Electric vehicle charging management method and device, and electronic device

Abstract

The application discloses an electric vehicle charging management method, device and electronic equipment. The method comprises the following steps: obtaining internal current data, external current data, real-time charging power of a charging pile and departure time of next trip set by a user; generating user current trip habit data according to the internal current data and the external current data; generating target data according to the internal current data and the external current data; predicting trip power consumption of the electric vehicle in the next trip according to the internal current data, the external current data and the user current trip habit data; and / or predicting required charging time of the electric vehicle in the next trip according to the target data, the real-time charging power and the departure time; and determining a charging strategy of the electric vehicle in a corresponding trip mode and charging mode according to the predicted trip power consumption and / or required charging time of the electric vehicle in the next trip. The application can customize the charging strategy in different trip modes and different charging modes.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle charging management technology, and specifically relates to an electric vehicle charging management method, device and electronic equipment. Background Technology

[0002] In recent years, the new energy vehicle industry has flourished, and the market share of electric vehicles has continued to increase.

[0003] However, the charging management of electric vehicles has become a key factor restricting their further development. While existing Battery Management Systems (BMS) can ensure basic battery safety, their limitations are becoming increasingly apparent when faced with complex and ever-changing usage scenarios. Traditional charging methods fail to fully consider users' personalized charging needs, the real-time status of the battery, and the dynamic changes in external environmental factors, leading to problems such as shortened battery life, low charging efficiency, and battery damage caused by high-rate charging, thus increasing the risk of thermal runaway. Summary of the Invention

[0004] To address the aforementioned problems, embodiments of the present invention provide an electric vehicle charging management method, apparatus, and electronic device.

[0005] In a first aspect, embodiments of the present invention provide an electric vehicle charging management method, comprising:

[0006] The system acquires the electric vehicle's internal current data, external current data, real-time charging power of the charging pile, and the user-set departure time for the next trip. The internal current data includes: the current cell voltage, cell temperature, charging and discharging current, remaining power, battery health status, mileage, fault information, and charging history data of the electric vehicle's battery. The external current data includes: weather conditions, ambient temperature, geographical location information, and the date and time corresponding to the ambient temperature.

[0007] Based on the internal current data and the external current data, and on the trained user travel behavior model, user current travel habit data is generated. The user current travel habit data includes: the travel time and distance of the next trip of the electric vehicle.

[0008] Based on the internal current data and the external current data, target data is generated based on the trained battery performance change model. The target data includes the remaining power and battery health status.

[0009] Based on the internal current data, the external current data, and the user's current travel habit data, and using a trained trip power consumption prediction model, predict the power consumption of the electric vehicle for the next trip; and / or based on the target data, the real-time charging power, and the departure time, and using a trained charging time prediction model, predict the required charging time for the electric vehicle for the next trip.

[0010] Obtain the travel mode and charging method for the electric vehicle's next trip;

[0011] Based on the predicted energy consumption and / or charging time required for the next trip of the electric vehicle, a charging strategy for the electric vehicle under the corresponding travel mode and charging method is determined.

[0012] In one possible implementation, the travel mode includes emergency travel and regular travel, and the charging method includes fast charging and slow charging.

[0013] The step of determining the charging strategy for the electric vehicle under the corresponding travel mode and charging method based on the power consumption and / or required charging time for the next trip includes:

[0014] If the travel mode is emergency travel and the charging method is slow charging, the charging current is adjusted according to the predicted required charging time and the current battery charge of the electric vehicle to determine that the minimum available power is reached before the electric vehicle departs.

[0015] If the travel mode is regular travel and the charging method is fast charging, then the remaining charge value of the electric vehicle's battery before charging is cut off is determined based on the predicted power consumption of the trip and the battery discharge depth range.

[0016] If the travel mode is regular travel and the charging method is slow charging, then the charging current and the remaining charge value before charging is cut off for the electric vehicle's battery are determined based on the predicted power consumption of the trip and the required charging time.

[0017] In one possible implementation, the method further includes:

[0018] If the travel mode is emergency travel and the charging method is fast charging, then the charging power of the electric vehicle's battery is determined based on the maximum allowable charging current of the electric vehicle's battery, the current battery voltage, and the maximum output power of the charging pile.

[0019] In one possible implementation, determining the charging power of the electric vehicle's battery based on the maximum allowable charging current of the electric vehicle's battery, the current battery voltage, and the maximum output power of the charging pile includes:

[0020] The product of the maximum charging current and the current battery voltage is determined as the current charging power;

[0021] The smaller of the current charging power and the maximum output power of the charging pile is determined as the charging power of the electric vehicle's battery.

[0022] In one possible implementation, adjusting the charging current based on the predicted required charging time and the current battery charge of the electric vehicle to determine the minimum available charge level before the electric vehicle departs includes:

[0023] Based on the predicted required charging time and the current battery charge of the electric vehicle, an adaptive control algorithm is used to adjust the charging current to determine the minimum available charge before the electric vehicle departs.

[0024] The adaptive control algorithm formula is as follows:

[0025]

[0026] Where I0 is the initial charging current, α is the adaptive adjustment coefficient, and SOC is... min For minimum available power, SOC current t represents the current battery charge of the electric vehicle. charge The predicted required charging time.

[0027] In one possible implementation, determining the remaining charge capacity of the electric vehicle's battery before charging cutoff, based on the predicted energy consumption during the trip and the battery's depth of discharge range, includes:

[0028] Based on the predicted power consumption during the trip and the battery discharge depth range, the remaining charge value of the electric vehicle's battery at the charging cutoff point is determined using the following formula;

[0029]

[0030] Among them, Q total Q represents the total battery capacity. predict For predicted trip power consumption, SOC end-fast This refers to the remaining battery level when the travel mode is regular travel and the charging method is fast charging.

[0031] In one possible implementation, determining the charging current and remaining charge level of the electric vehicle's battery based on the predicted energy consumption during the trip and the required charging time includes:

[0032] Based on the predicted power consumption during the trip and the required charging time, the charging current and remaining power value of the electric vehicle's battery are determined using a multi-objective optimization algorithm.

[0033] In one possible implementation, the method further includes:

[0034] The battery management system is controlled according to the charging strategy, and the charging parameters are adjusted.

[0035] In a second aspect, embodiments of the present invention provide an electric vehicle charging management device, comprising:

[0036] The first acquisition module is used to acquire the electric vehicle's internal current data, external current data, the real-time charging power of the charging pile, and the departure time of the user's next trip. The internal current data includes: the current cell voltage, cell temperature, charging and discharging current, remaining power, battery health status, driving range, fault information, and charging history data of the electric vehicle's battery. The external current data includes: weather conditions, ambient temperature, geographical location information, and the date and time corresponding to the ambient temperature.

[0037] The user current travel habit data generation module is used to generate user current travel habit data based on the internal current data and the external current data, and on the user travel behavior model. The user current travel habit data includes: the travel time and travel distance of the next trip of the electric vehicle.

[0038] The target data generation module is used to generate target data based on the internal current data and the external current data, and on the basis of the battery performance change model. The target data includes the remaining power and the battery health status.

[0039] The trip power consumption prediction module is used to predict the power consumption of the electric vehicle for the next trip based on the internal current data, the external current data, and the user's current travel habit data, using a trip power consumption prediction model; and / or

[0040] The required charging time prediction module is used to predict the required charging time for the electric vehicle's next trip based on the target data, the real-time charging power, and the departure time, using a charging time prediction model.

[0041] The second acquisition module is used to acquire the travel mode and charging method of the electric vehicle's next trip;

[0042] The charging strategy determination module is used to determine the charging strategy of the electric vehicle under the corresponding travel mode and charging method based on the power consumption of the electric vehicle's next trip and / or the required charging time.

[0043] Thirdly, embodiments of the present invention provide an electronic device, comprising:

[0044] The system includes a memory and a processor, which communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, which can invoke the program instructions to perform the steps of the method described in the first aspect and various possible implementations.

[0045] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect and various possible implementations.

[0046] Fifthly, embodiments of the present invention provide a computer program product containing instructions that, when the computer program product is run on a computer, cause the steps of the method described in the first aspect and various possible implementations to be executed by the computer.

[0047] The beneficial effects of the technical solution provided by the embodiments of the present invention are as follows: Personalized charging strategies can accurately match user needs with optimal battery performance under different travel modes and charging methods, significantly improving charging efficiency; innovative predictive models and dynamic charging control mechanisms effectively reduce the battery's charge / discharge rate, keeping the battery's operating conditions within a range of low lifespan degradation, thereby significantly extending the battery's lifespan; real-time monitoring of various battery parameters, such as voltage, temperature, and SOC, provides feedback for adjusting the BMS's charging parameters, enhancing the safety and stability of the charging process; users can check the charging status at any time through the APP, improving the user experience and providing strong support for the widespread application of electric vehicles. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating an electric vehicle charging management method provided in an embodiment of the present invention.

[0049] Figure 2 A schematic block diagram of an electric vehicle charging management device provided in an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0053] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0054] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if monitoring (the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when monitoring (the stated condition or event)," or "in response to monitoring (the stated condition or event)."

[0055] In recent years, the new energy vehicle industry has flourished, and the market share of electric vehicles has continued to increase. However, the charging management of electric vehicles has become a key factor restricting their further development. While existing Battery Management Systems (BMS) can ensure basic battery safety, their limitations are becoming increasingly apparent when facing complex and ever-changing usage scenarios. Traditional charging methods fail to fully consider users' personalized charging needs, the real-time status of the battery, and the dynamic changes in external environmental factors, leading to problems such as shortened battery life, low charging efficiency, and battery damage from high-rate charging, thus increasing the risk of thermal runaway. For example, different users have significantly different travel habits, and fixed charging strategies are difficult to meet their personalized charging needs; at the same time, the optimal charging parameters of the battery change under different temperature conditions and usage stages, and existing technologies cannot optimize and adjust these parameters in real time.

[0056] In view of this, embodiments of the present invention provide a flowchart of an electric vehicle charging management method. For example... Figure 1 As shown, the method may include the following steps:

[0057] Step 101: Obtain the electric vehicle's internal current data, external current data, real-time charging power of the charging pile, and the user's set departure time for the next trip. The internal current data includes: the current cell voltage, cell temperature, charging and discharging current, remaining power, battery health status, mileage, fault information, and charging history data of the electric vehicle's battery. The external current data includes: weather conditions, ambient temperature, geographical location information, and the date and time corresponding to the ambient temperature.

[0058] Step 102: Based on the current internal and external data, and the user's travel behavior model, generate the user's current travel habit data, which includes the travel time and distance of the next trip by electric vehicle.

[0059] Step 103: Based on the current internal and external data, generate target data according to the battery performance change model. The target data includes the remaining power and battery health status.

[0060] Step 104: Based on current internal data, current external data, and user's current travel habits data, predict the energy consumption of the electric vehicle for the next trip using a trip energy consumption prediction model; and / or based on target data, real-time charging power, and departure time, predict the required charging time for the electric vehicle for the next trip using a charging time prediction model.

[0061] Step 105: Obtain the travel mode and charging method for the electric vehicle's next trip.

[0062] Step 106: Determine the charging strategy for the electric vehicle under the corresponding travel mode and charging method based on the power consumption of the electric vehicle's next trip and / or the required charging time.

[0063] The following describes in detail each step of the above process and the effects that can be further produced, with reference to the embodiments of the present invention.

[0064] First, in conjunction with the embodiments of the present invention, a detailed description will be given of step 101 above, namely, "acquiring the internal current data, external current data, real-time charging power of the charging pile, and the departure time of the next trip set by the user. The internal current data includes: the current cell voltage, cell temperature, charging and discharging current, remaining power, battery health status, driving mileage, fault information, and charging history data of the electric vehicle's battery. The external current data includes: weather conditions, ambient temperature, geographical location information, and the date and time corresponding to the ambient temperature."

[0065] In this embodiment of the invention, the departure time for the next trip can be set on the user terminal APP.

[0066] In this embodiment of the invention, the following data are first acquired: the electric vehicle's internal current data, external current data, the real-time charging power of the charging pile, and the user-set departure time for the next trip. Exemplarily, the internal current data includes: the current individual cell voltage, individual cell temperature, charging / discharging current, remaining battery capacity, battery health status, driving mileage, fault information, and charging history data of the electric vehicle's battery. The external current data includes: weather conditions, ambient temperature, geographical location information, and the date and time corresponding to the ambient temperature. Fault information refers to issues such as excessively high individual cell voltage or temperature in the electric vehicle's battery. In this embodiment of the invention, the specific definition of fault information is not limited.

[0067] The following describes in detail step 102, namely, "based on internal current data and external current data, generating user current travel habit data based on the user travel behavior model, the user current travel habit data includes: the travel time and travel distance of the next trip of the electric vehicle", with reference to the embodiments of the present invention.

[0068] In this embodiment of the invention, user current travel habit data is generated based on internal and external current data and a user travel behavior model. This user current travel habit data includes: the travel time and distance of the next trip using an electric vehicle.

[0069] One possible approach is to generate analytical data based on current internal and external data. Then, based on this analytical data and a user travel behavior model, data on the user's current travel habits is generated.

[0070] One possible implementation is to generate analytical data based on internal and external current data. Specifically, this can be done by generating support and confidence scores based on the internal and external current data. Support can be understood as the number of transactions containing the set of data items divided by the total number of transactions, and confidence as the number of transactions containing both the preceding and following items divided by the number of transactions containing only the preceding item. Here, the set of data items refers to the set containing internal current data, external current data, and user input data items (travel mode selection, such as emergency or regular travel, personalized needs, such as set departure time or charging cutoff battery level). The total number of transactions refers to the sum of all collected data records. If "containing the preceding item" means containing a specific data item, then "containing the preceding and following items" means containing both the preceding and following data items.

[0071] As one possible approach, generating user current travel habit data based on the user travel behavior model, according to the analyzed data, can be specifically done by generating user current travel habit data based on the user travel behavior model, according to the support and confidence levels.

[0072] It should be noted that the user travel behavior model can be trained using historical support, historical confidence, and user historical travel habit data as training samples.

[0073] The following describes step 103, namely, "generating target data based on the battery performance change model according to the internal current data and the external current data, the target data including the remaining power and the battery health status," in detail with reference to the embodiments of the present invention.

[0074] In this embodiment of the invention, target data is generated based on the battery performance change model, according to the internal current data and the external current data. The target data includes the remaining power and the battery health status.

[0075] One possible approach is to generate analytical data based on current internal and external data. Then, based on this analytical data and a battery performance variation model, target data is generated.

[0076] As one possible approach, generating target data based on the battery performance change model, according to the analyzed data, can be specifically done by generating target data based on the support and confidence levels, according to the battery performance change model.

[0077] It should be noted that the battery performance change model can be trained using historical support, historical confidence, and historical target data as training samples.

[0078] The following describes in detail step 104, namely, "predicting the energy consumption of the electric vehicle for the next trip based on the trip energy consumption prediction model according to the internal current data, external current data and user current travel habit data; and / or predicting the required charging time for the electric vehicle for the next trip based on the charging time prediction model according to the target data, real-time charging power and departure time".

[0079] In this embodiment of the invention, based on internal current data, external current data, and user current travel habit data, a trip power consumption prediction model is used to predict the power consumption of the electric vehicle for the next trip; and / or based on target data, real-time charging power, and departure time, a charging time prediction model is used to predict the required charging time for the electric vehicle for the next trip.

[0080] As one possible implementation, the trip energy consumption prediction model is trained using a Long Short-Term Memory network, and its core formula is as follows:

[0081] Input gate: i t =σ(W ii x t +b ii +W hi h t-1 +bhi )

[0082] Forgotten Gate: f t =σ(W if x t +b if +W h fh t-1 +b hf )

[0083] Memory unit: c t =f t ⊙c t-1 +i t ⊙tanh(W ic x t +b ic +W hc h t-1 +b hc )

[0084] Output gate: o t =σ(W io x t +b io +W ho h t-1 +b ho )

[0085] Output gate: h t =o t ⊙tanh(c t )

[0086] Where, x t For inputs at different times, namely internal historical data, external historical data, and user historical travel habit data, h t The hidden layer output at the current moment represents the battery consumption for the next trip in the electric vehicle's history. tThe memory unit is σ, the sigmoid function is σ, ⊙ represents element-wise multiplication, and W and b are the weights and bias parameters, respectively. Internal historical data includes the electric vehicle's battery's historical cell voltage, temperature, charge / discharge current, remaining charge, battery health status, mileage, fault information, and charging data. External historical data includes historical weather conditions, ambient temperature, geographical location information, and the corresponding date and time for the ambient temperature. User travel history data includes the electric vehicle's next trip's travel time and distance. If the difference between the energy consumption for the electric vehicle's next trip (output by the Long Short-Term Memory network) and the actual energy consumption for the next trip exceeds a preset threshold, the Long Short-Term Memory network's model parameters are adjusted until the difference is less than or equal to the preset threshold.

[0087] As one possible implementation, the charging time prediction model can employ an autoregressive integral moving average model, the formula of which is as follows:

[0088]

[0089] Among them, X t Given time series data, B is the shift operator, and φ i For autoregressive coefficients, θ j ε is the moving average coefficient. t The sequence is white noise, where p is the autoregressive order, d is the differencing order, and q is the moving average order. The time series data includes historical target data, historical charging power, historical departure time, and historical required charging current. The historical target data is obtained based on internal and external historical data, using a battery performance variation model. Internal historical data includes historical cell voltage, historical cell temperature, historical charge / discharge current, historical remaining capacity, historical battery health status, historical mileage, historical fault information, and historical charging data for the electric vehicle's battery. External historical data includes historical weather conditions, historical ambient temperature, historical geographical location information, and the date and time corresponding to the historical ambient temperature.

[0090] The following describes step 105, namely "obtaining the travel mode and charging method for the next trip of the electric vehicle", in detail with reference to the embodiments of the present invention.

[0091] In this embodiment of the invention, the travel mode and charging method for the next trip of the electric vehicle are obtained.

[0092] As one possible implementation, travel modes can include emergency travel and regular travel, and charging methods can include fast charging and slow charging.

[0093] One possible implementation is to include travel mode selection buttons and charging method selection buttons on the user's mobile app. Users can select the appropriate travel mode using the travel mode selection button and the appropriate charging method using the charging method selection button.

[0094] The following describes in detail step 106, namely, "determining the charging strategy of the electric vehicle under the corresponding travel mode and charging method based on the power consumption and / or required charging time of the electric vehicle's next trip," with reference to embodiments of the present invention.

[0095] In this embodiment of the invention, the charging strategy for the electric vehicle under the corresponding travel mode and charging method is determined based on the power consumption of the electric vehicle's next trip and / or the required charging time.

[0096] As one possible implementation, if the travel mode is emergency travel and the charging method is fast charging, the charging power of the electric vehicle's battery is determined based on the maximum allowable charging current of the electric vehicle's battery, the current battery voltage, and the maximum output power of the charging station. For example, the product of the maximum charging current and the current battery voltage is determined as the current charging power. The smaller of the current charging power and the maximum output power of the charging station is then determined as the charging power of the electric vehicle's battery.

[0097] As one possible implementation, if the travel mode is emergency travel and the charging method is slow charging, the charging current is adjusted based on the predicted required charging time and the current battery level of the electric vehicle to determine the minimum usable battery level before the electric vehicle departs. For example, an adaptive control algorithm is used to adjust the charging current based on the predicted required charging time and the current battery level of the electric vehicle to determine the minimum usable battery level before the electric vehicle departs. The formula for the adaptive control algorithm is as follows:

[0098]

[0099] Where I0 is the initial charging current, α is the adaptive adjustment coefficient, and SOC is... min For minimum available power, SOC current t represents the current battery charge of the electric vehicle. charge The predicted required charging time.

[0100] As one possible implementation, if the travel mode is regular travel and the charging method is fast charging, the remaining charge level of the electric vehicle's battery before charging is determined based on the predicted energy consumption during the trip and the battery's depth of discharge range. For example, the remaining charge level of the electric vehicle's battery before charging is determined based on the following formula, according to the predicted energy consumption during the trip and the battery's depth of discharge range:

[0101]

[0102] Among them, Q total Q represents the total battery capacity. predict For predicted trip power consumption, SOC end-fast This refers to the remaining battery level when the travel mode is regular travel and the charging method is fast charging.

[0103] As one possible implementation, if the travel mode is regular travel and the charging method is slow charging, the charging current and remaining charge level of the electric vehicle's battery are determined based on the predicted energy consumption during the trip and the required charging time. For example, the charging current and remaining charge level of the electric vehicle's battery are determined based on a multi-objective optimization algorithm, according to the predicted energy consumption during the trip and the required charging time.

[0104] It should be noted that during the charging process of an electric vehicle's battery, various battery parameters, such as voltage, temperature, and state of charge (SOC), are monitored in real time. If any abnormality is detected in the battery parameters, the charging parameters are immediately adjusted or charging is stopped, and an alarm message is sent to the user's terminal.

[0105] Based on the real-time monitored battery temperature, a fuzzy control algorithm is used to dynamically adjust the charging current I. adjust When the temperature is too high, the charging current should be appropriately reduced; when the temperature is too low, the current should be moderately increased to accelerate the charging speed, provided that battery safety is ensured. The fuzzy control rules are shown in the table below:

[0106] <![CDATA[T<T low ]]> <![CDATA[I adjust =I current (1+β1)]]> <![CDATA[T LOW ≤T≤T high ]]> <![CDATA[I adjust =I current ]]> <![CDATA[T>T high ]]> <![CDATA[I adjust =I current (1-β2)]]>

[0107] Among them, T low T high For the set temperature threshold, I current β1 and β2 are the current charging current and adjustment coefficients, respectively.

[0108] The beneficial effects of the technical solution provided by the embodiments of the present invention are as follows: Personalized charging strategies can accurately match user needs with optimal battery performance under different travel modes and charging methods, significantly improving charging efficiency; innovative predictive models and dynamic charging control mechanisms effectively reduce the battery's charge / discharge rate, keeping the battery's operating conditions within a range of low lifespan degradation, thereby significantly extending the battery's lifespan; real-time monitoring of various battery parameters, such as voltage, temperature, and SOC, provides feedback for adjusting the BMS's charging parameters, enhancing the safety and stability of the charging process; users can check the charging status at any time through the APP, improving the user experience and providing strong support for the widespread application of electric vehicles.

[0109] According to another embodiment, an electric vehicle charging management device is provided. Figure 2 A schematic block diagram of an electric vehicle charging management device according to one embodiment is shown. Figure 2 As shown, the device 200 may include: a first acquisition module 201, a user's current travel habit data generation module 202, a target data generation module 203, a trip power consumption prediction module 204, a required charging time prediction module 205, a second acquisition module 206, and a first charging strategy determination module 207. The main functions of each component module are as follows:

[0110] The first acquisition module 201 is used to acquire the electric vehicle's internal current data, external current data, the real-time charging power of the charging pile, and the departure time of the next trip set by the user. The internal current data includes: the current cell voltage, cell temperature, charging and discharging current, remaining power, battery health status, driving mileage, fault information, and charging history data of the electric vehicle's battery. The external current data includes: weather conditions, ambient temperature, geographical location information, and the date and time corresponding to the ambient temperature.

[0111] The user current travel habit data generation module 202 is used to generate user current travel habit data based on the user travel behavior model according to the internal current data and the external current data. The user current travel habit data includes: the travel time and travel distance of the next trip of the electric vehicle.

[0112] The target data generation module 203 is used to generate target data based on the internal current data and the external current data, and on the basis of the battery performance change model. The target data includes the remaining power and the battery health status.

[0113] The trip power consumption prediction module 204 is used to predict the power consumption of the electric vehicle for the next trip based on the internal current data, the external current data, and the user's current travel habit data, using a trip power consumption prediction model; and / or

[0114] The required charging time prediction module 205 is used to predict the required charging time for the next trip of the electric vehicle based on the target data, the real-time charging power and the departure time, and a charging time prediction model.

[0115] The second acquisition module 206 is used to acquire the travel mode and charging method of the electric vehicle for the next trip;

[0116] The first charging strategy determination module 207 is used to determine the charging strategy of the electric vehicle under the corresponding travel mode and charging method based on the power consumption and / or required charging time of the electric vehicle's next trip.

[0117] In one possible implementation, the travel mode includes emergency travel and regular travel, and the charging method includes fast charging and slow charging.

[0118] The charging strategy determination module 207 is specifically used to adjust the charging current based on the predicted required charging time and the current battery charge of the electric vehicle if the travel mode is emergency travel and the charging method is slow charging, so as to determine that the minimum available power is reached before the electric vehicle departs.

[0119] If the travel mode is regular travel and the charging method is fast charging, then the remaining charge value of the electric vehicle's battery before charging is cut off is determined based on the predicted power consumption of the trip and the battery discharge depth range.

[0120] If the travel mode is regular travel and the charging method is slow charging, then the charging current and the remaining charge value before charging is cut off for the electric vehicle's battery are determined based on the predicted power consumption of the trip and the required charging time.

[0121] In one possible implementation, the device 200 further includes: a second charging strategy determination module, configured to determine the charging power of the electric vehicle's battery based on the maximum allowable charging current of the electric vehicle's battery, the current battery voltage, and the maximum output power of the charging pile if the travel mode is emergency travel and the charging method is fast charging.

[0122] In one possible implementation, the second charging strategy determination module is specifically used to determine the product of the maximum charging current and the current battery voltage as the current charging power; and to determine the smaller of the current charging power and the maximum output power of the charging pile as the charging power of the electric vehicle's battery.

[0123] In one possible implementation, the first charging strategy determination module 207 is specifically used to adjust the charging current using an adaptive control algorithm based on the predicted required charging time and the current battery charge of the electric vehicle, and to determine the minimum available charge before the electric vehicle departs.

[0124] The adaptive control algorithm formula is as follows:

[0125]

[0126] Where I0 is the initial charging current, α is the adaptive adjustment coefficient, and SOC is... min For minimum available power, SOC current t represents the current battery charge of the electric vehicle. charge The predicted required charging time.

[0127] In one possible implementation, the first charging strategy determination module 207 is specifically used to determine the remaining charge value of the electric vehicle's battery before charging cutoff based on the following formula, according to the predicted travel power consumption and the battery discharge depth range.

[0128]

[0129] Among them, Q total Q represents the total battery capacity. predict For predicted trip power consumption, SOC end-fast This refers to the remaining battery level when the travel mode is regular travel and the charging method is fast charging.

[0130] In one possible implementation, the first charging strategy determination module 207 is specifically used to determine the charging current and remaining charge value of the electric vehicle's battery based on a multi-objective optimization algorithm, according to the predicted power consumption during the trip and the required charging time.

[0131] In one possible implementation, the device 200 further includes a charging parameter adjustment module for controlling the battery management system and adjusting the charging parameters according to the charging strategy.

[0132] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0133] In addition, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0134] And an electronic device, comprising:

[0135] One or more processors; and

[0136] A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any of the foregoing method embodiments.

[0137] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0138] in, Figure 3 The architecture of an electronic device is illustrated by way of example, which may include a processor 310, a video display adapter 311, a disk drive 312, an input / output interface 313, a network interface 314, and a memory 320. The processor 310, video display adapter 311, disk drive 312, input / output interface 313, network interface 314, and memory 320 can communicate with each other via a communication bus 330.

[0139] The processor 310 can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention.

[0140] The memory 320 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 320 can store the operating system 321 for controlling the operation of the electronic device 300, and the basic input / output system (BIOS) 322 for controlling the low-level operations of the electronic device 300. Additionally, it can store a web browser 323, a data storage management system 324, and an electric vehicle charging management device 325, etc. The aforementioned electric vehicle charging management device 325 can be the application program that specifically implements the aforementioned steps in this embodiment of the invention. In summary, when the technical solution provided in this embodiment of the invention is implemented through software or firmware, the relevant program code is stored in the memory 320 and is called and executed by the processor 310.

[0141] Input / output interface 313 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0142] Network interface 314 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (e.g., USB, Ethernet cable) or wireless means (e.g., mobile network, Wi-Fi, Bluetooth).

[0143] Bus 330 includes a pathway for transmitting information between various components of the device, such as processor 310, video display adapter 311, disk drive 312, input / output interface 313, network interface 314, and memory 320.

[0144] It should be noted that although the above-described device only shows the processor 310, video display adapter 311, disk drive 312, input / output interface 313, network interface 314, memory 320, bus 330, etc., in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the solution of this application, and does not necessarily include all the components shown in the figures.

[0145] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the embodiments of the present invention have been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for managing electric vehicle charging, characterized in that, include: The system acquires the electric vehicle's internal current data, external current data, real-time charging power of the charging pile, and the user-set departure time for the next trip. The internal current data includes: the current cell voltage, cell temperature, charging and discharging current, remaining power, battery health status, mileage, fault information, and charging history data of the electric vehicle's battery. The external current data includes: weather conditions, ambient temperature, geographical location information, and the date and time corresponding to the ambient temperature. Based on the internal current data and the external current data, and on the trained user travel behavior model, user current travel habit data is generated. The user current travel habit data includes: the travel time and distance of the next trip of the electric vehicle. Based on the internal current data and the external current data, target data is generated based on the trained battery performance change model. The target data includes the remaining power and battery health status. Based on the internal current data, the external current data, and the user's current travel habit data, and using a trained trip power consumption prediction model, predict the power consumption of the electric vehicle for the next trip; and / or based on the target data, the real-time charging power, and the departure time, and using a trained charging time prediction model, predict the required charging time for the electric vehicle for the next trip. Obtain the travel mode and charging method for the electric vehicle's next trip; Based on the predicted energy consumption and / or required charging time for the next trip of the electric vehicle, determine the charging strategy for the electric vehicle under the corresponding travel mode and charging method; The travel modes include emergency travel and regular travel, and the charging methods include fast charging and slow charging. The step of determining the charging strategy for the electric vehicle under the corresponding travel mode and charging method based on the power consumption and / or required charging time for the next trip includes: If the travel mode is emergency travel and the charging method is slow charging, the charging current is adjusted according to the predicted required charging time and the current battery charge of the electric vehicle to determine that the minimum available power is reached before the electric vehicle departs. If the travel mode is regular travel and the charging method is fast charging, then the remaining charge value of the electric vehicle's battery before charging is cut off is determined based on the predicted power consumption of the trip and the battery discharge depth range. If the travel mode is regular travel and the charging method is slow charging, then the charging current and the remaining charge value before charging is cut off are determined based on the predicted power consumption of the trip and the required charging time. The step of adjusting the charging current based on the predicted required charging time and the current battery charge of the electric vehicle to determine the minimum available battery charge before the electric vehicle departs includes: Based on the predicted required charging time and the current battery charge of the electric vehicle, an adaptive control algorithm is used to adjust the charging current to determine the minimum available charge before the electric vehicle departs. The adaptive control algorithm formula is as follows: Where I0 is the initial charging current and α is the adaptive adjustment coefficient. Minimum available power. The current battery charge of the electric vehicle. For the predicted required charging time; The step of determining the remaining charge capacity of the electric vehicle's battery before charging cutoff, based on the predicted power consumption during the trip and the battery discharge depth range, includes: Based on the predicted power consumption during the trip and the battery discharge depth range, the remaining charge value of the electric vehicle's battery at the charging cutoff point is determined using the following formula. Among them, Q total This refers to the total battery capacity. For the predicted trip power consumption, This refers to the remaining battery level when the travel mode is regular travel and the charging method is fast charging.

2. The method according to claim 1, characterized in that, The method further includes: If the travel mode is emergency travel and the charging method is fast charging, then the charging power of the electric vehicle's battery is determined based on the maximum allowable charging current of the electric vehicle's battery, the current battery voltage, and the maximum output power of the charging pile.

3. The method according to claim 2, characterized in that, Determining the charging power of the electric vehicle's battery based on the maximum allowable charging current of the electric vehicle's battery, the current battery voltage, and the maximum output power of the charging pile includes: The product of the maximum charging current and the current battery voltage is determined as the current charging power; The smaller of the current charging power and the maximum output power of the charging pile is determined as the charging power of the electric vehicle's battery.

4. The method according to claim 1, characterized in that, The step of determining the charging current and remaining charge level of the electric vehicle's battery based on the predicted power consumption during the trip and the required charging time includes: Based on the predicted power consumption during the trip and the required charging time, the charging current and remaining power value of the electric vehicle's battery are determined using a multi-objective optimization algorithm.

5. The method according to any one of claims 1-4, characterized in that, The method further includes: The battery management system is controlled according to the charging strategy, and the charging parameters are adjusted.

6. An electric vehicle charging management device, characterized in that, include: The first acquisition module is used to acquire the electric vehicle's internal current data, external current data, the real-time charging power of the charging pile, and the departure time of the user's next trip. The internal current data includes: the current cell voltage, cell temperature, charging and discharging current, remaining power, battery health status, driving range, fault information, and charging history data of the electric vehicle's battery. The external current data includes: weather conditions, ambient temperature, geographical location information, and the date and time corresponding to the ambient temperature. The user current travel habit data generation module is used to generate user current travel habit data based on the internal current data and the external current data, and on the user travel behavior model. The user current travel habit data includes: the travel time and travel distance of the next trip of the electric vehicle. The target data generation module is used to generate target data based on the internal current data and the external current data, and on the basis of the battery performance change model. The target data includes the remaining power and the battery health status. The trip power consumption prediction module is used to predict the power consumption of the electric vehicle for the next trip based on the internal current data, the external current data, and the user's current travel habit data, using a trip power consumption prediction model; and / or The required charging time prediction module is used to predict the required charging time for the electric vehicle's next trip based on the target data, the real-time charging power, and the departure time, using a charging time prediction model. The second acquisition module is used to acquire the travel mode and charging method of the electric vehicle's next trip; The first charging strategy determination module is used to determine the charging strategy of the electric vehicle under the corresponding travel mode and charging method based on the power consumption of the electric vehicle's next trip and / or the required charging time. The travel modes include emergency travel and regular travel, and the charging methods include fast charging and slow charging. The first charging strategy determination module is specifically used to adjust the charging current based on the predicted required charging time and the current battery charge of the electric vehicle if the travel mode is emergency travel and the charging method is slow charging, so as to determine that the minimum available power is reached before the electric vehicle departs. If the travel mode is regular travel and the charging method is fast charging, then the remaining charge value of the electric vehicle's battery before charging is cut off is determined based on the predicted power consumption of the trip and the battery discharge depth range. If the travel mode is regular travel and the charging method is slow charging, then the charging current and the remaining charge value before charging is cut off are determined based on the predicted power consumption of the trip and the required charging time. The first charging strategy determination module is specifically used to adjust the charging current using an adaptive control algorithm based on the predicted required charging time and the current battery charge of the electric vehicle, in order to determine the minimum available charge before the electric vehicle departs. The adaptive control algorithm formula is as follows: Where I0 is the initial charging current and α is the adaptive adjustment coefficient. Minimum available power. The current battery charge of the electric vehicle. For the predicted required charging time; The first charging strategy determination module is specifically used to determine the remaining charge value of the electric vehicle's battery before charging cutoff based on the predicted power consumption during the trip and the battery discharge depth range, according to the following formula. Among them, Q total This refers to the total battery capacity. For the predicted trip power consumption, This refers to the remaining battery level when the travel mode is regular travel and the charging method is fast charging.

7. An electronic device, characterized in that, include: The memory and the processor communicate with each other via a bus; The memory stores program instructions that can be executed by the processor, and the processor can execute the method as described in any one of claims 1 to 5 by calling the program instructions.

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