CN114154107A - Method and device for calculating average energy consumption - Google Patents
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Abstract
本发明提出了一种平均能耗计算方法，应用于增程式电动车，预先存储有电池电量参数范围值和燃料状态参数范围值所对应的加权计算系数，所述方法包括：获取行驶N个里程系列的M个不同里程段的能耗数据，其中，各里程系列有各自的单位长度；获取所述增程式电动车当前电池电量参数和燃料状态参数；根据所述当前电池电量参数和燃料状态参数，获取对应的加权计算系数；根据所述加权系数以及所述能耗数据，加权计算单位公里平均能耗。本发明为了避免分段的数据统计时跳变，对于分段能耗的数据限定了统计和存储方式，本发明通过这种方法，提高了平均能耗的准确性，为计算续航里程提供了重要参考。
The present invention proposes a method for calculating average energy consumption, which is applied to extended-range electric vehicles, and pre-stores weighted calculation coefficients corresponding to the range values of battery power parameters and the range values of fuel state parameters. The method includes: obtaining N driving distances. Energy consumption data of M different mileage segments of a series, wherein each mileage series has its own unit length; obtain the current battery power parameters and fuel state parameters of the extended-range electric vehicle; according to the current battery power parameters and fuel state parameters , obtain the corresponding weighted calculation coefficient; according to the weighted coefficient and the energy consumption data, weighted calculation of the average energy consumption per unit kilometer. In order to avoid the jump in the statistics of the segmented data, the present invention defines the statistics and storage methods for the data of the segmented energy consumption. Through this method, the present invention improves the accuracy of the average energy consumption and provides an important factor for calculating the cruising range. refer to.
Description
[ technical field ] A method for producing a semiconductor device
The present application relates to the field of average energy consumption calculation technologies, and in particular, to an average energy consumption calculation method and apparatus.
[ background of the invention ]
The average energy consumption of the extended range electric vehicle is related to electricity and fuel consumption, and the calculation of the average energy consumption has certain difficulty. Because the range-extended electric vehicle has two 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 provides power support for the vehicle, thereby effectively improving the endurance mileage of the vehicle.
The driving range calculation accuracy of the extended range electric vehicle is an important index for improving the use experience of a user, the driving range at the present stage is calculated by adopting an average energy consumption method, but the average energy consumption of the whole extended range electric vehicle is still represented by the average oil consumption of the traditional fuel vehicle. The characteristics of the extended range electric vehicle are not considered in the calculation method, the calculated oil consumption data are often lower than the real oil consumption, and the average oil consumption jumps due to the jump of the oil consumption in the current period when the calculation period is switched, so that the average energy consumption state of the whole vehicle cannot be well represented.
Therefore, how to more accurately calculate the average energy consumption of the extended range electric vehicle so as to improve the accuracy of predicting the driving mileage is an important problem to be solved at present.
[ summary of the invention ]
The embodiment of the invention provides an average energy consumption method and electronic equipment, which can be used for more accurately calculating the average energy consumption of an extended range electric vehicle, so that the accuracy of predicting the endurance mileage is improved.
In a first aspect, an embodiment of the present invention provides an average energy consumption calculation method applied to an extended range electric vehicle, where weighting calculation coefficients corresponding to a battery power parameter range value and a fuel state parameter range value are stored in advance, where the method includes: acquiring energy consumption data of M different mileage sections of N mileage series, wherein each mileage series has a unit length, and N and M are positive integers; acquiring a current battery electric quantity parameter and a current fuel state parameter of the extended range electric vehicle; selecting P energy consumption data from the energy consumption data of the M different mileage sections, wherein P is a positive integer, and M is less than P; acquiring a weighted calculation coefficient of the average energy consumption of the whole vehicle per kilometer 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 weighting calculation coefficient and the P energy consumption data, carrying out weighting calculation on the average energy consumption of the whole vehicle per kilometer.
Energy consumption of different mileage sections is calculated through the battery electric quantity parameter and the fuel state parameter, and the weighting coefficient is 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 endurance mileage judgment can be better reduced.
In one possible design, in N mileage series, acquiring corresponding energy consumption data from 0 for any mileage section corresponding to each mileage series and recording the energy consumption data corresponding to the mileage section; and when any one mileage segment reaches a preset upper limit value of the mileage segment, acquiring the energy consumption data corresponding to the recorded mileage segment at the moment, and acquiring the energy consumption data corresponding to the next mileage segment of the mileage segment from 0 until acquiring the energy consumption data corresponding to M different mileage segments from different mileage series.
One section of mileage is started from 0 and the corresponding energy consumption is started from 0, so that the energy consumption of one section of mileage can be conveniently and completely counted.
In one possible design, the energy consumption data obtained from any mileage series is stored in the order of time of the acquisition.
Chronological order may facilitate selective invocation of historical data.
In one possible design, the method for weighted calculation of the average energy consumption according to the weighted calculation coefficients and the P energy consumption data includes: the energy consumption a1 full at the time of the full travel of the latest mileage segment of the first mileage series is obtained by: acquiring the driving distance D1 of the latest mileage section of the first mileage series, the accumulated energy consumption data A1 corresponding to the actual driving distance D1 of the latest mileage section of the first mileage series, and the energy consumption data A11 of the 11 th mileage section of the first mileage series; a1 full-a 1 × D1+ (1-D1) × a11, wherein a1 full represents the energy consumption of the last mileage segment of the first mileage series, 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 per unit mileage of the first mileage range series is obtained byFlat plate：
Wherein i is 2, 3 … …, M; wherein A1 full represents the energy consumption of the first mileage series when the last mileage segment is full, A level represents the energy consumption per mileage of the first mileage series, and M represents the number of mileage of the first mileage series.
In one possible design, the method for weighted calculation of the average energy consumption according to the weighted calculation coefficients and the P energy consumption data further includes: the energy consumption B1 full at the time of the full travel of the latest one mileage segment of the second mileage series is obtained by: acquiring the driving distance D2 of the latest mileage section of the second mileage series, the accumulated energy consumption data B1 corresponding to the actual driving distance D2 of the latest mileage section of the second mileage series, and the energy consumption data B11 of the 11 th mileage section of the second mileage series; b1 is full-B1 × D2+ (1-D2) × B11, wherein B1 is full-B represents energy consumption data corresponding to the actual travel distance D2 of the latest mileage section of the second mileage series, D2 represents the actual travel distance of the latest mileage section of the second mileage series, and B11 represents current energy consumption data of the latest mileage section of the second mileage series;
the energy consumption B of the unit mileage of the second mileage series is obtained in the following mannerFlat plate：
Wherein i is 2, 3, … …, M; wherein B1 full represents the energy consumption at which the last mileage segment of the second mileage series has run to completion, BFlat plateAnd M represents the unit mileage energy consumption of the second mileage series, and M represents the unit mileage number of the first mileage series.
In one possible design, the mileage series includes a first mileage series and a second mileage series, the preset upper limit value of the mileage section of the first mileage series is a first upper limit value, the preset upper limit value of the mileage section 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 value of the weighting coefficient of the second mileage series minus the weighting coefficient of the first mileage series is larger as the battery charge parameter is higher.
In a second aspect, an embodiment of the present invention provides an average energy consumption calculation apparatus for an extended range electric vehicle, in which weighting coefficients corresponding to a battery power parameter range value and a fuel status parameter range value are stored in advance, the apparatus including: the system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring energy consumption data of M different mileage sections of N mileage series, each mileage series has a unit length, and the N, M is a positive integer; the acquisition module is also used for acquiring the current battery electric quantity parameter and the current 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 sections, wherein P is a positive integer, and M is less than P; the acquiring module is further used for acquiring the weighted calculation coefficient of the average energy consumption per kilometer of the whole vehicle 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 calculating module is used for calculating the average energy consumption of the whole vehicle per kilometer in a weighting manner according to the weighting calculation coefficient and the energy consumption data.
In a third aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium for storing computer instructions for causing the computer to perform any one of the methods described in 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, and wherein the processor is capable of performing any of the methods of the first aspect when invoked by the program instructions.
It should be understood that the second to fourth aspects of the embodiment of the present application are consistent with the technical solution of the first aspect of the embodiment of the present invention, and the beneficial effects obtained by the aspects and the corresponding possible implementation are similar, and are not described again.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments will be briefly described below, and the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for calculating average energy consumption according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an embodiment of energy consumption statistics;
FIG. 3 is a schematic diagram of an embodiment of energy consumption statistics;
FIG. 4 is a schematic diagram of an electronic computing device according to an embodiment of the invention;
fig. 5 is a schematic diagram of an electronic device according to an embodiment of the invention.
[ detailed description ] embodiments
For better understanding of the technical solutions of the present application, the following detailed descriptions of the embodiments of the present application are provided with reference to the accompanying drawings. It should be understood that the embodiments described are only a few embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," and the like in the embodiments of the present invention are used for descriptive purposes only and not for purposes of indicating or implying relative importance, nor for purposes of indicating or implying order. "first certain" may be used for illustration, and may mean that there are also second certain, third certain, 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 object of the invention is to improve the accuracy of calculating the average energy consumption of the extended range electric vehicle.
In the process of driving the extended range electric automobile, the mileage changes, the battery state and the fuel state also change, and the energy consumption rate is influenced when the electric quantity changes. The invention provides an embodiment of an average energy consumption calculation method, which is characterized in that a battery electric quantity parameter range value and a fuel state parameter range value are stored in advance, and the characteristics related to energy consumption of different mileage, battery electric quantity parameters, fuel state parameters and the like are utilized. As shown in fig. 1, the battery electric quantity 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: acquiring energy consumption data of M different mileage sections of N mileage series, wherein each mileage series has a unit length, and N and M are positive integers;
s102: acquiring a current battery electric quantity parameter and a current fuel state parameter of the extended range electric vehicle;
s103: selecting P energy consumption data from the energy consumption data of the M different mileage sections, wherein P is a positive integer, and M is less than P;
s104: acquiring a weighted calculation coefficient of the average energy consumption of the whole vehicle per kilometer 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 weighting calculation coefficient and the P energy consumption data, carrying out weighting calculation on the average energy consumption of the whole vehicle per kilometer.
For the extended range electric vehicle, it is not comprehensive enough to consider only the battery power, and the fuel state, such as the remaining fuel amount and the fuel type, needs to be considered. Energy consumption of different mileage sections is calculated through the battery electric quantity parameter and the fuel state parameter, and the weighting calculation coefficient is comprehensively considered and determined along with the battery electric quantity and the fuel, so that the influence of the battery electric quantity and the fuel on the endurance mileage judgment can be better reduced. The energy consumption of the whole vehicle is calculated by considering the energy supply side of the whole vehicle, including the power battery and the range extender.
Step S101, energy consumption data of M different mileage sections of N mileage series are obtained, wherein each mileage series has a unit length, and N and M are positive integers.
In some preferred embodiments of the present invention, energy consumption data of M different mileage sections of N mileage series are obtained, wherein each mileage series has a respective unit length, and N and M are positive integers;
the N is 2, M is 15, the N mileage series is 2 mileage series A and B, A has energy consumption data A1, A2 and A3 … A10 of 10 different mileage sections in total, B has energy consumption data B1, B2, B3, B4 and B5 of 5 different mileage sections in total. Wherein, the unit length of the mileage series A is 1km, and the unit length of the mileage series B is 10 km. Referring to FIG. 2, A1 is the most recent energy consumption data from 0km to 1km, A2 is the most recent energy consumption data from 1km to 2km, … A10 is the most recent energy consumption data from 9 km to 10 km; b1 is the nearest 0-10km of energy consumption data, B2 is the nearest 10-20km of energy consumption data, … B5 is the nearest 40-50km of energy consumption data. Namely, energy consumption data A1, A2, A3 … A10 and B1, B2, B3, B4 and B5 of 15 different mileage sections of 2 mileage series A and B are obtained.
The data of the A1, the A2, the A3 …, the A10, the B1, the B2, the B3, the B4 and the B5 are updated in real time, and the workload is large. A method of updating every other mileage may be adopted, and then, the average energy consumption calculation method includes: acquiring corresponding energy consumption data from 0 for any mileage section corresponding to each mileage series in the N mileage series, and recording the energy consumption data corresponding to the mileage section; and when any one mileage segment reaches a preset upper limit value of the mileage segment, acquiring the energy consumption data corresponding to the recorded mileage segment at the moment, and acquiring the energy consumption data corresponding to the next mileage segment of the mileage segment from 0 until acquiring the energy consumption data corresponding to M different mileage segments 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 is 2 and M is 20, and the N mileage series is 2 mileage series a and B. The first mileage series a includes the following parameters: recording a first mileage section D1 along with the driving, wherein the upper limit value of the first mileage section is 1 km; the energy consumption data includes: the first energy consumption data corresponding to the first mileage segment recorded along with the driving is A1, and 10 corresponding recorded result energy consumption data A2 and A3 … A11 are recorded when the upper limit value of the first mileage segment is reached.
The mileage covered by the second mileage series B may be determined according to the cruising ability in a fully charged state, and if the electric vehicle travels about 100km when fully charged, and B covers at least 100km, it covers exactly one charging period, in which case the energy consumption data may cover the entire cruising, and then the second mileage series B includes the following parameters: the second mileage section D2 recorded along with the driving and the upper limit value of the second mileage section are 10km, and the energy consumption data comprises: the second energy consumption data corresponding to the second mileage section recorded along with the driving is B1, and 10 corresponding recorded result energy consumption data B2 and B3 … B11 cover 100km when the upper limit value of the second mileage section is reached.
In one period of the driving process, D1 and A1 increase from 0 along with the driving; when the D1 is increased to 1km, A2 is obtained, then D1 and A1 are increased from 0 to follow the running again, when the D1 is increased to 1km, A3 is obtained, and the process is circulated.
In one period of the driving process, D2 and B1 increase from 0 along with the driving; when the D2 is increased to 10km, B2 is obtained, then D2 and B1 are increased from 0 to follow the running again, when the D2 is increased to 10km, B3 is obtained, and the process is circulated.
In the above loop, if the storage of a2, A3 … a11, B2 and B3 … B11 is not time-sequential, the selection of data is difficult to call. In one embodiment, the energy consumption data acquired from any one mileage series is stored in the acquired time sequence. 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 increase from 0 along with the driving; when D1 increases to 1km, a10 value is given to a11, a9 value … … is given to a10, a2 value is given to A3, a1 value is given to a2, a1 is cleared, and then D1 and a1 increase again with the travel from 0, and the loop is repeated.
Step S102, acquiring a current battery electric quantity parameter and a current 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 sections, wherein P is a positive integer and M is less than P;
step S104, acquiring a weighted calculation coefficient of the average energy consumption of the whole vehicle per kilometer 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, and B3 … B11 are energy consumptions of different mileage sections, the energy consumption data of each mileage section may differ depending on the vehicle speed, the vehicle load, and the like. Because the vehicle characteristics of the extended-range electric vehicle need to be considered, the battery state of charge and the fuel state need to be comprehensively considered. Therefore, the current battery electric quantity parameter and the current fuel state parameter of the extended range electric vehicle are obtained firstly, and a corresponding average energy consumption weighting calculation coefficient per kilometer of the whole vehicle is determined comprehensively according to the current battery electric quantity state and the current fuel state, so that the energy consumption calculation of the whole vehicle is more accurate.
Step S105 is a method for calculating an average energy consumption in a weighted manner according to the weighted calculation coefficient and the P energy consumption data, and the method includes:
the energy consumption a1 full at the time of the full travel of the latest mileage segment of the first mileage series is obtained by:
acquiring the driving distance D1 of the latest mileage section of the first mileage series, the accumulated energy consumption data A1 corresponding to the actual driving distance D1 of the latest mileage section of the first mileage series, and the energy consumption data A11 of the 11 th mileage section of the first mileage series;
a1 full ═ a1 × D1+ (1-D1) × a11
Wherein A1 full represents the energy consumption of the first mileage section of the first mileage series, A1 represents the energy consumption data corresponding to the actual travel distance D1 of the latest mileage section of the first mileage series, D1 represents the actual travel distance of the latest mileage section of the first mileage series, and A11 represents the current energy consumption data of the last mileage section of the first mileage series;
the energy consumption A average of the unit mileage of the first mileage series is obtained by:
wherein i is 2, 3 … …, M;
wherein A1 full represents the energy consumption of the first mileage series when the last mileage segment is full, A level represents the energy consumption per mileage of the first mileage series, and M represents the number of mileage of the first mileage series.
The energy consumption B1 full at the time of the full travel of the latest one mileage segment of the second mileage series is obtained by:
acquiring the driving distance D2 of the latest mileage section of the second mileage series, the accumulated energy consumption data B1 corresponding to the actual driving distance D2 of the latest mileage section of the second mileage series, and the energy consumption data B11 of the 11 th mileage section of the second mileage series;
b1 full ═ B1 × D2+ (1-D2) × B11
Wherein, B1 fully represents the energy consumption data corresponding to the actual travel distance D2 of the latest mileage section of the second mileage series, D2 represents the actual travel distance of the latest mileage section of the second mileage series, and B11 represents the current energy consumption data of the latest mileage section of the second mileage series; the energy consumption B average of the unit mileage of the second mileage series is obtained by:
wherein i is 2, 3, … …, M;
wherein, B1 represents the energy consumption when the latest mileage segment of the second mileage series runs to full, B level represents the energy consumption per unit mileage of the second mileage series, and M represents the number of unit mileage 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 for 11 different mileage segments of the 2-mileage series a and B to be defined below are obtained, and when the latest segment of the first mileage series is full, 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. For each assignment, A1 and B1 are reset to 0, and the energy consumption value is obtained again.
The first mileage series a includes: the driving 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 comprises: energy consumption data A1 corresponding to the actual driving distance D1 of the latest mileage segment of the first mileage series, 9 corresponding recorded result energy consumption data A2, A3, … … and A10 when the upper limit value of the first mileage segment is reached, and energy consumption data A11 of the 11 th mileage segment of the first mileage series.
The second mileage series B includes: the driving 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 comprises: energy consumption data B1 corresponding to the actual driving distance D2 of the latest mileage segment of the first mileage series, 9 corresponding recorded result energy consumption data B2, B3, … … and B10 when the upper limit value of the first mileage segment is reached, and energy consumption data B11 of the 11 th mileage segment of the first mileage series.
In one period of the driving process, D1 and A1 increase from 0 along with the driving; when the D1 is increased to 1km, A2 is obtained, then D1 and A1 are increased from 0 to follow the running again, when the D1 is increased to 1km, new A2 is obtained, and the process is circulated.
In one period of the driving process, D2 and B1 increase from 0 along with the driving; when the D2 is increased to 10km, B2 is obtained, then D2 and B1 are increased from 0 to follow the running again, when the D2 is increased to 10km, new B2 is obtained, and the process is circulated.
In one period, the data A1, B1, D1 and D2 start from 0, and the data A1, B1, D1 and D2 increase in real time along with driving. When the vehicle runs to x km (x <1), A1 is the nearest energy consumption data from 0 th to xkm th, A2 is the nearest energy consumption data from (x +1) th to xkm th as shown in FIG. 3; b1 is the most recent energy consumption data from 0 th to xkm th, and B2 is the most recent energy consumption data from (x +10) -xkm th.
Since A1 and D are slowly changed in real time along with the running process and have continuity, the energy consumption value used for weighting obtained by the method does not jump, and the result of average energy consumption does not jump. The embodiment of the invention realizes that the average energy consumption of the whole vehicle is obtained by using fewer stored values through an algorithm, and the average energy consumption is calculated in real time and can be output in real time or output in whole kilometers.
In one scenario, when the total endurance is 100km, the charging gun is fully charged and is just pulled out, once the driver drives a section of fierce driving, the statistical energy consumption of the latest 1-10km will rapidly rise, and if the weight of the statistical energy consumption is too large, the endurance mileage calculated according to the energy consumption will rapidly decrease. However, when the driving is actually performed to a low level, the driving is very gentle, and the battery is not likely to output high power as when fully charged, so that the real driving range should not be rapidly reduced as in such a situation. The endurance is calculated according to the energy consumption, and if the endurance is calculated only according to the energy consumption of the recent driving condition, the extended range electric vehicle is not suitable for the extended range electric vehicle in the state of being fully charged. The energy consumption data covers 100km, so that the endurance mileage can be calculated more flexibly, 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 section of mileage which is smaller recently can reflect the current and the next energy consumption better than the energy consumption of a section of mileage which is larger recently, so that the energy consumption of a section of mileage which is smaller recently is weighted more, and the energy consumption of a section of mileage which is longer is weighted more heavily; when the electric quantity is high, the output power of the battery is higher, and the energy consumption of a small section of recent mileage is not as high as that of a large section of recent mileage to reflect the current and the next energy consumption, so that the energy consumption of the small section of recent mileage takes a smaller weight and the energy consumption of the long section of recent mileage takes a larger weight. 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 the energy consumption of a section of mileage which is small recently can be weighted less, and the energy consumption of a section of mileage which is long can be weighted more. In one embodiment, the mileage series includes at least a first mileage series and a second mileage series, the preset upper limit value of the mileage segment of the first mileage series is a first upper limit value, the preset upper limit value of the mileage segment of the second mileage series is a second upper limit value, and the first upper limit value is smaller than the second upper limit value.
It should be noted that if the initial values of the energy consumption data such as a 1-a 11 are all 0, the calculated average energy consumption will be 0, and the calculated endurance mileage is the energy reserve/average energy consumption, which will result in a calculation error of 0 at the denominator. Therefore, the initial value of A1-A11 is non-0 data, and can be set as the average energy consumption calculated according to the bulletin pure electric endurance and the bulletin battery power, so as to ensure the normal calculation of endurance mileage. An alternative embodiment is as follows:
1. the setting storage unit stores the following parameters:
(1) d1 is the running distance of the vehicle, D1 increases along with the running in real time from zero, clears when the running speed increases to 1km, and restarts the mileage counting of the next round of 0-1 km;
(2) d2 is the running distance of the vehicle, D2 increases along with the running in real time from zero, clears when the running speed increases to 10km, and restarts the mileage counting of the next round of 0-10 km;
(3) a1 is the energy consumption of the vehicle within the distance D1;
(4) b1 is the energy consumption of the vehicle within the distance D2;
(5) a2 represents the energy consumption corresponding to the latest recorded 1km of the vehicle, and both A1 and A2 are provided with initial values different from 0; when D1 increased to 1km, a2 was assigned a1 value and a1 was cleared.
(6) B2 represents the energy consumption corresponding to the 10km recorded by the vehicle recently; both B1 and B2 are set to initial values other than 0; when D2 increased to 10km, B2 was assigned a B1 value and B1 was cleared.
2. Calculating the average energy consumption of the last 10km and 100km,
acquiring the actual mileage A real of the latest 1km driving;
when the driving distance is more than 10km and the nearest 1km is less than the whole kilometer, the energy consumption of the nearest 1km is as follows: a1 full ═ a true + (1-D1) x a 11;
acquiring the actual mileage B real of the latest 10km driving;
when the driving distance is more than 100km and the nearest 10km driving distance is less than 10km completely, the energy consumption of the nearest 10km is as follows: b1 full ═ B true + (10-D2) x B11;
average energy consumption per kilometer of the last 10 km:
namely:
wherein i is 2, 3 … …, M;
wherein A1 full represents the energy consumption of the first mileage series when the last mileage segment is full, A level represents the energy consumption per mileage of the first mileage series, and M represents the number of mileage of the first mileage series.
Average energy consumption per kilometer of recent 100 km:
namely:
wherein i is 2, 3, … …, M;
wherein, B1 represents the energy consumption when the latest mileage segment of the second mileage series runs to full, B level represents the energy consumption per unit mileage of the second mileage series, and M represents the number of unit mileage of the first mileage series.
3. Calculating average energy consumption AP per kilometer according to a weighting coefficient Q, determining Q according to the remaining capacity and the remaining oil quantity, wherein the remaining capacity can be obtained by one or more parameters of SOE, SOC, SOP and SOH,
AP＝Aflat plate×(1-Q)+BFlat plate×Q
Wherein, the relationship between Q and the residual electric quantity and the residual oil quantity is shown in the following table, wherein Q1-12As a constant, the calculation of the bulleted pure electric endurance and the bulleted battery electric quantity is as follows:
the residual electric quantity and the oil quantity jointly determine a weighting coefficient, the coefficient is 1 when the oil is full, and the weighting coefficient is reduced along with the reduction of the electric quantity and the oil quantity. However, when the weighting factor drops to a certain value, the weighting factor remains unchanged, for example: when both the amount of electricity and the amount of oil become 0, the weighting coefficient becomes 0.7 from 1.
The weighted calculation mode considers the average energy consumption of the whole vehicle of the nearest 100 kilometers and the nearest 10 kilometers, and comprehensively considers and takes proper weighted calculation coefficients to perform weighted calculation according to the electric quantity state and the fuel state of the battery, so that the calculation result is more accurate, and reliable technical support is provided for the calculation of the endurance mileage. The weighting calculation coefficient of the embodiment of the invention is determined by the residual capacity of the power battery and the residual oil quantity of the oil tank, and the proportion of average energy consumption is improved along with the reduction of the residual capacity and the residual fuel of the vehicle, so that the current real energy consumption state of the vehicle can be reflected to a certain extent.
Referring to fig. 4, an embodiment of an electronic computing device for calculating average energy consumption of an extended range electric vehicle according to the present invention includes: an obtaining module 401, configured to obtain energy consumption data of M different mileage segments of N mileage series, where each mileage series has a unit length, and N, M is a positive integer, and further configured to obtain a current battery power parameter and a fuel state parameter of the extended range electric vehicle, and further configured to obtain, according to the current battery power parameter and the fuel state parameter, an average energy consumption weighting calculation coefficient per kilometer of the entire vehicle 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 is less than P; a calculating module 403, configured to obtain P weighting coefficients corresponding to the P energy consumption data according to the current battery power parameter, and perform weighted calculation on the average energy consumption per kilometer according to the weighting coefficients and the energy consumption data.
Referring to fig. 5, an embodiment 500 of an electronic device is provided, 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 calls to perform any of the methods described herein. For the implementation principle and the technical effect, reference may be made to the description related to the embodiments of the method, which is not repeated 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. For the implementation principle and the technical effect, reference may be made to the description related to the embodiments of the method, which is not repeated herein.
As will be appreciated by one skilled in the art, 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 system, apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Each functional unit in the embodiments of the present description may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods described in the embodiments of the present disclosure. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The present invention has not been 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
As mentioned above, the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and not to limit the same; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An average energy consumption calculation method is applied to an extended range electric vehicle and is characterized in that weighting calculation coefficients corresponding to a battery electric quantity parameter range value and a fuel state parameter range value are stored in advance, and the method comprises the following steps:
acquiring energy consumption data of M different mileage sections of N mileage series, wherein each mileage series has a unit length, and N and M are positive integers;
acquiring a current battery electric quantity parameter and a current fuel state parameter of the extended range electric vehicle;
selecting P energy consumption data from the energy consumption data of the M different mileage sections, wherein P is a positive integer, and M is less than P;
acquiring a weighted calculation coefficient of the average energy consumption of the whole vehicle per kilometer 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 weighting calculation coefficient and the P energy consumption data, carrying out weighting calculation on the average energy consumption of the whole vehicle per kilometer.
2. The method of claim 1, wherein the method of obtaining energy consumption data for M different mileage segments of a series of N miles driven comprises:
acquiring corresponding energy consumption data from 0 for any mileage section corresponding to each mileage series in the N mileage series, and recording the energy consumption data corresponding to the mileage section; and when any one mileage segment reaches a preset upper limit value of the mileage segment, acquiring the energy consumption data corresponding to the recorded mileage segment at the moment, and acquiring the energy consumption data corresponding to the next mileage segment of the mileage segment from 0 until acquiring the energy consumption data corresponding to M different mileage segments from different mileage series.
3. The method of claim 2, comprising storing the energy consumption data obtained from any one of the mileage series in the order of time of the obtaining.
4. The method of claim 2, wherein the step of weighted computing the average energy consumption according to the weighted computing coefficients and the P energy consumption data comprises:
the energy consumption a1 full at the time of the full travel of the latest mileage segment of the first mileage series is obtained by:
acquiring the driving distance D1 of the latest mileage section of the first mileage series, the accumulated energy consumption data A1 corresponding to the actual driving distance D1 of the latest mileage section of the first mileage series, and the energy consumption data A11 of the 11 th mileage section of the first mileage series;
a1 full ═ a1 × D1+ (1-D1) × a11
Wherein a1 full represents the energy consumption of the first mileage series when the last mileage segment 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 average of the unit mileage of the first mileage series is obtained by:
wherein i is 2, 3 … …, M;
wherein A1 full represents the energy consumption of the first mileage series when the last mileage segment is full, A level represents the energy consumption per mileage of the first mileage series, and M represents the number of mileage of the first mileage series.
5. The method of claim 2, wherein the step of weighted computing the average energy consumption according to the weighted computing coefficients and the P energy consumption data further comprises:
the energy consumption B1 full at the time of the full travel of the latest one mileage segment of the second mileage series is obtained by:
acquiring the driving distance D2 of the latest mileage section of the second mileage series, the accumulated energy consumption data B1 corresponding to the actual driving distance D2 of the latest mileage section of the second mileage series, and the energy consumption data B11 of the 11 th mileage section of the second mileage series;
b1 full ═ B1 × D2+ (1-D2) × B11
Wherein, B1 fully represents the energy consumption data corresponding to the actual travel distance D2 of the latest mileage section of the second mileage series, D2 represents the actual travel distance of the latest mileage section of the second mileage series, and B11 represents the current energy consumption data of the latest mileage section of the second mileage series;
the energy consumption B average of the unit mileage of the second mileage series is obtained by:
wherein i is 2, 3, … …, M;
wherein, B1 full represents the energy consumption when the latest mileage segment of the second mileage series runs to full, B level represents the energy consumption per unit mileage of the second mileage series, and M represents the number of unit mileage of the first mileage series.
6. The method of claim 2, wherein the method for calculating the average energy consumption per kilometer of the whole vehicle in a weighted manner according to the weighted calculation coefficients and the energy consumption data further comprises:
the average energy consumption per kilometer of the whole vehicle is obtained through the following modes:
acquiring a weighting coefficient Q corresponding to the current battery electric quantity parameter and the current fuel state parameter;
AP＝Aflat plate×Q+BFlat plate×(1-Q)
Wherein AP represents average energy consumption per kilometer of the whole vehicle, AFlat plateEnergy consumption per mileage representing a first mileage series, BFlat plateRepresents the energy consumption per unit mileage of the second mileage series, and Q represents a weighting calculation coefficient.
7. The method according to claim 5, wherein the mileage series includes a first mileage series and a second mileage series, the mileage section of the first mileage series has a preset upper limit value of a first upper limit value, the mileage section of the second mileage series has a preset upper limit value of a second upper limit value, and the first upper limit value < the second upper limit value; the value of the weighting coefficient of the second mileage series minus the weighting coefficient of the first mileage series is larger as the battery charge parameter is higher.
8. An average energy consumption calculation device for an extended range electric vehicle, wherein weighting coefficients corresponding to a battery power parameter range value and a fuel state parameter range value are stored in advance, the device comprising:
the system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring energy consumption data of M different mileage sections of N mileage series, each mileage series has a unit length, and the N, M is a positive integer;
the acquisition module is also used for acquiring the current battery electric quantity parameter and the current 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 sections, wherein P is a positive integer, and M is less than P;
the acquiring module is further used for acquiring the weighted calculation coefficient of the average energy consumption per kilometer of the whole vehicle 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 calculating module is used for calculating the average energy consumption of the whole vehicle per kilometer in a weighting manner according to the weighting calculation coefficient and the energy consumption data.
9. A non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the method of any one of claims 1 to 6.
10. An electronic device, characterized by 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 to 6.
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