Safe charging control method and system based on battery identity authentication

Abstract

The invention provides a safe charging control method and system based on battery identity authentication, relates to the field of energy supply of electric vehicles, and solves the problems that in the prior art, the identity authenticity of a battery cannot be effectively verified, and the real-time sensing capability on the electrochemical state in the battery is lacked. And therefore, access of an unauthorized or inferior battery is difficult to prevent and a microscopic failure risk is difficult to warn in advance in a high-rate fast charging process. The method comprises the following steps: dynamically authenticating the identity of a battery, acquiring an initial impedance characteristic parameter representing an electrochemical state, and sending the initial impedance characteristic parameter to a charging pile; the charging pile generates an initial charging strategy adaptive to the current battery state; in the charging process, the battery management system periodically reports comprehensive state information to the charging pile; and the charging pile evaluates the safety risk state of the battery based on the comprehensive state information, and dynamically adjusts charging parameters or stops charging according to an evaluation result. The method and the device are used in a safe charging process.

Description

Technical Field

[0001] This application relates to the field of energy replenishment, and in particular to a safe charging control method and system based on battery identity authentication. Background Technology

[0002] With the rapid popularization of electric bicycles, electric two-wheelers, and swappable power battery systems, users' demand for high-rate fast charging (supercharging) is increasing. To improve user experience, charging power is constantly increasing and charging time is continuously shortening. However, lithium-ion batteries are prone to electrochemical anomalies such as local overheating, lithium dendrite precipitation, and impaired solid-phase diffusion under high-current charging conditions. If these phenomena are not identified and addressed in time, they may lead to thermal runaway or even fire and explosion accidents.

[0003] Currently, some charging systems have basic voltage, current, and temperature monitoring functions and can cut off charging when thresholds are exceeded. However, existing technologies generally suffer from a key deficiency: the lack of an effective verification mechanism for the authenticity of the battery and the inability to obtain electrochemical characteristic parameters reflecting the internal microstate of the battery in real time during charging. This results in safety control strategies relying solely on static or macroscopic data, making it difficult to achieve differentiated and proactive risk prevention and control for different individual batteries.

[0004] Therefore, this application proposes a safe charging system based on battery identity authentication, real-time battery status data interaction, and joint monitoring between the charging station and the cloud, which improves the safety, intelligence, and traceability of the fast charging process. Summary of the Invention

[0005] This application provides a safe charging control method and system based on battery identity authentication, which solves the technical problem that the existing technology cannot effectively verify the authenticity of the battery identity and lacks the ability to perceive the internal electrochemical state of the battery in real time, which makes it difficult to prevent unauthorized or inferior batteries from being connected and to provide early warning of microscopic failure risks during high-rate fast charging.

[0006] To achieve the above objectives, this application adopts the following technical solution: Firstly, a safe charging control method and system based on battery identity authentication are provided, including: After the user initiates a charging operation, the battery management system establishes a communication connection with the charging pile and performs dynamic authentication of the battery identity based on the built-in physical non-cloning function. At the same time, it obtains the initial impedance characteristic parameters characterizing the electrochemical state by applying a small-signal AC excitation to the battery. The battery management system sends handshake information, including the authentication result and the initial impedance characteristic parameters, to the charging pile; The charging pile verifies the battery's legitimacy based on the authentication result, and generates an initial charging strategy adapted to the current battery state by combining the initial impedance characteristic parameters and the battery's historical health data stored in the cloud. During the charging process, the battery management system continuously applies small-signal AC excitation to update the dynamic impedance characteristic parameters, and periodically reports comprehensive status information, including voltage, current, temperature, state of charge, and the dynamic impedance characteristic parameters, to the charging pile. The charging pile assesses the safety risk status of the battery based on the comprehensive status information, and dynamically adjusts the charging parameters or terminates charging according to the assessment results. After charging is completed, the charging pile will upload the impedance characteristic data, safety assessment records and identity authentication information of this charging process to the cloud for battery health management and anti-counterfeiting traceability.

[0007] Based on the above technical solutions, in the safe charging control method based on battery identity authentication provided in this application, with the surge in demand for high-rate fast charging of electric bicycles and removable power batteries, charging safety issues are becoming increasingly prominent: under high current conditions, it is easy to induce hidden dangers such as overheating, lithium plating, and abnormal internal resistance, while existing systems generally lack effective battery identity verification mechanisms, making it difficult to prevent unauthorized or inferior batteries from accessing the system; at the same time, safety monitoring is mostly limited to a single perspective at the vehicle end or charging pile end, and cannot integrate the battery's microscopic electrochemical state and macroscopic operating data, resulting in delayed risk warnings or "one-size-fits-all" strategies. To address the above problems, this application adopts a technical path of "trustworthy identity + knowable state + collaborative decision-making": using a physically unclonable function (PUF) to achieve dynamic, anti-cloning identity authentication, ensuring that only legitimate devices participate in charging; continuously injecting micro-amplitude AC excitation during fast charging, and extracting key parameters reflecting the battery's internal health status, such as charge transfer impedance and Warburg diffusion coefficient, in real time; and through efficient collaboration between the BMS, charging pile, and cloud—the charging pile end provides millisecond-level local response based on multi-dimensional state information, while the cloud aggregates full lifecycle data for strategy optimization, health assessment, and anti-counterfeiting traceability. This solution effectively overcomes the core bottlenecks of traditional fast charging systems, such as "unrecognizable, unclear, and inaccurate control." While ensuring high-power charging efficiency, it significantly improves safety, personalization, and manageability, laying a reliable foundation for large-scale battery swapping and intelligent battery operation.

[0008] In conjunction with the first aspect above, in one possible implementation, the dynamic authentication of the battery identity includes: After the charging connection is established, the charging pile sends an authentication request containing a random challenge value to the battery management system; The battery management system generates an original response signal based on a physically unclonable function, and performs encryption operations on the original response signal according to the random challenge value to generate a one-time authentication token; The battery management system sends the one-time authentication token to the charging station; The charging station uploads the one-time authentication token, the random challenge value, and the battery identification information to the cloud authentication server. The cloud authentication server verifies the validity of the one-time authentication token based on the pre-stored physical non-cloning function benchmark characteristics of the battery, the random challenge value, and the device behavior context information, and returns the authentication result.

[0009] In conjunction with the first aspect above, in one possible implementation, the physically unclonable function is set in an internal memory cell, which is an SRAM array in a battery management chip. The original response signal is composed of the stable flipping state of each memory cell in the SRAM array during power-on initialization, and a consistent physically unclonable function output is generated after each power-on through error correction code calibration.

[0010] In conjunction with the first aspect above, in one possible implementation, obtaining the initial impedance characteristic parameters characterizing the electrochemical state includes: During the charging initialization phase, the battery management system applies a small-signal AC current excitation to the battery, which contains multiple preset test frequency components. While applying the excitation, the battery management system simultaneously acquires the voltage response signal at the battery terminal and the actual injected current signal, and calculates the complex impedance value at each preset test frequency; based on the complex impedance value at the preset test frequency, it extracts the initial impedance characteristic parameters characterizing the dynamic properties of the battery electrode interface and the lithium-ion diffusion behavior.

[0011] In conjunction with the first aspect above, in one possible implementation, the calculation of the complex impedance value at each frequency point includes: For each of the plurality of preset test frequencies, the battery management system generates a pair of orthogonal reference signals based on the preset test frequency, and uses the orthogonal reference signals to synchronously demodulate the voltage response signal and the current signal respectively, to obtain the real and imaginary components of the voltage and current at the corresponding preset test frequency. Based on the complex voltage and complex current components corresponding to each preset test frequency, the complex impedance value of the battery at that frequency is obtained through complex division.

[0012] In conjunction with the first aspect above, in one possible implementation, the charging pile assesses the safety risk status of the battery based on the comprehensive status information, including: Obtain the current charge transfer impedance of the battery Warburg diffusion coefficient Individual voltage change rate and temperature Wherein, the charge transfer impedance and Warburg diffusion coefficient The complex impedance spectrum is measured based on the small-signal AC excitation applied to the battery and extracted by fitting a preset electrochemical equivalent circuit model; the single-cell voltage change rate is obtained by numerical differentiation calculation of the periodically sampled single-cell voltage sequence; the temperature is directly collected by temperature sensors arranged on the surface of the battery cells. Calculate the safety risk criterion value : ; in, and These are the reference charge transfer impedance and initial diffusion coefficient of the battery in its initial healthy state, respectively. For ambient temperature, As the reference temperature difference, For the cumulative number of battery cycles, As a scaling factor, These are the weighting coefficients; Determine the security risk criterion value Is it greater than or equal to the safety threshold? If yes, the battery is determined to be in a high-risk state, and the charging power is reduced or charging is terminated; otherwise, the charging operation continues. The safety threshold is dynamically adjusted according to the battery's state of charge and temperature.

[0013] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the security threshold includes: Obtain the battery's current state of charge (SOC) and temperature. ; Based on the SOC, calculate the state of charge sensitivity factor. : ; in, , , For the preset charge state segmentation points, The shape factor; Based on the temperature Calculate the temperature decay factor : ; Among them, by analyzing the critical The critical temperature. This is the temperature sensitivity coefficient; Based on the state of charge sensitivity factor and temperature decay factor Through formula The current security threshold is calculated. ;in, This is the baseline safety criterion threshold for the battery under standard conditions.

[0014] In conjunction with the first aspect above, in one possible implementation, the step of dynamically adjusting charging parameters or terminating charging based on the evaluation results includes: Compare the safety risk criterion value that characterizes the battery safety risk state with the dynamic safety threshold; When the safety risk criterion value is less than the first preset proportional threshold, the current charging parameters are maintained. When the safety risk criterion value is greater than or equal to the first preset ratio threshold but does not reach the safety threshold, a new charging strategy is requested from the remote management platform, and the reduced-order charging parameters generated by the remote management platform based on the historical electrochemical state data of similar batteries are received. The charging current or charging stage switching point is adjusted according to the reduced-order charging parameters. When the safety risk criterion value is greater than or equal to the dynamic safety threshold, the charging circuit is cut off and an alarm is triggered. During the execution of the downgraded charging parameters, the changing trend of the quantization criterion value is continuously monitored. If it does not decrease to the safe range within the preset monitoring period, the charging termination operation is executed.

[0015] In conjunction with the first aspect above, in one possible implementation, the cloud constructs a digital twin of the battery based on the uploaded impedance characteristic data, security assessment records, and identity authentication information, uses a graph neural network to predict the remaining cycle life, and generates a suggested maximum allowable charging rate for the next charge, which is then fed back to the user terminal.

[0016] Secondly, this application provides a safe charging control system based on battery identity authentication, including: an identity authentication module, a charging monitoring module, and a management module; wherein, the identity authentication module is used to establish a communication connection between the battery management system and the charging pile after the user initiates a charging operation, and to dynamically authenticate the battery identity and obtain initial impedance characteristic parameters characterizing the electrochemical state; the battery management system sends handshake information including the authentication result and the initial impedance characteristic parameters to the charging pile; the charging pile verifies the legality of the battery according to the authentication result, and generates an initial charging strategy adapted to the current battery state by combining the initial impedance characteristic parameters and the historical health data of the battery stored in the cloud; the charging monitoring module is used to continuously apply small-signal AC excitation to update the dynamic impedance characteristic parameters during the charging process, and periodically report comprehensive status information to the charging pile; the charging pile assesses the safety risk status of the battery based on the comprehensive status information, and dynamically adjusts the charging parameters or terminates charging according to the assessment result; the management module is used to upload the impedance characteristic data, safety assessment record, and identity authentication information of this charging process to the cloud after the charging is completed, for battery health management and anti-counterfeiting traceability.

[0017] This application provides a secure charging control method and system based on battery identity authentication, which effectively solves problems such as high battery safety risks, untrusted identities, coarse strategies, and isolated monitoring in high-rate fast charging scenarios. By integrating a physically unclonable function hardware security module into the battery management system, dynamic identity authentication at the battery level is achieved. During each charging session, the charging pile initiates a random challenge to the battery management system. The battery management system generates a unique response based on the inherent physical microstructure of the chip during manufacturing, forming a one-time authentication token. This mechanism does not require stored keys and possesses anti-copying and anti-replay characteristics, fundamentally eliminating the risk of counterfeit, refurbished, or illegally modified batteries impersonating genuine products to access the supercharging network, providing a highly reliable identity foundation for subsequent implementation of personalized and differentiated charging strategies. Throughout the fast charging process, the system continuously applies millivolt-level multi-frequency small-signal AC excitation, combined with synchronous sampling and orthogonal demodulation technology, to extract electrochemical characteristic parameters with well-defined mechanisms, such as charge transfer impedance and Warburg diffusion coefficient, in real time. These parameters are directly related to the reaction kinetics and solid-phase diffusion capability of lithium ions at the electrode interface, and can sensitively capture early failure signs such as lithium plating precursors, abnormal SEI film growth, or particle contact degradation. By integrating this data with traditional parameters such as voltage, current, temperature, and SOC, a multi-dimensional state perception system is constructed, significantly outperforming traditional solutions that rely solely on macroscopic data from one side (vehicle or charging pile), effectively filling blind spots in safety monitoring. More importantly, this solution breaks down data silos between vehicles and charging piles, establishing a three-way collaborative mechanism between the BMS, charging piles, and the cloud management platform: the charging pile achieves millisecond-level rapid response based on local comprehensive state information (such as dynamic power reduction, early switching to constant voltage, or emergency charging stop), while simultaneously encrypting and uploading key data such as identity authentication results, impedance evolution trajectories, and safety assessment logs to the cloud. The cloud then uses this data to model battery health, mine risk patterns, and perform anti-counterfeiting traceability, feeding back optimized strategy parameters or thresholds to the edge, forming a closed loop of "perception—decision—execution—learning." Thus, this application achieves four layers of security protection: "trusted identity access + real-time state perception + edge-cloud joint decision-making + full lifecycle closed loop," significantly improving the safety and intelligence of the fast charging process and providing a scalable and auditable technical foundation for future large-scale battery swapping networks, digital management of battery assets, and carbon footprint tracking.

[0018] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0019] Figure 1 A system architecture diagram of a safe charging control system based on battery identity authentication is provided for embodiments of this application; Figure 2 A flowchart illustrating a safe charging control method based on battery identity authentication provided in an embodiment of this application; Figure 3 This is a flowchart illustrating a dynamic authentication method for battery identity provided in an embodiment of this application. Detailed Implementation

[0020] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] The safe charging control method based on battery identity authentication provided in this application embodiment can be applied to, for example... Figure 1 The safety charging control system based on battery identity authentication shown includes: an identity authentication module, a charging monitoring module, and a management module. The identity authentication module is used to establish a communication connection between the battery management system and the charging pile after the user initiates a charging operation, and to dynamically authenticate the battery identity and obtain the initial impedance characteristic parameters that characterize the electrochemical state. The battery management system sends handshake information, including authentication results and initial impedance characteristic parameters, to the charging station; The charging station verifies the battery's legitimacy based on the authentication results and, in conjunction with the initial impedance characteristic parameters and the battery's historical health data stored in the cloud, generates an initial charging strategy adapted to the current battery state. The charging monitoring module is used to continuously apply small-signal AC excitation to the battery management system to update the dynamic impedance characteristic parameters during the charging process, and periodically report comprehensive status information to the charging pile. The charging station assesses the safety risk status of the battery based on comprehensive status information and dynamically adjusts the charging parameters or terminates charging according to the assessment results. The management module is used to upload impedance characteristic data, safety assessment records and identity authentication information of the charging process to the cloud after the charging is completed, for battery health management and anti-counterfeiting traceability.

[0022] To address the technical problems of existing technologies failing to effectively verify battery identity and lacking real-time sensing capabilities of the battery's internal electrochemical state, thus hindering the prevention of unauthorized or substandard batteries from being connected and providing early warnings of microscopic failure risks during high-rate fast charging, this application provides a safe charging control method based on battery identity authentication. This method includes: After the user initiates a charging operation, the battery management system establishes a communication connection with the charging pile, performs dynamic authentication of the battery identity based on the built-in physical non-cloning function, and obtains the initial impedance characteristic parameters characterizing the electrochemical state by applying a small-signal AC excitation to the battery. The battery management system sends handshake information, including authentication results and initial impedance characteristic parameters, to the charging station; The charging station verifies the battery's legitimacy based on the authentication results and, in conjunction with the initial impedance characteristic parameters and the battery's historical health data stored in the cloud, generates an initial charging strategy adapted to the current battery state. During the charging process, the battery management system continuously applies small-signal AC excitation to update the dynamic impedance characteristic parameters, and periodically reports comprehensive status information, including voltage, current, temperature, state of charge and dynamic impedance characteristic parameters, to the charging pile. The charging station assesses the safety risk status of the battery based on comprehensive status information and dynamically adjusts the charging parameters or terminates charging according to the assessment results. After charging is completed, the charging station will upload the impedance characteristic data, safety assessment records and identity authentication information of this charging process to the cloud for battery health management and anti-counterfeiting traceability.

[0023] Based on this, this application constructs a closed-loop charging security system that integrates native hardware security capabilities with electrochemical mechanism perception. Its core advantages lie in: no longer relying on easily copied software identifiers or static keys, but instead using a physical fingerprint generated by the PUF as the root of trust, ensuring that only batteries with legitimate hardware identities can access the system; simultaneously, it breaks through the limitations of traditional BMS systems that only monitor macroscopic electrical quantities, continuously "seeing through" the internal state of the battery using embedded small-signal excitation technology without interrupting charging, transforming invisible diffusion impedance, interface reactions, and other key aging indicators into quantifiable, transmittable, and decision-making security inputs. Furthermore, the system incorporates single-charge behavior into the entire battery lifecycle management—the initial strategy is personalized based on historical health data, process control is dynamically optimized based on real-time risks, and post-charge data feeds back into the cloud model iteration. This closed-loop mechanism of "authentication—perception—decision—feedback" not only effectively blocks the access path of unauthorized devices but also achieves accurate identification and flexible control of aging and deteriorating batteries, balancing security, compatibility, and user experience, providing a feasible technical paradigm for the safe and large-scale application of high-density lithium batteries in consumer scenarios.

[0024] like Figure 2 As shown in the embodiment of this application, a safe charging control method based on battery identity authentication is provided, including: S201. After the user initiates a charging operation, the Battery Management System (BMS) establishes a communication connection with the charging pile, and performs dynamic authentication of the battery identity based on the built-in Physical Unclonable Function (PUF), and obtains the initial impedance characteristic parameters characterizing the electrochemical state by applying a small-signal AC excitation to the battery.

[0025] In one embodiment of this application, when a user initiates a charging operation (e.g., inserting the battery into a charging station or connecting a charging gun), a communication connection is first established between the BMS and the charging station. The communication connection can be implemented via a wired interface (such as an isolated CAN bus or UART) or a wireless method (such as a power line carrier PLC or Bluetooth Low Energy BLE), and enters the security initialization phase after the physical layer handshake is completed.

[0026] It should be noted that the excitation signal is a multi-frequency composite sine wave signal, containing four frequency components: 10Hz, 50Hz, 200Hz and 1kHz. The total amplitude does not exceed 1% of the current open-circuit voltage of the battery to ensure that it does not interfere with the subsequent main charging process.

[0027] S202, the battery management system sends handshake information, including authentication results and initial impedance characteristic parameters, to the charging pile.

[0028] It should be noted that after completing dynamic battery identity authentication and initial electrochemical state perception, the BMS integrates the results into structured handshake information and sends it to the charging station via the established communication link. The handshake information includes, but is not limited to: the identity authentication result (e.g., a status indicator of "authentication successful" or "authentication failed"), a summary of the one-time authentication token generated by a physically unclonable function (PUF), and initial impedance characteristic parameters characterizing the battery's current electrochemical state. These initial impedance characteristic parameters include at least charge transfer impedance and the Warburg diffusion coefficient, both extracted from complex impedance spectra measured by multi-frequency small-signal AC excitation.

[0029] In a preferred embodiment, the handshake information is encapsulated in a predefined data frame format, including a verification field to ensure transmission integrity. The BMS generates and sends this information within the initialization window before the main charging circuit is activated, ensuring that the charging pile has obtained both the battery's identity credibility and micro-health status before initiating the charging strategy. Upon receiving the handshake information, the charging pile determines whether to allow entry into the subsequent charging phase and uses it as one of the input conditions for generating initial charging parameters. This mechanism achieves synergy between secure access control and state awareness, providing fundamental data support for building a highly reliable charging system.

[0030] S203. The charging pile verifies the legality of the battery identity based on the authentication result; if not, the charging pile will alert the user of charging failure via voice prompt or light; if yes, the user interface will prompt whether to confirm charging; if yes, an initial charging strategy adapted to the current battery state will be generated based on the initial impedance characteristic parameters and the historical health data of the battery stored in the cloud; if not, charging will not proceed. It should be noted that by authorizing or using trusted genuine devices to determine the effectiveness of the battery, the system analyzes the authentication results of the charging pile after receiving the handshake information sent by the BMS, and verifies the battery's legitimacy accordingly. Here, "legitimacy" refers to whether the battery is a genuine device authorized by the manufacturer, possesses a unique and trusted identifier, and is not listed in the list of prohibited or abnormal devices, rather than merely referring to the validity of the communication data format.

[0031] If the authentication result indicates that the battery identity is invalid (e.g., authentication failed, PUF response does not match cloud record, or the device is blacklisted), the charging station will trigger an alarm through the local human-machine interaction unit. The alarm includes, but is not limited to: a voice announcement of "Charging failed, battery unauthorized", LED indicator lights displaying the abnormal status with a specific color or flashing frequency, and prohibiting the start of the charging main circuit.

[0032] If the authentication result indicates that the battery is legitimate, the charging station prompts the user through the user interface (such as a display screen or mobile app) with "Legitimate battery detected, start charging?", and waits for the user's confirmation. If the user selects "No" or there is no response after a timeout, the station remains in standby mode and charging is not performed. If the user confirms "Yes", the charging station further combines the initial impedance characteristic parameters (such as charge transfer impedance and Warburg diffusion coefficient) contained in the handshake information, as well as the battery's historical health data (including cumulative cycle count, historical impedance evolution trend, aging rate, and usage environment records) obtained from the cloud management platform, to calculate an initial charging strategy adapted to the current battery state through a preset strategy generation model. The initial charging strategy includes parameters such as the constant current stage current amplitude, constant voltage switching threshold, and maximum allowable charging power, aiming to balance charging efficiency and electrochemical safety.

[0033] S204. During the charging process, the battery management system continuously applies small-signal AC excitation to update the dynamic impedance characteristic parameters, and periodically reports comprehensive status information, including voltage, current, temperature, state of charge and dynamic impedance characteristic parameters, to the charging pile.

[0034] S205. The charging pile assesses the safety risk status of the battery based on comprehensive status information, and dynamically adjusts the charging parameters or terminates charging according to the assessment results.

[0035] S206. After charging is completed, the charging station will upload the impedance characteristic data, safety assessment records and identity authentication information of this charging process to the cloud for battery health management and anti-counterfeiting traceability.

[0036] Based on the above technical solutions, this application provides a safe charging control method based on battery identity authentication. In the context of the rapid popularization of electric vehicles and replaceable power batteries, while high-rate fast charging (supercharging) improves the user experience, it significantly amplifies safety risks: high current can easily cause irreversible damage such as local overheating, lithium crystal deposition, and sudden changes in internal resistance. Existing charging systems generally lack the ability to verify the true identity of the battery, making it difficult to distinguish between original batteries and high-risk counterfeit products. At the same time, safety monitoring largely relies on data from charging piles or BMS alone, failing to integrate microscopic electrochemical states and macroscopic operating parameters for comprehensive judgment, leading to delayed or misjudged risk identification. Therefore, this application proposes a safe charging control scheme that integrates hardware-level identity authentication, real-time impedance sensing, and cloud-edge collaborative decision-making. Dynamic and unclonable authentication is achieved through Physically Unclonable Functions (PUFs), ensuring that only legitimate batteries are connected. Embedded small-signal excitation technology continuously acquires impedance characteristic parameters representing interface reactions and ion diffusion during fast charging, enabling mechanism-level perception of early failures. A closed-loop data system connecting the battery management system (BMS), charging pile, and cloud enables the charging pile to adjust or terminate charging in real time based on its local multi-dimensional status, while the cloud accumulates full lifecycle data for strategy optimization and anti-counterfeiting traceability. This solution fundamentally solves the industry challenges of "untrustworthy identity, unknowable status, and uncoordinated decision-making," not only improving the safety and personalization of fast charging but also providing key technological support for building a trustworthy, traceable, and intelligent battery ecosystem.

[0037] In one possible implementation of the embodiments of this application, such as Figure 3 As shown, the dynamic authentication of the battery identity in S201 can be implemented through the following steps: S301, S302, S303, S304, and S305. These steps are explained in detail below: S301. After the charging connection is established, the charging pile sends an authentication request containing a random challenge value to the battery management system. S302 The battery management system generates the original response signal based on the physical non-cloning function, and performs encryption calculation on the original response signal according to the random challenge value to generate a one-time authentication token; S303, the battery management system sends a one-time authentication token to the charging station; S304. The charging station uploads the one-time authentication token, random challenge value and battery identification information to the cloud authentication server. S305 The cloud authentication server verifies the validity of the one-time authentication token based on the pre-stored PUF baseline characteristics of the battery, random challenge value, and device behavior context information, and returns the authentication result.

[0038] In one embodiment of this application, the charging safety authentication process is initiated after the battery and the charging pile have completed the physical connection and established a communication link.

[0039] Specifically, the charging pile generates a random number as a challenge value and encapsulates this challenge value in an authentication request message, sending it to the BMS. The BMS has a built-in security module based on PUF. In a preferred implementation, PUF utilizes the stable toggling state of each storage bit in the SRAM cell within the BMS main control chip during power-on initialization as a source of physical entropy. Due to the microscopic randomness of the manufacturing process, the power-on initial state of the SRAM of each chip is unique and cannot be copied, thus constituting a device-level hardware fingerprint.

[0040] The Battery Management System (BMS) first reads the original response signal from the SRAM PUF and calibrates it using pre-stored error-correcting codes (such as BCH or LDPC codes) to eliminate bit flips caused by environmental noise, generating a stable baseline PUF key. Subsequently, the BMS concatenates the received random challenge value with the calibrated PUF key and inputs it into a lightweight cryptographic unit. In one embodiment, the SHA-3 series hash algorithm is used to perform a one-way mapping on the concatenated data, generating a fixed-length one-time authentication token. This token is only valid for the current charging session and cannot be reverse-engineered to recover the PUF key or predict the next response. The BMS returns the one-time authentication token to the charging station via an established communication channel (such as a CAN bus or power line carrier). Upon receiving the authentication token, the charging station encapsulates it along with the originally sent random challenge value and battery identification information obtained from the BMS (such as battery serial number, model code, or device unique ID), and uploads it to a cloud authentication server via a wireless network (such as 4G / 5G or Wi-Fi). The cloud-based authentication server maintains a trusted database of registered batteries. For each legitimate battery, it stores its corresponding PUF baseline characteristics (i.e., the calibrated original PUF key or its bound digest), historical charge / discharge records, geographical location trajectory, and abnormal behavior markers, among other device behavior context information. Upon receiving an authentication request, the server first retrieves the corresponding PUF baseline characteristics based on the battery identification information. Then, using the same hash algorithm and concatenation rules, it recalculates the expected authentication token based on the PUF baseline characteristics and the received random challenge value. If the calculation result matches the uploaded one-time authentication token, and the device behavior context does not trigger risk rules (such as frequent charging across regions within a short period), the authentication is considered successful; otherwise, the device is deemed illegal or a high-risk device.

[0041] The cloud-based authentication server returns the authentication result (e.g., "legitimate," "illegal," or "requires manual review") to the charging station. The charging station then determines whether to allow the user to proceed to the next charging stage based on this result.

[0042] Based on the aforementioned technical solutions, lithium-ion batteries are widely used in electric vehicles and portable energy storage devices. However, the market is flooded with refurbished, cloned, or substandard batteries, which often impersonate legitimate devices by copying genuine serial numbers, easily leading to overheating, fires, and other safety accidents. Traditional authentication methods based on static IDs or pre-stored keys are easily stolen or reused, failing to effectively prevent hardware-level forgery. Furthermore, existing charging systems lack the ability to perceive the microscopic electrochemical state of batteries, making intervention difficult in early failure stages such as lithium plating. Therefore, this application proposes a dynamic authentication mechanism integrating PUF and cloud collaboration: utilizing the inherent physical randomness of chip manufacturing to generate unclonable hardware fingerprints, combined with a random challenge-response protocol and one-time tokens, fundamentally eliminating identity spoofing; and identifying abnormal usage patterns by comparing device behavior context in the cloud. This solution requires no key storage, resists physical detection and replay attacks, and is suitable for low-cost BMS platforms. Compared to traditional methods, it significantly improves the credibility of battery identity authentication and the overall system security boundary, providing key technical support for building a traceable, anti-counterfeiting, and intelligent charging ecosystem.

[0043] In one possible implementation of this application embodiment, the acquisition of the initial impedance characteristic parameters in S201 can be specifically achieved through the following S401, S402, and S403, which are described in detail below: S401. During the charging initialization phase, the battery management system applies a small-signal AC current excitation to the battery, which contains multiple preset test frequency components.

[0044] Among them, multiple preset test frequencies are configured to cover the key frequency bands of the battery electrochemical response, so as to excite ohmic resistance, charge transfer reaction and ion diffusion process respectively; S402. While applying the excitation, the battery management system simultaneously collects the voltage response signal at the battery terminal and the actual injected current signal, and calculates the complex impedance value at each preset test frequency. Specifically, for each of the multiple preset test frequencies, the battery management system generates a pair of orthogonal reference signals based on the preset test frequency; Generate a quadrature reference signal with the same frequency as the preset test frequency, including one reference signal in phase with the excitation and one reference signal with the same phase. The voltage response signal is multiplied by two orthogonal reference signals and then low-pass filtered to obtain the real and imaginary response components of the voltage at the preset test frequency. The actual injected current signal is multiplied by two orthogonal reference signals and then low-pass filtered to obtain the real and imaginary excitation components of the current at the preset test frequency. Based on the real and imaginary parts of voltage and current at a preset test frequency, the complex impedance value corresponding to the preset test frequency is calculated through complex number division.

[0045] S403. Based on the complex impedance value at a preset test frequency, extract the initial impedance characteristic parameters that characterize the dynamic properties of the battery electrode interface and the lithium-ion diffusion behavior.

[0046] The above technical solution uses discrete functions to assess the initial impedance characteristic parameters for safety risk status evaluation, creating a battery health assessment system. Based on parameter adjustments using the impedance error function, it analyzes complex impedance values, the ion diffusion process, and adjusts parameters in conjunction with the safety risk assessment results. This system addresses whether the battery should be charged and provides comprehensive analysis of voltage and current signals.

[0047] In one embodiment of this application, during the charging initialization phase, before the main charging current is initiated, the BMS performs an initial electrochemical impedance spectroscopy (EIS) measurement to obtain key parameters reflecting the battery's microstate. This process includes the following steps: The BMS controls its built-in constant current excitation source to inject a small-signal AC current excitation into the battery cell or battery pack. The excitation signal is a composite sinusoidal signal containing multiple preset test frequency components, such as four frequency points: 10Hz, 50Hz, 200Hz, and 1kHz. These preset test frequencies are configured to cover key frequency bands of the battery's electrochemical response: the high-frequency band (e.g., 1kHz) is used to excite the ohmic resistance (R_Omega), the mid-frequency band (e.g., 50–200Hz) is used to characterize charge transfer reaction kinetics, and the low-frequency band (e.g., 10Hz) is used to detect the solid-phase diffusion behavior of lithium ions in the electrode material.

[0048] While applying the excitation, the analog front-end (AFE) module of the BMS synchronously acquires the voltage response signal at the battery terminal and the actual injected current signal, with a sampling rate of no less than 10 times the highest excitation frequency to satisfy the Nyquist sampling theorem.

[0049] For each preset test frequency ( The BMS's microcontroller unit (MCU) generates a pair of orthogonal reference signals with the same frequency: one is a cosine signal (in phase with the excitation), and the other is a sine signal (90° orthogonal in phase). The voltage response signal is digitally multiplied by the two orthogonal reference signals, and the DC component is extracted by a digital low-pass filter, thus obtaining the real part of the voltage response at that frequency. and imaginary response components ( Similarly, performing the same operation on the actual injected current signal yields the real excitation component of the current. ) and imaginary excitation components ( ).

[0050] Based on the above components, using the formula The impedance composite value at this frequency was calculated. j represents the imaginary unit and is the complex number operator. Based on the complex impedance values ​​at various preset test frequencies, and combined with a preset electrochemical equivalent circuit model (such as the Randles model), the system extracts initial impedance characteristic parameters that characterize the battery state through a parameter fitting algorithm. These initial impedance characteristic parameters include charge transfer impedance (reflecting the electrode interface reaction rate) and Warburg diffusion coefficient (reflecting lithium-ion diffusion capability), which are used for subsequent safety risk assessment and charging strategy generation.

[0051] Based on the above technical solutions, in electric vehicles and light energy storage applications, battery safety accidents often stem from early lithium plating, abnormal SEI film growth, or electrode diffusion obstruction—microscopic electrochemical failures. Traditional charging systems rely solely on macroscopic parameters such as voltage, current, and temperature, making it difficult to provide timely warnings before thermal runaway. Furthermore, existing impedance measurement methods often require dedicated equipment, power-off operation, or long-term scanning, making them unsuitable for real-time operation within low-cost, high-noise consumer-grade BMSs. Therefore, this application proposes a technical solution that synchronously injects multi-frequency small-signal excitation during the charging initialization phase and rapidly extracts complex impedance based on orthogonal demodulation. By pre-setting test frequencies covering key frequency bands of ohms, charge transfer, and diffusion, and employing phase-locked loop detection to perform orthogonal demodulation of voltage / current signals at the same frequency, complex impedance values ​​with high signal-to-noise ratios at various frequency points can be obtained within milliseconds. This allows for the fitting of physically meaningful characteristic parameters such as charge transfer impedance and Warburg diffusion coefficient. This solution requires no additional hardware, reuses existing AFE and MCU resources in the BMS, and combines real-time performance, low cost, and electrochemical mechanism rationality. Compared to traditional threshold alarms, it can identify internal battery degradation trends in advance, providing a scientific basis for dynamically adjusting charging strategies and significantly improving charging safety and battery lifespan.

[0052] In one possible implementation of this application embodiment, the initial impedance characteristic parameters in S204 can be obtained through the following S501, S502, and S503, which are described in detail below: S501, Obtain the current charge transfer impedance of the battery. Warburg diffusion coefficient Individual voltage change rate and temperature .

[0053] Among them, charge transfer impedance and Warburg diffusion coefficient The complex impedance spectrum was measured based on the small-signal AC excitation applied to the battery and extracted by fitting a pre-defined electrochemical equivalent circuit model. Specifically, based on the measured complex impedance spectrum, the system uses a pre-defined electrochemical equivalent circuit model (such as the classic Randles model) to fit its parameters. The Randles model typically includes ohmic resistance. Charge transfer resistance With double-layer capacitance The parallel branches formed, and the Warburg impedance element characterizing the ion diffusion process. The Warburg impedance is expressed in the frequency domain as: ;in, The angular frequency of the excitation signal; By employing nonlinear least squares, the Levenberg-Marquardt algorithm, or other optimization methods, the parameters of each component in the model are adjusted to minimize the error between the impedance curve calculated by the model and the measured complex impedance spectrum. After fitting, the obtained... This refers to the charge transfer impedance, which reflects the rate of charge transfer kinetics at the electrode / electrolyte interface. The Warburg diffusion coefficient is used to characterize the solid-phase diffusion resistance of lithium ions in electrode active materials.

[0054] The rate of change of cell voltage is obtained by numerical differentiation of the periodically sampled cell voltage sequence; the temperature is directly collected by temperature sensors placed on the surface of the cell. S502, Calculate the safety risk criterion value : ; in, and These are the reference charge transfer impedance and initial diffusion coefficient of the battery in its initial healthy state, respectively. For ambient temperature, As the reference temperature difference, For the cumulative number of battery cycles, As a scaling factor, These are weighting coefficients, all of which follow... Increased and adaptively adjusted to enhance sensitivity to aging batteries; It should be noted that the scaling factor The adjustment parameter, used to characterize the impact of lithium-ion diffusion degradation on safety risks, is pre-calibrated based on the battery chemistry system and historical fault data. For example, for a graphite anode / ternary cathode system, It can be set to 3.0; for lithium iron phosphate systems, due to the more stable diffusion path, It can be set to 1.8. This parameter can be written into the battery's identity file at the factory and dynamically optimized by the cloud management platform based on specific operating data.

[0055] It should be noted that the reference temperature difference This is a preset constant used to measure the current temperature rise. Normalized to a dimensionless thermal risk factor. Its value is determined based on the safe temperature rise boundary of the battery chemistry system, for example, for ternary lithium-ion batteries. This setting can be set to 20°C, indicating that when the battery surface temperature exceeds the ambient temperature by 20°C, the heat-related risks increase significantly. This parameter can be written into the device configuration file according to the battery model, or dynamically optimized by the cloud management platform based on group thermal behavior data.

[0056] S503, Criterion for Determining Safety Risks Is it greater than or equal to the safety threshold? If yes, the battery is determined to be in a high-risk state, and the charging power is reduced or charging is terminated; otherwise, the charging operation continues.

[0057] The safety threshold is dynamically adjusted based on the battery's state of charge and temperature. The method for obtaining this threshold includes the following steps: Step P1: Obtain the battery's current state of charge (SOC) and temperature. ; Step P2: Calculate the state of charge sensitivity factor based on SOC. ; Specifically, when SOC is in the low range ( When the SOC is high, a Gaussian function is used to reflect the characteristic that the risk of lithium plating increases as the SOC decreases; when the SOC is in the high range ( When the SOC increases, another Gaussian function is used to reflect the trend that the risk of cathode oxidation and gas generation increases with increasing SOC: ; in, , , Preset charge state segmentation points (e.g., 20%, 50%, 90%). The shape factor; It should be pointed out that, Controlling the SOC range (e.g., <50%) Sensitivity to SOC.

[0058] The larger the value, the narrower the Gaussian curve becomes, and the safety threshold is significantly reduced only at extremely low SOC (e.g., <10%) (emphasizing the concentrated area of ​​lithium plating risk). The smaller the value, the wider the curve becomes, and the safety boundary is gradually tightened starting from 30% SOC.

[0059] : Control the response strength to overcharging / gas production risks in high SOC ranges (e.g., >50%).

[0060] The larger the value, the lower the threshold will be when the battery is close to full charge (e.g., >95%). The smaller the value, the more likely an early warning will be issued starting from 80% SOC.

[0061] therefore, Essentially, it involves mapping the battery material system and failure mechanism to adjustable parameters in the safety strategy.

[0062] Shape adjustment coefficient and These are not fixed constants, but rather configuration parameters determined based on the battery chemistry system, manufacturing process, and historical operating data. In one embodiment, their initial values ​​are obtained through laboratory accelerated aging and lithium plating boundary tests and stored in the battery identification file. For example, shape adjustment factor and Based on the pre-calibrated positive and negative electrode material system of the battery. For example, for a graphite negative electrode / ternary positive electrode system, , For lithium iron phosphate systems, the lithium plating window is relatively narrow. The value is higher.

[0063] In another embodiment, the cloud management platform uses a statistical model of battery failure to... and Periodic optimizations are performed, and firmware updates are distributed to charging stations or BMS to achieve continuous evolution of safety policies.

[0064] For example, the shape adjustment factor is dynamically updated by the cloud management platform based on the historical safety event distribution of similar batteries. If a batch of batteries frequently experiences lithium plating alarms around SOC=15%, the factor is automatically increased. This makes the safety threshold more sensitive in that area.

[0065] Based on temperature Calculate the temperature decay factor : ; Among them, by analyzing the critical The critical temperature. This is the temperature sensitivity coefficient; the Sigmoid function ensures that the temperature is close to or exceeds [a certain threshold]. At that time, the safety threshold is significantly reduced.

[0066] It should be noted that the temperature sensitivity coefficient This is an adjustable parameter used to characterize the sensitivity of a battery's thermal risk to increasing temperature near its critical temperature. Its value is pre-calibrated based on the battery's chemistry, manufacturing process, and historical thermal event data. For example, for ternary lithium-ion batteries, It can be set to 0.6–1.0; for lithium iron phosphate batteries, It can be set to 0.1–0.3. This parameter can be stored in the battery identity profile and periodically optimized by the cloud management platform based on group operation data.

[0067] Step P3: Based on the state of charge sensitivity factor and temperature decay factor Through formula The current security threshold is calculated. ;in, This is the baseline safety criterion threshold for the battery under standard conditions (e.g., 25°C, SOC=50%, new battery).

[0068] Based on the above technical solutions, in electric vehicles and light energy storage applications, battery safety accidents often originate from microscopic electrochemical anomalies such as early lithium plating, interface degradation, or diffusion obstruction. Traditional charging systems rely solely on macroscopic parameters such as voltage, current, and temperature, making it difficult to provide accurate early warnings before thermal runaway. This is especially true for aged batteries, whose safety margin is significantly reduced, and fixed threshold strategies are prone to missed detections or misjudgments. Therefore, this application proposes an intelligent risk assessment mechanism that integrates multidimensional electrochemical characteristics and dynamic safety boundaries: charge transfer impedance and Warburg diffusion coefficient are extracted online through small-signal excitation, and combined with voltage change rate and temperature rise to construct a nonlinear weighted safety risk criterion; simultaneously, a weighting coefficient that adaptively adjusts with the number of cycles is introduced to enhance sensitivity to aged batteries. More importantly, the safety threshold is not fixed but dynamically generated based on SOC and temperature—automatically tightening the safety boundary in low / high SOC ranges and high-temperature regions, truly reflecting the failure risk distribution of the battery under different operating conditions. This solution represents a leap from "static threshold" to "mechanism-driven, state-adaptive" approaches, significantly improving charging safety and policy accuracy, and providing an engineerable, highly reliable safety control framework for low-cost consumer-grade BMS.

[0069] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

Claims

1. A safe charging control method based on battery identity authentication, characterized in that, include: After the user initiates a charging operation, the battery management system establishes a communication connection with the charging pile, performs dynamic authentication of the battery identity based on the built-in physical non-cloning function, and obtains the initial impedance characteristic parameters characterizing the electrochemical state by applying a small-signal AC excitation to the battery. The battery management system sends handshake information, including the authentication result and the initial impedance characteristic parameters, to the charging pile; The charging pile verifies the battery's legitimacy based on the authentication result, and generates an initial charging strategy adapted to the current battery state by combining the initial impedance characteristic parameters and the battery's historical health data stored in the cloud. During the charging process, the battery management system continuously applies small-signal AC excitation to update the dynamic impedance characteristic parameters, and periodically reports comprehensive status information, including voltage, current, temperature, state of charge, and the dynamic impedance characteristic parameters, to the charging pile. The charging pile assesses the safety risk status of the battery based on the comprehensive status information, and dynamically adjusts the charging parameters or terminates charging according to the assessment results. After charging is completed, the charging pile will upload the impedance characteristic data, safety assessment records and identity authentication information of this charging process to the cloud for battery health management and anti-counterfeiting traceability.

2. The safe charging control method based on battery identity authentication according to claim 1, characterized in that, The dynamic authentication of the battery identity includes: After the charging connection is established, the charging pile sends an authentication request containing a random challenge value to the battery management system; The battery management system generates an original response signal based on a physically unclonable function, and performs encryption operations on the original response signal according to the random challenge value to generate a one-time authentication token; The battery management system sends the one-time authentication token to the charging station; The charging station uploads the one-time authentication token, the random challenge value, and the battery identification information to the cloud authentication server. The cloud authentication server verifies the validity of the one-time authentication token based on the pre-stored physical non-cloning function benchmark characteristics of the battery, the random challenge value, and the device behavior context information, and returns the authentication result.

3. The safe charging control method based on battery identity authentication according to claim 1, characterized in that, The physically unclonable function is located in an internal memory cell, which is an SRAM array in the battery management chip. The original response signal is composed of the stable flipping state of each memory cell in the SRAM array during power-on initialization, and a consistent physically unclonable function output is generated after each power-on through error correction code calibration.

4. The safe charging control method based on battery identity authentication according to claim 1, characterized in that, The acquisition of initial impedance characteristic parameters characterizing the electrochemical state includes: During the charging initialization phase, the battery management system applies a small-signal AC current excitation to the battery, which contains multiple preset test frequency components. While applying the excitation, the battery management system simultaneously acquires the voltage response signal at the battery terminal and the actual injected current signal, and calculates the complex impedance value at each preset test frequency; based on the complex impedance value at the preset test frequency, it extracts the initial impedance characteristic parameters characterizing the dynamic properties of the battery electrode interface and the lithium-ion diffusion behavior.

5. The safe charging control method based on battery identity authentication according to claim 4, characterized in that, The calculation of the complex impedance value at each frequency point includes: For each of the plurality of preset test frequencies, the battery management system generates a pair of orthogonal reference signals based on the preset test frequency, and uses the orthogonal reference signals to synchronously demodulate the voltage response signal and the current signal respectively, to obtain the real and imaginary components of the voltage and current at the corresponding preset test frequency. Based on the complex voltage and complex current components corresponding to each preset test frequency, the complex impedance value of the battery at that frequency is obtained through complex division.

6. The safe charging control method based on battery identity authentication according to claim 1, characterized in that, The charging pile assesses the battery's safety risk status based on the comprehensive status information, including: Obtain the current charge transfer impedance of the battery Warburg diffusion coefficient Individual voltage change rate and temperature Wherein, the charge transfer impedance and Warburg diffusion coefficient The complex impedance spectrum is measured based on the small-signal AC excitation applied to the battery and extracted by fitting a preset electrochemical equivalent circuit model; the single-cell voltage change rate is obtained by numerical differentiation calculation of the periodically sampled single-cell voltage sequence; the temperature is directly collected by temperature sensors arranged on the surface of the battery cells. Calculate the safety risk criterion value : ； in, and These are the reference charge transfer impedance and initial diffusion coefficient of the battery in its initial healthy state, respectively. For ambient temperature, As the reference temperature difference, For the cumulative number of battery cycles, As a scaling factor, These are the weighting coefficients; Determine the security risk criterion value Is it greater than or equal to the safety threshold? If yes, the battery is determined to be in a high-risk state, and the charging power is reduced or charging is terminated; otherwise, the charging operation continues. The safety threshold is dynamically adjusted according to the battery's state of charge and temperature.

7. The safe charging control method based on battery identity authentication according to claim 6, characterized in that, The method for obtaining the security threshold includes: Obtain the battery's current state of charge (SOC) and temperature. ; Based on the SOC, calculate the state of charge sensitivity factor. : ； in, , , For the preset charge state segmentation points, The shape factor; Based on the temperature Calculate the temperature decay factor : ； Among them, by analyzing the critical The critical temperature. This is the temperature sensitivity coefficient; Based on the state of charge sensitivity factor and temperature decay factor Through formula The current security threshold is calculated. ;in, This is the baseline safety criterion threshold for the battery under standard conditions.

8. The safe charging control method based on battery identity authentication according to claim 1, characterized in that, The method of dynamically adjusting charging parameters or terminating charging based on evaluation results includes: Compare the safety risk criterion value that characterizes the battery safety risk state with the dynamic safety threshold; When the safety risk criterion value is less than the first preset proportional threshold, the current charging parameters are maintained. When the safety risk criterion value is greater than or equal to the first preset ratio threshold but does not reach the safety threshold, a new charging strategy is requested from the remote management platform, and the reduced-order charging parameters generated by the remote management platform based on the historical electrochemical state data of similar batteries are received. The charging current or charging stage switching point is adjusted according to the reduced-order charging parameters. When the safety risk criterion value is greater than or equal to the dynamic safety threshold, the charging circuit is cut off and an alarm is triggered. During the execution of the downgraded charging parameters, the changing trend of the quantization criterion value is continuously monitored. If it does not decrease to the safe range within the preset monitoring period, the charging termination operation is executed.

9. A safe charging control method based on battery identity authentication according to claim 1, characterized in that, Based on the uploaded impedance characteristic data, safety assessment records, and identity authentication information, the cloud platform constructs a digital twin of the battery, uses a graph neural network to predict the remaining cycle life, and generates a suggested maximum allowable charging rate for the next charge, which is then fed back to the user terminal.

10. A safe charging control system based on battery identity authentication, operating based on the safe charging control method based on battery identity authentication according to any one of claims 1-9, characterized in that, Includes an identity authentication module, a charging monitoring module, and a management module; The identity authentication module is used to establish a communication connection between the battery management system and the charging pile after the user initiates a charging operation, and to dynamically authenticate the battery identity and obtain the initial impedance characteristic parameters characterizing the electrochemical state. The battery management system sends handshake information, including the authentication result and the initial impedance characteristic parameters, to the charging pile; The charging pile verifies the battery's legitimacy based on the authentication result, and generates an initial charging strategy adapted to the current battery state by combining the initial impedance characteristic parameters and the battery's historical health data stored in the cloud. The charging monitoring module is used to continuously apply small-signal AC excitation to update dynamic impedance characteristic parameters during the charging process, and periodically report comprehensive status information to the charging pile. The charging pile assesses the safety risk status of the battery based on the comprehensive status information, and dynamically adjusts the charging parameters or terminates charging according to the assessment results. The management module is used to upload the impedance characteristic data, safety assessment records and identity authentication information of the charging process to the cloud after the charging is completed, for battery health management and anti-counterfeiting traceability.

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