Electric vehicle charging control method

Abstract

The present invention discloses a charging control method for an electric vehicle, which relates to the technical field of electric vehicle charging. The method includes the following components: battery status monitoring: using a battery management system (BMS) to monitor the current status of the battery in real time, obtaining battery temperature data through sensors, and evaluating the health status of the battery. The present invention fully considers the user's charging needs, such as charging time and charging priority, through the charging control method. By setting objective functions and constraints, the PID control algorithm is combined to achieve dynamic adjustment of the charging rate, ensuring that the actual charging rate gradually approaches the expected charging rate. This personalized charging service not only improves the user experience, but also demonstrates the high intelligence and adaptability of the system. By visually displaying the prediction results and charging strategies, it provides intelligent decision support for decision makers or operators, further improving the overall performance of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging, and in particular to an electric vehicle charging control method. Background Art

[0002] Traditional charging control methods are often based on fixed charging strategies and parameters, lacking comprehensive consideration of the real-time status of the battery and grid. This lack of dynamic adaptability makes it difficult to effectively handle complex conditions such as battery aging, temperature fluctuations, and grid load fluctuations, resulting in low charging efficiency and increasing safety risks such as overcharging, over-discharging, and overheating.

[0003] Secondly, traditional charging control methods ignore the personalized needs of users. Different users may have different requirements for charging time, charging priority, etc., and traditional charging control methods cannot provide flexible and personalized charging services, which reduces the user's charging experience.

[0004] Furthermore, traditional charging control methods lack effective intelligent decision-making support mechanisms. Due to the lack of accurate predictions of the future states of batteries and the grid, as well as real-time evaluation of charging strategy effectiveness, decision makers or operators often struggle to make optimal charging decisions, thus impacting the overall performance and charging efficiency of the system.

[0005] Therefore, in view of the deficiencies and shortcomings of the existing technology, the present invention proposes an adaptive charging control method to solve the above problems. Summary of the Invention

[0006] The present invention aims to address the shortcomings of existing technologies by providing an electric vehicle charging control method. This method collects and analyzes real-time battery and grid status data, using regression analysis models to predict the future battery state and grid load. Then, combined with a linear programming algorithm, this method calculates the optimal charging rate that maximizes charging efficiency or meets specific user requirements, while satisfying constraints such as battery temperature and grid load.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a charging control method for an electric vehicle, the specific steps of the charging control method are:

[0008] Battery status monitoring: Utilizes a battery management system (BMS) to monitor the battery's current status in real time. Sensors are used to obtain battery temperature data, consider the impact of different temperatures on battery charging characteristics, assess the battery's health status, and factor in battery aging and remaining life to develop an appropriate charging strategy.

[0009] Grid load monitoring: Through charging equipment or grid management systems connected to the grid, the grid load is monitored in real time, the remaining capacity and stability of the grid are assessed, and high-power charging is avoided during peak load periods to ensure stable grid operation.

[0010] Receiving user charging requests: Receive user charging requests through the vehicle system, mobile phone app, or other user interaction interfaces, allowing users to customize charging modes;

[0011] Adaptive charging rate calculation: Based on the current state of the battery, the load of the power grid, and the user's charging needs, linear programming is used to calculate the optimal charging rate. A reasonable charging plan is formulated by comprehensively considering the fluctuation range of the power grid and the user's charging priority.

[0012] Charging rate control: The calculated optimal charging rate is sent to the charging device, and the output power is controlled by the charging device's control system to adapt to changes in battery and grid conditions, achieving adaptive charging rate control.

[0013] Continuous monitoring and adjustment: During the charging process, the battery and grid status are continuously monitored, and the charging rate is adjusted according to actual conditions to maintain optimal charging efficiency and battery life. If any abnormal situation occurs, charging is stopped in time and an alarm is issued.

[0014] Furthermore, the battery status monitoring measures the charge and discharge current and voltage of the battery in real time through the current sensor and voltage sensor in the BMS, and estimates the remaining capacity SOC of the battery using the ampere-hour integration method. The mathematical formula of the ampere-hour integration method is: Among them: SOC represents the current remaining capacity of the battery, SOC0 represents the initial state of charge of the battery, C n It represents the rated capacity of the battery, I represents the current passing through the battery, which is positive during charging and negative during discharging, and t represents time. The data collected from thermistors or thermocouples to monitor the surface and internal temperature of the battery are used to predict the battery's capacity decay, internal resistance increase, and temperature distribution parameters, and to evaluate the battery's health status. Based on the real-time monitoring and evaluation results of the battery status, a suitable charging strategy is formulated.

[0015] Furthermore, the grid load monitoring utilizes charging equipment or a grid management system connected to the grid to collect the power, voltage, current, and frequency of the grid in real time, extracts features related to the load data after cleaning and standardizing the data, and uses a weighted moving average method to predict future grid load conditions. The prediction results are displayed in a visual manner to decision makers or operators. During peak grid load periods, high-power charging is avoided to reduce the burden on the grid and ensure stable operation of the grid. At the same time, during low grid load periods, the charging power can be appropriately increased to improve charging efficiency.

[0016] Furthermore, the weighted moving average method is used to predict the future grid load situation, and the collected grid load situation is recorded as (Y1, Y2, ..., Y T ), determine the number of periods n of the moving average, and assign a weight (w1, w2, ..., w n ), and ensure that i =1 n w i =1, for each period t, calculate the weighted moving average F t , and its weighted moving average formula is: And t≥n, where: Y is the actual value of the grid load, w is the corresponding weight, F t It is the predicted value for one or more periods in the future. The prediction results are verified and evaluated using actual data using the mean square error. After the evaluation, the future grid load conditions can be predicted.

[0017] Furthermore, the user charging demand receiving user can input the expected charging completion time and charging priority information through the vehicle system, mobile phone app or other user interaction interface. After the user enters the charging demand, the input content is verified to ensure that the input data is valid and reasonable, allowing the user to customize the charging mode.

[0018] Furthermore, the adaptive charging rate calculation is based on the collected data, evaluates the battery status and the remaining capacity and stability of the power grid, and uses linear programming to calculate the optimal charging rate while considering the battery status, the load of the power grid and the user's charging needs. This allows the charging process to maximize charging efficiency or meet the user's specific needs while satisfying all constraints (such as battery temperature limits and power grid load limits). The charging rate is dynamically adjusted according to the real-time status of the battery and the power grid, as well as the real-time needs of the user, to ensure the safety and efficiency of the charging process.

[0019] Furthermore, the linear programming is used to calculate the optimal charging rate by defining the charging rate r ι (charge amount per unit time) is the decision variable, and the objective function Z = ∑ t -1 T c t ·r t , where: T is the total number of time intervals, t is the length of each time interval, define the constraints, battery status constraints: Grid load constraints: User demand constraints: j = 1, 2, ..., nr t ∈r ≥0, t = 1, 2, …, T, the objective function and constraints are input into the linear programming solver to obtain the value that maximizes the charging efficiency Z, and the optimal charging rate sequence is obtained. The charging rate is dynamically adjusted according to the real-time status of the battery and the power grid and the real-time needs of the user.

[0020] Furthermore, the charging rate control determines the goal of maximizing charging efficiency and minimizing charging time based on the battery status, grid status and user needs (such as charging time and charging priority). Based on the goal, a control algorithm is selected to calculate the optimal charging rate. The calculated optimal charging rate is converted into a control instruction and sent to the control system of the charging device. After receiving the control instruction, the control system of the charging device controls the charging rate by adjusting the power.

[0021] Furthermore, the PID control algorithm is selected to set a desired charging rate target value based on the battery status, grid status, and user needs, and compares the actual charging rate detected in real time with the set desired charging rate to calculate the difference. According to the PID control algorithm, the difference is subjected to proportional (P), integral (I), and differential (D) operations to calculate the control variable. According to the control variable calculated by the PID control, the output power of the charging device is adjusted, thereby achieving adaptive control of the charging rate. During the charging process, the difference between the charging rate and the set desired charging rate is detected in real time, so that the actual charging rate gradually approaches the desired charging rate. The mathematical formula of the PID control algorithm is: K p is the proportionality coefficient, (K i ) is the integration coefficient (K d ).

[0022] Furthermore, according to the electric vehicle charging control method described in claim 1, it is characterized in that the continuous monitoring and adjustment is carried out by monitoring the status of the battery and the power grid, as well as the operation of the charging equipment during the charging process, to determine whether abnormal conditions such as overcharging, over-discharging, and overheating occur during the charging process. If it is found that the current charging rate does not match the actual situation or an abnormal situation occurs, real-time adjustment is performed, and the optimal charging rate is recalculated according to the real-time status of the battery (such as SOC, temperature) and the real-time load of the power grid, and a new charging plan is formulated. At the new charging rate, the status of the battery and the power grid continues to be monitored to verify whether the new charging rate achieves the expected effect. If serious abnormal conditions (such as battery overheating, power grid failure) are found during the monitoring process, corresponding emergency measures are taken, such as disconnecting the charging connection, starting the cooling system and issuing an alarm.

[0023] Compared with the existing technology, this electric vehicle charging control method has the following beneficial effects:

[0024] 1. This invention collects and analyzes battery and grid status data in real time, uses a regression analysis model to predict the future battery state and grid load, and combines this with a linear programming algorithm to calculate the optimal charging rate. This method maximizes charging efficiency or meets specific user needs while ensuring that constraints such as battery temperature and grid load are met. This adaptive charging control method not only improves charging efficiency but also ensures charging safety through real-time monitoring and adjustment, effectively avoiding potential risks of overcharging, over-discharging, and overheating.

[0025] 2. The present invention fully considers the user's charging needs, such as charging time and charging priority, through the charging control method. By setting the objective function and constraints, combined with the PID control algorithm, dynamic adjustment of the charging rate is achieved to ensure that the actual charging rate gradually approaches the expected charging rate. This personalized charging service not only improves the user experience, but also demonstrates the high intelligence and adaptability of the system. By visually displaying the prediction results and charging strategies, it provides intelligent decision-making support for decision makers or operators, further improving the overall performance of the system.

[0026] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0028] Figure 1 The present invention is a flow chart of a charging control method for an electric vehicle.

[0029] Figure 2 This is a flow chart of Example 1 of an electric vehicle charging control method. DETAILED DESCRIPTION

[0030] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0031] Example 1

[0032] A method for controlling charging of an electric vehicle comprises the following steps: battery status monitoring, grid load monitoring, receiving user charging requirements, adaptive charging rate calculation, charging rate control, and continuous monitoring and adjustment.

[0033] First, the battery status monitoring uses the current sensor and voltage sensor in the BMS to measure the battery's charge and discharge, current, and voltage in real time, and uses the ampere-hour integration method to estimate the battery's state of charge (SOC). The mathematical formula of the ampere-hour integration method is: Use the data collected from thermistors or thermocouples to monitor the surface and internal temperature of the battery to establish a battery aging model to predict the battery's capacity decay, internal resistance increase, and temperature distribution parameters, evaluate the battery's health status, and formulate a suitable charging strategy based on the real-time monitoring and evaluation results of the battery.

[0034] Next, grid load monitoring is performed by collecting real-time grid power, voltage, current, and frequency through charging equipment or grid management systems connected to the grid. After cleaning and standardizing the data, features related to the load data are extracted and the future grid load is predicted using the weighted moving average method. The collected grid load conditions are recorded as Y1, Y2, ..., Y T ), determine the number of periods n of the moving average, and assign a weight w1, w2, ..., w to each period of data points n ), and ensure that i =1 n w i =1, for each period t, calculate the weighted moving average F t , and its weighted moving average formula is: And t ≥ n, use actual data to verify and evaluate the prediction results using the mean square error. The mathematical formula of the mean square error is: After the evaluation, future grid load conditions can be predicted.

[0035] Subsequently, the user's charging demand is received. The user can input the expected charging completion time and charging priority information through the vehicle system, mobile phone App or other user interaction interface. After the user enters the charging demand, the input content is verified to ensure that the input data is valid and reasonable, allowing users to customize charging modes such as fast charging, slow charging, and timed charging. For fast charging mode, the system needs to ensure that the constraints of battery temperature and grid load are met while maximizing charging efficiency. For slow charging mode, the system can simply set a lower charging current and voltage to extend battery life and reduce energy consumption. For timed charging mode, the system needs to start or end charging at a specified time according to the user's settings.

[0036] Then, the adaptive charging rate calculation uses linear programming to calculate the optimal charging rate by considering the battery status, grid load and user charging demand. ι (charge amount per unit time) is the decision variable, and the objective function Z = ∑ t -1 T c t ·r t , where: T is the total number of time intervals, t is the length of each time interval, define the constraints, battery status constraints: Grid load constraints: User demand constraints: j = 1, 2, ..., nr t ∈r ≥0 , t=1,2,…,T, input the objective function and constraints into the linear programming solver to obtain the value that maximizes the charging efficiency Z,

[0037] And according to the real-time status of the battery and the power grid, as well as the real-time needs of the user, the charging rate is dynamically adjusted to ensure the safety and efficiency of the charging process.

[0038] Next, the charging rate control determines the goal of maximizing charging efficiency based on the battery status, grid status, and user needs (such as charging time and charging priority). The actual charging rate detected in real time is compared with the set expected charging rate, and the difference is calculated. According to the PID control algorithm, the difference is proportional (P), integral (I), and differential (D) operations are performed on the difference to calculate the control variable. Based on the control variable calculated by PID control, the output power of the charging device is adjusted to achieve adaptive control of the charging rate. During the charging process, the difference between the charging rate and the set expected charging rate is detected in real time, so that the actual charging rate gradually approaches the expected charging rate. The mathematical formula of the PID control algorithm is: K p 0 is the proportional coefficient, (K i ) is the integration coefficient (K d ), converts the calculated optimal charging rate into a control instruction and sends it to the control system of the charging device. After receiving the control instruction, the control system of the charging device controls the charging rate by adjusting the power.

[0039] Finally, continuous monitoring and adjustment are carried out by monitoring the status of the battery and the power grid, as well as the operation of the charging equipment during the charging process, to determine whether any abnormal conditions such as overcharging, over-discharging, or overheating occur during the charging process. If an abnormality is found in the current charging rate, the optimal charging rate is recalculated based on the real-time status of the battery (such as SOC, temperature) and the real-time load of the power grid, and a new charging plan is formulated. At the new charging rate, the status of the battery and the power grid continues to be monitored. If a serious abnormality is found during the monitoring process (such as battery overheating or power grid failure), appropriate emergency measures are taken, such as disconnecting the charging connection, starting the cooling system, and issuing an alarm.

[0040] By implementing this electric vehicle charging control method, charging efficiency has been successfully improved, grid load pressure has been reduced, and battery life has been extended. At the same time, users can also choose the appropriate charging mode according to their needs, improving the charging experience.

[0041] Example 2

[0042] Application of Adaptive Charging Rate Calculation in Electric Vehicle Charging Control Method

[0043] This embodiment aims to describe in detail the adaptive charging rate calculation by collecting battery status (such as SOC, temperature), grid status (such as power, voltage, current, frequency) and user-set charging requirements (such as charging time, charging priority) data in real time through sensors and monitoring equipment to evaluate the battery status and the remaining capacity and stability of the grid.

[0044] Taking into account the battery status, grid load, and user charging needs, linear programming is used to calculate the optimal charging rate. The charging rate (charge amount per unit time) is defined as the decision variable, and an objective function that maximizes charging efficiency is created. The objective function takes into account factors such as the total number of time intervals and the length of each time interval. Constraints are defined, including battery status constraints (such as battery temperature limits), grid load constraints (to ensure grid stability), and user demand constraints (such as charging time, charging priority, etc.). The objective function and constraints are input into the linear programming solver to obtain the optimal charging rate sequence that maximizes charging efficiency. The charging rate is dynamically adjusted according to the real-time status of the battery and grid, as well as the real-time needs of the user, to ensure the safety and efficiency of the charging process.

[0045] The desired charging rate target value is set based on the battery status, grid status, and user needs. The actual charging rate of the charging device is detected in real time and compared with the desired charging rate to calculate the difference. The PID control algorithm is used to perform proportional (P), integral (I), and differential (D) operations on the difference to calculate the control quantity. Based on the control quantity calculated by PID control, the output power of the charging device is adjusted to achieve adaptive control of the charging rate. During the charging process, the difference between the charging rate and the set desired charging rate is detected in real time, so that the actual charging rate gradually approaches the desired charging rate.

[0046] During the charging process, the system continuously monitors the real-time changes in battery status, grid status, and user needs. If any abnormal situation occurs (such as high battery temperature, excessive grid load, etc.), corresponding adjustments are made according to the preset safety strategy. Based on real-time data, the charging rate is dynamically adjusted to ensure the safety and efficiency of the charging process and meet user needs.

[0047] In summary, the optimal charging rate sequence is calculated through the linear programming algorithm to ensure that the charging efficiency is maximized while meeting all constraints. The PID control algorithm is used to achieve adaptive control of the charging rate, so that the actual charging rate gradually approaches the expected charging rate, thereby improving the stability and efficiency of the charging process.

[0048] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A method for controlling charging of an electric vehicle, characterized in that: The specific steps of the charging control are: Battery status monitoring: Utilize a battery management system (BMS) to monitor the current status of the battery in real time. Sensors are used to obtain battery temperature data, consider the impact of different temperatures on battery charging characteristics, assess the battery's health status, and factor in battery aging and remaining life to develop an appropriate charging strategy. Grid load monitoring: Through charging equipment or grid management systems connected to the grid, the grid load is monitored in real time, the remaining capacity and stability of the grid are assessed, and high-power charging is avoided during peak load periods to ensure stable grid operation. Receiving user charging requests: Receive user charging requests through the vehicle system, mobile phone app, or other user interaction interfaces, allowing users to customize charging modes; Adaptive charging rate calculation: Based on the current state of the battery, the load of the power grid, and the user's charging needs, linear programming is used to calculate the optimal charging rate. A reasonable charging plan is formulated by comprehensively considering the fluctuation range of the power grid and the user's charging priority. Charging rate control: The calculated optimal charging rate is sent to the charging device, and the output power is controlled by the charging device's control system to adapt to changes in battery and grid conditions, achieving adaptive charging rate control. Continuous monitoring and adjustment: During the charging process, the battery and grid status are continuously monitored, and the charging rate is adjusted according to the actual situation to maintain optimal charging efficiency and battery life. If any abnormal situation occurs, charging is stopped in time and an alarm is issued; The grid load monitoring system utilizes charging equipment or a grid management system connected to the grid to collect real-time grid power, voltage, current, and frequency. After cleaning and standardizing the data, it extracts features related to the load data and uses a weighted moving average method to predict future grid load conditions. The prediction results are presented to decision makers or operators in a visual manner. During peak grid load periods, high-power charging is avoided to reduce the burden on the grid and ensure stable operation of the grid. At the same time, during low grid load periods, the charging power can be appropriately increased to improve charging efficiency. The weighted moving average method is used to predict the future grid load situation, and the collected grid load situation is recorded as , determine the number of periods n of the moving average and assign a weight to the data points in each period and ensure , for each period , calculate the weighted moving average , and its weighted moving average formula is: ,in: is the actual value of the grid load condition, is the corresponding weight, It is the predicted value for one or more periods in the future. The prediction results are verified and evaluated using actual data using mean square error. After the evaluation, the future grid load can be predicted. The adaptive charging rate calculation evaluates the battery status and the remaining capacity and stability of the power grid based on the collected data. Taking into account the battery status, the load of the power grid, and the user's charging needs, linear programming is used to calculate the optimal charging rate so that the charging process meets all constraints (such as battery temperature limits and power grid load limits) while maximizing charging efficiency or meeting the user's specific needs. The charging rate is dynamically adjusted according to the real-time status of the battery and power grid, as well as the real-time needs of the user, to ensure a safe and efficient charging process. The linear programming is used to calculate the optimal charging rate, by defining the charging rate (Charge amount per unit time) is the decision variable, and the objective function of maximizing charging efficiency is created ,in: is the total number of time intervals, is the length of each time interval, defining the constraints, battery status constraints: , grid load constraints: )=0, user demand constraints: , the objective function and constraints are input into the linear programming solver to obtain the value that maximizes the charging efficiency Z, and the optimal charging rate sequence is obtained. The charging rate is dynamically adjusted according to the real-time status of the battery and the power grid and the real-time needs of the user.

2. The electric vehicle charging control method according to claim 1, characterized in that: The battery status monitoring uses the current sensor and voltage sensor in the BMS to measure the battery's charge and discharge current and voltage in real time, and uses the ampere-hour integration method to estimate the battery's remaining capacity (SOC). The mathematical formula of the ampere-hour integration method is: ,in: Indicates the current remaining battery power. Indicates the initial state of charge of the battery, Indicates the rated capacity of the battery. Indicates the current passing through the battery, which is positive when charging and negative when discharging. Indicates time, uses the data collected from thermistors or thermocouples to monitor the surface and internal temperature of the battery to predict the battery's capacity decay, internal resistance increase, and temperature distribution parameters, evaluates the battery's health status, and formulates a suitable charging strategy based on the real-time monitoring and evaluation results of the battery status.

3. The electric vehicle charging control method according to claim 1, characterized in that: The user charging demand receiving user can input the expected charging completion time and charging priority information through the vehicle system, mobile phone app or other user interaction interface. After the user enters the charging demand, the input content is verified to ensure that the input data is valid and reasonable, allowing the user to customize the charging mode, such as fast charging, slow charging, and timed charging.

4. The electric vehicle charging control method according to claim 1, characterized in that: The charging rate control determines the goal of maximizing charging efficiency and minimizing charging time based on the battery status, grid status, and user needs (such as charging time and charging priority). Based on this goal, a PID control algorithm is selected to calculate the optimal charging rate. The calculated optimal charging rate is converted into a control instruction and sent to the control system of the charging device. After receiving the control instruction, the control system of the charging device controls the charging rate by adjusting the power.

5. The electric vehicle charging control method according to claim 4, characterized in that: The PID control algorithm is selected to set a desired charging rate target value based on the battery status, grid status, and user needs. The actual charging rate detected in real time is compared with the set desired charging rate to calculate the difference. According to the PID control algorithm, the difference is subjected to proportional (P), integral (I), and differential (D) operations to calculate the control variable. The output power of the charging device is adjusted based on the control variable calculated by the PID control, thereby achieving adaptive control of the charging rate. During the charging process, the difference between the charging rate and the set desired charging rate is detected in real time, so that the actual charging rate gradually approaches the desired charging rate. The mathematical formula of the PID control algorithm is: , is the integral coefficient ).

6. The electric vehicle charging control method according to claim 1, characterized in that: The continuous monitoring and adjustment is achieved by monitoring the status of the battery and the power grid, as well as the operation of the charging equipment during the charging process, to determine whether any abnormal conditions such as overcharging, over-discharging, or overheating occur during the charging process. If it is found that the current charging rate does not match the actual situation or an abnormality occurs, real-time adjustments are made. Based on the real-time status of the battery (such as SOC, temperature) and the real-time load of the power grid, the optimal charging rate is recalculated and a new charging plan is formulated. At the new charging rate, the status of the battery and the power grid are continuously monitored to verify whether the new charging rate achieves the expected effect. If a serious abnormality (such as battery overheating or power grid failure) is found during the monitoring process, corresponding emergency measures are taken, such as disconnecting the charging connection, activating the cooling system, and issuing an alarm.

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