A new energy vehicle charging process supervision method and system

Abstract

The application relates to the technical field of automobile charging supervision, in particular to a new energy automobile charging process supervision method and system, which comprises the following steps: collecting basic charging data and owner charging basic information of a target electric automobile during charging; using a temperature conversion method to calculate the actual temperature of a battery of the electric automobile during charging according to the basic charging data; establishing an owner charging database by using the collected and calculated data; performing real-time monitoring on a current charging process according to the owner charging database and a charging comparison model, and analyzing the charging behavior tendency of the owner according to the owner charging database; pushing charging suggestions for the owner according to the charging behavior tendency, and taking emergency measures according to the real-time monitoring result. The application can accurately perform real-time monitoring on the charging process of the electric automobile, push charging suggestions for the owner by using the charging behavior tendency of the owner, effectively reduce the probability of electric automobile charging accidents, and improve the satisfaction of the owner.

Description

Technical Field

[0001] This invention relates to the field of vehicle charging supervision technology, and in particular to a method and system for supervising the charging process of new energy vehicles. Background Technology

[0002] With the rapid development of electric vehicles, the safety and reliability of electric vehicle charging have received widespread attention. Electric vehicles are powered by various types of batteries. If a battery malfunctions during charging, it can damage the electric vehicle or the charging station, and in severe cases, even cause a fire. Therefore, regulating the electric vehicle charging process is crucial. However, to date, the regulation of electric vehicle charging is still in its early stages, and research results are relatively limited.

[0003] The regulation of electric vehicle (EV) charging primarily involves monitoring and managing basic charging information such as charging voltage, charging current, charging temperature, charging time, and battery model. While charging voltage and current are readily measurable in current technologies, charging temperature is difficult to measure accurately due to the enclosed structure of the battery and the application environment, increasing the probability of spontaneous combustion in EVs. Although some researchers have proposed methods for measuring charging temperature, these methods are often computationally complex and prone to significant errors; others are only suitable for laboratory use, making them unsuitable for accurate temperature monitoring. Furthermore, existing EV charging monitoring methods monitor each parameter independently, failing to consider the overall charging risks arising from changes in parameter values, thus reducing the reliability of EV charging process regulation. In addition, current EV charging management often focuses solely on data collection and utilization by operators, neglecting to provide concise charging data based on owner behavior to help regulate charging practices. This hinders the improvement of charging station operators' service quality and indirectly increases the probability of EV charging malfunctions. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for monitoring the charging process of new energy vehicles.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a method for monitoring the charging process of new energy vehicles. The method includes the following steps: collecting basic charging data and basic charging information of the target electric vehicle during charging; calculating the actual battery temperature of the electric vehicle during charging using a temperature conversion method based on the basic charging data; establishing a vehicle owner charging database using the collected and calculated historical basic charging data, historical vehicle owner charging information, and historical actual temperatures of the target electric vehicle; monitoring the current charging process of the target electric vehicle in real time based on the vehicle owner charging database and a charging comparison model, and analyzing the charging behavior tendencies of the vehicle owner based on the vehicle owner charging database; providing charging suggestions to the vehicle owner based on the charging behavior tendencies, and taking emergency measures based on the results of real-time monitoring. The present invention can accurately monitor the charging process of electric vehicles in real time and can provide charging suggestions to the vehicle owner based on the charging behavior tendencies of the vehicle owner, effectively reducing the probability of electric vehicle charging accidents and improving vehicle owner satisfaction.

[0006] Optionally, the basic charging data includes the input voltage, input current, battery shell temperature, battery external temperature and battery internal resistance during battery charging, and the vehicle owner's basic charging information includes, but is not limited to, battery model, remaining battery power at the start of charging, final battery power at the end of charging and charging frequency. The process of calculating the actual battery temperature during electric vehicle charging using the basic charging data and the temperature conversion method includes the following steps: The battery casing resistance during battery charging is predicted using the aforementioned basic charging data. The actual temperature of the battery during electric vehicle charging is calculated using a temperature conversion model based on the battery shell temperature, external temperature, battery internal resistance, and battery shell resistance.

[0007] Furthermore, the cell resistance changes with temperature, so using baseline charging data to predict the cell resistance during battery charging can improve the accuracy of the calculated actual temperature.

[0008] Optionally, predicting the battery casing resistance during charging using the basic charging data includes the following steps: In the laboratory, an external resistance prediction database is established based on the aforementioned basic charging data and battery shell resistance. The BP neural network is trained using the external resistance prediction database to obtain a battery shell resistance prediction model, and the battery shell resistance prediction model is used to predict the battery shell resistance during electric vehicle charging.

[0009] Furthermore, the powerful nonlinear mapping capability of the BP neural network can accurately predict the tank resistance, thereby improving the accuracy of the calculated actual temperature.

[0010] Optionally, the temperature conversion model satisfies the following relationship:

[0011] Where T is the actual temperature. The temperature of the pool shell, The temperature outside the pool is... The internal resistance of the battery is... The resistance of the pool shell is denoted as .

[0012] Furthermore, this temperature conversion model calculates the actual temperature of an electric vehicle during charging using a series of directly measurable data. Not only are the parameters easy to obtain, but the calculation is also simple and convenient for practical applications.

[0013] Optionally, the step of monitoring the current charging process of the target electric vehicle in real time based on the vehicle owner charging database and the charging comparison model, and analyzing the charging behavior tendencies of vehicle owners based on the vehicle owner charging database, includes the following steps: Based on the current input voltage, current input current, current actual temperature during the current charging process, and the vehicle owner charging database, the charging matching degree of the current charging process is calculated using the charging comparison model. The charging process of an electric vehicle can be monitored in real time using the current input voltage, the current input current, the current actual temperature, and the charging matching degree. The charging behavior tendencies of car owners are determined based on the aforementioned car owner charging database.

[0014] Furthermore, real-time monitoring of the electric vehicle charging process using current input voltage, current input current, current actual temperature, and charging matching degree can improve the reliability of supervision, while judging the owner's charging behavior tendencies can help push appropriate charging suggestions to the owner.

[0015] Optionally, determining a vehicle owner's charging behavior tendency based on the vehicle owner charging database includes the following steps: Determine the criteria for classifying the charging behavior tendency; Based on the aforementioned classification criteria, the charging behavior tendencies of car owners are determined using the car owner charging database.

[0016] Furthermore, judging the charging behavior tendencies of car owners can help push appropriate charging suggestions to them, enabling them to optimize their charging behavior and reduce the probability of electric vehicle charging failures.

[0017] Optionally, the charging comparison model satisfies the following relationship:

[0018] in, For the charging compatibility, The current input voltage, The current input current, The current actual temperature is N, and the number of data sets in the vehicle owner charging database is N. The charging voltage in the i-th group of data in the vehicle owner charging database. The charging current in the i-th group of data in the vehicle owner charging database. The historical actual temperature in the i-th group of data in the vehicle owner charging database.

[0019] Furthermore, the charging comparison model takes into account the charging risks that may arise from changes in various parameter values ​​as a whole, which helps to improve the reliability of the supervision of the electric vehicle charging process.

[0020] Optionally, the step of pushing charging suggestions to the vehicle owner based on the charging behavior tendency and taking emergency measures based on the real-time monitoring results includes the following steps: A charging feedback database is built, and when charging begins, charging feedback is retrieved from the database based on the charging behavior tendencies and pushed to the vehicle owner. Multiple parameter thresholds are set, and emergency measures are taken when the current input voltage, the current input current, the current actual temperature, or the charging matching degree exceeds the parameter thresholds.

[0021] Furthermore, providing car owners with appropriate charging suggestions can help them optimize their charging behavior, improve the service quality of charging station operators, increase car owners' satisfaction during charging, and reduce the probability of electric vehicle charging failures. Setting multiple parameter thresholds can help prevent charging accidents based on single or multiple parameters, thereby improving charging safety.

[0022] Optionally, the parameter thresholds include an input voltage threshold, an input current threshold, an actual temperature threshold, a first matching degree threshold, and a second matching degree threshold. The step of setting multiple parameter thresholds and taking emergency measures when the current input voltage, current input current, current actual temperature, or charging matching degree exceeds the parameter thresholds includes the following steps: Set the input voltage threshold, the input current threshold, the actual temperature threshold, the first matching degree threshold, and the second matching degree threshold; A notification message is sent to the vehicle owner when the charging compatibility exceeds a first compatibility threshold. An alarm will be issued and charging will be stopped when any one of the following conditions is met: the current input voltage is greater than the input voltage threshold, the current input current is greater than the input current threshold, the current actual temperature is greater than the actual temperature threshold, and the charging matching degree is greater than the second matching degree threshold.

[0023] In summary, the method provided by this invention uses a temperature conversion method to accurately obtain the actual battery temperature during electric vehicle charging, improving the accuracy and reliability of monitoring. Based on input voltage, input current, and actual temperature monitoring, it uses a charging comparison model to comprehensively consider the charging risks that may arise from changes in various parameter values, further enhancing the accuracy and reliability of monitoring. By analyzing the charging behavior tendencies of vehicle owners, it provides appropriate charging suggestions, enabling owners to optimize their charging behavior and reduce the probability of electric vehicle charging failures. Furthermore, by setting multiple parameter thresholds and taking timely emergency measures based on the detection results, it improves charging safety.

[0024] Secondly, this invention provides a new energy vehicle charging process monitoring system. The system uses a new energy vehicle charging process monitoring method provided by this invention. The system includes: a data acquisition module for collecting basic charging data and basic charging information of the target electric vehicle during charging; a processing module for calculating the actual battery temperature of the electric vehicle during charging using a temperature conversion method based on the basic charging data; establishing a vehicle owner charging database using the collected and calculated historical basic charging data, historical battery information, and historical actual temperatures of the target electric vehicle; real-time monitoring of the current charging process of the target electric vehicle based on the vehicle owner charging database and a charging comparison model, and analysis of the vehicle owner's charging behavior tendencies based on the vehicle owner charging database; an output module for pushing charging suggestions to the vehicle owner based on the charging behavior tendencies and taking emergency measures based on the real-time monitoring results; and a storage module for storing the data collected by the data acquisition module and the data generated by the processing module.

[0025] Furthermore, in addition to having the same advantages as the method provided by this invention, the system provided by this invention can also improve the intelligence level of charging piles, further enhance the service quality of charging pile operators, and increase the satisfaction of car owners when charging. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of a method for monitoring the charging process of new energy vehicles according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the framework of a new energy vehicle charging process monitoring system according to an embodiment of the present invention. Detailed Implementation

[0028] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0029] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0030] It should be noted in advance that, in one alternative embodiment, except for independent descriptions, the same symbols or letters appearing in all formulas have the same meaning and value.

[0031] In one optional embodiment, please refer to Figure 1 This invention provides a method for monitoring the charging process of new energy vehicles, the method comprising the following steps: S1. Collect basic charging data and basic charging information of the target electric vehicle during charging.

[0032] Specifically, in this embodiment, the basic charging data includes the input voltage, input current, battery casing temperature, external battery temperature, and battery internal resistance during battery charging. The vehicle owner's basic charging information includes, but is not limited to, the battery model, the remaining battery power at the start of charging, the final battery power at the end of charging, and the charging frequency.

[0033] Furthermore, the battery casing temperature refers to the temperature of the electric vehicle's battery outer shell, the external battery temperature refers to the air temperature 4cm away from the battery casing, and the charging frequency refers to the monthly charging frequency of the target electric vehicle. Both the battery casing temperature and the external battery temperature can be directly measured using temperature sensors; the type of temperature sensor is not specified here. In addition, the input voltage, input current, battery internal resistance, battery type, remaining battery charge at the start of charging, final battery charge at the end of charging, and charging frequency can all be obtained using existing technologies, and therefore will not be detailed here.

[0034] S2. Calculate the actual battery temperature during electric vehicle charging using the temperature conversion method based on the basic charging data.

[0035] Step S2 specifically includes the following steps: S21. Use the aforementioned basic charging data to predict the battery casing resistance during battery charging.

[0036] Specifically, step S21 includes the following steps: S211. Establish an external resistance prediction database in the laboratory based on the basic charging data and the battery shell resistance.

[0037] Specifically, in this embodiment, 100 sets of data on the input voltage, input current, battery casing temperature, battery external temperature, battery internal resistance, and battery casing resistance of the electric vehicle battery are obtained through experimental measurements in the laboratory. Then, these 100 sets of data are used to establish an external resistance prediction database.

[0038] Furthermore, considering the variety of electric vehicle battery models, it is necessary to establish separate external resistance prediction databases for different battery models. In addition, obtaining electric vehicle battery input voltage, input current, battery casing temperature, external temperature, internal resistance, and casing resistance through laboratory experiments is existing technology and will not be detailed here.

[0039] S212. The BP neural network is trained using the external resistance prediction database to obtain a battery shell resistance prediction model, and the battery shell resistance prediction model is used to predict the battery shell resistance when the electric vehicle is charging.

[0040] Specifically, in this embodiment, 100 sets of data in the external resistance prediction database are divided into a training set and a test set in a 4:1 ratio. Then, a backpropagation (BP) neural network for predicting the tank resistance is constructed. Finally, the training and test sets are used to train and test the BP neural network. If the test error of the obtained BP neural network is too large, the training and test sets are reallocated, and the BP neural network is retrained until the test error of the trained BP neural network meets the requirements. The finally trained BP neural network is used as the tank resistance prediction model. The training and testing of the BP neural network are existing technologies and will not be described in detail here.

[0041] Furthermore, obtaining accurate battery casing resistance in real time during charging helps improve the accuracy of the calculated actual temperature. However, battery casing resistance changes with temperature, and there is no clear linear relationship between battery casing resistance and directly obtainable input voltage, input current, battery casing temperature, external battery temperature, and battery internal resistance. Therefore, the powerful nonlinear mapping function of BP neural networks is used to accurately predict battery casing resistance, thereby improving the accuracy of the subsequent calculated actual temperature and ultimately enhancing the accuracy and reliability of monitoring the electric vehicle charging process.

[0042] S22. Based on the battery shell temperature, battery outside temperature, battery internal resistance, and battery shell resistance, calculate the actual battery temperature during electric vehicle charging using a temperature conversion model.

[0043] Specifically, in this embodiment, the temperature conversion model satisfies the following relationship:

[0044] Where T is the actual temperature. For the temperature of the pool shell, The temperature outside the pool, This refers to the battery's internal resistance. The resistance is the tank casing resistance.

[0045] Furthermore, the battery casing temperature, external battery temperature, and internal battery resistance are easily and accurately obtained, as is the battery casing resistance obtained through the method in step S21. Therefore, the parameters of this temperature conversion model are easy to obtain, and the accuracy and reliability of the calculated actual temperature are good. In addition, this temperature conversion model is simple to calculate and easy to apply in practice.

[0046] S3. Establish a vehicle owner charging database using the historical basic charging data, historical owner charging information, and historical actual temperature of the target electric vehicle obtained through collection and calculation.

[0047] Specifically, in this embodiment, the basic charging data, owner charging information, and actual temperature of the target electric vehicle are collected and saved for each charging session. For a given target electric vehicle, if it has been charged normally no more than 20 times previously, the basic charging data, owner charging information, and actual temperature of each of its previous normal charging sessions are used as historical basic charging data, historical owner charging information, and historical actual temperatures to establish the owner charging database. If it has been charged normally more than 20 times previously, the basic charging data, owner charging information, and actual temperature of its most recent 20 normal charging sessions are used as historical basic charging data, historical owner charging information, and historical actual temperatures to establish the owner charging database. Therefore, the owner charging database contains a maximum of 20 sets of data. Each set of data includes the basic charging data, owner charging information, and actual temperature collected or calculated during that normal charging session. The different sets of data in the owner charging database are arranged in chronological order, meaning that the last set of data in the owner charging database is the basic charging data, owner charging information, and actual temperature of the target electric vehicle's most recent normal charging session.

[0048] Furthermore, normal charging of the target electric vehicle means that the owner does not receive any notification information during a single charging process, and the charging station does not issue any alarms or stop charging.

[0049] S4. Based on the vehicle owner charging database and the charging comparison model, monitor the current charging process of the target electric vehicle in real time, and analyze the charging behavior tendencies of the vehicle owners based on the vehicle owner charging database.

[0050] Step S4 specifically includes the following steps: S41. Based on the current input voltage, current input current, current actual temperature and the vehicle owner charging database during the current charging process, calculate the charging matching degree of the current charging process using the charging comparison model.

[0051] Specifically, in this embodiment, the charging matching degree satisfies the following relationship:

[0052] in, For charging compatibility, The current input voltage. The current input current, The current actual temperature is N, and the number of data sets in the vehicle owner charging database is N. The charging voltage in the i-th data set of the car owner charging database. The charging current in the i-th data set of the car owner charging database. The historical actual temperature in the i-th data set of the car owner charging database.

[0053] Furthermore, the charging comparison model takes into account the charging risks that may arise from changes in various parameter values ​​as a whole, which helps to improve the reliability of the supervision of the electric vehicle charging process.

[0054] S42. Real-time monitoring of the charging process of electric vehicles is achieved by using the current input voltage, the current input current, the current actual temperature, and the charging matching degree.

[0055] Specifically, in this embodiment, the current input voltage, current input current, and current actual temperature are used to monitor the electric vehicle charging process in real time from the perspective of individual parameters. That is, if any one or more of the current input voltage, current input current, and current actual temperature exceed a set threshold, corresponding emergency measures will be triggered to prevent charging accidents. However, if any one or more of the current input voltage, current input current, and current actual temperature deviate from the corresponding values ​​during normal charging but do not exceed the set threshold, the probability of a charging accident will greatly increase, but the corresponding emergency measures will not be implemented, ultimately leading to a charging accident, such as battery damage or spontaneous combustion. Charging matching degree, on the other hand, reflects the overall fluctuation of the current input voltage, current input current, and current actual temperature compared to normal charging, which helps increase the accuracy and reliability of monitoring the electric vehicle charging process and reduces safety hazards during charging.

[0056] S43. Determine the charging behavior tendencies of car owners based on the car owner charging database.

[0057] Specifically, step S43 includes the following steps: S431. Determine the criteria for classifying the charging behavior tendency.

[0058] Specifically, in this embodiment, charging behavior tendencies are divided into four levels: Level 1, Level 2, Level 3, and Level 4. The main basis for the division is the remaining battery power at the start of charging and the final battery power at the end of charging.

[0059] Furthermore, based on the owner's basic charging information from the last normal charge of the target electric vehicle, if the remaining battery charge at the start of charging is less than 50% and the final battery charge at the end of charging is greater than 95%, the owner's charging behavior tendency is classified as Level 1; if the remaining battery charge at the start of charging is less than 50% and the final battery charge at the end of charging is less than 95%, the owner's charging behavior tendency is classified as Level 2; if the remaining battery charge at the start of charging is greater than 50% and the final battery charge at the end of charging is greater than 95%, the owner's charging behavior tendency is classified as Level 3; and if the remaining battery charge at the start of charging is greater than 50% and the final battery charge at the end of charging is less than 95%, the owner's charging behavior tendency is classified as Level 4.

[0060] Furthermore, in other alternative embodiments, other criteria can be selected to determine the classification of charging behavior tendencies. However, these criteria all exist in the vehicle owner charging database, and the more main criteria there are, the more complex the classification criteria will become.

[0061] S432. Based on the classification criteria, use the vehicle owner charging database to determine the vehicle owner's charging behavior tendency.

[0062] Specifically, in this embodiment, the charging behavior tendency of the car owner is determined by using the remaining battery power at the start of charging and the final battery power at the end of charging from the last set of data in the car owner charging database, according to a classification standard. Determining the car owner's charging behavior tendency helps to push appropriate charging suggestions to the car owner, enabling the car owner to optimize their charging behavior and reducing the probability of electric vehicle charging failures.

[0063] S5. Based on the charging behavior tendencies, push charging suggestions to the vehicle owner and take emergency measures based on the results of real-time monitoring.

[0064] Step S5 specifically includes the following steps: S51. Build a charging opinion database, and when charging starts, retrieve charging opinions from the charging opinion database based on the charging behavior tendency and push them to the vehicle owner.

[0065] Specifically, in this embodiment, the charging opinion database includes charging opinions for different charging behavior tendencies. After determining the charging behavior tendency of the car owner in step S432, the corresponding charging opinion in the charging opinion database can be called and pushed to the car owner when the target electric vehicle starts charging.

[0066] Furthermore, the charging feedback is set by the charging station operator. For example, for car owners whose charging behavior is rated as Level 1, the feedback could be set as "Your last charging behavior was good, please continue." This helps improve the service quality of the charging station operator, increases car owner satisfaction while charging, and also encourages car owners to optimize their charging behavior, which helps reduce the probability of electric vehicle charging malfunctions.

[0067] Furthermore, in other alternative embodiments, the setting of charging opinions may vary depending on the criteria for classifying charging behavior tendencies.

[0068] S52. Set multiple parameter thresholds, and take emergency measures when the current input voltage, the current input current, the current actual temperature, or the charging matching degree exceeds the parameter thresholds.

[0069] The parameter thresholds include the input voltage threshold, input current threshold, actual temperature threshold, first matching degree threshold, and second matching degree threshold. Step S52 specifically includes the following steps: S521. Set the input voltage threshold, the input current threshold, the actual temperature threshold, the first matching degree threshold, and the second matching degree threshold.

[0070] Specifically, in this embodiment, the input voltage threshold, input current threshold, and actual temperature threshold of the battery will vary depending on the battery model. Therefore, the input voltage threshold, input current threshold, and actual temperature threshold of different battery models need to be obtained by relevant technicians through experimental measurement in a laboratory. This embodiment does not impose any limitations on these parameters. The first matching degree threshold and the second matching degree threshold are set to 0.15 and 0.25, respectively.

[0071] S522, When the charging matching degree exceeds the first matching degree threshold, a prompt message is sent to the vehicle owner.

[0072] Specifically, in this embodiment, when the charging matching degree exceeds the first matching degree threshold, it indicates that the overall fluctuation of the input voltage, input current, and actual temperature is larger than that during normal charging. At this time, the charging pile issues a prompt voice to inform the car owner that the overall fluctuation of the current input voltage, current, and actual temperature is larger than that during normal charging, reminding the car owner to pay attention to the input voltage, current, and actual temperature to avoid charging failures that could lead to spontaneous combustion of the target electric vehicle, thereby reducing safety hazards.

[0073] S523. When any one of the following conditions is met: the current input voltage is greater than the input voltage threshold, the current input current is greater than the input current threshold, the current actual temperature is greater than the actual temperature threshold, and the charging matching degree is greater than the second matching degree threshold, an alarm is issued and charging is stopped.

[0074] Specifically, in this embodiment, the charging pile will issue an alarm and stop charging when the current input voltage is greater than the input voltage threshold; the charging pile will issue an alarm and stop charging when the current input current is greater than the input current threshold; the charging pile will issue an alarm and stop charging when the current actual temperature is greater than the actual temperature threshold; and the charging pile will issue an alarm and stop charging when the charging matching degree is greater than the second matching degree threshold.

[0075] Furthermore, if one or more of the current input voltage, current input current, and current actual temperature exceed their respective thresholds, an alarm will be issued and charging will be stopped, thereby effectively reducing the possibility of charging accidents. Simultaneously, if the overall fluctuations of the current input voltage, current input current, and actual temperature compared to those during normal charging are significant—that is, when the charging matching degree exceeds the second matching degree threshold—an alarm will be issued and charging will be stopped. This further helps to reduce the possibility of charging accidents and improves the accuracy and reliability of monitoring the electric vehicle charging process.

[0076] It should be noted that in some cases, the actions described in the specification can be performed in different orders and still achieve the desired results. In this embodiment, the order of steps is given only to make the embodiment clearer and easier to explain, and not to limit it.

[0077] In an alternative embodiment, please refer to Figure 2 The present invention also provides a new energy vehicle charging process monitoring system, which uses a new energy vehicle charging process monitoring method provided by the present invention. The system includes a data acquisition module A1, a processing module A2, an output module A3, and a storage module A4.

[0078] The acquisition module A1 is used to collect basic charging data and basic charging information of the owner when the target electric vehicle is charging.

[0079] Specifically, in this embodiment, the acquisition module A1 performs the content described in step S1, which will not be repeated here.

[0080] The processing module A2 is used to calculate the actual battery temperature of the electric vehicle during charging using the temperature conversion method based on the basic charging data; to establish a car owner charging database using the collected and calculated historical basic charging data, historical battery basic information and historical actual temperature of the target electric vehicle; to monitor the current charging process of the target electric vehicle in real time based on the car owner charging database and the charging comparison model, and to analyze the charging behavior tendencies of the car owner based on the car owner charging database.

[0081] Specifically, in this embodiment, the processing module A2 is connected to the acquisition module A1 via a data cable. The processing module A2 performs the contents described in steps S2-S4, which will not be elaborated here.

[0082] Furthermore, in other alternative embodiments, the processing module A2 and the acquisition module A1 can also be connected in other ways, and specific connection methods will not be listed here.

[0083] The output module A3 is used to push charging suggestions to the car owner based on the charging behavior tendency, and to take emergency measures based on the results of real-time monitoring.

[0084] Specifically, in this embodiment, the output module A3 is connected to the processing module A2 via a data cable. The output module A3 performs the contents described in step S5, which will not be elaborated here.

[0085] Furthermore, in other alternative embodiments, the output module A3 and the processing module A2 can also be connected in other ways, and specific connection methods will not be listed here.

[0086] The storage module A4 is used to store the data collected by the acquisition module A1 and the data generated by the processing module A2.

[0087] Specifically, in this embodiment, the storage module A4 is connected to the acquisition module A1 and the processing module A2 via data lines. The storage module A4 is specifically used to store the basic charging data and vehicle owner charging basic information collected by the acquisition module A1, as well as all the data obtained from the processing module A2.

[0088] Furthermore, in other alternative embodiments, the data stored in the storage module A4 can be used to supplement and update the vehicle owner charging database.

[0089] In summary, the method provided by this invention uses a temperature conversion method to accurately obtain the actual battery temperature during electric vehicle charging, improving the accuracy and reliability of monitoring. Based on input voltage, input current, and actual temperature monitoring, it uses a charging comparison model to comprehensively consider the charging risks that may arise from changes in various parameter values, further enhancing the accuracy and reliability of monitoring. By analyzing the charging behavior tendencies of vehicle owners, it provides appropriate charging suggestions, enabling them to optimize their charging behavior and reduce the probability of electric vehicle charging failures. By setting multiple parameter thresholds and taking timely emergency measures based on the detection results, it improves charging safety. Furthermore, the system provided by this invention, in addition to having the same advantages as the method provided by this invention, can also improve the intelligence level of charging piles and further enhance the service quality of charging pile operators, increasing vehicle owner satisfaction during charging.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for monitoring the charging process of new energy vehicles, characterized in that, Includes the following steps: Collect basic charging data and owner charging information of the target electric vehicle during charging. The basic charging data includes the input voltage, input current, battery shell temperature, battery external temperature, and battery internal resistance during battery charging. The basic charging information of the vehicle owner includes at least the remaining battery power at the start of charging and the final battery power at the end of charging. The battery casing resistance during battery charging is predicted using the aforementioned basic charging data. The actual temperature of the battery during electric vehicle charging is calculated using a temperature conversion model based on the battery shell temperature, the external temperature of the battery, the battery internal resistance, and the battery shell resistance. The temperature conversion model satisfies the following relationship: Where T is the actual temperature. The temperature of the pool shell, The temperature outside the pool is... The internal resistance of the battery is... The resistance of the pool shell; A vehicle owner charging database is established using historical basic charging data, historical owner charging information, and historical actual temperature data of the target electric vehicles collected and calculated. Based on the current input voltage, current input current, current actual temperature, and the vehicle owner charging database during the current charging process, the charging matching degree of the current charging process is calculated using a charging comparison model. The charging comparison model satisfies the following relationship: in, For the charging compatibility, The current input voltage, The current input current, The current actual temperature is N, and the number of data sets in the vehicle owner charging database is N. The charging voltage in the i-th group of data in the vehicle owner charging database. The charging current in the i-th group of data in the vehicle owner charging database. The historical actual temperature in the i-th group of data in the vehicle owner charging database; The charging process of an electric vehicle can be monitored in real time using the current input voltage, the current input current, the current actual temperature, and the charging matching degree. Based on the aforementioned vehicle owner charging database, determine the charging behavior tendencies of vehicle owners; Based on the charging behavior tendencies, charging suggestions are pushed to the car owner, and emergency measures are taken based on the results of real-time monitoring.

2. The method for monitoring the charging process of new energy vehicles according to claim 1, characterized in that, The process of predicting the battery casing resistance during battery charging using the basic charging data includes the following steps: In the laboratory, an external resistance prediction database is established based on the aforementioned basic charging data and battery shell resistance. The BP neural network is trained using the external resistance prediction database to obtain a battery shell resistance prediction model, and the battery shell resistance prediction model is used to predict the battery shell resistance during electric vehicle charging.

3. The method for monitoring the charging process of new energy vehicles according to claim 1, characterized in that, The process of determining a car owner's charging behavior tendency based on the car owner charging database includes the following steps: Determine the criteria for classifying the charging behavior tendency; Based on the aforementioned classification criteria, the charging behavior tendencies of car owners are determined using the car owner charging database.

4. The method for monitoring the charging process of new energy vehicles according to claim 1, characterized in that, The process of pushing charging suggestions to vehicle owners based on their charging behavior tendencies and taking emergency measures based on real-time monitoring results includes the following steps: A charging feedback database is built, and when charging begins, charging feedback is retrieved from the database based on the charging behavior tendencies and pushed to the vehicle owner. Multiple parameter thresholds are set, and emergency measures are taken when the current input voltage, the current input current, the current actual temperature, or the charging matching degree exceed the parameter thresholds.

5. A method for monitoring the charging process of new energy vehicles according to claim 4, characterized in that: The parameter thresholds include the input voltage threshold, the input current threshold, the actual temperature threshold, the first matching degree threshold, and the second matching degree threshold. The step of setting multiple parameter thresholds and taking emergency measures when the current input voltage, the current input current, the current actual temperature, and the charging matching degree exceed the parameter thresholds includes the following steps: Set the input voltage threshold, the input current threshold, the actual temperature threshold, the first matching degree threshold, and the second matching degree threshold; A notification message is sent to the vehicle owner when the charging compatibility exceeds a first compatibility threshold. An alarm will be issued and charging will be stopped when any one of the following conditions is met: the current input voltage is greater than the input voltage threshold, the current input current is greater than the input current threshold, the current actual temperature is greater than the actual temperature threshold, and the charging matching degree is greater than the second matching degree threshold.

6. A new energy vehicle charging process monitoring system, wherein the system uses the new energy vehicle charging process monitoring method according to any one of claims 1-5, the system comprising: The data acquisition module is used to collect basic charging data and basic charging information of the vehicle owner during the charging of the target electric vehicle. The basic charging data includes the input voltage, input current, battery shell temperature, battery external temperature, and battery internal resistance during battery charging. The basic charging information of the vehicle owner includes at least the remaining battery power at the start of charging and the final battery power at the end of charging. The processing module uses the basic charging data to predict the battery casing resistance during battery charging. Based on the battery casing temperature, the external battery temperature, the battery internal resistance, and the battery casing resistance, a temperature conversion model is used to calculate the actual battery temperature during electric vehicle charging; the temperature conversion model satisfies the following relationship: Where T is the actual temperature. The temperature of the pool shell, The temperature outside the pool is... The internal resistance of the battery is... The tank casing resistance is defined as follows: A vehicle owner charging database is established using historical basic charging data of the target electric vehicle, historical owner charging information, and historical actual temperature obtained through collection and calculation; based on the current input voltage, current input current, current actual temperature, and the vehicle owner charging database during the current charging process, a charging comparison model is used to calculate the charging matching degree of the current charging process; the charging comparison model satisfies the following relationship: in, For the charging compatibility, The current input voltage, The current input current, The current actual temperature is N, and the number of data sets in the vehicle owner charging database is N. The charging voltage in the i-th group of data in the vehicle owner charging database. The charging current in the i-th group of data in the vehicle owner charging database. The system uses the historical actual temperature from the i-th data set in the vehicle owner charging database; it utilizes the current input voltage, current input current, current actual temperature, and charging matching degree to monitor the charging process of the electric vehicle in real time; and it determines the charging behavior tendencies of the vehicle owner based on the vehicle owner charging database. The output module is used to push charging suggestions to the vehicle owner based on the charging behavior tendency, and to take emergency measures based on the results of real-time monitoring. A storage module is provided for storing the data collected by the acquisition module and the data generated by the processing module.

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