CN110406427B - Self-learning method for remaining mileage of electric automobile - Google Patents
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- CN110406427B CN110406427B CN201910507603.3A CN201910507603A CN110406427B CN 110406427 B CN110406427 B CN 110406427B CN 201910507603 A CN201910507603 A CN 201910507603A CN 110406427 B CN110406427 B CN 110406427B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/50—Control modes by future state prediction
- B60L2260/52—Control modes by future state prediction drive range estimation, e.g. of estimation of available travel distance
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
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Abstract
The invention relates to the technical field of electric automobiles, and discloses a self-learning method for remaining mileage of an electric automobile, which comprises the following steps: when the prior power consumption coefficient does not exist, calculating an initial value of the power consumption coefficient and taking the initial value as the prior power consumption coefficient, and when the prior power consumption coefficient exists, acquiring the prior power consumption coefficient; when a first difference between the current battery residual capacity and the previous battery residual capacity is larger than a first threshold and a second difference between the current total mileage of travel and the previous total mileage of travel is larger than a second threshold, calculating a current power consumption coefficient, and calculating an average power consumption coefficient according to the current power consumption coefficient and the previous power consumption coefficient; and calculating the remaining mileage value at the current moment according to the average power consumption coefficient, the current battery remaining capacity and the battery health degree. The method has good adaptivity and robustness to the difference of the driving mileage of the whole vehicle caused by the environmental temperature, the driving working condition and the driving habit, so that the current remaining mileage of the whole vehicle can be accurately calculated.
Description
Technical Field
The invention belongs to the technical field of electric automobiles, and particularly relates to a self-learning method for remaining mileage of an electric automobile.
Background
The traditional method for calculating the remaining traveled mileage of the electric automobile comprises the steps of calculating energy consumption according to voltage and current integrals, calculating traveled mileage according to speed integrals, and calculating the remaining mileage according to the energy consumption, the traveled mileage, battery energy and remaining electric quantity.
Because the voltage and current fluctuation of the vehicle is large in the driving process, the calculation result of each time has large fluctuation, and the calculation accuracy of the remaining mileage is low. In addition, when the whole vehicle is stopped and powered off every time, data can be cleared, and when the vehicle is powered on again, the calculation can be carried out only by using a default value, so that the calculation of the remaining mileage at the initial stage of the vehicle is inaccurate. Meanwhile, the traditional remaining mileage calculation method is based on the current driving data for calculation, no history data is recorded, the calculation result of each time is independent, no continuity exists, and no self-learning function exists.
Patent CN201410103913.6 proposes a method for calculating energy consumption of driving by using voltage and current integrals, and calculating mileage of driving by using a rotating speed integral, thereby calculating average energy consumption and remaining mileage of a vehicle. The method is influenced by voltage and current fluctuation, so that the calculated remaining mileage generates large fluctuation, and a driver feels inaccurate calculation. Patent CN201510513893.4 proposes a method for dividing the state of charge of a power battery into 10 equally divided state of charge intervals, integrating the vehicle speed to obtain the driving mileage of each state of charge interval, recording the driving mileage, and calculating the remaining driving mileage according to the current state of charge lookup table. The remaining mileage calculated by the method is determined by the last mileage with the same charge state, and the remaining mileage is not calculated accurately when the current driving conditions of the last two times are different; in addition, the working condition that the air conditioner consumes power when the vehicle does not run is not considered, the vehicle actually does not run under the working condition, and the remaining mileage is reduced.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a self-learning method for the remaining mileage of the electric automobile, which is used for performing fusion calculation based on historical driving data and current driving data and deducing the current remaining mileage.
In order to achieve the above purpose, the solution adopted by the invention is as follows:
specifically, the self-learning method for the remaining mileage of the electric automobile comprises the following steps:
s1, judging whether a previous power consumption coefficient exists or not;
s2, when no current power consumption coefficient exists, calculating an initial value of the power consumption coefficient according to the default cruising mileage of the battery, taking the initial value as the current power consumption coefficient, and executing S3;
when the prior power consumption coefficient exists, acquiring the prior power consumption coefficient, and executing S3;
s3, acquiring the residual electric quantity of the battery at the current moment and the residual electric quantity of the battery at the previous moment, judging whether a first difference value between the residual electric quantity of the battery at the current moment and the residual electric quantity of the battery at the previous moment is larger than a first threshold value, if so, executing S4, and if not, executing S3;
s4, acquiring the total mileage of the current time and the total mileage of the previous time, judging whether a second difference value between the total mileage of the current time and the total mileage of the previous time is greater than a second threshold value, if so, executing S5, and if not, executing S4;
s5, calculating a first ratio of the second difference value to the first difference value, and taking the first ratio as a power consumption coefficient of the current moment;
s6, calculating an average power consumption coefficient according to the power consumption coefficient at the current moment and the historical power consumption coefficient;
and S7, acquiring the current capacity and the current health degree of the battery, and calculating the product of the average power consumption coefficient, the residual electric quantity at the current moment and the current health degree of the battery to obtain the residual mileage value at the current moment.
Further, the initial value is calculated according to the following formula:
initial value = battery default mileage/battery initial charge.
Further, the average power consumption coefficient is calculated by using a weighted summation formula, where the weighted summation formula is as follows:
wherein, K i Is the power consumption coefficient at time i, mu i As a weighting coefficient, K average The average power consumption coefficient is shown in the specification, wherein i =1,2, \ 8230and n is a positive integer.
Further, the weighting coefficients are determined according to a least squares filtering algorithm.
Further, when i is greater than 3, selecting the current power consumption coefficient at the time i, the power consumption coefficient at the time i-1 and the power consumption coefficient at the time i-2, and performing weighted summation on the current power consumption coefficient at the time i, the power consumption coefficient at the time i-1 and the power consumption coefficient at the time i-2 through the weighted summation formula to obtain the average power consumption coefficient.
The invention has the beneficial effects that:
the method calculates the power consumption coefficient of the current moment in real time based on the difference value of the driving mileage and the remaining power, obtains an average power consumption coefficient by weighting and summing the historical power consumption coefficients, calculates the remaining mileage according to the average power consumption coefficient and the remaining power, performs fusion calculation by combining historical driving data and current driving data, has self-learning capability, and can accurately calculate the current remaining mileage of the whole vehicle; based on the change calculation of the driving mileage, the phenomenon that the obtained remaining mileage jumps due to voltage and current fluctuation in the driving process of the vehicle to influence the calculation accuracy is avoided, and meanwhile, the method has good adaptivity and robustness to the difference of the driving mileage of the whole vehicle caused by the environmental temperature, the driving working condition and the driving habit; the driver can conveniently make a driving path decision, the phenomenon that the electric automobile is broken down due to the fact that the power battery is discharged in the driving process is avoided, meanwhile, the user is reminded of charging the battery in time, the possibility of over-discharging of the battery is avoided, and the service life of the battery is prolonged.
Drawings
FIG. 1 is a schematic diagram of a system for self-learning remaining mileage of an electric vehicle according to a preferred embodiment of the present invention;
FIG. 2 is a block diagram of functional units of a processor according to a preferred embodiment of the present invention;
FIG. 3 is a flowchart of a self-learning method for remaining mileage of an electric vehicle according to a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The terms "first", "second", "third", etc. are used merely to distinguish between descriptions and are not to be construed as indicating or implying a relative importance, the term "preceding" referring to a sample time instant preceding a current sample time instant.
The following describes a self-learning method for remaining mileage of an electric vehicle according to an embodiment of the present invention.
Referring to fig. 1, the self-learning method for the remaining mileage of the electric vehicle is applied to a self-learning system for the remaining mileage of the electric vehicle, and the system comprises a Vehicle Control Unit (VCU) of the electric vehicle, wherein the vehicle control unit of the electric vehicle comprises a CAN transceiver module, a processor and a storage module, the CAN transceiver module and the storage module are respectively connected with the processor, the storage module is an EEPROM and is used for storing a power consumption coefficient, and when the EEPROM is used for parking and powering off, data cannot be cleared, historical data CAN be effectively stored, and the accuracy of calculating the remaining mileage is ensured. The whole electric vehicle controller carries out data interaction with a Battery Management System (BMS) and an anti-lock braking system (ABS) through a CAN transceiver module, the battery management system is used for collecting the residual electric quantity information of the power battery, the anti-lock braking system calculates the traveling mileage data through monitoring wheel speed pulses, the VCU is also in communication connection with the instrument, and the data such as the residual electric quantity and the residual mileage are displayed through the instrument.
Referring to fig. 2, a first determining unit, a second determining unit, a first calculating unit, a second calculating unit, a third calculating unit and a fourth calculating unit are integrated in the processor.
A first judgment unit for judging whether there is a previous power consumption coefficient.
And the first calculation unit is used for calculating the initial value of the power consumption coefficient of the battery.
And the data acquisition unit is used for acquiring the residual capacity of the battery at the current moment, the residual capacity of the battery at the previous moment, the current capacity of the battery and the current health degree of the battery from the battery management system, and acquiring the total driving mileage data at the current moment and the total driving mileage at the previous moment from the anti-lock system.
And the second judgment unit is used for calculating a first difference value between the residual electric quantity of the battery at the current moment and the residual electric quantity of the battery at the previous moment, judging whether the difference value is greater than a set first threshold value, calculating a second difference value between the total mileage data at the current moment and the total mileage data at the previous moment, and judging whether the difference value is greater than a set second threshold value.
And a second calculation unit for calculating a current power consumption coefficient, the current power consumption coefficient = a second difference/the first difference.
And the third calculating unit is used for calculating the average power consumption coefficient according to the power consumption coefficient at the current moment and the historical power consumption coefficient.
And the fourth calculating unit is used for calculating the remaining mileage value according to the average power consumption coefficient, the remaining power at the current moment and the health state data of the battery.
In this embodiment, each of the functional units may be an independent unit or a combination of units, for example, the first determining unit and the second determining unit may be combined into one functional unit, and the first calculating unit, the second calculating unit, the third calculating unit and the fourth calculating unit may also be combined into one functional unit, each functional unit may represent one program segment or a portion of code, where the program segment or the portion of code includes one or more executable instructions for implementing a specified logical function.
Referring to fig. 3, the method includes the steps of:
s1, judging whether a previous power consumption coefficient exists in the EEPROM through a first judging unit, wherein the previous power consumption coefficient is a sampling time before the current sampling time, and the power consumption coefficient is the number of kilometers of running per unit energy consumption.
S2, when no current power consumption coefficient exists, a first calculation unit calculates an initial value of the power consumption coefficient according to the default cruising mileage of the battery, wherein the initial value of the power consumption coefficient = the default cruising mileage of the battery/the initial electric quantity of the battery, and if the theoretical cruising mileage of the whole vehicle 400KM is taken as an example, the residual electric quantity of the battery is 100%, the initial value of the power consumption coefficient is 400/100=4, the initial value is taken as the current power consumption coefficient and stored in an EEPROM, and S3 is executed;
when the previous power consumption coefficient exists, the data acquisition unit acquires the previous power consumption coefficient from the EEPROM, and S3 is performed.
S3, the data acquisition unit acquires the residual electric quantity of the battery at the current moment and the residual electric quantity of the battery at the previous moment through the BMS and sends the residual electric quantity to the second judgment unit, the second judgment unit calculates a first difference value between the residual electric quantity of the battery at the current moment and the residual electric quantity of the battery at the previous moment according to the following formula and judges whether the first difference value is larger than a set first threshold value or not, if the first difference value is larger than the first threshold value, S4 is executed, and if the first difference value is smaller than the first threshold value, S3 is executed;
for example, if the first threshold value is set to 20%, the remaining capacity of the battery at the current time is set to 40%, and the remaining capacity of the battery at the previous time is set to 65%, the first difference value is 65-40=25.
S4, the data acquisition unit acquires the total mileage of the current time and the total mileage of the previous time through the ABS system and sends the data to the second judgment unit, the second judgment unit calculates a second difference value between the total mileage of the current time and the total mileage of the previous time and judges whether the second difference value is greater than a set second threshold value, if so, S5 is executed, and if not, S4 is executed;
for example, if the second threshold value is set to 1KM, the total mileage traveled currently is 60KM, and the total mileage traveled previously is 30KM, the second difference value is 60-30=30km.
If the first difference is larger than the first threshold value, but the second difference is far smaller than the second threshold value (if the second difference is 0.3 KM), the vehicle is judged to be not moved, power is used for high-voltage equipment such as a vehicle-mounted air conditioner and PTC, at the moment, the calculation of the power consumption coefficient is not counted, and the accuracy of calculating the remaining mileage is effectively improved.
S5, calculating the current power consumption coefficient through a second calculating unit, namely calculating a first ratio of a second difference value to a first difference value, wherein the calculation formula is as follows:
S6, calculating an average power consumption coefficient by a third calculation unit according to the power consumption coefficient at the current moment and historical power consumption coefficients, wherein the historical power consumption coefficients comprise the power consumption coefficient at each sampling moment before the current moment; the average power consumption coefficient is calculated by adopting a weighted summation formula, wherein the weighted summation formula is as follows:
wherein, K i Is the power consumption coefficient at time i, mu i The weighting coefficients are determined according to a least square filtering algorithm, which is a conventional technical means and is not described herein, K average The average power consumption coefficient is shown in the specification, wherein i =1,2, \ 8230and n is a positive integer.
In this embodiment, the average power consumption coefficient is obtained by taking 3 power consumption coefficients to calculate the average power consumption coefficient in a weighted manner, that is, when i >3, the power consumption coefficient at the current time i, the power consumption coefficient at the time i-1 and the power consumption coefficient at the time i-2 are selected, and the power consumption coefficient at the current time i, the power consumption coefficient at the time i-1 and the power consumption coefficient at the time i-2 are weighted and summed through a weighted summation formula to obtain the average power consumption coefficient.
The process of calculating the average power consumption coefficient can also take different numbers of power consumption coefficients to perform weighted averaging, the number of values can be adjusted according to actual conditions, for example, 5 power consumption coefficients at the current moment and before can be taken, in the embodiment, taking 3 power consumption coefficients is only a preferred implementation mode, and the preferred implementation mode mainly aims to reduce the dependence of the remaining mileage calculation on historical data, if the values are too many, the dependence of the remaining mileage calculation on the historical data is stronger, and if the difference between the previous driving environment and the next driving environment is larger, the remaining mileage calculation is possibly inaccurate.
S7, the data acquisition unit acquires the current capacity and the current health degree of the battery through the BMS, the fourth calculation unit calculates the remaining mileage value at the current time according to the average power consumption coefficient, the remaining electric quantity at the current time and the current health degree of the battery, and the calculation formula is as follows:
S rem ＝K average * SOC SOH, wherein S rem Is the remaining mileageThe value, SOC is the current remaining capacity, SOH is the current health data of the battery.
The following examples illustrate:
a method for weighting and solving an average power consumption coefficient by taking 3 power consumption coefficients is adopted for a brand new electric automobile, the SOH is 1 because the automobile is a new automobile, the theoretical endurance of the battery of the whole automobile is 400km, the initial setting is that the first threshold value of SOC change is 20%, and the second threshold value of total mileage change is 10km; when the SOC is 100%, the VCU reads the historical information K from the EEPROM 1 、K 2 And K 3 Since there is no history information value for a new vehicle, K 1 、K 2 、K 3 Are all 400/100=4. In the embodiment, equal time interval sampling is adopted, and the weighting is carried out by applying a least square method 3 point 2 order filter coefficient to obtain K average ＝1/7*K 3 +3/7*K 2 +3/7*K 1 Then K is average And =4. According to S rem ＝K average * SOC SOH =4 SOH 100 1, i.e. SOC =100%, the initial endurance is 400km.
When the SOC is reduced to 39% after a certain period of running, for example, the power consumption coefficient K 1 、K 2 And K 3 Updating the iteration according to the above process if K 1 ＝3.4，K 2 ＝4.2，K 3 =3.6, and therefore K is calculated by weighting average =3.77, then according to S rem ＝K average * SOC SOH =3.77 SOH 391 =147, the remaining mileage is 147km.
When the vehicle has been used for 1 year, the SOH becomes 98%; powering up at SOC =86%, reading K of EEPROM 1 ＝3.18，K 2 ＝3.48，K 3 =2.89, so the weighting calculates K average =3.267, then S rem ＝K average * SOC SOH =3.267 SOH 86 0.98=275, so the remaining mileage is 275km.
By testing an electric vehicle with a certain driving range of 460km, the driving range is compared with the actual driving range from 100% to 35.2%, and the maximum error does not exceed 5%.
In conclusion, the method can combine historical driving data and current driving data to perform fusion calculation, has self-learning capability, and has good adaptivity and robustness to the difference of the driving mileage of the whole vehicle caused by environmental temperature, driving conditions and driving habits, so that the current remaining mileage of the whole vehicle can be accurately calculated, a driver can make a decision on a driving path conveniently, the phenomenon that the electric vehicle is anchored due to the fact that the power battery is discharged in the driving process is avoided, a user is reminded to charge the battery in time, the possibility of over-discharge of the battery is avoided, and the service life of the battery is prolonged.
In the embodiments provided in the present application, it should be understood that the present invention, if implemented in the form of software functional modules and sold or used as a stand-alone product, can be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, and various media capable of storing program codes.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A self-learning method for remaining mileage of an electric vehicle is characterized by comprising the following steps:
s1, judging whether a previous power consumption coefficient exists or not;
s2, when no current power consumption coefficient exists, calculating an initial value of the power consumption coefficient according to the default cruising mileage of the battery, taking the initial value as the current power consumption coefficient, and executing S3;
when the prior power consumption coefficient exists, acquiring the prior power consumption coefficient, and executing S3;
s3, acquiring the residual electric quantity of the battery at the current moment and the residual electric quantity of the battery at the previous moment, judging whether a first difference value between the residual electric quantity of the battery at the current moment and the residual electric quantity of the battery at the previous moment is larger than a first threshold value, if so, executing S4, and if not, executing S3;
s4, acquiring the total mileage of the current time and the total mileage of the previous time, judging whether a second difference value between the total mileage of the current time and the total mileage of the previous time is greater than a second threshold value, if so, executing S5, and if not, executing S4;
s5, calculating a first ratio of the second difference value to the first difference value, and taking the first ratio as a power consumption coefficient of the current moment;
s6, calculating an average power consumption coefficient according to the power consumption coefficient at the current moment and the historical power consumption coefficient;
and S7, acquiring the current capacity and the current health degree of the battery, and calculating the product of the average power consumption coefficient, the residual electric quantity at the current moment and the current health degree of the battery to obtain the residual mileage value at the current moment.
2. The self-learning method for the remaining mileage of electric vehicle as recited in claim 1, wherein the initial value is calculated according to the following formula:
initial value = battery default mileage/battery initial charge.
3. The self-learning method for the remaining mileage of electric vehicle as recited in claim 1, wherein the average power consumption coefficient is calculated by a weighted sum formula as follows:
wherein, K i Is the power consumption coefficient at time i, mu i As a weighting coefficient, K average The average power consumption coefficient is shown in the specification, wherein i =1,2, \ 8230and n is a positive integer.
4. The self-learning method for the remaining mileage of electric vehicle as recited in claim 3, wherein the weighting factor is determined according to a least square filtering algorithm.
5. The self-learning method for the remaining mileage of the electric vehicle as recited in claim 3, wherein when i >3, the current power consumption coefficient at the time i-1, and the current power consumption coefficient at the time i-2 are selected, and the current power consumption coefficient at the time i, the current power consumption coefficient at the time i-1, and the current power consumption coefficient at the time i-2 are weighted and summed by the weighted summation formula to obtain the average power consumption coefficient.
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| CN110901645A (en) * | 2019-11-11 | 2020-03-24 | 东风航盛（武汉）汽车控制***有限公司 | Method for calculating endurance mileage of extended-range hybrid electric vehicle |
| CN110861501A (en) * | 2019-11-19 | 2020-03-06 | 东风航盛（武汉）汽车控制***有限公司 | Method for estimating endurance mileage of electric automobile based on self-learning |
| CN110901468B (en) * | 2019-11-26 | 2022-11-29 | 神龙汽车有限公司 | Method for calculating remaining endurance mileage of electric learner-driven vehicle |
| EP3835115A1 (en) * | 2019-12-12 | 2021-06-16 | Permobil AB | Method of estimating the remaining driving distance available from a charge for a powered mobility device |
| CN112124147A (en) * | 2020-09-11 | 2020-12-25 | 东风华神汽车有限公司 | Method and system for estimating remaining driving range of pure electric vehicle adapting to driving habits |
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| CN112319307A (en) * | 2020-10-30 | 2021-02-05 | 东风襄阳旅行车有限公司 | K value-based electric automobile driving range estimation method |
| CN113306448A (en) * | 2021-06-17 | 2021-08-27 | 江苏云快充新能源科技有限公司 | Charging prompting method, device, equipment and storage medium |
| CN114407662B (en) * | 2022-02-11 | 2024-05-03 | 西北工业大学 | Residual mileage prediction method and device, electric automobile and storage medium |
| CN115465152A (en) * | 2022-10-21 | 2022-12-13 | 广东邦普循环科技有限公司 | Power battery monitoring method, device, equipment and storage medium |
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| CN105235543B (en) * | 2015-10-27 | 2017-11-14 | 北京新能源汽车股份有限公司 | Method, device and system for processing remaining driving mileage of electric automobile |
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