Method, device and system for calculating remaining cruising range based on mass coefficient

Abstract

The present invention provides a method, device and system for calculating the remaining cruising range based on a mass coefficient, which relates to the technical field of pure electric logistics vehicles, including: obtaining the driving information and cargo status information of the electric logistics vehicle in real time; performing energy consumption calculation processing and logistics vehicle mass calculation processing on the driving information, and determining the average energy consumption per 100 kilometers of the electric logistics vehicle and the average mass per 100 kilometers of the electric logistics vehicle; selecting a target vehicle weight calculation strategy from a preset vehicle weight calculation strategy set according to the cargo status information, and determining the vehicle weight information at the current moment based on the target vehicle weight calculation strategy; using the quotient of the vehicle weight information at the current moment and the average mass per 100 kilometers as the mass weighting coefficient, and using the mass weighting coefficient, the battery remaining energy information at the current moment and the average energy consumption per 100 kilometers of the electric logistics vehicle to perform cruising range analysis and processing, and determining the remaining cruising range of the electric logistics vehicle at the current moment. The present invention can significantly improve the accuracy of the calculation of the remaining cruising range of the vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of pure electric logistics vehicles, and in particular to a method, device and system for calculating remaining cruising range based on a mass coefficient. Background Art

[0002] At present, most pure electric logistics vehicles calculate the remaining range of the vehicle based on the current available battery power and the historical energy consumption per 100 kilometers. Relevant technologies suggest that since the total mass of the pure electric logistics vehicle each time it travels will vary depending on the amount of cargo loaded on the vehicle, the difference in weight between an empty and fully loaded vehicle is relatively large. Therefore, the energy consumption of the vehicle in the two operating conditions will also be relatively large. At this time, if the historical energy consumption is simply used to calculate the remaining range of the vehicle, when the vehicle load differs greatly from the average mass within the historical energy consumption calculation range per 100 kilometers, the calculated remaining range will deviate significantly from the actual mileage, thereby misleading the driver's judgment of the vehicle's current remaining range and affecting the driver's normal use of the vehicle. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a method, device and system for calculating the remaining cruising range based on a mass coefficient, which can significantly improve the accuracy of calculating the remaining cruising range of a vehicle.

[0004] In a first aspect, an embodiment of the present invention provides a method for calculating the remaining cruising range based on a mass coefficient, and the method is applied to a remaining cruising range calculation system based on a mass coefficient, and the method includes: acquiring driving information and cargo status information of an electric logistics vehicle in real time, wherein the driving information includes: driving distance information, driving time information, battery output power information, battery remaining energy information and vehicle weight information of the electric logistics vehicle, and the cargo status information includes: vehicle load and vehicle empty; performing energy consumption calculation processing and logistics vehicle mass calculation processing on the driving information to determine the average energy consumption per 100 kilometers of the electric logistics vehicle and the average mass per 100 kilometers of the electric logistics vehicle; selecting a target vehicle weight calculation strategy from a preset vehicle weight calculation strategy set according to the cargo status information, and determining the vehicle weight information at the current moment based on the target vehicle weight calculation strategy; taking the quotient of the vehicle weight information at the current moment and the average mass per 100 kilometers of the electric logistics vehicle as the mass weighting coefficient, and using the mass weighting coefficient, the current battery remaining energy information and the average energy consumption per 100 kilometers of the electric logistics vehicle to perform cruising range analysis processing to determine the remaining cruising range of the electric logistics vehicle at the current moment.

[0005] In one embodiment, energy consumption calculation and logistics vehicle mass calculation are performed based on driving information to determine the average energy consumption and average mass of the electric logistics vehicle per 100 kilometers, including: energy consumption calculation is performed based on battery output power information, driving distance information and driving time information to obtain the average energy consumption of the electric logistics vehicle per 100 kilometers; logistics vehicle mass calculation is performed based on vehicle weight information and driving distance information to obtain the average mass of the electric logistics vehicle per 100 kilometers.

[0006] In one embodiment, energy consumption is calculated and processed according to battery output power information, driving distance information and driving time information to obtain the average energy consumption per 100 kilometers of the electric logistics vehicle, including: using the driving time information corresponding to the preset energy consumption monitoring segment, integrating the battery output power information consumed in the preset energy consumption monitoring segment to obtain the total energy consumed by the current energy consumption monitoring segment; summing the total energy of each preset energy consumption monitoring segment within 100 kilometers and the quotient of the driving distance to obtain the sum of the average energy consumption of each energy consumption monitoring segment, and dividing the sum of the average energy consumption of each energy consumption monitoring segment by the number of preset energy consumption monitoring segments included in the 100 kilometers and multiplying by 100 to determine the average energy consumption of the electric logistics vehicle per 100 kilometers.

[0007] In one embodiment, the weight of the logistics vehicle is calculated and processed based on the vehicle weight information and the driving distance information to obtain the step of obtaining the average weight per 100 kilometers of the electric logistics vehicle, including: when a switch signal of any cargo box door in the electric logistics vehicle is detected, the vehicle weight information at that moment and the driving distance information of the current vehicle driving segment are recorded, wherein the current vehicle driving segment is the vehicle driving segment between the current switch signal and the adjacent last detected switch signal; the sum of the products of the vehicle weight information and the driving distance information of each vehicle driving segment contained in 100 kilometers is divided by 100 to determine the average weight per 100 kilometers of the electric logistics vehicle.

[0008] In one embodiment, according to the cargo status information, a target vehicle weight calculation strategy is selected from a set of preset vehicle weight calculation strategies, and based on the target vehicle weight calculation strategy, the step of determining the vehicle weight information at the current moment includes: when the cargo status information is the vehicle load, directly obtaining the vehicle weight information at the current moment through a preset load sensor; when the cargo status information is that the vehicle is unloaded, performing vehicle weight calculation processing based on the motor torque information and acceleration information of the electric logistics vehicle to obtain the unloaded vehicle weight information, and according to the wind resistance information of the electric logistics vehicle, adding the drag coefficient to the unloaded vehicle weight information to determine the vehicle weight information at the current moment.

[0009] In one embodiment, the step of calculating and processing the vehicle weight based on the motor torque information and acceleration information of the electric logistics vehicle to obtain the empty vehicle weight information includes: acquiring the motor torque information and acceleration information of the electric logistics vehicle; determining the traction force of the electric logistics vehicle based on the motor torque information, and determining the quotient of the traction force and the acceleration information as the empty vehicle weight information.

[0010] In one embodiment, a range analysis is performed using a mass weighting coefficient, the current battery remaining energy information, and the average energy consumption per 100 kilometers of the electric logistics vehicle to determine the remaining range of the electric logistics vehicle at the current moment, including: multiplying the mass weighting coefficient by the current average energy consumption per 100 kilometers of the electric logistics vehicle to determine the target average energy consumption corresponding to the vehicle weight at the current moment; and determining the quotient of the current battery remaining energy information and the target average energy consumption as the remaining range.

[0011] In a second aspect, an embodiment of the present invention further provides a device for calculating the remaining cruising range based on a mass coefficient, the device being applied to a system for calculating the remaining cruising range based on a mass coefficient, the device comprising: an information acquisition module for acquiring in real time the driving information and cargo status information of an electric logistics vehicle, wherein the driving information comprises: driving distance information, driving time information, battery output power information, battery remaining energy information and vehicle weight information of the electric logistics vehicle, and the cargo status information comprises: vehicle load and vehicle empty load; an information analysis module for performing energy consumption calculation processing and logistics vehicle mass calculation processing on the driving information, and determining the average energy consumption per 100 kilometers of the electric logistics vehicle and the average mass per 100 kilometers of the electric logistics vehicle; a vehicle weight calculation module for selecting a target vehicle weight calculation strategy from a set of preset vehicle weight calculation strategies according to the cargo status information, and determining the vehicle weight information at the current moment based on the target vehicle weight calculation strategy; a mileage calculation module for taking the quotient of the vehicle weight information at the current moment and the average mass per 100 kilometers of the electric logistics vehicle as a mass weighting coefficient, and performing cruising range analysis processing using the mass weighting coefficient, the current battery remaining energy information and the average energy consumption per 100 kilometers of the electric logistics vehicle, and determining the remaining cruising range of the electric logistics vehicle at the current moment.

[0012] In the third aspect, an embodiment of the present invention also provides a remaining cruising range calculation system based on a mass coefficient, including: a vehicle controller, a load sensor, a battery management unit and a vehicle instrument, wherein the vehicle controller is connected to the load sensor, the battery management unit and the vehicle instrument respectively; wherein the load sensor is used to obtain vehicle weight information in real time, and send the vehicle weight information to the vehicle controller; the battery management unit is used to obtain battery output power information and battery remaining energy information in real time, and send the battery output power information and battery remaining energy information to the vehicle controller; the vehicle controller is used to perform mileage calculation processing on the vehicle weight information, battery output power information and battery remaining energy information, determine the remaining cruising range of the electric logistics vehicle at the current moment, and send the remaining cruising range to the vehicle instrument via the CAN bus, so as to present the remaining cruising range to the driver in real time.

[0013] In one embodiment, the remaining cruising range calculation system based on the mass coefficient also includes: a peripheral sensor set, which is connected to the vehicle controller; wherein the peripheral sensor set includes: a speed sensor, an acceleration sensor and a wind resistance sensor, and the peripheral sensor set is used to send the speed information, acceleration information and wind resistance information of the electric logistics vehicle to the vehicle controller, so that the vehicle controller can calculate the driving distance information and the vehicle's empty weight of the electric logistics vehicle.

[0014] In a fourth aspect, an embodiment of the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement any one of the methods provided in the first aspect.

[0015] In a fifth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any one of the methods provided in the first aspect.

[0016] The embodiments of the present invention bring the following beneficial effects:

[0017] An embodiment of the present invention provides a method, device and system for calculating the remaining cruising range based on a mass coefficient. After acquiring the driving information and cargo status information of an electric logistics vehicle in real time, the method performs energy consumption calculation processing and logistics vehicle mass calculation processing on the driving information, determines the average energy consumption per 100 kilometers of the electric logistics vehicle and the average mass per 100 kilometers of the electric logistics vehicle, and selects a target vehicle weight calculation strategy from a preset vehicle weight calculation strategy set according to the cargo status information, and determines the vehicle weight information at the current moment based on the target vehicle weight calculation strategy, and finally uses the quotient of the vehicle weight information at the current moment and the average mass per 100 kilometers of the electric logistics vehicle as the mass weighting coefficient, and uses the mass weighting coefficient, the battery remaining energy information at the current moment and the average energy consumption per 100 kilometers of the electric logistics vehicle to perform cruising range analysis processing to determine the remaining cruising range of the electric logistics vehicle at the current moment. The embodiment of the present invention can significantly improve the accuracy of the calculation of the remaining cruising range of the vehicle.

[0018] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 A schematic diagram of the structure of a system for calculating remaining cruising range based on a mass coefficient provided by an embodiment of the present invention;

[0022] Figure 2 A flowchart of a method for calculating remaining cruising range based on a mass coefficient provided by an embodiment of the present invention;

[0023] Figure 3 A schematic diagram of a specific flow chart of a method for calculating remaining cruising range based on a mass coefficient provided by an embodiment of the present invention;

[0024] Figure 4 A schematic diagram of the structure of a device for calculating remaining cruising range based on a mass coefficient provided by an embodiment of the present invention;

[0025] Figure 5 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] At present, most pure electric logistics vehicles calculate the remaining range of the vehicle based on the current available battery power and the historical energy consumption per 100 kilometers. Relevant technologies propose that since the total vehicle mass of the pure electric logistics vehicle each time it travels will vary according to the amount of cargo loaded on the vehicle, the difference in vehicle weight when empty and fully loaded is relatively large. Therefore, the vehicle energy consumption in the two working conditions will also be relatively large. At this time, if the remaining range of the vehicle is calculated simply by using the historical energy consumption, when the vehicle load differs greatly from the average mass within the historical energy consumption calculation range per 100 kilometers, the calculated remaining range will deviate greatly from the actual mileage, thereby misleading the driver's judgment on the current remaining range of the vehicle and affecting the driver's normal use of the vehicle. Based on this, the remaining range calculation method, device and system based on the mass coefficient provided by the present invention can significantly improve the accuracy of the calculation of the remaining range of the vehicle.

[0028] To facilitate understanding of this embodiment, first, a method for calculating the remaining cruising range based on a mass coefficient disclosed in an embodiment of the present invention is introduced in detail. The method is applied to a remaining cruising range calculation system based on a mass coefficient. To facilitate understanding of the remaining cruising range calculation system based on a mass coefficient, an embodiment of the present invention provides a structural schematic diagram of a remaining cruising range calculation system based on a mass coefficient, as shown in FIG. Figure 1 As shown, the remaining cruising range calculation system based on the mass coefficient includes: a vehicle controller VCU, a load sensor, a battery management unit BMS and a vehicle instrument, wherein the vehicle controller is connected to the load sensor, the battery management unit and the vehicle instrument respectively, wherein the load sensor is used to obtain the vehicle weight information in real time and send the vehicle weight information to the vehicle controller, the battery management unit is used to obtain the battery output power information and the battery remaining energy information in real time, and send the battery output power information and the battery remaining energy information to the vehicle controller, the vehicle controller is used to perform mileage calculation processing on the vehicle weight information, the battery output power information and the battery remaining energy information, determine the remaining cruising range of the electric logistics vehicle at the current moment, and send the remaining cruising range to the vehicle instrument through the CAN bus, so as to present the remaining cruising range to the driver in real time.

[0029] In addition, the remaining cruising range calculation system based on the mass coefficient also includes: a peripheral sensor set, which is connected to the vehicle controller, wherein the peripheral sensor set includes: a speed sensor, an acceleration sensor and a wind resistance sensor, and the peripheral sensor set is used to send the speed information, acceleration information and wind resistance information of the electric logistics vehicle to the vehicle controller, so that the vehicle controller can calculate the driving distance information and the vehicle's empty weight of the electric logistics vehicle. Furthermore, the peripheral sensor set can also include a dynamic torque sensor, which can detect the torque of the vehicle's motor, thereby calculating the traction based on the torque, and determining the mass of the electric logistics vehicle based on the traction and acceleration.

[0030] based on Figure 1 The structure diagram of a remaining cruising range calculation system based on a mass coefficient is shown in FIG. 1 . The remaining cruising range calculation method based on a mass coefficient is described in detail in the embodiment of the present invention. Figure 2 A flow chart of a method for calculating remaining cruising range based on a mass coefficient is shown, and the method mainly includes the following steps S202 to S208:

[0031] Step S202, obtaining the driving information and cargo status information of the electric logistics vehicle in real time, wherein the driving information includes: the driving distance information, driving time information, battery output power information, battery remaining energy information and vehicle weight information of the electric logistics vehicle, and the cargo status information includes: vehicle load and vehicle empty. In one embodiment, the driving information of the electric logistics vehicle is collected by the load sensor and the battery management unit in the remaining cruising range calculation system, the door opening and closing signals are collected by the peripheral sensor set, and the loading and unloading conditions of the electric logistics vehicle are counted, so as to determine the cargo status information of the electric logistics vehicle.

[0032] Step S204, perform energy consumption calculation and logistics vehicle mass calculation based on driving information to determine the average energy consumption per 100 kilometers and the average mass of the electric logistics vehicle per 100 kilometers. In one embodiment, the information collected by the load sensor, battery management unit and peripheral sensor set can be processed by the vehicle controller to determine the average energy consumption per 100 kilometers and the average mass of the electric logistics vehicle per 100 kilometers. The specific measurement distance (100 kilometers) can be adjusted according to the vehicle freight distance.

[0033] Step S206, according to the cargo state information, select the target vehicle weight calculation strategy from the preset vehicle weight calculation strategy set, and determine the vehicle weight information at the current moment based on the target vehicle weight calculation strategy. In one embodiment, when the electric logistics vehicle is in a vehicle load state, the vehicle weight is large, and the impact of wind resistance on the energy consumption of the electric logistics vehicle is small. Therefore, the weight of the vehicle can be directly obtained through the load sensor. When the electric logistics vehicle is in a vehicle unloaded state, the vehicle weight is small. At this time, it is necessary to add the resistance weighting coefficient of wind resistance (i.e., drag coefficient) to the current mass in combination with wind resistance information (wind force, windward area and air density, etc.) before calculating the remaining mileage. In order to avoid the error in the weight obtained by the load sensor affecting the calculation effect after the weighted drag coefficient is calculated, the weight of the vehicle is calculated based on traction and acceleration when it is unloaded. If this weight remains unchanged with the change of acceleration, it can be determined that the weight calculation result at this time is relatively accurate.

[0034] Step S208, taking the quotient of the vehicle weight information at the current moment and the average mass of the electric logistics vehicle per 100 kilometers as the mass weighting coefficient, and using the mass weighting coefficient, the battery remaining energy information at the current moment and the average energy consumption of the electric logistics vehicle per 100 kilometers to perform cruising range analysis and processing, to determine the remaining cruising range of the electric logistics vehicle at the current moment. Specifically, the current remaining range calculation formula is:

[0035]

[0036] Among them, E is the current remaining battery energy, S is the current remaining mileage, Eav is the average energy consumption of the vehicle in the past 100km, and K is the mass weighting coefficient.

[0037]

[0038] Among them, M is the current mass of the vehicle, and Mav is the average mass of the vehicle within the past 100 km.

[0039] The above-mentioned remaining cruising range calculation method based on mass coefficient provided in the embodiment of the present invention can significantly improve the accuracy of calculating the remaining cruising range of the vehicle.

[0040] See also Figure 3 The specific flow chart of a method for calculating the remaining cruising range based on the mass coefficient is shown. The embodiment of the present invention also provides an implementation method for calculating the remaining cruising range of an electric logistics vehicle based on the mass coefficient. For details, see the following steps S302 to S312:

[0041] In step S302, the vehicle controller calculates the average energy consumption of the vehicle over a distance of nearly 100 km. It needs to perform energy consumption calculation based on the battery output power information, driving distance information and driving time information to obtain the average energy consumption per 100 kilometers of the electric logistics vehicle. Specifically, firstly, the driving time information corresponding to the preset energy consumption monitoring segment is used to integrate the battery output power information consumed in the preset energy consumption monitoring segment to obtain the total energy consumed by the current energy consumption monitoring segment. Then, the quotient of the total energy of each preset energy consumption monitoring segment within 100 kilometers and the driving distance is summed to obtain the sum of the average energy consumption of each energy consumption monitoring segment. The sum of the average energy consumption of each energy consumption monitoring segment is divided by the number of preset energy consumption monitoring segments contained in the 100 kilometers and then multiplied by 100 to determine the average energy consumption of the electric logistics vehicle per 100 kilometers.

[0042] In one embodiment, the output power of the battery management unit BMS when it is working is:

[0043] PBMS=U(t)I(t)

[0044] The mileage counted by the vehicle controller VCU is C_SamplePeriod (i.e., the preset energy consumption monitoring segment), where the default value of the preset energy consumption monitoring segment is 10km, which can be manually calibrated. The driving time information corresponding to the preset energy consumption monitoring segment is used to integrate the battery output power information consumed in the preset energy consumption monitoring segment to obtain the total energy consumed by the current energy consumption monitoring segment. The total energy consumed in the preset energy consumption monitoring segment is expressed as:

[0045] Ecost = ∫T2T1 PBMSdt

[0046] The average energy consumption AvgEc of the electric logistics vehicle in the preset energy consumption monitoring segment is expressed as:

[0047] AvgEc=Ecost / C_SamplePeriod

[0048] The unit of AvgEc is kWh / km.

[0049] The average energy consumption of the 0-10 km is recorded by the vehicle controller VCU as AvgEc1, and the energy consumption of the 10-20 km is calculated by the same method as AvgEc2... until the energy consumption of the 90-100 km is AvgEc10. The total energy of each preset energy consumption monitoring segment within 100 km and the quotient of the driving distance are summed to obtain the sum of the average energy consumption of each energy consumption monitoring segment, and the sum of the average energy consumption of each energy consumption monitoring segment is divided by the number of preset energy consumption monitoring segments included in 100 kilometers and multiplied by 100 to determine the average energy consumption of the electric logistics vehicle per 100 kilometers, and then the energy consumption of nearly 100 kilometers is obtained:

[0050] VCU_VehAvrgEgyCnse=(AvgEc1+AvgEc2+......+AvgEc10) / 10*100

[0051] The unit is kWh / 100km.

[0052] Step S304, the vehicle controller obtains the current remaining energy of the battery, that is, the vehicle controller can directly and conveniently obtain the current remaining energy of the battery calculated by the BMS through the CAN bus.

[0053] Step S306, the vehicle controller calculates the average mass of the vehicle in the past 100 km. It is necessary to perform logistics vehicle mass calculation processing based on the vehicle weight information and the driving distance information to obtain the average mass of the electric logistics vehicle per 100 kilometers. Specifically, when a switch signal of any cargo box door in the electric logistics vehicle is detected, the vehicle weight information at that moment and the driving distance information of the current vehicle driving segment are recorded, wherein the current vehicle driving segment is the vehicle driving segment between the current switch signal and the adjacent last detected switch signal, and then the sum of the products of the vehicle weight information and the driving distance information of each vehicle driving segment contained in 100 kilometers is divided by 100 to determine the average mass of the electric logistics vehicle per 100 kilometers. That is to say, when the vehicle controller calculates the average mass of the vehicle in the past 100 km, the current mass of the vehicle sensed by the load sensor can be conveniently obtained through the CAN bus. When any door of the cargo box is switched, the VCU register will store the mass of the last vehicle and the corresponding vehicle mileage, and the average mass of the vehicle in the past 100 km is calculated by continuous cumulative averaging.

[0054] Step S308: The vehicle controller calculates the weighted load coefficient by obtaining the ratio of the current vehicle mass (the current vehicle weight information) of the load sensor to the average mass of the vehicle in the past 100 km. In one embodiment, the quotient of the current vehicle weight information and the average mass of the electric logistics vehicle in the past 100 km can be used as the mass weight coefficient. The method for obtaining the current vehicle weight information is determined by the vehicle loading state. For details, see (1) to (2) below:

[0055] (1) When the cargo state information is the vehicle load, the vehicle weight information at the current moment is directly obtained through the preset load sensor. Specifically, when the electric logistics vehicle is in the vehicle load state, the vehicle weight is large and the effect of wind resistance on the energy consumption of the electric logistics vehicle is small. Therefore, the weight of the vehicle can be directly obtained through the load sensor.

[0056] (2) When the loading status information indicates that the vehicle is unloaded, the vehicle weight is calculated based on the motor torque information and acceleration information of the electric logistics vehicle to obtain the unloaded vehicle weight information, and the drag coefficient is added to the unloaded vehicle weight information according to the wind resistance information of the electric logistics vehicle to determine the vehicle weight information at the current moment. Specifically, after obtaining the motor torque information and acceleration information of the electric logistics vehicle, the traction of the electric logistics vehicle is determined according to the motor torque information, and the quotient of the traction and the acceleration information is determined as the unloaded vehicle weight information. Specifically, when the electric logistics vehicle is in an unloaded state, the vehicle weight is relatively small. At this time, it is necessary to add the resistance weighting coefficient (i.e., the drag coefficient) of the wind resistance to the current mass in combination with the wind resistance information (wind force, windward area, and air density, etc.) before calculating the remaining mileage. In order to avoid the error in the weight obtained by the load sensor affecting the calculation effect after the weighted drag coefficient is calculated, the weight of the vehicle is calculated according to the traction and acceleration when it is unloaded. If this weight remains unchanged with the change of acceleration, it can be determined that the weight calculation result at this time is relatively accurate.

[0057] Step S310, the vehicle controller calculates the current remaining mileage of the vehicle based on the average energy consumption of nearly 100 km, the weighted load factor and the current remaining energy of the battery. In one embodiment, the product of the mass weighted coefficient and the average energy consumption per 100 kilometers of the electric logistics vehicle at the current moment can be determined as the target average energy consumption corresponding to the vehicle weight at the current moment, and the quotient of the current battery remaining energy information and the target average energy consumption is determined as the remaining cruising range. That is, the vehicle controller first obtains the weighted average energy consumption based on the vehicle mass coefficient based on the product of the average energy consumption of nearly 100 km and the weighted load coefficient, and then calculates the current remaining mileage of the vehicle based on the current remaining energy of the battery.

[0058] Step S312, the vehicle controller outputs the current remaining mileage and displays it through the instrument. Specifically, the vehicle controller transmits the latest calculated remaining vehicle range to the vehicle instrument through the CAN bus for display, so that the instrument presents the remaining range to the driver.

[0059] In summary, the present invention uses the vehicle's historical energy consumption per 100 kilometers, the average vehicle weight corresponding to the vehicle's historical energy consumption per 100 kilometers, the current available energy of the power battery, the current vehicle weight and other physical quantities through the vehicle controller, and adds the vehicle mass coefficient when calculating the current remaining mileage, which can more accurately feedback the current remaining mileage of the pure electric logistics vehicle. The remaining mileage displayed on the instrument is closer to the actual value of the driver's upcoming trip, solving the problem that the historical energy consumption of the driver cannot quickly approach the current energy consumption due to the difference in load, causing the vehicle controller to calculate the current remaining mileage inaccurately.

[0060] For the remaining cruising range calculation method based on the mass coefficient provided in the above-mentioned embodiment, an embodiment of the present invention provides a remaining cruising range calculation device based on the mass coefficient, and the device is applied to the remaining cruising range calculation system based on the mass coefficient, see Figure 4 A schematic diagram of the structure of a device for calculating remaining cruising range based on a mass coefficient is shown, and the device includes the following parts:

[0061] The information acquisition module 402 acquires the driving information and cargo status information of the electric logistics vehicle in real time, wherein the driving information includes: driving distance information, driving time information, battery output power information, battery remaining energy information and vehicle weight information of the electric logistics vehicle, and the cargo status information includes: vehicle load and vehicle empty;

[0062] The information analysis module 404 performs energy consumption calculation and logistics vehicle mass calculation based on the driving information to determine the average energy consumption per 100 kilometers of the electric logistics vehicle and the average mass per 100 kilometers of the electric logistics vehicle;

[0063] The vehicle weight calculation module 406 selects a target vehicle weight calculation strategy from a preset vehicle weight calculation strategy set according to the cargo state information, and determines the vehicle weight information at the current moment based on the target vehicle weight calculation strategy;

[0064] The mileage calculation module 408 uses the quotient of the current vehicle weight information and the average mass of the electric logistics vehicle per 100 kilometers as the mass weighting coefficient, and uses the mass weighting coefficient, the current battery remaining energy information and the average energy consumption of the electric logistics vehicle per 100 kilometers to perform cruising range analysis and processing to determine the remaining cruising range of the electric logistics vehicle at the current moment.

[0065] The above-mentioned remaining cruising range calculation device based on mass coefficient provided in the embodiment of the present application can significantly improve the accuracy of the calculation of the remaining cruising range of the vehicle.

[0066] In one embodiment, when performing energy consumption calculation processing and logistics vehicle mass calculation processing based on driving information to determine the average energy consumption and average mass of the electric logistics vehicle per 100 kilometers, the above-mentioned information analysis module 404 is also used to: perform energy consumption calculation processing based on battery output power information, driving distance information and driving time information to obtain the average energy consumption of the electric logistics vehicle per 100 kilometers; perform logistics vehicle mass calculation processing based on vehicle weight information and driving distance information to obtain the average mass of the electric logistics vehicle per 100 kilometers.

[0067] In one embodiment, when performing the step of calculating energy consumption based on battery output power information, driving distance information and driving time information to obtain the average energy consumption per 100 kilometers of the electric logistics vehicle, the above-mentioned information analysis module 404 is also used to: utilize the driving time information corresponding to the preset energy consumption monitoring segment, integrate the battery output power information consumed in the preset energy consumption monitoring segment, and obtain the total energy consumed by the current energy consumption monitoring segment; sum the total energy of each preset energy consumption monitoring segment within 100 kilometers and the quotient of the driving distance to obtain the sum of the average energy consumption of each energy consumption monitoring segment; and divide the sum of the average energy consumption of each energy consumption monitoring segment by the number of segments of the preset energy consumption monitoring segment contained in the 100 kilometers and multiply it by 100 to determine the average energy consumption of the electric logistics vehicle per 100 kilometers.

[0068] In one embodiment, when calculating and processing the mass of the logistics vehicle according to the vehicle weight information and the driving distance information to obtain the average mass per 100 kilometers of the electric logistics vehicle, the above-mentioned information analysis module 404 is also used for: when a switch signal of any cargo box door in the electric logistics vehicle is detected, recording the vehicle weight information at that moment and the driving distance information of the current vehicle driving segment, wherein the current vehicle driving segment is the vehicle driving segment between the current switch signal and the adjacent last detected switch signal; dividing the sum of the products of the vehicle weight information and the driving distance information of each vehicle driving segment contained in 100 kilometers by 100 to determine the average mass per 100 kilometers of the electric logistics vehicle.

[0069] In one embodiment, when performing the step of selecting a target vehicle weight calculation strategy from a preset vehicle weight calculation strategy set according to the cargo status information, and determining the vehicle weight information at the current moment based on the target vehicle weight calculation strategy, the above-mentioned vehicle weight calculation module 406 is also used for: when the cargo status information is the vehicle load, directly obtaining the vehicle weight information at the current moment through a preset load sensor; when the cargo status information is that the vehicle is unloaded, performing vehicle weight calculation processing based on the motor torque information and acceleration information of the electric logistics vehicle to obtain the unloaded vehicle weight information, and adding the drag coefficient to the unloaded vehicle weight information according to the wind resistance information of the electric logistics vehicle to determine the vehicle weight information at the current moment.

[0070] In one embodiment, when performing the step of calculating the vehicle weight based on the motor torque information and acceleration information of the electric logistics vehicle to obtain the empty vehicle weight information, the above-mentioned vehicle weight calculation module 406 is also used to: obtain the motor torque information and acceleration information of the electric logistics vehicle; determine the traction force of the electric logistics vehicle based on the motor torque information, and determine the quotient of the traction force and the acceleration information as the empty vehicle weight information.

[0071] In one embodiment, when performing a mileage analysis and processing using a mass weighting coefficient, the current battery remaining energy information, and the average energy consumption per 100 kilometers of the electric logistics vehicle to determine the remaining mileage of the electric logistics vehicle at the current moment, the mileage calculation module 408 is also used to: determine the product of the mass weighting coefficient and the current average energy consumption per 100 kilometers of the electric logistics vehicle as the target average energy consumption corresponding to the vehicle weight at the current moment; and determine the quotient of the current battery remaining energy information and the target average energy consumption as the remaining mileage.

[0072] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0073] An embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned implementation methods.

[0074] Figure 5 A structural diagram of an electronic device provided in an embodiment of the present invention, the electronic device 100 includes: a processor 50, a memory 51, a bus 52 and a communication interface 53, wherein the processor 50, the communication interface 53 and the memory 51 are connected via the bus 52; the processor 50 is used to execute an executable module stored in the memory 51, such as a computer program.

[0075] The memory 51 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 53 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0076] The bus 52 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0077] Among them, the memory 51 is used to store programs, and the processor 50 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 50 or implemented by the processor 50.

[0078] The processor 50 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 50. The above processor 50 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to be executed, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 51, and the processor 50 reads the information in the memory 51 and completes the steps of the above method in combination with its hardware.

[0079] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0080] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, electronic device, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0081] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for calculating remaining cruising range based on mass coefficient, characterized in that: The method comprises: Acquire the driving information and cargo status information of the electric logistics vehicle in real time, wherein the driving information includes: driving distance information, driving time information, battery output power information, battery remaining energy information and vehicle weight information of the electric logistics vehicle, and the cargo status information includes: vehicle load and vehicle empty; Perform energy consumption calculation and logistics vehicle mass calculation on the driving information to determine the average energy consumption per 100 kilometers of the electric logistics vehicle and the average mass per 100 kilometers of the electric logistics vehicle; According to the cargo state information, a target vehicle weight calculation strategy is selected from a preset vehicle weight calculation strategy set, and based on the target vehicle weight calculation strategy, the vehicle weight information at the current moment is determined; The quotient of the vehicle weight information at the current moment and the average mass per 100 kilometers of the electric logistics vehicle is used as the mass weighting coefficient, and the mass weighting coefficient, the battery remaining energy information at the current moment and the average energy consumption per 100 kilometers of the electric logistics vehicle are used to perform cruising range analysis and processing to determine the remaining cruising range of the electric logistics vehicle at the current moment; Among them, the step of selecting a target vehicle weight calculation strategy from a preset vehicle weight calculation strategy set according to the cargo state information, and determining the vehicle weight information at the current moment based on the target vehicle weight calculation strategy includes: when the cargo state information is vehicle load, directly obtaining the vehicle weight information at the current moment through a preset load sensor; when the cargo state information is vehicle empty, performing vehicle weight calculation processing based on the motor torque information and acceleration information of the electric logistics vehicle to obtain empty vehicle weight information, and adding the drag coefficient to the empty vehicle weight information according to the wind resistance information of the electric logistics vehicle to determine the vehicle weight information at the current moment; Among them, the step of calculating and processing the vehicle weight based on the motor torque information and acceleration information of the electric logistics vehicle to obtain the empty vehicle weight information includes: acquiring the motor torque information and the acceleration information of the electric logistics vehicle; determining the traction force of the electric logistics vehicle according to the motor torque information, and determining the quotient of the traction force and the acceleration information as the empty vehicle weight information.

2. The method for calculating the remaining cruising range based on the mass coefficient according to claim 1, characterized in that: The step of performing energy consumption calculation processing and logistics vehicle mass calculation processing on the driving information to determine the average energy consumption per 100 kilometers of the electric logistics vehicle and the average mass per 100 kilometers of the electric logistics vehicle includes: Perform energy consumption calculation according to the battery output power information, the driving distance information and the driving time information to obtain the average energy consumption per 100 kilometers of the electric logistics vehicle; The mass of the logistics vehicle is calculated based on the vehicle weight information and the driving distance information to obtain the average mass of the electric logistics vehicle per 100 kilometers.

3. The method for calculating the remaining cruising range based on the mass coefficient according to claim 2, characterized in that: The step of performing energy consumption calculation processing according to the battery output power information, the driving distance information and the driving time information to obtain the average energy consumption per 100 kilometers of the electric logistics vehicle includes: Using the driving time information corresponding to the preset energy consumption monitoring segment, the battery output power information consumed in the preset energy consumption monitoring segment is integrated to obtain the total energy consumed in the current energy consumption monitoring segment; The quotient of the total energy of each preset energy consumption monitoring segment within 100 kilometers and the driving distance is summed to obtain the sum of the average energy consumption of each energy consumption monitoring segment, and the sum of the average energy consumption of each energy consumption monitoring segment is divided by the number of preset energy consumption monitoring segments contained in 100 kilometers and multiplied by 100 to determine the average energy consumption of the electric logistics vehicle per 100 kilometers.

4. The method for calculating the remaining cruising range based on the mass coefficient according to claim 2, characterized in that: The step of calculating the mass of the logistics vehicle according to the vehicle weight information and the travel distance information to obtain the average mass of the electric logistics vehicle per 100 kilometers includes: When a switch signal of any cargo box door in the electric logistics vehicle is detected, the vehicle weight information at that moment and the travel distance information of the current vehicle travel segment are recorded, wherein the current vehicle travel segment is the vehicle travel segment between the current switch signal and the adjacent last detected switch signal; The sum of the products of the vehicle weight information and the driving distance information of each vehicle driving segment contained in 100 kilometers is divided by 100 to determine the average weight of the electric logistics vehicle per 100 kilometers.

5. The method for calculating remaining cruising range based on mass coefficient according to claim 1, characterized in that: The step of performing cruising range analysis and processing by using the mass weighting coefficient, the battery remaining energy information at the current moment, and the average energy consumption per 100 kilometers of the electric logistics vehicle to determine the remaining cruising range of the electric logistics vehicle at the current moment includes: The product of the mass weighting coefficient and the average energy consumption per 100 kilometers of the electric logistics vehicle at the current moment is determined as the target average energy consumption corresponding to the vehicle weight at the current moment; The quotient of the battery remaining energy information at the current moment and the target average energy consumption is determined as the remaining cruising range.

6. A device for calculating remaining cruising range based on mass coefficient, characterized in that: The device is applied to a remaining cruising range calculation system based on a mass coefficient, and the device comprises: An information acquisition module is used to acquire the driving information and cargo status information of the electric logistics vehicle in real time, wherein the driving information includes: driving distance information, driving time information, battery output power information, battery remaining energy information and vehicle weight information of the electric logistics vehicle, and the cargo status information includes: vehicle load and vehicle empty; An information analysis module performs energy consumption calculation processing and logistics vehicle mass calculation processing on the driving information to determine the average energy consumption per 100 kilometers of the electric logistics vehicle and the average mass per 100 kilometers of the electric logistics vehicle; A vehicle weight calculation module selects a target vehicle weight calculation strategy from a preset vehicle weight calculation strategy set according to the cargo state information, and determines the vehicle weight information at the current moment based on the target vehicle weight calculation strategy; The mileage calculation module uses the quotient of the vehicle weight information at the current moment and the average mass of the electric logistics vehicle per 100 kilometers as the mass weighting coefficient, and uses the mass weighting coefficient, the battery remaining energy information at the current moment, and the average energy consumption of the electric logistics vehicle per 100 kilometers to perform cruising range analysis and processing to determine the remaining cruising range of the electric logistics vehicle at the current moment; Among them, according to the cargo state information, a target vehicle weight calculation strategy is selected from a preset vehicle weight calculation strategy set, and based on the target vehicle weight calculation strategy, the vehicle weight information at the current moment is determined, including: when the cargo state information is vehicle load, the vehicle weight information at the current moment is directly obtained through a preset load sensor; when the cargo state information is vehicle empty, the vehicle weight calculation is performed based on the motor torque information and acceleration information of the electric logistics vehicle to obtain the empty vehicle weight information, and according to the wind resistance information of the electric logistics vehicle, the wind resistance coefficient is added to the empty vehicle weight information to determine the vehicle weight information at the current moment; Among them, the vehicle weight calculation and processing is performed based on the motor torque information and acceleration information of the electric logistics vehicle to obtain the empty vehicle weight information, including: acquiring the motor torque information and the acceleration information of the electric logistics vehicle; determining the traction force of the electric logistics vehicle according to the motor torque information, and determining the quotient of the traction force and the acceleration information as the empty vehicle weight information.

7. A system for calculating remaining cruising range based on mass coefficient, the system being used to implement the method for calculating remaining cruising range based on mass coefficient as claimed in claim 1, characterized in that: The remaining cruising range calculation system based on mass coefficient includes: a vehicle controller, a load sensor, a battery management unit and a vehicle instrument, wherein the vehicle controller is connected to the load sensor, the battery management unit and the vehicle instrument respectively; wherein, The load sensor is used to obtain vehicle weight information in real time and send the vehicle weight information to the vehicle controller; The battery management unit is used to obtain battery output power information and battery remaining energy information in real time, and send the battery output power information and the battery remaining energy information to the vehicle controller; The vehicle controller is used to perform mileage calculation and processing based on the vehicle weight information, the battery output power information and the battery remaining energy information, determine the remaining cruising range of the electric logistics vehicle at the current moment, and send the remaining cruising range to the vehicle instrument via the CAN bus to present the remaining cruising range to the driver in real time.

8. The remaining cruising range calculation system based on mass coefficient according to claim 7, characterized in that: The remaining cruising range calculation system based on the mass coefficient further includes: a peripheral sensor set, the peripheral sensor set is connected to the vehicle controller; wherein, The peripheral sensor set includes: a speed sensor, an acceleration sensor and a wind resistance sensor. The peripheral sensor set is used to send the speed information, acceleration information and wind resistance information of the electric logistics vehicle to the vehicle controller, so that the vehicle controller can calculate the driving distance information and the vehicle's empty weight of the electric logistics vehicle.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 5.

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