging of the vehicle, wherein the control strategy process is as follows:
preferably, a control strategy of the hybrid electric vehicle can be formulated according to the vehicle speed state, the SOC capacity state and the required torque, seven modes of the vehicle in the running process are reasonably switched by utilizing the predicted working condition, and the charging and discharging control of the vehicle is adjusted, wherein the control strategy process comprises the following steps:
(1) the system acquires the current SOC state, the current vehicle speed and the predicted vehicle speed, and solves the required torque of the system through the predicted working condition information;
(2) judging whether the current required torque is larger than 0, if so, entering the step (3), otherwise, switching the automobile to a braking energy recovery mode;
(3) if the current SOC value is larger than the SOC1 in the pure electric driving mode and the required torque TqGreater than the upper limit T of the high-efficiency torque of the enginemaxIf the driving mode is the pure electric mode, the driving mode is switched to the pure electric mode; if the current SOC value is larger than the SOC1 in the pure electric driving mode and the required torque TqLess than the high-efficiency torque upper limit T of the enginemaxIf the driving mode is the engine independent driving mode, switching to the engine independent driving mode for driving; if the current SOC value is smaller than the SOC1 in the pure electric driving mode, entering the step (4);
(4) judging whether the current SOC is larger than the SOC2 in the electric quantity consumption mode, if so, entering the step (5), and otherwise, entering the step (8);
(5) judging whether the predicted vehicle speed is greater than the current vehicle speed, if so, determining whether the predicted vehicle speed v is greater than the current vehicle speedfGreater than the current vehicle speed vnEntering the step (6), otherwise entering the step (7);
(6) if the required torque TqGreater than the lower limit T of the torque when the engine operates efficientlyminIf not, switching to a series hybrid driving mode;
(7) at this time, the predicted vehicle speed vfLess than the current vehicle speed vnIf the torque T is requiredqLess than the upper limit T of the torque when the engine operates efficientlymaxIf not, switching to a parallel hybrid driving mode, otherwise, switching to a series hybrid driving mode;
(8) judging whether the predicted vehicle speed is greater than the current vehicle speed, if so, determining whether the predicted vehicle speed v is greater than the current vehicle speedfGreater than the current vehicle speed vnEntering the step (9), otherwise entering the step (10);
(9) if the required torque TqLess than the upper limit T of the torque when the engine operates efficientlymaxIf not, switching to a series hybrid drive charging mode;
(10) at this time, the predicted vehicle speed vfLess than the current vehicle speed vnIf the torque T is requiredqGreater than the upper limit T of the torque when the engine operates efficientlymaxAnd if not, switching to a series hybrid drive charging mode to maintain the battery capacity and prolong the service life of the battery.
The present embodiment is not intended to limit the shape, material, structure, method, etc. of the present invention in any manner, and any simple modification, equivalent change and modification of the above embodiments according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.

Claims (9)

1. The plug-in hybrid electric vehicle energy optimization control method based on cooperative sensing is characterized by comprising the following steps of:
step 1: the method comprises the following steps that an automobile starts to run on a road, and GPS-BDS positioning modules installed on the automobile are utilized to accurately position and generate real-time position information of the automobile;
step 2: the method comprises the following steps that a multilayer sensor module arranged on an automobile monitors road information around the automobile in real time, wherein the road information comprises the positions and motion states of other automobiles and pedestrians, road traffic signals and position information of obstacles, the information processing module is used for finishing the processing and fusion of the information, and a local environment grid map of the automobile is constructed;
and step 3: the wireless communication module in the cooperation perception system installed on the automobile is used for establishing transmission connection with other vehicles in a communication range, so that the real-time sharing of local environment grid maps generated by all vehicles is completed, and information collected by a CAN bus system installed on the automobile is uploaded;
and 4, step 4: an information processing module in the cooperative sensing system is used for combining an environment grid map shared by other vehicles with an own environment grid map, and finally generating an accurate environment grid map reflecting the surrounding environment of the own vehicle;
and 5: according to the accurate environment grid map generated by combination, the information processing module generates a path optimization function according to the combination result of the accurate environment grid map, and reasonably predicts the acceleration of the automobile by combining the self motion state information acquired by the CAN bus system, so that the driving condition in a future period of time is predicted in real time;
step 6: the method has the advantages that the speed change is acquired according to the predicted driving condition, the torque and power requirements of the automobile in a period of time in the future can be acquired, and the energy consumption requirements of a pure electric driving mode, an engine direct driving mode, a series hybrid driving charging mode, a parallel hybrid driving charging mode and a braking energy recovery mode are combined, so that the seven modes can be reasonably switched in time, and the purposes of improving fuel economy, saving energy and reducing emission are achieved;
in the step 3, after a single vehicle detects the environment and generates a local environment grid map, a plurality of local incomplete environment grid maps are integrated through wireless communication network sharing to form a relatively complete environment grid map;
in the step 4, the environment grid maps constructed by multiple vehicles are merged, the merged environment grid map solves the problem of blind areas perceived by a single vehicle, the distribution situation of objects around the vehicle is reflected, and the merging process is as follows:
constructing a local map M i And M j And inputting a local map M i And M j ；
Initializing a populationq ij Each individual represents a parameter λ, γ of the transfer function, which is calculated as:
Figure DEST_PATH_IMAGE001
performing fitness function for each individualf n Calculation, using it as an optimization metric, fitness functionf n The calculation formula of (a) is as follows:
Figure DEST_PATH_IMAGE002
in the formulaN ij As mapsM i And mapM j The number of grids that are completely fused,αas a function of conversionF s The weight coefficient of (a);
generating new individuals according to selection, crossover and variation;
starting genetic iterative operation, judging whether a termination condition is met, if so, continuing to perform the next step, otherwise, returning to the second step; the setting of the termination condition is to complete iteration within an effective time to realize the correct fusion of the map, and an evaluation function is required
Figure DEST_PATH_IMAGE003
To determine a mapM i AndM j whether the fusion of (1) failed, the merit function is expressed as follows:
Figure DEST_PATH_IMAGE004
defining composite computations
Figure DEST_PATH_IMAGE005
The following were used:
Figure DEST_PATH_IMAGE006
in the formula (I), the compound is shown in the specification,x、yrespectively, are the coordinate values of the map grid,θis the relative position angle of the two grids,
Figure DEST_PATH_IMAGE007
and
Figure DEST_PATH_IMAGE008
respectively representM i AndM j the number of cells with the same median value and different values; when in use
Figure DEST_PATH_IMAGE009
The overlap area representing the map can be completely matched when =1,
Figure DEST_PATH_IMAGE010
the smaller the value of (a), the smaller the matching degree of the overlapping region;
outputting an optimal transfer function F s ；
According to the optimal transfer function pairM j Perform translation and rotation, andM i and fusing to obtain a new map.
2. The plug-in hybrid electric vehicle energy optimization control method based on cooperative sensing as claimed in claim 1, wherein the method based on cooperative sensing is based on real-time communication of the vehicle, an environment grid map around the vehicle is obtained through cooperative sensing among multiple vehicles on a road, information of vehicles, pedestrians, road marking lines and traffic signals in the driving process is represented on the grid map, a path optimization function is generated by utilizing the collected environment information, and finally the driving condition in the future period of time is predicted by combining the running state of the vehicle.
3. The energy optimization control method for the plug-in hybrid electric vehicle based on the cooperative sensing as claimed in claim 2, wherein the information of the driving conditions predicted by the result of the cooperative sensing includes a braking start state, an acceleration driving state and a deceleration driving state, and a reasonable operation mode switching scheme can be formulated according to the speed change condition of the vehicle in a future period of time by combining the power battery capacity state and the driving torque requirement of the plug-in hybrid electric vehicle.
4. The plug-in hybrid electric vehicle energy optimization control method based on cooperative sensing as claimed in claim 2, wherein the process of obtaining the precise environment grid map of the surrounding environment of the plug-in hybrid electric vehicle through cooperative sensing is completed through a cooperative sensing system installed on the vehicle, and the cooperative sensing system comprises: the GPS-BDS positioning module, the multilayer sensor module, the wireless communication module and the information processing module; wherein the content of the first and second substances,
the GPS-BDS positioning module is used for realizing the accurate positioning of the vehicle, acquiring the position information of the vehicle and improving the acquisition precision of the sensor;
the multilayer sensor module comprises a three-dimensional laser radar and a CCD camera sensor, and the CCD camera sensor is divided into three layers: the first layer is used for collecting the position information of surrounding obstacles; the second layer is used for collecting dynamic changes of pedestrians and vehicles; the third layer is used for collecting traffic environment information, including lane line and traffic signal lamp information;
the wireless communication module is used for networking with other vehicles, adopts a 2.5GHZ communication protocol, has a communication range of 50m in radius and a transmission speed of 60MB/s, and shares the acquired environmental information;
the information processing module is used for fusing information detected by the radar and the camera to generate an environment grid map on one hand, and is used for fusing an environment grid map shared by other vehicles around on the other hand.
5. The plug-in hybrid electric vehicle energy optimization control method based on cooperative sensing according to claim 2, characterized in that: the CAN bus system installed on the automobile CAN be used for acquiring the motion parameters of the automobile, wherein the motion parameters comprise the speed, the state of a power battery, power, torque and control signals, the motion parameters are connected with the cooperation sensing system to transmit the information of the automobile to the information processing module, and the state information of the automobile is transmitted to other automobiles through the wireless communication module;
the environment grid map information obtained by the cooperation sensing system is combined with the speed information, the power battery state information and the torque information of the automobile obtained by the CAN bus system, and the prediction of the running condition of the automobile in the next period of time is finished through the information processing module;
the power and torque requirements of the automobile can be obtained through calculation in advance by acquiring the speed change state in a period of time in the future, and the energy optimization control system can reasonably regulate and control the driving mode and the charging and discharging states of the energy optimization control system according to the power battery state of the energy optimization control system by combining the power and torque requirements and comparing the power and torque requirements with the high-efficiency running output torque of an engine in a power system of the plug-in hybrid electric vehicle.
6. The plug-in hybrid electric vehicle energy optimization control method based on cooperative sensing according to claim 1, characterized in that: in the step 1, the position information of the road vehicle is accurately positioned and obtained by using a method of combining two positioning systems of a GPS and a BDS, and the positioning accuracy of the two positioning systems is improved by fusing data of the two positioning systems.
7. The plug-in hybrid electric vehicle energy optimization control method based on cooperative sensing according to claim 4, characterized in that: in the step 2, the perception of the single vehicle to the road and field environment is enriched through the information fusion technology of the bottom layer, and the local environment grid map of the vehicle is constructed by the method for detecting the state of the vehicle, and the process is as follows:
establishing a space coordinate system model according to the installation positions of the three-dimensional laser radar and the CCD camera sensor, and importing information collected by the three-dimensional laser radar and the CCD camera sensor into the coordinate system model;
preprocessing information acquired by a three-dimensional laser radar and a CCD camera sensor, filtering out non-useful information, further extracting data characteristics of the information, and performing normalization processing on the data characteristics;
aiming at the processed data, performing matching calculation by using a Kalman filtering algorithm and combining a real map, and combining the characteristic information of the data;
updating the merged target position information by combining with the grid map constructed last time;
and performing filtering, merging, matching operation and updating processes for target data which is continuously updated for multiple times, and finally constructing a complete environment grid map.
8. The plug-in hybrid electric vehicle energy optimization control method based on cooperative sensing according to claim 1, characterized in that: in the step 5, the real-time prediction of the optimal acceleration of the vehicle is completed by combining the merging results of the accurate environment grid maps and a model prediction control algorithm and formulating a prediction rule, and the optimal acceleration is solved by taking the destination as a constraint condition as fast and safe as possible, so that the running state of the vehicle in a future period of time, namely the real-time working condition, is reasonably predicted; solving the optimal acceleration formula as follows:
obtaining self vehicle from environment digital mapiSurrounding vehiclejPosition ofs i 、s j And obstacle positionz i And the current acceleration is obtained by the CAN busu i Speed of vehiclev i 、v j Andjvehicle relativeiSpeed of the vehiclev ji ；
Calculating the intention track of cooperative perception in the running process of the automobileK i (t)Is of the formula
Figure DEST_PATH_IMAGE011
；
Calculating vehiclei、jThe distance betweenS ij Whereint h In order to predict the time interval that is set,
Figure DEST_PATH_IMAGE012
；
the predicted optimal acceleration value is calculated by a model predictive control algorithm,
Figure DEST_PATH_IMAGE013
wherein the content of the first and second substances,t d t is the total length of the prediction time for a certain moment in the prediction time period,nin order to communicate the number of vehicles,α k (k =1,2,3,4) are weight coefficients,δtin order to iterate the step size,βis the obstacle position error coefficient; after the acceleration is obtained through solving, the speed distribution condition within the time T, namely the running condition, can be obtained through derivation of the acceleration.
9. The plug-in hybrid electric vehicle energy optimization control method based on cooperative sensing according to claim 1, characterized in that: in the step 6, the control strategy of the hybrid electric vehicle is formulated, the ratios of seven modes of the vehicle in the running process are reasonably combined by utilizing the predicted working conditions, and the control of charging and discharging of the vehicle is adjusted, wherein the process is as follows:
(1) the system acquires the current SOC state, the current vehicle speed and the predicted vehicle speed, and solves the required torque of the system through the predicted working condition information;
(2) judging whether the current required torque is larger than 0, if so, entering the step (3), otherwise, switching the automobile to a braking energy recovery mode;
(3) if the current SOC value is larger than the SOC1 in the pure electric driving mode and the required torque TqGreater than the upper limit T of the high-efficiency torque of the enginemaxIf the driving mode is the pure electric mode, the driving mode is switched to the pure electric mode; if the current SOC value is larger than the SOC1 in the pure electric drive mode and needs to beTorque T is obtainedqLess than the high-efficiency torque upper limit T of the enginemaxIf the driving mode is the engine independent driving mode, switching to the engine independent driving mode for driving; if the current SOC value is smaller than the SOC1 in the pure electric driving mode, entering the step (4);
(4) judging whether the current SOC is larger than the SOC2 in the electric quantity consumption mode, if so, entering the step (5), and otherwise, entering the step (8);
(5) judging whether the predicted vehicle speed is greater than the current vehicle speed, if so, determining whether the predicted vehicle speed v is greater than the current vehicle speedfGreater than the current vehicle speed vnEntering the step (6), otherwise entering the step (7);
(6) if the required torque TqGreater than the lower limit T of the torque when the engine operates efficientlyminIf not, switching to a series hybrid driving mode;
(7) at this time, the predicted vehicle speed vfLess than the current vehicle speed vnIf the torque T is requiredqLess than the upper limit T of the torque when the engine operates efficientlymaxIf not, switching to a parallel hybrid driving mode, otherwise, switching to a series hybrid driving mode;
(8) judging whether the predicted vehicle speed is greater than the current vehicle speed, if so, determining whether the predicted vehicle speed v is greater than the current vehicle speedfGreater than the current vehicle speed vnEntering the step (9), otherwise entering the step (10);
(9) if the required torque TqLess than the upper limit T of the torque when the engine operates efficientlymaxIf not, switching to a series hybrid drive charging mode;
(10) at this time, the predicted vehicle speed vfLess than the current vehicle speed vnIf the torque T is requiredqGreater than the upper limit T of the torque when the engine operates efficientlymaxAnd if not, switching to a series hybrid drive charging mode to maintain the battery capacity and prolong the service life of the battery.
CN201811121379.6A 2018-09-26 2018-09-26 Energy optimization control method for plug-in hybrid electric vehicle based on cooperative sensing Active CN109278752B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811121379.6A CN109278752B (en) 2018-09-26 2018-09-26 Energy optimization control method for plug-in hybrid electric vehicle based on cooperative sensing

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811121379.6A CN109278752B (en) 2018-09-26 2018-09-26 Energy optimization control method for plug-in hybrid electric vehicle based on cooperative sensing

Publications (2)

Publication Number Publication Date
CN109278752A CN109278752A (en) 2019-01-29
CN109278752B true CN109278752B (en) 2020-06-05

Family

ID=65181596

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811121379.6A Active CN109278752B (en) 2018-09-26 2018-09-26 Energy optimization control method for plug-in hybrid electric vehicle based on cooperative sensing

Country Status (1)

Country Link
CN (1) CN109278752B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP4013077A1 (en) * 2020-12-11 2022-06-15 Volkswagen Ag Inter-vehicle communication based dynamic sensor operation

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109895766B (en) * 2019-03-13 2021-08-31 刘乐 Active obstacle avoidance system of electric automobile
CN114144345B (en) * 2019-05-13 2024-03-29 康明斯公司 Method and system for improving fuel economy of a hybrid powertrain in a vehicle
CN110717218B (en) * 2019-09-18 2021-08-03 北京理工大学 Electric drive vehicle distributed power drive system reconstruction control method and vehicle
CN110667565B (en) * 2019-09-25 2021-01-19 重庆大学 Intelligent network connection plug-in hybrid electric vehicle collaborative optimization energy management method
CN111002975B (en) * 2019-12-27 2022-02-08 延锋汽车饰件系统有限公司 Vehicle energy management method, system, electronic device, and storage medium
CN113147755A (en) * 2021-03-10 2021-07-23 云度新能源汽车股份有限公司 Anti-collision system and method for vehicle video network
CN113570911A (en) * 2021-06-11 2021-10-29 广东职业技术学院 Energy management and control method, device and system for new energy automobile teaching platform
CN113978470B (en) * 2021-12-13 2024-01-12 郑州轻工业大学 On-line quick estimation method for friction force between tire and road surface
CN116878487B (en) * 2023-09-07 2024-01-19 河北全道科技有限公司 Method and device for establishing automatic driving map, vehicle and server
CN116985778B (en) * 2023-09-27 2023-12-08 北京航空航天大学 Hybrid vehicle global energy management optimization method under multitasking cross-core deployment

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102008014580B4 (en) * 2008-03-14 2020-06-04 Volkswagen Ag Economy system and method for vehicles
CN104859647B (en) * 2015-06-01 2017-06-09 河南理工大学 Plug-in hybrid-power automobile energy-saving control method
CN105946857B (en) * 2016-05-16 2017-02-15 吉林大学 Parallel plug-in hybrid electric vehicle (PHEV) energy management method based on intelligent transportation system
CN106969779B (en) * 2017-03-17 2020-05-12 重庆邮电大学 DSRC-based intelligent vehicle map fusion system and method
CN107845261B (en) * 2017-11-03 2020-05-12 北京理工大学 Tensor fusion traffic information-based automobile global working condition dynamic reconstruction method

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP4013077A1 (en) * 2020-12-11 2022-06-15 Volkswagen Ag Inter-vehicle communication based dynamic sensor operation

Also Published As

Publication number Publication date
CN109278752A (en) 2019-01-29

Similar Documents

Publication Publication Date Title
CN109278752B (en) Energy optimization control method for plug-in hybrid electric vehicle based on cooperative sensing
US11155258B2 (en) System and method for radar cross traffic tracking and maneuver risk estimation
CN110543814B (en) Traffic light identification method and device
US20220332348A1 (en) Autonomous driving method, related device, and computer-readable storage medium
CN110901648A (en) Vehicle, system, and logic for real-time ecological routing and adaptive drive control
US8605947B2 (en) Method for detecting a clear path of travel for a vehicle enhanced by object detection
WO2021000800A1 (en) Reasoning method for road drivable region and device
CN110930323B (en) Method and device for removing reflection of image
CN108583578A (en) The track decision-making technique based on multiobjective decision-making matrix for automatic driving vehicle
JP7125915B2 (en) Information processing device, information processing method, and program
CN113792566A (en) Laser point cloud processing method and related equipment
CN112512887B (en) Driving decision selection method and device
Zhang et al. A cognitively inspired system architecture for the Mengshi cognitive vehicle
CN116685874A (en) Camera-laser radar fusion object detection system and method
CN117440908A (en) Method and system for pedestrian action prediction based on graph neural network in automatic driving system
CN112550272B (en) Intelligent hybrid electric vehicle hierarchical control method based on visual perception and deep reinforcement learning
CN112258850A (en) Edge side multi-sensor data fusion system of vehicle-road cooperative system
US20210237737A1 (en) Method for Determining a Lane Change Indication of a Vehicle
WO2022051951A1 (en) Lane line detection method, related device, and computer readable storage medium
US20230048680A1 (en) Method and apparatus for passing through barrier gate crossbar by vehicle
CN110481534A (en) Vehicle control system, control method for vehicle and storage medium
US20230111354A1 (en) Method and system for determining a mover model for motion forecasting in autonomous vehicle control
CN114693540A (en) Image processing method and device and intelligent automobile
CN114056347A (en) Vehicle motion state identification method and device
Meng et al. Configuration and design schemes of environmental sensing and vehicle computing systems for automated driving: A review

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant