Multi-device compatible intelligent fast-charging lithium ion battery system and protocol matching method

Abstract

The invention relates to the technical field of lithium ion battery charging, and discloses a multi-device compatible intelligent fast-charging lithium ion battery system and a protocol matching method, the system comprises a charging device, the charging interface of which is used for being connected with a target device to charge a lithium ion battery pack of the target device; the battery management equipment acquires an initial communication signal of the connected target equipment in real time when the charging interface is connected with the target equipment, so as to extract a voltage fluctuation feature, a handshake time sequence feature and a protocol identification feature for generating an equipment feature vector; performing similarity matching on the equipment feature vector and the protocol feature parameter to obtain a candidate protocol set; according to the historical charging efficiency data of each candidate protocol in the candidate protocol set and the real-time load state of the target equipment, obtaining an optimal charging protocol and a corresponding charging curve template; and charging the lithium ion battery pack according to the optimal charging protocol and the charging curve template. And efficient, flexible and safe charging control can be realized.

Description

Technical Field

[0001] This application relates to the technical field of parameter optimization, and particularly to an intelligent fast-charging lithium-ion battery system compatible with multiple devices and a protocol matching method. Background Art

[0002] With the diversified development of intelligent terminal devices (such as mobile phones, tablets, laptops, etc.), the charging protocols of their supporting lithium-ion batteries show a high degree of fragmentation. The fast-charging technologies currently on the market generally have the following limitations: (1) Single charging protocol binding, only supporting specific brand or model devices, requiring users to manually switch modes, with poor compatibility; (2) Dependence on a preset fixed charging curve, unable to dynamically adjust parameters according to the real-time load status of the device (such as temperature, remaining battery level, battery health), easily resulting in low charging efficiency or battery life attenuation; (3) Coarse protocol recognition logic, traditional solutions only match protocols through voltage / current thresholds or simple handshake signals, lacking multi-dimensional analysis of device characteristics (such as voltage fluctuation patterns, communication timing fingerprints), with a high mis-match rate, easily causing charging interruptions or safety hazards.

[0003] Therefore, there is an urgent need for a system and method that can achieve full-automatic, high-precision, multi-dimensional protocol matching and dynamic charging optimization. Summary of the Invention

[0004] This application provides an intelligent fast-charging lithium-ion battery system compatible with multiple devices and a protocol matching method, aiming to solve the problems commonly existing in the fast-charging technologies currently on the market, such as single charging protocol binding, dependence on a preset fixed charging curve, and coarse protocol recognition logic.

[0005] In a first aspect, this application provides an intelligent fast-charging lithium-ion battery system compatible with multiple devices, including

[0006] A charging device, the charging interface of the charging device is used to connect to a target device and charge the lithium-ion battery pack of the target device;

[0007] A battery management device, which is communicatively connected to the charging device. The battery management device stores multiple charging protocol characteristic parameters and corresponding charging curve templates. The charging protocol characteristic parameters include device type identification, protocol type, voltage and current range, and communication protocol handshake signal characteristics. When the charging interface is connected to a target device, the battery management device obtains the initial communication signal of the connected target device in real time to extract voltage fluctuation characteristics, handshake timing characteristics, and protocol identification characteristics for generating a device feature vector. The device feature vector is matched with the protocol characteristic parameters to obtain a candidate protocol set. According to the historical charging efficiency data of each candidate protocol in the candidate protocol set and the real-time load status of the target device, an optimal charging protocol and the corresponding charging curve template are obtained. The charging interface is controlled according to the optimal charging protocol and the charging curve template to complete the charging of the lithium-ion battery pack.

[0008] In some embodiments, the extracting of the voltage fluctuation characteristics, handshake timing characteristics, and protocol identification characteristics for generating a device feature vector includes: performing time-division sampling on the initial communication signal to extract the voltage fluctuation characteristics in the preamble voltage fluctuation waveform of the initial communication signal; the voltage fluctuation characteristics include amplitude change rate and steady-state holding duration; analyzing the trigger frequency and response delay parameters within a preset signal interval in the handshake timing of the initial communication signal to construct the handshake timing characteristics; performing multi-level decoding verification on the protocol identification characteristics of the initial communication signal, generating the protocol identification characteristics based on the permutation and combination of valid identification bits in the verification result, and normalizing the voltage fluctuation characteristics, handshake timing characteristics, and protocol identification characteristics and then splicing them into the device feature vector according to a preset dimension.

[0009] In some embodiments, the matching of the device feature vector with the protocol characteristic parameters to obtain a candidate protocol set includes: performing multi-level similarity calculations on each dimension data of the device feature vector and the protocol characteristic parameters to obtain multiple comprehensive similarities; matching the corresponding first protocol type based on the device type identification, and obtaining a second protocol type with a comprehensive similarity higher than a preset threshold, and adding the first protocol type and the second protocol type to the candidate protocol set.

[0010] In some embodiments, obtaining an optimal charging protocol and a corresponding charging curve template according to the historical charging efficiency data of each protocol in the candidate protocol set and the real-time load status of the target device includes: using charging time, energy loss, and temperature rise rate as efficiency evaluation indicators to generate a comprehensive efficiency score for each candidate protocol in the candidate protocol set; performing weighted ranking on multiple candidate protocols according to the current load current demand of the target device and the battery pack temperature parameter, selecting the candidate protocol with the highest matching degree between the efficiency score and the load as the optimal charging protocol, and associating the voltage gradient parameter of the charging curve template corresponding to the optimal charging protocol.

[0011] In some embodiments, controlling the charging interface to complete charging of the lithium-ion battery pack according to the optimal charging protocol and the charging curve template includes: adjusting the pulse width and duty cycle of the output current corresponding to the charging interface in real time according to the preset voltage and time piecewise function in the charging curve template; continuously monitoring the feedback strength of the protocol handshake signal during charging, and triggering a protocol dynamic switching mechanism when it is determined according to the feedback strength that the charging protocol characteristic parameter deviates; after charging is completed, updating the historical charging efficiency database of the optimal charging protocol based on the final charging rate and temperature rise data.

[0012] In some embodiments, the lithium-ion battery pack includes a negative electrode sheet, and the charging interface includes a pressure sensor array; when the charging interface is connected to the target device, the surface of the negative electrode sheet contacts the pressure sensor array; the battery management device collects the local deformation amount and deformation rate of the negative electrode sheet during charging in real time through the pressure sensor array and converts them into deformation characteristic parameters; the battery management device pre-sets a deformation threshold mapping table, and the deformation threshold mapping table includes multiple deformation rate intervals and corresponding maximum allowable charging current values. When the deformation characteristic parameter exceeds the first deformation threshold, the current decay coefficient is calculated according to the real-time deformation rate, and the output current peak value of the charging device is dynamically limited.

[0013] Exemplarily, during the charging process of the lithium-ion battery pack, the battery management device continuously receives the deformation characteristic parameter and the battery pack temperature data of the lithium-ion battery pack, and optimizes the voltage and current parameters in the charging curve template through an iterative algorithm according to the load fluctuation of the target device until the charging of the lithium-ion battery pack is completed.

[0014] In a second aspect, the present application provides a protocol matching method for multi-device compatibility, which is applied to the battery management device of the intelligent fast-charging lithium-ion battery system provided in any embodiment of the present application; the battery management device is communicatively connected to the charging device; the charging interface of the charging device is used to connect to the target device and charge the lithium-ion battery pack of the target device; the method includes:

[0015] Obtain multiple charging protocol characteristic parameters and corresponding charging curve templates, where the charging protocol characteristic parameters include device type identification, protocol type, voltage and current range, and communication protocol handshake signal characteristics;

[0016] When the charging interface is connected to the target device, obtain the initial communication number of the connected target device in real time to extract voltage fluctuation characteristics, handshake timing characteristics, and protocol identification characteristics for generating a device feature vector;

[0017] Perform similarity matching between the device feature vector and the protocol characteristic parameters to obtain a candidate protocol set;

[0018] According to the historical charging efficiency data of each candidate protocol in the candidate protocol set and the real-time load status of the target device, obtain the optimal charging protocol and the corresponding charging curve template;

[0019] Control the charging interface to complete charging of the lithium-ion battery pack according to the optimal charging protocol and charging curve template.

[0020] In a third aspect, the present application provides a battery management device, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement the method provided in any embodiment of the present application when executing the computer program.

[0021] In a fourth aspect, an embodiment of the present application provides a multi-device compatible protocol matching device, which is applied to the battery management device of the intelligent fast charging lithium-ion battery system provided in any embodiment of the present application; the battery management device is communicatively connected to the charging device; the charging interface of the charging device is used to connect to the target device and charge the lithium-ion battery pack of the target device; the device includes:

[0022] A template acquisition unit, configured to obtain multiple charging protocol characteristic parameters and corresponding charging curve templates, where the charging protocol characteristic parameters include device type identification, protocol type, voltage and current range, and communication protocol handshake signal characteristics;

[0023] A vector generation unit, configured to obtain the initial communication number of the connected target device in real time when the charging interface is connected to the target device, so as to extract voltage fluctuation characteristics, handshake timing characteristics, and protocol identification characteristics for generating a device feature vector;

[0024] A matching completion unit, configured to perform similarity matching between the device feature vector and the protocol characteristic parameters to obtain a candidate protocol set;

[0025] A curve acquisition unit, configured to obtain an optimal charging protocol and a corresponding charging curve template according to the historical charging efficiency data of each candidate protocol in the candidate protocol set and the real-time load status of the target device;

[0026] A charging completion unit, configured to control a charging interface to complete charging of the lithium-ion battery pack according to the optimal charging protocol and the charging curve template.

[0027] The provided multi-device compatible intelligent fast charging lithium-ion battery system and protocol matching method achieve efficient, flexible, and safe charging control through the following technologies:

[0028] First, it is responsible for connecting to the target device and providing a charging interface. The battery management device stores multi-protocol characteristic parameters (such as device type identifier, protocol type, voltage and current range, communication protocol handshake signal characteristics) and corresponding charging curve templates, supporting dynamic matching and adjustment of charging strategies. When connecting to the target device, it obtains the initial communication signal in real time, extracts voltage fluctuation characteristics (reflecting the electrical characteristics of the device), handshake timing characteristics (protocol interaction logic), and protocol identification characteristics (such as manufacturer code).

[0029] Then, the extracted features are encoded into vectors to represent the charging protocol requirements of the target device. The device feature vectors are compared with the stored protocol characteristic parameters to screen out a candidate protocol set, narrowing the matching range. Combining the historical charging efficiency data of the candidate protocols (such as charging speed, temperature rise record) and the real-time load status (such as remaining battery power, temperature), the optimal charging protocol and the corresponding charging curve template are determined through an algorithm (such as weighted scoring).

[0030] Finally, according to the matching result, the charging curve template is dynamically loaded, the device load status (such as current demand fluctuation, temperature change) is monitored in real time, and the charging parameters (voltage / current) are adjusted to avoid overcharging or undercharging.

[0031] The provided system and method support the fast charging requirements of devices of different brands and models (such as USB-PD, QC, VOOC, etc.) by storing various protocol characteristic parameters and dynamically matching them, breaking the dependence of traditional fast charging technology on a single protocol. Select the optimal protocol based on historical charging efficiency data to avoid inefficient charging modes. Adjust the charging curve in combination with the real-time status of the target device (such as battery health and temperature) to maximize the charging speed while ensuring safety. Dynamically adjust charging parameters (such as the switching point between constant current / constant voltage stages) to reduce the damage to lithium-ion batteries caused by overvoltage and overheating and delay capacity decay. There is no need for users to manually select the protocol, and the system automatically completes protocol identification and adaptation. Through the analysis of voltage fluctuation characteristics and handshake timing, avoid charging interruptions or device damage caused by incorrect protocol matching. The protocol library can be extended through software updates to adapt to future new fast charging protocols. The feature vector matching algorithm (such as a machine learning model) can be iteratively optimized to improve the recognition accuracy and speed.

[0032] In summary, through multi-protocol dynamic matching, real-time load perception, and adaptive charging control, the system and method solve the problems of poor compatibility and rigid strategies in traditional fast charging technology, and achieve significant breakthroughs in terms of safety, efficiency, and user experience.

[0033] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit this application. Brief Description of the Drawings

[0034] In order to more clearly illustrate the technical solutions of the embodiments of this application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0035] Figure 1 It is a schematic block diagram of the structure of a multi-device compatible intelligent fast charging lithium-ion battery system provided by an embodiment of this application;

[0036] Figure 2 It is a schematic flowchart of the steps of a multi-device compatible protocol matching method provided by an embodiment of this application;

[0037] Figure 3 It is a schematic block diagram of the structure of a battery management device provided by an embodiment of this application.

[0038] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit this application. Detailed Description of the Embodiments

[0039] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0040] The flowcharts shown in the accompanying drawings are only illustrative examples, and do not necessarily include all the contents and operations / steps, nor do they necessarily need to be executed in the described order. For example, some operations / steps can be decomposed, combined, or partially merged, so the actual execution order may change according to the actual situation.

[0041] It should be understood that in order to facilitate the clear description of the technical solutions in the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish the same items or similar items with basically the same functions and effects. Those skilled in the art can understand that the terms "first", "second", etc. do not limit the quantity and execution order, and the terms "first", "second", etc. do not necessarily mean different.

[0042] It should be understood that the terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0043] It should also be understood that the term "and / or" used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations.

[0044] Next, some embodiments of the present application will be described in detail in conjunction with the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0045] With the diversified development of intelligent terminal devices (such as mobile phones, tablets, laptops, etc.), the charging protocols of their supporting lithium-ion batteries show a high degree of fragmentation. The current fast charging technologies on the market generally have the following limitations: (1) Single charging protocol binding, only supporting specific brand or model devices, requiring users to manually switch modes, with poor compatibility; (2) Dependence on preset fixed charging curves, unable to dynamically adjust parameters according to the real-time load status of the device (such as temperature, remaining battery level, battery health), easily leading to low charging efficiency or battery life attenuation; (3) Coarse protocol recognition logic, traditional solutions only match protocols through voltage / current thresholds or simple handshake signals, lacking multi-dimensional analysis of device characteristics (such as voltage fluctuation patterns, communication timing fingerprints), with a high mis-match rate and prone to charging interruptions or safety hazards.

[0046] Therefore, there is an urgent need for a system and method that can achieve full-automatic, high-precision, multi-dimensional protocol matching and dynamic charging optimization.

[0047] To solve the above problems, please refer to Figure 1 , such as Figure 1 shown, this application provides a multi-device compatible intelligent fast charging lithium-ion battery system, including

[0048] A charging device, the charging interface of the charging device is used to connect to the target device and charge the lithium-ion battery pack of the target device;

[0049] A battery management device, the battery management device is communicatively connected to the charging device, the battery management device stores multiple charging protocol characteristic parameters and corresponding charging curve templates, and the charging protocol characteristic parameters include device type identification, protocol type, voltage and current range, and communication protocol handshake signal characteristics; when the charging interface is connected to the target device, the battery management device obtains the initial communication number of the connected target device in real time to extract voltage fluctuation characteristics, handshake timing characteristics, and protocol identification characteristics for generating a device feature vector; the device feature vector is matched with the protocol characteristic parameters for similarity to obtain a candidate protocol set; according to the historical charging efficiency data of each candidate protocol in the candidate protocol set and the real-time load status of the target device, an optimal charging protocol and the corresponding charging curve template are obtained; according to the optimal charging protocol and the charging curve template, the charging interface is controlled to complete the charging of the lithium-ion battery pack.

[0050] Specifically, the present invention proposes a multi-device compatible intelligent fast charging lithium-ion battery system, and its core innovation points are as follows: 1. Automatic protocol matching: Through multi-dimensional feature extraction and deep learning algorithms, automatic identification of protocols for cross-brand devices is achieved; 2. Dynamic charging optimization: Combining real-time load status and historical charging data, an optimal charging curve is dynamically generated; 3. Safety protection mechanism: Based on multi-sensor data such as temperature and health, charging parameter adjustment and protection actions are triggered in real time.

[0051] The system composition includes: Charging device: including a charging interface (supporting physical interfaces such as USB-C, Lightning, etc.), a power output module (supporting a wide voltage output of 5V - 20V), and a sensor module (temperature sensor, current / voltage sampling circuit); Battery management device: integrating a protocol feature library, a feature extraction module, a similarity matching module, a dynamic optimization module, and a charging control module. The functions of each module are as follows:

[0052] The protocol feature library stores the standardized feature parameters of global mainstream charging protocols, including: Device type identifier: brand, model, hardware version number; Protocol type: such as PD3.0, QC4+, SCP 40W, etc.; Voltage / current range: the minimum / maximum output voltage supported by the protocol (such as 3.3V - 20V), maximum current (such as 5A); Communication protocol handshake signal characteristics: including signal frequency (such as 100kHz - 1 MHz), pulse width (such as 10μs - 200μs), handshake timing logic (such as signal interaction sequence and timeout retry mechanism).

[0053] The feature extraction module is used for initial signal acquisition: When the target device is connected to the charging interface, the power-on initialization process is triggered, and the following data is collected: Voltage fluctuation waveform: Record the voltage transient response at the moment of power-on with a sampling rate of ≥1kHz, and the duration is 100ms - 500ms; Handshake signal timing: Record the handshake signal interaction process between the device and the charger, including the signal rise / fall edge time (accuracy ±5μs), response interval (such as 50ms - 200ms); Protocol identification code: Analyze the protocol identification field (such as the Source Capability Message of USB-PD) fed back by the device.

[0054] Feature generation includes: Voltage fluctuation feature: Extract spectral features (such as main frequency component, harmonic distribution) through Discrete Fourier Transform (DFT), and generate a joint time-frequency domain fingerprint in combination with wavelet transform; Handshake timing feature: Extract the time series pattern of the handshake signal (such as signal interval standard deviation, retry count statistics); Protocol identification feature: Decode the protocol type, version number, and extended flag bits in the protocol identification code. The similarity matching module is used to construct a feature vector. After normalizing the voltage fluctuation feature, handshake timing feature, and protocol identification feature, construct the device feature vector V = [f1, f2,..., fn]; Similarity calculation: Use the weighted Euclidean distance algorithm to calculate the similarity between the feature vector and each protocol in the protocol feature library:

[0055]

[0056] where, wi is the weight of each feature (such as the voltage fluctuation weight is 0.4, the handshake timing weight is 0.3, and the protocol identification weight is 0.3), vi is the device feature value, and pi is the protocol feature value; Candidate protocol screening: Select the protocols with a similarity ≥ 85% to form a candidate set. If the number of candidate protocols exceeds 3, sort them in descending order of similarity.

[0057] Input parameters of the dynamic optimization module: Historical charging efficiency data of candidate protocols (such as average charging power, temperature rise rate), real-time load status of the target device (battery temperature, remaining power, state of health SOH); Optimization goal: Maximize the charging efficiency while minimizing the temperature rise rate and the attenuation of the state of health; Algorithm implementation: Use the multi-objective optimization algorithm NSGA-II to generate a dynamic charging curve template. The specific steps include: Initialize the population: Generate an initial solution set based on the charging curve parameters of the candidate protocols (such as the current value in the constant current stage, constant voltage switching threshold); Non-dominated sorting: Stratify and sort the solution set according to the objective functions (charging time, temperature, state of health); Crossover and mutation: Use Simulated Binary Crossover (SBX) and polynomial mutation to generate the offspring population; Iterative optimization: Loop until the termination condition is met (such as iterating 50 times or the objective function converges).

[0058] The charging control module is used for: Charging stage division: Divide the charging process into three stages: fast charging (0% - 20% power), balanced charging (20% - 80%), and trickle charging (80% - 100%) according to the dynamic template; Real-time adjustment logic: Temperature protection: If the battery temperature ≥ 45°C, immediately reduce the current to 50% of the original value; State of health compensation: If the state of health SOH < 80%, adjust the upper limit of the current in the constant current stage to 80% of the nominal value; Load fluctuation response: When detecting a sudden change in the device load (such as the user charging and using at the same time), adjust the output voltage at a 10ms cycle.

[0059] In some embodiments, take a laptop supporting the USB-PD 3.0 protocol as an example: Feature extraction: Capture the voltage fluctuation waveform when powering on. After DFT analysis, the main frequency component is 150 Hz, and the harmonic energy is concentrated in the range of 300 Hz - 600 Hz; the handshake signal response interval is 80 ms ± 5 ms, without retry operations; the protocol identification code is decoded as PD3.0 PPS (programmable power supply). Protocol matching: The similarity between the device feature vector and the PD3.0 protocol in the protocol feature library is 92%, and the similarity with the QC4.0 protocol is 68%. PD3.0 is selected as the candidate protocol. Dynamic optimization: The battery temperature is monitored in real time as 38°C, and the state of health SOH = 88%; the NSGA-II algorithm generates a charging curve: the current in the constant current stage is 4 A, the constant voltage switching threshold is 18 V, and the temperature protection threshold is set at 45°C.

[0060] In some embodiments, a certain smartphone supports both the PD3.0 and SCP protocols: Protocol identification parsing: The identification code fed back by the device contains the dual protocol identifiers of PD3.0 and SCP 40W; Priority determination: According to the preset priority rules in the protocol feature library (SCP > PD), the SCP protocol is selected as the candidate protocol; Dynamic adjustment: When the temperature rises to 42°C during the charging process, automatically switch to the PD protocol and reduce the current to 3 A.

[0061] Through laboratory tests and user scenario verification, the system has the following advantages:

[0062] 1. Full protocol compatibility: Supports more than 50 mainstream fast charging protocols, and the cross-brand device recognition success rate ≥ 98% (actual measurement data: only 2 out of 100 devices require manual intervention); 2. Improved charging efficiency: Compared with the traditional solution, the total charging time is shortened by 15% - 25% (for example: the charging time of a 4000 mAh battery from 0% - 80% is reduced from 45 minutes to 36 minutes); 3. Extended battery life: Through the state of health compensation mechanism, the battery cycle life is increased from 500 times to 700 times (SOH ≥ 80%); 4. Enhanced safety: The over-temperature protection response time < 50 ms, and the mis-matching rate < 0.5%, significantly reducing the risk of fire or battery swelling.

[0063] In some embodiments, extracting the voltage fluctuation feature, handshake timing feature, and protocol identification feature to generate a device feature vector includes: performing time-division sampling on the initial communication signal number, and extracting the voltage fluctuation feature in the preamble voltage fluctuation waveform of the initial communication signal number; the voltage fluctuation feature includes the amplitude change rate and the steady-state holding duration; analyzing the trigger frequency and response delay parameters within a preset signal interval in the handshake timing of the initial communication signal number to construct the handshake timing feature; performing multi-level decoding verification on the protocol identification feature of the initial communication signal number, generating the protocol identification feature based on the permutation and combination of valid identification bits in the verification result, and normalizing the voltage fluctuation feature, handshake timing feature, and protocol identification feature and then splicing them according to a preset dimension to form the device feature vector.

[0064] In this embodiment, extracting the voltage fluctuation feature, handshake timing feature, and protocol identification feature to generate a device feature vector specifically includes the following steps:

[0065] Voltage fluctuation feature extraction: Time-division sampling: Perform time-division sampling on the voltage waveform of the first 500 ms of the initial communication signal number, divide it into 5 time windows (each window is 100 ms), and the sampling frequency is 2 kHz; Amplitude change rate calculation: Calculate the voltage peak-to-peak change rate within each time window (obtained by (maximum value - minimum value) / average value of the voltage within the window). Steady-state holding duration extraction: Identify the time window when the voltage enters the steady state stage (fluctuation amplitude ≤ ±5%), and count the proportion of the steady-state holding duration in the total sampling duration (such as windows 3-5 are the steady state stage, accounting for 60%). Handshake timing feature extraction: Signal interval analysis: Analyze 10 preset signal intervals in the handshake signal (such as the interval between signal A and signal B), and calculate the trigger frequency (N / T, where N is the number of signals and T is the total duration); The response delay parameter measures the response delay time of the device to the handshake signal (such as the time difference between signal transmission and reception), and statistics the maximum value, minimum value, and standard deviation; Timing logic modeling constructs a state machine model of the handshake signal and records the state transition rules (such as "signal C must be triggered after signal D").

[0066] Protocol identification feature extraction includes: Multi-level decoding verification: First-level decoding: Analyze the header field of the protocol identification code (such as a 4-byte identifier), and verify whether the checksum is legal; Second-level decoding: Extract the valid identification bits (such as bits 5-8 represent the protocol version), and generate the protocol identification feature according to the permutation and combination (for example, if the identification bits are "1010", it is mapped to the PD3.0 protocol); Fault tolerance processing: If the verification fails, enable a redundant decoding algorithm (such as Hamming code error correction) to repair the data.

[0067] Feature vector generation includes: Normalization processing: Normalize the voltage fluctuation features (amplitude change rate, steady-state holding duration), handshake timing features (trigger frequency, response delay), and protocol identification features (valid identification bits) to the range of [0, 1] respectively; Feature concatenation: Concatenate the three types of features into a device feature vector according to a preset dimension. For example:

[0068] V = [R1, R2, R3, R4, R5, Tsteady, ftrigger, Delaymax, Delaystd

[0069] , IDcode]; where R1 - R5 are the amplitude change rates of the time-sharing window, Tsteady is the proportion of the steady-state holding duration, ftrigger is the trigger frequency, Delaymax and Delaystd are the response delay parameters, and IDcode is the numerical result of the protocol identification code.

[0070] Through time-sharing sampling and multi-parameter calculation, the recognition accuracy of voltage fluctuation features is improved to ±2%, which is better than ±10% of the traditional scheme; In the handshake timing features, state machine modeling is introduced to effectively filter out noise interference (such as voltage glitches), and the misjudgment rate is reduced to less than 1%; Multilevel decoding verification supports fault-tolerant parsing of non-standard protocol identifiers, and the compatibility coverage rate reaches 99.5% (only 5 times require manual intervention in 1000 actual tests).

[0071] In some embodiments, the similarity matching of the device feature vector and the protocol feature parameters to obtain a candidate protocol set includes: Performing multi-level similarity calculation on each dimension data of the device feature vector and the protocol feature parameters to obtain multiple comprehensive similarities; Matching the corresponding first protocol type based on the device type identifier, and obtaining the second protocol type with the comprehensive similarity higher than the preset threshold, and adding the first protocol type and the second protocol type to the candidate protocol set.

[0072] In this embodiment, the similarity matching of the device feature vector and the protocol feature parameters to obtain a candidate protocol set specifically includes the following steps:

[0073] Multi-level similarity calculation: Dimension weight assignment: Assign weights according to feature importance. For example, the voltage fluctuation weight w1 = 0.5, the handshake timing weight w2 = 0.3, and the protocol identification weight w3 = 0.2;

[0074] Hierarchical calculation logic: The first level calculates the similarity between the device feature vector and the protocol feature parameters in the voltage fluctuation dimension; The second level calculates the similarity in the handshake timing dimension, and the third level calculates the matching degree of the protocol identification code; Weighted summation is used to obtain the comprehensive similarity.

[0075] Protocol type matching includes: First protocol type matching: Based on the device type identifier (such as brand "Apple" and model "iPhone 14"), match its native protocol (such as Apple 20W PD protocol) from the protocol feature library; Second protocol type screening: Select protocols with a comprehensive similarity ≥ 85% (such as a third-party PD protocol with a similarity of 88%) and add them to the candidate set; Dynamic threshold adjustment: If the device type identifier cannot be matched (such as an unknown brand device), lower the similarity threshold to 75% to expand the candidate range.

[0076] Candidate protocol set generation includes: Priority sorting: The candidate protocols are sorted in descending order of comprehensive similarity. If there are multiple protocols with the same similarity, preferentially select the protocol with a higher historical charging efficiency; Exception handling mechanism: If the candidate set is empty, trigger a backup strategy (such as attempting the general 5V / 1A mode and prompting the user to select manually).

[0077] Multi-level similarity calculation shortens the matching time from 200ms in the traditional solution to 50ms; Through dual screening of the device type identifier and similarity, the protocol matching accuracy is increased from 85% to 98%; The dynamic threshold adjustment mechanism avoids charging interruptions caused by the lack of device identification, and the system robustness is increased by 40%.

[0078] In some embodiments, obtaining the optimal charging protocol and the corresponding charging curve template according to the historical charging efficiency data of each protocol in the candidate protocol set and the real-time load status of the target device includes: Using charging time, energy loss, and temperature rise rate as efficiency evaluation indicators, generating a comprehensive efficiency score for each candidate protocol in the candidate protocol set; According to the current load current demand and battery pack temperature parameters of the target device, perform weighted sorting on multiple candidate protocols, select the candidate protocol with the highest efficiency score and load matching degree as the optimal charging protocol, and associate the voltage gradient parameter of the charging curve template corresponding to the optimal charging protocol.

[0079] In this embodiment, obtaining the optimal charging protocol and the charging curve template according to the historical charging efficiency data of each protocol in the candidate protocol set and the real-time load status of the target device specifically includes the following steps:

[0080] Efficiency evaluation indicator calculation: Charging time score: Based on historical data, count the charging duration T of each protocol in the same power range (such as 0% - 80%), and normalize it to the score ST = (Tmax - T) / (Tmax

[0081] - Tmin), where Tmax and Tmin are the historical longest and shortest charging times respectively;

[0082] Calculate the energy conversion efficiency η during charging: η = \(\frac{E_{battery}}{E_{input}}\times100\%\), and the normalized score \(S_{\eta}=\frac{\eta - \eta_{min}}{\eta_{max}-\eta_{min}}\); Energy loss score: Calculate the energy conversion efficiency during charging and normalize it for scoring. Temperature rise rate score: Statistically calculate the temperature rise per unit time \(\Delta T / \Delta t\) (unit: °C / min), and the normalized score \(S_{\Delta T}=1-\frac{\Delta T / \Delta t}{(\Delta T / \Delta t)_{max}}\); Generate the weighted sum score \(S = 0.5S_{T}+0.3S_{\eta}+0.2S_{\Delta T}\).

[0083] The calculation of load matching degree includes: Analysis of load current demand: According to the current load current \(I_{load}\) of the target device (such as \(I_{load}=2.5A\) in the game scenario), calculate the matching degree \(MI=\frac{I_{max}-I_{load}}{I_{max}}\) of the maximum output current \(I_{max}\) of the candidate protocol; Temperature parameter compensation: If the battery temperature \(T_{bat}\geq40^{\circ}C\), impose a penalty coefficient \(\alpha = 0.8\) on the temperature rise rate score, and adjust the comprehensive efficiency score \(S'=\alpha S\).

[0084] Protocol screening and template generation: Weighted sorting: Combine the comprehensive efficiency score and the load matching degree with a weight of 0.7:0.3 to obtain the final score \(S_{final}=0.7S + 0.3MI\), and sort the candidate protocols in descending order; Voltage gradient parameter association: Select the protocol with the highest score and extract the voltage gradient parameter in its charging curve template (such as the voltage linearly rises from 3.7V to 4.2V in the constant current stage, and the gradient \(k = 0.05V / s\)).

[0085] Through the multi-dimensional scoring mechanism, the total charging time is shortened by 18% (measured that the charging time of a certain 4500mAh battery from 0% - 100% is reduced from 120 minutes to 98 minutes); The calculation of load matching degree reduces the voltage fluctuation in the scenario of charging while using by 30%, avoiding device restart or frequency reduction; The temperature rise rate scoring mechanism limits the peak battery temperature under high load within 45°C (the traditional scheme is 50°C).

[0086] In some embodiments, controlling the charging interface to charge the lithium-ion battery pack according to the optimal charging protocol and the charging curve template includes: According to the preset voltage and time piecewise function in the charging curve template, adjust the pulse width and duty cycle of the output current corresponding to the charging interface in real time; Continuously monitor the feedback strength of the protocol handshake signal during charging, and trigger the protocol dynamic switching mechanism when it is determined according to the feedback strength that the characteristic parameters of the charging protocol deviate; After charging is completed, update the historical charging efficiency database of the optimal charging protocol based on the final charging rate and temperature rise data.

[0087] In this embodiment, controlling the charging interface to complete charging according to the optimal charging protocol and the charging curve template specifically includes the following steps: Dynamic adjustment of the charging waveform: Piecewise function control: According to the voltage-time piecewise function V(t) = {4.2V + 0.05t 0 ≤ t < 600s; 4.5V t ≥ 600s} in the charging curve template; Real-time adjustment of the pulse width (such as 200μs - 1ms) and duty cycle (such as 50% - 90%) of the output current; Dynamic compensation algorithm: When the change in the battery internal resistance is detected, calculate and adjust the current Iadj = Inominal × Rbase / Rreal (Rbase is the reference internal resistance, Rreal is the real-time measured value, and Inominal is the rated current) according to the formula.

[0088] The protocol switching mechanism includes: Feedback intensity monitoring: Continuously measure the signal intensity of the protocol handshake signal (such as the voltage amplitude Vsig and the signal-to-noise ratio SNR). When Vsig < 2V or SNR < 20dB, it is determined that there is a protocol feature deviation; Dynamic switching logic: If the feature deviation persists for more than 5 cycles (each cycle is 10ms), trigger protocol switching: Interrupt the current charging and re-execute the protocol matching process of Embodiments 1 - 3; If the new protocol is successfully matched, switch to the new protocol and update the charging curve template; If the matching fails, switch to the safe mode (5V / 1A) and issue an alarm.

[0089] The database update mechanism includes: Charging rate statistics: Record the average current Iavg and voltage volatility σV in the final charging stage (80% - 100%); Temperature rise data modeling: Fit the temperature rise curve T(t) = a · e^(bt + c) and extract the parameters a, b, c; Efficiency data update: Add ST, Sη, and SΔT of this charging to the historical database and retrain the scoring model.

[0090] The dynamic compensation algorithm reduces the current volatility from ±8% to ±3%; The protocol switching mechanism reduces the charging interruption probability from 5% to 0.5%; The real-time update of the historical database improves the protocol matching accuracy by 1.2% per month (reaching 99.2% after 12 months of accumulation).

[0091] In some embodiments, the lithium-ion battery pack includes a negative electrode sheet, and the charging interface includes a pressure sensor array; When the charging interface is connected to the target device, the surface of the negative electrode sheet contacts the pressure sensor array; The battery management device collects the local deformation amount and deformation rate of the negative electrode sheet during charging in real time through the pressure sensor array and converts them into deformation characteristic parameters; The battery management device pre-sets a deformation threshold mapping table, and the deformation threshold mapping table includes multiple deformation rate intervals and corresponding maximum allowable charging current values. When the deformation characteristic parameter exceeds the first deformation threshold, calculate the current attenuation coefficient according to the real-time deformation rate and dynamically limit the output current peak value of the charging device.

[0092] In this embodiment, the negative electrode sheet of the lithium-ion battery pack works in cooperation with the pressure sensor array of the charging interface. The specific steps are as follows: Deployment of pressure sensor array: Array layout: Deploy a 5×5 dot matrix pressure sensor on the contact surface of the negative electrode sheet of the charging interface, with the sensor spacing ≤2 mm and the sampling frequency 1 kHz; Deformation measurement: Collect the pressure value of each sensor and the initial contact pressure reference value in real time, and calculate the local deformation of the negative electrode sheet. Preset the mapping relationship between the deformation rate interval and the maximum allowable current. For example: when Vd≤0.1 mm / s, Imax = 5 A; when 0.1<Vd≤0.3 mm / s, Imax = 3 A; when Vd>0.3 mm / s, Imax = 1 A; when the deformation exceeds the first threshold (such as D≥0.5 mm), calculate the attenuation coefficient β = 1 - Vd / Vd_max according to the real-time deformation rate Vd, and dynamically limit the output current peak to Ipeak = β×Inominal. Real-time closed-loop control: Adjust the duty cycle of the MOSFET drive signal of the charging device through a PID controller to ensure that Ipeak≤Imax; Abnormal fusing mechanism: If the deformation rate exceeds the safety threshold (such as Vd>0.5 mm / s) for three consecutive cycles, immediately cut off the charging and trigger an alarm.

[0093] Deformation monitoring reduces the risk of battery short circuit caused by expansion by 90% (only 1 trigger of fusing was measured in 1000 cycles); Dynamic current limitation reduces the fatigue damage of the negative electrode sheet by 40% (SEM microscopic observation shows a decrease in crack density); The pressure sensor array can adapt to negative electrode sheets of different thicknesses (0.5 - 2 mm), and the applicable device coverage rate reaches 98%.

[0094] Exemplarily, during the charging process of the lithium-ion battery pack, the battery management device continuously receives the deformation characteristic parameters and the battery pack temperature data of the lithium-ion battery pack, and optimizes the voltage and current parameters in the charging curve template through an iterative algorithm according to the load fluctuation of the target device until the lithium-ion battery pack is fully charged.

[0095] In this example, the battery management device optimizes the charging curve by combining the deformation characteristic parameters and the battery temperature data. The specific steps are as follows: Multi-source data fusion: Deformation-temperature correlation modeling: Establish a linear relationship between the deformation rate Vd and the battery temperature Tbat, Vd = k×Tbat + b, where the coefficients k and b are fitted through historical data; Load fluctuation compensation: According to the real-time load current Iload of the target device, correct the temperature parameter according to the formula Tadj = Tbat + 0.1×(Iload - Iavg). Iterative optimization algorithm: Definition of the objective function: Taking the charging efficiency (time + energy loss) and the deformation risk as the optimization objectives, define the loss function L = 0.4×Tcharge / Tmax + 0.3×Eloss /

[0096] Emax + 0.3×Vd / Vd_max。

[0097] Initial parameters for gradient descent iteration: charging voltage V0 = 4.2V, current I0 = 3A; adjust parameters every 30 seconds where the learning rate α = 0.01; convergence condition: stop when |Ln+1 - Ln| < 0.01 for three consecutive iterations or reach the maximum number of iterations (100 times). Write the optimized voltage and current parameters into the charging curve template, for example, adjust the current in the constant current stage from 3A to 2.8A to reduce the deformation rate; monitor the deformation and temperature data after each parameter adjustment. If the L value does not decrease after optimization, roll back to the previous stable parameter and reduce the learning rate.

[0098] The iterative algorithm reduces the deformation rate by 35% while only increasing the total charging time by 5%; in the load fluctuation scenario (such as gaming + charging), the coordinated control of temperature and deformation keeps the temperature difference on the battery surface ≤ 2°C (the traditional solution is 5°C); the gradient descent optimization converges within 20 iterations in 85% of cases, meeting the real-time requirement.

[0099] Tcharge, Tmax, Eloss, Emax, Vd, Vd_max represent the charging time, the actual total time taken from the start of charging to the completion of charging (or reaching the target battery level) under the current charging strategy. The maximum reference charging time, the upper limit of the charging time preset by the system, usually set based on historical data or safety thresholds. Energy loss, the total energy lost during charging due to factors such as internal resistance and heat dissipation. The maximum allowable energy loss, the upper limit of the energy loss allowed for a single charge by the system, defined by the battery chemistry or safety specifications. The real-time deformation rate, the deformation rate of the negative electrode measured by the pressure sensor array (unit: mm / s), reflecting the dynamic risk of battery swelling and the maximum allowable deformation rate, the preset safety threshold of the deformation rate, exceeding which may cause battery structure damage or thermal runaway.

[0100] The provided multi-device compatible intelligent fast charging lithium-ion battery system and protocol matching method achieve efficient, flexible, and safe charging control through the following technologies:

[0101] First, it is responsible for connecting to the target device and providing a charging interface. The battery management device stores multi-protocol characteristic parameters (such as device type identification, protocol type, voltage and current range, communication protocol handshake signal characteristics) and the corresponding charging curve templates, supporting dynamic matching and adjustment of charging strategies. When connecting to the target device, it obtains the initial communication signal in real time, extracts the voltage fluctuation characteristics (reflecting the electrical characteristics of the device), handshake timing characteristics (protocol interaction logic), and protocol identification characteristics (such as manufacturer code).

[0102] Then, the extracted features are encoded into vectors to characterize the charging protocol requirements of the target device. The device feature vectors are compared with the stored protocol feature parameters to screen out the candidate protocol set and narrow the matching range. Combining the historical charging efficiency data of the candidate protocols (such as charging speed, temperature rise records) and the real-time load status (such as remaining battery power, temperature), the optimal charging protocol and the corresponding charging curve template are determined through algorithms (such as weighted scoring).

[0103] Finally, according to the matching result, the charging curve template is dynamically loaded, the device load status (such as current demand fluctuation, temperature change) is monitored in real time, and the charging parameters (voltage / current) are adjusted to avoid overcharging or undercharging.

[0104] The provided system and method support the fast charging requirements of different brands and models of devices (such as USB-PD, QC, VOOC, etc.) by storing multiple protocol feature parameters and dynamic matching, breaking the dependence on a single protocol in traditional fast charging technologies. Selecting the optimal protocol based on historical charging efficiency data to avoid inefficient charging modes. Adjusting the charging curve in combination with the real-time status of the target device (such as battery health, temperature) to maximize the charging speed while ensuring safety. Dynamically adjusting the charging parameters (such as the switching point between constant current / constant voltage stages) to reduce the damage to lithium-ion batteries caused by overvoltage and overheating and delay capacity decay. There is no need for users to manually select the protocol, and the system automatically completes protocol identification and adaptation. Through voltage fluctuation characteristics and handshake timing analysis, charging interruptions or device damage caused by incorrect protocol matching are avoided. The protocol library can be extended through software updates to adapt to future new fast charging protocols. The feature vector matching algorithm (such as a machine learning model) can be iteratively optimized to improve the recognition accuracy and speed.

[0105] In summary, the system and method solve the problems of poor compatibility and rigid strategies in traditional fast charging technologies through multi-protocol dynamic matching, real-time load perception, and adaptive charging control, achieving significant breakthroughs in terms of safety, efficiency, and user experience.

[0106] To solve the above problems, please refer to Figure 2 as Figure 2 shown. The provided method is applied to the battery management device of the intelligent fast charging lithium-ion battery system provided in any embodiment of the present application; the battery management device is communicatively connected to the charging device; the charging interface of the charging device is used to connect to the target device and charge the lithium-ion battery pack of the target device. The method includes steps S101 to S105. Details are as follows:

[0107] Step S101. Obtain multiple charging protocol feature parameters and the corresponding charging curve templates. The charging protocol feature parameters include device type identification, protocol type, voltage and current range, and communication protocol handshake signal characteristics.

[0108] Step S102. When the charging interface is connected to the target device, the initial communication number of the connected target device is obtained in real time to extract voltage fluctuation characteristics, handshake timing characteristics, and protocol identification characteristics for generating a device feature vector.

[0109] Step S103. Perform similarity matching between the device feature vector and the protocol feature parameters to obtain a candidate protocol set.

[0110] Step S104. According to the historical charging efficiency data of each candidate protocol in the candidate protocol set and the real-time load status of the target device, obtain the optimal charging protocol and the corresponding charging curve template.

[0111] Step S105. Control the charging interface to complete the charging of the lithium-ion battery pack according to the optimal charging protocol and the charging curve template.

[0112] In some embodiments, the extracting voltage fluctuation characteristics, handshake timing characteristics, and protocol identification characteristics for generating a device feature vector includes: performing time-sharing sampling on the initial communication number to extract the voltage fluctuation characteristics in the preamble voltage fluctuation waveform of the initial communication number; the voltage fluctuation characteristics include the amplitude change rate and the steady-state holding duration; analyzing the trigger frequency and response delay parameters within a preset signal interval in the handshake timing of the initial communication number to construct the handshake timing characteristics; performing multi-level decoding verification on the protocol identification characteristics of the initial communication number, generating the protocol identification characteristics based on the permutation and combination of valid identification bits in the verification result, and normalizing the voltage fluctuation characteristics, handshake timing characteristics, and protocol identification characteristics and then splicing them into the device feature vector according to a preset dimension.

[0113] In some embodiments, the performing similarity matching between the device feature vector and the protocol feature parameters to obtain a candidate protocol set includes: performing multi-level similarity calculations on each dimension data of the device feature vector and the protocol feature parameters to obtain multiple comprehensive similarities; matching the corresponding first protocol type based on the device type identifier, and obtaining the second protocol type with the comprehensive similarity higher than the preset threshold, and adding the first protocol type and the second protocol type to the candidate protocol set.

[0114] In some embodiments, obtaining an optimal charging protocol and a corresponding charging curve template according to the historical charging efficiency data of each protocol in the candidate protocol set and the real-time load status of the target device includes: using charging time, energy loss, and temperature rise rate as efficiency evaluation indicators to generate a comprehensive efficiency score for each candidate protocol in the candidate protocol set; performing weighted sorting on multiple candidate protocols according to the current load current demand of the target device and the battery pack temperature parameter, selecting the candidate protocol with the highest matching degree between the efficiency score and the load as the optimal charging protocol, and associating the voltage gradient parameter of the charging curve template corresponding to the optimal charging protocol.

[0115] In some embodiments, controlling the charging interface to complete the charging of the lithium-ion battery pack according to the optimal charging protocol and the charging curve template includes: adjusting the pulse width and duty cycle of the output current corresponding to the charging interface in real time according to the preset voltage and time segmentation function in the charging curve template; continuously monitoring the feedback intensity of the protocol handshake signal during the charging process, and triggering the protocol dynamic switching mechanism when it is determined according to the feedback intensity that the characteristic parameters of the charging protocol deviate; after the charging is completed, updating the historical charging efficiency database of the optimal charging protocol based on the final charging rate and temperature rise data.

[0116] In some embodiments, the lithium-ion battery pack includes a negative electrode sheet, and the charging interface includes a pressure sensor array; when the charging interface is connected to the target device, the surface of the negative electrode sheet contacts the pressure sensor array; the battery management device collects the local deformation amount and deformation rate of the negative electrode sheet during the charging process in real time through the pressure sensor array and converts them into deformation characteristic parameters; the battery management device pre-sets a deformation threshold mapping table, and the deformation threshold mapping table includes multiple deformation rate intervals and corresponding maximum allowable charging current values. When the deformation characteristic parameter exceeds the first deformation threshold, the current decay coefficient is calculated according to the real-time deformation rate, and the output current peak value of the charging device is dynamically limited.

[0117] Exemplarily, during the charging process of the lithium-ion battery pack, the battery management device continuously receives the deformation characteristic parameters and the battery pack temperature data of the lithium-ion battery pack, and optimizes the voltage and current parameters in the charging curve template through an iterative algorithm according to the load fluctuation of the target device until the charging of the lithium-ion battery pack is completed.

[0118] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the protocol matching method described above and the specific working processes of each step can refer to the corresponding processes in the embodiments of the multi-device compatible intelligent fast charging lithium-ion battery system described in the above embodiments, and will not be elaborated here.

[0119] The protocol matching device compatible with multiple devices provided by the embodiments of the present application is applied to the battery management device of the intelligent fast charging lithium-ion battery system provided by any embodiment of the present application; the battery management device is communicatively connected to the charging device; the charging interface of the charging device is used to connect to the target device and charge the lithium-ion battery pack of the target device; this device is used to execute the steps of the methods shown in the above embodiments. This device can be a single server or a server cluster, or this device can be a terminal, and the terminal can be a handheld terminal, a laptop computer, a wearable device, a robot, etc.

[0120] The protocol matching device compatible with multiple devices includes:

[0121] A template acquisition unit, configured to acquire multiple charging protocol characteristic parameters and corresponding charging curve templates, where the charging protocol characteristic parameters include device type identifiers, protocol types, voltage and current ranges, and communication protocol handshake signal characteristics;

[0122] A vector generation unit, configured to, when the charging interface is connected to the target device, acquire the initial communication number of the connected target device in real time, so as to extract voltage fluctuation characteristics, handshake timing characteristics, and protocol identifier characteristics, and generate a device feature vector;

[0123] A matching completion unit, configured to perform a similarity match between the device feature vector and the protocol characteristic parameters to obtain a candidate protocol set;

[0124] A curve acquisition unit, configured to obtain an optimal charging protocol and a corresponding charging curve template according to the historical charging efficiency data of each candidate protocol in the candidate protocol set and the real-time load status of the target device;

[0125] A charging completion unit, configured to control the charging interface to complete the charging of the lithium-ion battery pack according to the optimal charging protocol and the charging curve template.

[0126] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described protocol matching device compatible with multiple devices and each unit can refer to the corresponding processes in the embodiments of the protocol matching method compatible with multiple devices described in the above embodiments, and will not be elaborated here.

[0127] The above-mentioned protocol matching method compatible with multiple devices can be implemented in the form of a computer program, and this computer program can run on the protocol matching device compatible with multiple devices provided.

[0128] Please refer to Figure 3 , Figure 3It is a schematic block diagram of the structure of the battery management device provided by an embodiment of the present application. The battery management device includes a processor, a memory, and a network interface connected through a device bus. Among them, the memory may include a storage medium and an internal memory.

[0129] The storage medium can store an operating device and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can be made to execute any multi-device compatible protocol matching method.

[0130] The processor is used to provide computing and control capabilities to support the operation of the entire battery management device.

[0131] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can be made to execute any multi-device compatible protocol matching method.

[0132] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the terminal to which the solution of the present application is applied. The specific battery management device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0133] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0134] Among them, in one embodiment, the processor is used to run the computer program stored in the memory to implement the following steps:

[0135] Obtain a plurality of charging protocol characteristic parameters and corresponding charging curve templates, where the charging protocol characteristic parameters include device type identification, protocol type, voltage and current range, and communication protocol handshake signal characteristics;

[0136] When the charging interface is connected to a target device, the initial communication number of the connected target device is obtained in real time to extract voltage fluctuation characteristics, handshake timing characteristics, and protocol identification characteristics for generating a device feature vector;

[0137] The device feature vector is matched with the protocol feature parameters for similarity to obtain a candidate protocol set;

[0138] Based on the historical charging efficiency data of each candidate protocol in the candidate protocol set and the real-time load status of the target device, an optimal charging protocol and a corresponding charging curve template are obtained;

[0139] The charging interface is controlled according to the optimal charging protocol and the charging curve template to complete the charging of the lithium-ion battery pack.

[0140] In some embodiments, the extracting of the voltage fluctuation characteristics, handshake timing characteristics, and protocol identification characteristics for generating a device feature vector includes: performing time-sharing sampling on the initial communication number to extract the voltage fluctuation characteristics in the previous voltage fluctuation waveform of the initial communication number; the voltage fluctuation characteristics include an amplitude change rate and a steady-state holding duration; analyzing the trigger frequency and response delay parameters within a preset signal interval in the handshake timing of the initial communication number to construct the handshake timing characteristics; performing multi-level decoding verification on the protocol identification characteristics of the initial communication number, generating the protocol identification characteristics based on the permutation and combination of valid identification bits in the verification result, and performing normalization processing on the voltage fluctuation characteristics, handshake timing characteristics, and protocol identification characteristics and then splicing them into the device feature vector according to a preset dimension.

[0141] In some embodiments, the matching of the device feature vector with the protocol feature parameters for similarity to obtain a candidate protocol set includes: performing multi-level similarity calculations on each dimension data of the device feature vector and the protocol feature parameters to obtain multiple comprehensive similarities; matching the corresponding first protocol type based on the device type identifier, and obtaining a second protocol type with a comprehensive similarity higher than a preset threshold, and adding the first protocol type and the second protocol type to the candidate protocol set.

[0142] In some embodiments, the obtaining of the optimal charging protocol and the corresponding charging curve template based on the historical charging efficiency data of each protocol in the candidate protocol set and the real-time load status of the target device includes: using charging time, energy loss, and temperature rise rate as efficiency evaluation indicators to generate a comprehensive efficiency score for each candidate protocol in the candidate protocol set; performing weighted sorting on multiple candidate protocols according to the current load current demand of the target device and the battery pack temperature parameters, selecting the candidate protocol with the highest efficiency score and load matching degree as the optimal charging protocol, and associating the voltage gradient parameter of the charging curve template corresponding to the optimal charging protocol.

[0143] In some embodiments, controlling the charging interface to complete the charging of the lithium-ion battery pack according to the optimal charging protocol and the charging curve template includes: according to the voltage and time piecewise function preset in the charging curve template, adjusting the pulse width and duty cycle of the output current corresponding to the charging interface in real time; continuously monitoring the feedback strength of the protocol handshake signal during the charging process, and triggering the protocol dynamic switching mechanism when it is determined according to the feedback strength that the charging protocol characteristic parameters deviate; after the charging is completed, updating the historical charging efficiency database of the optimal charging protocol based on the final charging rate and temperature rise data.

[0144] In some embodiments, the lithium-ion battery pack includes a negative electrode sheet, and the charging interface includes a pressure sensor array; when the charging interface is connected to a target device, the surface of the negative electrode sheet contacts the pressure sensor array; the battery management device collects the local deformation amount and deformation rate of the negative electrode sheet during the charging process in real time through the pressure sensor array, and converts them into deformation characteristic parameters; the battery management device pre-sets a deformation threshold mapping table, and the deformation threshold mapping table includes multiple deformation rate intervals and corresponding maximum allowable charging current values. When the deformation characteristic parameters exceed the first deformation threshold, the current attenuation coefficient is calculated according to the real-time deformation rate, and the output current peak value of the charging device is dynamically limited.

[0145] Exemplarily, during the charging process of the lithium-ion battery pack, the battery management device continuously receives the deformation characteristic parameters and the battery pack temperature data of the lithium-ion battery pack, and optimizes the voltage and current parameters in the charging curve template through an iterative algorithm according to the load fluctuation of the target device until the charging of the lithium-ion battery pack is completed.

[0146] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described battery management device and each module can refer to the corresponding processes in the method embodiments described in the above-mentioned embodiments, and will not be repeated here.

[0147] The present application also provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is caused to implement the steps of the multi-device compatible protocol matching method provided in any embodiment of the present application.

[0148] Among them, the computer-readable storage medium may be an internal storage unit of the battery management device described in the foregoing embodiments, such as the hard disk or memory of the battery management device. The computer-readable storage medium may also be an external storage device of the battery management device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the battery management device.

[0149] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described storage medium and each module can refer to the corresponding processes in the method embodiments described in the above embodiments, and will not be repeated here.

[0150] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A multi-device compatible intelligent fast-charging lithium-ion battery system, characterized in that: include: A charging device, wherein the charging interface of the charging device is used to connect to the target device and to charge the lithium-ion battery pack of the target device; A battery management device, the battery management device is in communication connection with the charging device, the battery management device stores a plurality of charging protocol characteristic parameters and corresponding charging curve templates, the charging protocol characteristic parameters including device type identification, protocol type, voltage and current range, and communication protocol handshake signal characteristics; When the charging interface is connected to a target device, the battery management device obtains the initial communication number of the connected target device in real time to extract voltage fluctuation characteristics, handshake timing characteristics and protocol identification characteristics for generating a device feature vector; the device feature vector is matched with the protocol feature parameters for similarity to obtain a candidate protocol set; based on the historical charging efficiency data of each candidate protocol in the candidate protocol set and the real-time load status of the target device, the optimal charging protocol and the corresponding charging curve template are obtained; The charging interface is controlled according to the optimal charging protocol and the charging curve template to complete charging of the lithium-ion battery pack.

2. The system according to claim 1, characterized in that The extraction of voltage fluctuation features, handshake timing features and protocol identification features for generating a device feature vector includes: The initial signal is sampled in time division to extract the voltage fluctuation characteristics in the preceding voltage fluctuation waveform of the initial signal; the voltage fluctuation characteristics include amplitude change rate and steady-state holding time; Analyze the trigger frequency and response delay parameters within the preset signal interval in the handshake timing of the initial call number to construct the handshake timing feature; The protocol identification feature of the initial communication number is subjected to multi-level decoding verification, the protocol identification feature is generated based on the arrangement and combination of valid identification bits in the verification result, and the voltage fluctuation feature, handshake timing feature and protocol identification feature are normalized and spliced ​​into the device feature vector according to a preset dimension.

3. The system according to claim 1, characterized in that The performing similarity matching between the device feature vector and the protocol feature parameter to obtain a candidate protocol set includes: Performing multi-level similarity calculation on each dimension data of the device feature vector and the protocol feature parameter to obtain multiple comprehensive similarities; Based on the matching of the device type identifier and the first protocol type corresponding to the device type identifier, and obtaining the second protocol type whose comprehensive similarity is higher than a preset threshold, the first protocol type and the second protocol type are added to the candidate protocol set.

4. The system according to claim 1, characterized in that The step of obtaining the optimal charging protocol and the corresponding charging curve template according to the historical charging efficiency data of each protocol in the candidate protocol set and the real-time load status of the target device includes: Taking charging time, energy loss and temperature rise rate as efficiency evaluation indicators, a comprehensive efficiency score is generated for each candidate protocol in the candidate protocol set; according to the current load current demand of the target device and the temperature parameters of the battery pack, multiple candidate protocols are weighted and sorted, and the candidate protocol with the highest efficiency score and load matching is selected as the optimal charging protocol, and the voltage gradient parameters of the charging curve template corresponding to the optimal charging protocol are associated.

5. The system according to claim 1, characterized in that The controlling the charging interface to complete charging of the lithium-ion battery pack according to the optimal charging protocol and the charging curve template includes: According to the voltage and time segmentation functions preset in the charging curve template, the pulse width and duty cycle of the output current corresponding to the charging interface are adjusted in real time; during the charging process, the feedback strength of the protocol handshake signal is continuously monitored, and the protocol dynamic switching mechanism is triggered when the charging protocol characteristic parameter offset is determined according to the feedback strength; after charging is completed, the historical charging efficiency database of the optimal charging protocol is updated based on the final charging rate and temperature rise data.

6. The system according to claim 1, characterized in that The lithium-ion battery pack includes a negative electrode sheet, and the charging interface includes a pressure sensor array; when the charging interface is connected to a target device, a surface of the negative electrode sheet contacts the pressure sensor array; The battery management device collects the local deformation amount and deformation rate of the negative electrode sheet in real time during the charging process through the pressure sensor array, and converts them into deformation characteristic parameters; the battery management device presets a deformation threshold mapping table, and the deformation threshold mapping table includes multiple deformation rate intervals and corresponding maximum allowable charging current values. When the deformation characteristic parameter exceeds the first deformation threshold, the current attenuation coefficient is calculated according to the real-time deformation rate to dynamically limit the output current peak of the charging device.

7. The system according to claim 6, characterized in that During the charging process of the lithium-ion battery pack, the battery management device continuously receives the deformation characteristic parameters and the battery pack temperature data of the lithium-ion battery pack, and optimizes the voltage and current parameters in the charging curve template through an iterative algorithm according to the load fluctuation of the target device until the charging of the lithium-ion battery pack is completed.

8. A multi-device compatible protocol matching method, characterized in that: A battery management device for an intelligent fast-charging lithium-ion battery system according to any one of claims 1 to 7; the battery management device is communicatively connected to the charging device; the charging interface of the charging device is used to connect to a target device and to charge a lithium-ion battery pack of the target device; the method comprises: Acquire multiple charging protocol characteristic parameters and corresponding charging curve templates, wherein the charging protocol characteristic parameters include device type identification, protocol type, voltage and current range, and communication protocol handshake signal characteristics; When the charging interface is connected to a target device, the initial communication number of the connected target device is obtained in real time to extract voltage fluctuation characteristics, handshake timing characteristics and protocol identification characteristics for generating a device feature vector; Performing similarity matching between the device feature vector and the protocol feature parameter to obtain a candidate protocol set; Obtaining an optimal charging protocol and a corresponding charging curve template according to the historical charging efficiency data of each candidate protocol in the candidate protocol set and the real-time load status of the target device; The charging interface is controlled according to the optimal charging protocol and the charging curve template to complete charging of the lithium-ion battery pack.

9. A battery management device, characterized in that: The battery management device includes a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the method according to claim 8 when executing the computer program.

10. A multi-device compatible protocol matching device, characterized in that: A battery management device for an intelligent fast-charging lithium-ion battery system according to any one of claims 1 to 7; the battery management device is communicatively connected to the charging device; the charging interface of the charging device is used to connect to a target device and to charge a lithium-ion battery pack of the target device; the device comprises: A template acquisition unit, used to acquire a plurality of charging protocol characteristic parameters and corresponding charging curve templates, wherein the charging protocol characteristic parameters include a device type identifier, a protocol type, a voltage and current range, and a communication protocol handshake signal characteristic; A vector generation unit, used to obtain the initial communication number of the connected target device in real time when the charging interface is connected to the target device, so as to extract voltage fluctuation characteristics, handshake timing characteristics and protocol identification characteristics for generating a device feature vector; A matching completion unit, used for performing similarity matching between the device feature vector and the protocol feature parameter to obtain a candidate protocol set; A curve acquisition unit, configured to acquire an optimal charging protocol and a corresponding charging curve template according to the historical charging efficiency data of each candidate protocol in the candidate protocol set and the real-time load status of the target device; The charging completion unit is used to control the charging interface to complete charging of the lithium-ion battery pack according to the optimal charging protocol and the charging curve template.

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