CN110077274B - Estimation method, device and equipment for travelling distance of logistics electric vehicle - Google Patents
Estimation method, device and equipment for travelling distance of logistics electric vehicle Download PDFInfo
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- CN110077274B CN110077274B CN201910248988.6A CN201910248988A CN110077274B CN 110077274 B CN110077274 B CN 110077274B CN 201910248988 A CN201910248988 A CN 201910248988A CN 110077274 B CN110077274 B CN 110077274B
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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
- 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
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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
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/50—Control modes by future state prediction
- B60L2260/52—Control modes by future state prediction drive range estimation, e.g. of estimation of available travel distance
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Abstract
The embodiment of the invention provides a method, a device and equipment for estimating the travelable distance of a logistics electric vehicle, and relates to the technical field of new energy electric vehicles. The method comprises the following steps: acquiring a departure point, a destination and environmental information of a driving path between the departure point and the destination; acquiring the current load capacity and battery information of the logistics electric vehicle; and determining the actual driving distance of the logistics electric vehicle in the driving process based on the driving path according to the current load capacity, the battery information and the environment information. According to the invention, the actual driving range of the logistics electric vehicle can be more accurately estimated by adding the consideration of multiple factors such as the current load capacity, the battery information and the environmental information in the calculation process of the driving range of the logistics electric vehicle.
Description
Technical Field
The invention relates to the technical field of new energy electric vehicles, in particular to a method, a device and equipment for estimating the travelable distance of a logistics electric vehicle.
Background
The electric motor car is as green sunward industry, has developed in china for ten years, and chinese electric bicycle quantity increases with 30% speed every year, but it often needs to charge at public electric pile that charges, and the point of charging is few, and charging speed is slower, and public charging stake is owing to lack unified management standard, often appears that the parking stall that charges is occupied, the socket mismatch scheduling problem, and it brings very big inconvenience to charge for the vehicle in long-distance driving process.
For the user of the electric bicycle carrying cargo, it is very important information to estimate the distance that the electric bicycle can travel, and further plan his or her own journey and route. The load capacity of the battery of the electric vehicle, battery information and environmental factors have great influence on the travelable distance of the electric vehicle, and the estimation of the travelable distance of the electric vehicle only according to the rated electric quantity or the current residual electric quantity of the battery may cause great errors, so that the problems of incapability of normally traveling due to the fact that the battery is dead in the transportation process and the like are caused.
Disclosure of Invention
In view of the above, an object of the embodiments of the present invention is to provide a method, an apparatus and a device for estimating a distance that an electric vehicle can travel, so as to solve the problem of large estimation error of the distance that a cargo electric vehicle can travel in the prior art.
The embodiment of the invention provides a method for estimating the travelable distance of a logistics electric vehicle, which comprises the following steps:
acquiring a departure point, a destination and environmental information of a driving path between the departure point and the destination;
acquiring the current load capacity and battery information of the logistics electric vehicle;
and determining the actual driving distance of the logistics electric vehicle in the driving process based on the driving path according to the current load capacity, the battery information and the environment information.
Preferably, the environment information between the departure point and the destination includes:
road conditions and ambient temperature.
Preferably, the battery information includes a battery SOC, a battery capacity, and a battery energy efficiency obtained by multiplying the battery capacity by an energy consumption efficiency of the battery, wherein the battery consumption efficiency is a rate of change of a travel distance with a battery capacity when the battery capacity is 100%.
Preferably, the determining an actual distance that the logistics electric vehicle can travel based on the travel path according to the current load capacity, the battery information, and the environment information specifically includes:
obtaining theoretical driving mileage; wherein the theoretical mileage is obtained by multiplying the battery SOC and the battery energy efficiency;
and configuring weight parameters for the loading capacity and the ambient temperature of the logistics electric vehicle, and performing weighting correction on the theoretical driving range according to the weight parameters to generate the weighted actual driving distance of the logistics electric vehicle.
Preferably, the theoretical mileage is modified in a weighted manner according to the weighting parameters, specifically as follows:
where C is the battery SOC, η is the battery energy efficiency, m0Is the rated load capacity of the battery, m1Is the actual load capacity of the battery, T0As reference temperature, T1Is ambient temperature, A1As a weight parameter for the current payload, A2As a weighting parameter for the ambient temperature, A3Is a constant number 0<Ai<1, and A1+A3＝1。
Preferably, before acquiring the current load capacity and the battery information of the logistics electric vehicle, the method further includes:
and establishing a mapping table of the energy consumption efficiency of the battery under different average driving speeds so as to obtain the energy consumption efficiency of the battery according to the average driving speed of the logistics electric vehicle in the driving process of the logistics electric vehicle.
Preferably, the method further comprises the following steps:
acquiring the length and the gradient of an uphill road section within a preset distance in front of the current driving position;
determining an actual distance corresponding to the section of the uphill slope according to the length and the gradient of the section of the uphill slope; and
and correcting the actual distance to be travelled according to the actual distance.
The embodiment of the invention also provides a device for estimating the travelable distance of the logistics electric vehicle, which comprises the following components:
the data reading module is used for acquiring a departure point, a destination and environment information of a driving path between the departure point and the destination;
the detection module is used for acquiring the current load capacity and battery information of the logistics electric vehicle;
and the estimation module is used for determining the actual driving distance of the logistics electric vehicle in the driving process based on the driving path according to the current load capacity, the battery information and the environment information.
Preferably, the method further comprises the following steps:
and the database construction module is used for establishing a mapping table of the energy consumption efficiency of the battery with different carrying capacity under different average driving speeds so as to obtain the energy consumption efficiency of the battery according to the average driving speed of the logistics electric vehicle in the driving process of the logistics electric vehicle.
The embodiment of the invention also provides a device for estimating the travelable distance of the logistics electric vehicle, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the method for estimating the travelable distance of the logistics electric vehicle.
In the embodiment, the consideration of multiple factors such as the current load capacity, the battery information and the environmental information is added in the calculation process of the travelable distance of the logistics electric vehicle, so that the actual travelable distance of the logistics electric vehicle can be more accurately estimated, a driver can conveniently and reasonably arrange own trip according to the actual situation of a freight order, and the influence of insufficient electric power or even exhaustion of the electric vehicle on the freight process is avoided.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of a method for estimating a physical distribution electric vehicle travelable distance according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of an estimation apparatus for a physical distribution electric vehicle distance to be traveled according to a second embodiment of the present invention.
Icon: 201-data reading module; 202-a detection module; 203-estimation module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
As shown in fig. 1, a first embodiment of the present invention provides a method for estimating a travelable distance of a logistics electric vehicle, which can be performed by a logistics electric device, and in particular, can be performed by one or more processors or controllers in the logistics electric device, and includes the following steps:
s101, acquiring environmental information of a departure point, a destination and a driving path between the departure point and the destination;
it should be understood that the logistics electric vehicle is an electrically driven vehicle, uses a battery as its energy source, obtains electric power by connecting with an external power source, may be an electric bicycle, an electric motorcycle, an electric truck, or a pure electric vehicle, an electric moped, a solar electric vehicle or a hybrid electric vehicle, and generally comprises a storage battery, an electric hub, a controller, a charger and a vehicle body, and is at least provided with a sensor and a data processing device to implement the estimation method of the travelable distance of the logistics electric vehicle.
In the present embodiment, the data processing device of the electric vehicle is configured to obtain the input information of the departure point, the destination, and the measurement information of the environmental information by the sensor as the input of the estimation method. Further, the environment information between the departure point and the destination includes: road conditions and ambient temperature. The road section conditions comprise a ramp road section, a congested road section in an urban area, a high-speed road section, a bumpy road section or sand blown by wind, rain, snow, haze and the like.
S102, acquiring the current load capacity and battery information of the logistics electric vehicle;
in this embodiment, before the logistics electric vehicle starts, current load capacity and battery information are detected, where the battery information includes a battery SOC, a battery capacity, and a battery energy efficiency, and the battery energy efficiency is obtained by multiplying the battery capacity by an energy consumption efficiency of a battery, where the battery consumption efficiency is a rate of change of a driving distance with a battery capacity when the battery capacity is 100%.
In the embodiment, the SOC of the battery is the percentage of the remaining capacity of the battery, and the electromagnetic field capacity is the rated capacity of the battery. Furthermore, the actual available capacity of the battery decreases along with the increase of the number of charging and discharging cycles in the use process of the battery, and in order to reflect the influence of the battery state on the driving range in real time, the energy consumption efficiency is defined to reflect the consumption of electric quantity in the driving process of the electric vehicle. The energy consumption efficiency is the change rate of the vehicle driving distance and the battery electric quantity in the previous driving period, and the energy efficiency of the battery can be calculated based on the rated electric quantity of the battery and the energy consumption efficiency of the battery.
S103, determining the actual driving distance of the logistics electric vehicle in the driving process based on the driving path according to the current load capacity, the battery information and the environment information.
In the embodiment, the actual driving range of the electric vehicle can be estimated more accurately by receiving the load capacity, the battery information and the environment information and adding multi-factor consideration in the driving range calculation. The calculation result is displayed on the instrument of the logistics electric vehicle before going out, or is transmitted to a freight driver through other wireless devices, so that the driver can conveniently and reasonably arrange own going out according to the actual situation of the freight order.
On the basis of the first embodiment of the present invention, in a preferred embodiment, the determining an actual travelable distance of the logistics electric vehicle during traveling based on the travel path according to the current load capacity, the battery information and the environment information specifically includes:
obtaining theoretical driving mileage; wherein the theoretical mileage is obtained by multiplying the battery SOC and the battery energy efficiency;
and configuring weight parameters for the loading capacity and the ambient temperature of the logistics electric vehicle, and performing weighting correction on the theoretical driving range according to the weight parameters to generate the weighted actual driving distance of the logistics electric vehicle.
In the embodiment, considering that the self weight of the electric vehicle is constant, the vehicle-mounted weight and the ambient temperature have a large influence on the travelable distance. When the load is increased, the bearing pressure of the wheels is increased, and the power consumption in the running process is increased; when the temperature decreases, the discharge amount of the battery increases, and the battery consumption increases. In order to reduce the influence of the load capacity and the ambient temperature of the electric vehicle on the estimation result, the estimation result of the theoretical driving mileage is weighted and adjusted to provide more accurate information for a driver, so that the electric vehicle can be charged in time at the next charging point,
On the basis of the first embodiment of the present invention, in a preferred embodiment, the theoretical mileage is modified in a weighted manner according to the weighting parameter, specifically as follows:
where C is the battery SOC, η is the battery energy efficiency, m0Is the rated load capacity of the battery, m1Is the actual load capacity of the battery, T0As reference temperature, T1Is ambient temperature, A1As a weight parameter for the current payload, A2As a weighting parameter for the ambient temperature, A3Is a constant number 0<Ai<1, and A1+A3＝1。
In one embodiment, the parameter A may be calculated by a model operation1、A2And A3And calibrating to perform weighting correction on the theoretical driving mileage. Specifically, through a battery working condition test performed in advance, data such as battery SOC, battery energy efficiency, battery rated load, actual load of the battery, reference and ambient temperature are imported into MatlabAnd performing dynamic modeling, and adjusting the value of each parameter through model operation.
On the basis of the first embodiment of the present invention, in a preferred embodiment, before acquiring the current load capacity and the battery information of the logistics electric vehicle, the method further includes:
and establishing a mapping table of the energy consumption efficiency of the battery under different average driving speeds so as to obtain the energy consumption efficiency of the battery according to the average driving speed of the logistics electric vehicle in the driving process of the logistics electric vehicle.
In this embodiment, a corresponding relationship between the consumption efficiency of the battery and the average driving speed of the electric vehicle may be established in advance and stored in a database of the data processing device for calculating the real-time effective operating distance, wherein the average driving speed is obtained by obtaining the distance between the starting point and the destination and the freight time for estimation. Optionally, battery consumption efficiency and other environmental information may be established such as: the invention is not limited in particular by the storage database of the mapping table data of the relations such as the gradient, the wind, the sand, the rain, the snow and the like. More preferably, the average value of the battery consumption efficiency corresponding to the environment information is obtained in the establishment correspondence data network.
Furthermore, the battery state information of the database is updated by storing the vehicle driving distance and the change rate of the battery electric quantity in the previous driving period, so that the influence of the battery state change on the calculation result is reduced to the maximum extent.
On the basis of the first embodiment of the present invention, in a preferred embodiment, the method for estimating the distance that the logistics electric vehicle can travel further comprises:
acquiring the length and the gradient of an uphill road section within a preset distance in front of the current driving position;
determining an actual distance corresponding to the section of the uphill slope according to the length and the gradient of the section of the uphill slope; and
and correcting the actual distance to be travelled according to the actual distance.
In a specific implementation mode, the environmental information ramp road section has a large influence on the energy consumption of the battery of the electric vehicle, in the actual driving process, if the ramp is more and the electric vehicle climbs the slope and needs to consume large energy, the energy consumption of the battery is larger, the length and the gradient of the uphill road section are blended into the theoretical driving mileage for weighting correction, and a more accurate estimation result can be obtained:
where C is the battery SOC, η is the battery energy efficiency, m0Is the rated load capacity of the battery, m1Is the actual load capacity of the battery, T0As reference temperature, T1Is ambient temperature, A1As a weight parameter for the current payload, A2As a weighting parameter for the ambient temperature, A4Is a weight parameter of the slope segment, A3Is constant, theta is the gradient, L is the length of the uphill road segment, 0<A1-3<1, and A1+A3＝0。。
In summary, the estimation method for the feasible driving distance of the logistics electric vehicle provided by the embodiment of the invention can estimate the actual feasible driving distance of the logistics electric vehicle more accurately by adding the consideration of multiple factors such as the current load capacity, the battery information and the environmental information in the calculation process of the feasible driving distance. Before the logistics electric vehicle runs, a driver can judge whether the electric power reserve of the electric vehicle can complete a given task according to an estimation result, and the battery state of the electric vehicle is better grasped, so that the next charging time and charging point of the logistics electric vehicle are planned.
A second embodiment of the present invention provides an estimation apparatus for a distance that a logistics electric vehicle can travel, including:
a data reading module 201, configured to obtain a departure point, a destination, and environment information of a driving path between the departure point and the destination;
the detection module 202 is used for acquiring the current load capacity and battery information of the logistics electric vehicle;
and the estimation module 203 is used for determining the actual driving distance of the logistics electric vehicle in the driving process based on the driving path according to the current load capacity, the battery information and the environment information.
Preferably, the environment information between the departure point and the destination includes:
road conditions and ambient temperature.
Preferably, the battery information includes a battery SOC, a battery capacity, and a battery energy efficiency obtained by multiplying the battery capacity by an energy consumption efficiency of the battery, wherein the battery consumption efficiency is a rate of change of a travel distance with a battery capacity when the battery capacity is 100%.
Preferably, the estimation module 203 specifically includes:
the theoretical mileage acquisition module is used for acquiring theoretical driving mileage; wherein the theoretical mileage is obtained by multiplying the battery SOC and the battery energy efficiency;
and the corrected mileage acquisition module is used for configuring weight parameters for the loading capacity and the environmental temperature of the logistics electric vehicle, so as to perform weighted correction on the theoretical driving mileage according to the weight parameters and generate the weighted actual driving distance of the logistics electric vehicle.
Preferably, the theoretical mileage is modified in a weighted manner according to the weighting parameters, specifically as follows:
where C is the battery SOC, η is the battery energy efficiency, m0Is the rated load capacity of the battery, m1Is the actual load capacity of the battery, T0As reference temperature, T1Is ambient temperature, A1As a weight parameter for the current payload, A2As a weighting parameter for the ambient temperature, A3Is a constant number 0<Ai<1, and A1+A3＝1。
Preferably, the method further comprises the following steps:
and the database construction module is used for establishing a mapping table of the energy consumption efficiency of the battery under different average driving speeds so as to obtain the energy consumption efficiency of the battery according to the average driving speed of the logistics electric vehicle in the driving process of the logistics electric vehicle.
Preferably, the method further comprises the following steps:
a slope segment correction module; the system comprises a road grade acquisition unit, a road grade acquisition unit and a control unit, wherein the road grade acquisition unit is used for acquiring the length and the gradient of an uphill road section within a preset distance in front of a current driving position; determining an actual distance corresponding to the section of the uphill slope according to the length and the gradient of the section of the uphill slope; and correcting the actual distance to be travelled according to the actual distance.
A third embodiment of the present invention provides an estimation apparatus for a physical distribution electric vehicle travelable distance, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the estimation method for the physical distribution electric vehicle travelable distance as described above when executing the program.
The memories and processors are electrically connected to each other, directly or indirectly, to enable data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory stores a logistics electric vehicle distance-to-empty estimation device, the estimation device comprises at least one software functional module which can be stored in the memory in the form of software or firmware (firmware), and the processor executes various functional applications and data processing by running software programs and modules stored in the memory, such as the logistics electric vehicle distance-to-empty monitoring device in the embodiment of the invention, so as to realize the logistics electric vehicle distance-to-empty method in the embodiment of the invention.
The Memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory is used for storing programs, and the processor executes the programs after receiving the execution instructions.
The processor may be an integrated circuit chip having signal processing capabilities. The processor may be a general-purpose processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. But may also be a Digital Signal Processor (DSP)), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A method for estimating the travelable distance of a logistics electric vehicle is characterized by comprising the following steps:
acquiring a departure point, a destination and environmental information of a driving path between the departure point and the destination;
acquiring the current load capacity and battery information of the logistics electric vehicle;
obtaining theoretical driving mileage; the theoretical driving mileage is obtained by multiplying the battery SOC and the battery energy efficiency;
configuring weight parameters for the loading capacity and the ambient temperature of the logistics electric vehicle, and performing weighting correction on the theoretical driving mileage according to the weight parameters to generate a weighted actual driving distance of the logistics electric vehicle;
and carrying out weighting correction on the theoretical driving mileage according to the weighting parameters, which specifically comprises the following steps:
where C is the battery SOC, η is the battery energy efficiency, m0Is the rated load capacity of the battery, m1Is the actual load capacity of the battery, T0As reference temperature, T1Is ambient temperature, A1As a weight parameter for the current payload, A2As a weighting parameter for the ambient temperature, A3Is constant, and A1+A3＝1。
2. The method of estimating a physical distribution electric vehicle travelable distance according to claim 1, wherein the environmental information between the departure point and the destination includes: road conditions and ambient temperature.
3. The method for estimating the travelable distance of a logistics electric vehicle as claimed in claim 1, wherein before obtaining the current load capacity and the battery information of the logistics electric vehicle, the method further comprises:
and establishing a mapping table of the energy consumption efficiency of the battery under different average driving speeds so as to obtain the energy consumption efficiency of the battery according to the average driving speed of the logistics electric vehicle in the driving process of the logistics electric vehicle.
4. The method for estimating the travelable distance of a logistics electric vehicle as claimed in claim 1, further comprising:
acquiring the length and the gradient of an uphill road section within a preset distance in front of the current driving position;
determining an actual distance corresponding to the section of the uphill slope according to the length and the gradient of the section of the uphill slope; and
and correcting the actual distance to be travelled according to the actual distance.
5. An estimation device of a physical distribution electric vehicle travelable distance, characterized by comprising:
the data reading module is used for acquiring a departure point, a destination and environment information of a driving path between the departure point and the destination;
the detection module is used for acquiring the current load capacity and battery information of the logistics electric vehicle;
the estimation module is used for determining the actual driving distance of the logistics electric vehicle in the driving process based on the driving path according to the current load capacity, the battery information and the environment information;
the estimation device adopts the estimation method of the physical distribution electric vehicle travelable distance according to any one of claims 1 to 4.
6. The estimation apparatus of physical distribution electric vehicle travelable distance of claim 5, characterized by further comprising:
and the database construction module is used for establishing a mapping table of the energy consumption efficiency of the battery with different carrying capacity under different average driving speeds so as to obtain the energy consumption efficiency of the battery according to the average driving speed of the logistics electric vehicle in the driving process of the logistics electric vehicle.
7. A device for estimating a physical distribution electric vehicle travelable distance, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method for estimating a physical distribution electric vehicle travelable distance according to any one of claims 1 to 4 when executing the program.
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