CN111717072A - Intelligent charging optimization method for electric vehicle battery - Google Patents

Intelligent charging optimization method for electric vehicle battery Download PDF

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Publication number
CN111717072A
CN111717072A CN202010454582.6A CN202010454582A CN111717072A CN 111717072 A CN111717072 A CN 111717072A CN 202010454582 A CN202010454582 A CN 202010454582A CN 111717072 A CN111717072 A CN 111717072A
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charging
battery
soc
electric
historical
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CN111717072B (en
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徐玉杰
翟树军
杨霞
袁海洲
吕岳
马梅
胡本哲
彭婧
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
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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]
    • B60L58/15—Preventing overcharging
    • 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
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/54—Drive Train control parameters related to batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

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  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Secondary Cells (AREA)

Abstract

The invention relates to an intelligent charging optimization method for an electric vehicle battery, which comprises the following steps of S1, obtaining historical charging data and historical driving data of the electric vehicle battery, wherein the historical charging data and the historical driving data comprise residual electric quantity before charging each time and initial electric quantity after charging, daily driving mileage of the electric vehicle, driving mileage of each charging and discharging period and average air temperature in the charging and discharging period, and S2, obtaining a conversion coefficient η between the driving mileage of the electric vehicle and the discharging electric quantity of the batteryi: s3, obtaining the travel distance x of the electric automobile on the next day according to the historical travel dataj+1(ii) a S4, judging whether the current remaining electric quantity of the battery of the electric automobile meets the travel requirement of the next day or not; s5, determining the optimal charging electric quantity required by the i +1 th charging: according to the invention, an optimal battery charging plan is scientifically determined according to historical use data and future travel requirements of the electric automobile, the charging and discharging period of the electric automobile is properly prolonged, and the charging times and the daily long-time deep charging frequency are reduced.

Description

Intelligent charging optimization method for electric vehicle battery
Technical Field
The invention belongs to the technical field of electric vehicle battery charging optimization, and particularly relates to an intelligent charging optimization method for an electric vehicle battery.
Background
With the continuous maturity of electric automobile technology, electric automobile keeps promoting rapidly, and battery continuation of the journey mileage also continuously increases, and some motorcycle types have reached single charge and have stably continued the journey more than 400 kilometers. At present, the main purpose of a private electric automobile is to go on duty and replace walking, the travel requirement in a city is met, the daily travel distance generally does not exceed 50 kilometers and is far lower than the designed endurance mileage, and more electric quantity is still stored in an electric automobile battery when the daily travel is finished. However, in reality, the car owner of the electric vehicle generally has a battery phobia (there is a fear that the battery capacity is not enough to maintain the next trip), and therefore, the car owner often charges the electric vehicle at will after the trip of the day is finished (the charging peak period is at night). This means that electric automobile is full of electricity state when beginning to use every day, still holds more electric quantity when finishing using the day, and the actual charge demand of daily is lower, because the car owner can't in time cut off the power night, and electric automobile is under 100% state of charge for a long time, can let the battery continuously be in high-pressure state, causes the battery temperature too high even, and in the past, can shorten electric automobile battery life, and there is the extravagant phenomenon of electric power, increases electric automobile use cost.
Therefore, according to historical use data of the electric automobile and future travel demands, an optimal battery charging plan is scientifically determined, the charging and discharging period of the electric automobile is properly prolonged, the charging times and daily long-time deep charging frequency are reduced, and the method is an important means for prolonging the service life and improving the use economy of the battery of the electric automobile.
Therefore, based on the problems, the intelligent charging optimization method for the electric vehicle battery, which optimizes the charging capacity and the charging behavior, has important practical significance.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an intelligent charging optimization method for an electric vehicle battery, which is used for optimizing the charging capacity and the charging behavior.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
the intelligent charging optimization method for the electric vehicle battery comprises the following steps:
s1, obtaining historical charging data and historical driving data of the electric automobile battery, wherein the historical charging data and the historical driving data comprise the residual electric quantity before each charging and the initial electric quantity after the charging is completed, the daily driving mileage of the electric automobile, the driving mileage of each charging and discharging period and the average temperature in the charging and discharging period;
wherein, the adjacent two charging periods are a complete charging and discharging period;
s2, obtaining a conversion coefficient η between the driving mileage of the electric automobile and the discharge electric quantity of the batteryi：
Figure BDA0002508744520000021
Wherein, ηiRepresents the driving distance, SOC, of the electric vehicle which can be driven every 1 degree of electricity effectively dischargedi,sRepresents the initial charge amount after the ith charging, SOCi,eRepresents the remaining capacity before the i +1 th charge, xj∑ x for the daily mileage of an electric vehiclejIndicating that the historical driving range is summed, ∑ ((SOC)i,s-SOCi,e)·μi) Represents the summation of historical discharge capacities;
μirepresents the average discharge efficiency, mu, in the i-th charge-discharge cycleiThe calculation formula of (2) is as follows:
Figure BDA0002508744520000022
n is the rated discharge frequency of the battery; t is0α is the discharging efficiency of the battery in the initial state;β, the discharge efficiency of the battery at the optimum discharge temperature, sigma is the influence degree of the temperature on the discharge efficiency;
s3, obtaining the travel distance x of the electric automobile on the next day according to the historical travel dataj+1；
S4, judging whether the current remaining electric quantity of the battery of the electric automobile meets the travel requirement of the next day or not;
obtaining the battery discharge quantity Q meeting the requirement of the j +1 sunriseout: according to the use safety and service life of the battery, determining the battery power storage demand Q: judging whether the electric automobile needs to be charged on the jth day:
if SOCi,eThe storage capacity of the storage battery of the electric automobile meets the requirement of sunrise at j +1 and the safety requirement of the battery, and at the moment, the storage battery of the electric automobile does not need to be charged at j;
if SOCi,eIf the current storage capacity of the storage battery of the electric automobile does not meet the requirement of sunrise +1 and the safety requirement of the battery, the storage battery of the electric automobile needs to be charged at the jth day;
s5, determining the optimal charging electric quantity required by the i +1 th charging:
s501, obtaining the driving mileage x of the electric vehicle in the (i + 1) th charging and discharging cycle according to historical trip datai+1：
S502, obtaining a predicted value SOC of the charging electric quantity according to the predicted value of the trip mileage, the conversion coefficient and the discharging efficiencyi+1,s：
Figure BDA0002508744520000031
Therein, SOCupIs the upper limit of rated capacity, k, of the storage battery1Is the battery capacity margin coefficient, k2The safety coefficient of the battery is set;
s503, determining a charging electric quantity correction value SOC 'according to the upper limit of the battery capacity'i+1,s：
Figure BDA0002508744520000032
S504, according to the expected trip mileage of the vehicle ownerDetermining the final electric quantity SOC' of the electric automobile after the charging for the (i + 1) th timei+1,s：
SOC″i+1,s＝max(SOC′i+1,s,SOCexp) (9)
In the formula, SOCexpThe battery charge after the charging is completed is expected for the user.
Further, the next-day driving mileage x of the electric automobilej+1The acquisition formula of (1) is as follows:
Figure BDA0002508744520000041
j1＝0,1,…,5，w1for the mileage traveled the same day of the week to the weight of the prediction, w2And the weights of the driving mileage on other days to the prediction result meet the following requirements: w is a1+6w21 and w1＞＞w2。
Further, the battery discharge amount Q meeting the requirement of the j +1 th sunrise is acquired in the step S4out：
Figure BDA0002508744520000042
In the formula (I), the compound is shown in the specification,
Figure BDA0002508744520000043
represents the amount of power required to complete the expected driving range; k is a radical of1The battery capacity surplus coefficient;
according to the use safety and service life of the battery, determining the battery power storage demand Q:
Q＝k2·SOCup+Qout(5)
in the formula, SOCupIs the upper limit of rated capacity, k, of the storage battery2To the cell safety factor, k2·SOCupRepresenting the lowest charge limit of the battery charge at the cut-off voltage.
Further, in S501, the driving distance x of the electric vehicle in the (i + 1) th charging and discharging cycle is acquired according to the historical trip datai+1：
Figure BDA0002508744520000044
In the formula i10,1, …, m-1, m is the continuous historical charge and discharge times before the i +1 th charge and discharge.
Further, m is more than or equal to 1.
Further, in step S504, if the owner has a long distance travel demand in the future, the SOC may be takenexp＝SOCupIf the owner has no high expected charge, then SOC is takenexp＝SOC′i+1,s。
The invention has the advantages and positive effects that:
according to the method, whether the current battery capacity meets the next-day requirement or not is predicted by simulating the historical charging and discharging rules and the trip rules of the electric vehicle, the optimal charging capacity is reasonably determined according to the future trip requirement, and the charging and discharging period of the electric vehicle is properly prolonged.
Detailed Description
First, it should be noted that the specific structures, features, advantages, etc. of the present invention will be specifically described below by way of example, but all the descriptions are for illustrative purposes only and should not be construed as limiting the present invention in any way.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The present invention will be specifically described below.
The intelligent charging optimization method for the electric vehicle battery comprises the following steps:
s1, obtaining historical charging data and historical driving data of the electric automobile battery, wherein the historical charging data and the historical driving data comprise the residual electric quantity before each charging and the initial electric quantity after the charging is completed, the daily driving mileage of the electric automobile, the driving mileage of each charging and discharging period and the average temperature in the charging and discharging period;
wherein, the adjacent two charging periods are a complete charging and discharging period; there may be 1 or more natural days during each charge-discharge cycle.
S2, obtaining a conversion coefficient η between the driving mileage of the electric automobile and the discharge electric quantity of the batteryi：
Figure BDA0002508744520000051
Wherein, ηiRepresents the driving distance, SOC, of the electric vehicle which can be driven every 1 degree of electricity effectively dischargedi,sRepresents the initial charge amount after the ith charging, SOCi,eRepresents the remaining capacity before the i +1 th charge, xj∑ x for the daily mileage of an electric vehiclejIndicating that the historical driving range is summed, ∑ ((SOC)i,s-SOCi,e)·μi) Represents the summation of historical discharge capacities;
μirepresents the average discharge efficiency, mu, in the i-th charge-discharge cycleiThe calculation formula of (2) is as follows:
Figure BDA0002508744520000061
n is the rated discharge times (same as the rated charge times) of the battery and can be determined by the factory parameters of the battery; t isiIs the average temperature in the charge-discharge period; t is0The method comprises the steps of representing an optimal discharge temperature at which the battery discharge efficiency is highest and can be determined by battery factory parameters, α representing the discharge efficiency in an initial state of the battery and can be determined by the battery factory parameters, β representing the battery discharge efficiency at the optimal discharge temperature and can be determined by the battery factory parameters, sigma being the influence degree of the temperature on the discharge efficiency and being obtained through simulation according to historical temperature data and the discharge efficiency, wherein in a normal condition, the relation between the discharge efficiency and the temperature approximately meets normal distribution, at the moment, sigma is the standard deviation of the average temperature change during the running of the electric automobile and can be the average temperature standard deviation of about 7 days, and k is an adjustable parameter and can be determined by the battery factory parameters according to the performance of the battery.
Under normal conditionsIn the adjacent charging period, the temperature change is not obvious, the battery service life (discharging times) is only different by 1 time, the two have little influence on the discharging efficiency, and at the moment, the parameters are basically kept unchanged. Meanwhile, with the progress of the electric automobile storage battery technology, the discharge efficiency muiThe discharge efficiency mu is basically kept stable in the economic life, so that the discharge efficiency mu can be determined according to the factory parameters of the storage battery in practical applicationiIs a constant value.
S3, obtaining the driving mileage xj of the electric automobile on the next day according to the historical travel data+1；
S4, judging whether the current remaining electric quantity of the battery of the electric automobile meets the travel requirement of the next day or not;
obtaining the battery discharge quantity Q meeting the requirement of the j +1 sunriseout: according to the use safety and service life of the battery, determining the battery power storage demand Q: judging whether the electric automobile needs to be charged on the jth day:
if SOCi,eThe storage capacity of the storage battery of the electric automobile meets the requirement of sunrise at j +1 and the safety requirement of the battery, and at the moment, the storage battery of the electric automobile does not need to be charged at j;
if SOCi,eIf the current storage capacity of the storage battery of the electric automobile does not meet the requirement of sunrise +1 and the safety requirement of the battery, the storage battery of the electric automobile needs to be charged at the jth day;
s5, determining the optimal charging electric quantity required by the i +1 th charging:
s501, obtaining the driving mileage x of the electric vehicle in the (i + 1) th charging and discharging cycle according to historical trip datai+1：
S502, obtaining a predicted value SOC of the charging electric quantity according to the predicted value of the trip mileage, the conversion coefficient and the discharging efficiencyi+1,s：
Figure BDA0002508744520000071
Therein, SOCupIs the upper limit of rated capacity, k, of the storage battery1Is the battery capacity margin coefficient, k2The safety coefficient of the battery is set;
s503, according to the batteryDetermining the upper limit of the capacity and determining a corrected value SOC of the charging electric quantity'i+1,s：
Figure BDA0002508744520000072
S504, determining the final electric quantity SOC' of the electric automobile after the charging for the (i + 1) th time is finished according to the expected trip mileage and the charging requirement of the automobile owneri+1,s：
SOC″i+1,s＝max(SOC′i+1,s,SOCexp) (9)
In the formula, SOCexpThe battery electric quantity after the charging is expected for the user, if the vehicle owner has a long-distance travel demand in the future, the SOC can be takenexp＝SOCupIf the owner has no high expected charge, then SOC is takenexp＝SOC′i+1,s。。
It should be noted that the following-day mileage x of the electric vehiclej+1The acquisition formula of (1) is as follows:
Figure BDA0002508744520000081
j1＝0,1,…,5，w1for the mileage traveled the same day of the week to the weight of the prediction, w2And the weights of the driving mileage on other days to the prediction result meet the following requirements: w is a1+6w21 and w1＞＞w2；w1、w2The numerical value of (a) can be simulated according to historical data, the simulation method can adopt the existing mature multivariate linear regression method and the like, and can also be set according to the travel rule characteristics, the stronger the travel rule of each week is, the higher the w is1The larger. It should be noted that private electric vehicles have strong regularity, each week (7 days) is generally used as a change cycle, and the travel regularity on the same day of each week has higher similarity, so the corresponding weight can be properly increased.
It should be noted that the battery discharge amount Q that satisfies the requirement of the j +1 th sunrise is acquired in step S4out：
Figure BDA0002508744520000082
In the formula (I), the compound is shown in the specification,
Figure BDA0002508744520000083
represents the amount of power required to complete the expected driving range; k is a radical of1The storage capacity margin coefficient of the battery can be set according to the risk bearing capacity of the vehicle owner, and is generally 1.0-1.5.
According to the use safety and service life of the battery, determining the battery power storage demand Q:
Q＝k2·SOCup+Qout(5)
in the formula, SOCupIs the upper limit of rated capacity, k, of the storage battery2The battery safety coefficient can be determined by battery delivery parameters; k is a radical of2·SOCupAnd the charge capacity of the battery is the lowest charge capacity limit under the cut-off voltage, and when the charge capacity is lower than the limit, the battery cannot be discharged continuously or the battery is damaged by continuous discharge.
It should be noted that in S501, the driving distance x of the electric vehicle in the i +1 th charging and discharging cycle is obtained according to the historical trip datai+1：
Figure BDA0002508744520000091
In the formula i1Where m is 0,1, …, and m-1, m is the number of historical charging and discharging times that continue before the i +1 th charging and discharging, and equation (6) represents the driving distance corresponding to the latest m charging and discharging cycles, and the driving distance of the electric vehicle in the i +1 th charging and discharging cycle is predicted. And m can be 1-10 generally, and if m is 3, the driving mileage corresponding to the latest 3 charge-discharge cycles is adopted, and the driving mileage of the electric automobile in the (i + 1) th charge-discharge cycle is predicted.
For example, in this embodiment, a private electric vehicle in a certain cell is charged, and the result of optimizing intelligent charging of the battery of the electric vehicle is simulated, according to the usage rule of the private electric vehicle, it is assumed that the private electric vehicle is charged after work every day, and the power supply is cut off before work the next day; in the embodiment, 4 electric automobiles with different specifications are selected, and the next day charging capacity is determined; the basic parameters of the electric vehicle battery are shown in table 1.
TABLE 1
Figure BDA0002508744520000092
Figure BDA0002508744520000101
Assuming that the current time point j is Tuesday, four electric vehicles are all in the ith charge-discharge cycle, and the ith charge of the EV1 has satisfied the driving requirement of the last 3 days, the ith charge of the EV2 has satisfied the driving requirement of the last 7 days, the ith charge of the EV3 has satisfied the driving requirement of the last 6 days, the ith charge of the EV4 has satisfied the driving requirement of the last 6 days, and the data of the relevant daily driving mileage are as shown in Table 2:
TABLE 2
Figure BDA0002508744520000102
Predicting the expected mileage on Wednesday, w may be set1＝0.4、w2Meanwhile, due to data limitation, the present embodiment calculates the conversion coefficient between the mileage of each electric vehicle and the discharged battery capacity in the discharge and mileage data in the ith charge and discharge cycle, and predicts the mileage in the (i + 1) th charge and discharge cycle by using equation (6), where m is 1. The related measurement results are as follows:
Figure BDA0002508744520000103
Figure BDA0002508744520000111
the EV2 owner expects a long-distance travel demand the next day, and then order the SOCexp＝SOCupAt this time, the calculation result of the charging electric quantity after the charging of each electric vehicle is shown in the following table 3:
TABLE 3
Figure BDA0002508744520000112
It can be seen that the battery storage demand of the j +1 th battery calculated by the EV1 is 8.75kWh, which is lower than the current battery storage capacity, indicating that the current storage capacity can meet the next trip demand of the electric vehicle and the battery safety requirement, and therefore, charging is not required; in the same way, the other three electric vehicles need to be charged in time so as to meet the requirements of travel and battery safety. At this time, the battery charge SOC calculated by EV2i+1,s24.55kWh below the upper charge capacity limit, the battery charge constraint is met, but since the vehicle owner expects a high charge (SOC)exp25kWh), the electric quantity after charging is 25 kWh; battery charge SOC calculated by EV3i+1,sWhen the battery capacity is higher than the upper limit of the battery capacity, the maximum limit of the electric quantity after charging is 20 kWh; battery charge SOC calculated by EV4i+1,sAnd when the charging capacity is lower than the upper limit of the charging capacity, the battery charging constraint is met, and the vehicle owner has no expected high charging capacity, the electric quantity after the charging is finished is 24.55 kWh.
The present invention has been described in detail with reference to the above examples, but the description is only for the preferred examples of the present invention and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.

Claims (6)

1. The intelligent charging optimization method for the electric vehicle battery is characterized by comprising the following steps: the method comprises the following steps:
s1, obtaining historical charging data and historical driving data of the electric automobile battery, wherein the historical charging data and the historical driving data comprise the residual electric quantity before each charging and the initial electric quantity after the charging is completed, the daily driving mileage of the electric automobile, the driving mileage of each charging and discharging period and the average temperature in the charging and discharging period;
wherein, the adjacent two charging periods are a complete charging and discharging period;
s2, obtaining the driving mileage of the electric automobile andconversion factor η between discharged electric quantities of batteryi：
Figure FDA0002508744510000011
Wherein, ηiRepresents the driving distance, SOC, of the electric vehicle which can be driven every 1 degree of electricity effectively dischargedi,sRepresents the initial charge amount after the ith charging, SOCi,eRepresents the remaining capacity before the i +1 th charge, xj∑ x for the daily mileage of an electric vehiclejIndicating that the historical driving range is summed, ∑ ((SOC)i,s-SOCi,e)·μi) Represents the summation of historical discharge capacities;
μirepresents the average discharge efficiency, mu, in the i-th charge-discharge cycleiThe calculation formula of (2) is as follows:
Figure FDA0002508744510000012
n is the rated discharge frequency of the battery; t is0Representing the optimal discharge temperature, α representing the discharge efficiency of the battery in the initial state, β representing the discharge efficiency of the battery at the optimal discharge temperature, and sigma representing the influence degree of the temperature on the discharge efficiency;
s3, obtaining the travel distance x of the electric automobile on the next day according to the historical travel dataj+1；
S4, judging whether the current remaining electric quantity of the battery of the electric automobile meets the travel requirement of the next day or not;
obtaining the battery discharge quantity Q meeting the requirement of the j +1 sunriseout: according to the use safety and service life of the battery, determining the battery power storage demand Q: judging whether the electric automobile needs to be charged on the jth day:
if SOCi,eThe storage capacity of the storage battery of the electric automobile meets the requirement of sunrise at j +1 and the safety requirement of the battery, and at the moment, the storage battery of the electric automobile does not need to be charged at j;
if SOCi,eIf the charge capacity of the storage battery of the electric automobile does not meet the sunrise requirement of the j +1 th day and the safety requirement of the battery, the charge capacity of the storage battery of the electric automobile does not meet the sunrise requirement of the j +1 th day and the safety requirement of the batteryThen, the j day needs charging;
s5, determining the optimal charging electric quantity required by the i +1 th charging:
s501, obtaining the driving mileage x of the electric vehicle in the (i + 1) th charging and discharging cycle according to historical trip datai+1：
S502, obtaining a predicted value SOC of the charging electric quantity according to the predicted value of the trip mileage, the conversion coefficient and the discharging efficiencyi+1,s：
Figure FDA0002508744510000021
Therein, SOCupIs the upper limit of rated capacity, k, of the storage battery1Is the battery capacity margin coefficient, k2The safety coefficient of the battery is set;
s503, determining a charging electric quantity correction value SOC 'according to the upper limit of the battery capacity'i+1,s：
Figure FDA0002508744510000022
S504, determining the final electric quantity SOC' of the electric automobile after the charging for the (i + 1) th time is finished according to the expected trip mileage and the charging requirement of the automobile owneri+1,s：
SOC″i+1,s＝max(SOC′i+1,s,SOCexp) (9)
In the formula, SOCexpThe battery charge after the charging is completed is expected for the user.
2. The intelligent charging optimization method for the electric vehicle battery according to claim 1, characterized in that:
electric automobile next-day mileage xj+1The acquisition formula of (1) is as follows:
Figure FDA0002508744510000031
j1＝0,1,…,5，w1for the mileage traveled the same day of the week to the weight of the prediction, w2And the weights of the driving mileage on other days to the prediction result meet the following requirements: w is a1+6w21 and w1＞＞w2。
3. The intelligent charging optimization method for the electric vehicle battery according to claim 1, characterized in that: the battery discharge amount Q meeting the requirement of the j +1 th sunrise is obtained in the step S4out：
Figure FDA0002508744510000032
In the formula (I), the compound is shown in the specification,
Figure FDA0002508744510000033
represents the amount of power required to complete the expected driving range; k is a radical of1The battery capacity surplus coefficient;
according to the use safety and service life of the battery, determining the battery power storage demand Q:
Q＝k2·SOCup+Qout(5)
in the formula, SOCupIs the upper limit of rated capacity, k, of the storage battery2To the cell safety factor, k2·SOCupRepresenting the lowest charge limit of the battery charge at the cut-off voltage.
4. The intelligent charging optimization method for the electric vehicle battery according to claim 1, characterized in that: in the step S501, the driving distance x of the electric vehicle in the (i + 1) th charging and discharging cycle is acquired according to the historical trip datai+1：
Figure FDA0002508744510000034
In the formula i10,1, …, m-1, m is the continuous historical charge and discharge times before the i +1 th charge and discharge.
5. The intelligent charging optimization method for the batteries of the electric vehicles according to claim 4, characterized in that: and m is more than or equal to 1.
6. The intelligent charging optimization method for the electric vehicle battery according to claim 1, characterized in that: in step S504, if the owner has a long distance travel demand in the future, the SOC may be takenexp＝SOCupIf the owner has no high expected charge, then SOC is takenexp＝SOC′i+1,s。
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