Multi-scene adaptation method and system for mobile charging pile

Abstract

The application discloses a multi-scene adaptation method and system for a mobile charging pile, relates to the technical field of charging, and comprises the following steps: establishing a device self-checking data set; after the target vehicle is connected with the charging gun, sending an information sharing request to the target vehicle; if the request is passed, a scene feature set is established; after reading the charging demand of the target vehicle, the target control optimization of the charging demand is executed under the scene feature set, the control optimization result is established, and the mapping state response curve of the control optimization result is configured; the mobile charging pile is controlled to charge the target vehicle based on the control optimization result, the battery monitoring feedback is read, the transient follow-up compensation is performed by using the battery monitoring feedback and the mapping state response curve, and the scene adaptation charging management of the mobile charging pile is performed by using the transient follow-up compensation. The application solves the technical problem that the mobile charging pile in the prior art cannot be efficiently adapted and managed in different charging scenes, and achieves the technical effect of improving charging adaptability and charging efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging, in particular to a multi-scene adaptation method and system for mobile charging piles. BACKGROUND

[0002] Mobile charging piles face the diversity of charging demand and environmental changes in actual application. In different charging scenes, mobile charging piles often have difficulty in adapting to the diversified charging demand of target vehicles and the state of equipment, especially in the absence of equipment self-checking and real-time monitoring, the charging process is prone to abnormalities, affecting the charging efficiency. In addition, mobile charging piles usually cannot dynamically adjust the charging process according to environmental factors such as temperature and humidity. Since the control system of the charging pile relies on the preset program, it lacks intelligent adaptation to different charging scenes and tasks, so that the charging management cannot realize flexible and efficient adjustment, thereby limiting its application effect in complex environments. SUMMARY

[0003] The present application provides a multi-scene adaptation method and system for mobile charging piles, which is used to solve the technical problem that the mobile charging pile in the prior art cannot be efficiently adapted and managed in different charging scenes.

[0004] In view of the above problems, the present application provides a multi-scene adaptation method and system for mobile charging piles.

[0005] The first aspect of the present application provides a multi-scene adaptation method for mobile charging piles, the method comprising:

[0006] When the mobile charging pile arrives at the target position based on the remote scheduling command, the device self-checking of the mobile charging pile is performed, and the device self-checking data set is established; when the target vehicle is connected through the charging gun, the information sharing request is sent to the target vehicle; if the receiving request is passed, after obtaining the vehicle sharing data of the target vehicle, the scene feature set is established based on the device self-checking data set, the vehicle sharing data and the environmental data; after reading the charging demand of the target vehicle, the target control optimization of the charging demand is performed under the scene feature set, the control optimization result is established, and the mapping state response curve of the control optimization result is configured; the target vehicle is charged based on the control optimization result, and the battery monitoring feedback is read, the transient pursuit compensation is performed using the battery monitoring feedback and the mapping state response curve, and the scene adaptation charging management of the mobile charging pile is performed using the transient pursuit compensation.

[0007] The second aspect of the present application provides a multi-scene adaptation system for mobile charging piles, the system comprising:

[0008] The system includes a device self-test module, which performs a device self-test and establishes a device self-test dataset after the mobile charging pile arrives at the target location based on a remote scheduling command; a sharing request sending module, which sends an information sharing request to the target vehicle after connecting with it via the charging gun; a feature set establishment module, which, if the request is acknowledged, establishes a scene feature set based on the device self-test dataset, vehicle sharing data, and environmental data after acquiring the target vehicle's vehicle sharing data; a target control optimization module, which reads the target vehicle's charging needs, performs target control optimization for the charging needs under the scene feature set, establishes the control optimization result, and configures the mapping state response curve of the control optimization result; and a charging management module, which controls the mobile charging pile to charge the target vehicle based on the control optimization result, reads battery monitoring feedback, performs transient follow-up compensation using the battery monitoring feedback and the mapping state response curve, and performs scene-adaptive charging management of the mobile charging pile using the transient follow-up compensation.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] This application, upon the arrival of a mobile charging pile at a target location based on a remote scheduling command, performs a device self-test and establishes a device self-test dataset. After connecting with a target vehicle via the charging gun, it sends an information sharing request to the target vehicle. If the request is acknowledged, after acquiring the target vehicle's vehicle-shared data, it establishes a scene feature set based on the device self-test dataset, vehicle-shared data, and environmental data. After reading the target vehicle's charging needs, it performs target control optimization of the charging needs under the scene feature set, establishes the control optimization result, and configures the mapping state response curve of the control optimization result. Based on the control optimization result, it controls the mobile charging pile to charge the target vehicle and reads battery monitoring feedback. It then uses the battery monitoring feedback and the mapping state response curve to perform transient follow-up compensation, and uses this transient follow-up compensation to perform scene-adaptive charging management of the mobile charging pile. This invention solves the technical problem of inefficient adaptation and management of mobile charging piles in different charging scenarios in the prior art. By establishing a scene feature set based on the device self-test dataset, vehicle-shared data, and environmental data, and performing target control optimization and transient follow-up compensation, it achieves the technical effect of improving charging adaptability and charging efficiency. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart of a multi-scenario adaptation method for a mobile charging pile is provided for the embodiments of the present application.

[0013] Figure 2 A system structure diagram of a multi-scenario adaptation system for a mobile charging pile is provided for the embodiments of the present application.

[0014] Legend: device self-checking module 11, shared request sending module 12, feature set establishing module 13, target control optimization module 14, and charging management module 15. DETAILED DESCRIPTION

[0015] The present application provides a multi-scenario adaptation method and system for a mobile charging pile, aiming to solve the technical problem that the mobile charging pile in the prior art cannot be efficiently adapted and managed in different charging scenarios. The technical effect of improving charging adaptability and charging efficiency is achieved by establishing a scenario feature set based on a device self-checking data set, vehicle sharing data, and environmental data, and performing target control optimization and transient pursuit compensation.

[0016] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0017] It should be noted that any variation of the terms "comprise" and "have" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0018] Embodiment one, as shown in the present application provides a multi-scenario adaptation method for a mobile charging pile, which comprises: Figure 1

[0019] Step S100: After the mobile charging pile reaches the target position based on the remote scheduling command, device self-checking of the mobile charging pile is performed, and a device self-checking data set is established.

[0020] Further, the method provided by the embodiments of the present application further comprises:

[0021] The device self-checking data set comprises power module self-checking data, charging gun and connection interface self-checking data, heat dissipation self-checking data, and safety protection self-checking data.

[0022] ​In the embodiments of the present application, when the mobile charging pile arrives at the target position based on the remote scheduling command, the device self-checking of the mobile charging pile is started. This process occurs before the charging pile is connected with the target vehicle, and the purpose is to ensure that each module of the charging pile is in a normal working state, so as to avoid faults or unsafe conditions during the charging process. The device self-checking collects the state data of each module of the charging pile by using a series of sensors and monitoring devices, and these data form a device self-checking data set.

[0023] Specifically, the power module self-checking collects the voltage, current and power data of the power module by using the voltage sensor, current sensor and power monitoring device. The collected voltage, current and power data are taken as the power module self-checking data and recorded in the device self-checking data set.

[0024] During the charging gun and connection interface self-checking process, the charging pile collects the connection data between the charging gun and the charging pile interface by using the electrical signal monitor on the connection interface. Specifically, the connection data includes the electrical contact quality, insertion depth and contact resistance of the interface. The collected related data are taken as the charging gun and connection interface self-checking data and recorded in the device self-checking data set.

[0025] The heat dissipation self-checking monitors the working temperature of the charging pile in real time by using the temperature sensor, so as to ensure that the charging pile can effectively dissipate heat during the charging process or standby state and prevent the device from overheating. By monitoring the temperature changes inside and outside the charging pile, the data collected by the temperature sensor are taken as the heat dissipation self-checking data and recorded in the device self-checking data set.

[0026] During the safety protection self-checking process, the charging pile monitors the overload protection, short-circuit protection and leakage protection and other safety protection functions of the charging pile in real time by using the current sensor, voltage sensor and temperature sensor. By collecting the related current, voltage and temperature data, whether there is a current anomaly, voltage fluctuation or temperature overload and other safety hazards is detected. These data are taken as the safety protection self-checking data and recorded in the device self-checking data set.

[0027] Finally, all the collected data, including the power module self-checking data, charging gun and connection interface self-checking data, heat dissipation self-checking data and safety protection self-checking data, are sorted to form the device self-checking data set.

[0028] Step S200: After the charging gun is connected with the target vehicle, an information sharing request is sent to the target vehicle.

[0029] In the embodiments of the present application, when the charging gun is connected with the target vehicle, the charging pile confirms that the charging gun and the charging interface of the vehicle have been successfully connected through electrical signals. At this time, the charging pile sends an information sharing request to the target vehicle, which is transmitted through a communication protocol (such as CAN bus or PLC communication), aiming to request the target vehicle to share its vehicle sharing data, such as battery capacity, charging demand and other related information.

[0030] Further, the method provided by the embodiments of the present application further comprises:

[0031] If the receiving request does not pass the receipt, a protocol matching instruction is generated; after reading the charging protocol of the target vehicle according to the protocol matching instruction, a safe charging response scheme is configured; and the charging management of the target vehicle is performed based on the safe charging response scheme.

[0032] In the embodiments of the present application, when the charging pile establishes a physical connection with the target vehicle through the charging gun, the charging pile sends an information sharing request to the target vehicle, requiring the target vehicle to share its battery status, charging demand and other related data. If the target vehicle does not return a confirmation receipt within a specified time, i.e. the receiving request does not pass the receipt, the charging pile cannot obtain the necessary charging information from the vehicle. At this time, the charging pile automatically generates a protocol matching instruction. The function of the protocol matching instruction is to let the charging pile check whether the charging protocol of the target vehicle is compatible with the protocol supported by the charging pile itself. The charging pile will re-negotiate the protocol with the target vehicle through a communication protocol such as CAN bus or PLC communication, to ensure that both parties can reach an agreement and enable the charging pile to correctly understand the charging demand of the target vehicle.

[0033] Once the protocol matching is successful, the charging pile reads the charging protocol of the target vehicle, and obtains the charging demand of the vehicle according to the read data, such as the required voltage, current, power and current SOC of the battery and other information. These information will be used as the basis for subsequent charging management of the charging pile, to ensure that the charging process meets the requirements of the target vehicle.

[0034] Then, the charging pile configures a safe charging response scheme according to the charging protocol data of the target vehicle. The process of configuring the scheme is carried out according to the preset charging rules and standards. First, the charging pile sets the maximum voltage and current output limit according to the charging voltage and current requirements of the target vehicle. Assuming that the battery of the target vehicle supports fast charging, the charging pile will provide higher charging power within the allowed maximum current range, ensuring that the charging speed meets the vehicle's requirements. If the battery is close to full, the charging pile will gradually reduce the charging current and enter the constant voltage charging mode. In addition, the charging pile sets a temperature upper limit during the charging process according to the battery temperature data of the target vehicle, ensuring that overheating does not occur during the charging process. When the temperature is higher than the set safety value, the charging pile will automatically reduce the charging power to prevent the battery from overheating. At the same time, the charging pile will also configure other charging safety protection measures, including overcharge protection, overcurrent protection, short circuit protection, etc. For example, if the current exceeds the preset maximum charging current value during the charging process, the charging pile will immediately adjust the output current through the internal current detection circuit to prevent the battery from being damaged due to overcharging.

[0035] Once the safe charging response scheme is configured, the charging pile starts the charging management of the target vehicle according to the scheme. During the charging process, the charging pile continuously monitors the key parameters of the battery such as voltage, current, temperature, etc. For example, the charging pile measures the voltage change of the battery in real time and adjusts the charging current according to the battery state. When the battery power is close to full, the charging pile automatically reduces the current output to ensure that the battery charges at a lower current in the last stage of the charging process, thereby reducing the negative impact of charging on the battery. If any data monitored during the charging process exceeds the preset safety range, such as high battery temperature or excessive current, the charging pile will take measures immediately, such as reducing the charging power or stopping the charging, to ensure the safety of the charging process and protect the battery. Through these specific control means, the charging pile ensures the safety of the battery during the charging process and the efficient operation of the charging pile.

[0036] Step S300: If the receipt of the request is received, after obtaining the vehicle sharing data of the target vehicle, a scene feature set is established based on the device self-check data set, the vehicle sharing data, and the environment data.

[0037] In the embodiments of the present application, after receiving the receipt of the target vehicle information sharing request, the charging pile obtains the vehicle sharing data of the target vehicle, including the battery state (SOC) of the vehicle, charging requirements (such as required charging voltage, current, power, etc.), and other related information.

[0038] Next, the device self-checking data set, vehicle sharing data and environmental data are integrated to establish a scene feature set. The environmental data includes temperature information collected by a temperature sensor built in the charging pile. The scene feature set integrates the working state of the charging pile, the charging demand of the target vehicle and the environmental temperature to provide support for decision-making in the subsequent charging process of the charging pile.

[0039] Step S400: After reading the charging demand of the target vehicle, target control optimization of the charging demand is performed under the scene feature set, a control optimization result is established, and a mapping state response curve of the control optimization result is configured.

[0040] In the embodiments of the present application, after reading the charging demand of the target vehicle, the mobile charging pile performs target control optimization of the charging demand based on the scene feature set. Specifically, first, the mobile charging pile obtains its task queue and establishes a time influence factor according to the queue. Then, an environmental thermal stress influence factor is established according to the environmental data. Next, a target optimization function is configured based on the time influence factor, the environmental thermal stress influence factor and the charging demand. Subsequently, the scene feature set is analyzed to extract SOC features, rated capacity features, output power features, voltage ranges, residual energy, real-time battery states and environmental features to construct charging constraints. Finally, these analysis results are used as inputs to perform charging fitting control optimization based on the target optimization function to obtain the control optimization result.

[0041] After the control optimization result is established, the mapping state response curve of the control optimization result is configured. Specifically, first, the key parameters such as target voltage, target current and power in the control optimization result are extracted, combined with the battery temperature data obtained by the temperature sensor in real time, and according to the power adaptation rules of the battery at different temperature intervals, a battery temperature response curve is constructed to describe the corresponding relationship between temperature and power, and temperature and current. For example, when the battery temperature is lower than 15℃, the charging power output is limited, and when it is higher than 35℃, the current output is reduced to avoid overheating of the battery. Subsequently, according to the power target value in the control optimization result and the voltage working range allowed by the target vehicle, the relationship between the corresponding power and voltage is fitted respectively to form a charging power-voltage curve for describing the corresponding output power under different charging voltage conditions. Finally, the battery temperature response curve and the charging power-voltage curve are used as the mapping state response curve to realize real-time dynamic response control of voltage, current and power output, thereby obtaining the mapping state response curve.

[0042] Further, in the method provided by the embodiments of the present application, after reading the charging demand of the target vehicle, the target control optimization of the charging demand is performed under the scene feature set to establish the control optimization result, which further includes:

[0043] obtain a task queue of the mobile charging pile, establish a time influence factor according to the task queue; establish an environmental thermal stress influence factor according to the environmental data; dynamically configure an optimization target function based on the time influence factor, the environmental thermal stress influence factor and the charging demand; analyze the scene feature set to establish SOC features, rated capacity features, output power features, voltage ranges, residual energy, real-time battery status and environmental features; after configuring charging constraints by using the analysis result as input features, perform charging fitting control optimization under the charging constraints based on the optimization target function, and establish a control optimization result.

[0044] In the embodiments of the present application, the mobile charging pile first obtains a task queue in the task management system, where each task represents a charging request. The mobile charging pile identifies the start time, end time and scheduled charging duration of each task by sorting the task queue. Based on the order of the task queue and the charging duration, the charging pile calculates the time influence factor of each task, which represents the urgency of the task. The time influence factor is obtained by normalizing the ratio of the remaining waiting time of each task to the total scheduling time. For example, the remaining waiting time is the interval between the task and the start time, and the total scheduling time is the total charging duration of all tasks. Through this step, the charging pile obtains a time factor for optimizing scheduling.

[0045] Then the charging pile establishes an environmental thermal stress influence factor based on environmental data. A temperature sensor is used to collect environmental temperature data, and the charging pile compares the environmental temperature with the safe working temperature range of the battery through simple calculation. If the environmental temperature is high, the charging pile will generate a higher environmental thermal stress influence factor, indicating that special attention should be paid to heat dissipation management when the battery is charged in this environment to avoid overheating of the battery. Conversely, low temperature environment may cause charging efficiency to decrease, so the environmental thermal stress influence factor will also be adjusted accordingly. Through this step, the environmental thermal stress influence factor is obtained.

[0046] Then the time influence factor and the environmental thermal stress influence factor are combined to consider the charging demand of the target vehicle (such as charging voltage, current, etc.), and the optimization target function is dynamically adjusted. The function of this target function is to optimize the charging strategy of the charging pile to balance the charging time, charging efficiency and battery safety. In this process, according to the time influence factor and the environmental thermal stress influence factor, the weights of each target in the target function are dynamically adjusted, for example, when the task is urgent, the target function is biased towards shortening the charging time; when the environmental temperature is high, the target function is biased towards protecting the battery safety and slowing down the charging rate. Through this method, the optimization target function is obtained.

[0047] Next, the scene feature set is parsed, which includes device self-check data, target vehicle battery status, and environmental features, etc. The SOC feature (battery power state), rated capacity feature (maximum capacity of the battery), output power feature (maximum charging power that the battery can accept), voltage range (safe voltage range of the battery), remaining energy (current remaining chargeable energy of the battery), real-time battery status (battery health status, temperature, etc.), and environmental features (such as ambient temperature) are extracted. Through this step, detailed information of all input features is obtained.

[0048] Then, the parsed results obtained from the scene feature set are used to configure charging constraints. These constraints include the upper limit of the charging voltage, the maximum charging current of the battery, the maximum value of the charging temperature, etc. For example, the voltage of the battery cannot exceed the maximum voltage value, the current cannot exceed the maximum charging current value, and the charging temperature cannot exceed the safe range. If the battery temperature is too high, the charging pile will automatically reduce the charging power.

[0049] Finally, the parsed results are used as input features to perform charging fitting control optimization under the charging constraints based on the optimization target function. In this process, based on the optimization target function and the charging constraints, a multi-level weight model is constructed through the dynamic coupling relationship of the time influence factor and the environmental thermal pressure influence factor. The influence of task queue and environmental data evolution fitting on the charging process is obtained. Then, the weight of the influence factor is updated using the evolution fitting result. Then, in the optimization process, the weight of the optimization target function is dynamically adjusted according to the updated weight of the influence factor, so as to optimize the charging process and obtain the final control optimization result.

[0050] Further, the method provided by the application embodiment further comprises:

[0051] Based on the dynamic coupling relationship of the time influence factor and the environmental thermal pressure influence factor, a multi-level weight model is established. The evolution fitting of the task queue and the environmental data is performed, and the evolution fitting result is used as input data to perform influence factor weight updating based on the multi-level weight model. The weight evolution management of the optimization target function in the optimization process is performed using the influence factor weight updating result, so as to complete the charging fitting control optimization.

[0052] In the embodiments of the present application, a multi-level weight model is constructed based on the dynamic coupling relationship of the time influence factor and the environmental thermal stress influence factor. First, the time influence factor and the environmental thermal stress influence factor are calculated in real time as input data through the task queue and external environmental data, respectively. These two factors reflect the urgency of the task and the influence of the environmental temperature on the charging process, respectively. By combining these two factors together, a multi-level weight model is constructed using a weighted algorithm. The weights in this model are dynamically changing, and they are adjusted in real time according to the time effectiveness of the task and the change of the environmental temperature. In a high-temperature environment, even if the task queue is relatively urgent, the charging power of the charging pile will be limited to ensure the safety of the battery. In a low-temperature environment, if the task is urgent and there are few vehicles, the charging power is preferentially increased to ensure the charging efficiency.

[0053] Then the evolution fitting of the task queue and environmental data is performed. By analyzing historical task data and real-time environmental data, a fitting algorithm is executed to predict the influence of task and environmental changes on the charging process. The evolution fitting of the task queue considers the queuing order of the task, the charging demand, and the estimated charging time. The evolution fitting of the environmental data is based on the influence of changes in external factors such as temperature on the charging process. These evolution fitting results will be used as input data to provide a basis for the subsequent weight update.

[0054] Based on the evolution fitting results, the influence factor weight update based on the multi-level weight model is performed. According to the evolution fitting results, the charging pile adjusts the weights of the time influence factor and the environmental thermal stress influence factor in real time. If the environmental temperature is high, the weight of the environmental thermal stress influence factor is increased, and the charging power is reduced. If the task is urgent, the weight of the time influence factor will be increased, and the charging rate will be increased. In this process, the influence factor weight is adjusted through the multi-level weight model to ensure that the task can be reasonably scheduled according to the environmental conditions and the demand of the battery.

[0055] Finally, the influence factor weight update result is used to dynamically adjust the weight evolution management of the optimization objective function. By adjusting the weights in the optimization objective function, various parameters in the charging process such as voltage, current, and charging power are optimized. The weight evolution management ensures that the charging pile can flexibly adjust the control parameters according to different task urgency and environmental conditions to achieve the best balance between charging efficiency and safety. Finally, through this process, the charging fitting control optimization is completed, and the optimal charging parameter configuration, i.e., the control optimization result, is obtained.

[0056] Step S500: Control the mobile charging pile to charge the target vehicle based on the control optimization result, read the battery monitoring feedback, use the battery monitoring feedback and the mapping state response curve for transient pursuit compensation, and use the transient pursuit compensation to perform scene adaptive charging management of the mobile charging pile.

[0057] In the embodiment of the present application, based on the control optimization result, the mobile charging pile is controlled to charge the target vehicle. First, the control optimization result is used to guide the charging of the target vehicle. When charging is performed, real-time battery monitoring feedback is read. The battery monitoring feedback includes two parts: one is from the charging battery of the target vehicle, and the other is from the discharging battery of the mobile charging pile. The charging battery feedback of the target vehicle includes the voltage, current, temperature and SOC state of the battery. The feedback of the discharging battery of the mobile charging pile mainly includes the discharging state of the battery, such as battery power and voltage.

[0058] After reading the battery monitoring feedback, transient following compensation is performed on the charging process by combining the mapping state response curve. Specifically, first, the time sequence node transient state deviation is established by combining the battery monitoring feedback and the mapping state response curve. Then, the deviation is analyzed to obtain the battery temperature deviation and the power target deviation. Based on the battery temperature deviation, the battery safety risk constraint of transient compensation is configured to ensure that the temperature during the charging process is always controlled within a safe range. According to the power target deviation, the following intensity constraint is established to determine how to adjust the response speed of the power output according to the actual state of the battery. Through this dynamic adjustment, the battery safety risk constraint and the following intensity constraint are used to perform transient following compensation, and the charging power and voltage are flexibly adjusted to cope with the transient fluctuations in the charging process, so as to ensure that the charging process is efficient and safe. Finally, based on the compensation results, scene adaptation charging management is performed to optimize various parameters in the charging process, so as to ensure that the charging process can be efficiently and safely completed under different environmental and task conditions.

[0059] Further, the method provided by the embodiment of the application further comprises:

[0060] According to the battery monitoring feedback and the mapping state response curve, the time sequence node transient state deviation is established. The time sequence node transient state deviation is analyzed to obtain the battery temperature deviation and the power target deviation. According to the battery temperature deviation, the battery safety risk constraint of transient compensation is configured. According to the power target deviation, the following intensity constraint is established. The battery safety risk constraint and the following intensity constraint are used to perform transient following compensation.

[0061] In the embodiments of the present application, first, the charging pile calculates the time node transient state deviation according to the real-time acquired battery state data and the mapping state response curve. Specifically, first, the real-time data of the target vehicle battery and the charging pile battery are acquired from the battery monitoring feedback, including the voltage, current, temperature and SOC (battery state of charge) and other information of the battery. These data are compared with the target set value (such as the predetermined voltage, current and temperature range) in the charging process, so as to obtain the deviation of the time node transient state. The time node transient state deviation refers to the difference between the actual charging process and the target value of each battery state (such as temperature, current, voltage, etc.).

[0062] After obtaining the time node transient state deviation, the deviation is analyzed to obtain the battery temperature deviation and the power target deviation. The battery temperature deviation refers to the difference between the actual battery temperature and the predetermined safe temperature. If the battery temperature is higher than the predetermined target, the deviation is positive, indicating that the temperature is too high, and the charging pile needs to take measures to reduce the temperature. Conversely, if the temperature is lower, the deviation is negative, and the charging pile can appropriately increase the power output to speed up the charging. Similarly, the power target deviation is the difference between the actual charging power and the target charging power. If the actual power is greater than the target power, the deviation is positive, and the charging pile will reduce the output power to avoid battery overload. If the actual power is less than the target power, the deviation is negative, and the charging pile will increase the charging power to increase the charging speed.

[0063] Then, based on the battery temperature deviation, the transient compensation battery safety risk constraint is configured. The battery safety risk constraint refers to when the temperature deviation of the battery exceeds the set safety threshold, the charging pile will adjust the charging strategy to ensure that the battery will not overheat, damage or safety accident due to high temperature. This constraint controls the charging pile to reduce the charging power when the temperature is too high to avoid overheating. Specifically, when the battery temperature deviation is greater than the predetermined threshold (for example, when the temperature exceeds 40℃), the charging pile will automatically reduce the charging current or voltage according to the constraint to avoid the battery temperature continuing to rise and ensure the temperature control safety in the charging process.

[0064] Similarly, the pursuit intensity constraint is established according to the power target deviation. The pursuit intensity constraint is a constraint for controlling the speed and intensity of the charging pile in response to the power target change. This constraint ensures that the charging pile adjusts the response speed of the charging current or voltage according to the size of the power deviation. For example, if the power target deviation is large, the charging pile will increase the charging response speed to quickly adjust the charging power and restore to the target value as soon as possible; if the deviation is small, a slower adjustment method is adopted to avoid other problems of the battery caused by too fast adjustment.

[0065] Finally, transient follow-up compensation is performed using battery safety risk constraints and follow-up strength constraints. This process begins by acquiring a real-time task queue, evaluating the urgency of task execution, and establishing task impact constraints to determine task priorities and charging requirements. Based on this, the battery temperature curve is acquired, and predictive battery feedforward constraints are established by predicting battery temperature trends to adjust the charging strategy in advance and avoid risks caused by excessively high or low temperatures. Finally, by combining battery safety risk constraints, follow-up strength constraints, task impact constraints, and predictive battery feedforward constraints, the charging process is dynamically adjusted to perform transient follow-up compensation.

[0066] Furthermore, in the method provided in the application embodiments, the step of performing transient follow-up compensation using the battery safety risk constraints and follow-up strength constraints further includes:

[0067] Obtain a real-time task queue, perform an emergency evaluation of task execution based on the real-time task queue, and establish task impact constraints; obtain a battery temperature curve, perform trend prediction based on the battery temperature curve, and establish predictive battery feedforward constraints; perform transient follow-up compensation using the battery safety risk constraints, follow-up strength constraints, task impact constraints, and predictive battery feedforward constraints.

[0068] In this embodiment, a real-time task queue is first acquired. This queue contains task information for vehicles awaiting charging, including each vehicle's charging needs, task queuing order, and task priority. The charging station employs a task priority sorting method, ranking tasks based on their urgency and charging needs, and assessing the urgency of task execution. Specifically, the urgency of task execution considers the battery SOC of the charging task, the task queuing order, and the estimated charging time. Vehicles with lower SOCs require priority charging to reduce waiting time. Tasks arriving earlier are processed first. For tasks with longer charging needs, the charging station increases resource allocation to ensure completion within the predetermined time. By evaluating this data, the charging station establishes task impact constraints for each task. These constraints reflect the priority of each task during the charging process, ensuring that high-priority tasks receive timely charging, while low-priority tasks can be appropriately postponed to optimize the allocation of charging resources.

[0069] Next, the battery temperature profile is obtained, which shows the change in battery temperature over time during charging. The trend of battery temperature change is predicted using regression analysis or time series prediction algorithms (such as ARIMA models or LSTM networks). By analyzing historical temperature data and real-time temperature changes, the trend of battery temperature change over a future period is predicted, particularly whether it will exceed a safety threshold. For example, if the current battery temperature is 38°C, the charging station predicts that the temperature will rise to 42°C (exceeding the 40°C safety threshold) within the next few minutes. Based on this trend prediction, predictive battery feedforward constraints are established, and responses are made in advance, adjusting the charging power or current to prevent the battery temperature from continuing to rise. For example, when a temperature increase is predicted, the temperature rise is slowed down by reducing the charging current or charging power, keeping the battery within a safe temperature range.

[0070] Then, based on the aforementioned analysis results, and in conjunction with battery safety risk constraints and follow-through strength constraints, the parameters during the charging process are adjusted. Battery safety risk constraints refer to the safety operation limits set by the charging station based on the battery temperature deviation and battery type requirements. For example, when the temperature deviation exceeds a safety threshold (e.g., temperature above 40°C), the charging station will automatically reduce the current or power to prevent battery damage due to overheating. If the temperature rises further, the charging station may also stop charging until the temperature returns to a safe range. This constraint ensures that the battery is not affected by excessive temperature during charging, maintaining battery health.

[0071] The follow-through strength constraint refers to the charging pile's response rate to deviations from the power target. The charging pile adjusts its charging power response speed based on task priority and battery temperature. For example, when there is a large difference between the actual and target charging power, the charging pile will increase its response speed and quickly adjust the power output; when the deviation is small, the charging pile will adopt a gentler adjustment strategy to avoid overloading the battery and ensure the smoothness and safety of the charging process.

[0072] Finally, transient follow-up compensation is comprehensively implemented by utilizing battery safety risk constraints, follow-up strength constraints, task impact constraints, and predictive battery feedforward constraints. During charging, the charging station adjusts the charging current, voltage, and power in real time based on battery monitoring feedback data and predictive information to cope with transient changes. For example, when the battery temperature exceeds the safe range, the charging station will immediately take measures to reduce the charging power and lower the battery temperature. Simultaneously, if the task is urgent, the charging station will prioritize adjusting the charging power to ensure the task is completed on time. During real-time compensation, the charging station flexibly adjusts the charging strategy by combining all constraints, ensuring both charging efficiency and maximizing battery safety.

[0073] Furthermore, in the method provided in the application embodiments, the step of performing scenario-adaptive charging management of mobile charging piles using the transient follow-up compensation further includes:

[0074] A scene library is established based on the scene feature set and charging control data; the scene feature set is mapped and reconstructed within the scene library, and scene templates are configured; multi-scene adaptive charging management is performed based on the scene templates.

[0075] In this embodiment, a scenario library is first established based on a scenario feature set and charging control data. The scenario feature set includes real-time battery status information (such as battery SOC, voltage, temperature, etc.), task priority, environmental conditions (such as ambient temperature), and the charging capacity of the charging pile, collected during the charging process. This real-time data is combined with charging control data (such as charging current, charging power, charging voltage, etc.) to establish a scenario library. The scenario library is a storage structure used to store the optimal control strategies under different charging scenarios. Through this data integration, the charging pile can select an appropriate charging strategy and optimize the control parameters during the charging process based on the real-time collected scenario features.

[0076] Next, scene feature sets are mapped and reconstructed within the scene library, and scene templates are configured. In the mapping and control reconstruction step, a data mapping algorithm is used to reconstruct the charging control model based on the relationship between the real-time collected scene feature sets and historical control data. This process analyzes historical and real-time data, combined with the charging requirements of the charging pile and the battery status, to adjust various parameters of the control strategy, such as charging current, voltage, and power. Through this mapping, the charging pile can dynamically adjust its charging strategy to adapt to different charging scenarios. For example, in high-temperature environments, the charging pile may adjust the upper limit of the current output to ensure the safety of the battery charging process. Based on this, the charging pile generates scene templates. These templates organize and store the optimal parameters under various conditions during the charging process as a reference strategy for future charging processes. Each scene template contains the optimal charging current, voltage settings, and power control strategy under specific environmental conditions.

[0077] Finally, the charging station employs multi-scenario adaptive charging management based on scenario templates. During this process, the charging station selects the most suitable scenario template for the current task based on real-time monitoring data such as the target vehicle's battery status, task urgency, and ambient temperature. Through dynamic scenario adaptation, the charging station can flexibly choose charging strategies based on factors such as the priority of the charging task, environmental changes, and the vehicle's battery status. In urgent tasks, the charging station may select a template that increases charging power to ensure timely task completion. Conversely, when the temperature is too high, the charging station will select a scenario template that reduces power to prevent battery overheating. Through this multi-scenario adaptive charging management method, the charging station adjusts charging parameters in real time during the charging process, ensuring efficient and safe charging.

[0078] Furthermore, in the method provided in the application embodiments, after performing scenario-adaptive charging management of mobile charging piles using the transient follow-up compensation, it further includes:

[0079] A charging record is established, which stores the charging execution data of the current charging task and the status data of the mobile charging pile; a historical charging dataset is retrieved, and charging anomalies in the charging record are identified based on the historical charging dataset; anomaly backtracking is performed based on the charging anomaly identification results, and a backtracking state chain is established; root cause analysis and management of the mobile charging pile are performed based on the backtracking state chain.

[0080] In this embodiment, a charging record is first established, which includes charging execution data for the current charging task and mobile charging pile status data. The charging execution data includes real-time recorded information such as current, voltage, power, charging time, and target charging status during the charging process, used to track various parameters during charging. The mobile charging pile status data records the operating status of the charging pile, such as its health status (e.g., whether there is a fault), battery temperature, location data, and task execution status.

[0081] Next, the historical charging dataset is retrieved from the historical database. This dataset contains data on all charging tasks performed by the charging pile in the past, including historical charging current, voltage, power, temperature, and SOC parameters. Using a data comparison analysis method, the charging data of the current charging task is compared with the data in the historical charging dataset to identify any anomalies in the current charging task. Specifically, a comparison is made with historical data based on preset thresholds to check whether the real-time charging data deviates from the historical normal range. For example, if the battery temperature rises in the current charging task beyond the temperature variation range in the historical charging data (e.g., the temperature in historical data is usually maintained at 30-40℃, while the temperature in the current task has risen to 45℃), this situation is marked as a charging anomaly.

[0082] After identifying a charging anomaly, anomaly backtracking is performed based on the identification results. Time-series backtracking methods are used to trace the time and relevant stages of the charging anomaly based on timestamps and related status data during the charging process. By analyzing the data during the backtracking process, the electrical parameters and equipment status at the time of the anomaly are identified. For example, when an abnormal rise in battery temperature is detected, the temperature change curve, current, and voltage data are traced back, and other abnormal behaviors, such as excessive current or charging power fluctuations, are progressively checked before the temperature rise. Based on time-series backtracking, the current, voltage, and temperature data at all critical moments are examined to determine if any behavior exceeds the normal range and to identify the root cause of the problem.

[0083] A backtracking state chain is established through the backtracking process. The backtracking state chain refers to the state data of all key time points obtained by the charging pile during the charging process. Each state node contains information such as current, voltage, power, temperature, and SOC at a specific time point, as well as the health status and task execution status of the device. The backtracking state chain provides a complete chain of anomalies, and by analyzing each state node, the root cause of the problem can be diagnosed. For example, if the backtracking reveals that the state node corresponding to the excessively high battery temperature corresponds to excessive current, it confirms that the problem may be due to excessive current causing the temperature increase.

[0084] Finally, root cause analysis management is performed based on the backtracking state chain. Root cause analysis uses causal relationship analysis methods to identify the root cause of charging anomalies by analyzing the data at each node in the backtracking state chain. For example, if a node in the backtracking process indicates an abnormal rise in battery temperature and current exceeding the preset safety range, the charging station can determine that the problem originates from excessive charging current, and then adjust the charging strategy or optimize the equipment hardware to prevent similar problems from recurring. Through this root cause analysis management, the charging station can accurately locate the root cause of the problem and take appropriate remedial measures to ensure long-term stable and efficient charging operation.

[0085] In summary, the embodiments of this application have at least the following technical effects:

[0086] This application, upon the arrival of a mobile charging pile at a target location based on a remote scheduling command, performs a device self-test and establishes a device self-test dataset. After connecting with a target vehicle via the charging gun, it sends an information sharing request to the target vehicle. If the request is acknowledged, after acquiring the target vehicle's vehicle-shared data, it establishes a scene feature set based on the device self-test dataset, vehicle-shared data, and environmental data. After reading the target vehicle's charging needs, it performs target control optimization of the charging needs under the scene feature set, establishes the control optimization result, and configures the mapping state response curve of the control optimization result. Based on the control optimization result, it controls the mobile charging pile to charge the target vehicle and reads battery monitoring feedback. It then uses the battery monitoring feedback and the mapping state response curve to perform transient follow-up compensation, and uses this transient follow-up compensation to perform scene-adaptive charging management of the mobile charging pile. This invention solves the technical problem of inefficient adaptation and management of mobile charging piles in different charging scenarios in the prior art. By establishing a scene feature set based on the device self-test dataset, vehicle-shared data, and environmental data, and performing target control optimization and transient follow-up compensation, it achieves the technical effect of improving charging adaptability and charging efficiency.

[0087] Example 2, based on the same inventive concept as the multi-scenario adaptation method for mobile charging piles in the foregoing examples, such as... Figure 2 As shown, this application provides a multi-scenario adaptation system for mobile charging piles. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0088] The device self-test module 11 is used to perform a device self-test of the mobile charging pile after it arrives at the target location based on a remote scheduling command, and to establish a device self-test dataset. The sharing request sending module 12 is used to send an information sharing request to the target vehicle after it is connected to the target vehicle through the charging gun. The feature set establishment module 13 is used to establish a scene feature set based on the device self-test dataset, vehicle sharing data, and environmental data after obtaining the vehicle sharing data of the target vehicle if the request is received with an acknowledgment. The target control optimization module 14 is used to read the charging demand of the target vehicle, perform target control optimization of the charging demand under the scene feature set, establish the control optimization result, and configure the mapping state response curve of the control optimization result. The charging management module 15 is used to control the mobile charging pile to charge the target vehicle based on the control optimization result, read the battery monitoring feedback, use the battery monitoring feedback and the mapping state response curve to perform transient follow-up compensation, and use the transient follow-up compensation to perform scene-adaptive charging management of the mobile charging pile.

[0089] Furthermore, the system is also used to implement the following functions:

[0090] Obtain the task queue of mobile charging piles, and establish a time influence factor based on the task queue; establish an environmental thermal pressure influence factor based on the environmental data; dynamically configure an optimization objective function based on the time influence factor, the environmental thermal pressure influence factor, and the charging demand; analyze the scene feature set to establish SOC features, rated capacity features, output power features, voltage range, remaining energy, real-time battery status, and environmental features; configure charging constraints using the analysis results, use the analysis results as input features, and perform charging fitting control optimization under charging constraints based on the optimization objective function to establish control optimization results.

[0091] Furthermore, the system is also used to implement the following functions:

[0092] Based on the dynamic coupling relationship between time-related factors and environmental thermal pressure factors, a multi-level weight model is established. Evolutionary fitting of task queues and environmental data is performed, and the evolutionary fitting results are used as input data to update the weights of the influence factors based on the multi-level weight model. The weight evolution management of the objective function is carried out using the updated influence factor weights during the optimization process to complete the charging fitting control optimization.

[0093] Furthermore, the system is also used to implement the following functions:

[0094] The transient state deviation of the time-series nodes is established based on the battery monitoring feedback and the mapped state response curve; the transient state deviation of the time-series nodes is analyzed to obtain the battery temperature deviation and the power target deviation; battery safety risk constraints for transient compensation are configured based on the battery temperature deviation; a following strength constraint is established based on the power target deviation; and transient following compensation is performed using the battery safety risk constraint and the following strength constraint.

[0095] Furthermore, the system is also used to implement the following functions:

[0096] Obtain a real-time task queue, perform an emergency evaluation of task execution based on the real-time task queue, and establish task impact constraints; obtain a battery temperature curve, perform trend prediction based on the battery temperature curve, and establish predictive battery feedforward constraints; perform transient follow-up compensation using the battery safety risk constraints, follow-up strength constraints, task impact constraints, and predictive battery feedforward constraints.

[0097] Furthermore, the system is also used to implement the following functions:

[0098] A charging record is established, which stores the charging execution data of the current charging task and the status data of the mobile charging pile; a historical charging dataset is retrieved, and charging anomalies in the charging record are identified based on the historical charging dataset; anomaly backtracking is performed based on the charging anomaly identification results, and a backtracking state chain is established; root cause analysis and management of the mobile charging pile are performed based on the backtracking state chain.

[0099] Furthermore, the system is also used to implement the following functions:

[0100] If the received request does not receive a receipt, a protocol matching instruction is generated; after reading the target vehicle's charging protocol according to the protocol matching instruction, a safe charging response scheme is configured; and charging management of the target vehicle is performed based on the safe charging response scheme.

[0101] Furthermore, the system is also used to implement the following functions:

[0102] The device self-test dataset includes power module self-test data, charging gun and connection interface self-test data, heat dissipation self-test data, and safety protection self-test data.

[0103] Furthermore, the system is also used to implement the following functions:

[0104] A scene library is established based on the scene feature set and charging control data; the scene feature set is mapped and reconstructed within the scene library, and scene templates are configured; multi-scene adaptive charging management is performed based on the scene templates.

[0105] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0106] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0107] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A multi-scenario adaptation method for mobile charging piles, characterized in that, The method includes: When the mobile charging pile arrives at the target location based on the remote dispatch command, it performs a device self-test and establishes a device self-test dataset. Once connected to the target vehicle via the charging gun, an information sharing request is sent to the target vehicle. If the request is acknowledged, then after obtaining the vehicle-sharing data of the target vehicle, a scene feature set is established based on the device self-test dataset, vehicle-sharing data, and environmental data. After reading the charging requirements of the target vehicle, target control optimization of the charging requirements is performed under the scene feature set, control optimization results are established, and the mapping state response curve of the control optimization results is configured. Based on the control optimization results, the mobile charging pile is controlled to charge the target vehicle, and the battery monitoring feedback is read. The battery monitoring feedback and the mapped state response curve are used to perform transient follow-up compensation, and the transient follow-up compensation is used to perform scenario-adaptive charging management of the mobile charging pile. After reading the charging requirements of the target vehicle, target control optimization for the charging requirements is performed under the scene feature set, and the control optimization results are established, including: Obtain the task queue of mobile charging piles, and establish a time influence factor based on the task queue; An environmental thermal stress influence factor is established based on the aforementioned environmental data; Based on the time influence factor, the environmental thermal pressure influence factor, and the dynamic configuration optimization objective function of charging demand; The scene feature set is analyzed to establish SOC features, rated capacity features, output power features, voltage range, remaining energy, real-time battery status, and environmental features; After configuring charging constraints using the analytical results, the analytical results are used as input features. Based on the optimization objective function, charging fitting control optimization is performed under the charging constraints to establish the control optimization results. Based on the aforementioned objective function, charging fitting control optimization is performed under charging constraints, and the control optimization results are established, including: A multi-level weighted model is established based on the dynamic coupling relationship between time-related factors and environmental thermal stress factors. The system performs evolutionary fitting of task queues and environmental data, uses the evolutionary fitting results as input data, and performs influence factor weight updates based on a multi-level weight model. By utilizing the updated results of the influence factor weights, the weight evolution management of the objective function is carried out during the optimization process to complete the charging fitting control optimization.

2. The multi-scenario adaptation method for mobile charging piles as described in claim 1, characterized in that, The transient tracking compensation using the battery monitoring feedback and the mapped state response curve includes: Establish the transient state deviation of the timing node based on the battery monitoring feedback and the mapped state response curve; Analyze the transient state deviation of the time-series nodes to obtain the battery temperature deviation and power target deviation; Battery safety risk constraints with transient compensation are configured based on the battery temperature deviation. Establish a following strength constraint based on the power target deviation; Transient following compensation is performed using the battery safety risk constraints and following strength constraints.

3. The multi-scenario adaptation method for mobile charging piles as described in claim 2, characterized in that, The transient follow-up compensation using the battery safety risk constraints and follow-up strength constraints includes: Obtain the real-time task queue, perform an urgent evaluation of task execution based on the real-time task queue, and establish task impact constraints; Obtain the battery temperature curve, perform trend prediction based on the battery temperature curve, and establish predictive battery feedforward constraints. Transient following compensation is performed using the aforementioned battery safety risk constraints, following strength constraints, task impact constraints, and predictive battery feedforward constraints.

4. The multi-scenario adaptation method for mobile charging piles as described in claim 1, characterized in that, The process of performing scenario-adaptive charging management for mobile charging piles using the transient follow-up compensation includes: Establish a charging record, which stores the charging execution data of the current charging task and the status data of the mobile charging pile; Call the historical charging dataset, identify charging anomalies in the charging records based on the historical charging dataset, perform anomaly backtracking based on the charging anomaly identification results, and establish a backtracking state chain; Root cause analysis management of mobile charging stations is performed based on the backtracking state chain.

5. The multi-scenario adaptation method for mobile charging piles as described in claim 1, characterized in that, After sending the information sharing request to the target vehicle, the process includes: If the received request does not receive a receipt, a protocol matching instruction is generated; After reading the target vehicle's charging protocol according to the protocol matching instruction, a safe charging response scheme is configured. Charging management of the target vehicle is based on the aforementioned safe charging response scheme.

6. The multi-scenario adaptation method for mobile charging piles as described in claim 1, characterized in that, The device self-test dataset includes power module self-test data, charging gun and connection interface self-test data, heat dissipation self-test data, and safety protection self-test data.

7. The multi-scenario adaptation method for mobile charging piles as described in claim 1, characterized in that, The scenario-adaptive charging management of mobile charging piles using the transient follow-up compensation also includes: A scenario library is established based on the scenario feature set and charging control data; Within the scene library, the scene feature set is mapped and reconstructed, and scene templates are configured. Multi-scenario adaptive charging management is performed based on the aforementioned scenario template.

8. A multi-scenario adaptation system for mobile charging piles, characterized in that, The system is used to execute the multi-scenario adaptation method for mobile charging piles as described in any one of claims 1-7, the system comprising: The device self-test module is used to perform device self-tests on the mobile charging pile after it arrives at the target location based on a remote scheduling command, and to establish a device self-test dataset. The sharing request sending module is used to send an information sharing request to the target vehicle after the charging gun is connected to the target vehicle. The feature set building module is used to build a scene feature set based on the device self-test dataset, vehicle shared data, and environmental data after obtaining the vehicle-shared data of the target vehicle, if the received request passes the receipt. The target control optimization module is used to read the charging demand of the target vehicle, perform target control optimization of the charging demand under the scene feature set, establish the control optimization result, and configure the mapping state response curve of the control optimization result. The charging management module is used to control the mobile charging pile to charge the target vehicle based on the control optimization result, read the battery monitoring feedback, use the battery monitoring feedback and the mapped state response curve to perform transient follow-up compensation, and use the transient follow-up compensation to perform scenario-adaptive charging management of the mobile charging pile.

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