Charging pile information monitoring control system and method thereof

Abstract

The invention discloses a charging pile information monitoring control method and system, and the method is realized through the following four steps: state estimation: based on real-time measurement data, driving a reduced-order electrochemical model, and estimating the internal core state of a battery; boundary calculation: reversely solving a safe charging boundary at the next moment in combination with the internal state and a safe threshold value; power distribution: on the basis of a safety boundary and a battery health state, optimizing, solving and generating a power instruction of each charging pile within a power upper limit of a power grid; state updating: calculating and updating a battery health state factor according to the power instruction and the internal state; the core of the method is that the battery is converted from an external black box to a white box with a transparent internal state through the electrochemical model, the internal state influencing the safety and the service life can be accurately observed in real time, and a scientific basis is provided for refined and safe charging control.

Description

Technical Field

[0001] This invention relates to the field of charging pile information monitoring and control technology, specifically to a charging pile information monitoring and control system and its method. Background Technology

[0002] With the popularization of electric vehicles and the large-scale construction of charging facilities, the safety, efficiency, and battery life maintenance of the charging process face severe challenges. At present, traditional charging strategies mostly adopt fixed charging modes and are mainly controlled based on external macroscopic parameters such as voltage and current provided by the battery management system. However, such methods treat the battery as a black box and cannot see its complex internal electrochemical state. This leads to charging strategies that are often too conservative to ensure safety or accelerate battery aging in pursuit of efficiency, making it difficult to achieve a balance between charging speed, safety, and long-term battery health. Therefore, how to accurately sense the internal state of the battery and thereby achieve personalized and refined closed-loop control of the charging process to balance safety, efficiency and battery asset preservation has become a technical challenge that urgently needs to be solved in this field. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a charging pile information monitoring and control system and method. Specifically, the technical solution of this invention is as follows: A method for monitoring and controlling charging pile information, comprising: Step 1: Based on the real-time collected measurement data of the charging pile and battery management system, drive the preset reduced-order electrochemical state-space model to estimate the internal state of the battery. Step 2: Combining the internal state with the preset physical safety threshold, calculate the safe charging boundary at the next moment by solving the state estimation equation in reverse algebra. Step 3: Based on the safe charging boundary and the battery health state factor, and with the upper limit of the total power of the power grid as a constraint, solve the constraint optimization problem to generate charging power instructions allocated to each charging pile. Step 4: Based on the charging power command and internal state, calculate the loss of battery health status through the preset health degradation cost equation, and update the health status factor using the loss.

[0004] Preferably, the measurement data includes the measured charging current and battery surface temperature; the internal state includes the estimated value of the lithium ion concentration on the negative electrode surface and the estimated value of the core region temperature.

[0005] Preferably, the physical safety thresholds include the maximum permissible lithium-ion concentration on the negative electrode surface and the maximum permissible core temperature; the safe charging boundary is the maximum permissible charging current value at the next moment.

[0006] Preferably, solving constrained optimization problems includes: The overall efficiency of the charging station cluster is optimized by taking the safe charging boundary corresponding to each charging pile as a hard constraint and the total power limit of the power grid as a premise.

[0007] Preferred overall efficiency optimization processing includes: In constrained optimization problems, a pre-defined health tendency weight coefficient is introduced, and combined with a health state factor, the limited power grid power is allocated.

[0008] Preferably, the initial values ​​of the health status factor are given by historical data.

[0009] Preferably, the health degradation cost equation is an empirical model that combines current stress with temperature stress based on the Arrhenius formula.

[0010] A charging pile information monitoring and control system includes: The state estimation module is used to drive a preset reduced-order electrochemical state-space model based on the measurement data of the charging pile and battery management system collected in real time, so as to estimate the internal state of the battery. The safety boundary calculation module is used to calculate the safe charging boundary at the next moment by combining the internal state with the preset physical safety threshold and solving the state estimation equation by inverse algebra. The collaborative scheduling module is used to solve the constraint optimization problem based on the safe charging boundary and the battery health state factor, with the total power limit of the power grid as a constraint, in order to generate charging power instructions allocated to each charging pile. The health assessment module is used to calculate the amount of loss in battery health status based on charging power commands and internal state, through a preset health degradation cost equation, and to update the health status factor using the amount of loss.

[0011] Preferably, the internal state output by the state estimation module is the input to the safety boundary calculation module; the safe charging boundary output by the safety boundary calculation module is the constraint condition for the cooperative scheduling module to solve the constraint optimization problem.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention transforms the battery from a black box in terms of external electrical characteristics to a white box with transparent internal state by constructing an electrochemical state-space model. This allows for real-time and precise observation of the core internal states that affect safety and lifespan, such as the lithium-ion concentration on the negative electrode surface and the temperature of the core region, thus providing a scientific basis for refined control. 2. This invention overturns the traditional fixed charging mode. By solving the state estimation equation in reverse, it dynamically and individually calculates the physically permissible maximum safe charging current for each battery at the current moment. This method can maximize the charging potential of the battery while ensuring that dangers such as lithium plating and thermal runaway do not occur, thus solving the problem of balancing safety and efficiency. 3. This invention introduces a collaborative scheduling optimization mechanism for charging station clusters. It not only uses the individual safe charging boundary of each vehicle as an insurmountable hard constraint, but also uses the total power of the power grid as the upper limit, and combines the health status of the battery to carry out intelligent power allocation. This enables the limited power grid resources to be preferentially allocated to vehicles with better charging potential and health status, thereby optimizing the overall operating efficiency and throughput of the charging station. 4. This invention constructs a complete closed-loop feedback system from charging behavior to health degradation quantification and scheduling strategy adjustment; it quantifies the long-term impact of current charging behavior on battery life through the health degradation cost equation and dynamically updates its health factor, taking into account the full life cycle value of the battery, and achieving the best balance between short-term charging efficiency and long-term asset preservation. Attached Figure Description

[0013] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of the method of the present invention.

[0014] Figure 2 This is a structural diagram of the system of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0016] Example 1: Please see Figure 1 A method for monitoring and controlling charging pile information, comprising: Step 1: Based on the real-time collected measurement data of the charging pile and battery management system, drive the preset reduced-order electrochemical state-space model to estimate the internal state of the battery. Step 2: Combining the internal state with the preset physical safety threshold, calculate the safe charging boundary at the next moment by solving the state estimation equation in reverse algebra. Step 3: Based on the safe charging boundary and the battery health state factor, and with the upper limit of the total power of the power grid as a constraint, solve the constraint optimization problem to generate charging power instructions allocated to each charging pile. Step 4: Based on the charging power command and internal state, calculate the loss of battery health status through the preset health degradation cost equation, and update the health status factor using the loss.

[0017] This embodiment provides a charging pile information monitoring and control method, the purpose of which is to achieve personalized, safe and efficient charging process through precise insight into the internal state of the battery and closed-loop feedback control; the method constitutes a complete and self-consistent technical closed loop; The first step of this method is to drive a preset reduced-order electrochemical state-space model based on real-time collected measurement data from the charging pile and battery management system to estimate the internal state of the battery. This reduced-order electrochemical state-space model is a mathematical model used to infer the internal microstate of a battery in real time with lower computational resource consumption. Its technical motivation is to transform the battery from a black box in terms of external electrical characteristics into a white box with a transparent internal state. In this embodiment, the model is implemented through a set of state estimation equations, the structure of which is as follows: and As shown; in the above model, the definitions, physical meanings, and sources of each parameter are clear: The value representing the estimated lithium-ion concentration on the negative electrode surface at time t is a core indicator for assessing the risk of lithium plating and is calculated iteratively by this equation. The concentration estimate from the previous moment is used as the initial condition for this calculation. The measured charging current at time t is collected in real time by the current sensor of the charging pile and is the core driving input of the model. The average lithium-ion concentration is calculated by the battery management system (BMS) according to the ampere-hour integration method, reflecting the overall state of charge (SOC) of the battery. The value of the estimated temperature of the core region of the battery at time t is a key indicator for assessing the risk of thermal runaway, and it is obtained by iterative calculation of this equation. This is the temperature estimate from the previous moment; The measured battery surface temperature was collected by the temperature sensor in the BMS. The preset calculation step size for the system; These are all model state coefficients, calibration parameters related to the materials and structure of a specific battery model. They are obtained by conducting multi-condition cyclic testing of the battery model in the laboratory and collecting data, followed by offline identification and calibration using a system identification algorithm. The calculation logic for this step is as follows: at each calculation step... Inside, using real-time measured current and surface temperature As input, the above equations are used for iterative calculations to continuously output the key internal states of the battery. and High-fidelity estimation; The second step of this method is to calculate the safe charging boundary at the next moment by combining the internal state with a preset physical safety threshold and solving the state estimation equation in reverse algebra. Here, the safe charging boundary refers to the maximum charging current allowed to ensure that the battery does not suffer physical damage at any time. Its technical motivation is to replace the traditional fixed charging strategy and dynamically and individually find the physically permissible fastest safe charging rate for each battery in its current specific state. The calculation of this boundary is achieved by inversely deriving the state estimation equation from the first step. It is assumed that at the next moment… Internal state and It just reached its safety limit, that is, the preset physical security threshold. Maximum permissible lithium-ion concentration on the negative electrode surface and Maximum permissible core temperature; to solve for the maximum permissible current at the next moment, it is necessary to predict the battery surface temperature at the next moment using the measured value at the current moment. Here, a reasonable simplification is adopted for engineering purposes, that is, assuming a very short calculation step. The surface temperature of the internal battery remains constant, based on the measured surface temperature at the current moment. Approximate surface temperature at the next moment ;Will Replace with , Replace with The equation is then solved algebraically in reverse to obtain the maximum input current that allows the state to reach the critical value. The calculation formula is as follows: in, and Directly derived from the aforementioned state estimation equations at the current time The output, and It is based on the physical safety threshold preset according to the battery materials science mechanism and industry safety standards, while the gain coefficient and The parameters are completely consistent with those in the state estimation equation, ensuring model consistency. In each control cycle, this step dynamically generates a unique safe charging limit for each battery using the real-time internal state estimated in the first step. ; The third step of this method is to solve a constrained optimization problem based on the safe charging boundary and the battery health state factor, with the total power limit of the power grid as a constraint, to generate charging power instructions allocated to each charging pile. The purpose of solving this constrained optimization problem is to introduce an intelligent resource allocation mechanism that, while ensuring the absolute safety of all vehicles and meeting grid load limits, prioritizes the allocation of limited grid power to vehicles with greater charging potential or those requiring faster charging, thereby achieving overall efficiency optimization of the charging station cluster. The objective function of this collaborative scheduling optimization model is... Its constraints are as well as ;in, It is to be solved, assigned to the first Power command for each charging station; This refers to the total number of charging stations; This is the current maximum allowable power capacity of the power grid, provided by the power grid dispatching system; It is the first The vehicle's current actual voltage is provided in real time by the BMS; The output of the second step constitutes the core individual constraints of this optimization problem; It is the first The State of Health (SOH) factor of the vehicle battery, the initial value of which is given by historical data; This is a health tendency weighting coefficient that can be set by the operator according to its operational strategy. The calculation logic of this step is to maximize the weighted total power of all charging piles within a scheduling cycle, while satisfying the total power constraint of the power grid and the safe charging power limit of each charging pile. By solving this linear programming problem, a set of optimal power allocation instructions is obtained. And distribute it to each charging station for implementation; The fourth step of this method is to calculate the loss in battery health status based on the charging power command and internal state, using a preset health degradation cost equation, and then update the health status factor using this loss. The health degradation cost equation is an empirical model used to quantify the impact of current charging behavior on the long-term battery life. Its technical motivation is to establish a quantitative relationship between charging rate and health cost, thereby incorporating the battery's full life-cycle value into consideration and forming a closed-loop control aimed at maximizing long-term value. This equation is structured as follows: As shown; where, Within a time step, the first The amount of health depletion of the vehicle battery; It is based on the power command output in the third step. and current voltage The calculated actual execution current; It is the absolute internal temperature of the battery estimated in the first step; This refers to the rated current of this battery model; Reference absolute temperature; and The attenuation coefficients of current stress and temperature stress are respectively characterized. The equivalent activation energy for battery aging; It is the ideal gas constant; the calculation logic of this step is that after the charging pile executes the power command, based on the actual current generated and the internal temperature of the battery, the minor health loss caused by this charging behavior is calculated using the above equation. Dynamically update the battery's health status factor: This updated version It will be used in the next round of collaborative scheduling optimization model, thus forming a complete closed-loop feedback between the preceding steps; By organically combining the above steps, this method realizes a complete closed-loop control system, from internal state perception to individual safety boundary calculation, to cluster collaborative scheduling, to long-term health impact assessment. It not only ensures the absolute safety of each vehicle during the charging process, but also intelligently optimizes the operating efficiency of the entire charging station while meeting grid constraints. More importantly, by quantitatively linking charging behavior with battery health degradation and feeding it back to the scheduling strategy, this method can significantly extend the full life cycle value of the battery, solving the technical problem of traditional charging strategies struggling to balance safety, efficiency, and battery life, and achieving an overall technical effect of safety, high efficiency, and value preservation.

[0018] Example 2: The measurement data includes the measured charging current and battery surface temperature; the internal state includes the estimated lithium-ion concentration on the negative electrode surface and the estimated temperature of the core region.

[0019] This embodiment is a further specification of the method in Embodiment 1; in this embodiment, the measurement data includes the measured charging current and the battery surface temperature; the internal state includes the estimated value of the lithium-ion concentration on the negative electrode surface and the estimated value of the core region temperature; specifically, the measured charging current... and battery surface temperature This is the most fundamental and easily accurately obtained physical quantity in modern charging piles and battery management systems. Using it as input to the state estimation model ensures the high universality and feasibility of the technical solution of this invention; simultaneously, the estimated value of lithium-ion concentration on the negative electrode surface... It is the most direct physical indicator for determining whether lithium plating has occurred, while the estimated temperature of the core region... These are key safety indicators for preventing thermal runaway. Choosing these two physical quantities as the estimated internal state means that this method directly targets the two most critical internal factors affecting battery safety and lifespan. By clearly defining the specific physical quantities of the measurement data and internal state, this embodiment ensures that the input of the state estimation model is easily obtainable and reliable, and its output consists of core parameters most directly related to battery safety and lifespan. This specificity makes the goal of the entire closed-loop control system more focused, directly addressing the two core pain points of lithium plating and overheating for prevention and control, thereby greatly improving the accuracy, robustness, and practical application value of the entire control method.

[0020] Example 3: The physical safety thresholds include the maximum permissible lithium-ion concentration on the negative electrode surface and the highest permissible core temperature; the safe charging boundary is the maximum permissible charging current value at the next moment.

[0021] This embodiment is a further specification of the method in Embodiment 1; in this embodiment, the physical safety threshold includes the maximum permissible lithium-ion concentration on the negative electrode surface and the maximum permissible core temperature; the safe charging boundary is the maximum permissible charging current value at the next moment; specifically, the maximum permissible lithium-ion concentration on the negative electrode surface... This is a critical value set based on electrochemical mechanisms. When the lithium-ion concentration on the negative electrode surface exceeds this value, lithium metal is easily deposited on the negative electrode surface, leading to irreversible capacity decay and potentially causing an internal short circuit; maximum allowable core temperature. This is a threshold set based on material stability and safety regulations. Exceeding this temperature will accelerate the chemical reactions inside the battery, increasing the risk of thermal runaway. Using these two core physical quantities as hard thresholds eliminates the two most dangerous failure modes at the source. Correspondingly, the maximum allowable charging current value for the next moment is calculated. This becomes a dynamic, real-time protection, ensuring that no matter how the current is scheduled, the current applied to the battery will not cause its internal state to exceed the aforementioned safety threshold. This embodiment directly links the definition of the safety boundary with the underlying physical safety mechanism, so that safety control is no longer based on a rough estimate of external macroscopic parameters, but on a precise prediction of the internal microscopic state. This makes the setting of the safety boundary more scientific and accurate, and can maximize the release of the battery's charging potential in different states while ensuring absolute safety, thus optimizing both safety and charging efficiency.

[0022] Example 4: Solving constrained optimization problems includes: The overall efficiency of the charging station cluster is optimized by taking the safe charging boundary corresponding to each charging pile as a hard constraint and the total power limit of the power grid as a premise.

[0023] Overall efficiency optimization includes: In constrained optimization problems, a pre-defined health tendency weight coefficient is introduced, and combined with a health state factor, the limited power grid power is allocated.

[0024] This embodiment is a further specification of the third step in Embodiment 1, which involves solving the constrained optimization problem. Specifically, in this embodiment, this step is manifested as follows: using the safe charging boundary corresponding to each charging pile as a hard constraint, and taking the upper limit of the total power of the power grid as a premise, the overall efficiency of the charging station cluster is optimized. To achieve overall efficiency optimization, this embodiment introduces a preset health tendency weight coefficient into the constrained optimization problem, and combines it with a health state factor to allocate the limited power grid power. Its underlying logic lies in optimizing the objective function... The design does not simply maximize the total charging power, but rather maximizes a weighted power; this weight is determined by the battery's own health status. The charging potential represented and the strategic weight set by the operator. The value orientation of the representatives is jointly determined; specifically, operators can adjust this weighting coefficient according to different operational objectives: for example, when pursuing maximum throughput and short-term profits of charging stations, a relatively low weighting coefficient can be set. The value is set to prioritize service for all vehicles; when the operational strategy focuses on extending the battery life of partner vehicles, enhancing long-term user value, or responding to green and energy-saving dispatch, a relatively higher value can be set. This value allows power allocation to favor batteries in better health and those whose charging causes less lifespan loss; simultaneously... This constraint ensures that the power allocated to each vehicle never exceeds its individual safety boundary, and This ensures that the total load of the charging station will not impact the power grid. Through this multi-objective collaborative optimization design, this method no longer treats all charging vehicles as homogeneous charging loads, but as heterogeneous intelligent agents with different health states and different safety boundaries. By introducing health state factors and policy weights, the scarce resource of grid power can be intelligently and preferentially allocated, which not only improves the overall throughput and economic benefits of the charging station, but also achieves more refined operation and management, thereby generating a synergistic gain effect.

[0025] Example 5: The initial values ​​for the health status factor are given by historical data.

[0026] The health degradation cost equation is an empirical model that combines current stress with temperature stress based on the Arrhenius formula.

[0027] This embodiment further specifies the fourth step of the health assessment closed loop in Embodiment 1. In this embodiment, the initial value of the health status factor is given by historical data. This means that when the vehicle first connects to the system, a reasonable value can be set for it by querying the historical charging and discharging data recorded in the vehicle's BMS or the historical data of the vehicle model stored in the cloud. Initial values ​​were provided, addressing the cold start problem. The health degradation cost equation is an empirical model combining current stress and temperature stress based on the Arrhenius equation. This model has a solid physicochemical foundation, where the current stress term accurately characterizes the physical impact of excessive current on the battery structure, while the temperature stress term is a classic application of the Arrhenius equation, precisely describing the exponential effect of temperature on the aging reaction rate. To ensure the feasibility of the model parameters, the parameter calibration process clearly distinguishes between variables in the calibration dataset and input variables during model runtime; for example, parameters... The calibration is based on a set of independent offline aging experiment data; in this calibration dataset, independent symbols are used, such as... Refers to the first Battery experimental life under constant current stress conditions, using Refers to the first Battery lifetime under constant ambient temperature; parameters It uses mathematical methods such as least squares to analyze multiple groups of data. The data points are fitted together; similarly, the parameters... and By multiple groups The data points are fitted by regression analysis; this distinction ensures the objectivity and reproducibility of the model parameters; by providing reasonable initial values ​​for the health status factor and adopting a health degradation model based on physical mechanisms and a clear calibration process, the quantitative assessment of the long-term impact of charging behavior is no longer a black box guess, but a scientific calculation with evidence, thereby achieving the best balance between charging efficiency and battery life and maximizing the value of user assets.

[0028] Example 6: Please see Figure 2 A charging pile information monitoring and control system, comprising: The state estimation module is used to drive a preset reduced-order electrochemical state-space model based on the measurement data of the charging pile and battery management system collected in real time, so as to estimate the internal state of the battery. The safety boundary calculation module is used to calculate the safe charging boundary at the next moment by combining the internal state with the preset physical safety threshold and solving the state estimation equation by inverse algebra. The collaborative scheduling module is used to solve the constraint optimization problem based on the safe charging boundary and the battery health state factor, with the total power limit of the power grid as a constraint, in order to generate charging power instructions allocated to each charging pile. The health assessment module is used to calculate the amount of loss in battery health status based on charging power commands and internal state, through a preset health degradation cost equation, and to update the health status factor using the amount of loss.

[0029] This embodiment provides a charging pile information monitoring and control system designed to execute the methods of any of the foregoing embodiments. The system includes a state estimation module, designed to monitor the microscopic health state of the battery in real time. This module receives measurement data and drives a reduced-order electrochemical state-space model to output the battery's internal state. The system also includes a safety boundary calculation module, designed to dynamically generate personalized safe charging limits for each battery. This module receives the internal state and, in conjunction with a preset physical safety threshold, calculates the maximum allowable charging current value for the next moment by performing an inverse algebraic solution to the state estimation equation. The system further includes a collaborative scheduling module, designed to optimize the overall efficiency of the charging station cluster. This module receives the safe charging boundary, battery health state factors, and grid data. The total power limit is determined by solving a constrained optimization problem to generate and issue charging power commands. The system also includes a health assessment module, which aims to quantify the long-term impact of charging behavior on battery life and form a closed-loop feedback. This module receives charging power commands and internal states, calculates the loss of battery health status through a preset health degradation cost equation, and uses this loss to update the corresponding battery's health status factor for use by the collaborative scheduling module in the next round of decision-making. Through the functional division and collaborative work of the above four modules, this system constitutes a complete and intelligent charging control system, integrating bottom-level data acquisition, mid-level state estimation and safety calculation, upper-level cluster scheduling, and top-level long-term value assessment, providing a complete system-level solution for the intelligent upgrading of charging facilities.

[0030] Example 7: The internal state output by the state estimation module serves as the input to the safety boundary calculation module; the safe charging boundary output by the safety boundary calculation module serves as the constraint condition for the cooperative scheduling module to solve the constraint optimization problem.

[0031] This embodiment further defines the internal data flow and inter-module coupling relationships of the system in Embodiment 6. In this system, the internal state output by the state estimation module serves as the input to the safety boundary calculation module. This data flow is the logical foundation for the system to achieve precise safety control, ensuring that the calculation of the safety boundary is always based on the latest and most accurate internal state of the battery, thus making the setting of the safety boundary highly timely and targeted. Simultaneously, the safe charging boundary output by the safety boundary calculation module serves as the constraint condition for the collaborative scheduling module to solve the constraint optimization problem. This data flow is the core bridge connecting individual safety and group optimization. Its inherent logic is that no matter how the collaborative scheduling module allocates power for overall efficiency, the power command it assigns to any charging pile can never exceed the physical safety limit of the battery corresponding to that pile in its current state. By clearly defining the input-output relationships and data dependencies between the modules, this embodiment constructs a clear and rigorous system. The perception-decision-execution logic chain, namely state estimation, is the cornerstone of safe computation, and safe computation is the red line of collaborative scheduling. This layered and interconnected system architecture ensures the logical consistency and inherent security of the entire system operation, enabling complex cluster optimization problems to be reliably solved while ensuring the safety of individual components, thereby improving the stability and reliability of the entire system. To further enhance the robustness and security of the system, the state estimation module can incorporate a data preprocessing unit to perform validity verification, filtering, and outlier handling on real-time acquired measurement data to prevent erroneous sensor readings from impacting the system. Simultaneously, the collaborative scheduling module can be configured with a safety redundancy strategy. When upstream module data interruption or sudden changes in key parameters are detected, the system can automatically switch to a preset, more conservative charging mode to ensure the safety of the battery and the power grid under any circumstances. To further optimize model accuracy, the health assessment module outputs health status factors... It can also be fed back to the state estimation module; specifically, the model state coefficients can be designed to correlate with the battery health state. The relevant functions enable the state estimation algorithm to adapt to changes in model parameters caused by battery aging, further improving the accuracy of internal state estimation. This data stream corresponds to... Figure 2 The arrow points from the health assessment module to the state estimation module.

[0032] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A charging pile information monitoring control method, characterized in that, The method comprises the following steps: Step 1, based on the real-time collected measurement data of the charging pile and the battery management system, a preset reduced-order electrochemical state space model is driven to estimate the internal state of the battery; Step 2, in combination with the internal state and the preset physical safety threshold, the safety charging boundary at the next moment is calculated by inversely algebraically solving the state estimation equation; Step 3, based on the safety charging boundary and the health state factor of the battery, a constrained optimization problem is solved to generate the charging power instruction allocated to each charging pile, with the upper limit of the total power of the power grid as the constraint; Step 4, based on the charging power instruction and the internal state, the loss amount of the battery health state is calculated by a preset health degradation cost equation, and the health state factor is updated using the loss amount. 2.The charging pile information monitoring control method of claim 1, wherein, The measurement data includes the measured charging current and the battery surface temperature; the internal state includes the estimated value of the negative electrode surface lithium ion concentration and the estimated value of the core region temperature. 3.The charging pile information monitoring control method of claim 1, wherein, The physical safety threshold includes the maximum allowed negative electrode surface lithium ion concentration and the highest allowed core temperature; the safety charging boundary is the maximum charging current value allowed at the next moment. 4.The charging pile information monitoring control method of claim 1, wherein, Solving the constrained optimization problem comprises: Optimizing the overall efficiency of the charging station cluster by taking the safety charging boundary corresponding to each charging pile as a hard constraint and taking the upper limit of the total power of the power grid as a premise.

5. The charging pile information monitoring control method according to claim 4, characterized in that, The overall efficiency optimization processing comprises: In the constrained optimization problem, a preset health tendency weight coefficient is introduced, and the limited power grid power is allocated in combination with the health state factor. 6.The charging pile information monitoring control method of claim 1, wherein, The initial value of the health state factor is given by historical data.

7. The charging pile information monitoring control method according to claim 1, characterized in that, The health degradation cost equation is an empirical model combining the current stress and the temperature stress based on the Arrhenius formula. 8.A charging pile information monitoring control system applied to the charging pile information monitoring control method of any one of claims 1-7, characterized in that, The method comprises: A state estimation module for driving a preset reduced-order electrochemical state space model based on real-time collected measurement data of the charging pile and the battery management system to estimate the internal state of the battery; A safety boundary calculation module for calculating the safety charging boundary at the next moment by inversely algebraically solving the state estimation equation in combination with the internal state and the preset physical safety threshold; A collaborative scheduling module for solving a constrained optimization problem to generate a charging power instruction allocated to each charging pile, with the upper limit of the total power of the power grid as the constraint, based on the safety charging boundary and the health state factor of the battery; A health evaluation module for calculating the loss amount of the battery health state by a preset health degradation cost equation based on the charging power instruction and the internal state, and updating the health state factor using the loss amount.

9. The charging pile information monitoring control system according to claim 8, characterized in that, The internal state output by the state estimation module is the input of the safety boundary calculation module; the safety charging boundary output by the safety boundary calculation module is the constraint condition for solving the constrained optimization problem of the collaborative scheduling module.

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