A charging pile system optimization solution based on user behavior habits

Abstract

The present invention discloses a charging pile system optimization scheme based on user behavior habits, which includes: collecting historical charging data of users: the remaining power D during initial charging; e1 , remaining power at the end of charging D e2 ; Then, based on the distribution analysis model, the following users are assigned corresponding labels: a. The frequency of the remaining power less than 20% during initial charging exceeds 70%; b. The frequency of the remaining power higher than 80% during initial charging exceeds 70%; c. The frequency of the remaining power lower than 80% at the end of charging exceeds 70%. Finally, users with the above labels are reminded to charge when the remaining power of the battery is 30%-60% and to charge to more than 80%.

Description

Technical Field

[0001] The present invention relates to the technical field of charging systems for charging piles, and in particular to a charging pile system optimization solution based on user behavior habits. Background Art

[0002] With the gradual promotion and popularization of electric vehicles, the proportion of electric vehicles in the total number of cars is increasing. Compared with traditional internal combustion engine vehicles, electric vehicles take longer to charge and battery maintenance is more complicated than internal combustion engine vehicles. In addition, the battery replacement cost of electric vehicles is high and expensive. For users, how to effectively extend the life of the battery is an issue that needs to be considered. At present, the batteries of electric vehicles generally use high-energy-density lithium-ion batteries, which have the characteristics of high energy density, long life, high rated voltage and high power tolerance. But at the same time, lithium-ion batteries are also relatively fragile and afraid of high temperature and rain. Improper charging behavior by users will also reduce the life of lithium-ion batteries.

[0003] In actual use, users need electric vehicles to be charged at all times to meet their daily commuting needs. However, compared to charging once a day or irregularly, the life of lithium batteries will be reduced. Many users also wait until the car consumes very little power before charging, which will undoubtedly cause damage to lithium batteries. Among the existing technical solutions related to charging piles, few solutions take into account the impact of users' actual charging habits on car batteries. For example, the patent (publication number: CN109977325A) uses public charging pile information and personal charging pile information to solve the negative impact of uneven distribution of charging piles on users; the patent (publication number: CN110225081A) collects charging pile information and electric vehicle information to select charging piles for users to improve the charging efficiency of users. It can be seen that the existing solutions do not realize personalized user charging services in combination with the user's personal charging characteristics. Summary of the invention

[0004] In order to solve the above-mentioned problems of the prior art, the present invention provides a charging pile system optimization solution based on user behavior habits. Based on the historical charging data of users on the charging platform, the solution provides users with personalized charging management services for the bad charging habits of users. It not only meets the daily commuting needs of users and determines when the user's electric vehicle needs to be charged, but also effectively extends the battery life of the user's electric vehicle. In this solution, through a mathematical statistics solution, based on the distribution analysis model, a user charging behavior portrait is constructed to help the system determine whether the user's charging habits are reasonable. And on this basis, according to the user's daily charging habits, it predicts when the user should charge next time, and predicts the required charging time, to bring users a better experience.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A charging pile system optimization solution based on user behavior habits, including a charging behavior analysis module, specifically:

[0007] (1) Collect the user's historical charging data: the remaining power D during initial charging e1 , remaining power at the end of charging D e2 ;

[0008] (2) Based on the statistics of the distribution analysis model, the following users with unhealthy charging habits are assigned corresponding labels:

[0009] Label a: The frequency of the remaining power being less than 20% during initial charging exceeds 70%;

[0010] Label b: The frequency of the remaining power exceeding 80% during initial charging exceeds 70%;

[0011] Label c: The frequency of remaining power being less than 80% at the end of charging exceeds 70%;

[0012] (3) Remind users with the above labels to make them aware of their unhealthy charging habits.

[0013] Preferably, the statistical method in step (2) is as follows:

[0014]

[0015] Preferably, the content of the reminder in step (3) is:

[0016] It is unhealthy to charge when the remaining power is less than 20% or higher than 80% during the initial charge, or lower than 80% when the charge ends. It is recommended to charge when the remaining power is 30%-60% and fully charge to more than 80%.

[0017] Preferably, the time of the reminder in step (3) is the time when the user next uses the charging pile.

[0018] Preferably, the solution further includes a power consumption ratio prediction module, which includes:

[0019] (1) Add the user's historical charging data: two adjacent charging time points Time1 and Time2;

[0020] (2) Calculate the average daily electric vehicle power consumption E of the user average :

[0021]

[0022] Preferably, the scheme further includes an optimal charging date prediction module, which includes:

[0023] (1) Charging when the remaining power is 30%-60% is a solution that is more friendly to the life of the lithium battery. Calculate the most appropriate charging days x.

[0024] (2) Remind each user to charge the battery again according to the number of days after each charge, and prompt the user to charge the battery to 80% or above.

[0025] Preferably, the power consumption ratio prediction module calculates the most suitable charging days in step (1), and the specific calculation method is:

[0026]

[0027] Preferably, the solution further includes an optimal charging time prediction module, which includes:

[0028] (1) Add the collection of historical charging data of users: charging power Cp n ,The remaining battery power D during this initial charging;

[0029] (2) If the total battery storage energy of the user's electric vehicle is E all , the calculated average charging power is Cp, and the optimal charging time T is:

[0030]

[0031] (3) Feedback to users that the optimal charging time is T or longer.

[0032] Preferably, the charging power C p The calculation method is:

[0033] (1) Collect the charging power data of electric vehicles multiple times, denoted as Cp1, Cp2...Cp n ;

[0034] (2) Remove the maximum value Cp in the collected power max ;

[0035] (3) Calculate the charging power C p for:

[0036] Preferably, the multiple collection of charging power data of the electric vehicle is set to collect the power data of the electric vehicle once every five minutes.

[0037] The beneficial effects of the present invention are embodied in: based on the user's historical charging data on the charging platform, personalized charging management services are provided to the user according to the user's bad charging habits. This not only meets the user's daily commuting needs and determines when the user's electric vehicle needs to be charged and maintained daily, but also can effectively extend the battery life of the user's electric vehicle, thereby reducing the user's additional expenses caused by replacing and repairing the electric vehicle power supply battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a schematic diagram of the modules included in the present invention;

[0039] Figure 2 It is a schematic diagram of the implementation process of the present invention;

[0040] Figure 3 This is a flow chart of the optimal charging date prediction module;

[0041] Figure 4 This is a flow chart of the optimal charging time prediction module;

[0042] Figure 5 A schematic diagram of the process for calculating the average charging power; DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] See also Figure 1 As shown, the specific embodiments provided by the present invention are as follows:

[0045] Embodiment 1:

[0046] A charging pile system optimization solution based on user behavior habits, including a charging behavior analysis module, specifically:

[0047] (1) Collect the user's historical charging data: the remaining power D during initial charging e1 , remaining power at the end of charging D e2 ;

[0048] (2) Based on the distribution analysis model, corresponding tags are assigned to the following users:

[0049] a. The frequency of the remaining power being less than 20% during initial charging exceeds 70%;

[0050] b. The frequency of the remaining power exceeding 80% during initial charging exceeds 70%;

[0051] c. The frequency of remaining power being less than 80% at the end of charging exceeds 70%;

[0052] (3) Remind users of the above labels that they should charge their batteries when the remaining power is between 30% and 60%, and fully charge them to more than 80%.

[0053] In this embodiment, the user's historical charging data is used to screen out users with unhealthy charging habits based on the distribution analysis model, and corresponding labels are added according to different habits. At the same time, users with the above labels are reminded to charge when the remaining battery power is 30%-60%, and to charge to more than 80%. Compared with the traditional charging pile system, this solution effectively utilizes the user's charging data, can specifically remind users with unhealthy charging habits, prompting them to correct their charging habits, and can also increase the service life of the rechargeable battery.

[0054] Embodiment 2:

[0055] The solution further includes a module for predicting the best charging date, which includes:

[0056] (1) Add the user's historical charging data: two adjacent charging time points Time1 and Time2;

[0057] (2) Calculate the average daily electric vehicle power consumption E of the user average :

[0058]

[0059] (3) Charging when the remaining power is 30%-60% is a solution that is more friendly to the life of the lithium battery. The most suitable charging days x are:

[0060]

[0061] (4) Remind each user to charge the battery again according to the number of days after each charge, and prompt the user to charge the battery to 80% or above.

[0062] This solution combines more user charging data, calculates the average daily power consumption of electric vehicles, and takes charging when the remaining power is 30%-60% as a solution that is more friendly to the life of lithium batteries. It predicts when the user should charge next time, which is more friendly to the life of lithium batteries. Targeted reminders are made to effectively prevent users from damaging the battery by charging when the battery power is higher than 80% or lower than 20% due to no plans.

[0063] Embodiment 3:

[0064] This solution further includes a module for predicting the optimal charging time, which includes:

[0065] (1) Add the user's historical charging data: charging power C p ,The remaining battery power D during this initial charging;

[0066] (2) If the total battery storage energy of the user's electric vehicle is E all , then the charging time T is:

[0067]

[0068] (3) Feedback to users that the optimal charging time is T or longer.

[0069] This embodiment collects the historical charging power when the charging pile charges the user's battery, and the remaining battery power D during the initial charging, and calculates the average charging power C p The optimal charging time is calculated with the battery charged to 80% or above as the optimal charging power. After starting charging, the user only needs to arrange the time to pick up the car based on the optimal charging time, so as to avoid picking up the car too early and having to wait.

[0070] Embodiment 4:

[0071] In this solution, the charging power is calculated as follows:

[0072] (1) Collect the charging power data of electric vehicles multiple times, denoted as Cp1, Cp2...Cp n ;

[0073] (2) Remove the maximum value Cp in the collected power max ;

[0074] (3) Calculate the charging power C p for:

[0075] Because charging power is a dynamically changing process, this embodiment proposes a mean power calculation scheme that removes the peak value to calculate the average charging power of the vehicle during the charging process, so as to make the prediction of the optimal charging time more accurate.

[0076] Embodiment 5:

[0077] In this solution, the multiple collection of charging power data of the electric vehicle is set to collect the power data of the electric vehicle once every five minutes.

[0078] This plan stipulates that the power data of electric vehicles is collected every five minutes to avoid increased energy consumption due to excessive data collection frequency; the charging power detection module built into the charging pile collects data, and the data is collected at a time point, that is, the power data of the electric vehicle charging at the time when the data is collected.

[0079] In the description of the embodiments of the present invention, it needs to be understood that terms such as “up”, “down”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “center”, “top”, “bottom”, “top”, “bottom”, “inside”, “outside”, “inside”, and “outside” indicate orientation or positional relationships.

[0080] In the description of the embodiments of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "install", "connect", "connect", and "assemble" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0081] In the description of the embodiments of the present invention, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0082] In the description of the embodiments of the present invention, it is to be understood that "-" and "~" represent the range between two values, and the range includes the endpoints. For example: "AB" represents a range greater than or equal to A and less than or equal to B. "A~B" represents a range greater than or equal to A and less than or equal to B.

[0083] In the description of the embodiments of the present invention, the term "and / or" herein is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " herein generally indicates that the associated objects before and after are in an "or" relationship.

[0084] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A charging pile system optimization solution based on user behavior habits, where: Includes charging behavior analysis module, specifically: (1) Collect the user's historical charging data: the remaining power during initial charging , Remaining power when charging is finished ; (2) Using the distribution analysis model, we assign corresponding labels to the following users with unhealthy charging habits: Label a: The frequency of the remaining power being less than 20% during initial charging exceeds 70%; Label b: The frequency of the remaining power exceeding 80% during initial charging exceeds 70%; Label c: The frequency of remaining power being less than 80% at the end of charging exceeds 70%; (3) Remind users with the above labels to make them aware of their unhealthy charging habits; It further includes a power consumption ratio prediction module, which includes: adding historical charging data of users: two adjacent charging time points and ; Calculate the average daily electric vehicle power consumption of the user : ； It further includes an optimal charging date prediction module, which includes: charging when the remaining power is 30%-60% as a solution that is more friendly to the life of the lithium battery, and calculating the most suitable charging days ; Remind each user to charge the battery again according to the number of days after each charge, and remind the user to charge the battery to 80% or above; The most suitable charging days are calculated as follows: 。 2. According to the charging pile system optimization solution based on user behavior habits according to claim 1, the statistical method of step (2) is as follows: 。 3. According to the charging pile system optimization solution based on user behavior habits according to claim 1, the content of the reminder in step (3) is: It is unhealthy to charge when the remaining power is less than 20% or higher than 80% during the initial charging, or lower than 80% when the charging ends. It is recommended to charge when the remaining power is 30%-60% and fully charge to more than 80%.

4. According to the charging pile system optimization solution based on user behavior habits as described in claim 3, the time of the reminder in step (3) is: the time when the user uses the charging pile next time.

5. The charging pile system optimization scheme based on user behavior habits according to claim 1 further comprises an optimal charging time prediction module, wherein: include: (1) Add the user's historical charging data: charging power, remaining battery power D during initial charging; (2) The calculated average charging power is , if the total battery storage energy of the user's electric vehicle is , then the optimal charging time T is: ； (3) Provide feedback to users that the optimal charging time is T or above.

6. A charging pile system optimization solution based on user behavior habits according to claim 5, wherein: The average charging power The calculation method is: (1) Collect the charging power data of electric vehicles multiple times, denoted as Cp1, Cp2, ...Cp n ; (2) Remove the maximum value Cp in the collected power max ; (3) Calculate the average charging power for: .

7. A charging pile system optimization solution based on user behavior habits according to claim 6, wherein: The multiple collection of charging power data of the electric vehicle is set to collect the power data of the electric vehicle once every five minutes.

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