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CN115257407B - 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
CN115257407B
CN115257407B CN202210982420.9A CN202210982420A CN115257407B CN 115257407 B CN115257407 B CN 115257407B CN 202210982420 A CN202210982420 A CN 202210982420A CN 115257407 B CN115257407 B CN 115257407B
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China
Prior art keywords
vehicle
road condition
historical
energy consumption
determining
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CN202210982420.9A
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Chinese (zh)
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CN115257407A (en
Inventor
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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Priority to CN202210982420.9A priority Critical patent/CN115257407B/en
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Classifications

    • 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

Landscapes

  • 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 engine output power of the vehicle according to the battery state of charge value and the predicted energy consumption value of the vehicle and combining the fuel consumption target and the battery state of charge target. The energy management method, the terminal and the computer storage medium of the extended-range electric vehicle can increase the low-load working time of the engine, reduce the oil consumption, keep the battery charge state value of the power battery in a proper range, ensure that the vehicle can fully recover the energy during the 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, a terminal and a computer storage medium of an extended range electric vehicle.
Background
When the automobile is decelerating or braking, compared with the traditional automobile, the function of the motor of the extended range electric automobile is equivalent to that of a generator, the energy for restraining the front of the automobile is reversely converted into electric energy, the electric energy is transmitted to the power battery through the converter, the energy generated by the recovery and braking of the power battery is used for driving, the utilization efficiency of the energy is improved, and the extended range electric automobile has the advantages of low emission, low oil consumption and the like.
However, in the current energy recovery process of the extended-range electric vehicle, when the battery state of charge value of the power battery reaches the upper limit, reverse charging can be stopped to protect the safety of the battery system, and redundant braking energy is consumed through heat energy, so that energy loss is caused.
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, and the energy utilization rate of the vehicle is improved.
The application provides an energy management method of an extended range electric vehicle, which 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 engine output power of the vehicle according to the battery state of charge value and the predicted energy consumption value of the vehicle and combining the fuel consumption target and the battery state of charge target.
In one embodiment, the step of creating the road condition database of the vehicle driving route includes: 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 steps: if the historical vehicle speeds of the vehicle at the historical positioning points with the distance smaller than the preset distance are all zero, any positioning point in the historical positioning points is reserved, and other positioning points in the historical positioning points are removed; if the distance between any two adjacent historical locating points is larger than the maximum running distance between the two corresponding adjacent historical locating points, eliminating the jump points in the two corresponding adjacent historical locating points; if the coordinates of any historical locating point do not meet the preset coordinate range, eliminating the corresponding historical locating point; and carrying out smoothing treatment on the historical locating points, and eliminating singular points in the historical locating points.
In one embodiment, the current working condition data of the vehicle comprises the current position and the current speed of the vehicle; the road condition data comprise 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 the 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 of the vehicle in the preset time according to the position of the current road condition point and the current speed of the vehicle.
In an embodiment, the road condition data further includes a gradient 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: according to the current working condition data of the vehicle and the gradient value of the current road condition point, determining a predicted energy consumption value of the vehicle in a first preset period of time within the preset time;
and determining a predicted energy consumption value of the vehicle in a second preset period of time within the preset time according to the current working condition data of the vehicle and the gradient value of the predicted road condition point.
In an embodiment, the step of determining the predicted energy consumption value of the vehicle according to the current working condition data and the road condition data in the road condition database further includes: acquiring an actual energy consumption value of the vehicle in the first preset period; and correcting the predicted energy consumption value of the second preset period according to the deviation of the predicted energy consumption value and the actual energy consumption value of the first preset 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 and in combination with the fuel consumption target and the battery state of charge target includes: and determining the second engine output power of the vehicle in each preset period according to the first engine output power of the vehicle in the first preset period and the first engine output power of the second preset period and the oil consumption target.
In one embodiment, before determining the second engine output power of the vehicle for each preset period, the method includes: determining the first engine output power of the vehicle in the second preset period according to the battery state of charge value of the vehicle in the predicted road condition point and the predicted energy consumption value of the second preset period and combining the battery state of charge target;
Before determining a 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 last road condition point of the predicted road condition point and the first engine output power of the last preset period corresponding to the predicted road condition point;
Before determining a battery state of charge value of the vehicle at the predicted road point, comprising: and determining the first engine output power of the vehicle in the first preset period according to the battery state of charge value of the vehicle in the current road condition point and the predicted energy consumption value of the first preset period and combining the battery state of charge target.
In one embodiment, before 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, 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 of the historical predicted energy consumption value and the historical actual energy consumption value.
The application also 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 application also provides a computer storage medium storing 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 of the extended range electric vehicle provided by the application can increase the low-load working time of the engine, reduce the oil consumption, keep the battery state of charge value of the power battery in a proper range, ensure that the vehicle can fully recover the 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 of an embodiment of the present application;
FIG. 2 is a schematic flow chart of an energy management method according to a first 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 scheme of the application is further elaborated below by referring to the drawings in the specification and the specific embodiments. 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 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 according to an embodiment of the present application, and an energy management method according to an embodiment of the present application is implemented based on 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 whole vehicle control unit VCU and a detection module, wherein the whole vehicle control unit VCU and the detection module 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 a CAN signal wire (comprising two signal wires of CAN_ H, CAN _L); the whole vehicle control unit VCU, the driving motor control unit MCU, the engine control unit ECU, the battery management system BMS and the generator control unit GCU are communicated through CAN signal lines.
On the one hand, the whole vehicle control unit VCU gives a control instruction to the generator control unit GCU and the battery management system BMS according to the whole vehicle required power, and the generator control unit GCU and the battery management system BMS respectively control the generator and the power battery to provide energy for the vehicle according to the control instruction; on the other hand, the whole vehicle control unit VCU combines the vehicle working condition data such as the vehicle speed, the vehicle position information and the like obtained by the detection module and the battery state of charge value of the power battery to control the output power of the engine so as to reduce the oil consumption of the vehicle and keep the state of charge value of the power battery in a proper range.
Fig. 2 is a schematic flow chart of an energy management method according to an embodiment of the 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 road condition database includes road condition data such as a position of a road condition point on a driving route of the vehicle, a gradient value of the road condition point, and the like; wherein the driving route of the vehicle is known, such as the route from origin a to destination B.
In one embodiment, step S101 includes:
acquiring historical working condition data of a vehicle, wherein the historical working condition data comprises historical positioning information and historical vehicle 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 running route;
preprocessing historical working condition data of a vehicle;
and determining road condition data according to the preprocessed historical working condition data.
In one embodiment, preprocessing historical operating condition data of a vehicle includes at least one of:
If the historical vehicle speed of the vehicle at the historical locating points with the distance smaller than the preset distance is zero, any locating point in the historical locating points is reserved, and other locating points in the historical locating points are removed;
If the distance between any two adjacent history locating points is larger than the maximum running distance between the two adjacent history locating points, eliminating the jump points in the two adjacent history locating points;
if the coordinates of any historical locating point do not meet the preset coordinate range, eliminating the corresponding historical locating point;
And carrying out smoothing treatment on the historical locating points, and eliminating singular points in the historical locating points.
The vehicle positioning device continuously uploads the positioning data of the vehicle along with the time according to a mechanism of uploading the data by the vehicle positioning device, when the vehicle is in a static state, the positioning coordinates of the vehicle cannot change or change in a very small range, the instantaneous speed of the vehicle is continuously 0, and only one positioning point corresponding to the 0 instantaneous speed is reserved; in the running process of the vehicle, assuming that two adjacent positioning points P n,Pn+1 on the running route are v n,vn+1 and t n,tn+1 respectively, taking the maximum value v max＝max{vn,vn+1 of the two points of instantaneous speed, obtaining DeltaL max＝vmax×(tn+1-tn according to a calculation formula of speed and displacement), if DeltaL is more than DeltaL max, judging the point P n+1 as a transition point, and deleting the point P n+1; and (3) calibrating the longitude and latitude range of the vehicle driving route, comparing the coordinates of the historical positioning points with the longitude and latitude range, and deleting the historical positioning points of which the coordinates do not meet the longitude and latitude range.
It should be noted that, due to factors such as sensor precision, signal interference and the like, rough singular points exist in the historical working condition data, and the actual road fluctuation change should be smooth and continuous, so that the historical working condition data needs to be subjected to smoothing treatment to remove the singular points. Optionally, a five-point three-time smoothing method is adopted to carry out smoothing noise reduction treatment on the historical working condition data.
In one embodiment, determining the road condition data in the road condition database according to the preprocessed history condition data includes:
and taking the position of the history working point in the preprocessed history working condition data as the position of the road condition point in the road condition data.
Calculating the gradient value of the road condition point according to the following formula:
Wherein θ is the gradient value of the kth-1 road condition point, h k is the altitude of the kth road condition point, h k-1 is the altitude of the kth-1 road condition point, v is the vehicle speed of the vehicle in the horizontal direction of the kth-1 road condition point, and Δt is the time interval from the kth-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 the road condition data in the road condition database;
Optionally, the current operating condition data of the vehicle includes a current position of the vehicle, a current vehicle speed and 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 the 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 of the vehicle in 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 which is 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; acquiring a road section formed by road condition points within a preset distance range, further analyzing the included angle between the direction of the current locating point of the vehicle and the direction of the road section, screening out the road section with the included angle between the direction of the current locating point of the vehicle and the direction of the current locating point of the vehicle within the preset included angle range, and selecting the position of the road condition point closest to the current locating 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 GPS navigation, and the road section direction is calculated according to the position coordinates of road condition points forming the road section.
Optionally, determining a running distance=current speed of the vehicle in a preset time according to the current speed and the preset time of the vehicle, comparing the position of the current road condition point, the running distance in the preset time and the road condition data, determining the running distance of the vehicle in the preset time along the running direction from the position of the current road condition point, 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 an embodiment, step S102 further includes:
according to the current working condition data of the vehicle and the gradient value of the current road condition point, determining a predicted energy consumption value of the vehicle in a first preset period within a preset time;
And determining the predicted energy consumption value of the vehicle in a second preset period within the preset time according to the current working condition data of the vehicle and the gradient value of the predicted road condition point.
Optionally, the preset time is divided at preset time intervals to obtain a plurality of preset time periods. If the preset time is 4S, the time for collecting the current working condition data is t k, and the preset time interval is 2S, the following preset time period is obtained: t k～tk+2、tk+2～tk +4; wherein k is a positive integer.
Optionally, the predicted energy consumption value is calculated according to the following formula:
Wherein delta SOC i is the predicted energy consumption value of the ith preset period, F i is the predicted power of the ith preset period, v is the current vehicle speed in the current working condition data, For the power factor, t i is the start time of the ith preset period, t i+tc is the end time of the ith preset period, i is a positive integer, and t c is a preset time interval;
Wherein C w is the air resistance coefficient, A is the windward 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 is the gradient value of the road condition point of the vehicle in the ith preset period, delta is the rotating mass coefficient, and a is the current acceleration in the current working condition data.
Preferably, if the preset time interval is set to be the time required for the vehicle to travel from the current road condition point to the next adjacent road condition point, θ i is the gradient value of the road condition point where the vehicle is located at the initial time t i in the ith preset period, that is, the ith preset period corresponds to the road condition point where the vehicle is located at the initial time t i in the ith preset period.
In an embodiment, step S102 further includes:
Acquiring an actual energy consumption value of a vehicle in a first preset period;
and correcting the predicted energy consumption value of the second preset period according to the deviation of the predicted energy consumption value and the actual energy consumption value of the first preset period.
Optionally, the predicted energy consumption value is modified by the following formula:
YP(s)＝Ym(s)+βe(s-1)
e(s-1)＝Y(s-1)-Ym(s-1)
Wherein Y P(s) is the predicted energy consumption value of the vehicle after the correction of the s preset period; y m(s) is a predicted energy consumption value of the vehicle before the correction of the s preset period; beta represents a correction coefficient; e (s-1) is a predicted energy consumption error value of the vehicle in the s-1 th preset period, Y (s-1) is an actual energy consumption value of the vehicle in the s-1 th preset period, and Y m (s-1) is a predicted energy consumption value of the vehicle in the s-1 th preset period; s is a positive integer greater than 1.
Step S103: and 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 and 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 period according to the first engine output power of the vehicle in the first preset period and the first engine output power of the second preset period and the oil consumption target.
Optionally, determining the first engine output power of the vehicle in the first preset period according to the battery state of charge value of the vehicle in the current road condition point and the predicted energy consumption value of the first preset period in combination with the battery state of charge target.
Optionally, according to the battery state of charge value of the last road condition point of the predicted road condition point and the first engine output power of the last preset period corresponding to the predicted road condition point, determining the battery state of charge value of the vehicle at the predicted road condition point according to the following formula:
SOC(k+1)＝ASOC(k)+BPg(k)
The SOC (k+1) is a battery state of charge value of the vehicle at the (k+1) th predicted road condition point, the SOC (k) is a battery state of charge value of the vehicle at the (k+1) th predicted road condition point, P g (k) is a first engine output power of the vehicle at the (k+1) th predicted road condition point in a preset period corresponding to the (k+1) th predicted road condition point, and A, B parameters are determined according to experimental calibration data.
Optionally, determining the first engine output power of the vehicle in the second preset period according to the battery state of charge value of the vehicle in the predicted road condition point and the predicted energy consumption value of the second preset period in combination with the battery state of charge target.
Optionally, the fuel consumption target is that the sum of fuel consumption of each preset time period is the lowest; the fuel consumption is calculated according to the following formula:
wherein, P g (i) is the output power of the first engine of the vehicle in the ith preset period, t i、ti+tc is the starting time and the ending time of the ith preset period respectively, and fuel (i) is the fuel consumption of the vehicle in the ith preset period.
Optionally, the battery state of charge target is SOC H≥SOCi0+SOCiQ-ΔSOCi≥SOCL; the SOC H is a first preset battery state of charge maximum value, the SOC L is a first preset battery state of charge minimum value, Δsoc i is a predicted energy consumption value of the vehicle in an i-th preset period, the SOC i0 is a battery state of charge value of an initial road condition point of the vehicle in the i-th preset period, and the SOC iQ is a battery state of charge value provided by output power of a first engine of the vehicle in the i-th preset period.
The method includes the steps that an output power of a first engine of a vehicle in a first preset period is determined to be a range value according to a battery state of charge value of the vehicle in a current road condition point and a predicted energy consumption value of the first preset period in combination with a battery state of charge target; 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 of a first preset period; according to a battery state of charge value of the vehicle at a first predicted road condition point and a predicted energy consumption value of a second preset period, determining a first engine output power of the vehicle at the second preset period as a range value by combining a battery state of charge target; and then determining the second engine output power of each preset period which enables the sum of the oil consumption to be the lowest from the first engine output power of the first preset period and the first engine output power of the vehicle in the second preset period.
Optionally, in determining the engine output power of the vehicle, the following conditions are also satisfied:
SOCmin≥SOC(k)≥SOCmax
Temin≥Te(k)≥Temax
Tmmin≥Tm(k)≥Tmmax
Tnmin≥Tn(k)≥Tnmax
The SOC (k) is a battery state of charge value of the vehicle at the k moment, the SOC min、SOCmax is a second preset battery state of charge minimum value and a maximum value respectively, the T e (k) is an engine output torque of the vehicle at the k moment, the T emin、Temax is a preset engine output torque minimum value and a maximum value respectively, the T n (k) is a generator output torque of the vehicle at the k moment, the T mmin、Tmmax is a preset generator output torque minimum value and a maximum value respectively, the T m (k) is a motor output torque of the vehicle at the k moment, and the T nmin、Tnmax is a preset motor output torque minimum value and a maximum value respectively; alternatively, the preset first battery state of charge maximum value and the preset second battery state of charge maximum value may be different values or the same value, and the preset first battery state of charge minimum value and the preset second battery state of charge minimum value may 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 road condition data in the road condition 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 of 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 to re-record the road condition data in the actual driving process, and a reward value is setAnd according to the reward value r, updating the road condition data in the road condition database by combining with the Belman equation. Wherein Δsoc Actual practice is that of is a historical actual energy consumption value, and Δsoc prediction is a historical predicted energy consumption value.
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 of the vehicle and the battery charge state value are combined, the position of the road condition point where the vehicle passes in the preset time and the energy consumption value of 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 of the preset time period and by combining the fuel consumption target and the battery charge state target, the low-load working time of the engine is prolonged, the fuel consumption is reduced, the battery charge state value of the power battery is kept in a proper range, the vehicle is ensured to fully recover the energy during downhill braking, and the energy utilization rate of the vehicle is improved.
Fig. 3 is a schematic structural diagram of a terminal according to a second embodiment of the present application. The terminal of the present application comprises: a processor 110, a memory 111 and a computer program 112 stored in the memory 111 and executable on the processor 110. The steps in the energy management method embodiments described above are implemented by the processor 110 when executing the computer program 112.
Terminals may include, but are not limited to, a processor 110, a memory 111. It will be appreciated by those skilled in the art that fig. 3 is merely an example of a terminal and is not intended to be limiting, and that more or fewer components than shown may be included, or certain components may be combined, or different components may be included, for example, a terminal may also include input and output devices, network access devices, buses, etc.
The Processor 110 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 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 memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), or the like. Further, the memory 111 may also include both an internal storage unit and an external storage device of the terminal. The memory 111 is used to store computer programs and other programs and data required for the terminal. The memory 111 may also be used to temporarily store data that has been output or is to be output.
The application also provides a computer storage medium having stored thereon a computer program which when executed by a processor performs the steps of the energy management method as above.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
In this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a list of elements is included, and may include other elements not expressly listed.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within 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 (7)

1. An energy management method 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;
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 and combining an oil consumption target and the battery state of charge target;
The current working condition data of the vehicle comprise the current position and the current speed of the vehicle; the road condition data comprise 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 the road condition point on the driving route, and determining the position of the current road condition point of the vehicle;
determining the position of a predicted road condition point of the vehicle passing in a preset time according to the position of the current road condition point and the current speed of the vehicle;
The road condition data also comprises gradient values 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:
According to the current working condition data of the vehicle and the gradient value of the current road condition point, determining a predicted energy consumption value of the vehicle in a first preset period of time within the preset time;
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 period of time within the preset time
The step of determining the predicted energy consumption value of the vehicle according to the current working condition data and the road condition data in the road condition database further comprises the following steps:
acquiring an actual energy consumption value of the vehicle in the first preset period;
And correcting the predicted energy consumption value of the second preset period according to the deviation of the predicted energy consumption value and the actual energy consumption value of the first preset period.
2. The method of claim 1, wherein the step of creating a database of road conditions for the travel route of the vehicle 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 steps:
If the historical vehicle speeds of the vehicle at the historical positioning points with the distance smaller than the preset distance are all zero, any positioning point in the historical positioning points is reserved, and other positioning points in the historical positioning points are removed;
If the distance between any two adjacent historical locating points is larger than the maximum running distance between the two corresponding adjacent historical locating points, eliminating the jump points in the two corresponding adjacent historical locating points;
if the coordinates of any historical locating point do not meet the preset coordinate range, eliminating the corresponding historical locating point;
And carrying out smoothing treatment on the historical locating points, and eliminating singular points in the historical locating points.
3. The method of claim 1, 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 the second engine output power of the vehicle in each preset period according to the first engine output power of the vehicle in the first preset period and the first engine output power of the second preset period and the oil consumption target.
4. A method as claimed in claim 3, comprising, prior to determining the second engine output power of the vehicle for each preset period:
Determining the first engine output power of the vehicle in the second preset period according to the battery state of charge value of the vehicle in the predicted road condition point and the predicted energy consumption value of the second preset period and combining the battery state of charge target;
before determining a 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 last road condition point of the predicted road condition point and the first engine output power of the last preset period corresponding to the predicted road condition point;
Before determining a battery state of charge value of the vehicle at the predicted road point, comprising:
And determining the first engine output power of the vehicle in the first preset period according to the battery state of charge value of the vehicle in the current road condition point and the predicted energy consumption value of the first preset period and combining the battery state of charge target.
5. The method of claim 1, comprising, prior to determining the predicted energy consumption value of the vehicle based on the current operating condition data of the vehicle and the road condition data in the road condition database:
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 of the historical predicted energy consumption value and the historical actual energy consumption value.
6. 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 method according to any one of claims 1 to 5 when the computer program is executed.
7. A computer storage medium storing a computer program, which when executed by a processor performs the steps of the method according to any one of claims 1 to 5.
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