CN114154107B - Average energy consumption calculation method and device - Google Patents
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Abstract
The invention provides an average energy consumption calculation method which is applied to an extended range electric vehicle, wherein a weighting calculation coefficient corresponding to a battery electric quantity parameter range value and a fuel state parameter range value is stored in advance, and the method comprises the following steps: acquiring energy consumption data of M different mileage segments of N mileage series, wherein each mileage series has a unit length; acquiring the current battery electric quantity parameter and the fuel state parameter of the extended-range electric vehicle; acquiring a corresponding weighted calculation coefficient according to the current battery electric quantity parameter and the fuel state parameter; and according to the weighting coefficient and the energy consumption data, weighting and calculating average energy consumption per kilometer. In order to avoid jump during data statistics of the segments and limit statistics and storage modes for the data of the segment energy consumption, the method improves the accuracy of average energy consumption and provides an important reference for calculating the endurance mileage.
Description
[ Field of technology ]
The present application relates to the field of average energy consumption computing technologies, and in particular, to a method and an apparatus for computing average energy consumption.
[ Background Art ]
The average energy consumption of the range-extending electric vehicle relates to electricity consumption and fuel consumption, and certain difficulty exists in calculating the average energy consumption. Because the range-extending electric vehicle is provided with two sets of energy sources of the power battery and the range extender, the range extender can generate electric energy by burning fuel when the residual electric quantity of the power battery is insufficient, and provide power support for the vehicle, so that the endurance mileage of the vehicle can be effectively improved.
The accuracy of calculation of the range-extending electric vehicle is an important index for improving the use experience of a user, an average energy consumption method is adopted for calculation of the range-extending electric vehicle at the present stage, but the average energy consumption of the whole vehicle of the existing range-extending electric vehicle is still represented by the average fuel consumption of the traditional fuel vehicle. The calculation method does not consider the characteristics of the extended range electric vehicle, the calculated oil consumption data is often lower than the actual oil consumption, and the oil consumption in the current period jumps when the period is switched, so that the average oil consumption jumps, and the average energy consumption state of the whole vehicle cannot be well represented.
Therefore, how to calculate the average energy consumption of the extended range electric vehicle more accurately, so as to improve the accuracy of the predicted range, is an important problem to be solved at present.
[ Invention ]
The embodiment of the invention provides an average energy consumption method and electronic equipment, which can more accurately calculate the average energy consumption of an extended range electric vehicle, thereby improving the accuracy of predicted endurance mileage.
In a first aspect, an embodiment of the present invention provides a method for calculating average energy consumption of an extended-range electric vehicle, which is applied to an extended-range electric vehicle and stores in advance a weighted calculation coefficient corresponding to a battery power parameter range value and a fuel state parameter range value, the method including: obtaining energy consumption data of M different mileage segments of N mileage series, wherein each mileage series has respective unit length, and N and M are positive integers; acquiring the current battery electric quantity parameter and the fuel state parameter of the extended-range electric vehicle; p energy consumption data are selected from the energy consumption data of the M different mileage segments, wherein P is a positive integer, and M is smaller than P; acquiring a whole vehicle unit kilometer average energy consumption weighting calculation coefficient corresponding to the current battery electric quantity parameter and the fuel state parameter according to the current battery electric quantity parameter and the fuel state parameter; and according to the weighted calculation coefficient and the P energy consumption data, weighting and calculating the average energy consumption of the whole vehicle unit kilometer.
The energy consumption of different mileage segments is calculated through the battery electric quantity parameter and the fuel state parameter, and the weighting coefficient is also determined along with the battery electric quantity parameter range value and the fuel state parameter range value, so that the influence of the battery electric quantity on the determination of the endurance mileage can be reduced better.
In one possible design, acquiring corresponding energy consumption data from 0 for any mileage segment corresponding to each mileage series in N mileage series, and recording the corresponding energy consumption data of the mileage segment; when any one of the mileage segments reaches the preset upper limit value of the mileage segment, the energy consumption data corresponding to the mileage segment recorded at the moment is acquired, and the energy consumption data corresponding to the next mileage segment of the any one mileage segment is acquired from 0 until the energy consumption data corresponding to M different mileage segments are acquired from different mileage series.
The mileage starts from 0 and the corresponding energy consumption starts from 0, so that the energy consumption of the mileage can be conveniently and completely counted.
In one possible design, the energy consumption data obtained in any one mileage series is stored in the time sequence of the obtaining.
Chronological order may facilitate selective invocation of historical data.
In one possible design, the method for weighted calculating average energy consumption according to the weighted calculation coefficient and the P energy consumption data includes: the energy consumption A1 of the first mileage series when the latest mileage is full is obtained by the following steps: acquiring the driving distance D1 of the latest one mileage segment of the first mileage series, accumulated energy consumption data A1 corresponding to the actual driving distance D1 of the latest one mileage segment of the first mileage series, and energy consumption data A11 of the 11 th mileage segment of the first mileage series; a1full=a1xd1+ (1-D1) ×a11, where a1 full represents the energy consumption of the last mileage segment of the first mileage series when the last mileage segment of the first mileage series is full, A1 represents the energy consumption data corresponding to the actual travel distance D1 of the last mileage segment of the first mileage series, D1 represents the actual travel distance of the last mileage segment of the first mileage series, and a11 represents the current energy consumption data of the last mileage segment of the first mileage series;
The energy consumption a Flat plate per unit mileage of the first mileage series is obtained by: Wherein i=2, 3 … …, M; wherein A1 is full, A level is the energy consumption of the last mileage of the first mileage series, A level is the unit mileage energy consumption of the first mileage series, and M is the unit mileage number of the first mileage series.
In one possible design, the method for weighted calculating average energy consumption according to the weighted calculation coefficient and the P energy consumption data further includes: the energy consumption B1 of the second mileage series when the latest mileage is full is obtained by: acquiring the driving distance D2 of the latest mileage segment of the second mileage series, accumulated energy consumption data B1 corresponding to the actual driving distance D2 of the latest mileage segment of the second mileage series, and energy consumption data B11 of the 11 th mileage segment of the second mileage series; b1full=b1×d2+ (1-D2) ×b11, where B1 full represents the energy consumption data corresponding to the actual travel distance D2 of the last mileage segment of the second mileage series, D2 represents the actual travel distance of the last mileage segment of the second mileage series, and B11 represents the current energy consumption data of the last mileage segment of the second mileage series;
The energy consumption B Flat plate per unit mileage of the second mileage series is obtained by: Wherein i=2, 3, … …, M; wherein B1 is full and represents the energy consumption of the second mileage series when the latest mileage is full, B Flat plate is the unit mileage energy consumption of the second mileage series, and M is the unit mileage number of the first mileage series.
In a possible design, the mileage series includes a first mileage series and a second mileage series, wherein a preset upper limit value of a mileage segment of the first mileage series is a first upper limit value, a preset upper limit value of a mileage segment of the second mileage series is a second upper limit value, and the first upper limit value is less than the second upper limit value; the higher the battery charge parameter, the greater the value of the weighting coefficient of the second range series minus the weighting coefficient of the first range series.
In a second aspect, an embodiment of the present invention provides an average energy consumption calculating device for an extended-range electric vehicle, in which weighting coefficients corresponding to a battery power parameter range value and a fuel state parameter range value are stored in advance, the device including: the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring energy consumption data of M different mileage segments of N mileage series, wherein each mileage series has a unit length, and N, M is a positive integer; the acquisition module is also used for acquiring the current battery electric quantity parameter and the fuel state parameter of the extended-range electric vehicle; the selecting module is used for selecting P energy consumption data from the energy consumption data of the M different mileage segments, wherein P is a positive integer, and M is smaller than P; the acquisition module is further used for acquiring a whole vehicle unit kilometer average energy consumption weighting calculation coefficient corresponding to the current battery electric quantity parameter and the fuel state parameter according to the current battery electric quantity parameter and the fuel state parameter; and the calculation module is used for calculating the average energy consumption of the whole vehicle unit kilometer in a weighting manner according to the weighting calculation coefficient and the energy consumption data.
In a third aspect, embodiments of the present invention provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform any of the methods of the first aspect.
In a fourth aspect, an embodiment of the present invention provides an electronic device, including: at least one processor, at least one memory communicatively coupled to the processor, wherein the memory stores program instructions executable by the processor, the processor invoking the program instructions capable of performing any of the methods of the first aspect.
It should be understood that, the second to fourth aspects of the embodiments of the present application are consistent with the technical solutions of the first aspect of the embodiments of the present application, and the beneficial effects obtained by each aspect and the corresponding possible implementation manner are similar, and are not repeated.
[ Description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an average energy consumption calculation method according to an embodiment of the invention;
FIG. 2 is a diagram illustrating an energy consumption statistics according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating an energy consumption statistics according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an electronic computing device in accordance with an embodiment of the invention;
fig. 5 is a schematic diagram of an electronic device according to an embodiment of the invention.
[ Detailed description ] of the invention
For a better understanding of the technical solution of the present application, the following detailed description of the embodiments of the present application refers to the accompanying drawings. It should be understood that the described embodiments are merely some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first," "second," and the like of embodiments of the present invention are used solely for the purpose of distinguishing between descriptions and not necessarily for the purpose of indicating or implying a relative importance or order. "first somewhere" may be used for illustration, and may mean that there are also second somewhere, third somewhere, etc. that exist in parallel.
The accuracy of predicting the endurance mileage depends on the accuracy of calculating the average energy consumption of the extended range electric vehicle, and one purpose of the invention is to improve the accuracy of calculating the average energy consumption of the extended range electric vehicle.
In the running process of the extended range electric automobile, the mileage changes, the battery state and the fuel state also change, and the energy consumption rate can be influenced when the electric quantity changes. The invention provides an embodiment of an average energy consumption calculation method, which is used for storing battery electric quantity parameter range values and fuel state parameter range values in advance and utilizing the related characteristics of energy consumption, battery electric quantity parameters, fuel state parameters and the like of different sections of mileage. As shown in fig. 1, the battery power parameter range value and the weighting calculation coefficient corresponding to the fuel are stored in advance, and the average energy consumption calculation method includes:
s101: obtaining energy consumption data of M different mileage segments of N mileage series, wherein each mileage series has respective unit length, and N and M are positive integers;
s102: acquiring the current battery electric quantity parameter and the fuel state parameter of the extended-range electric vehicle;
s103: p energy consumption data are selected from the energy consumption data of the M different mileage segments, wherein P is a positive integer, and M is smaller than P;
s104: acquiring a whole vehicle unit kilometer average energy consumption weighting calculation coefficient corresponding to the current battery electric quantity parameter and the fuel state parameter according to the current battery electric quantity parameter and the fuel state parameter;
s105: and according to the weighted calculation coefficient and the P energy consumption data, weighting and calculating the average energy consumption of the whole vehicle unit kilometer.
In the case of extended range electric vehicles, only the battery power is considered, but the fuel state, such as the residual oil amount, the fuel type, etc., needs to be considered. The energy consumption of different mileage segments is calculated through the battery electric quantity parameter and the fuel state parameter, and the weighting calculation coefficient is also determined along with the comprehensive consideration of the battery electric quantity and the fuel, so that the influence of the battery electric quantity and the fuel on the judgment of the endurance mileage can be reduced better. And the accuracy of calculating the energy consumption of the whole vehicle is improved by considering the energy supply side of the whole vehicle and comprising a power battery and a range extender.
Step S101, obtaining energy consumption data of M different mileage segments of N mileage series, wherein each mileage series has respective unit length, and N and M are positive integers.
In some preferred embodiments of the present invention, energy consumption data of M different mileage segments of N mileage series are obtained, where each mileage series has a respective unit length, and N, M are positive integers;
n=2, m=15, the N mileage series are 2 mileage series a and B, a has total energy consumption data A1, A2, A3 … a10 of 10 different mileage segments, and B has total energy consumption data B1, B2, B3, B4, B5 of 5 different mileage segments. Wherein, the unit length of the mileage series A is 1km, and the unit length of the mileage series B is 10km. As shown in FIG. 2, A1 is the nearest 0-1km energy consumption data, A2 is the nearest 1-2km energy consumption data, … A10 is the nearest 9-10km energy consumption data; b1 is the nearest energy consumption data of 0-10km, B2 is the nearest energy consumption data of 10-20km, … B5 is the nearest energy consumption data of 40-50 km. That is, energy consumption data A1, A2, A3 … a10 and B1, B2, B3, B4, B5 of 15 different mileage segments of 2 mileage series a and B are acquired.
The data of the A1, A2, A3 … A10, B1, B2, B3, B4 and B5 are updated in real time, and the workload is high. A method of updating every other mileage may be adopted, and thus, the average energy consumption calculation method includes: acquiring corresponding energy consumption data from 0 for any mileage segment corresponding to each mileage series in the N mileage series, and recording the energy consumption data corresponding to the mileage segment; when any one of the mileage segments reaches the preset upper limit value of the mileage segment, the energy consumption data corresponding to the mileage segment recorded at the moment is acquired, and the energy consumption data corresponding to the next mileage segment of the any one mileage segment is acquired from 0 until the energy consumption data corresponding to M different mileage segments are acquired from different mileage series. The method of updating every other mileage is adopted, so that the updating frequency is reduced, and the workload is reduced.
In some preferred embodiments of the present invention, n=2, m=20, and the N mileage series are2 mileage series a and B. The first mileage series a includes the following parameters: the first mileage D1 recorded along with the running and the upper limit value of the first mileage are 1km; the energy consumption data includes: and the first energy consumption data corresponding to the first mileage of the following record is A1, and the 10 result energy consumption data A2, A3 … A11 corresponding to the record when the first mileage reaches the upper limit value of the first mileage.
The mileage covered by the second mileage series B may be determined according to the cruising ability in the fully charged state, if the electric vehicle runs about 100km when fully charged, and the mileage covered by B covers exactly one charging period, in this case, the energy consumption data of the whole cruising can be covered, so the second mileage series B includes the following parameters: the second mileage D2 recorded along with the driving and the upper limit value of the second mileage are 10km, and the energy consumption data comprise: and the second energy consumption data corresponding to the second mileage recorded during traveling are B1, and the result energy consumption data B2, B3 … B11 correspondingly recorded when the 10 second mileage reaches the upper limit value of the second mileage cover 100km.
In one period of the driving process, D1 and A1 start to increase along with the driving from 0; when D1 grows to 1km, A2 is obtained, then D1 and A1 start to grow along with the running again from 0, and when D1 grows to 1km, A3 is obtained, and the cycle is repeated.
In one period of the driving process, D2 and B1 start to increase along with the driving from 0; when D2 grows to 10km, B2 is obtained, then D2 and B1 start to grow along with the running again from 0, and when D2 grows to 10km, B3 is obtained, and the cycle is repeated.
As in the loop described above, if the storage of A2, A3 … A11, B2, B3 … B11 is time-free, the selective invocation of data is difficult. In one embodiment, the energy consumption data obtained in any one of the mileage series is then stored in the time sequence of the obtaining. Chronological order may facilitate selective invocation of historical data. One embodiment is designed as follows: in one period of the driving process, D1 and A1 start to increase along with the driving from 0; when D1 increases to 1km, a10 is given to a11, A9 is given to a … … to A3, A1 is given to A2, A1 is cleared, and D1 and A1 start to increase again with running from 0, and the cycle is repeated.
Step S102, acquiring the current battery electric quantity parameter and the fuel state parameter of the extended range electric vehicle;
step S103, selecting P energy consumption data from the energy consumption data of the M different mileage segments, wherein P is a positive integer, and M is smaller than P;
Step S104, acquiring a whole vehicle unit kilometer average energy consumption weighted calculation coefficient corresponding to the current battery electric quantity parameter and the fuel state parameter according to the current battery electric quantity parameter and the fuel state parameter;
For the data of the above embodiment, since A1, A2, A3 … a11, B1, B2, B3 … B11 are energy consumption of different mileage segments, the energy consumption data of each mileage segment may be different according to factors such as vehicle speed, vehicle load, and the like. Because the electric vehicle is an extended range electric vehicle, the vehicle characteristics need to be considered, and therefore, the battery state of charge and the fuel state need to be comprehensively considered. Therefore, the current battery electric quantity parameter and the fuel state parameter of the extended range electric vehicle are firstly obtained, and the average energy consumption weighting calculation coefficient of a corresponding whole vehicle unit kilometer is comprehensively determined through the current battery electric quantity state and the fuel state, so that the energy consumption calculation of the whole vehicle is more accurate.
Step S105 is a method for weighted calculation of average energy consumption according to the weighted calculation coefficient and the P energy consumption data, including:
the energy consumption A1 of the first mileage series when the latest mileage is full is obtained by the following steps:
acquiring the driving distance D1 of the latest one mileage segment of the first mileage series, accumulated energy consumption data A1 corresponding to the actual driving distance D1 of the latest one mileage segment of the first mileage series, and energy consumption data A11 of the 11 th mileage segment of the first mileage series;
A1full=a1×d1+ (1-D1) ×a11
Wherein, A1 is full to represent the energy consumption of the last mileage segment of the first mileage series, A1 represents the energy consumption data corresponding to the actual driving distance D1 of the last mileage segment of the first mileage series, D1 represents the actual driving distance of the last mileage segment of the first mileage series, and a11 represents the current energy consumption data of the last mileage segment of the first mileage series;
The energy consumption a-plane of the unit mileage of the first mileage series is obtained by:
wherein i=2, 3 … …, M;
Wherein A1 is full, A level is the energy consumption of the last mileage of the first mileage series, A level is the unit mileage energy consumption of the first mileage series, and M is the unit mileage number of the first mileage series.
The energy consumption B1 of the second mileage series when the latest mileage is full is obtained by:
Acquiring the driving distance D2 of the latest mileage segment of the second mileage series, accumulated energy consumption data B1 corresponding to the actual driving distance D2 of the latest mileage segment of the second mileage series, and energy consumption data B11 of the 11 th mileage segment of the second mileage series;
b1full=b1×d2+ (1-D2) ×b11
Wherein B1 represents the energy consumption data corresponding to the actual driving distance D2 of the latest one mileage segment of the second mileage series, D2 represents the actual driving distance of the latest one mileage segment of the second mileage series, and B11 represents the current energy consumption data of the last mileage segment of the second mileage series; the energy consumption B level of the unit mileage of the second mileage series is obtained by:
Wherein i=2, 3, … …, M;
wherein B1 is full and represents the energy consumption of the second mileage series when the latest mileage is full, B is flat and represents the unit mileage energy consumption of the second mileage series, and M is the unit mileage number of the first mileage series.
In some preferred embodiments of the invention, energy consumption data A1, A2, A3, … …, a10, a11, B1, B2, B3 … … B10, B11 of 11 different mileage segments of the following defined 2 mileage series a and B are obtained, and when the first mileage series is full of the most recent one, the energy consumption is assigned to the next one, so A1 is assigned to A2, A2 is assigned to A3, … …, a10 is assigned to a11, B1 is assigned to B2, B2 is assigned to B3, … …, B10 is assigned to B11. Each assignment, A1 and B1 are reset to 0 and the energy consumption value is retrieved.
The first mileage series a includes: the running distance D1 of the latest mileage section and the unit mileage upper limit value of the first mileage section are 1km, and the energy consumption data comprise: and 9 pieces of energy consumption data A1 corresponding to the actual driving distance D1 of the latest mileage segment of the first mileage series correspond to the recorded result energy consumption data A2, A3, … … and A10 when the actual driving distance D1 reaches the upper limit value of the first mileage segment, and the 11 th mileage segment of the first mileage series corresponds to the energy consumption data A11.
The second mileage series B includes: the running distance D2 of the latest mileage section and the unit mileage upper limit value of the first mileage section are 10km, and the energy consumption data comprise: and the 9 energy consumption data B1 and 9 corresponding to the actual driving distance D2 of the latest mileage segment of the first mileage series reach the upper limit value of the first mileage segment, and the recorded result energy consumption data B2, B3, … … and B10 correspond to the 11 th mileage segment of the first mileage series.
In one period of the driving process, D1 and A1 start to increase along with the driving from 0; when D1 grows to 1km, A2 is obtained, then D1 and A1 start to grow along with the running again from 0, and when D1 grows to 1km, new A2 is obtained, and the cycle is repeated.
In one period of the driving process, D2 and B1 start to increase along with the driving from 0; when D2 grows to 10km, B2 is obtained, then D2 and B1 start to grow along with the running again from 0, when D2 grows to 10km, new B2 is obtained, and the cycle is repeated.
In one cycle, A1, B1, D2 starts from 0, and as the vehicle travels, the A1, B1, D2 data grows in real time. When the vehicle runs to x km (x < 1), as shown in fig. 3, A1 is the nearest energy consumption data of 0-xkm, and A2 is the nearest energy consumption data of (x+1) -xkm; b1 is the most recent energy consumption data of 0-xkm, and B2 is the most recent energy consumption data of (x+10) -xkm.
Because A1 and D are slowly changed in real time along with the running process and have the carrying capacity, the energy consumption value used by the weighting obtained by the method cannot jump, so that the average energy consumption result cannot jump. The embodiment of the invention obtains the average energy consumption of the whole vehicle by using fewer stored values through an algorithm, calculates the average energy consumption in real time, can output in real time, and can also select the whole kilometer output.
In one scenario, when the total endurance is 100km and the charging gun is fully charged and just pulled out, once the driver drives for a period of intense driving, the energy consumption counted by 1-10km recently rises rapidly, and if the weight of the energy consumption counted at the moment is too large, the endurance mileage calculated according to the energy consumption drops rapidly. However, when the actual driving is performed to have a low electric quantity, the driving is relaxed greatly, and the battery does not output high power as easily as when full power is supplied, so that the actual endurance mileage should not be reduced rapidly as in the case. The cruising is calculated according to the energy consumption, if the cruising is calculated according to the energy consumption of the latest driving situation, the cruising is not suitable for the extended range electric vehicle in the just-fully charged state. The energy consumption data is covered for 100km, so that the calculation of the endurance mileage is more flexible, and the calculation can be adjusted by using a weight modifying method, so that the accuracy of the endurance mileage is improved.
When the electric quantity is low, the output power of the battery is usually lower, and the energy consumption of a shorter mileage can reflect the current and the next energy consumption more than the energy consumption of a longer mileage, so that the energy consumption of the shorter mileage is weighted more heavily and the energy consumption of the longer mileage is weighted less heavily; when the electricity quantity is high, the output power of the battery is usually higher, the energy consumption of a short-term small mileage is not better reflected than the energy consumption of a long-term large mileage, so that the energy consumption of the short-term small mileage is weighted more heavily, and the energy consumption of a long-term mileage is weighted more heavily. In the design, the weighting coefficient of the first mileage series is Q, and the weighting coefficient of the second mileage series is (1-Q). When the battery electric quantity parameter is high, the weighting coefficient of the second mileage series is larger than that of the first mileage series, so that smaller weight can be used for energy consumption of a short mileage and larger weight can be used for energy consumption of a longer mileage. Thus, in one embodiment, the mileage series includes at least a first mileage series and a second mileage series, where the preset upper limit value of the mileage of the first mileage series is a first upper limit value, the preset upper limit value of the mileage of the second mileage series is a second upper limit value, and the first upper limit value is less than the second upper limit value.
It should be noted that if the initial values of the energy consumption data such as A1 to a11 are all 0, the calculated average energy consumption will be 0, and the calculated range=energy reserve/average energy consumption will result in a calculation error of 0 in the denominator. Therefore, the initial values of A1 to a11 are non-0 data, and may be set to average energy consumption calculated from the announced pure electric endurance and the announced battery power amount, so as to ensure normal calculation of the endurance mileage. An alternative embodiment is as follows:
1. the setting storage unit stores the following parameters:
(1) D1 is the driving distance of the vehicle, D1 starts from zero, grows along with the driving in real time, clears when the driving distance is increased to 1km, and restarts the mileage of the next round of 0-1 km;
(2) D2 is the distance of the vehicle, D2 starts from zero, grows along with the running in real time, clears when the distance grows to 10km, and resumes the mileage of the next round of 0-10 km;
(3) A1 is the energy consumption of the vehicle in the D1 driving distance;
(4) B1 is the energy consumption of the vehicle in the D2 driving distance;
(5) A2 represents the energy consumption corresponding to 1km recently recorded by the vehicle, and both A1 and A2 are provided with initial values which are not 0; when D1 grows to 1km, A2 is given an A1 value, and A1 is cleared.
(6) B2 represents the energy consumption corresponding to the last recorded 10km of the vehicle; b1 and B2 are respectively provided with an initial value which is not 0; when D2 grows to 10km, B1 value is given to B2, and B1 is cleared.
2. The average energy consumption of the last 10 kilometers and 100 kilometers is calculated,
Acquiring an actual mileage A real of the latest 1km running;
when the running distance is more than 10km and the latest 1km runs for less than the whole kilometer, the energy consumption of the latest 1 km: a1full=areal+ (1-D1) ×a11;
Acquiring the actual mileage Breal of the latest 10km running;
when the running distance is more than 100km, the latest 10km is less than the whole 10km, and the latest 10km has the energy consumption: b1full=breal+ (10-D2) ×b11;
average energy consumption per kilometer of the last 10 km:
Namely:
wherein i=2, 3 … …, M;
Wherein A1 is full, A level is the energy consumption of the last mileage of the first mileage series, A level is the unit mileage energy consumption of the first mileage series, and M is the unit mileage number of the first mileage series.
Average energy consumption per kilometer of the last 100 km:
Namely:
Wherein i=2, 3, … …, M;
wherein B1 is full and represents the energy consumption of the second mileage series when the latest mileage is full, B is flat and represents the unit mileage energy consumption of the second mileage series, and M is the unit mileage number of the first mileage series.
3. Calculating average energy consumption per kilometer AP according to a weighting coefficient Q, determining the Q according to the residual quantity and the residual quantity, wherein the residual quantity can be obtained through one or more parameters in SOE, SOC, SOP, SOH,
AP＝A Flat plate ×(1-Q)+B Flat plate ×Q
Wherein, the relation between Q and the remaining electric quantity and the remaining oil quantity is as follows, wherein Q 1-12 is a constant, and is calculated by noticing pure electric endurance and noticing the battery electric quantity:
and the residual electric quantity and the oil quantity jointly confirm a weighting coefficient, wherein the coefficient is 1 when the electric quantity and the oil quantity are full, and the weighting coefficient is reduced along with the reduction of the electric quantity and the oil quantity. But after the weighting factor drops to a certain value, the weighting factor remains unchanged, for example: when both the electric quantity and the oil quantity become 0, the weighting coefficient becomes 0.7 from 1.
The weighting calculation mode considers the average energy consumption of the whole vehicle at the nearest 100 km and the nearest 10 km, comprehensively considers the proper weighting calculation coefficient to carry out weighting calculation according to the battery state of charge and the fuel state, ensures that the calculation result is more accurate, and provides reliable technical support for the calculation of the endurance mileage. The weighting calculation coefficient of the embodiment of the invention is determined by the residual electric quantity of the power battery and the residual oil quantity of the oil tank, and the ratio of average energy consumption is increased along with the reduction of the residual electric quantity of the vehicle and the residual fuel, so that the current real energy consumption state of the vehicle can be reflected to a certain extent.
Referring to fig. 4, the present invention provides an embodiment of an electronic computing device for calculating average energy consumption of an extended-range electric vehicle, including: the acquiring module 401 is configured to acquire energy consumption data of M different mileage segments of N mileage series, where each mileage series has a respective unit length, and N, M is a positive integer, and is further configured to acquire a current battery power parameter and a fuel state parameter of the extended-range electric vehicle, and further configured to acquire, according to the current battery power parameter and the fuel state parameter, a whole-vehicle unit kilometer average energy consumption weighting calculation coefficient corresponding to the current battery power parameter and the fuel state parameter; a selecting module 402, configured to select P energy consumption data from the energy consumption data of the M different mileage segments, where P is a positive integer, and M < P; and the calculating module 403 is configured to obtain P weighting coefficients corresponding to the P energy consumption data according to the current battery power parameter, and weight and calculate average energy consumption per kilometer according to the weighting coefficients and the energy consumption data.
Referring to fig. 5, the present invention provides an electronic device embodiment 500, including: at least one processor 501, at least one memory 502 communicatively coupled to the processor, wherein: the memory 502 stores program instructions executable by the processor 501, which the processor 501 invokes to perform any of the methods of the present invention. The principle and technical effects of the method may be described with reference to the related descriptions in the embodiments described in the method, and are not described herein.
The present invention provides a non-transitory computer-readable storage medium embodiment storing computer instructions that cause the computer to perform any of the methods described herein. The principle and technical effects of the method may be described with reference to the related descriptions in the embodiments described in the method, and are not described herein.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. In the several embodiments provided in this specification, the disclosed systems, devices, and methods may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interface, indirect coupling or communication connection of devices or units, electrical, mechanical, or other form.
The functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.
The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform part of the steps of the methods described in the embodiments of the present specification. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a read-only memory (ROM), a random-access memory (RAM), a magnetic disk, or an optical disk, etc.
The present invention is not described with reference to flowchart illustrations and/or block diagrams of methods, apparatus, systems, and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing embodiments are merely illustrative of the technical solutions of the embodiments of the present invention, and are not limiting thereof; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. An average energy consumption calculation method applied to an extended range electric vehicle, characterized in that a weighted calculation coefficient corresponding to a battery power parameter range value and a fuel state parameter range value is stored in advance, the method comprising:
obtaining energy consumption data of M different mileage segments of N mileage series, wherein each mileage series has respective unit length, and N and M are positive integers;
acquiring the current battery electric quantity parameter and the fuel state parameter of the extended-range electric vehicle;
p energy consumption data are selected from the energy consumption data of the M different mileage segments, wherein P is a positive integer, and M is smaller than P;
Acquiring a whole vehicle unit kilometer average energy consumption weighting calculation coefficient corresponding to the current battery electric quantity parameter and the fuel state parameter according to the current battery electric quantity parameter and the fuel state parameter;
according to the weighting calculation coefficient and the P energy consumption data, weighting calculation is carried out on the average energy consumption of the whole vehicle unit kilometer;
the method for weighted calculation of average energy consumption according to the weighted calculation coefficient and the P energy consumption data comprises the following steps:
the energy consumption A1 of the first mileage series when the latest mileage is full is obtained by the following steps:
acquiring the driving distance D1 of the latest one mileage segment of the first mileage series, accumulated energy consumption data A1 corresponding to the actual driving distance D1 of the latest one mileage segment of the first mileage series, and energy consumption data A11 of the 11 th mileage segment of the first mileage series;
A1full=a1×d1+ (1-D1) ×a11
Wherein, A1 is full and represents the energy consumption of the last mileage segment of the first mileage series, A1 represents the energy consumption data corresponding to the actual driving distance D1 of the last mileage segment of the first mileage series, D1 represents the actual driving distance of the last mileage segment of the first mileage series, and a11 represents the current energy consumption data of the last mileage segment of the first mileage series;
The energy consumption a-plane of the unit mileage of the first mileage series is obtained by:
wherein i=2, 3 … …, M;
Wherein, A1 is full and represents the energy consumption of the last mileage of the first mileage series, A level represents the unit mileage energy consumption of the first mileage series, M represents the unit mileage number of the first mileage series;
the energy consumption B1 of the second mileage series when the latest mileage is full is obtained by:
Acquiring the driving distance D2 of the latest mileage segment of the second mileage series, accumulated energy consumption data B1 corresponding to the actual driving distance D2 of the latest mileage segment of the second mileage series, and energy consumption data B11 of the 11 th mileage segment of the second mileage series;
b1full=b1×d2+ (1-D2) ×b11
Wherein B1 represents the energy consumption data corresponding to the actual driving distance D2 of the latest one mileage segment of the second mileage series, D2 represents the actual driving distance of the latest one mileage segment of the second mileage series, and B11 represents the current energy consumption data of the last mileage segment of the second mileage series;
The energy consumption B level of the unit mileage of the second mileage series is obtained by:
Wherein i=2, 3, … …, M;
wherein, B1 is full and represents the energy consumption of the latest mileage of the second mileage series when the mileage of the latest mileage is full, B level represents the unit mileage energy consumption of the second mileage series, M represents the unit mileage number of the second mileage series;
the average energy consumption of the whole vehicle per kilometer is obtained by the following steps:
acquiring a weighting coefficient Q corresponding to the current battery electric quantity parameter and the fuel state parameter;
AP＝A Flat plate ×Q+B Flat plate ×(1-Q)
Wherein, AP represents the average energy consumption of the whole vehicle per kilometer, A Flat plate represents the energy consumption of the unit mileage of the first mileage series, B Flat plate represents the energy consumption of the unit mileage of the second mileage series, and Q represents the weighting calculation coefficient.
2. The method of claim 1, wherein the method of obtaining energy consumption data for M different mileage segments of the N mileage series comprises:
acquiring corresponding energy consumption data from 0 for any mileage segment corresponding to each mileage series in the N mileage series, and recording the energy consumption data corresponding to the mileage segment; when any one of the mileage segments reaches the preset upper limit value of the mileage segment, the energy consumption data corresponding to the mileage segment recorded at the moment is acquired, and the energy consumption data corresponding to the next mileage segment of the any one mileage segment is acquired from 0 until the energy consumption data corresponding to M different mileage segments are acquired from different mileage series.
3. The method of claim 2, comprising storing the energy consumption data acquired in any of the mileage series in the time sequence of acquisition.
4. The method of claim 1, wherein the mileage series includes a first mileage series, a second mileage series, wherein a preset upper limit value of mileage segments of the first mileage series is a first upper limit value, wherein a preset upper limit value of mileage segments of the second mileage series is a second upper limit value, and wherein the first upper limit value is < the second upper limit value; the higher the battery charge parameter, the greater the value of the weighting coefficient of the second range series minus the weighting coefficient of the first range series.
5. An average energy consumption calculation apparatus for an extended range electric vehicle, characterized in that weighting coefficients corresponding to a battery power parameter range value and a fuel state parameter range value are stored in advance, the apparatus comprising:
The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring energy consumption data of M different mileage segments of N mileage series, wherein each mileage series has a unit length, and N, M is a positive integer;
The acquisition module is also used for acquiring the current battery electric quantity parameter and the fuel state parameter of the extended-range electric vehicle;
The selecting module is used for selecting P energy consumption data from the energy consumption data of the M different mileage segments, wherein P is a positive integer, and M is smaller than P;
the acquisition module is further used for acquiring a whole vehicle unit kilometer average energy consumption weighting calculation coefficient corresponding to the current battery electric quantity parameter and the fuel state parameter according to the current battery electric quantity parameter and the fuel state parameter;
the calculation module is used for calculating the average energy consumption of the whole vehicle unit kilometer in a weighting manner according to the weighting calculation coefficient and the energy consumption data;
the method for weighted calculation of average energy consumption according to the weighted calculation coefficient and the P energy consumption data comprises the following steps:
the energy consumption A1 of the first mileage series when the latest mileage is full is obtained by the following steps:
acquiring the driving distance D1 of the latest one mileage segment of the first mileage series, accumulated energy consumption data A1 corresponding to the actual driving distance D1 of the latest one mileage segment of the first mileage series, and energy consumption data A11 of the 11 th mileage segment of the first mileage series;
A1full=a1×d1+ (1-D1) ×a11
Wherein, A1 is full and represents the energy consumption of the last mileage segment of the first mileage series, A1 represents the energy consumption data corresponding to the actual driving distance D1 of the last mileage segment of the first mileage series, D1 represents the actual driving distance of the last mileage segment of the first mileage series, and a11 represents the current energy consumption data of the last mileage segment of the first mileage series;
The energy consumption a-plane of the unit mileage of the first mileage series is obtained by:
wherein i=2, 3 … …, M;
Wherein, A1 is full and represents the energy consumption of the last mileage of the first mileage series, A level represents the unit mileage energy consumption of the first mileage series, M represents the unit mileage number of the first mileage series;
the energy consumption B1 of the second mileage series when the latest mileage is full is obtained by:
Acquiring the driving distance D2 of the latest mileage segment of the second mileage series, accumulated energy consumption data B1 corresponding to the actual driving distance D2 of the latest mileage segment of the second mileage series, and energy consumption data B11 of the 11 th mileage segment of the second mileage series;
b1full=b1×d2+ (1-D2) ×b11
Wherein B1 represents the energy consumption data corresponding to the actual driving distance D2 of the latest one mileage segment of the second mileage series, D2 represents the actual driving distance of the latest one mileage segment of the second mileage series, and B11 represents the current energy consumption data of the last mileage segment of the second mileage series;
The energy consumption B level of the unit mileage of the second mileage series is obtained by:
Wherein i=2, 3, … …, M;
wherein, B1 is full and represents the energy consumption of the latest mileage of the second mileage series when the mileage of the latest mileage is full, B level represents the unit mileage energy consumption of the second mileage series, M represents the unit mileage number of the second mileage series;
the average energy consumption of the whole vehicle per kilometer is obtained by the following steps:
acquiring a weighting coefficient Q corresponding to the current battery electric quantity parameter and the fuel state parameter;
AP＝A Flat plate ×Q+B Flat plate ×(1-Q)
Wherein, AP represents the average energy consumption of the whole vehicle per kilometer, A Flat plate represents the energy consumption of the unit mileage of the first mileage series, B Flat plate represents the energy consumption of the unit mileage of the second mileage series, and Q represents the weighting calculation coefficient.
6. A non-transitory computer readable storage medium storing computer instructions that cause the computer to perform the method of any one of claims 1 to 4.
7. An electronic device, comprising: at least one processor, at least one memory communicatively coupled to the processor, wherein:
The memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1-4.
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