On-line battery terminal power metering system for dc chargers

Abstract

The present application relates to the technical field of battery monitoring, in particular to an online battery terminal electric energy metering system for a direct current charger, comprising a battery terminal data acquisition module, a cloud battery digital twin module, a state estimation and parameter updating module, a battery terminal online electric energy metering module, a station terminal energy monitoring and prediction module, an energy distribution and node scheduling module, a direct current charging control module and a user interaction and result issuing module. By collecting power battery operation data, a battery digital twin is constructed in the cloud to complete state estimation, parameter updating and online electric energy metering, and in combination with station terminal available energy prediction, target energy supply, target charging current and node distribution results are generated for multiple charging requests, charging control is executed and distribution results, predicted waiting time and charging state are fed back, thereby improving electric energy metering, energy distribution and node scheduling accuracy.

Description

Technical Field

[0001] This invention relates to the field of battery monitoring technology, and more particularly to an online battery-side energy metering system for DC chargers. Background Technology

[0002] With the continuous growth of electric vehicle ownership, charging stations are prone to problems such as concentrated vehicle access during peak hours, increased queuing times, and increased pressure on energy allocation within the station. While existing technologies offer solutions for charging management based on priority, station navigation, occupancy prediction, or power allocation, they generally suffer from inaccurate understanding of the actual state of vehicle batteries, significant deviations in estimating charging duration and energy demand, and difficulty in balancing fluctuations in available energy at the station with concurrent requests from multiple vehicles. Especially after batteries have experienced different operating conditions and life stages, their internal resistance, capacity, and state of charge change. Using coarse estimation methods can easily lead to a mismatch between charging decisions and actual demand, thus affecting charging efficiency, user experience, and the utilization of station resources. Summary of the Invention

[0003] In view of the above technical problems, the present invention provides an online battery-side power metering system for DC chargers, which aims to solve the problems of inaccurate battery state estimation, insufficient accuracy in charging demand prediction, and unreasonable resource allocation within the station in the current electric vehicle charging process.

[0004] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0005] According to one aspect of the present invention, an online battery-side energy metering system for a DC charger is proposed, comprising a battery-side data acquisition module, a cloud-based battery digital twin module, a state estimation and parameter update module, a battery-side online energy metering module, a station-side energy monitoring and prediction module, an energy allocation and node scheduling module, a DC charging control module, and a user interaction and result distribution module. The battery-side data acquisition module is used to continuously collect data in real time on the power battery of the vehicle to be charged connected to the DC charger, obtain voltage, current, temperature, state of charge, depth of discharge and operating data reflecting attenuation characteristics, and upload the operating data to the cloud. The cloud-based battery digital twin module is communicatively connected to the battery-side data acquisition module. It is used to establish a battery digital twin that maps to the corresponding physical power battery for each vehicle to be charged, and outputs battery capacity, internal resistance characteristics, charging characteristic parameters and battery state parameters corresponding to the preset target charging endpoint based on the operating data. The state estimation and parameter update module is connected to the cloud-based battery digital twin module and is used to perform state estimation and parameter iterative correction on the battery digital twin. The battery-side online energy metering module is connected to the state estimation and parameter update module, and is used to measure the amount of energy to be replenished, the amount of energy already input, the remaining amount of energy to be replenished, and the estimated charging time based on the updated battery state parameters, the preset target charge endpoint, and the battery capacity. The station-side energy monitoring and prediction module is used to acquire the real-time available energy of the DC charger, the node availability status and historical power supply data, and to generate a prediction result of the station-side available energy. The energy allocation and node scheduling module is connected to the battery-side online energy metering module and the station-side energy monitoring and prediction module, respectively. When there are multiple charging requests, it generates the target energy supply, target charging current and node allocation results of each DC charger based on the amount of electricity to be replenished, the expected charging time, the real-time available energy and the prediction results of each vehicle to be charged. The DC charging control module is connected to the energy distribution and node scheduling module, and is used to charge the corresponding vehicle to be charged according to the target energy supply and the target charging current, and release the corresponding DC charger after charging is completed. The user interaction and result delivery module is connected to the energy allocation and node scheduling module, and is used to receive the charging request corresponding to the vehicle identifier, and to provide feedback on the node allocation result, the estimated waiting time and the charging status.

[0006] Furthermore, the cloud-based battery digital twin module includes an open-circuit voltage and state of charge relationship modeling submodule, an equivalent circuit modeling submodule, and a parameter lookup table submodule. The open-circuit voltage and state of charge relationship modeling submodule is used to perform curve fitting on the correspondence between open-circuit voltage and state of charge based on experimental data and online operating data of the power battery. The equivalent circuit modeling submodule is used to establish an equivalent model of the power battery including ohmic internal resistance branches and multi-branch polarized RC branches. The parameter lookup table submodule is used to store and retrieve the internal resistance parameters and polarization parameters of the equivalent model of the power battery according to the state of charge and operating conditions, so that the battery digital twin can characterize the terminal voltage response and charging characteristics of the power battery under different charging and discharging stages.

[0007] Furthermore, the state estimation and parameter update module is used to execute a state of charge estimation process based on unscented Kalman filtering, including: generating a set of sample points based on the current state estimate and covariance; substituting the set of sample points into the power battery state equation for propagation; correcting the propagation result by combining voltage measurement, current measurement and temperature measurement; outputting the updated state of charge and covariance; and correcting the power battery capacity and expected charging time by combining driving cycle data and capacity estimation results based on current-time integration.

[0008] Furthermore, the state estimation and parameter update module is also used to extract voltage transition features and current response features when the power battery undergoes pulse discharge process and charge-discharge transient process, calculate the ohmic internal resistance parameter and the parameters of each polarization branch based on the voltage transition features and the current response features, compare the actual collected voltage and current curves with the predicted curves of the battery digital twin, and correct the internal resistance curve and polarization parameter curves online in a way that minimizes the sum of squared deviations to compensate for parameter drift caused by cycle aging.

[0009] Furthermore, the station-side energy monitoring and prediction module is used to simultaneously receive energy supply data, energy consumption data, and node availability data from the renewable energy interface, the distribution network interface, and the station-side sensors. Based on historical energy supply records and historical energy consumption records, it generates a prediction result of the station-side available energy for the future prediction period. The prediction result of the station-side available energy is then compared with the real-time measured station-side available energy to adjust the station-side energy prediction model according to the minimum mean square deviation criterion.

[0010] Furthermore, the energy allocation and node scheduling module is used to calculate the amount of electricity to be replenished for each vehicle based on the difference between the current state of charge of each vehicle and the preset target end point of charge, as well as the corresponding battery capacity. It then converts the amount of electricity to be replenished into the corresponding station-end energy demand based on the conversion loss of the DC charger, and sums the station-end energy demand to obtain the total energy demand. When the real-time available energy is not less than the total energy demand, the target energy supply for each vehicle is set to the corresponding station-end energy demand, and a corresponding target charging current and target charging time are generated based on the expected charging time of each vehicle. When the real-time available energy is less than the total energy demand, an energy allocation ratio is calculated according to the proportion of the station-end energy demand of each vehicle in the total energy demand. Based on the energy allocation ratio, the target energy supply for each vehicle is determined, and combined with the charging characteristics of the battery digital twin, the target energy supply is converted into the target charging current and target charging time of the corresponding DC charging node.

[0011] Furthermore, the energy allocation and node scheduling module is also used to poll newly arriving charging requests, maintain the count of unserved requests and the remaining occupancy time of each DC charging node; when there are idle DC charging nodes, the idle DC charging nodes are preferentially allocated to vehicles with greater energy demand, and the occupancy time of the DC charging node is set to the target charging time of the corresponding vehicle; when there are no idle DC charging nodes, the current vehicle is allocated to the DC charging node with the shortest remaining occupancy time, and the occupancy time of the DC charging node is updated to the sum of the original occupancy time and the target charging time of the corresponding vehicle; after charging is completed, the number of available DC charging nodes is updated and the corresponding DC charging node is released.

[0012] Furthermore, the user interaction and result delivery module includes a mobile terminal, a cloud database, a local controller, and a station-end indicator unit; the mobile terminal is used to submit a charging application containing a vehicle identifier and receive node allocation results; the cloud database is used to store request status, DC charging node allocation information, node identifier information, and charging status information; the local controller is used to read the node identifier information and the DC charging node allocation information from the cloud database and send control commands to the station-end control board through a serial communication protocol; the station-end indicator unit is used to visually display the occupancy status, availability status, and allocation results of the corresponding DC charging node according to the control commands.

[0013] Furthermore, the cloud-based battery digital twin module and the energy allocation and node scheduling module jointly receive state of charge, temperature, charge / discharge rate, and depth of discharge as constraint parameters, and receive user feedback data and historical request time series data from the user interaction and result delivery module. They limit the value range of the target energy supply, the target charging current, and the target charging duration according to the constraint parameters, and update the node allocation priority parameters and station-side energy prediction parameters based on the user feedback data and the historical request time series data to improve the accuracy of subsequent energy metering and node scheduling.

[0014] The technical solution of the present invention has the following beneficial effects: This invention, based on the dynamic characterization of battery status and the collaborative analysis of station operation status, can more accurately obtain information related to vehicle charging demand and the expected charging process, and accordingly allocate charging resources within the station more rationally. Therefore, it helps to improve the accuracy of charging time prediction and energy allocation. When multiple vehicles request charging at the same time and the available energy in the station is limited, it can also take into account user waiting time, energy utilization efficiency and station load pressure, and reduce resource waste caused by unreasonable allocation. Attached Figure Description

[0015] Figure 1This is a structural block diagram of an online battery-side energy metering system for a DC charger, as described in one of the embodiments of this specification. Detailed Implementation

[0016] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make the invention more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of the invention. However, those skilled in the art will recognize that the technical solutions of the invention may be practiced with one or more of these specific details omitted, or other methods, components, apparatus, steps, etc., may be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the invention.

[0017] Furthermore, the accompanying drawings are merely illustrative of the invention. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0018] This invention provides an online battery-side energy metering system for use in DC chargers. (See reference...) Figure 1 As shown, an online battery-side energy metering system for a DC charger is provided in an embodiment of the present invention, including a battery-side data acquisition module 101, a cloud-based battery digital twin module 102, a state estimation and parameter update module 103, a battery-side online energy metering module 104, a station-side energy monitoring and prediction module 105, an energy allocation and node scheduling module 106, a DC charging control module 107, and a user interaction and result distribution module 108. The battery-side data acquisition module 101 is used to continuously collect data in real time on the power battery of the vehicle to be charged connected to the DC charger, obtain voltage, current, temperature, state of charge, depth of discharge, and operating data reflecting attenuation characteristics, and upload the operating data to the cloud.

[0019] Voltage and current data are used to characterize the changes in electrical characteristics of the power battery during charging and discharging, while temperature data reflects the thermal environment of the power battery and its impact on the accuracy of state estimation. Simultaneously, the battery-side data acquisition module 101 also acquires the state of charge (SOC) and depth of discharge, and collects operational data that characterizes the battery's degradation trend. This operational data includes internal resistance data that changes with driving cycles and historical response data during charge-discharge cycles. Since the internal resistance of the power battery changes with SOC and lifespan stages, and evolves towards the end of its lifespan after multiple driving cycles, the operational data reflecting degradation characteristics can be used for subsequent characterization and updating of the power battery's degradation state. Real-time continuous acquisition can be achieved through sensors or a data recording system. The acquired input and output data, such as voltage and current, can be further used for power battery model calibration. Temperature control and temperature acquisition ensure data integrity under different operating conditions. The SOC can be directly provided by the battery management system, or it can be combined with open-circuit voltage, current, and temperature data for subsequent correction. After obtaining the aforementioned operational data, the battery-side data acquisition module 101 uploads it to the cloud. This allows the cloud to establish a digital twin of the battery corresponding to the physical power battery, providing a data foundation for subsequent state of charge estimation, internal resistance parameter updates, charging energy calculation, and estimated charging time calculation. The state of charge can be characterized by the following formula: ; Where OCV is the open-circuit voltage. Minimum open-circuit voltage, The maximum open-circuit voltage; in another representation, the state of charge can also be expressed as ,in, The initial state of charge, For rated capacity, This represents the current changing over time. Through the above data collection and uploading, the cloud can continuously receive data related to the physical power battery and obtain more accurate information on state of charge and charging time.

[0020] The cloud-based battery digital twin module 102 is communicatively connected to the battery-side data acquisition module 101. It is used to establish a battery digital twin that maps to the corresponding physical power battery for each vehicle to be charged, and to output battery capacity, internal resistance characteristics, charging characteristic parameters, and battery state parameters corresponding to the preset target charging endpoint based on the operating data.

[0021] Specifically, the cloud-based battery digital twin module includes an open-circuit voltage and state of charge relationship modeling submodule, an equivalent circuit modeling submodule, and a parameter lookup table submodule. The open-circuit voltage and state of charge relationship modeling submodule is used to perform curve fitting on the correspondence between open-circuit voltage and state of charge based on experimental data and online operating data of the power battery. The equivalent circuit modeling submodule is used to establish an equivalent model of the power battery including ohmic internal resistance branches and multi-branch polarized RC branches. The parameter lookup table submodule is used to store and retrieve the internal resistance parameters and polarization parameters of the equivalent model of the power battery according to the state of charge and operating conditions, so that the battery digital twin can characterize the terminal voltage response and charging characteristics of the power battery under different charging and discharging stages.

[0022] The battery digital twin is constructed based on the operating information of the power battery, such as voltage, current, and temperature, during charging and discharging. It also incorporates the characteristic of the battery's internal resistance gradually changing with driving cycles to dynamically characterize the power battery's state under different operating conditions. By establishing corresponding battery digital twins for each vehicle in the cloud, the battery capacity, internal resistance characteristics, and charging characteristic parameters of the power battery can be continuously updated. Furthermore, it outputs battery state parameters corresponding to a preset target charging endpoint for subsequent charging control or duration prediction. The battery digital twin can simulate the power battery behavior using an equivalent circuit model. The collected operating data is used for model calibration, and the calibrated model is used to simulate the power battery's terminal voltage response and charging characteristics under different operating conditions.

[0023] The open-circuit voltage and state of charge (SOC) modeling submodule is used to establish the correspondence between open-circuit voltage and SOC based on experimental and online operating data of the power battery. Since the relationship between open-circuit voltage and SOC of a power battery is usually not strictly linear, this submodule can further employ curve fitting to establish a more accurate mapping relationship. In one implementation, polynomial fitting is used to establish the open-circuit voltage and SOC curve, and its fitting function can be expressed as: ; in, The normalized independent variable is... to The fitting coefficients are used. Through the above fitting relationships, the cloud-based battery digital twin can obtain a more refined open-circuit voltage mapping of the power battery under different states of charge, thus providing a foundation for subsequent equivalent circuit parameter calls, terminal voltage response calculations, and the determination of the target charge endpoint state.

[0024] The equivalent circuit modeling submodule is used to establish an equivalent model of the power battery, including ohmic internal resistance branches and multi-branch polarized RC branches. In one implementation, the equivalent model uses an ohmic internal resistance R_{0} and three sets of polarized RC branches. - , - , - A 3RC equivalent model was used to characterize the dynamic response characteristics of the power battery under different time constants. The ohmic internal resistance... The voltage and current response during the pulse discharge process can be determined by the following formula: ; in, Let U_{1}, U_{2}, and U_{3} represent the pulse load current, U_{4} represent the pulse edge voltage, and U_{5} represent the voltage generated by the polarization branch response. Correspondingly, the first polarization branch can be represented as: ; ; ; The second and third polarization branches can be represented as follows: ; ; ; in, This indicates the combination relationship of parallel branches. Through the joint modeling of the aforementioned ohmic internal resistance branches and multi-branch polarized RC branches, the battery digital twin can characterize the terminal voltage response characteristics of the power battery under different charge and discharge stages. The parameter lookup table submodule stores and retrieves parameters according to state of charge and operating conditions. , , , , , , These parameters enable the rapid retrieval of corresponding internal resistance and polarization parameters to update the battery status when the power battery is in different SOC ranges, temperature conditions, or cycling stages. Furthermore, to reflect internal resistance shifts caused by battery aging, the cloud-based battery digital twin module 102 can also update the internal resistance parameters based on deviation data after multiple driving cycles. Its objective function can be expressed as: ; in, This represents the internal resistance parameter to be estimated. Indicates the input quantity. This represents the internal resistance deviation observed under the corresponding input. By minimizing this objective function, the internal resistance curve in the digital twin can be corrected, thereby improving the output accuracy of the battery capacity, internal resistance characteristics, charging characteristic parameters, and battery state parameters corresponding to the preset target charging endpoint.

[0025] The state estimation and parameter update module 103 is connected to the cloud-based battery digital twin module 102 and is used to perform state estimation and parameter iterative correction on the battery digital twin.

[0026] The state estimation and parameter update module is used to execute a state of charge estimation process based on unscented Kalman filtering, including: generating a set of sample points based on the current state estimate and covariance; substituting the set of sample points into the power battery state equation for propagation; correcting the propagation result by combining voltage measurement, current measurement and temperature measurement; outputting the updated state of charge and covariance; and correcting the power battery capacity and expected charging time by combining driving cycle data and capacity estimation results based on current-time integration.

[0027] The state estimation and parameter update module is also used to extract voltage transition features and current response features when the power battery undergoes pulse discharge process and charge-discharge transient process. Based on the voltage transition features and the current response features, the ohmic internal resistance parameter and the parameters of each polarization branch are obtained. The actual collected voltage and current curves are compared with the predicted curves of the battery digital twin, and the internal resistance curve and polarization parameter curve are corrected online in the manner of minimizing the sum of squared deviations to compensate for the parameter drift caused by cycle aging.

[0028] The state estimation is primarily used to obtain the real-time state of charge (SOC) of the power battery and output time-related parameters corresponding to the current battery SOC. The parameter iteration correction is mainly used to update the model parameters in the digital twin online in response to the internal resistance shift and polarization response changes generated during the cyclic use of the power battery. Since the traditional ampere-hour integral estimation method is highly dependent on the accuracy of current measurement and cannot fully reflect the impact of changes in the internal parameters of the power battery on the estimation results, the state estimation and parameter update module 103 uses the unscented Kalman filter method to estimate the SOC and combines driving cycle data, pulse discharge response data, and real-time measured voltage, current, and temperature data to continuously correct the battery digital twin to improve the accuracy of SOC estimation and estimated charging time estimation.

[0029] In some implementations, the state estimation and parameter update module 103 performs a charge state estimation process based on unscented Kalman filtering. In this process, a set of sample points is first generated based on the current state estimate and covariance. This set of sample points can be represented as: ; in, This represents the estimated value of the current state. Let n denote the covariance matrix, and n denote the dimension of the state space. This represents the scaling factor. Subsequently, the set of sample points is substituted into the power battery state equation for propagation. The propagation process can be expressed as: ; in, Representing the system model, This represents the control input. After propagation is complete, the propagation results are corrected by combining the voltage, current, and temperature measurements. The correction process can be expressed as follows: ; ; in, Indicates the measured value. Represents the measurement model. Represents the measurement noise covariance matrix. Indicates the measured noise term. This represents the Kalman gain. Through the above prediction and correction process, the updated state of charge and covariance are output. This method comprehensively considers multi-source information such as battery voltage, current, and temperature, and compared to estimation methods that rely solely on current integration, it has better nonlinear processing capabilities and resistance to measurement noise.

[0030] The state estimation and parameter update module 103 compares the actual collected voltage and current curves with the predicted curves of the battery digital twin, and corrects the internal resistance curve and polarization parameter curves online in a way that minimizes the sum of squared deviations, so as to compensate for the parameter drift caused by cyclic aging.

[0031] The battery-side online energy metering module 104 is connected to the state estimation and parameter update module 103, and is used to measure the amount of energy to be replenished, the amount of energy already input, the amount of energy remaining to be replenished, and the estimated charging time based on the updated battery state parameters, the preset target charge endpoint, and the battery capacity.

[0032] The battery-side online energy metering module 104 does not directly estimate the battery state. Instead, based on the state of charge and related state parameters already output by the state estimation and parameter update module 103, it calculates the amount of energy required to reach the preset target charge endpoint and uses this calculation result as the amount of energy to be replenished. Simultaneously, it continuously updates the input energy and the remaining amount of energy to be replenished, taking into account real-time input during the charging process. Since the battery digital twin can output the energy required for charging and the charging time based on the current state of charge, the battery-side online energy metering module 104 can use the updated battery state parameters for online metering, and the metering results dynamically change with the charging process.

[0033] Specifically, in one implementation, the amount of electricity to be replenished can be determined according to the following formula: ; in, This represents the battery capacity of the i-th vehicle. This represents the current state of charge of the i-th vehicle. This represents the amount of electricity required for the i-th vehicle to reach the target charging state. Therefore, the online energy metering module 104 at the battery end can determine the amount of electricity needed to be replenished for a single vehicle based on the current state of charge and battery capacity; for multiple vehicles to be charged, the total required electricity can be expressed as: ; in, This indicates the total electricity demand of vehicles that have submitted charging requests and entered the metering process. The input electricity can be recorded based on the cumulative input during the real-time charging process, and the remaining electricity to be replenished can be obtained by subtracting the input electricity from the current electricity to be replenished, and is continuously updated as the charging process progresses, thereby forming an online metering result.

[0034] When external power supply conditions change, causing an adjustment in the actual power supply available to the vehicle, the battery-side online energy metering module 104 can update the metering results based on the adjusted available power. The actual power supply allocated to the i-th vehicle can be expressed as: ; in, Indicates the energy distribution ratio. This indicates the current available energy at the charging station. This represents the updated power supply allocated to the i-th vehicle. Based on this, the estimated charging time can be determined by the battery charging characteristics corresponding to the updated power supply. Specifically, the time parameter corresponding to this power supply is obtained from the charging characteristic curve corresponding to the battery digital twin, and dynamically updated in conjunction with the current charging process. In this way, the battery-side online energy metering module 104 can generate online metering results for each vehicle, including the amount of power to be replenished, the amount of power already input, the remaining amount of power to be replenished, and the estimated charging time, providing a basis for subsequent charging control.

[0035] The station-side energy monitoring and prediction module 105 is used to acquire the real-time available energy of the DC charger, the node availability status and historical power supply data, and to generate a prediction result of the station-side available energy.

[0036] The station-end energy monitoring and prediction module is used to simultaneously receive energy supply data, energy consumption data, and node availability data from renewable energy interfaces, distribution network interfaces, and station-end sensors. Based on historical energy supply records and historical energy consumption records, it generates station-end available energy prediction results for the future prediction period and compares the deviation of the station-end available energy prediction results with the real-time measured station-end available energy to adjust the station-end energy prediction model according to the minimum mean square deviation criterion.

[0037] The station-side energy monitoring and prediction module 105 is used to collect, summarize, and predict the available energy status at the charging station. The module receives data from the renewable energy side, the distribution network side, and the station-side sensor side. The renewable energy side data characterizes the output of energy supply units such as hybrid photovoltaic and wind power; the distribution network side data characterizes the feed-in or draw-out between the station and the grid; and the station-side sensor data characterizes the current actual available energy at the charging station and the availability status of each charging node. Since the energy at the charging station is affected by both the energy access situation within the station and historical energy consumption patterns and current node occupancy, the station-side energy monitoring and prediction module 105 processes the aforementioned energy supply data, energy consumption data, and node availability data in a unified manner to generate a real-time available energy result corresponding to the current station-side operating status. This result is then used as input for subsequent station-side energy prediction and node scheduling. The availability status of charging nodes can be updated according to whether the node is idle, occupied, or in a pending access state after being released. After a vehicle finishes charging, the corresponding node becomes an available node again, and the number of nodes and the availability status of nodes are updated before the next request is processed.

[0038] The station-side energy monitoring and prediction module 105 is also used to generate a prediction result of the station's available energy for the future prediction period based on historical energy supply records and historical energy consumption records. Historical station-side data includes energy input, energy usage, and user inflow information uploaded to the cloud by the charging station over time. The historical records of station-side energy input or consumption can be used to predict the station's available energy for future periods, and historical changes in node status can reflect the station's resource occupancy trends at different times. The station-side energy monitoring and prediction module 105 predicts the station's available energy based on historical data and obtains the predicted available energy. Simultaneously, station-side sensors continuously acquire the current actual available energy of the charging station, thus obtaining the real-time measured available energy. By comparing the deviation between the predicted available energy and the real-time measured actual available energy, the station-side prediction result can be verified, and the cloud prediction process can be adjusted accordingly to make the prediction result closer to the current station-side operating conditions. This deviation is characterized by mean square error, and the station-side energy prediction process is adjusted with the minimum mean square error as the target, thereby improving the accuracy of grasping the station's available energy during subsequent energy allocation.

[0039] The station-side energy monitoring and prediction module 105 outputs a predicted energy availability result that is not directly used for vehicle battery status estimation or battery parameter updates. Instead, it is limited to the station side and used to reflect the changing trend of the charging station's energy supply capacity within a future predicted period. This setting provides subsequent modules with the station's current available energy and its prediction results, and also enables dynamic perception of station resource status in scenarios with multiple vehicles concurrently accessing the station, in conjunction with node availability. When real-time available energy is sufficient, the station-side available energy result directly reflects the station's current energy supply capacity to meet vehicle charging needs. When real-time available energy is limited, the prediction result helps reflect the changing trend of station energy supply in subsequent periods, thus providing a data foundation for subsequent modules to allocate energy and nodes based on the station's energy status.

[0040] The energy allocation and node scheduling module 106 is connected to the battery-side online energy metering module 104 and the station-side energy monitoring and prediction module 105, respectively. When there are multiple charging requests, it generates the target energy supply, target charging current and node allocation results of each DC charger based on the amount of electricity to be replenished, the expected charging time, the real-time available energy and the prediction results of each vehicle to be charged.

[0041] The energy allocation and node scheduling module is used to calculate the amount of electricity to be replenished for each vehicle based on the difference between the current state of charge of each vehicle and the preset target end point of charge, as well as the corresponding battery capacity. It then converts the amount of electricity to be replenished into the corresponding station-end energy demand based on the conversion loss of the DC charger, and sums the station-end energy demands to obtain the total energy demand. When the real-time available energy is not less than the total energy demand, the target energy supply for each vehicle is set to the corresponding station-end energy demand, and a corresponding target charging current and target charging time are generated based on the expected charging time of each vehicle. When the real-time available energy is less than the total energy demand, an energy allocation ratio is calculated according to the proportion of the station-end energy demand of each vehicle in the total energy demand. Based on the energy allocation ratio, the target energy supply for each vehicle is determined, and the target energy supply is converted into the target charging current and target charging time of the corresponding DC charging node in conjunction with the charging characteristics of the battery digital twin.

[0042] The energy allocation and node scheduling module is also used to poll newly arrived charging requests, maintain the count of unserved requests and the remaining occupancy time of each DC charging node; when there are idle DC charging nodes, the idle DC charging nodes are preferentially allocated to vehicles with greater energy demand, and the occupancy time of the DC charging node is set to the target charging time of the corresponding vehicle; when there are no idle DC charging nodes, the current vehicle is allocated to the DC charging node with the shortest remaining occupancy time, and the occupancy time of the DC charging node is updated to the sum of the original occupancy time and the target charging time of the corresponding vehicle; after charging is completed, the number of available DC charging nodes is updated and the corresponding DC charging node is released.

[0043] The energy allocation and node scheduling module 106 is used to jointly process the energy demand of each vehicle to be charged and the charging node resources when there are multiple charging requests. This module receives online energy metering results from the battery and energy monitoring and prediction results from the charging station. It uses the current state of charge (SOC), battery capacity, estimated charging time, real-time available energy at the charging station, and predicted available energy during the prediction period for each vehicle to be charged as inputs. It polls multiple pending requests and maintains a count of unserved requests. For each vehicle to be charged, the difference between its current SOC and a preset target SOC end point is first used to determine its required energy supply. Then, considering the conversion loss of the DC charger, the required energy supply on the vehicle side is converted into the energy demand at the charging station. The total energy demand under the current multi-request scenario is obtained by summing the energy demands of each vehicle. The key processing point of this module is that, on the one hand, it determines the target energy supply for each vehicle based on the currently available energy at the charging station; on the other hand, it determines the target charging current and target charging time for the corresponding node based on the charging characteristics output by the battery digital twin, ensuring that energy allocation is consistent with node occupancy time.

[0044] When the real-time available energy is not less than the total energy demand, proportional allocation under limited conditions is not required. Instead, the target energy supply for each vehicle to be charged is set to its corresponding station-end energy demand, and the target charging time is formed based on the expected charging time of the corresponding vehicle. The corresponding target charging current is given by the charging characteristics of the battery digital twin, that is, the charging current that can be output at the node is determined according to the charging process characteristics and state of charge change relationship corresponding to the target energy supply. When the real-time available energy is less than the total energy demand, an energy allocation ratio calculation is performed for each vehicle, and the energy allocation ratio can be expressed as: ; in, This represents the energy demand at the charging station corresponding to the i-th vehicle to be charged. This indicates the total energy demand of multiple vehicles currently awaiting charging. This represents the proportion of the total energy demand for the i-th vehicle to be charged. Based on this energy allocation ratio, the currently available energy at the station can be proportionally distributed to each vehicle to be charged, thus obtaining the target energy supply for each vehicle. Subsequently, combined with the charging characteristics of the battery digital twin, the target energy supply is converted into the target charging current and target charging time for the corresponding DC charging node. With this setting, the output current at the node is not fixed, but dynamically updated according to changes in the station's energy supply conditions and vehicle demand, so that the charging current of each node can be adjusted in real time according to new requests, changes in station energy, and changes in the allocation ratio.

[0045] Regarding node scheduling, the energy allocation and node scheduling module 106 periodically checks the availability and remaining occupancy time of each DC charging node. When an idle DC charging node exists, it is preferentially allocated to vehicles with higher energy demand, and the occupancy time of that node is set to the target charging time of the corresponding vehicle. The relationship can be expressed as follows: ; in, This indicates the duration of time node n is occupied. Let represent the target charging time for the i-th vehicle to be charged. When no idle DC charging node exists, the vehicle to be charged is assigned to the DC charging node with the shortest remaining occupancy time, and the occupancy time of that node is updated to the sum of the original remaining occupancy time and the vehicle's target charging time. This relationship can be expressed as: ; In other words, when there are no idle nodes, arriving vehicles do not leave the existing node system but are instead connected to the node expected to be released first, thus shortening the overall waiting time. Whenever a new charging request arrives, the current energy demand, the real-time available energy at the station, and the energy allocation ratio are recalculated, and the node charging current is dynamically updated. After a vehicle completes charging, the corresponding DC charging node is released, and the number of available DC charging nodes and their remaining occupancy time are updated for the next vehicle to be charged. Thus, the energy allocation and node scheduling module 106, within its scope, implements the generation of target energy supply, target charging current, target charging duration, and node allocation results in scenarios with concurrent requests from multiple vehicles.

[0046] The DC charging control module 107 is connected to the energy distribution and node scheduling module 106, and is used to charge the corresponding vehicle to be charged according to the target energy supply and the target charging current, and release the corresponding DC charger after charging is completed.

[0047] The DC charging control module 107 primarily functions at the execution layer, implementing the allocation results generated by the preceding modules to specific charging nodes. This ensures that each node outputs charging current to the corresponding vehicle according to the allocated energy requirements. Since the charging current of each node is influenced by both the available energy at the station and the vehicle's energy demand, the DC charging control module 107 does not employ a fixed output method during the control process. Instead, it adjusts the output of each node based on the received target charging current, ensuring that the actual charging process at each node remains consistent with the energy allocation results generated by the preceding modules.

[0048] The DC charging control module 107 can continuously charge vehicles assigned to specific nodes until the assigned charging task is completed. The charging duration associated with each node is determined by the front-end module based on vehicle status parameters and charging characteristics. During this period, the DC charging control module 107 maintains the corresponding node in a controlled charging state. When a new charging request arrives, requiring an update to the node's charging current, the DC charging control module 107 can also synchronously adjust the output current of the corresponding node, allowing the node output to dynamically change with the station's energy status and vehicle demand. With this configuration, the node can complete the charging of the corresponding vehicle according to the target energy supply and target charging current.

[0049] Once the corresponding vehicle has finished charging, the DC charging control module 107 releases the corresponding DC charger, changing its state from occupied to available, allowing subsequent vehicles to connect. This release process can be synchronized with the node state update process; that is, after charging is complete, the available state of the corresponding node is updated, and the node re-enters the set of allocable nodes, ensuring that subsequent node scheduling continues based on the latest node state. In this way, the DC charging control module 107 completes the execution control of the target energy supply and target charging current, as well as the node release process after charging is complete, within its module scope.

[0050] The user interaction and result delivery module 108 is connected to the energy allocation and node scheduling module 106, and is used to receive the charging request corresponding to the vehicle identifier, and to feed back the node allocation result, the estimated waiting time and the charging status.

[0051] The user interaction and result delivery module includes a mobile terminal, a cloud database, a local controller, and a station-end indicator unit. The mobile terminal is used to submit a charging application containing a vehicle identifier and receive node allocation results. The cloud database is used to store request status, DC charging node allocation information, node identifier information, and charging status information. The local controller is used to read the node identifier information and the DC charging node allocation information from the cloud database and send control commands to the station-end control board via a serial communication protocol. The station-end indicator unit is used to visually display the occupancy status, availability status, and allocation results of the corresponding DC charging node according to the control commands.

[0052] The user interaction and result delivery module 108 can use a mobile terminal as the user request entry point. After the user enters the vehicle's unique identifier through the mobile terminal, they initiate a charging application. After the energy allocation and node scheduling module 106 polls for a new arrival request, it obtains the corresponding node allocation result and related time information for that vehicle, and feeds it back to the mobile terminal via the cloud so that the user can know the current node allocation status. In the system architecture, the mobile terminal is also used to receive node allocation status information. User-side data is obtained from the cloud, and data on user inflow and available energy at the charging station are continuously uploaded to the cloud, thus forming a closed loop between request reception and result feedback.

[0053] In one embodiment, the cloud-based battery digital twin module and the energy allocation and node scheduling module jointly receive state of charge, temperature, charge / discharge rate, and depth of discharge as constraint parameters, and receive user feedback data and historical request time series data from the user interaction and result delivery module. They limit the value range of the target energy supply, the target charging current, and the target charging duration according to the constraint parameters, and update the node allocation priority parameters and station-side energy prediction parameters based on the user feedback data and the historical request time series data to improve the accuracy of subsequent energy metering and node scheduling.

[0054] The system incorporates several parameters: state of charge (SOC), temperature, charge / discharge rate, and depth of discharge (DCD) to reflect the current state and operational boundaries of the battery. These constraint parameters serve as common inputs for battery state modeling on the cloud side and energy allocation on the node side. The charge / discharge rate characterizes the charge / discharge rate relative to the battery capacity, while SOC, temperature, and DCD define the acceptable charging range and charging response characteristics of the battery under current operating conditions. With this configuration, the cloud-based battery digital twin module can combine these constraint parameters to generate results consistent with the current battery state when outputting time-related parameters and required charging energy. Similarly, the energy allocation and node scheduling module can limit the range of output values ​​based on these constraint parameters when generating target energy supply, target charging current, and target charging duration, ensuring that the node-side charging control matches the actual state of the battery. In the system architecture, these constraint parameters are introduced as constraint inputs in the digital twin implementation and used to coordinate with the station's energy state and vehicle energy requirements to complete subsequent allocation processing. Furthermore, the determination of the optimal charging current for each node is itself based on parameters such as SOC, DCD, and battery capacity.

[0055] Based on the dynamic representation of battery status and the collaborative analysis of station operation status, this system can more accurately obtain information related to vehicle charging demand and the expected charging process, and make more reasonable allocation of charging resources within the station. Therefore, it helps to improve the accuracy of charging time prediction and energy allocation. When multiple vehicles request charging at the same time and the available energy in the station is limited, it can also take into account user waiting time, energy utilization efficiency and station load pressure, and reduce resource waste caused by unreasonable allocation.

[0056] It should be noted that although several modules or units of the system have been mentioned in the detailed description above, this division is not mandatory. In fact, according to exemplary embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0057] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0058] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. An online battery-side energy metering system for a DC charger, characterized in that, It includes a battery-side data acquisition module, a cloud-based battery digital twin module, a state estimation and parameter update module, a battery-side online energy metering module, a station-side energy monitoring and prediction module, an energy distribution and node scheduling module, a DC charging control module, and a user interaction and result distribution module. The battery-side data acquisition module is used to continuously collect data in real time on the power battery of the vehicle to be charged connected to the DC charger, obtain voltage, current, temperature, state of charge, depth of discharge and operating data reflecting attenuation characteristics, and upload the operating data to the cloud. The cloud-based battery digital twin module is communicatively connected to the battery-side data acquisition module. It is used to establish a battery digital twin that maps to the corresponding physical power battery for each vehicle to be charged, and outputs battery capacity, internal resistance characteristics, charging characteristic parameters and battery state parameters corresponding to the preset target charging endpoint based on the operating data. The state estimation and parameter update module is connected to the cloud-based battery digital twin module and is used to perform state estimation and parameter iterative correction on the battery digital twin. The battery-side online energy metering module is connected to the state estimation and parameter update module, and is used to measure the amount of energy to be replenished, the amount of energy already input, the remaining amount of energy to be replenished, and the estimated charging time based on the updated battery state parameters, the preset target charge endpoint, and the battery capacity. The station-side energy monitoring and prediction module is used to acquire the real-time available energy of the DC charger, the node availability status and historical power supply data, and to generate a prediction result of the station-side available energy. The energy allocation and node scheduling module is connected to the battery-side online energy metering module and the station-side energy monitoring and prediction module, respectively. When there are multiple charging requests, it generates the target energy supply, target charging current and node allocation results of each DC charger based on the amount of electricity to be replenished, the expected charging time, the real-time available energy and the prediction results of each vehicle to be charged. The DC charging control module is connected to the energy distribution and node scheduling module, and is used to charge the corresponding vehicle to be charged according to the target energy supply and the target charging current, and release the corresponding DC charger after charging is completed. The user interaction and result delivery module is connected to the energy allocation and node scheduling module, and is used to receive the charging request corresponding to the vehicle identifier, and to provide feedback on the node allocation result, the estimated waiting time and the charging status.

2. The online battery-side energy metering system for a DC charger according to claim 1, characterized in that, The cloud-based battery digital twin module includes an open-circuit voltage and state-of-charge (POC) modeling submodule, an equivalent circuit modeling submodule, and a parameter lookup table submodule. The open-circuit voltage and POC modeling submodule is used to perform curve fitting on the correspondence between open-circuit voltage and POC based on experimental and online operating data of the power battery. The equivalent circuit modeling submodule is used to establish an equivalent model of the power battery that includes ohmic internal resistance branches and multi-branch polarized RC branches. The parameter lookup table submodule is used to store and retrieve the internal resistance and polarization parameters of the equivalent model of the power battery according to POC and operating conditions, so that the battery digital twin can characterize the terminal voltage response and charging characteristics of the power battery under different charging and discharging stages.

3. The online battery-side energy metering system for a DC charger according to claim 1, characterized in that, The state estimation and parameter update module is used to execute the state of charge estimation process based on unscented Kalman filtering, including: generating a set of sample points based on the current state estimate and covariance; substituting the set of sample points into the power battery state equation for propagation; correcting the propagation result by combining voltage measurement, current measurement and temperature measurement; outputting the updated state of charge and covariance; and correcting the power battery capacity and expected charging time by combining driving cycle data and capacity estimation results based on current-time integration.

4. The online battery-side energy metering system for a DC charger according to claim 3, characterized in that, The state estimation and parameter update module is also used to extract voltage transition features and current response features when the power battery undergoes pulse discharge process and charge-discharge transient process. Based on the voltage transition features and the current response features, the ohmic internal resistance parameter and the parameters of each polarization branch are obtained. The actual collected voltage and current curves are compared with the predicted curves of the battery digital twin, and the internal resistance curve and polarization parameter curve are corrected online in the manner of minimizing the sum of squared deviations to compensate for the parameter drift caused by cycle aging.

5. The online battery-side energy metering system for a DC charger according to claim 1, characterized in that, The station-end energy monitoring and prediction module is used to simultaneously receive energy supply data, energy consumption data, and node availability data from renewable energy interfaces, distribution network interfaces, and station-end sensors. Based on historical energy supply records and historical energy consumption records, it generates station-end available energy prediction results for the future prediction period and compares the deviation of the station-end available energy prediction results with the real-time measured station-end available energy to adjust the station-end energy prediction model according to the minimum mean square deviation criterion.

6. The online battery-side energy metering system for a DC charger according to claim 1, characterized in that, The energy distribution and node scheduling module is used to calculate the amount of electricity to be replenished for each vehicle based on the difference between the current state of charge of each vehicle and the preset target end point of charge, as well as the corresponding battery capacity. It then converts the amount of electricity to be replenished into the corresponding station-end energy demand based on the conversion loss of the DC charger, and sums the station-end energy demand to obtain the total energy demand. When the real-time available energy is not less than the total energy demand, the target energy supply for each vehicle is set to the corresponding station-end energy demand, and the corresponding target charging current and target charging duration are generated based on the expected charging duration of each vehicle. When the real-time available energy is less than the total energy demand, the energy allocation ratio is calculated according to the proportion of the station-end energy demand of each vehicle to be charged in the total energy demand. Based on the energy allocation ratio, the target energy supply corresponding to each vehicle to be charged is determined, and the target energy supply is converted into the target charging current and target charging time of the corresponding DC charging node in combination with the charging characteristics of the battery digital twin.

7. The online battery-side energy metering system for a DC charger according to claim 6, characterized in that, The energy allocation and node scheduling module is also used to poll newly arrived charging requests, maintain the count of unserved requests and the remaining occupancy time of each DC charging node; when there are idle DC charging nodes, the idle DC charging nodes are preferentially allocated to vehicles with greater energy demand, and the occupancy time of the DC charging nodes is set to the target charging time of the corresponding vehicles. When there are no available DC charging nodes, the vehicle to be charged is assigned to the DC charging node with the shortest remaining occupancy time, and the occupancy time of the DC charging node is updated to the sum of the original occupancy time and the target charging time of the corresponding vehicle to be charged. After charging is complete, update the number of available DC charging nodes and release the corresponding DC charging nodes.

8. The online battery-side energy metering system for a DC charger according to claim 1, characterized in that, The user interaction and result delivery module includes a mobile terminal, a cloud database, a local controller, and a station-end indicator unit. The mobile terminal is used to submit a charging application containing a vehicle identifier and receive node allocation results. The cloud database is used to store request status, DC charging node allocation information, node identifier information, and charging status information. The local controller is used to read the node identifier information and the DC charging node allocation information from the cloud database and send control commands to the station-end control board via a serial communication protocol. The station-end indicator unit is used to visually display the occupancy status, availability status, and allocation results of the corresponding DC charging node according to the control commands.

9. The online battery-side energy metering system for a DC charger according to claim 1, characterized in that, The cloud-based battery digital twin module and the energy allocation and node scheduling module jointly receive state of charge, temperature, charge / discharge rate, and depth of discharge as constraint parameters. They also receive user feedback data and historical request time-series data from the user interaction and result delivery module. Based on the constraint parameters, they limit the range of values ​​for the target energy supply, the target charging current, and the target charging duration. Furthermore, they update the node allocation priority parameters and station-side energy prediction parameters according to the user feedback data and historical request time-series data to improve the accuracy of subsequent energy metering and node scheduling.

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