Adaptive charging control method for electric vehicle, and electric vehicle

Abstract

The present application provides an adaptive charging control method for an electric vehicle, and an electric vehicle. The method is applied to an electric vehicle that performs data interaction with electric vehicle supply equipment by means of a PLC signal coupled to a charging control guidance signal, and comprises: when a connection with the electric vehicle supply equipment is confirmed and a communicational connection has been established on the basis of a communication address of the electric vehicle supply equipment, executing a service information interaction procedure; executing a charging parameter acquisition procedure to acquire equipment charging parameters, a target charging mode and vehicle body charging information, and generating optimal charging parameters on the basis of the equipment charging parameters, the target charging mode and the vehicle body charging information by using a corresponding preset charging parameter optimization model; and after the optimal charging parameters are sent to the electric vehicle supply equipment, executing a charging task on the basis of the target charging mode and the optimal charging parameters. The present application can achieve intelligent management of alternating current charging processes, and effectively improve vehicle charging efficiency, charging safety and charging control flexibility.

Description

An adaptive charging control method for electric vehicles and electric vehicles

[0001] Cross-references to related applications

[0002] This application claims the benefit of Chinese Patent Application No. 202411760051.4, filed on December 2, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of electric vehicle charging control technology, and in particular to an adaptive charging control method for electric vehicles and an electric vehicle. Background Technology

[0004] With the increasing severity of global climate change and environmental pollution, reducing carbon emissions and promoting the development of clean energy have become a global consensus. Electric vehicles, as a clean and efficient mode of transportation, play a vital role in reducing air pollution and greenhouse gas emissions. However, the widespread application and popularization of electric vehicles still face many challenges, one of which is charging. Therefore, researching and developing efficient and intelligent electric vehicle charging control methods has significant practical implications and application value.

[0005] Current electric vehicle charging designs utilize CAN communication for DC charging to establish a connection and exchange information. During charging, the system updates charging information in real-time based on the data exchanged, enabling intelligent charging control. However, AC charging relies solely on the PWM signal duty cycle to determine the charging current from the power supply. Furthermore, it cannot dynamically update and control charging demand throughout the charging process, hindering intelligent adaptive charging control. For example, during AC charging, the control system cannot dynamically adjust charging parameters based on the actual state of the vehicle battery, the charging power from the power supply, and charging costs. This results in low charging efficiency and may even lead to undetected charging malfunctions due to inappropriate parameters. Therefore, there is an urgent need for an intelligent management method that can adaptively regulate charging parameters during AC charging to improve the charging efficiency and safety of electric vehicles.

[0006] Application content

[0007] The purpose of this application is to provide an adaptive charging control method for electric vehicles. The electric vehicle interacts with the power supply equipment through a PLC signal coupled to the charging control guidance signal to exchange data such as charging services and charging parameters. Based on the acquired charging parameters and the charging strategy optimization target under different charging modes, the electric vehicle calculates and generates the optimal charging parameters and feeds them back to the power supply equipment. During AC charging, the method can perceive changes in the charging environment and electric vehicle demand at the power supply equipment in real time and adaptively adjust the charging parameters at the electric vehicle end, thereby realizing intelligent management of the AC charging process of electric vehicles and effectively improving the charging efficiency and charging safety of electric vehicles.

[0008] To achieve the above objectives, it is necessary to provide an adaptive charging control method for electric vehicles and an electric vehicle in response to the aforementioned technical problems.

[0009] In a first aspect, embodiments of this application provide an adaptive charging control method for electric vehicles, applied to electric vehicles that interact with power supply equipment via a PLC signal coupled to a charging control guidance signal; the method includes the following steps:

[0010] S10. After confirming the connection with the power supply equipment, obtain the communication address of the power supply equipment, establish a communication connection with the power supply equipment based on the communication address, and execute the service information interaction process; the service information interaction process is used to perform various service information interactions with the power supply equipment, including application protocol requests, session establishment, service acquisition, and identification verification.

[0011] S20. Execute the charging parameter acquisition process to acquire device charging parameters, target charging mode and vehicle body charging information, and generate optimal charging parameters based on the device charging parameters, target charging mode and vehicle body charging information, according to the corresponding preset charging parameter optimization model; the preset charging parameter optimization model is an expected charging current optimization model constructed based on the charging optimization objectives of different target charging modes, with the device charging parameters and vehicle body charging information as optimization conditions.

[0012] S30. After sending the optimal charging parameters to the power supply equipment, the electric vehicle is charged according to the target charging mode and the optimal charging parameters.

[0013] Further, in step S20, the charging parameter acquisition process is executed, and the step of acquiring the target charging mode includes:

[0014] S201. Send a charging mode request to the corresponding vehicle-associated mobile terminal and start the corresponding request response timer.

[0015] S202. When the request response timer has not expired and a corresponding charging mode request response is received, the target charging mode is set according to the charging mode request response.

[0016] S203. When the request response timer expires and no charging mode request response is received, the target charging mode is set to the scheduled charging mode.

[0017] Further, in step S20, the target charging mode includes an immediate charging mode and a scheduled charging mode. When the target charging mode is the immediate charging mode, the step of generating optimal charging parameters based on the device charging parameters, the target charging mode, and the vehicle charging information, using a corresponding preset charging parameter optimization model, includes:

[0018] S204. Based on the device charging parameters and the vehicle charging information, the optimal charging parameters are generated according to the corresponding immediate charging parameter optimization model; the immediate charging parameter optimization model is constructed with minimizing charging time as the optimization objective; the objective function of the immediate charging model is expressed as:

[0019] In the formula, T represents the charging time; Soc s and Soc e These represent the current state of charge (SOC) and the target SOC, respectively; E represents the battery capacity; η represents the charging efficiency; and U and I represent the charging voltage and charging current, respectively.

[0020] Furthermore, the device charging parameters include maximum charging current, minimum charging current, maximum charging voltage, and minimum charging voltage; the vehicle body charging information includes battery capacity, allowable charging voltage, current battery state of charge, target battery state of charge, cable rated current, and maximum battery current requirement.

[0021] In step S204, the step of generating optimal charging parameters based on the corresponding immediate charging parameter optimization model according to the device charging parameters and the vehicle body charging information includes:

[0022] S2041. Determine the charging power constraint based on the device charging parameters;

[0023] S2042. Based on the battery capacity, allowable charging voltage, current battery state of charge and target battery state of charge in the vehicle charging information, solve the immediate charging parameter optimization model with the charging power constraint as the constraint condition to obtain the desired charging current.

[0024] S2043. Obtain the minimum current value among the expected charging current and the rated cable current and the maximum battery demand current in the vehicle body charging information, and generate the optimal charging parameters based on the minimum current value and the allowable charging voltage.

[0025] Further, in step S20, the target charging mode includes an immediate charging mode and a scheduled charging mode. When the target charging mode is a scheduled charging mode, the step of generating optimal charging parameters based on the device charging parameters, the target charging mode, and the vehicle charging information, using a corresponding preset charging parameter optimization model, includes:

[0026] S205. Based on the device charging parameters and the vehicle charging information, the optimal charging parameters are generated according to the corresponding scheduled charging parameter optimization model; the scheduled charging parameter optimization model includes a scheduled time period optimization model with the goal of minimizing user charging costs, and a time period charging parameter optimization model with the goal of minimizing grid load fluctuations.

[0027] Further, in step S205, the objective function of the appointment time slot optimization model is expressed as:

[0028] Where F1 represents the user's charging cost; B t P represents the electricity price per unit of electricity charged at time t; t t represents the charging power at time t; t1 and t2 represent the charging start time and charging end time, respectively.

[0029] Further, in step S205, the objective function of the time-period charging parameter optimization model is expressed as:

[0030] In the formula,

[0031] F2 represents the fluctuation of the power grid load; Indicates the target charging period; P s (t) represents the equivalent load power at time t; P avg P represents the average load power; load (t) and P EV (t) represent the base load power at time t when no electric vehicles are connected to the grid and the load power when electric vehicles are charging, respectively; X c (t) and P c The charging status and charging power of electric vehicle c at time t are represented; N represents the total number of electric vehicles connected to the power grid in the grid area at time t.

[0032] Furthermore, the device charging parameters include maximum charging current, minimum charging current, maximum charging voltage, minimum charging voltage, electricity price list for the power grid area, number of electric vehicles connected to the power grid area, and charging power and charging status of each electric vehicle; the vehicle body charging information includes battery capacity, allowable charging voltage, current battery state of charge, target battery state of charge, cable rated current, and maximum battery current requirement.

[0033] In step S205, the step of generating optimal charging parameters based on the corresponding scheduled charging parameter optimization model according to the device charging parameters and the vehicle body charging information includes:

[0034] S2051. Based on the battery capacity, current battery state of charge and target battery state of charge in the vehicle charging information, calculate the estimated charging time and obtain the minimum value between the estimated charging time and the user-set charging time as the scheduled charging time.

[0035] S2052. Based on the scheduled charging duration and the maximum charging current, minimum charging current, maximum charging voltage, minimum charging voltage and power grid area electricity price table in the device charging parameters, solve the scheduled time period optimization model to obtain at least one optimal scheduled charging time period;

[0036] S2053. Send all the best scheduled charging times to the corresponding vehicle-linked mobile terminal to obtain the user's desired charging time.

[0037] S2054. Based on the user's desired charging period, the allowable charging voltage in the vehicle charging information, and the device charging parameters including maximum charging current, minimum charging current, maximum charging voltage, minimum charging voltage, number of electric vehicles connected to the power grid area, and the charging power and charging status of each electric vehicle, solve the charging parameter optimization model for the period to obtain the desired charging current.

[0038] S2055. Obtain the minimum current value among the expected charging current and the rated cable current and maximum battery demand current in the vehicle body charging information, and generate the optimal charging parameters based on the minimum current value, the allowable charging voltage and the user's expected charging period.

[0039] Furthermore, in step S30, the method further includes:

[0040] S301. During the charging process, the latest charging parameters are periodically obtained from the power supply equipment through the PLC signal. Based on the latest charging parameters and the latest vehicle body charging information, real-time optimal charging parameters are generated based on the corresponding preset charging parameter optimization model. The real-time optimal charging parameters are then sent to the power supply equipment to adjust the charging power in real time.

[0041] Furthermore, the charging power constraint satisfies:

[0042] Among them, P t U represents the charging power of the electric vehicle at time t, and U t and I t P represents the charging voltage and charging current of the electric vehicle at time t, respectively; t,m ax and P t,min These represent the maximum and minimum charging power of the electric vehicle at time t, respectively, obtained based on the charging parameters of the device.

[0043] Furthermore, the formula for expressing the optimal charging parameters is: I t =min{I t,max ,I cc ,I EV}

[0044] Among them, I t I represents the required current for interaction between the electric vehicle and the power supply equipment at time t, and is the minimum current value among the expected charging current, the rated cable current, and the maximum required battery current; cc Indicates the rated current of the cable; I EV This indicates the current maximum current demand of the electric vehicle's battery.

[0045] Furthermore, the formula for calculating the average load power is as follows:

[0046] Furthermore, the formula for calculating the estimated charging time is as follows:

[0047] T2 indicates the estimated charging time; Soc s and Soc e These represent the current state of charge (SOC) and the target state of charge (SOC), respectively; E represents the battery capacity; η represents the charging efficiency; and P represents the preset charging power.

[0048] Furthermore, after step S30, the method further includes:

[0049] When the charging status of the electric vehicle is detected to meet the charging termination conditions, communication is established with the power supply equipment and the charging process is automatically terminated.

[0050] Secondly, embodiments of this application provide an electric vehicle that interacts with a power supply device via a PLC signal coupled to a charging control guidance signal, including an adaptive charging control device; the adaptive charging control device implements the steps of the above method when performing charging control.

[0051] This application provides an adaptive charging control method for electric vehicles and an electric vehicle in general. The method enables the electric vehicle to interact with a power supply device via a PLC signal coupled to a charging control guidance signal. After confirming a connection with the power supply device, the method obtains the communication address of the power supply device and establishes a communication connection based on the address. It then sequentially executes a service information interaction process including application protocol request, session establishment, service acquisition, and identification verification, as well as a charging parameter acquisition process. In response to a charging parameter acquisition response signal, the method acquires device charging parameters, the target charging mode, and vehicle charging information. Based on the device charging parameters, the target charging mode, and the vehicle charging information, and using a preset charging parameter optimization model constructed based on the device charging parameters and vehicle charging information as optimization conditions and different target charging modes, the method generates optimal charging parameters. Finally, the method sends the optimal charging parameters to the power supply device and executes the charging task according to the target charging mode and the optimal charging parameters. Compared with existing technologies, this adaptive charging control method for electric vehicles can sense changes in the charging environment and electric vehicle demand in real time during AC charging, and adaptively adjust the charging parameters at the electric vehicle end to achieve intelligent management of the AC charging process of electric vehicles. This not only effectively improves the charging efficiency and safety of electric vehicles, but also enables personalized management of charging needs and improves the flexibility of charging control. Attached Figure Description

[0052] Figure 1 is a flowchart illustrating the adaptive charging control method for electric vehicles in an embodiment of this application.

[0053] Figure 2 is a schematic diagram of the AC charging control design in an embodiment of this application, in which PLC modules are installed at both ends of the power supply equipment and the electric vehicle respectively.

[0054] Figure 3 is a detailed AC charging control design schematic diagram of adding PLC modules to both ends of the power supply equipment and the electric vehicle in an embodiment of this application;

[0055] Figure 4 is a schematic diagram of the architecture based on PLC signal communication in an embodiment of this application;

[0056] Figure 5 is a schematic diagram of the control and guidance circuit for AC charging in an embodiment of this application;

[0057] Figure 6 is a schematic diagram of the state transition implemented by the power supply equipment based on the control and guidance circuit shown in Figure 5 in an embodiment of this application;

[0058] Figure 7 is a schematic diagram of the communication interaction process of AC charging adaptive control in an embodiment of this application. Detailed Implementation

[0059] To make the objectives, technical solutions, and beneficial effects of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the embodiments described below are only part of the embodiments of this application and are used to illustrate this application only, but are not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0060] The adaptive charging control method for electric vehicles provided in this application can be understood as addressing the current application situation where existing electric vehicles, during AC charging, cannot effectively guarantee charging efficiency and safety because they cannot perceive changes in the power supply environment and charging demand in real time. This method proposes an adaptive charging parameter control method where the electric vehicle interacts with the power supply equipment via a PLC signal coupled to the charging control guidance signal to exchange charging service and charging parameter data. Furthermore, the electric vehicle calculates and generates optimal charging parameters based on the acquired charging parameters and the charging strategy optimization target under different charging modes, and feeds these parameters back to the power supply equipment. The following embodiments will provide a detailed description of the adaptive charging control method for electric vehicles of this application.

[0061] In one embodiment, as shown in Figure 1, an adaptive charging control method for electric vehicles is provided, applied to electric vehicles that interact with power supply equipment via a PLC signal coupled to a charging control guidance signal. The power supply equipment (EVSE) can be understood as a device that connects to the electric vehicle (EV) via a charging gun and supplies power to the electric vehicle, including charging piles or other charging support equipment. In practical applications, the AC charging control design shown in Figure 2 can be used to add a PLC module capable of sending a PLC signal coupled to the charging control guidance signal (CP signal) to both the power supply equipment and the electric vehicle. This enables data interaction between the electric vehicle and the power supply equipment based on the PLC signal, facilitating intelligent control of AC charging. The specific design for coupling the PLC signal and the CP signal is shown in Figure 3: In both the power supply equipment and the electric vehicle, a C-line is connected between the CP line and the ground terminal. PLC Capacitors and their relationship with C PLC In a PLC module with capacitors connected in series, the CP line is connected to the C... PLC The common terminal of the capacitor is connected to the inductor L, and a resistor R is connected in parallel across the inductor L. Damp Simultaneously, the other end of the inductor L is connected to the first detection point; in this design, the inductor L and the resistor R... Damp A low-pass filter can be used to prevent high-frequency signals from the PLC module from interfering with the status of the CP line and the original PWM signal, and through CPLC The capacitor isolates the PWM signal to prevent it from entering the PLC module, thus achieving safe interaction between the PLC signal and the charging control guidance signal (CP signal). The design elements shown in Figures 2 and 3, excluding the addition of PLC module communication, can be derived from existing AC charging control designs. The physical meanings of the letters and symbols can be explained using existing technical interpretations, and will not be repeated here.

[0062] It should be noted that the PLC signal communication structure, as shown in Figure 3, includes seven layers: physical layer, data link layer, network layer, transport layer, session layer, presentation layer, and application layer. The physical layer uses a charging cable, the data link layer uses a PLC carrier, the network layer uses IPv6 and includes IP, IMCP, and SLAAC (Stateless Address Autoconfiguration) mechanisms, the transport layer uses TCP / IP, UDP, or TLS (Transport Layer Security), the session layer uses GTPv2 (GPRS Tunneling Protocol version 2), the presentation layer uses EXI (Extensible Markup Language) encoding, and the application layer is implemented based on the SDP (Session Description Protocol). In practical applications, the method for generating PLC signals based on the PLC signal communication structure using the PCL module can be found in existing technologies and will not be detailed here.

[0063] Based on the PLC signal communication design between the electric vehicle and the power supply equipment shown in Figure 3, the adaptive charging control method for the AC charging process of the electric vehicle provided in this application includes the following steps:

[0064] S10. After confirming the connection with the power supply equipment, obtain the communication address of the power supply equipment, establish a communication connection with the power supply equipment based on the communication address, and execute the service information interaction process. The process of confirming the connection between the electric vehicle and the power supply equipment can be understood as the electric vehicle being woken up by the insertion of the charging gun. The charging control module in the vehicle controller of the electric vehicle guides the electric vehicle to a ready-to-receive-energy state based on the control guidance circuit shown in Figure 5. After the charging gun is inserted, R3 at the electric vehicle end performs voltage division, and the voltage at detection point 2 at the electric vehicle end changes from 0V to 9V. At the same time, the CC signal at detection point 3 is a valid value (valid values ​​are shown in Table 1). The voltage at monitoring point 1 at the power supply equipment end changes from 12V to 9V. Then the state changes from State1 to State2 as shown in Figure 6, that is, the electric vehicle and the power supply equipment are confirmed to be connected. The CP voltage at the electric vehicle end changes from 0V to 9V, the resistance of CC is a valid value, and the CP voltage at the power supply equipment end changes from 12V to 9V.

[0065] Table 1 Effective CC resistance values ​​when confirming connection between electric vehicles and power supply equipment.

[0066] In practical applications, the state transition process implemented by the control and guidance circuit at the power supply equipment end shown in Figure 6 is as follows:

[0067] State 1: The charging gun is not connected to the electric vehicle, and the PWM switch on the power supply side is not closed; State 1': The charging gun is not connected to the electric vehicle.

[0068] The PWM switch at the power supply equipment end is closed;

[0069] State 2: The charging gun is confirmed to be connected to the electric vehicle, and the PWM switch on the power supply equipment side is not closed; State 2': The charging gun is confirmed to be connected to the electric vehicle, and the PWM switch on the power supply equipment side is closed;

[0070] State 3: The electric vehicle is ready to receive energy, and the PWM switch on the power supply equipment is not closed; State 3': The electric vehicle is ready to receive energy, and the PWM switch on the power supply equipment is closed.

[0071] As shown in Figure 7, after both the electric vehicle and the power supply equipment confirm the connection, the switch S1 on the power supply equipment switches from 12V to 12V PWM, and the state of the power supply equipment changes from State 2 to State 2'. After obtaining the IP address and port number of the power supply equipment (which can be achieved through SDP technology), the electric vehicle quickly establishes a communication connection. It then obtains the application protocols (selectable protocols, such as 15118, 70121, etc.), protocol version numbers, and corresponding ID numbers on the power supply equipment via the supportAppProtocolRes signal, as well as the priority of each protocol when multiple protocols are satisfied simultaneously. Based on the obtained information, the electric vehicle selects its own matching protocol service. After obtaining the charging method service provided by the power supply equipment via the ServiceDiscoveryRes signal through communication with the PLC on the power supply equipment, it further realizes information interaction verification and payment method selection based on PLC communication. It should be noted that the signal descriptions involved in the communication process between the electric vehicle and the power supply equipment during AC charging are shown in Table 2. For ease of description, the processes of application protocol request, session establishment, service acquisition and identification verification are summarized as the service information interaction process (used to exchange various service information with the power supply equipment). The specific implementation of each process interaction can be referred to the relevant existing technology implementation, which will not be described in detail here.

[0072] Table 2 Communication Flow and Interaction Signals between Electric Vehicles and Power Supply Equipment

[0073] S20. Execute the charging parameter acquisition process to acquire device charging parameters, target charging mode, and vehicle charging information. Based on the device charging parameters, target charging mode, and vehicle charging information, generate optimal charging parameters using a corresponding preset charging parameter optimization model. The charging parameter acquisition process can be understood as the process by which an electric vehicle first acquires charging parameters from the power supply equipment after completing the service information interaction process, based on the interaction between the charging parameter acquisition request (ChargeParamterDiscoveryReq) signal and the charging parameter acquisition response (ChargeParamterDiscoveryRes) signal in Table 2. Specific implementation details can be found in existing technologies and will not be elaborated here. It should be noted that the charging parameters returned by the power supply equipment include the regional electricity price table, the maximum and minimum charging currents, maximum and minimum charging voltages that the current power grid can provide, and other charging-related parameters, which can be set according to the actual application scenario.

[0074] Once the electric vehicle receives the charging parameters and receives a response signal, and then parses the signal to obtain the transmitted charging parameters as described above, it can send a charging mode request to the mobile terminal (e.g., a mobile phone) bound to the electric vehicle. This allows the user to select the target charging mode (immediate charging mode or scheduled charging mode) based on their current needs. Vehicle charging information can be understood as the electric vehicle's own battery charging constraints, such as battery capacity, allowable charging voltage, current battery state of charge, target battery state of charge, cable rated current, and maximum battery current requirement. These parameters can be optimally set according to actual application requirements.

[0075] Specifically, in step S20, the charging parameter acquisition process is executed, and the steps to obtain the target charging mode include:

[0076] S201. Send a charging mode request to the corresponding vehicle-associated mobile terminal and start the corresponding request response timer; the timeout duration of the request response timer can be set according to actual application requirements and is not limited here.

[0077] S202. When the request response timer has not expired and the corresponding charging mode request response is received, the target charging mode is set according to the charging mode request response; that is, the immediate charging mode or scheduled charging mode returned by the mobile terminal before the request response timer expires is directly used as the target charging mode.

[0078] S203. When the request response timer expires and no charging mode request response is received, the target charging mode is set to the scheduled charging mode; that is, if the mobile terminal does not return to the target charging mode within a specified time, the target charging mode is directly set to the scheduled charging mode.

[0079] It should be noted that after obtaining the target charging mode through the above methods and steps, it needs to be stored so that when the scheduled charging vehicle is woken up again, the current charging mode can be directly read as the scheduled charging mode for use in optimizing charging parameters.

[0080] The preset charging parameter optimization model in this embodiment can be understood as an expected charging current optimization model constructed based on the preset charging optimization target under different target charging modes, using the device charging parameters and vehicle charging information as optimization conditions. That is, the specific preset charging parameter optimization model varies depending on the target charging mode, and the corresponding optimization target is also different. The following will describe in detail the embodiments of two application scenarios: immediate charging mode and scheduled charging mode.

[0081] In one embodiment, when the target charging mode in step S20 is an immediate charging mode, the step of generating optimal charging parameters based on the device charging parameters, the target charging mode, and the vehicle charging information, according to the corresponding preset charging parameter optimization model, includes:

[0082] S204. Based on the device charging parameters and the vehicle body charging information, generate the optimal charging parameters according to the corresponding immediate charging parameter optimization model; wherein, the immediate charging parameter optimization model can be understood as an optimization model constructed with the goal of reaching the target charge level in the shortest possible time when the plug-in time is taken as the starting charging time, that is, the immediate charging parameter optimization model is constructed with minimizing the charging time as the optimization objective, and the objective function of the immediate charging parameter optimization model is expressed as:

[0083] In the formula, T represents the charging time; Soc s and Soc e These represent the current battery state of charge (SOC) and the target battery SOC, respectively. The target battery SOC can be obtained from the vehicle charging requirements set by the user's mobile terminal. If no SOC is set, the default value (e.g., 80%) will be used. E represents the battery capacity. η represents the charging efficiency, and the maximum charging efficiency value can be selected according to the scenario. U and I represent the charging voltage and charging current, respectively.

[0084] In practical applications, when solving for the optimal charging current based on the objective function shown in equation (1), the charging power constraint shown in equation (2) must be satisfied:

[0085] In the formula, P t U represents the charging power of the electric vehicle at time t, and U t and I t P represents the charging voltage and charging current of the electric vehicle at time t, respectively; t,m ax and P t,min These represent the maximum and minimum charging power of the electric vehicle at time t, respectively, and are obtained based on the device charging parameters transmitted by the power supply equipment through PLC communication.

[0086] It should be noted that constant voltage charging is generally used in actual charging processes, i.e., U in equation (2) charging power t The value is fixed; we only need to make I... t Satisfying 0≤I t ≤I t,max That's it. Meanwhile, considering that charging efficiency is easily affected by temperature, to ensure the shortest charging time, the battery's operating temperature can be monitored and adjusted to achieve maximum charging efficiency. For example, if η is 100% at 25℃, the battery temperature can be controlled to remain at 25℃ using relevant technologies.

[0087] To ensure the effectiveness of the desired charging current solution based on the immediate charging parameter optimization model obtained from equations (1) and (2), this embodiment preferably sets the device charging parameters to include maximum charging current, minimum charging current, maximum charging voltage, and minimum charging voltage; the vehicle body charging information includes battery capacity, allowable charging voltage, current battery state of charge, target battery state of charge, cable rated current, and maximum battery demand current; specifically, in step S204, the step of generating optimal charging parameters based on the corresponding immediate charging parameter optimization model according to the device charging parameters and the vehicle body charging information includes:

[0088] S2041. Determine the charging power constraint based on the device charging parameters; wherein, determining the charging power constraint can be understood as determining P based on the maximum charging current, minimum charging current in the device charging parameters, and the allowable charging voltage in the vehicle body charging information. t,m ax and P t,min And the allowable charging power range at different times is obtained by formula (2).

[0089] S2042. Based on the battery capacity, allowable charging voltage, current battery state of charge, and target battery state of charge in the vehicle charging information, solve the immediate charging parameter optimization model with the charging power constraint as the constraint condition to obtain the desired charging current; wherein, the solution process of the desired charging current can be understood as using the charging current as a variable and minimizing the charging time as the objective, and solving the immediate charging parameter optimization model based on relevant algorithms to obtain the optimal variable value, the specific solution process is not limited here.

[0090] S2043. Obtain the minimum current value among the desired charging current and the cable rated current and the battery maximum required current in the vehicle body charging information, and generate the optimal charging parameters based on the minimum current value and the allowable charging voltage; wherein, the optimal charging parameters can be understood as the most suitable charging current determined by comprehensively analyzing the cable capacity and the vehicle end required current, taking into account charging safety issues in actual AC charging processes, and expressed as: I t =min{I t,max ,I cc ,I EV} (3)

[0091] In the formula, I t I represents the required current for interaction between the electric vehicle and the power supply equipment at time t, and is the minimum current value among the expected charging current, the rated cable current, and the maximum required battery current; cc This indicates the rated current (cable current capacity value) of the cable identified by the CC value at the electric vehicle end; I EV This indicates the maximum current demand of the electric vehicle's battery, which can be obtained based on the vehicle's existing functions and will not be detailed here.

[0092] By using the above methods and steps, the optimal charging parameters can be calculated based on the charging environment of the power supply equipment (the maximum charging current, minimum charging current, maximum charging voltage, and minimum charging voltage supported by the power supply equipment, etc.) and the electric vehicle's own situation before starting immediate charging. This satisfies the immediate charging strategy of reaching the charging target value in the shortest charging time, thereby ensuring the safety and effectiveness of starting immediate charging.

[0093] In one embodiment, when the target charging mode in step S20 is a scheduled charging mode, the step of generating optimal charging parameters based on the corresponding preset charging parameter optimization model according to the device charging parameters, the target charging mode, and the vehicle charging information includes:

[0094] S205. Based on the device charging parameters and the vehicle charging information, the optimal charging parameters are generated according to the corresponding scheduled charging parameter optimization model; the scheduled charging parameter optimization model includes a scheduled time period optimization model with the goal of minimizing user charging costs, and a time period charging parameter optimization model with the goal of minimizing grid load fluctuations.

[0095] In this embodiment, the reservation time slot optimization model can be understood as an optimization model constructed with the goal of obtaining the reservation charging time slot with the minimum charging cost, taking into account the different electricity prices at different times of the day, including peak and off-peak periods. Specifically, the objective function of the reservation time slot optimization model in step S205 is expressed as:

[0096] Where F1 represents the user's charging cost; B t The unit electricity price for charging at time t can be obtained from the regional electricity price table in the equipment charging parameters; P t The charging power at time t is represented by t1, which also needs to satisfy the power constraint shown in equation (2). t1 and t2 represent the charging start time and charging end time, respectively. The corresponding calculation time step can be set according to the actual application requirements, such as 1 hour. That is, when calculating the user's charging cost, the unit electricity price for charging per hour in the electricity price table of the power grid area is used for calculation. It should be noted that in actual applications, t1 and t2 need to satisfy an implicit charging time limit. First, the user-set charging time T1 set when the user selects the scheduled charging mode is obtained through the mobile terminal. At the same time, the expected charging time T2 is calculated according to the target battery state of charge set by the user, and the minimum value T1 of the two is used to calculate the charging time. op As a constraint on the selection of t1 and t2 in the process of solving equation (4).

[0097] In this embodiment, the time-slot charging parameter optimization model can be understood as considering that, based on the selection of the optimal charging time slot, there may be situations where electric vehicle charging is concentrated during a low-price period of the power grid, resulting in a large charging load of electric vehicles in the regional power grid during the off-peak period. This, in turn, forms a new load peak during the off-peak period of the regional power grid, causing load fluctuations in the regional power grid, affecting the stability of the regional power grid operation, and being detrimental to power grid management. Therefore, the preferred approach is to construct the charging parameter optimization model by performing regional power grid volatility analysis based on the number of electric vehicles connected to the selected scheduled charging time slot. Specifically, in step S205, the objective function of the time-slot charging parameter optimization model is expressed as:

[0098] In the formula,

[0099] F2 represents the fluctuation of the power grid load; The target charging period can be understood as the user's desired charging period determined based on equation (4); P s (t) represents the equivalent load power at time t; P avg P represents the average load power; load (t) and P EV(t) represent the base load power at time t when no electric vehicles are connected to the grid and the load power when electric vehicles are charging, respectively; X c (t) and P c The charging status (1 indicates connected and charging, 0 indicates connected but not charging) and charging power of electric vehicle c at time t are represented by N, which represents the total number of electric vehicles connected to the power grid in the power grid area at time t. Both can be obtained from the charging parameters transmitted by the power supply equipment connected to the electric vehicle.

[0100] To ensure the effectiveness of the desired charging current solution based on the reservation charging parameter optimization model obtained from equations (4)-(8), this embodiment preferably sets the equipment charging parameters to include the maximum charging current, minimum charging current, maximum charging voltage, minimum charging voltage, electricity price table for the power grid area, number of electric vehicles connected to the power grid area, and charging power and charging status of each electric vehicle; the vehicle body charging information includes battery capacity, allowable charging voltage, current battery state of charge, target battery state of charge, cable rated current, and maximum battery demand current; specifically, in step S205, the step of generating the optimal charging parameters based on the corresponding reservation charging parameter optimization model according to the equipment charging parameters and the vehicle body charging information includes:

[0101] S2051. Based on the battery capacity, current battery state of charge, and target battery state of charge in the vehicle charging information, calculate the estimated charging time and obtain the minimum value between the estimated charging time and the user-set charging time as the reserved charging time; wherein, the estimated charging time T2 is calculated based on equation (9), and the minimum value between the estimated charging time T2 and the user-set charging time T1 is used as the reserved charging time T. op Furthermore, the charging time constraint t2-t1≤T is obtained during the solution process of equation (4). op .

[0102] In the formula, T2 represents the estimated charging time; Soc s and Soc e These represent the current battery state of charge (SOC) and the target battery SOC, respectively. The target battery SOC can be obtained from the vehicle charging requirements set by the user's mobile terminal. If no SOC is set, the default value (e.g., 80%) will be used. E represents the battery capacity. η represents the charging efficiency, and the maximum charging efficiency value can be selected according to the scenario. P represents the preset charging power, which can be set according to actual application requirements.

[0103] S2052. Based on the reserved charging duration and the maximum charging current, minimum charging current, maximum charging voltage, minimum charging voltage and the power grid area price table in the device charging parameters, solve the reservation time period optimization model to obtain at least one optimal reservation charging time period; wherein, the optimal reservation charging time period can be understood as substituting the reserved charging duration, maximum charging current, minimum charging current, maximum charging voltage, minimum charging voltage and the power grid area price table into the reservation time period optimization model shown in equation (4), and using the corresponding solution algorithm to obtain the reservationable charging time period that minimizes the user's charging cost; in practical applications, the optimal reservation charging time period obtained can be one or multiple, and can be sent to the mobile terminal for the user to select.

[0104] S2053. Send all optimal scheduled charging time slots to the corresponding vehicle-associated mobile terminal to obtain the user's desired charging time slot. The user's desired charging time slot can be understood as the slowest but most satisfactory charging time slot selected by the user from all optimal scheduled charging time slots based on the received time and charging cost information, combined with the start time of the next trip or other filtering criteria. It should be noted that if the user does not select a charging start time (charging time slot) within the specified time, the earliest charging start time slot among all recommended time slots will be selected by default as the user's desired charging time slot. Additionally, if only one optimal scheduled charging time slot is actually obtained, the user can choose whether to use the recommended time slot or switch to immediate charging.

[0105] S2054. Based on the user's desired charging period, the allowable charging voltage in the vehicle charging information, and the device charging parameters including the maximum charging current, minimum charging current, maximum charging voltage, minimum charging voltage, number of electric vehicles connected to the power grid area, and the charging power and charging status of each electric vehicle, solve the time period charging parameter optimization model to obtain the scheduled desired charging current; wherein, the scheduled desired charging current can be understood as substituting the user's desired charging period, allowable charging voltage, maximum charging current, minimum charging current, maximum charging voltage, minimum charging voltage, number of electric vehicles connected to the power grid area, and the charging power and charging status of each electric vehicle into the time period charging parameter optimization model shown in equations (5)-(8), and using the corresponding solution algorithm to obtain the charging current that minimizes the fluctuation of the regional power grid.

[0106] S2055. Obtain the minimum current value among the expected charging current and the rated cable current and maximum battery demand current in the vehicle body charging information, and generate the optimal charging parameters based on the minimum current value, the allowable charging voltage and the user's expected charging period; wherein, the process of obtaining the optimal charging parameters can be understood as firstly, based on the charging safety issues in the actual AC charging process, combining the cable capacity and the vehicle end demand current, conducting a comprehensive analysis, and then generating the most suitable charging current demand value based on the formula (3), together with the allowable charging voltage of the vehicle body and the user's expected charging period, to generate the charging parameters that need to be transmitted to the power supply equipment.

[0107] This embodiment constructs an immediate charging parameter optimization model with the goal of minimizing charging time, a scheduled charging time optimization model with the goal of minimizing user charging costs, and a time-based charging parameter optimization model with the goal of minimizing grid load fluctuations. Finally, a scheduled charging parameter optimization model is obtained. This enables reliable selection of charging parameters under different charging modes to adapt to changes in the charging environment of the power supply equipment and the needs of electric vehicles, effectively improving the charging efficiency and charging safety of electric vehicles.

[0108] S30. After sending the optimal charging parameters to the power supply equipment, the electric vehicle is charged according to the target charging mode and the optimal charging parameters. Specifically, the electric vehicle generates optimal charging parameters for different target charging modes based on the device charging parameters and vehicle charging information obtained from the power supply equipment. It then sends the calculated optimal current requirement and other parameters to the power supply equipment via the PowerDeliveryReq signal to achieve the optimal charging target. Simultaneously, after the power supply equipment and the electric vehicle confirm the charging parameters, the electric vehicle's S2 switch (as shown in Figure 5) closes, and the power supply equipment changes its state from State2' to State3'. Switches C1 and C2 are then closed, and energy transmission begins. The charging task is executed according to the target charging mode and the corresponding optimal charging parameters. If the target charging mode is an immediate charging mode, the power supply equipment directly transmits energy to the electric vehicle based on the received optimal charging parameters. If the target charging mode is a scheduled charging mode, the power supply equipment stores the received optimal charging parameters and starts charging according to the corresponding charging current requirement value based on the user's desired charging period.

[0109] Furthermore, considering that the charging environment and electric vehicle demand at the power supply equipment end may change in real time during the actual charging process, if the optimal charging parameters at the start of charging are consistently used for continuous charging, there may be issues with low charging efficiency or charging safety. To facilitate real-time perception of changes in the charging environment and electric vehicle demand at the power supply equipment end, and to promptly adjust the charging parameters accordingly, ensuring charging efficiency in real time while avoiding safety hazards caused by the lag in identifying problems during the charging process, this embodiment preferably involves real-time PLC communication between the electric vehicle end and the power supply equipment end throughout the charging process. The electric vehicle end periodically sends a PowerDeliveryReq signal to the power supply equipment end. The electric vehicle end obtains real-time charging parameter information such as the current allowable charging current range and charging voltage range of the power supply equipment (the load rate of the power grid varies at different times, resulting in different limits on charge, which in turn causes changes in the allowable charging current and voltage ranges of the power supply equipment end) through the PowerDeliveryRes signal replied by the power supply equipment end. Based on the equipment charging parameter information and the vehicle charging information, the optimal charging parameters are generated based on a preset charging parameter optimization model for the corresponding charging mode, thereby achieving real-time intelligent control of the electric vehicle's charging power according to changes in real-time charging demand. Specifically, in step S30, the method further includes:

[0110] S301. During the charging process, the latest charging parameters are periodically obtained from the power supply equipment through the PLC signal. Based on the latest charging parameters and the latest vehicle body charging information, real-time optimal charging parameters are generated based on the corresponding preset charging parameter optimization model. The real-time optimal charging parameters are then sent to the power supply equipment to adjust the charging power in real time.

[0111] In practical applications, electric vehicles that have started charging periodically monitor and acquire vehicle charging information to determine whether the expected charging target has been reached. If the expected charging target has not been reached, a PowerDeliveryReq signal is sent to the power supply equipment according to a preset communication frequency. Based on the PowerDeliveryRes signal returned by the power supply equipment, changes in equipment charging parameters such as the allowable charging current range and charging voltage range at the power supply equipment end are sensed in real time. Based on this, the optimal demand current and other parameters for the corresponding charging mode are obtained and sent to the power supply equipment to achieve adaptive adjustment of charging power during AC charging. When the electric vehicle starts the charging process in immediate charging mode, the latest charging parameters and vehicle charging information acquired in real time during the charging process are used to adjust the charging power accordingly. The electric vehicle is charged in a manner that is based on the immediate charging parameter optimization model shown in Equations (1) and (2) to solve for the desired charging current. Based on the obtained desired charging current, the most suitable charging current demand value is obtained based on the processing procedure shown in Equation (3). The required optimal charging parameters are then generated and negotiated with the power supply equipment. When the electric vehicle starts the charging process in the reservation charging mode, the desired charging current is solved based on the latest charging parameters and vehicle charging information obtained in real time during the charging process, according to the time period charging parameter optimization model shown in Equations (5)-(8). Based on the obtained desired charging current, the most suitable charging current demand value is obtained based on the processing procedure shown in Equation (3). The required optimal charging parameters are then generated and negotiated with the power supply equipment.

[0112] Meanwhile, the power supply equipment will periodically check for grid limitations, user operation commands, and any abnormalities in the electric vehicle that require shutdown, in order to ensure the efficiency and safety of the charging process. When either the electric vehicle or the power supply equipment detects that the charging termination conditions are met, it will automatically end the charging process after communicating with the other party. That is, the electric vehicle will turn on the S2 switch, and the power supply equipment's state will change from State 3' to State 2'.

[0113] Furthermore, to better meet actual charging needs, satisfy adaptive management and control in diverse charging scenarios, and improve the flexibility of power supply control, this embodiment preferably allows switching of charging modes and adjustment of charging time under the scheduled charging mode before the scheduled charging is initiated. When an electric vehicle using the scheduled charging mode has not yet started charging, the charging mode can be switched through the vehicle-linked mobile terminal. When switching the target charging mode of the electric vehicle from the scheduled charging mode to the immediate charging mode, the optimal charging current needs to be generated according to the optimal charging parameter acquisition scheme before the immediate charging mode is initiated, and charging is started immediately accordingly. During the charging process, the charging power is intelligently optimized and controlled based on the corresponding immediate charging parameter optimization model, according to the latest charging parameters and vehicle charging information acquired in real time. At the same time, when an electric vehicle using the scheduled charging mode has already started charging, the start time of the next trip (adjustment of the charging end time) can be revised through the vehicle-linked mobile terminal to re-trigger the charging parameter optimization process. Based on this revision, combined with the latest charging parameters and vehicle charging information acquired in real time, the charging power is intelligently optimized and controlled based on the time-period charging parameter optimization model.

[0114] The electric vehicle provided in this application interacts with the power supply equipment via a PLC signal coupled to the charging control guidance signal to exchange data such as charging services and charging parameters. Furthermore, based on real-time acquired equipment charging parameters and vehicle charging information, the electric vehicle calculates and generates optimal charging parameters according to the charging strategy optimization target under different charging modes, and feeds these parameters back to the power supply equipment to achieve adaptive intelligent control of charging power. Compared with existing technologies, this approach can perceive changes in the charging environment and electric vehicle demands at the power supply equipment end in real time during AC charging, adaptively adjusting the charging parameters at the electric vehicle end. This achieves intelligent management of the AC charging process for electric vehicles, effectively improving charging efficiency and safety, and enabling personalized management of charging needs, thus enhancing the flexibility of charging control.

[0115] It should be noted that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order requirement for the execution of these steps, and they can be executed in other orders.

[0116] In one embodiment, an electric vehicle is provided that interacts with a power supply device via a PLC signal coupled to a charging control guidance signal; the vehicle includes an adaptive charging control device; the adaptive charging control device performs the steps of the above method when executing charging control. Optionally, the adaptive charging control device may be a controller, PC, tablet, server, or cloud platform, etc.

[0117] In summary, the adaptive charging control method and system for electric vehicles provided in this application embodiment enables the electric vehicle to interact with the power supply equipment via a PLC signal coupled to the charging control guidance signal. After confirming the connection with the power supply equipment, the system obtains the communication address of the power supply equipment and establishes a communication connection based on the communication address. Then, it sequentially executes a service information interaction process including application protocol request, session establishment, service acquisition, and identification verification, as well as a charging parameter acquisition process. In response to the charging parameter acquisition response signal, it acquires the equipment charging parameters, target charging mode, and vehicle charging information. Based on the equipment charging parameters, target charging mode, and vehicle charging information, and using the corresponding equipment charging parameters and vehicle charging information as optimization conditions, it constructs a preset charging optimization target based on different target charging modes. The method involves generating optimal charging parameters using a charging parameter optimization model, sending these parameters to the power supply equipment, executing the charging task based on the target charging mode and the optimal parameters, and periodically acquiring the latest charging parameters from the power supply equipment via PLC signals during the charging process. Based on these latest parameters and the latest vehicle charging information, the method generates real-time optimal charging parameters using a corresponding preset charging parameter optimization model and sends them to the power supply equipment to adjust the charging power in real time. This approach enables real-time sensing of changes in the charging environment and electric vehicle demands during AC charging, adaptively adjusting the charging parameters at the electric vehicle end, and achieving intelligent management of the AC charging process for electric vehicles. This not only effectively improves the charging efficiency and safety of electric vehicles but also allows for personalized management of charging needs, enhancing the flexibility of charging control.

[0118] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system 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. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0119] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. An adaptive charging control method for an electric vehicle, wherein, An electric vehicle that interacts with a power supply device via a PLC signal coupled to a charging control guidance signal; the method includes the following steps: S10. After confirming the connection with the power supply equipment, obtain the communication address of the power supply equipment, establish a communication connection with the power supply equipment based on the communication address, and execute the service information interaction process; the service information interaction process is used to perform various service information interactions with the power supply equipment, including application protocol requests, session establishment, service acquisition, and identification verification. S20. Execute the charging parameter acquisition process to acquire device charging parameters, target charging mode and vehicle body charging information, and generate optimal charging parameters based on the device charging parameters, target charging mode and vehicle body charging information, according to the corresponding preset charging parameter optimization model; the preset charging parameter optimization model is an expected charging current optimization model constructed based on the charging optimization objectives of different target charging modes, with the device charging parameters and vehicle body charging information as optimization conditions. S30. After sending the optimal charging parameters to the power supply equipment, the electric vehicle is charged according to the target charging mode and the optimal charging parameters.

2. The adaptive charging control method for electric vehicles as described in claim 1, wherein, In step S20, the charging parameter acquisition process is executed, and the steps to obtain the target charging mode include: S201. Send a charging mode request to the corresponding vehicle-associated mobile terminal and start the corresponding request response timer. S202. When the request response timer has not expired and a corresponding charging mode request response is received, the target charging mode is set according to the charging mode request response. S203. When the request response timer expires and no charging mode request response is received, the target charging mode is set to the scheduled charging mode.

3. The adaptive charging control method for electric vehicles as described in claim 1, wherein, In step S20, the target charging mode includes an immediate charging mode and a scheduled charging mode. When the target charging mode is the immediate charging mode, the step of generating optimal charging parameters based on the device charging parameters, the target charging mode, and the vehicle charging information, according to the corresponding preset charging parameter optimization model, includes: S204. Based on the device charging parameters and the vehicle charging information, the optimal charging parameters are generated according to the corresponding immediate charging parameter optimization model; the immediate charging parameter optimization model is constructed with minimizing charging time as the optimization objective; the objective function of the immediate charging model is expressed as: In the formula, T represents the charging time; Soc s and Soc e These represent the current state of charge (SOC) and the target SOC, respectively; E represents the battery capacity; η represents the charging efficiency; and U and I represent the charging voltage and charging current, respectively.

4. The adaptive charging control method for electric vehicles as described in claim 3, wherein, The device charging parameters include maximum charging current, minimum charging current, maximum charging voltage, and minimum charging voltage; the vehicle body charging information includes battery capacity, allowable charging voltage, current battery state of charge, target battery state of charge, cable rated current, and maximum battery current requirement. In step S204, the step of generating optimal charging parameters based on the corresponding immediate charging parameter optimization model according to the device charging parameters and the vehicle body charging information includes: S2041. Determine the charging power constraint based on the device charging parameters; S2042. Based on the battery capacity, allowable charging voltage, current battery state of charge and target battery state of charge in the vehicle charging information, solve the immediate charging parameter optimization model with the charging power constraint as the constraint condition to obtain the desired charging current. S2043. Obtain the minimum current value among the expected charging current and the rated cable current and the maximum battery demand current in the vehicle body charging information, and generate the optimal charging parameters based on the minimum current value and the allowable charging voltage.

5. The adaptive charging control method for electric vehicles as described in claim 1, wherein, In step S20, the target charging mode includes an immediate charging mode and a scheduled charging mode. When the target charging mode is a scheduled charging mode, the step of generating optimal charging parameters based on the device charging parameters, the target charging mode, and the vehicle charging information, using a corresponding preset charging parameter optimization model, includes: S205. Based on the device charging parameters and the vehicle charging information, the optimal charging parameters are generated according to the corresponding scheduled charging parameter optimization model; the scheduled charging parameter optimization model includes a scheduled time period optimization model with the goal of minimizing user charging costs, and a time period charging parameter optimization model with the goal of minimizing grid load fluctuations.

6. The adaptive charging control method for electric vehicles as described in claim 5, wherein, In step S205, the objective function of the appointment time slot optimization model is expressed as: Where F1 represents the user's charging cost; B t P represents the electricity price per unit of electricity charged at time t; t t represents the charging power at time t; t1 and t2 represent the charging start time and charging end time, respectively.

7. The adaptive charging control method for electric vehicles as described in claim 6, wherein, In step S205, the objective function of the time-period charging parameter optimization model is expressed as: In the formula, F2 represents the fluctuation of the power grid load; Indicates the target charging period; P s (t) represents the equivalent load power at time t; P avg P represents the average load power; load (t) and P EV (t) represent the base load power at time t when no electric vehicles are connected to the grid and the load power when electric vehicles are charging, respectively; X c (t) and P c The charging status and charging power of electric vehicle c at time t are represented; N represents the total number of electric vehicles connected to the power grid in the grid area at time t.

8. The adaptive charging control method for electric vehicles as described in claim 7, wherein, The device charging parameters include maximum charging current, minimum charging current, maximum charging voltage, minimum charging voltage, electricity price list for the power grid area, number of electric vehicles connected to the power grid area, and charging power and charging status of each electric vehicle; the vehicle body charging information includes battery capacity, allowable charging voltage, current battery state of charge, target battery state of charge, cable rated current, and maximum battery current requirement. In step S205, the step of generating optimal charging parameters based on the corresponding scheduled charging parameter optimization model according to the device charging parameters and the vehicle body charging information includes: S2051. Based on the battery capacity, current battery state of charge and target battery state of charge in the vehicle charging information, calculate the estimated charging time and obtain the minimum value between the estimated charging time and the user-set charging time as the scheduled charging time. S2052. Based on the scheduled charging duration and the maximum charging current, minimum charging current, maximum charging voltage, minimum charging voltage and power grid area electricity price table in the device charging parameters, solve the scheduled time period optimization model to obtain at least one optimal scheduled charging time period; S2053. Send all the best scheduled charging times to the corresponding vehicle-linked mobile terminal to obtain the user's desired charging time. S2054. Based on the user's desired charging period, the allowable charging voltage in the vehicle charging information, and the device charging parameters including maximum charging current, minimum charging current, maximum charging voltage, minimum charging voltage, number of electric vehicles connected to the power grid area, and the charging power and charging status of each electric vehicle, solve the charging parameter optimization model for the period to obtain the desired charging current. S2055. Obtain the minimum current value among the expected charging current and the rated cable current and maximum battery demand current in the vehicle body charging information, and generate the optimal charging parameters based on the minimum current value, the allowable charging voltage and the user's expected charging period.

9. The adaptive charging control method for electric vehicles as described in claim 1, wherein, In step S30, the method further includes: S301. During the charging process, the latest charging parameters are periodically obtained from the power supply equipment through the PLC signal. Based on the latest charging parameters and the latest vehicle body charging information, real-time optimal charging parameters are generated based on the corresponding preset charging parameter optimization model. The real-time optimal charging parameters are then sent to the power supply equipment to adjust the charging power in real time.

10. The adaptive charging control method for electric vehicles as described in claim 4, wherein, The charging power constraint satisfies: Among them, P t U represents the charging power of the electric vehicle at time t, and U t and I t P represents the charging voltage and charging current of the electric vehicle at time t, respectively; t,max and P t,min These represent the maximum and minimum charging power of the electric vehicle at time t, respectively, obtained based on the charging parameters of the device.

11. The adaptive charging control method for electric vehicles as described in claim 4, wherein, The formula for calculating the optimal charging parameters is: I t =min{I t,max ,I cc ,I EV } Among them, I t I represents the required current for interaction between the electric vehicle and the power supply equipment at time t, and is the minimum current value among the expected charging current, the rated cable current, and the maximum required battery current; cc Indicates the rated current of the cable; I EV This indicates the current maximum current demand of the electric vehicle's battery.

12. The adaptive charging control method for electric vehicles as described in claim 7, wherein, The formula for calculating the average load power is as follows:

13. The adaptive charging control method for electric vehicles as described in claim 8, wherein, The formula for calculating the expected charging time is as follows: T2 indicates the estimated charging time; Soc s and Soc e These represent the current state of charge (SOC) and the target state of charge (SOC), respectively; E represents the battery capacity; η represents the charging efficiency; and P represents the preset charging power.

14. The adaptive charging control method for electric vehicles as described in claim 1, wherein, After step S30, the method further includes: When the charging status of the electric vehicle is detected to meet the charging termination conditions, communication is established with the power supply equipment and the charging process is automatically terminated.

15. An electric vehicle, wherein the electric vehicle interacts with a power supply device via a PLC signal coupled to a charging control guidance signal, wherein, It includes an adaptive charging control device; the adaptive charging control device performs the steps of the method according to any one of claims 1 to 14 when executing charging control.

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