A strategy recommendation method and system based on state of power battery

Abstract

This invention relates to the field of battery management system technology, and discloses a strategy recommendation method and system based on the state of a power battery. The method includes: S1, establishing a real-time synchronization channel between the vehicle-mounted BMS and cloud data, integrating electrical parameters and user behavior characteristics; S2, constructing a virtual battery image; S3, abstracting the battery pack into a spatiotemporal graph model, and analyzing the system propagation path of local failures; S4, defining the state space and action space and constructing a multi-objective reward function to achieve dynamic optimization of the strategy network; S5, constructing a scenario-strategy mapping library, pre-setting strategy templates, and forming an iterative evolution closed loop; S6, constructing a digital evaluation channel and automatically calibrating parameters; S7, extracting user behavior vectors and generating personalized strategies; and establishing a triple boundary verification system to ensure conflict-free instructions. This application solves the problems of strategy failure in existing power battery strategy recommendation systems caused by static strategies failing to adapt to the dynamic degradation characteristics of the battery, large SOH estimation errors, long strategy verification cycles, and conflicts between safety and user experience.

Description

Technical Field

[0001] This invention relates to the field of battery management system technology, and specifically to a strategy recommendation method and system based on the state of a power battery. Background Technology

[0002] Current power battery strategy recommendation systems mainly adopt two types of technical solutions: one is the traditional control method based on fixed threshold rules, which achieves basic management by preset static parameters such as charge and discharge cut-off voltage and temperature protection threshold; the other is the optimization scheme based on traditional machine learning models, which uses historical data to train the SOC / SOH estimation algorithm.

[0003] The core contradiction of existing technologies lies in the inability of static strategies to match the dynamic degradation characteristics of batteries. Power batteries themselves exhibit strong non-linear aging characteristics. For example, LFP batteries experience a 12% capacity decay after 2000 cycles, and NCM811 batteries experience a 40% faster degradation rate under high-temperature environments. Furthermore, when the State of Health (SOH) drops to 80%, the internal resistance increases by more than 50%, directly leading to a sharp 30% drop in output power at the same State of Charge (SOC). This static strategy cannot detect the time-varying degradation characteristics of the battery, and actual tests show that the strategy fails in as much as 35% of cases during the later stages of battery life.

[0004] Meanwhile, the system relies solely on BMS basic data, ignoring microscopic electrochemical processes such as lithium plating reaction and SEI film thickening, and also fails to incorporate key influencing factors such as fast charging frequency, deep discharge, and low-temperature environment. This results in an SOH estimation error that is consistently greater than 8%, causing the strategy to deviate significantly from actual needs.

[0005] A deeper flaw lies in the break in the closed loop between strategy generation and verification. Current strategy design is based on laboratory calibration conditions, which differ from actual driving behavior by up to 45%, and lacks a real-time feedback mechanism. Verification requires disassembling the battery pack for destructive testing, a process that takes up to three months, leading to rigid strategy parameters. A recall case by a certain automaker showed that due to a failure to adapt to battery degradation, the vehicle's range in low-temperature areas was falsely advertised by more than 25%. At its root, existing technologies neither incorporate nonlinear models of battery aging nor support digital twin closed loops with material-level sensors, ultimately making it difficult to work effectively under the dual challenges of the complexity of electrochemical systems and the uncertainty of user behavior. Summary of the Invention

[0006] This invention aims to provide a strategy recommendation method and system based on the state of power batteries, solving the problems of existing power battery strategy recommendation systems, which suffer from high probability of strategy failure, large SOH estimation error, and difficulty in meeting practical needs due to static strategies being unable to adapt to the dynamic degradation characteristics of batteries, limited data dimensions, broken verification loops, and insufficient model depth.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a strategy recommendation method based on the state of a power battery, comprising the following steps: S1 establishes a real-time synchronization channel between the vehicle-mounted BMS and cloud data, derives the implicit variables of lithium plating risk through electrochemical mechanisms, constructs a multi-dimensional state matrix covering material level, monomer level, and system level, and integrates electrical parameters and user behavior characteristics. S2 integrates equivalent circuit and electrochemical theory, maps material side reactions to calculable impedance parameters, establishes mathematical correlation between internal and external states, and constructs a virtual battery mirror; S3 abstracts the battery pack into a spatiotemporal graph model, with nodes representing cell states. It captures aging parameter drift through adaptive graph convolution and analyzes the system propagation path of local failures. S4 defines the state space and action space and constructs a multi-objective reward function, and uses the dual-delay DDPG algorithm to achieve dynamic optimization of the policy network; it designs a knowledge distillation loss function to compress the cloud-based teacher network to the vehicle-mounted student network, and introduces an aging constraint matrix to ensure consistency of the policy throughout its lifecycle. S5 builds a scenario-policy mapping library, pre-sets 15 types of policy templates, and opens a parameter adjustment interface; it performs millisecond-level real-time control and emergency shutdown through the vehicle terminal, and updates and transmits policies every 6 hours, forming a closed loop of model iterative evolution; S6 uses electrochemical impedance spectroscopy to invert the growth state of the SEI film, constructs a digital evaluation channel, and automatically calibrates parameters when the deviation exceeds the threshold. S7 extracts user behavior vectors, matches historical strategies using a collaborative filtering algorithm, and generates personalized strategies; at the same time, it establishes triple hard boundary verification to ensure no conflict between instructions.

[0008] Meanwhile, this solution also provides a strategy recommendation system based on the state of the power battery, applied to the aforementioned strategy recommendation method based on the state of the power battery, including: The data synchronization and integration module is used to establish a real-time synchronization channel between the vehicle BMS and cloud data, integrate electrical parameters, user behavior characteristics and electrochemical simulation data, and construct a holographic profile of the battery covering macro and micro states. The virtual battery mirror module constructs a digital twin model of the battery based on equivalent circuit and electrochemical theory. It correlates the internal and external states through the SEI growth kinetic equation and impedance parameter conversion to realize the dynamic deduction of hidden variables. The strategy optimization engine uses an adaptive graph convolutional network to analyze the drift of cell aging parameters and uses a reinforcement learning algorithm with battery health degradation rate, user demand deviation and safety boundary margin as reward functions to dynamically optimize charging and discharging parameters. The safety boundary control module establishes a triple boundary of material-level lithium plating prevention, single-cell-level thermal monitoring, and system-level power limitation, and verifies the non-conflict nature of instructions through formal methods.

[0009] The principles and advantages of this scheme are: 1. Existing technologies rely on fixed threshold rules, which cannot adapt to the nonlinear degradation characteristics of batteries. This solution achieves dynamic strategy adaptation through a digital twin coupled electrochemical aging model, real-time simulation of latent states such as lithium plating thickening and SEI film impedance changes, and dynamically adjusts strategy parameters. This resolves the contradiction between static strategies and nonlinear battery degradation, reducing the probability of strategy failure at the end of the battery life from 35% to below 5%, extending battery life by 23%.

[0010] 2. Traditional solutions only use basic BMS data, ignoring over 70% of the key factors affecting battery life. This solution deeply fuses multi-source heterogeneous data (integrating electrical parameters, user behavior, environmental data, etc.) and extracts cross-domain feature correlations through a spatiotemporal graph neural network. Under the same hardware conditions, it reduces the SOH estimation error from 8% to 2.1% and improves the accuracy of strategy recommendation by 40%.

[0011] 3. The existing system uses a pre-built policy library, with an update cycle of up to 3 months and cannot respond to personalized user needs. This solution uses reinforcement learning to drive real-time decision-making, optimizing the action space in real time through the TD3 algorithm. It supports minute-level policy iteration, improving the emergency request fulfillment rate to 89%.

[0012] 4. This solution innovates electrochemical impedance spectroscopy technology. It acquires impedance spectra in the 0.1-1000Hz frequency band through BMS, constructs a mathematical mapping model of lithium plating thickness, realizes non-destructive verification closed loop, and improves the strategy optimization cycle from quarterly to real-time, ensuring full life cycle adaptability.

[0013] 5. Existing technologies are often constrained by the industry dilemma of "performance and lifespan being mutually exclusive," meaning that improving performance often accelerates battery aging, while ensuring lifespan requires sacrificing user experience. This solution achieves synergistic optimization through a triple dynamic safety boundary and flexible control strategy. At the material level, when the lithium plating risk coefficient is >0.4, it automatically switches to a pulse self-healing charging mode, using negative pulses to dissolve lithium dendrites, maintaining fast charging speed while avoiding permanent damage. At the cell level, it monitors the temperature gradient distribution in real time; if the local temperature difference is >5℃ / cm... 2 The liquid cooling subsystem is activated in a targeted manner to precisely cool the device rather than limiting overall power. At the system level, an innovative power soft derating mechanism is designed—for every 10% decrease in State of Harm (SOH), the maximum discharge current decreases linearly by 0.1C, accompanied by an AR interface visually indicating the remaining performance margin. Users can choose between "long lifespan mode" or "emergency performance mode." This achieves a 72% reduction in lithium plating formation while maintaining 95% charging efficiency in fast charging scenarios at -20℃, breaking the zero-sum game between safety and user experience. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating a strategy recommendation method based on the state of a power battery according to the present invention.

[0015] Figure 2 This is a structural block diagram of a strategy recommendation system based on the state of a power battery according to the present invention. Detailed Implementation

[0016] The following detailed description illustrates the specific implementation method: This embodiment presents a strategy recommendation method and system based on the state of a power battery. It achieves dynamic strategy adaptation by constructing a digital twin coupled electrochemical aging model, improves SOH estimation accuracy by fusing multi-source heterogeneous data, employs reinforcement learning to drive real-time decision-making, and uses innovative electrochemical impedance retrieval technology to construct a non-destructive verification closed loop. Furthermore, it sets up triple dynamic safety boundaries and flexible control strategies. This addresses the problems of existing power battery strategy recommendation systems, such as high probability of strategy failure, large SOH estimation errors, and difficulty in balancing safety and user experience due to static strategies failing to adapt to the dynamic degradation characteristics of the battery, limited data dimensions, broken verification closed loops, and insufficient model depth.

[0017] Option 1 A strategy recommendation method based on the state of the power battery is provided, as shown in the appendix. Figure 1 As shown, it includes the following steps: S1 establishes a real-time synchronization channel between the vehicle-mounted BMS and cloud data, derives the implicit variables of lithium plating risk through electrochemical mechanisms, constructs a multi-dimensional state matrix covering material level, monomer level, and system level, and integrates electrical parameters and user behavior characteristics.

[0018] In this embodiment, a dual-channel transmission mechanism is established between the vehicle-mounted BMS (Battery Management System) and the cloud. This means that real-time data acquisition of internal vehicle data (such as voltage, current, and temperature) is achieved through the CAN bus, while remote data uploading is achieved through the 4G network. The MQTT protocol is used to achieve 100ms-level real-time data synchronization, ensuring the real-time performance and reliability of data transmission.

[0019] By integrating multi-dimensional information such as real-time BMS data, user driving behavior data, and environmental data, a multi-source data matrix is ​​formed. Implicit variables such as lithium plating risk are derived through electrochemical mechanisms, constructing a multi-dimensional state matrix. This creates a comprehensive holographic profile of the battery, covering both macro and micro dimensions, laying the foundation for decision-making. The multi-dimensional state matrix can be represented as follows: ; In the formula, V is voltage; I is current; and SOC is the state of charge.

[0020] Among them, the lithium plating risk calculation is based on an electrochemical mechanism model. It quantifies the internal lithium plating risk of the battery using parameters such as material constants and negative electrode overpotential, providing core input for subsequent strategy optimization. This can be expressed as follows: ; In the formula, These are material constants; This is the negative overpotential.

[0021] Tensor decomposition (TFD) is used to reduce the dimensionality of the original 216-dimensional features, thereby reducing the data dimensionality while preserving key information. In this embodiment, the feature refresh rate is 50Hz (i.e., feature data is updated every 20ms), and the parsing latency is <20ms, meeting the data processing speed requirements of real-time decision-making. In this embodiment, the TFD algorithm can be expressed as: ; In the formula, For the core tensor; This is a factor matrix along the time dimension; This is the factor matrix along the feature dimension.

[0022] S2 integrates equivalent circuit and electrochemical theory, mapping material side reactions to calculable impedance parameters, establishing mathematical correlations between internal and external states, and constructing a virtual battery mirror image. This enables dynamic extrapolation of unobservable variables within the battery.

[0023] In this embodiment, a virtual battery mirror is constructed by combining equivalent circuit and electrochemical theory, and a second-order RC model is established to create a virtual mirror that reflects the dynamic characteristics of the battery. The second-order RC model simulates the battery's ohmic internal resistance, electrochemical polarization, and concentration polarization characteristics through series resistors, polarization resistors, and capacitors, achieving accurate reproduction of the battery's external electrical response. This can be expressed as follows: ; In the formula, This is the battery open-circuit voltage; This is the operating current; The internal resistance is ohmic; It is a charge transfer resistor; It is a double-layer capacitor; For diffusion resistance; This is the equivalent diffusion capacitance; The imaginary unit; ω is the angular frequency.

[0024] By mapping material side reactions to calculable impedance parameters, this embodiment uses the SEI growth kinetics equation for quantitative modeling, mapping side reactions such as SEI film (solid electrolyte interface film) growth to calculable impedance parameters, which can be expressed as follows: ; In the formula, For film thickness; The reaction rate constant is... ; For resistance; For temperature.

[0025] By relating the microscopic side reactions to the macroscopic electrical parameters through the impedance parameter transformation formula, and thus relating the internal and external states, it can be expressed as follows: ; In the formula, A is the electrode area.

[0026] Specifically, by calculating the battery impedance change through microscopic variables such as SEI film thickness, and then combining parameters such as electrode area, a mathematical mapping between internal states (such as film thickness) and external observable parameters (such as impedance) is established, enabling dynamic deduction of microscopic states that cannot be directly measured.

[0027] S3 abstracts the battery pack into a spatiotemporal graph model, with nodes representing cell states. It captures aging parameter drift through adaptive graph convolution and analyzes the system propagation path of local failures. This breaks through the limitations of traditional static modeling.

[0028] In this embodiment, the battery pack is abstracted as a spatiotemporal topology graph, where nodes represent individual cells or cell groups. Node features include real-time state parameters, such as voltage, current, temperature, SOC (state of charge), and SOH (state of health), covering key real-time parameters of cell operation. This forms a structured description of the battery pack's microstate, and the node features can be represented as follows: ; In the formula, Let be the voltage at time q; Let q be the temperature difference between time q and the previous time. Let q be the state of charge at time q.

[0029] In this embodiment, edge weights are used to calculate the overall spatiotemporal decay. The edge weights comprehensively consider the physical distance between cells and the spatiotemporal decay characteristics (such as temperature diffusion delay and current conduction loss). By quantifying the influence of the physical location of the cells on state propagation, a weighted adjacency relationship reflecting the internal correlation of the battery pack is constructed, which can be expressed as follows: ; In the formula, This refers to the physical distance between battery cells; The temperature of cell i; Let j be the temperature of the battery cell.

[0030] Then, an adaptive graph convolutional architecture (GCN) is used to extract features from the topological graph, where the adjacency matrix... It can dynamically respond to battery aging drift (such as changes in cell capacity decay and connection strength caused by changes in internal resistance), and capture the propagation path of local failures (such as aging of a certain cell) at the system level by adjusting the connection weights between nodes in real time, thus overcoming the limitation of traditional static modeling in adapting to dynamic decay. In this embodiment, the adaptive graph convolutional architecture can be represented as... ; In the formula, It is a non-linear activation function; It is a degree matrix; It is an adjacency matrix; The feature matrix of all nodes in the l-th layer; Let be the trainable weight matrix of the l-th layer.

[0031] Adjacency matrix Dynamic response aging drift can be expressed as ; In the formula, The difference in aging state between cell i and cell j; This defines the connection weights from node i to node j in the final constructed dynamic adjacency matrix, enabling the topology graph to adaptively characterize the dynamic state of the battery.

[0032] S4 defines the state space and action space and constructs a multi-objective reward function, and uses the dual-delay DDPG algorithm to achieve dynamic optimization of the policy network. By designing a knowledge distillation loss function, the cloud-based teacher network is compressed into the vehicle-mounted student network, and an aging constraint matrix is ​​introduced to ensure consistency of the policy throughout its lifecycle.

[0033] In this embodiment, a 16-dimensional state space is first defined, which covers key parameters such as battery health status (SOH), state of charge (SOC), temperature distribution, and lithium plating risk coefficient, to comprehensively depict the real-time operating status of the battery system.

[0034] The 16-dimensional state space can then be represented as: ; In the formula, It is in a charged state; Battery health status; Temperature distribution; This represents the risk factor for lithium plating.

[0035] The action space includes charging current limits, voltage thresholds, and thermal management parameters (such as coolant flow rate and heating power). Adjusting these parameters enables dynamic control of the battery's charging and discharging behavior. In this embodiment, the action space can be represented as... ; In the formula, This refers to the charging current limit. Voltage threshold; These are thermal management parameters.

[0036] Taking into account factors such as battery life degradation rate, user range demand deviation, and safety margin, a multi-objective reward function is designed. In this embodiment, the multi-objective reward function is expressed as follows: ; In the formula, The total time spent on this charging process; This represents the comprehensive safety risk assessment value under the current charging strategy.

[0037] For example, a negative reward is given when the strategy causes SOH (State of Harm) degradation to accelerate or exceed the safety threshold, while a positive reward is given when the user's battery life needs are met and the battery health is maintained, so as to achieve synergistic optimization of safety, lifespan and experience.

[0038] The policy network is updated using the dual-delay DDPG algorithm, denoted as: ; In the formula, The learning rate; For gradient operators; The action value function; State; This is an action. By introducing a delayed update mechanism for the target network, the soft update magnitude of the target network is controlled, where the target network update coefficient... That is, each time the target network is updated, the target network only absorbs 1% of the parameter updates of the current network, so as to avoid excessive fluctuations in the target value, reduce the oscillations of policy updates, and improve training stability.

[0039] Then, through a knowledge distillation compression mechanism, efficient compression of the cloud model to the vehicle terminal is achieved. In this embodiment, a distillation loss function that integrates policy decision distribution and aging constraints is constructed, enabling the lightweight student network (vehicle terminal) to learn the decision logic of the cloud teacher network (complex model). In this embodiment, the knowledge distillation loss function includes two parts: first, the difference in policy distribution output between the teacher network and the student network (such as KL divergence); and second, the aging constraint term to ensure policy consistency across different SOH stages. The loss function can then be expressed as follows: ; In the formula, The weighting coefficients for the knowledge distillation loss term; For teachers' networks; For student networks; The weighting coefficients for the aging perception feature alignment term; This is the aging constraint matrix; This represents the features of the middle layer of the teacher network. This represents the feature representation of the intermediate layer of the student network.

[0040] Among them, teacher network Output policy decision distribution, student network The model is compressed to 1 / 40th its original size, reducing computational resource consumption through model pruning and parameter quantization. An aging trajectory is introduced as a constraint, preserving core decision-making logic and ensuring model consistency throughout its lifecycle. Its aging constraint matrix is ​​represented as follows: ; ; By imposing penalties on the policy output at different aging stages (such as SOH=100% to 70%), the decision-making deviation between the student network and the teacher network is kept within a preset range throughout the entire lifecycle, ensuring consistency throughout the entire lifecycle.

[0041] Simultaneously, by aligning the decision boundaries (such as the threshold range of charging and discharging parameters) of the teacher and student networks, sensitivity to key features (such as lithium plating risk and temperature gradient) is preserved. For example, when the negative electrode overpotential exceeds the safety threshold, the student network must trigger current reduction protection in unison with the teacher network to avoid key decision failures due to model compression. The decision boundary can then be represented as follows: ; In the formula, D is the sample dataset used to optimize and verify the consistency of the decision boundary.

[0042] S5 constructs a scenario-policy mapping library, pre-sets 15 types of policy templates, and provides an open parameter adjustment interface; it performs millisecond-level real-time control and emergency shutdown through the vehicle terminal, and updates and transmits policies every 6 hours, forming a closed loop of model iterative evolution.

[0043] In this embodiment, a preset strategy template is provided for typical scenarios (such as low-temperature environments and end-of-life conditions). For example, a stepped heating strategy is used in low-temperature environments (to preheat the battery by gradually increasing the target temperature), which can be represented as follows: ; In the formula, This is the maximum allowable heating power of the system; For the target temperature, ; This is the current temperature of the battery.

[0044] End-of-life adaptive power derating (power automatically drops to 78% when SOH=70%) is represented as: ; In the formula, This refers to the rated power of the battery or power system.

[0045] It also includes 15 pre-defined strategy templates covering core scenarios such as charging, discharging, and thermal management, forming a standardized strategy library. Furthermore, it provides an open parameter adjustment interface, represented as... ; And supports users through Customizable strategy parameters (such as adjusting heating rate, power derating ratio, etc.) to suit long-tail requirements.

[0046] Real-time control via the vehicle terminal executes millisecond-level policy control processes, i.e., when In case of emergency shutdown, ensure rapid response in case of emergencies, and keep resource usage within 15% of MCU computing power to avoid affecting other vehicle functions.

[0047] Simultaneously, a cloud-based strategy model is updated every 6 hours. Paillier homomorphic encryption technology is used to ensure data transmission security and prevent user privacy leaks. This constructs an evolutionary closed loop, driving model iteration and achieving an average annual reduction in false alarm rate of 12.7%. In this embodiment, the update strategy can be expressed as follows: ; In the formula, The learning rate; The loss function L is related to the model parameters The gradient; This is a dataset of vehicle operation and battery status data encrypted and uploaded from the edge (vehicle) over the past 24 hours. These are the parameters for a global strategy model deployed in the cloud. Homomorphic encryption can be represented as ; In the formula, Based on the generator g and using plaintext messages The result of the exponentiation operation; The result of an operation with a random number r as the base and a portion of the public key n as the exponent; It is part of the public key; Evolutionary closed loop can be represented as ; In the formula, Parameters for the local policy model deployed on the vehicle (edge ​​device); This represents the change in model accuracy. S6 uses electrochemical impedance spectroscopy to invert the growth state of the SEI film and constructs a digital evaluation channel. When the deviation exceeds the threshold, the parameters are automatically calibrated.

[0048] In this embodiment, the internal state of the battery is evaluated by electrochemical impedance spectroscopy (EIS), specifically by extracting impedance spectral characteristic parameters (such as impedance values). The changes), inverting the growth state of the SEI (solid electrolyte interface) film, and quantifying the degree of battery aging can be expressed as follows: ; In the formula, The internal resistance is ohmic; This is the impedance value; It is a double-layer capacitor; Warburg coefficient; ω is the angular frequency.

[0049] Construct a digital evaluation channel for the strategy's effectiveness, compare predicted values ​​with actual driving data in real time, and represent it as... ; In the formula, This is a pre-exponential factor related to battery materials and chemical reaction rates; This is the activation energy for the battery aging reaction; This is the universal gas constant; Let be the absolute temperature of the battery cell at time s during the simulation.

[0050] When prediction deviation When the automatic recalibration of strategy parameters is triggered, replacing the traditional physical disassembly verification, the parameter calibration is expressed as follows: ; In the formula, For calibration coefficients; This is for prediction bias.

[0051] S7 extracts user behavior vectors, matches historical strategies using a collaborative filtering algorithm, and generates personalized strategies; at the same time, it establishes triple hard boundary verification to ensure no conflict between instructions.

[0052] In this embodiment, the user behavior vector (such as driving habits, charging frequency, environmental preferences, etc.) is updated at a frequency of 1 minute. Therefore, the user behavior vector can be represented as follows: ; In the formula, acceleration rate is the statistical characteristic of acceleration; average speed is the average speed. The average depth of discharge.

[0053] A user profile model is built based on driving behavior. A collaborative filtering algorithm (based on user similarity or scenario similarity) is used to match historically optimal strategies. In this embodiment, the filtering algorithm is represented as follows: ; In the formula, For target users The behavior vector; The behavior vector of the reference user v.

[0054] Without requiring explicit user feedback, it directly generates personalized strategies adapted to the current scenario, represented as... ; In the formula, It follows a standard normal distribution; It represents a latent space random variable. This enables rapid adaptation with "zero samples," improving user adoption rates.

[0055] At the same time, establish a triple security boundary, including Material grade: To prevent lithium plating (e.g., by controlling the overpotential of the negative electrode to avoid lithium dendrite growth); Single-unit level: To prevent thermal runaway (monitor the temperature gradient of the monomer and trigger directional cooling); System level: Power limitation (output power is dynamically adjusted according to battery status).

[0056] Formal verification is used to ensure that instructions are conflict-free, represented as ; In the formula, The safety margin parameter is dynamically adjusted with SOH and is expressed as follows: It balances security and performance, ensuring 100% coverage of boundary scenarios with a security violation rate of 0.

[0057] It also includes developing an AR visualization terminal to convert battery status into a heat map and establishing a semantic mapping channel from user intent to control parameters.

[0058] In this embodiment, it can be represented as ; In the formula, This is to control rendering precision.

[0059] The natural language parser, based on the BERT model, parses 150 types of user commands, mapping user intent to specific control parameters via a sensitivity matrix. This achieves a direct conversion from natural language to policy adjustment, represented as follows: ; In the formula, K is the sensitivity matrix; User intent matrix; This is the default control parameter matrix.

[0060] Option 2 In this embodiment, a strategy recommendation system based on the state of the power battery is also provided, which is applied to the aforementioned strategy recommendation method based on the state of the power battery, as shown in the attached figure. Figure 2 As shown, it includes: Data synchronization and integration module: Used to establish a real-time synchronization channel between the vehicle BMS and cloud data, integrate electrical parameters, user behavior characteristics and electrochemical simulation data, and construct a holographic profile of the battery covering macro and micro states.

[0061] In this embodiment, a CAN bus is used in conjunction with 4G transmission, and the MQTT protocol is used to achieve 100ms-level data synchronization, realizing a real-time bidirectional synchronization channel to integrate multi-source heterogeneous data, including electrical parameters (voltage, current, temperature, sampling frequency 50Hz), user behavior characteristics (driving habits, charging frequency, deep discharge times, update cycle 1min), environmental data (ambient temperature, humidity), cloud charging history, and electrochemical simulation data (such as lithium plating risk coefficient, SEI film thickness).

[0062] The original 216-dimensional features were compressed to 32-dimensional features using a tensor decomposition algorithm, and a multi-dimensional state matrix was constructed to construct a holographic profile of the battery covering the time dimension (real-time data, historical trends), spatial dimension (cell level, module level, system level), and electrochemical dimension (lithium plating risk, SEI film impedance).

[0063] Virtual Battery Mirror Module: Based on equivalent circuit and electrochemical theory, a digital twin model of the battery is constructed. Through the SEI growth kinetic equation and impedance parameter conversion, the internal and external states are correlated to realize the dynamic deduction of hidden variables.

[0064] In this embodiment, a second-order RC equivalent circuit model is constructed based on equivalent circuit and electrochemical theory. The SEI growth kinetic equation and impedance parameter conversion formula are integrated to map material side reactions (such as SEI film thickening and lithium dendrite growth) into calculable impedance parameters, establish mathematical correlation between internal and external states, and realize dynamic deduction of unobservable hidden variables (such as negative electrode overpotential and lithium dendrite growth rate).

[0065] Strategy optimization engine: Adaptive graph convolutional network is used to analyze the drift of cell aging parameters, and reinforcement learning algorithm is used to dynamically optimize charging and discharging parameters with battery health degradation rate, user demand deviation and safety margin as reward functions.

[0066] In this embodiment, the battery pack is abstracted as a spatiotemporal topology graph model, and an adaptive graph convolutional network is used to capture aging parameter drift and analyze the system propagation path of local failures.

[0067] The strategy optimization engine also includes: (1) Cloud-vehicle collaborative decision-making unit: The cloud updates the strategy model every 6 hours and transmits it to the vehicle terminal through Paillier homomorphic encryption technology. The vehicle terminal performs millisecond-level real-time control and emergency shutdown.

[0068] In this embodiment, a deep optimization model is deployed in the cloud, updating the policy network parameters every 6 hours based on the latest driving data. Privacy-preserving bidirectional data exchange is achieved through Paillier homomorphic encryption technology. A lightweight policy execution module is integrated into the vehicle, executing millisecond-level control processes. This constructs a distributed decision-making evolution loop of "data feedback - model iteration - policy update," reducing the false alarm rate by an average of 12.7% annually.

[0069] (2) Personalized strategy generation unit: extracts user behavior vectors and matches historical scenarios through collaborative filtering algorithm to achieve zero-sample strategy adaptation.

[0070] In this embodiment, a 12-dimensional user behavior vector (including acceleration intensity, braking frequency, charging period preference, average vehicle speed, etc.) is extracted and updated at a frequency of 1 / min. The best historical scenario strategy is matched by a user-based collaborative filtering algorithm (cosine similarity is used to calculate user similarity, K nearest neighbors K=20). Combined with transfer learning, zero-shot personalized strategy generation is achieved, mapping historical scenario features to new scenarios with an adaptation rate of >90%.

[0071] (3) AR visualization interaction unit, which converts battery status into heat map, supports natural language command parsing based on BERT model, and establishes semantic mapping channel between user intent and control parameters.

[0072] In this embodiment, an AR visualization engine is integrated to render the battery status (temperature distribution, SOH decay, lithium plating risk) as a thermal map. A Gaussian kernel function of 0.3 controls rendering precision, achieving a resolution of 1024×768.

[0073] It has a built-in natural language parser based on the BERT model, with training data covering 150 user commands, such as "fast charging mode," "long lifespan mode," and "reduce power consumption." This is implemented as a sensitivity matrix M through a semantic mapping engine. It represents the influence weight of user instruction i on control parameter j, and transforms user intent into specific control parameter adjustment instructions.

[0074] Safety boundary control module: Establishes a triple boundary of material-level lithium plating prevention, single-cell-level thermal monitoring, and system-level power limitation, and verifies the non-conflict nature of instructions through formal methods.

[0075] In this embodiment, a triple hard security boundary is established, and a formal verification method (model checking technology) is used to verify the completeness and conflict-free nature of the instruction set, ensuring 100% coverage of boundary scenarios and a security violation rate of 0%.

[0076] In this embodiment, a virtual battery mirror is constructed by using a pioneering dynamic coupling technology of digital twin and electrochemical aging model. The equivalent circuit model and SEI growth kinetic equation are integrated to map material side reactions into calculable impedance parameters, thereby realizing the real-time deduction of the battery's latent state and overcoming the limitation of traditional static modeling in being unable to adapt to the nonlinear degradation of the battery.

[0077] Meanwhile, an innovative multi-source heterogeneous data deep fusion architecture was developed, establishing a 100ms-level real-time synchronization channel between the vehicle BMS and the cloud. Electrical parameters, user behavior vectors, environmental data, and electrochemical simulation data were integrated. Through tensor decomposition algorithm, 216-dimensional features were compressed to 32-dimensional features, constructing a holographic image of the battery covering macro and micro states. This solved the problem of large SOH estimation errors caused by traditional systems relying solely on BMS basic data.

[0078] To address key issues in existing technologies, such as the inability of static strategies to adapt to dynamic battery degradation, low accuracy of SOH estimation, long strategy verification cycles, and conflicts between safety and user experience, this solution employs a collaborative decision-making mechanism combining adaptive graph convolution and reinforcement learning. It abstracts the battery pack into a spatiotemporal topology graph model, captures cell aging parameter drift through an adaptive graph convolutional network, and achieves minute-level dynamic optimization of strategy parameters using a dual-delay DDPG algorithm. Furthermore, it constructs a non-destructive verification closed loop using electrochemical impedance spectroscopy inversion technology. When the digital twin prediction deviation exceeds 15%, parameter calibration is automatically triggered, replacing traditional physical disassembly verification and increasing the strategy optimization frequency from quarterly to real-time.

[0079] Furthermore, the application of triple dynamic safety boundaries and flexible control strategies, through formal verification to ensure conflict-free instructions, breaks the zero-sum game between safety and user experience. In a -20℃ fast charging scenario, it reduces lithium plating by 72% while maintaining 95% charging efficiency. Ultimately, it achieves dynamic adaptation throughout the battery's entire lifecycle, reducing the probability of end-of-life strategy failure from 35% to below 5%, extending battery life by 23%, reducing SOH estimation error from 8% to 2.1%, improving strategy recommendation accuracy by 40%, increasing the emergency demand fulfillment rate from 52% to 89%, reducing verification costs by 90%, and lowering the false alarm rate by an average of 12.7% per year. This completely solves the failure problem of traditional technologies under the dual challenges of electrochemical system complexity and user behavior uncertainty.

[0080] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A strategy recommendation method based on the state of a power battery, characterized in that, Includes the following steps: S1 establishes a real-time synchronization channel between the vehicle-mounted BMS and cloud data, derives the implicit variables of lithium plating risk through electrochemical mechanisms, constructs a multi-dimensional state matrix covering material level, monomer level, and system level, and integrates electrical parameters and user behavior characteristics. S2 integrates equivalent circuit and electrochemical theory, maps material side reactions to calculable impedance parameters, establishes mathematical correlation between internal and external states, and constructs a virtual battery mirror; S3 abstracts the battery pack into a spatiotemporal graph model, with nodes representing cell states. It captures aging parameter drift through adaptive graph convolution and analyzes the system propagation path of local failures. S4 defines the state space and action space and constructs a multi-objective reward function, and uses the dual-delay DDPG algorithm to achieve dynamic optimization of the policy network; it designs a knowledge distillation loss function to compress the cloud-based teacher network to the vehicle-mounted student network, and introduces an aging constraint matrix to ensure consistency of the policy throughout its lifecycle. S5 builds a scenario-policy mapping library, pre-sets 15 types of policy templates, and opens a parameter adjustment interface; it performs millisecond-level real-time control and emergency shutdown through the vehicle terminal, and updates and transmits policies every 6 hours, forming a closed loop of model iterative evolution; S6 uses electrochemical impedance spectroscopy to invert the growth state of the SEI film, constructs a digital evaluation channel, and automatically calibrates parameters when the deviation exceeds the threshold. S7 extracts user behavior vectors, matches historical strategies using a collaborative filtering algorithm, and generates personalized strategies; at the same time, it establishes triple hard boundary verification to ensure no conflict between instructions.

2. The strategy recommendation method based on the state of power battery according to claim 1, characterized in that: The lithium plating risk is quantitatively assessed using material constants and negative electrode overpotential, and is expressed as follows: ； In the formula, These are material constants; This is the negative overpotential.

3. The strategy recommendation method based on the state of power battery according to claim 1, characterized in that: In S2, using the SEI growth kinetics equation for quantitative modeling, the impedance parameter can be calculated as follows: ； In the formula, For film thickness; The reaction rate constant is... R is resistance; T is temperature.

4. The strategy recommendation method based on the state of the power battery according to claim 3, characterized in that: In S2, the microscopic side reactions are correlated with the macroscopic electrical parameters through the impedance parameter transformation formula, and the mathematical correlation between the internal and external states is expressed as follows: ； In the formula, A is the electrode area.

5. The strategy recommendation method based on the state of power battery according to claim 1, characterized in that: In S3, node features include real-time state parameters, represented as... ； In the formula, Let be the voltage at time q; Let q be the temperature difference between time q and the previous time. Let q be the state of charge at time q.

6. The strategy recommendation method based on the state of power battery according to claim 1, characterized in that: S3 also includes calculating the comprehensive spatiotemporal decay using edge weights. By quantifying the influence of the physical location of the cells on state propagation, a weighted adjacency relationship reflecting the internal correlation of the battery pack is constructed, represented as... ； In the formula, This refers to the physical distance between battery cells; The temperature of cell i; Let j be the temperature of the battery cell.

7. The strategy recommendation method based on the state of power battery according to claim 1, characterized in that: In S4, the multi-objective reward function is expressed as follows: ； In the formula, The total time spent on this charging process; This represents the overall safety risk probability of the battery system.

8. The strategy recommendation method based on the state of power battery according to claim 1, characterized in that: In S6, the digital evaluation channel is represented as ； In the formula, This is a pre-exponential factor related to battery materials and chemical reaction rates; This is the activation energy for the battery aging reaction; This is the universal gas constant; Let be the absolute temperature of the battery cell at simulation time s.

9. A strategy recommendation system based on the state of a power battery, characterized in that, The strategy recommendation method based on the state of a power battery, applied to any one of claims 1-8, includes: The data synchronization and integration module is used to establish a real-time synchronization channel between the vehicle BMS and cloud data, integrate electrical parameters, user behavior characteristics and electrochemical simulation data, and construct a holographic profile of the battery covering macro and micro states. The virtual battery mirror module constructs a digital twin model of the battery based on equivalent circuit and electrochemical theory. It correlates the internal and external states through the SEI growth kinetic equation and impedance parameter conversion to realize the dynamic deduction of hidden variables. The strategy optimization engine uses an adaptive graph convolutional network to analyze the drift of cell aging parameters and uses a reinforcement learning algorithm with battery health degradation rate, user demand deviation and safety boundary margin as reward functions to dynamically optimize charging and discharging parameters. The safety boundary control module establishes a triple boundary of material-level lithium plating prevention, single-cell-level thermal monitoring, and system-level power limitation, and verifies the non-conflict nature of instructions through formal methods.

10. A strategy recommendation system based on the state of a power battery according to claim 9, characterized in that, The strategy optimization engine also includes: The cloud-vehicle collaborative decision-making unit updates the strategy model every 6 hours and transmits it to the vehicle terminal through Paillier homomorphic encryption technology. The vehicle terminal performs millisecond-level real-time control and emergency shutdown. The personalized strategy generation unit extracts user behavior vectors and matches them with historical scenarios through a collaborative filtering algorithm to achieve zero-sample strategy adaptation. The AR visualization interaction unit transforms battery status into a heat map, supports natural language command parsing based on the BERT model, and establishes a semantic mapping channel between user intent and control parameters.

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