CN111717072B - Intelligent charging optimization method for electric automobile battery - Google Patents
Intelligent charging optimization method for electric automobile battery Download PDFInfo
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- CN111717072B CN111717072B CN202010454582.6A CN202010454582A CN111717072B CN 111717072 B CN111717072 B CN 111717072B CN 202010454582 A CN202010454582 A CN 202010454582A CN 111717072 B CN111717072 B CN 111717072B
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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
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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
- 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
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- 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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- Charge And Discharge Circuits For Batteries Or The Like (AREA)
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Abstract
The invention relates to an intelligent charging optimization method for an electric automobile battery, which comprises the following steps: s1, acquiring historical charging data and historical driving data of a battery of an electric automobile, wherein the historical charging data and the historical driving data comprise before each chargingThe residual electric quantity of the electric vehicle, the initial electric quantity after the electric vehicle is charged, the daily driving mileage of the electric vehicle, the driving mileage of each charging and discharging period and the average air temperature in the charging and discharging period; s2, obtaining a conversion coefficient eta between the driving mileage of the electric automobile and the discharge electric quantity of the battery i : s3, acquiring the driving mileage x of the electric vehicle on the next day according to the historical trip data j+1 The method comprises the steps of carrying out a first treatment on the surface of the S4, judging whether the current residual electric quantity of the battery of the electric automobile meets the next-day travel requirement or not; s5, determining the optimal charging quantity required by the (i+1) th charging: according to the historical use data and the future travel demands of the electric automobile, the optimal battery charging plan is scientifically determined, 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
Technical Field
The invention belongs to the technical field of electric vehicle battery charging optimization, and particularly relates to an intelligent electric vehicle battery charging optimization method.
Background
With the continuous maturity of electric automobile technology, electric automobile keeps volume to promote fast, and battery continuation of journey mileage also constantly increases, and some motorcycle types have reached single charge and stably continued to journey more than 400 kilometers. At present, the main application of private electric vehicles is to travel on duty and off duty instead of walk, so as to meet the travel demands in urban areas, the daily travel distance is generally not more than 50 km and is far lower than the design endurance mileage, and more electric quantity is still stored in the electric vehicle battery at the end of the travel of the same day. However, in reality, battery phobia is commonly existed in electric automobile owners (there is concern that the battery power is insufficient to maintain the next-day travel demand), so the owners often charge the electric automobile at hand after the end of the current-day journey (at night, in the charge peak period). This means that the electric automobile is full state when beginning to use every day, still holds more electric quantity when ending the use in the same day, and daily actual charge demand is lower, because night car owner can't in time break off the power, and electric automobile can let the battery keep in the high-voltage state under the 100% state of charge for a long time, causes the battery temperature too high even, can shorten electric automobile battery life for a long time, and has the extravagant phenomenon of electric power, increases electric automobile use cost.
Therefore, according to historical use data and future travel demands of the electric automobile, an optimal battery charging plan is scientifically determined, the charging and discharging period of the electric automobile is properly prolonged, the charging times and the daily long-time deep charging frequency are reduced, and the method is an important means for improving the service life and the use economy of the battery of the electric automobile.
Therefore, based on the problems, the intelligent electric vehicle battery charging optimization method for optimizing 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 optimizes the charging capacity and the charging behavior.
The invention solves the technical problems by adopting the following technical scheme:
the intelligent charging optimization method for the electric automobile battery comprises the following steps:
s1, acquiring historical charging data and historical driving data of a battery of an electric vehicle, wherein the historical charging data and the historical driving data comprise the residual electric quantity before each charging, the initial electric quantity after the charging is completed, the daily driving mileage of the electric vehicle, the driving mileage of each charging and discharging period and the average air 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 eta between the driving mileage of the electric automobile and the discharge electric quantity of the battery i ：
Wherein eta i Indicating the driving distance of the electric vehicle capable of driving by releasing 1 degree of electricity per effective state of charge (SOC) i,s Indicating the initial electric quantity after the ith charge, SOC i,e Represents the residual capacity before the i+1st charge, x j Is the daily driving mileage of the electric automobile, sigma x j Representing the sum of historical mileage, Σ ((SOC) i,s -SOC i,e )·μ i ) Representing a summation of the historical discharge amounts;
μ i represents the average discharge efficiency, mu, in the ith charge-discharge period i The calculation formula of (2) is as follows:
n is the rated discharge times of the battery; t (T) 0 Indicating an optimal discharge temperature; alpha is the discharge efficiency of the battery in the initial state; beta represents the battery discharge efficiency at the optimal discharge temperature; sigma is the degree of influence of temperature on discharge efficiency;
s3, acquiring the driving mileage x of the electric vehicle on the next day according to the historical trip data j+1 ；
S4, judging whether the current residual electric quantity of the battery of the electric automobile meets the next-day travel requirement or not;
obtaining battery discharge quantity Q meeting j+1th sunrise demand out : according to the safety and service life of the battery, determining the storage demand Q of the battery: judging whether the electric automobile needs to be charged on the j th day or not:
if SOC is i,e Not less than Q, indicating that the current storage capacity of the storage battery of the electric automobile meets the j+1th sunrise demand and the battery safety demand, and at the moment, the charging is not needed on the j th day;
if SOC is i,e Q is less than or equal to the value of the current electric automobile accumulator, which indicates that the accumulator capacity of the accumulator of the electric automobile does not meet the j+1th sunrise demand and the battery safety demand, at the momentThe j day needs to be charged;
s5, determining the optimal charging quantity required by the (i+1) th charging:
s501, acquiring the driving mileage x of the electric vehicle in the (i+1) th charge-discharge period according to the historical trip data i+1 ：
S502, obtaining a charge quantity predicted value SOC according to the trip mileage predicted value, the conversion coefficient and the discharge efficiency i+1,s ：
Wherein SOC is up For the upper limit of rated capacity of the storage battery, k 1 For the margin coefficient, k of the battery charge capacity 2 Is the safety coefficient of the battery;
s503, determining a charge quantity correction value SOC 'according to the upper limit of the battery capacity' i+1,s ：
S504, determining the final electric quantity SOC' after the i+1th charge of the electric automobile is completed according to the expected trip mileage and the charging requirement of the automobile owner i+1,s ：
SOC″ i+1,s ＝max(SOC′ i+1,s ,SOC exp ) (9)
In SOC exp The battery power after the completion of the charging is expected for the user.
Further, the next day driving mileage x of the electric automobile j+1 The acquisition formula of (2) is as follows:
j 1 ＝0,1,…,5，w 1 weight of the driving mileage on the same day of the week on the prediction result, w 2 The weight of the predicted result for the driving mileage of other days is as follows: w (w) 1 +6w 2 =1 and w 1 ＞＞w 2 。
Further, in the step S4, the battery discharge Q satisfying the j+1st sunrise demand is obtained out ：
In the method, in the process of the invention,
representing the amount of power required to complete the intended range; k (k) 1 The storage capacity margin coefficient of the battery;
according to the safety and service life of the battery, determining the storage demand Q of the battery:
Q＝k 2 ·SOC up +Q out (5)
in SOC up For the upper limit of rated capacity of the storage battery, k 2 K is the safety factor of the battery 2 ·SOC up The lowest charge limit of the battery charge at the cut-off voltage is indicated.
Further, in S501, the driving mileage x of the electric vehicle in the (i+1) th charge/discharge cycle is obtained according to the historical trip data i+1 ：
Wherein i is 1 =0, 1, …, m-1, m is the number of consecutive historical charges and discharges before the (i+1) th charge and discharge.
Further, m is more than or equal to 1.
Further, in step S504, if the vehicle owner has a long-distance travel requirement in the future, the SOC is taken exp ＝SOC up If the vehicle owner does not have a high expected charge, the SOC is taken exp ＝SOC′ i+1,s 。
The invention has the advantages and positive effects that:
according to the method, the historical charge-discharge rule and trip rule of the electric automobile are simulated, whether the current battery storage capacity meets the next day requirement is predicted, the optimal charge capacity is reasonably determined according to the future trip requirement, the charge-discharge period of the electric automobile is properly prolonged, the damage of long-time repeated and deep charge to the battery can be effectively avoided, and the service life of the battery is prolonged.
Detailed Description
First, it should be noted that the following detailed description of the specific structure, characteristics, advantages, and the like of the present invention will be given by way of example, however, all descriptions are merely illustrative, and should not be construed as limiting the present invention in any way.
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.
The present invention will be specifically described below.
The intelligent charging optimization method for the electric automobile battery comprises the following steps:
s1, acquiring historical charging data and historical driving data of a battery of an electric vehicle, wherein the historical charging data and the historical driving data comprise the residual electric quantity before each charging, the initial electric quantity after the charging is completed, the daily driving mileage of the electric vehicle, the driving mileage of each charging and discharging period and the average air 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 in each charge-discharge cycle.
S2, obtaining a conversion coefficient eta between the driving mileage of the electric automobile and the discharge electric quantity of the battery i ：
Wherein eta i Indicating the driving distance of the electric vehicle capable of driving by releasing 1 degree of electricity per effective state of charge (SOC) i,s Indicating the initial electric quantity after the ith charge, SOC i,e Represents the residual capacity before the i+1st charge, x j For the daily driving mileage of the electric automobile, sigma x j Representing the sum of historical mileage, Σ ((SOC) i,s -SOC i,e )·μ i ) Representing a summation of the historical discharge amounts;
μ i represents the average discharge efficiency, mu, in the ith charge-discharge period i The calculation formula of (2) is as follows:
n is the rated discharge frequency (same as the rated charge frequency) of the battery, and can be determined by the factory parameters of the battery; t (T) i Is the average air temperature in the charge-discharge period; t (T) 0 The optimal discharging temperature is represented, at which the discharging efficiency of the battery is highest, and can be determined by the factory parameters of the battery; alpha is the discharge efficiency of the battery in the initial state and can be determined by the factory parameters of the battery; beta represents the battery discharge efficiency at the optimal discharge temperature and can be determined by battery delivery parameters; sigma is the influence degree of temperature on the discharge efficiency, can be obtained by simulation according to historical temperature data and the discharge efficiency, and in the normal case, the relationship between the discharge efficiency and the temperature approximately meets normal distribution, and at this time, sigma is the standard deviation of the average air temperature change during the running of the electric automobile, and the average air temperature standard deviation of nearly 7 days can be taken; k is an adjustable parameter, which can be determined from battery factory parameters depending on battery performance.
In general, in the adjacent charging cycle, the temperature change is not obvious, the service life (discharging times) of the battery is only 1 time, and the influence of the service life and the discharging times on the discharging efficiency is not great, and at this time, all the parameters are basically unchanged. Meanwhile, with the progress of the technology of the storage battery of the electric automobile, the discharge efficiency mu i Is basically 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 application i Is a fixed value.
S3, acquiring the driving mileage xj of the electric vehicle on the next day according to the historical trip data +1 ；
S4, judging whether the current residual electric quantity of the battery of the electric automobile meets the next-day travel requirement or not;
obtaining battery discharge quantity Q meeting j+1th sunrise demand out : according to the use safety of the batteryAnd service life, confirm battery and store the demand Q: judging whether the electric automobile needs to be charged on the j th day or not:
if SOC is i,e Not less than Q, indicating that the current storage capacity of the storage battery of the electric automobile meets the j+1th sunrise demand and the battery safety demand, and at the moment, the charging is not needed on the j th day;
if SOC is i,e Q is less than the value, indicate the accumulator capacity of the accumulator of the present electric automobile does not meet the j+1st sunrise and battery safety requirement, at this moment, the j-th day needs to charge;
s5, determining the optimal charging quantity required by the (i+1) th charging:
s501, acquiring the driving mileage x of the electric vehicle in the (i+1) th charge-discharge period according to the historical trip data i+1 ：
S502, obtaining a charge quantity predicted value SOC according to the trip mileage predicted value, the conversion coefficient and the discharge efficiency i+1,s ：
Wherein SOC is up For the upper limit of rated capacity of the storage battery, k 1 For the margin coefficient, k of the battery charge capacity 2 Is the safety coefficient of the battery;
s503, determining a charge quantity correction value SOC 'according to the upper limit of the battery capacity' i+1,s ：
S504, determining the final electric quantity SOC' after the i+1th charge of the electric automobile is completed according to the expected trip mileage and the charging requirement of the automobile owner i+1,s ：
SOC″ i+1,s ＝max(SOC′ i+1,s ,SOC exp ) (9)
In SOC exp For the user to expect the battery power after the charging is completed, if the vehicle owner has a long-distance travel requirement in the future, the SOC can be obtained exp ＝SOC up If the vehicle owner does not haveHigh expected charge, then take the SOC exp ＝SOC′ i+1,s 。。
It should be noted that the next day driving distance x of the electric vehicle j+1 The acquisition formula of (2) is as follows:
j 1 ＝0,1,…,5，w 1 weight of the driving mileage on the same day of the week on the prediction result, w 2 The weight of the predicted result for the driving mileage of other days is as follows: w (w) 1 +6w 2 =1 and w 1 ＞＞w 2 ；w 1 、w 2 The numerical value of (2) can be simulated according to historical data, the simulation method can adopt the existing mature multiple linear regression method and the like, and can also be set according to travel rule characteristics, and the stronger the travel rule per week is, the w is 1 The larger. It should be noted that, the private electric automobile has a strong regularity of travel, and generally takes a week (7 days) as a change period, and the travel rule of the same day of each week is more similar, so that the corresponding weight can be properly improved.
It should be noted that, in the step S4, the battery discharge amount Q satisfying the j+1th sunrise demand is obtained out ：
In the method, in the process of the invention,
representing the amount of power required to complete the intended range; k (k) 1 The storage capacity margin coefficient of the battery can be set according to the risk bearing capacity of a vehicle owner, and generally 1.0-1.5 is adopted.
According to the safety and service life of the battery, determining the storage demand Q of the battery:
Q＝k 2 ·SOC up +Q out (5)
in SOC up Rated for the accumulatorUpper limit of capacity, k 2 The safety coefficient of the battery can be determined by the factory parameters of the battery; k (k) 2 ·SOC up The lowest charge limit of the charge of the battery at the cut-off voltage is indicated, and when the charge is lower than the value, 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 charge/discharge cycle is obtained according to the historical trip data i+1 ：
Wherein i is 1 =0, 1, …, m-1, m is the number of continuous historical charge and discharge times before the i+1st charge and discharge, and equation (6) represents the driving mileage corresponding to the m nearest charge and discharge cycles, and predicts the driving mileage of the electric vehicle in the i+1st charge and discharge cycles. And m can generally be 1-10, if m is 3, the driving mileage corresponding to the 3 latest charging and discharging periods is adopted, and the driving mileage of the electric vehicle in the (i+1) th charging and discharging period is predicted.
In this embodiment, taking a private electric vehicle charging of a certain cell as an example, an intelligent charging optimization result of a simulated electric vehicle battery is simulated, and according to a use rule of the private electric vehicle, the charging is assumed to be performed after the next shift every day, and a power supply is cut off before the next shift. In the embodiment, 4 electric vehicles with different specifications are selected, and the next daily charging electric quantity is determined; the basic parameters of the electric automobile storage battery are shown in table 1.
TABLE 1
Assuming that the current time point j is tuesday, four electric vehicles are all in the ith charge-discharge period, the ith charge of EV1 meets the near-3-day driving requirement, the ith charge of EV2 meets the near-7-day driving requirement, the ith charge of EV3 meets the near-6-day driving requirement, the ith charge of EV4 meets the near-6-day driving requirement, and related daily driving mileage data are shown in Table 2:
TABLE 2
Predicting the expected driving mileage of the current Wednesday, w can be set 1 ＝0.4、w 2 Meanwhile, according to the data of the discharge amount and the driving mileage in the ith charge and discharge period, the conversion coefficient between the driving mileage of each electric vehicle and the discharge amount of the battery is calculated according to the data of the limit of the data of the discharge amount and the driving mileage in the ith charge and discharge period, and the driving mileage in the (i+1) th charge and discharge period is predicted by means of the formula (6), wherein m=1. The correlation measurement results are shown as follows:
the EV2 vehicle owner expects that the next day has long-distance travel demand, and then the SOC is made exp ＝SOC up At this time, the calculation results of the charge amount after the completion of the charging of each electric vehicle are shown in table 3 below:
TABLE 3 Table 3
The electricity storage requirement of the battery on the j+1th day calculated by EV1 is 8.75kWh, which is lower than the current electricity storage capacity of the battery, so that the current electricity storage capacity can meet the next-day travel requirement of the electric automobile and the battery safety requirement, and therefore, the electric automobile does not need to be charged; similarly, all three electric vehicles need to be charged in time so as to meet the travel and battery safety requirements. At this time, EV2 was calculatedIs the battery charge amount SOC of (a) i+1,s =24.55 kWh, below the upper limit of charge capacity, meets the battery charging constraints, but is expected to be high due to the vehicle owner's charge (SOC exp =25 kWh), the amount of electricity after the completion of charging takes an expected value of 25kWh; EV3 calculated battery charge amount SOC i+1,s =22.40 kWh, above the upper limit of battery capacity, the charge after completion takes the highest limit of 20kWh; EV4 calculated battery charge amount SOC i+1,s =24.55 kWh, below the upper limit of the charging capacity, satisfying the battery charging constraint, and the vehicle owner has no high expectation of the charging electric quantity, and the electric quantity after the charging is completed takes the expected value of 24.55kWh.
The foregoing examples illustrate the invention in detail, but are merely preferred embodiments of the invention and are not to be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (6)
1. The intelligent charging optimization method for the electric automobile battery is characterized by comprising the following steps of: the method comprises the following steps:
s1, acquiring historical charging data and historical driving data of a battery of an electric vehicle, wherein the historical charging data and the historical driving data comprise the residual electric quantity before each charging, the initial electric quantity after the charging is completed, the daily driving mileage of the electric vehicle, the driving mileage of each charging and discharging period and the average air 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 eta i between the driving mileage of the electric automobile and the discharge electric quantity of the battery:
wherein eta i Indicating the driving distance of the electric vehicle capable of driving by releasing 1 degree of electricity per effective state of charge (SOC) i,s Indicating the initial electric quantity after the ith charge, SOC i,e Represents the residual capacity before the i+1st charge, x j Is an electric automobileDaily mileage, Σx j Representing the sum of historical mileage, Σ ((SOC) i,s -SOC i,e )·μ i ) Representing a summation of the historical discharge amounts;
μ i represents the average discharge efficiency, mu, in the ith charge-discharge period i The calculation formula of (2) is as follows:
n is the rated discharge times of the battery; t (T) 0 Indicating an optimal discharge temperature; alpha is the discharge efficiency of the battery in the initial state; beta represents the battery discharge efficiency at the optimal discharge temperature; sigma is the degree of influence of temperature on discharge efficiency; t (T) i Is the average air temperature in the charge-discharge period; k is the battery discharge constant;
s3, acquiring the driving mileage x of the electric vehicle on the next day according to the historical trip data j+1 ；
S4, judging whether the current residual electric quantity of the battery of the electric automobile meets the next-day travel requirement or not;
obtaining battery discharge quantity Q meeting j+1th sunrise demand out : according to the safety and service life of the battery, determining the storage demand Q of the battery: judging whether the electric automobile needs to be charged on the j th day or not:
if SOC is i,e Not less than Q, indicating that the current storage capacity of the storage battery of the electric automobile meets the j+1th sunrise demand and the battery safety demand, and at the moment, the charging is not needed on the j th day;
if SOC is i,e Q is less than the value, indicate the accumulator capacity of the accumulator of the present electric automobile does not meet the j+1st sunrise and battery safety requirement, at this moment, the j-th day needs to charge;
s5, determining the optimal charging quantity required by the (i+1) th charging:
s501, acquiring the driving mileage x of the electric vehicle in the (i+1) th charge-discharge period according to the historical trip data i+1 ：
S502, obtaining a charge quantity predicted value SOC according to the trip mileage predicted value, the conversion coefficient and the discharge efficiency i+1,s ：
Wherein SOC is up For the upper limit of rated capacity of the storage battery, k 1 For the margin coefficient, k of the battery charge capacity 2 Is the safety coefficient of the battery; mu (mu) i+1 The average discharge efficiency of the (i+1) th charge-discharge period;
s503, determining a charge quantity correction value SOC 'according to the upper limit of the battery capacity' i+1,s ：
S504, determining the final electric quantity SOC' after the i+1th charge of the electric automobile is completed according to the expected trip mileage and the charging requirement of the automobile owner i+1,s ：
SOC″ i+1,s ＝max(SOC′ i+1,s ,SOC exp ) (9)
In SOC exp The battery power after the completion of the charging is expected for the user.
2. The intelligent charging optimization method for the electric automobile battery according to claim 1, wherein the method comprises the following steps:
next day driving mileage x of electric automobile j+1 The acquisition formula of (2) is as follows:
j 1 ＝0,1,…,5，w 1 weight of the driving mileage on the same day of the week on the prediction result, w 2 The weight of the predicted result for the driving mileage of other days is as follows: w (w) 1 +6w 2 =1 and w 1 ＞＞w 2 ；x j-6 The driving mileage of the electric automobile is the j-6 th day; x is x j-j1 Is j-j 1 Day mileage, j 1 The driving mileage of 0,1,2,3,4,5, corresponding to the j-th day, j-1-th day, j-2-th day, j-5-th day, and x can be taken j-6 Constitute 7 days a week.
3. The intelligent charging optimization method for the electric automobile battery according to claim 1, wherein the method comprises the following steps: the step S4 is to obtain the battery discharge Q meeting the j+1th sunrise demand out ：
In the method, in the process of the invention,
representing the amount of power required to complete the intended range; k (k) 1 The storage capacity margin coefficient of the battery;
according to the safety and service life of the battery, determining the storage demand Q of the battery:
Q＝k 2 ·SOC up +Q out (5)
in SOC up For the upper limit of rated capacity of the storage battery, k 2 K is the safety factor of the battery 2 ·SOC up The lowest charge limit of the battery charge at the cut-off voltage is indicated.
4. The intelligent charging optimization method for the electric automobile battery according to claim 1, wherein the method comprises the following steps: in S501, the driving mileage x of the electric vehicle in the (i+1) th charge-discharge period is obtained according to the historical trip data i+1 ：
Wherein i is 1 =0, 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 electric automobile battery according to claim 4, wherein the method comprises the following steps: and m is more than or equal to 1.
6. The intelligent charging optimization method for the electric automobile battery according to claim 1, wherein the method comprises the following steps: in the step S504, if the vehicle owner has a long-distance travel requirement in the future, the SOC is taken exp ＝SOC up If the vehicle owner does not have a high expected charge, the SOC is taken exp ＝SOC i ′ +1,s 。
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| CN112572229A (en) * | 2020-10-29 | 2021-03-30 | 广州小鹏自动驾驶科技有限公司 | Charging limit value adjusting method and device |
| CN112435053A (en) * | 2020-11-13 | 2021-03-02 | 睿驰电装(大连)电动系统有限公司 | Method and device for predicting charging behavior of electric vehicle and electronic equipment |
| CN113665436B (en) * | 2021-09-28 | 2022-11-29 | 蜂巢能源科技有限公司 | Battery management method and device |
| CN114094818B (en) * | 2021-11-29 | 2022-07-29 | 江苏晨朗电子集团有限公司 | Integrated electronic transformer of direct current converter for automobile charging |
| CN117436287B (en) * | 2023-12-20 | 2024-04-19 | 天津力神电池股份有限公司 | Battery pack life prediction method, device and storage medium |
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