Intelligent charging control method for electric vehicle

Abstract

The invention relates to an intelligent charging control method for an electric vehicle, and relates to the technical field of electric vehicle charging. The method comprises the following steps: acquiring a single charging gun insertion time length obtained based on historical data, and first time required for fully charging a battery at a maximum rate; the historical data comprises charging gun insertion duration corresponding to multiple times of charging; calculating an actual charging request coefficient based on a difference value between the single charging gun insertion duration and the first time; and adjusting the actual charging rate according to the actual charging request coefficient. According to the technical scheme, the charging rate can be adjusted according to the actual charging request coefficient, and the charging rate is reduced under the condition that the user time is sufficient and rapid charging is not needed. And the loss of the battery is relatively small due to the relatively low charging rate, so that the waste of electric energy is avoided, the service life of the battery is prolonged, the cost of replacing the battery by a user is reduced, and the overall use economy of the electric automobile is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicle charging, in particular to an intelligent electric vehicle charging control method. BACKGROUND

[0002] The fast charging technology of existing electric vehicles (EV) usually charges at a high charging rate (such as 2C). Although this high-rate charging can quickly fill the battery, it will cause a large amount of energy waste. For example, in the case where the user does not actually need fast charging, the battery is still charged at a high rate, which consumes excess energy.

[0003] Moreover, most existing charging systems select the fastest charging speed based on the physical characteristics of the battery, lack analysis and intelligent control of the user's actual charging behavior, and cannot dynamically adjust the charging rate according to the user's usage habits and charging scenarios to achieve the best charging effect. In some cases, the user may need fast charging, but in other cases, there is sufficient time and no need to charge at a high rate, and in such cases, charging at a lower rate can save energy and prolong the life of the battery. The existing charging system cannot automatically adjust the charging rate according to the user's actual usage needs. SUMMARY

[0004] The present application provides an intelligent electric vehicle charging control method, which can solve the problem of energy waste and affect the battery life caused by the existing charging system in the prior art, which cannot automatically adjust the charging rate according to the user's actual usage needs.

[0005] The present application provides an intelligent electric vehicle charging control method, which includes: obtaining a single charging gun insertion time based on historical data, and a first time required to fully charge the battery at the maximum rate; calculating an actual charging request coefficient based on the difference between the single charging gun insertion time and the first time; and adjusting the actual charging rate according to the actual charging request coefficient.

[0006] In some embodiments, the charging gun insertion time is longer than the first time.

[0007] In some embodiments, when the target vehicle needs to be charged at the maximum rate, the maximum rate charging can be manually selected; when the target vehicle does not select the maximum rate charging, the actual charging rate suitable for the target vehicle can be automatically adjusted according to the actual charging request coefficient.

[0008] In some embodiments, the charging gun insertion time is T1, the first time is T2, and the difference between the charging times of the two is , The calculation formula of Δt i=T1-T 2。

[0009] In some embodiments, the charging time difference of the target vehicle is recorded multiple times in the historical period. A data set is obtained based on the data set, and the standard deviation σ of the charging time difference data set of the target vehicle in the historical period is calculated based on the mean μ; wherein the calculation formula of the mean μ is: ; The calculation formula of standard deviation σ is: .

[0010] In some embodiments, the charging time difference is calculated based on the mean μ and the standard deviation σ. Normalized to a standard normal distribution , and the standard deviation is obtained ; Among them, the standard normal distribution The calculation formula is: .

[0011] In some embodiments, based on experience, a charging time difference Obey the standard normal distribution , the difference is converted into The distribution of ; The calculation formula is: .

[0012] In some embodiments, the cumulative distribution function of the difference Δt is calculated , according to the cumulative distribution function , combined with the erf error function, the target charging request coefficient is obtained ;in, The calculation formula is: , The calculation formula is: ,in, is the error function, which is defined as: .

[0013] In some embodiments, according to the target charge request coefficient , and the actual charging request coefficient is obtained , according to the actual charging request coefficient , combined with the maximum charge rate , and adjust the actual charging rate suitable for the target vehicle ;in, .

[0014] In some embodiments, when the target vehicle needs to be charged at the maximum rate, the maximum rate charging can be manually selected, at which time, .

[0015] The technical scheme provided by the embodiments of the present application has the beneficial effects that: The embodiments of the present application provide an intelligent charging control method for electric vehicles, comprising: obtaining a single charging gun insertion time length based on historical data and a first time required to fully charge the battery at the maximum rate; calculating an actual charging request coefficient based on the difference between a plurality of sets of the single charging gun insertion time length and the first time; and adjusting the actual charging rate according to the actual charging request coefficient.

[0016] In actual use, the existing charging system lacks analysis and intelligent control of the actual charging behavior of users and cannot dynamically adjust the charging rate according to the user's usage habits and charging scenarios. Most existing charging systems select the fastest charging speed based on the physical characteristics of the battery and provide fast food at the maximum rate. Although high-rate charging can quickly charge the battery, it will cause great damage to the battery, affect the battery life, and cannot automatically adjust according to the actual use requirements of users, resulting in waste of electric energy.

[0017] The technical scheme can adjust the charging rate according to the actual charging request coefficient. In the case where the user has sufficient time and does not need to charge quickly, the charging rate is reduced. The lower charging rate causes relatively less damage to the battery, which helps to avoid waste of electric energy, prolong the service life of the battery, reduce the cost of replacing the battery for the user, and improve the overall use economy of the electric vehicle.

[0018] Specifically, the technical scheme obtains the single charging gun insertion time length (based on historical data) and the first time required to fully charge the battery at the maximum rate, calculates the actual charging request coefficient based on the difference between the two, and then adjusts the actual charging rate, which is used as the basis for adjusting the charging rate. This way of calculating the coefficient and adjusting the charging rate based on historical data and actual demand realizes intelligent control of the charging process, better meets the charging needs of users in different scenarios, and improves the user's satisfaction with the charging experience of the electric vehicle. Under different charging scenarios and user needs, the best charging effect is different. When quick travel is needed, the user wants to fully charge the battery as soon as possible; when time is sufficient, the user pays more attention to electric energy saving and battery life.

[0019] When the user does not actually need fast charging, that is, the single charging gun insertion time is long, and the time exceeds the first time required to fully charge the battery at the maximum rate, the actual charging request coefficient reflects this demand, that is, the user has more time to charge and does not need a high charging rate. At this time, the system can reduce the charging rate according to the actual charging request coefficient to avoid consuming excess power when charging at a high rate, thereby effectively reducing power waste. The technical solution can dynamically adjust the charging rate according to the actual charging request coefficient, meet the user's demand for charging speed and battery protection, and achieve the best charging effect, making the charging process of the electric vehicle more reasonable. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0021] Figure 1 The control logic diagram provided by the embodiments of the present application. DETAILED DESCRIPTION

[0022] In order to make those skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0023] The embodiments of the present application provide an intelligent charging control method for electric vehicles, which can solve the problem of power waste and affect the battery life caused by the existing charging system in the prior art, which cannot automatically adjust the charging rate according to the actual use demand of the user.

[0024] Referring to Figure 1 The embodiments of the present application provide an intelligent charging control method for electric vehicles, which includes: Obtaining the single charging gun insertion time based on historical data and the first time required to fully charge the battery at the maximum rate; Based on the difference between the single charging gun insertion time and the first time, the actual charging request coefficient is calculated; and the actual charging rate is adjusted according to the actual charging request coefficient.

[0025] In actual use, the existing charging system lacks analysis and intelligent control of the actual charging behavior of users and cannot dynamically adjust the charging rate according to the user's use habits and charging scene. Most of the existing charging systems select the fastest charging speed based on the physical characteristics of the battery and use the maximum rate for fast food. Although high-rate charging can quickly charge the battery, it will cause great damage to the battery, affect the battery life, and cannot be automatically adjusted according to the actual use requirements of the user, resulting in waste of electric energy.

[0026] The technical solution can adjust the charging rate according to the actual charging request coefficient. In the case where the user has sufficient time and does not need fast charging, the charging rate is reduced. The lower charging rate causes relatively less damage to the battery, helps to avoid waste of electric energy, prolongs the service life of the battery, reduces the cost of replacing the battery for the user, and improves the overall use economy of the electric vehicle.

[0027] Specifically, the technical solution obtains the single charging gun insertion time length (based on historical data) and the first time required to fully charge the battery at the maximum rate, calculates the actual charging request coefficient based on the difference between the two, and adjusts the actual charging rate, which is used as the basis for adjusting the charging rate. This way of calculating the coefficient based on historical data and actual demand and adjusting the charging rate realizes intelligent control of the charging process and can better meet the charging needs of users in different scenarios, improving the user's satisfaction with the charging experience of electric vehicles. In different charging scenarios and user needs, the best charging effect is different. When fast travel is needed, the user wants to fully charge the battery as soon as possible; when time is sufficient, the user pays more attention to power saving and battery life.

[0028] When the user does not actually need fast charging, that is, the single charging gun insertion time length is longer than the first time required to fully charge the battery at the maximum rate, the actual charging request coefficient will reflect this demand, that is, the user has sufficient charging time and does not need a high charging rate. At this time, the system can reduce the charging rate according to the actual charging request coefficient to avoid charging with excessive rate to consume excess electric energy, thereby effectively reducing the waste of electric energy. The technical solution can dynamically adjust the charging rate according to the actual charging request coefficient, meet the user's demand for charging speed and battery protection, and achieve the best charging effect, making the charging process of the electric vehicle more reasonable.

[0029] In some optional embodiments, the obtained plug-in duration of the charging gun is longer than the first time. When the plug-in duration of the charging gun is longer than the first time required for charging the battery to full capacity at the maximum rate, it indicates that the user has enough time to complete the charging process and does not urgently need to quickly charge the battery. For example, the user parks the vehicle at home overnight for charging, or parks for a long time at the workplace for charging, at which time the user is more concerned about the charging cost and the battery health rather than the charging speed. This technical solution can accurately identify such scenarios and avoid using a high-rate charging method. Because high-rate charging can quickly charge the battery, but it will generate more heat, increase the loss of the battery, and also consume more electric energy. In this case, using a lower charging rate for charging can meet the user's demand for long-time charging, effectively save electric energy, reduce charging costs, prolong the service life of the battery, and improve the reliability and stability of the battery.

[0030] From the perspective of charging infrastructure, when a large number of electric vehicles use this intelligent adjustment of charging rate according to the plug-in duration, high-rate charging during peak periods can be avoided, and the load pressure on the power grid can be reduced. In the case of a long plug-in duration of the charging gun, the vehicle can choose to charge at a lower rate during a period of low load on the power grid, realize reasonable allocation and optimized utilization of charging resources, and improve the operating efficiency and stability of the entire charging system.

[0031] In some optional embodiments, when the target vehicle needs to be charged at the maximum rate, the maximum rate charging can be manually selected; when the target vehicle does not choose to be charged at the maximum rate, the actual charging rate suitable for the target vehicle can be automatically adjusted according to the actual charging request coefficient.

[0032] This technical solution takes into account the differences in charging needs of users in different situations. Some users may have urgent needs, such as needing to immediately travel to an important location, and hope to quickly charge the battery to full capacity. At this time, manually selecting the maximum rate charging function can meet their urgent needs and ensure that the vehicle can quickly obtain sufficient power without affecting the travel plan. In the absence of urgent needs, the system can intelligently control the charging process according to the actual charging request coefficient to achieve efficient use of electric energy and reasonable protection of the battery.

[0033] The combination of manually selecting the maximum rate charging and automatically adjusting the charging rate makes the charging system more flexible and controllable. Users can freely switch between charging modes according to their own judgment and actual situation, retaining the user's initiative control over the charging process while providing intelligent automatic adjustment functions. This flexible design can adapt to different user operation habits and usage preferences, improving user satisfaction and acceptance of the charging system.

[0034] In some optional embodiments, the plug-in duration of the charging gun is T1, the first time is T2, and the difference between the charging times of the two is , The calculation formula of Δt i =T1-T 2。 Since the plug-in duration T1 of the charging gun for each charging may vary due to different use scenarios and needs of the user, the will also dynamically change. Based on the calculation of , the charging system can perceive the changes in the user's charging time in real time and dynamically adjust the charging rate, making the charging process more adaptable to actual needs and improving the flexibility and adaptability of charging. In actual use, when the user of the target vehicle manually selects maximum rate charging, the data will not be recorded, i.e., special cases will not affect the judgment of the charging time difference .

[0035] Different charging rates have different degrees of battery wear, and high-rate charging generates more heat, accelerating the aging and degradation of the battery. Based on the dynamic charging control of , the appropriate charging rate can be selected according to the adequacy of the charging time, avoiding long-term high-rate charging of the battery, thereby effectively prolonging the service life of the battery, reducing the user's battery replacement cost, and improving the overall economy of electric vehicles.

[0036] In some optional embodiments, the charging time difference data set of the target vehicle multiple times in the historical period is recorded, the mean μ thereof is obtained according to the data set, and the standard deviation σ of the charging time difference data set of the target vehicle in the historical period is calculated according to the mean μ; wherein the calculation formula of the mean μ is: ; and the calculation formula of the standard deviation σ is: .

[0037] By recording the charging time difference Δt i data set of the target vehicle multiple times in the historical period, rich charging behavior data can be collected. These data reflect the charging time characteristics of the user at different times and in different scenarios, providing a basis for in-depth analysis of the user's charging habits.

[0038] Calculating the mean μ can obtain the average level of the historical charging time difference, which represents the adequacy of the user's charging time under normal circumstances. The standard deviation σ measures the dispersion of the charging time difference, i.e., the fluctuation of the data. If the standard deviation is large, it means that the user's charging time difference is large and may be affected by multiple factors; if the standard deviation is small, it means that the user's charging time is relatively stable. The mean μ and the standard deviation σ are important parameters for data standardization. Subsequently, the charging time difference When normalizing to a standard normal distribution, the mean μ and standard deviation σ are required for calculation. Therefore, accurately calculating these two parameters is key to ensuring the correctness of subsequent normalization processing and lays the foundation for further analysis of the distribution of charging time differences.

[0039] In some optional embodiments, the charging time difference is calculated based on the mean μ and the standard deviation σ. Normalized to a standard normal distribution , and the standard deviation is obtained ; Among them, the standard normal distribution The calculation formula is: Due to the difference in charging time between different users There may be large numerical differences, and direct comparison and analysis of these data may be affected by the dimensionality. By standardizing them to a standard normal distribution, data of different sizes and ranges can be converted to a unified scale, making the differences between different charging times comparable.

[0040] In some optional embodiments, based on experience, a charging time difference Obey the standard normal distribution , the difference is converted into The distribution of ; The calculation formula is: .

[0041] The probability density function can accurately describe the probability density of random variables at each value point. The distribution of the charge time difference is described as a specific probability density function, which clearly shows the distribution probability of the charge time difference within different intervals. During the charging control process, different charge time differences may correspond to different charging risks and costs. The probability density function can be used to evaluate the probability of different standard deviation values ​​Δt, thereby quantifying the risks in the charging process.

[0042] In some optional embodiments, the difference is calculated Cumulative distribution function of , according to the cumulative distribution function , combined with the erf error function, the target charging request coefficient is obtained ;in, The calculation formula is: , The calculation formula is: ,in, is the error function, which is defined as: .

[0043] The cumulative distribution function (CDF) represents the probability that a random variable is less than or equal to a certain value. By calculating the CDF of the difference Δt, we can comprehensively consider the probability distribution of charging time differences in different intervals and obtain a comprehensive probability indicator. Combining it with the ERF error function (ERF) makes it easier to calculate the CDF because the ERF error function has mature mathematical calculation methods and properties.

[0044] The target charging request coefficient is a quantitative representation of a user's charging needs. Through the above calculation process, the probability information of the charging time difference is converted into a specific coefficient value, which can more accurately reflect the user's actual demand for charging speed and time.

[0045] In some optional embodiments, according to the target charging request coefficient , and the actual charging request coefficient is obtained , according to the actual charging request coefficient , combined with the maximum charge rate , and adjust the actual charging rate suitable for the target vehicle ;in, The target charge request coefficient combines multiple factors, including historical charging time differences, to reflect the user's expectations for time and speed for this charging session. The actual charge request coefficient is derived from the target charge request coefficient, and then combined with the maximum charge rate to determine the actual charge rate. This process accurately translates the user's abstract charging requirements into specific charging parameters.

[0046] In some optional embodiments, when the target vehicle needs to be charged at the maximum rate, the maximum rate charging can be manually selected. When the target vehicle needs to be charged at the maximum rate, the user can manually select the maximum rate to charge, and then adjust the relevant parameters. , the actual charging rate is equal to the maximum charging rate. This design provides users with greater flexibility. In emergency situations, users do not need to rely on the system's automatic adjustment and can directly obtain the fastest charging speed, meeting the urgent needs of users in special scenarios.

[0047] In summary, the specific control steps of the control method of this application are: obtaining the single charging gun plug-in time T1 obtained based on historical data, and the first time T2 required to fully charge the battery at the maximum rate; the difference between the single charging gun plug-in time T1 and the first time T2 is the charging time difference of a certain time ; Record the charging time difference of target vehicles multiple times in the historical period A data set, based on which a mean μ is obtained, and based on the mean μ, a standard deviation σ of the charging time difference data set of the target vehicle in the historical period is calculated; According to the mean μ and the standard deviation σ, the charging time difference value of a certain time is standardized to the standard normal distribution Z i , and the standard deviation value is obtained ; According to experience, the charging time difference value of a certain time obeys the standard normal distribution Z i , and the distribution of the difference value is described by the probability density function ; The cumulative distribution function of the difference value is calculated , and according to the cumulative distribution function , the target charging request coefficient is obtained by combining the erf error function , and according to the target charging request coefficient , the actual charging request coefficient is obtained , and according to the actual charging request coefficient , the actual charging rate suitable for the target vehicle is obtained by combining the maximum charging rate ; In practice, when the target vehicle needs to be charged at the maximum rate, the maximum rate charging can be manually selected.

[0048] In the description of the present application, it should be noted that the orientation or position relationship indicated by the terms "upper", "lower" and the like is based on the orientation or position relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. Unless otherwise specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be a fixed connection, or a detachable connection, or an integral connection; it can be a mechanical connection, or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or the communication between two elements inside. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0049] ​It should be noted that, in this application, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element.

[0050] The foregoing is merely a list of specific embodiments of the present application, intended to enable those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the broadest scope consistent with the principles and novel features of the present application.

Claims

1. An electric vehicle intelligent charging control method, characterized in that: include: Obtain the single-charge plug-in duration based on historical data, as well as the initial time required to fully charge the battery at the maximum rate; An actual charging request coefficient is calculated based on the difference between the plurality of groups of single charging gun plug-in time lengths and the first time; and an actual charging rate is adjusted according to the actual charging request coefficient.

2. The electric vehicle intelligent charging control method according to claim 1, wherein: The obtained charging gun plug-in time is longer than the first time.

3. The electric vehicle intelligent charging control method according to claim 1, wherein: When the target vehicle needs to be charged at the maximum rate, the maximum rate can be manually selected; when the target vehicle does not choose to be charged at the maximum rate, the actual charging rate suitable for the target vehicle can be automatically adjusted according to the actual charging request coefficient.

4. The electric vehicle intelligent charging control method according to claim 3, wherein: The charging gun plug-in time is T1, the first time is T2, and the difference between the two charging times is , The calculation formula is Δt i =T1-T2.

5. The electric vehicle intelligent charging control method according to claim 4, characterized in that: Record the charging time difference of the target vehicle multiple times in the historical period A data set is obtained based on the data set, and the standard deviation σ of the charging time difference data set of the target vehicle in the historical period is calculated based on the mean μ; wherein the calculation formula of the mean μ is: ; The calculation formula of standard deviation σ is: .

6. The electric vehicle intelligent charging control method according to claim 5, characterized in that: According to the mean μ and standard deviation σ, the charging time difference Normalized to a standard normal distribution , and the standard deviation is obtained ; Among them, the standard normal distribution The calculation formula is: .

7. The electric vehicle intelligent charging control method according to claim 6, characterized in that: According to experience, the difference in charging time Obey the standard normal distribution , the difference is converted into The distribution of ; The calculation formula is: .

8. The electric vehicle intelligent charging control method according to claim 7, wherein: Calculate the difference Cumulative distribution function of , according to the cumulative distribution function , combined with the erf error function, the target charging request coefficient is obtained ;in, The calculation formula is: , The calculation formula is: ,in, is the error function, which is defined as: .

9. The electric vehicle intelligent charging control method according to claim 8, characterized in that: According to the target charging request coefficient , and the actual charging request coefficient is obtained , according to the actual charging request coefficient , combined with the maximum charge rate , and adjust the actual charging rate suitable for the target vehicle ;in, .

10. The electric vehicle intelligent charging control method according to claim 9, characterized in that: When the target vehicle needs to be charged at the maximum rate, the maximum rate can be manually selected. 。

Images