CN115257407A - Energy management method, terminal and computer storage medium for extended range electric vehicle - Google Patents

Energy management method, terminal and computer storage medium for extended range electric vehicle Download PDF

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Publication number
CN115257407A
CN115257407A CN202210982420.9A CN202210982420A CN115257407A CN 115257407 A CN115257407 A CN 115257407A CN 202210982420 A CN202210982420 A CN 202210982420A CN 115257407 A CN115257407 A CN 115257407A
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vehicle
road condition
energy consumption
determining
value
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CN115257407B (en
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高达
米捷
蔡威
李林志
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Zoomlion Heavy Industry Science and Technology Co Ltd
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Zoomlion Heavy Industry Science and Technology Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L50/00—Electric propulsion with power supplied within the vehicle
    • B60L50/50—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells
    • B60L50/60—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells using power supplied by batteries
    • B60L50/61—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells using power supplied by batteries by batteries charged by engine-driven generators, e.g. series hybrid electric vehicles
    • B60L50/62—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells using power supplied by batteries by batteries charged by engine-driven generators, e.g. series hybrid electric vehicles charged by low-power generators primarily intended to support the batteries, e.g. range extenders
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
    • B60L3/12—Recording operating variables ; Monitoring of operating variables
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • B60L58/13—Maintaining the SoC within a determined range
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L7/00—Electrodynamic brake systems for vehicles in general
    • B60L7/10—Dynamic electric regenerative braking
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2200/00—Type of vehicles
    • B60L2200/36—Vehicles designed to transport cargo, e.g. trucks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/10—Vehicle control parameters
    • B60L2240/12—Speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/54—Drive Train control parameters related to batteries
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/60—Navigation input
    • B60L2240/62—Vehicle position
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/60—Navigation input
    • B60L2240/64—Road conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/50—Control modes by future state prediction
    • B60L2260/54—Energy consumption estimation

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

The application relates to an energy management method, a terminal and a computer storage medium of an extended-range electric vehicle, wherein the energy management method comprises the following steps: establishing a road condition database of a vehicle driving route; after the road condition database is established, determining a predicted energy consumption value of the vehicle according to the current working condition data of the vehicle and the road condition data in the road condition database; and determining the output power of the engine of the vehicle according to the battery state of charge value of the vehicle and the predicted energy consumption value by combining the fuel consumption target and the battery state of charge target. The energy management method, the terminal and the computer storage medium for the extended-range electric vehicle can increase the low-load working time of an engine, reduce oil consumption, keep the battery charge state value of a power battery in a proper range, ensure that the vehicle can fully recover energy during downhill braking, and improve the energy utilization rate of the vehicle.

Description

Energy management method, terminal and computer storage medium for extended range electric vehicle
Technical Field
The application belongs to the field of mining trucks, and particularly relates to an energy management method and terminal for a range-extended electric vehicle and a computer storage medium.
Background
In the implementation suggestions of the state about accelerating the construction of green mines, the extended range electric automobile is added with auxiliary power equipment such as a battery, a generator, a motor and the like on the traditional fuel oil vehicle to realize the greening of mine equipment. When the automobile is decelerated or braked, compared with the traditional automobile, the motor of the extended-range electric automobile has the function of a generator, the energy for inhibiting the automobile from going forwards is reversely converted into electric energy, the electric energy is transmitted to the power battery through the converter, the power battery recovers the energy generated by braking and is used for driving, the utilization efficiency of energy is improved, and the extended-range electric automobile has the advantages of low emission, low oil consumption and the like.
However, in the energy recovery process of the extended range electric vehicle, when the battery state of charge value of the power battery reaches the upper limit, the reverse charging is stopped to protect the safety of the battery system, and the redundant braking energy is consumed through heat energy, thereby causing energy loss.
Disclosure of Invention
In view of the above technical problems, the present application provides an energy management method, a terminal and a computer storage medium for an extended range electric vehicle, so that the vehicle can fully recover energy during downhill braking, thereby improving the energy utilization rate of the vehicle.
The application provides an energy management method of an extended range electric vehicle, comprising the following steps: establishing a road condition database of a vehicle driving route; after the road condition database is established, determining a predicted energy consumption value of the vehicle according to the current working condition data of the vehicle and the road condition data in the road condition database; and determining the output power of the engine of the vehicle according to the battery state of charge value and the predicted energy consumption value of the vehicle and by combining the fuel consumption target and the battery state of charge target.
In one embodiment, the step of establishing the road condition database of the driving routes of the vehicle includes: acquiring historical working condition data of the vehicle, wherein the historical working condition data comprises historical positioning information and historical speed information of the vehicle on the whole driving route; preprocessing the historical working condition data; determining the road condition data according to the preprocessed historical working condition data;
the step of preprocessing the historical operating condition data comprises at least one of the following: if the historical speed of the vehicle at the historical positioning points with the distance less than the preset distance is zero, any one of the historical positioning points is reserved, and other positioning points in the historical positioning points are eliminated; if the distance between any two adjacent historical positioning points is greater than the maximum running distance of the vehicle between the two corresponding adjacent historical positioning points, eliminating jump points in the two corresponding adjacent historical positioning points; if the coordinates of any historical positioning point do not meet the preset coordinate range, the corresponding historical positioning point is removed; and carrying out smoothing treatment on the historical positioning points, and eliminating singular points in the historical positioning points.
In one embodiment, the current operating condition data of the vehicle comprises a current position and a current vehicle speed of the vehicle; the road condition data comprises positions of road condition points on the driving route; the step of determining the predicted energy consumption value of the vehicle according to the current working condition data of the vehicle and the road condition data in the road condition database comprises the following steps: matching the current position of the vehicle with the position of a road condition point on the driving route, and determining the position of the current road condition point of the vehicle; and determining the position of the predicted road condition point passed by the vehicle within the preset time according to the position of the current road condition point and the current speed of the vehicle.
In one embodiment, the traffic data further includes a grade value of a traffic point on the driving route; the step of determining the predicted energy consumption value of the vehicle according to the current working condition data of the vehicle and the road condition data in the road condition database comprises the following steps: determining a predicted energy consumption value of the vehicle in a first preset time period within the preset time according to the current working condition data of the vehicle and the gradient value of the current road condition point; and according to the current working condition data of the vehicle and the gradient value of the predicted road condition point, actually predicting the energy consumption value of the vehicle in a second preset time period within the preset time.
In an embodiment, the step of determining the predicted energy consumption value of the vehicle according to the current working condition data and the traffic data in the traffic database further includes: acquiring an actual energy consumption value of the vehicle in the first preset time period; and correcting the predicted energy consumption value of the second preset time period according to the deviation of the predicted energy consumption value and the actual energy consumption value of the first preset time period.
In one embodiment, the step of determining the engine output power of the vehicle according to the battery state of charge value and the predicted energy consumption value of the vehicle in combination with the fuel consumption target and the battery state of charge target includes: and determining second engine output power of the vehicle in each preset time period according to the first engine output power of the vehicle in the first preset time period and the first engine output power of the vehicle in the second preset time period and in combination with the oil consumption target.
In one embodiment, before determining the second engine output of the vehicle for each preset period, the method comprises: determining the first engine output power of the vehicle in the second preset time period according to the battery state of charge value of the vehicle at the predicted road condition point and the predicted energy consumption value in the second preset time period in combination with the battery state of charge target;
prior to determining the first engine output power of the vehicle for the second preset period, comprising: determining the battery state of charge value of the vehicle at the predicted road condition point according to the battery state of charge value of the previous road condition point of the predicted road condition point and the first engine output power of the previous preset time period corresponding to the predicted road condition point;
prior to determining the battery state of charge value of the vehicle at the predicted road condition point, comprising: and determining the first engine output power of the vehicle in the first preset time period according to the battery charge state value of the vehicle at the current road condition point and the predicted energy consumption value in the first preset time period in combination with the battery charge state target.
In one embodiment, before determining the predicted energy consumption value of the vehicle according to the current operating condition data of the vehicle and the traffic condition data in the traffic condition database, the method includes: acquiring a historical predicted energy consumption value and a historical actual energy consumption value of the vehicle on the driving route; and updating the road condition data in the road condition database according to the deviation between the historical predicted energy consumption value and the historical actual energy consumption value.
The present application further provides a terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.
The present application further provides a computer storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above method.
The energy management method, the terminal and the computer storage medium for the extended-range electric vehicle can increase the low-load working time of an engine, reduce oil consumption, keep the battery charge state value of a power battery in a proper range, ensure that the vehicle can fully recover energy during downhill braking, and improve the energy utilization rate of the vehicle.
Drawings
FIG. 1 is a schematic diagram of an energy management system according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of an energy management method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a terminal according to a second embodiment of the present application.
Detailed Description
The technical solution of the present application is further described in detail with reference to the drawings and specific embodiments of the specification. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, "and/or" includes any and all combinations of one or more of the associated listed items.
Fig. 1 is a schematic structural diagram of an energy management system provided in an embodiment of the present application, and an energy management method provided in the embodiment of the present application is implemented on the basis of the structure of the energy management system. The energy management system in the embodiment of the application comprises a sensing control system, a power system and an execution system.
The sensing control system comprises a vehicle control unit VCU and a detection module which are electrically connected; the power system comprises a battery management system BMS and a power battery which are electrically connected, a generator control unit GCU and a generator which are electrically connected, and an engine control unit ECU and an engine which are electrically connected; the execution system comprises a driving motor control unit MCU, a driving motor and an execution mechanism, wherein the driving motor control unit MCU is electrically connected with the driving motor, and the driving motor is mechanically connected with the execution mechanism. In addition, the engine is mechanically connected with the generator, the generator is electrically connected with the driving motor, and the power battery is communicated with the driving motor through CAN signal lines (comprising two signal lines of CAN _ H and CAN _ L); the VCU, the MCU, the ECU, the BMS and the GCU are communicated through CAN signal lines.
On one hand, the VCU issues control instructions to the GCU and the BMS according to the required power of the whole vehicle, and the GCU and the BMS respectively control the generator and the power battery to provide energy for the vehicle according to the control instructions; on the other hand, the vehicle control unit VCU controls the output power of the engine in combination with vehicle condition data such as the vehicle speed and the vehicle position information acquired by the detection module and the battery state of charge value of the power battery, so as to reduce the fuel consumption of the vehicle and keep the state of charge value of the power battery in a proper range.
Fig. 2 is a schematic flowchart of an energy management method according to an embodiment of the present application. As shown in fig. 2, the energy management method of the extended range electric vehicle of the present application may include the steps of:
step S101: establishing a road condition database of a vehicle driving route;
optionally, the traffic database includes traffic data such as a position of a traffic point on a driving route of the vehicle, a gradient value of the traffic point, and the like; where the driving route of the vehicle is known, such as the route from the origin a to the destination B.
In one embodiment, step S101 includes:
acquiring historical working condition data of the vehicle, wherein the historical working condition data comprises historical positioning information and historical speed information of the vehicle on the whole driving route; the historical positioning information comprises longitude and latitude coordinates, altitude and other information of the vehicle on a driving route;
preprocessing historical working condition data of the vehicle;
and determining road condition data according to the preprocessed historical working condition data.
In one embodiment, the vehicle historical operating condition data is pre-processed, including at least one of:
if the historical speed of the vehicle at the historical positioning points with the distance smaller than the preset distance is zero, any one of the historical positioning points is reserved, and other positioning points in the historical positioning points are eliminated;
if the distance between any two adjacent historical positioning points is greater than the maximum running distance of the vehicle between the two corresponding adjacent historical positioning points, eliminating jump points in the two corresponding adjacent historical positioning points;
if the coordinates of any historical positioning point do not meet the preset coordinate range, the corresponding historical positioning point is removed;
and smoothing the historical positioning points, and eliminating singular points in the historical positioning points.
Illustratively, according to a mechanism of uploading data by the vehicle-mounted positioning device, the vehicle-mounted positioning device continuously uploads the positioning data of the vehicle over time, when the vehicle is in a static state, the positioning coordinate of the vehicle does not change or changes within a very small range, the instantaneous speed of the vehicle is continuously 0, and at the moment, only the retention of the positioning coordinate of the vehicle is neededA positioning point corresponding to the 0 instantaneous speed is selected; during the running process of the vehicle, two adjacent positioning points P on the running route are assumed n ,P n+1 Corresponding to instantaneous velocity and time v n ,v n+1 And t n ,t n+1 Taking the maximum value v of the instantaneous speed of two points max ＝max{v n ，v n+1 Get Δ L according to the calculation formula of velocity and displacement max ＝v max ×(t n+1 -t n ) If Δ L is>ΔL max Then, the point P can be determined n+1 To jump the point, P n+1 Deleting points; and calibrating the latitude and longitude range of the vehicle driving route, comparing the coordinates of the historical positioning points with the latitude and longitude range, and deleting the historical positioning points of which the coordinates do not meet the latitude and longitude range.
It is worth mentioning that due to factors such as sensor precision and signal interference, rough singular points exist in the historical working condition data, and the real road fluctuation change should be smooth and continuous, so that the historical working condition data needs to be smoothed, and the singular points in the historical working condition data are eliminated. Optionally, a five-point three-time smoothing method is adopted to perform smoothing and noise reduction on the historical working condition data.
In one embodiment, determining the traffic data in the traffic database according to the preprocessed historical operating condition data includes:
and taking the position of the historical working condition point in the preprocessed historical working condition data as the position of the road condition point in the road condition data.
Calculating the slope value of the road condition point according to the following formula:
Figure BDA0003800656800000061
wherein theta is the gradient value of the kth road condition point-1, h k Altitude h of the kth road condition point k-1 The altitude of the kth-1 road condition point, v the horizontal vehicle speed of the vehicle at the kth-1 road condition point, and Δ t the time interval from the k-1 road condition point to the kth road condition point.
Step S102: determining a predicted energy consumption value of the vehicle according to the current working condition data of the vehicle and road condition data in a road condition database;
optionally, the current operating condition data of the vehicle includes a current position of the vehicle, a current vehicle speed and a current acceleration, a current mass, a current battery state of charge value, and the like.
In one embodiment, step S102 includes:
matching the current position of the vehicle with the position of a road condition point on a driving route, and determining the position of the current road condition point of the vehicle;
and determining the position of the predicted road condition point passed by the vehicle within the preset time according to the position of the current road condition point and the current speed of the vehicle.
Optionally, searching the road condition point within a preset distance range from the current positioning point of the vehicle according to the position of the current positioning point of the vehicle and the position of the road condition point; and acquiring a road section formed by road condition points within a preset distance range, further analyzing an included angle between the direction of the current positioning point of the vehicle and the direction of the road section, screening the road section with the included angle between the direction of the current positioning point of the vehicle and the direction of the current positioning point of the vehicle within the preset included angle range, and selecting the position of the road condition point closest to the current positioning point of the vehicle on the road section as the position of the current road condition point of the vehicle. The direction of the current positioning point of the vehicle is the direction of the positioning point in the GPS navigation, and the direction of the road section is obtained by calculation according to the position coordinates of the road condition points forming the road section.
Optionally, according to the current vehicle speed and the preset time of the vehicle, determining a driving distance of the vehicle within the preset time = the current vehicle speed for the preset time, comparing the position of the current road condition point, the driving distance within the preset time and the road condition data, determining the position of the vehicle from the current road condition point, continuing to drive along the driving direction for the driving distance within the preset time, and the position of the passing road condition point, and taking the position of the passing road condition point as the position of the predicted road condition point.
In one embodiment, step S102 further includes:
determining a predicted energy consumption value of the vehicle in a first preset time period within preset time according to the current working condition data of the vehicle and the gradient value of the current road condition point;
and according to the current working condition data of the vehicle and the gradient value of the predicted road condition point, the predicted energy consumption value of the vehicle in the second preset time period within the preset time is determined.
Optionally, the preset time is divided by a preset time interval to obtain a plurality of preset time periods. If the preset time is 4S, the moment of collecting the current working condition data is t k And the preset time interval is 2S, obtaining the following preset time interval: t is t k ～t k +2、t k +2～t k +4; wherein k is a positive integer.
Optionally, the predicted energy consumption value is calculated according to the following formula:
Figure BDA0003800656800000081
wherein, Δ SOC i Predicted energy consumption value for the ith predetermined period, F i Predicted power for the ith preset time interval, v is the current vehicle speed in the current working condition data,
Figure BDA0003800656800000082
Is the power factor, t i Is the starting time of the ith preset period, t i +t c Is the end time of the ith preset time interval, i is a positive integer, t c Is a preset time interval;
Figure BDA0003800656800000083
wherein, C w Is the air resistance coefficient, A is the frontal area of the vehicle, f is the resistance coefficient influenced by the road surface characteristics, m is the current mass of the vehicle in the current working condition data, g is the gravitational acceleration, theta i The gradient value of the vehicle road condition point in the ith preset time period, delta is a rotating mass coefficient, and a is the current acceleration in the current working condition data.
Preferably, the preset time interval is set to a time interval from the vehicle driving from the current road condition point to the vehicle drivingThe time required for the next adjacent road condition point, then theta i Is the initial time t in the ith preset period i The grade value of the road condition point of the vehicle, i.e. the ith preset time interval and the initial time t in the ith preset time interval i And the road condition point where the vehicle is located corresponds to the road condition point.
In one embodiment, step S102 further includes:
acquiring an actual energy consumption value of a vehicle in a first preset time period;
and correcting the predicted energy consumption value of the second preset time period according to the deviation between the predicted energy consumption value and the actual energy consumption value of the first preset time period.
Optionally, the predicted energy consumption value is modified by the following formula:
Y P (s)＝Y m (s)+βe(s-1)
e(s-1)＝Y(s-1)-Y m (s-1)
wherein Y is P (s) is the predicted energy consumption value of the vehicle after the vehicle is corrected in the s-th preset time period; y is m (s) is the predicted energy consumption value of the vehicle before modification at the s-th preset time period; β represents a correction coefficient; e (s-1) is a predicted energy consumption error value of the vehicle in the s-1 th preset time period, Y (s-1) is an actual energy consumption value of the vehicle in the s-1 th preset time period, and Y m (s-1) the predicted energy consumption value of the vehicle in the s-1 th preset time period; s is a positive integer greater than 1.
Step S103: and determining the output power of the engine of the vehicle according to the battery state of charge value and the predicted energy consumption value of the vehicle and by combining the fuel consumption target and the battery state of charge target.
In one embodiment, step S103 includes: and determining the second engine output power of the vehicle in each preset time period by combining the oil consumption target according to the first engine output power of the vehicle in the first preset time period and the first engine output power of the vehicle in the second preset time period.
Optionally, the first engine output power of the vehicle in the first preset time period is determined according to the battery state of charge value of the vehicle at the current road condition point and the predicted energy consumption value in the first preset time period in combination with the battery state of charge target.
Optionally, the battery state of charge value of the vehicle at the predicted road condition point is determined according to the battery state of charge value of the previous predicted road condition point and the first engine output power of the previous preset time period corresponding to the predicted road condition point by the following formula:
SOC(k+1)＝ASOC(k)+BP g (k)
wherein SOC (k + 1) is the battery state of charge value of the vehicle at the k +1 th predicted road condition point, SOC (k) is the battery state of charge value of the last road condition point of the k +1 th predicted road condition point, P g (k) And determining parameters A and B for the first engine output power of the last preset time period corresponding to the (k + 1) th predicted road condition point according to the experimental calibration data.
Optionally, the output power of the first engine of the vehicle in the second preset time period is determined according to the battery state of charge value of the vehicle in the predicted road condition point and the predicted energy consumption value in the second preset time period in combination with the battery state of charge target.
Optionally, the fuel consumption target is the lowest sum of the fuel consumption in each preset time period; the oil consumption is calculated according to the following formula:
Figure BDA0003800656800000101
wherein, P g (i) For a first engine output power, t, of the vehicle during an i-th predetermined period i 、t i +t c Respectively is the starting time and the ending time of the ith preset time period, and fuel (i) is the oil consumption of the vehicle in the ith preset time period.
Optionally, the battery state of charge target is SOC H ≥SOC i0 +SOC iQ -ΔSOC i ≥SOC L (ii) a Wherein, SOC H Is a first predetermined maximum value of the state of charge, SOC L Is a first predetermined minimum battery state of charge, Δ SOC i For the predicted energy consumption value, SOC, of the vehicle in the ith preset time period i0 The battery state of charge (SOC) value of the vehicle at the initial road condition point of the ith preset time period iQ A battery state of charge value provided for a first engine output power of the vehicle for an ith predetermined period.
Exemplarily, according to a battery state of charge value of the vehicle at a current road condition point and a predicted energy consumption value in a first preset time period, and in combination with a battery state of charge target, determining a first engine output power of the vehicle in the first preset time period, which is a range value; determining a battery state of charge value of the vehicle at a first predicted road condition point, which is a range value, according to the battery state of charge value of the vehicle at the current road condition point and the first engine output power in a first preset time period; determining the first engine output power of the vehicle in a second preset time period, which is a range value, by combining a battery charge state target according to the battery charge state value of the vehicle in a first predicted road condition point and the predicted energy consumption value in the second preset time period; and then determining the second engine output power of each preset time period which enables the sum of the oil consumption to be the lowest from the first engine output power of the first preset time period and the first engine output power of the vehicle in the second preset time period.
Alternatively, in determining the engine output power of the vehicle, the following condition is also satisfied:
SOC min ≥SOC(k)≥SOC max
T emin ≥T e (k)≥T emax
T mmin ≥T m (k)≥T mmax
T nmin ≥T n (k)≥T nmax
wherein SOC (k) is the battery state of charge (SOC) of the vehicle at the time k min 、SOC max Respectively, the minimum value and the maximum value of the second preset battery state of charge, T e (k) For the engine output torque of the vehicle at time k, T emin 、T emax Respectively, a preset minimum engine torque, T, and a preset maximum engine torque n (k) For the generator output torque at time k, T, of the vehicle mmin 、T mmax Respectively, a preset minimum value and a preset maximum value of the output torque of the generator, T m (k) For the motor output torque at time k, T, of the vehicle nmin 、T nmax Respectively presetting a minimum value and a maximum value of the output torque of the motor; alternatively,the preset maximum value of the state of charge of the first battery and the preset maximum value of the state of charge of the second battery can be different values or the same value, and the preset minimum value of the state of charge of the first battery and the preset minimum value of the state of charge of the second battery can be different values or the same value.
It should be noted that before determining the predicted energy consumption value of the vehicle according to the current working condition data of the vehicle and the traffic data in the traffic database, the method includes: acquiring a historical predicted energy consumption value and a historical actual energy consumption value of a vehicle on a driving route; and updating the road condition data in the road condition database according to the deviation between the historical predicted energy consumption value and the historical actual energy consumption value.
Optionally, a road condition self-learning strategy based on deep reinforcement learning is adopted, road condition data in the actual driving process are recorded again, and a reward value is set
Figure BDA0003800656800000111
And updating the road condition data in the road condition database according to the reward value r and by combining with a Bellman equation. Wherein, Δ SOC Practice of To historical actual energy consumption values, Δ SOC Prediction Energy consumption values are predicted for the history.
For example, if the reward value determined according to the road condition data in the road condition database is smaller than the reward value determined according to the road condition data recorded in the actual driving process, the road condition data in the road condition database is updated to the road condition data recorded in the actual driving process.
According to the energy management method provided by the embodiment of the application, the road condition database of the vehicle driving route is established in advance, the current working condition data and the battery charge state value of the vehicle are combined, the position of a road condition point passed by the vehicle in the preset time and the energy consumption value in the preset time period are determined, the output power of the engine is controlled according to the battery charge state value and the energy consumption value in the preset time period and the oil consumption target and the battery charge state target, the low-load working time of the engine is prolonged, the oil consumption is reduced, the battery charge state value of the power battery is kept in a proper range, the vehicle can fully recover the energy in downhill braking, and the energy utilization rate of the vehicle is improved.
Fig. 3 is a schematic structural diagram of a terminal according to the second embodiment of the present application. The terminal of the application includes: a processor 110, a memory 111, and a computer program 112 stored in the memory 111 and operable on the processor 110. The steps in the above-described energy management method embodiments are implemented when the processor 110 executes the computer program 112.
The terminal may include, but is not limited to, a processor 110, a memory 111. Those skilled in the art will appreciate that fig. 3 is merely an example of a terminal and is not intended to be limiting and may include more or fewer components than those shown, or some of the components may be combined, or different components, e.g., the terminal may also include input-output devices, network access devices, buses, etc.
The Processor 110 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 111 may be an internal storage unit of the terminal, such as a hard disk or a memory of the terminal. The memory 111 may also be an external storage device of the terminal, such as a plug-in hard disk provided on the terminal, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 111 may also include both an internal storage unit of the terminal and an external storage device. The memory 111 is used for storing computer programs and other programs and data required by the terminal. The memory 111 may also be used to temporarily store data that has been output or is to be output.
The present application also provides a computer storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of the above energy management method.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
As used herein, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, including not only those elements listed, but also other elements not expressly listed.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (10)

1. A method of energy management for an extended range electric vehicle, comprising:
establishing a road condition database of a vehicle driving route;
after the road condition database is established, determining a predicted energy consumption value of the vehicle according to the current working condition data of the vehicle and the road condition data in the road condition database;
and determining the output power of the engine of the vehicle according to the battery state of charge value of the vehicle and the predicted energy consumption value by combining the fuel consumption target and the battery state of charge target.
2. The method according to claim 1, wherein the step of creating the road condition database of the vehicle driving route comprises:
acquiring historical working condition data of the vehicle, wherein the historical working condition data comprises historical positioning information and historical vehicle speed information of the vehicle on the whole driving route;
preprocessing the historical working condition data;
determining the road condition data according to the preprocessed historical working condition data;
the step of preprocessing the historical operating condition data comprises at least one of the following:
if the historical speed of the vehicle at the historical positioning points with the distance less than the preset distance is zero, any one of the historical positioning points is reserved, and other positioning points in the historical positioning points are eliminated;
if the distance between any two adjacent historical positioning points is greater than the maximum running distance of the vehicle between the two corresponding adjacent historical positioning points, eliminating jump points in the two corresponding adjacent historical positioning points;
if the coordinates of any historical positioning point do not meet the preset coordinate range, the corresponding historical positioning point is removed;
and carrying out smoothing treatment on the historical positioning points, and eliminating singular points in the historical positioning points.
3. The method of claim 1, wherein the current operating condition data of the vehicle includes a current location of the vehicle, a current vehicle speed; the road condition data comprises the positions of road condition points on the driving route;
the step of determining the predicted energy consumption value of the vehicle according to the current working condition data of the vehicle and the road condition data in the road condition database comprises the following steps:
matching the current position of the vehicle with the position of a road condition point on the driving route, and determining the position of the current road condition point of the vehicle;
and determining the position of the predicted road condition point passed by the vehicle in the preset time according to the position of the current road condition point and the current speed of the vehicle.
4. The method according to claim 3, wherein the road condition data further includes a grade value of a road condition point on the driving route;
the step of determining the predicted energy consumption value of the vehicle according to the current working condition data of the vehicle and the road condition data in the road condition database comprises the following steps:
determining a predicted energy consumption value of the vehicle in a first preset time period within the preset time according to the current working condition data of the vehicle and the gradient value of the current road condition point;
and according to the current working condition data of the vehicle and the gradient value of the predicted road condition point, determining the predicted energy consumption value of the vehicle in a second preset time period within the preset time.
5. The method of claim 4, wherein the step of determining the predicted energy consumption value of the vehicle based on the current operating condition data and the traffic data in the traffic database further comprises:
acquiring an actual energy consumption value of the vehicle in the first preset time period;
and correcting the predicted energy consumption value of the second preset time period according to the deviation between the predicted energy consumption value and the actual energy consumption value of the first preset time period.
6. The method of claim 4 or 5, wherein the step of determining the engine output power of the vehicle based on the battery state of charge value and the predicted energy consumption value of the vehicle in combination with a fuel consumption target and a battery state of charge target comprises:
and determining second engine output power of the vehicle in each preset time period by combining the fuel consumption target according to the first engine output power of the vehicle in the first preset time period and the first engine output power of the vehicle in the second preset time period.
7. The method of claim 6, prior to determining the second engine output power of the vehicle for each preset period, comprising:
determining the first engine output power of the vehicle in the second preset time period according to the battery state of charge value of the vehicle at the predicted road condition point and the predicted energy consumption value in the second preset time period in combination with the battery state of charge target;
prior to determining the first engine output power of the vehicle for the second preset period, comprising:
determining the battery state of charge value of the vehicle at the predicted road condition point according to the battery state of charge value of the previous road condition point of the predicted road condition point and the first engine output power of the previous preset time period corresponding to the predicted road condition point;
prior to determining the battery state of charge value for the vehicle at the predicted road condition point, comprising:
and determining the first engine output power of the vehicle in the first preset time period according to the battery charge state value of the vehicle at the current road condition point and the predicted energy consumption value in the first preset time period in combination with the battery charge state target.
8. The method according to any of claims 3-5, wherein prior to determining the predicted energy consumption value of the vehicle based on the current operating condition data of the vehicle and the traffic data in the traffic database, comprises:
acquiring a historical predicted energy consumption value and a historical actual energy consumption value of the vehicle on the driving route;
and updating the road condition data in the road condition database according to the deviation between the historical predicted energy consumption value and the historical actual energy consumption value.
9. A terminal, characterized in that the terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to any of claims 1 to 8 when executing the computer program.
10. A computer storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 8.
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