Battery pack energy efficiency optimization method and system

Abstract

The invention provides a battery pack energy efficiency optimization method and system, and the method comprises the steps: recognizing the structure information of riding equipment, carrying out the structure matching with an equipment battery pack of the riding equipment, and outputting a battery adaptation parameter; acquiring riding data, and performing equipment power distribution on the riding data and the battery adaptive parameters to obtain an energy efficiency control threshold value; identifying battery state data of the equipment battery pack based on the battery adaptation parameter, performing thermal safety boundary calculation on the battery state data and the energy efficiency control threshold, and outputting attenuation trend data; and performing battery maintenance analysis on the equipment battery pack according to the attenuation trend data to generate subsequent maintenance planning information. The battery performance degradation or thermal runaway risk can be pre-warned, and the safety of battery use is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery pack, and particularly relates to a battery pack energy efficiency optimization method and system. BACKGROUND

[0002] With the popularity of electric riding devices, as the core energy component, the energy efficiency optimization and safety management of the battery pack become the key to improving user experience and device reliability. Especially in high-frequency use scenarios such as shared travel and logistics distribution, the long-term performance degradation, thermal safety risk and dynamic power distribution of the battery pack are increasingly prominent. In the current technology, the battery energy efficiency optimization depends on static parameter matching or single riding data feedback, and lacks the coordinated analysis of device structure differences, riding state and battery thermal safety boundaries. The existing method may only allocate power according to the battery capacity or fixed load model, and ignore the dynamic influence of the riding device structure (such as the weight of the vehicle body and the type of the motor) on the actual output of the battery, or does not combine the battery degradation trend for preventive maintenance, resulting in insufficient energy efficiency control precision, shortened battery life and even thermal runaway risk. SUMMARY

[0003] To overcome the problems in the related art, the present application provides a battery pack energy efficiency optimization method and system, which can early warn the battery performance degradation or thermal runaway risk and improve the safety of battery use.

[0004] The present application provides a battery pack energy efficiency optimization method, comprising: identifying the structure information of the riding device, and performing structure matching with the device battery pack of the riding device to output battery adaptation parameters; obtaining riding data, performing device power distribution with the battery adaptation parameters to obtain an energy efficiency control threshold; based on the battery adaptation parameters, identifying the battery state data of the device battery pack, and performing thermal safety boundary calculation with the energy efficiency control threshold to output degradation trend data; performing battery maintenance analysis on the device battery pack according to the degradation trend data to generate subsequent maintenance planning information.

[0005] Preferably, the identification of the structure information of the riding device and the structure matching with the device battery pack of the riding device to output the battery adaptation parameters comprises: parsing the frame space parameters and structure extension parameters in the structure information to generate cavity size parameters and mechanism displacement information; performing current path identification on the device battery pack according to the cavity size parameters to generate current distribution parameters; based on the mechanism displacement information, activating the energy supply partition state of the device battery pack to generate energy supply activation data; Integrating the current distribution parameters and the energy supply activation data, output the battery adaptation parameters.

[0006] Preferably, the partition state activation of the energy supply partition of the equipment battery pack based on the mechanism displacement information generates energy supply activation data, including: Extract the structure change information and extension range data of the structure extension parameters, and perform multi-axis motion analysis with the mechanism displacement information to output the motion phase parameters; Based on the motion phase parameters, perform activation calculation and position mapping on the energy supply partition to generate an activation instruction set and a cell connection relationship; Optimize the activation instruction set through the cell connection relationship to obtain energy distribution data; Integrate the energy distribution data and the motion phase parameters to output the energy supply activation data.

[0007] Preferably, the riding data is obtained, and the energy efficiency control threshold is obtained by performing equipment power distribution on the battery adaptation parameters, including: Collect and integrate the speed data, slope angle data and load pressure data of the riding equipment to generate the riding data; Perform motion load calculation on the riding data to generate power demand data; Distribute the cell cluster power of the equipment battery pack through the battery adaptation parameters to generate a current quota set; According to the power demand data and the current quota set, perform equipment energy efficiency distribution to output the energy efficiency control threshold.

[0008] Preferably, the battery state data of the equipment battery pack is identified based on the battery adaptation parameters, and the heat safety boundary calculation is performed on the energy efficiency control threshold to output the attenuation trend data, including: Based on the battery adaptation parameters, monitor the single temperature and voltage fluctuation of the equipment battery pack to obtain the battery state data; According to the battery state data and the energy efficiency control threshold, perform heat safety boundary analysis on the equipment battery pack to generate a protection boundary parameter; Identify the charge and discharge power of the protection boundary parameter to obtain a power limitation parameter; According to the power limitation parameter and the protection boundary parameter, perform attenuation identification on the equipment battery pack to output the attenuation trend data.

[0009] Preferably, the heat safety boundary analysis is performed on the equipment battery pack according to the battery state data and the energy efficiency control threshold to generate a protection boundary parameter, including: extracting a single cell temperature gradient and a voltage fluctuation rate in the battery state data, performing a thermal correlation analysis on the overheat, and generating overheat data; performing regional load distribution on the overheat data through the energy efficiency control threshold and the structure information, and generating a partitioned thermal load data set; performing thermal conduction path analysis on the partitioned thermal load data set according to heat dissipation structure information of the structure information, and obtaining local temperature rise data; performing protection parameter deduction on the local temperature rise data according to a preset temperature rise critical point, and outputting the protection boundary parameter.

[0010] Preferably, the battery maintenance analysis on the equipment battery pack according to the attenuation trend data generates subsequent maintenance planning information, including: extracting historical charge and discharge records of the equipment battery pack from the cycling equipment; performing mode matching on the battery attenuation mode and the historical charge and discharge records based on the battery attenuation mode, and obtaining battery attenuation correlation information; performing mode matching on the battery attenuation mode and the historical charge and discharge records based on the battery attenuation mode, and obtaining battery attenuation correlation information; adjusting the output power gradient of the equipment battery pack according to the battery attenuation correlation information, and generating subsequent maintenance planning information.

[0011] Preferably, the segmented fitting based on the battery attenuation mode and the historical charge and discharge records obtains battery attenuation correlation information, including: extracting core attenuation parameters including capacity attenuation, internal resistance drift and self-discharge anomaly from the battery attenuation mode; segmenting the historical charge and discharge records according to preset charge and discharge cycle period data to obtain charge and discharge stage parameter groups; correspondingly associating the charge and discharge stage parameter groups with the core attenuation parameters to form an association relationship structure of charge and discharge parameters and attenuation parameters; performing attenuation trajectory reconstruction through the association relationship structure to generate segmented reconstruction parameters of each segment; performing full-cycle fusion analysis on the segmented reconstruction parameters of all segments to output the battery attenuation correlation information.

[0012] The application also provides a battery pack energy efficiency optimization system, which is applied to the battery pack energy efficiency optimization method described above, and includes: An identification module is configured to identify structure information of cycling equipment, and perform structure matching with an equipment battery pack of the cycling equipment to output battery adaptation parameters; An analysis module is configured to obtain cycling data, perform device power distribution with the battery adaptation parameters, and obtain an energy efficiency control threshold. a processing module configured to identify battery state data of the device battery pack based on the battery adaptation parameter, perform thermal safety boundary calculation with the energy efficiency control threshold, and output attenuation trend data; a construction module configured to perform battery maintenance analysis on the device battery pack according to the attenuation trend data, and generate subsequent maintenance planning information.

[0013] The technical scheme provided by the present application can include the following beneficial effects: The present application solves the problem of insufficient structural adaptation in the prior art by identifying the structural information of the cycling device and matching it with the battery pack to output accurate battery adaptation parameters, so that the power distribution is more in line with the actual needs of the device, thereby improving the battery energy efficiency. By combining cycling data and battery adaptation parameters for device power distribution, the energy efficiency control threshold can be adjusted to avoid battery over-discharge or redundant power supply, enhance dynamic energy efficiency management capability, and improve the energy utilization efficiency of the battery. Based on the battery adaptation parameter, the battery state data is identified, and the thermal safety boundary calculation is performed in combination with the energy efficiency control threshold, so that the attenuation trend data can be accurately output, the battery performance degradation or thermal runaway risk can be early warned, and the safety of the battery use can be improved. Through analysis of the attenuation trend data and the device battery pack, targeted subsequent maintenance planning information is generated, realizing the transition from passive maintenance to active predictive maintenance, prolonging the battery life and reducing the maintenance cost. By comprehensively considering the cycling device structure, cycling data and battery state, the energy efficiency optimization strategy is more adaptable, meeting the battery management needs in different cycling modes and improving the user experience. BRIEF DESCRIPTION OF DRAWINGS

[0014] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which like reference characters refer to like parts throughout the figures, and in which:

[0015] Figure 1 A battery pack energy efficiency optimization method flowchart is provided for the present application; Figure 2 A battery pack energy efficiency optimization system structure diagram is provided for the present application. DETAILED DESCRIPTION

[0016] The preferred embodiments of the present application will be described in more detail by referring to the attached drawings. Although the preferred embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0017] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in this application and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0018] It should be understood that, although the terms "first," "second," "third," etc. can be used herein to describe various information, the information should not be limited by these terms. These terms are only used to distinguish one piece of information from another. For example, a first information can also be termed a second information, and, similarly, a second information can also be termed a first information, without departing from the scope of the present application. Therefore, the features defined with "first," "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0019] Referring to Figure 1 As shown in the above steps, the present application provides a battery pack energy efficiency optimization method, comprising: Step S1: identifying the structural information of the riding device, and performing structural matching with the device battery pack of the riding device, and outputting battery adaptation parameters; Step S2: obtaining riding data, performing device power distribution with the battery adaptation parameters, and obtaining an energy efficiency control threshold; Step S3: identifying battery state data of the device battery pack based on the battery adaptation parameters, performing thermal safety boundary calculation with the energy efficiency control threshold, and outputting attenuation trend data; Step S4: performing battery maintenance analysis on the attenuation trend data and the device battery pack, and generating subsequent maintenance planning information.

[0020] Based on the above steps, the detailed step process is as follows: Step S1: The system analyzes the 3D structural information of the cycling equipment to extract the spatial dimensions of the wheel hub cavity or the motion trajectory parameters of the folding mechanism. For wheel hub-type devices (such as the Lexing V9 unicycle), the wheel cavity diameter, spoke gap depth, and motor shaft positioning tolerance are measured to generate a wheel cavity spatial vector. For folding devices (such as the L6 scooter), the hinge rotation angle range and chassis unfolding thickness are deconstructed to output a folding degree-of-freedom matrix. Simultaneously, the internal topology of the battery pack is deconstructed, including the cell annular arrangement radius, electrode connection paths, and heat dissipation channel direction, generating a battery pack space occupancy tensor. The equipment structural parameters and the battery pack spatial tensor are input into a spatial matching algorithm to calculate the structural compatibility coefficient: if the wheel cavity diameter is ≥ battery pack outer diameter + 5mm tolerance and there is no interference between the heat dissipation channel and the spokes, an annular current distribution weight is generated; if there is no risk of battery pack compression deformation when the folding angle changes, a domain-specific power supply isolation protocol is output. The final battery adaptation parameter set includes current distribution weights, power supply protocols, and heat conduction path diagrams, providing a physical constraint benchmark for subsequent energy efficiency control.

[0021] Step S2: Signal streams from speed sensors, gyroscopes, and load pressure gauges are collected and filtered for noise reduction to generate cycling data vectors, including the rate of change of speed (dv / dt), slope angle (θ), and load value (m). Instantaneous power demand is calculated based on the vector components: climbing power = load × slope coefficient × speed; acceleration power = mass × acceleration × speed. The power demand is spatially decomposed using the current allocation weights from the battery adaptation parameters: hub-type devices allocate power to 12 cell clusters according to radial current weights; foldable devices activate the corresponding power supply domains according to the power supply protocol.

[0022] The system performs power-internal resistance co-optimization to find the optimal current path with the goal of minimizing Joule heat. It takes as input a battery pack internal resistance distribution map (from the S1 heat conduction path), constrains the single-cluster current to be less than or equal to the upper limit of the allocation set by the adaptation parameters, and outputs a cell cluster current quota matrix. The final energy efficiency control threshold is a two-dimensional matrix, with row vectors representing cell cluster IDs and column vectors representing [maximum charging current, maximum discharging current, energy recovery intensity]. The discharging current threshold is calculated as the quota value multiplied by the thermal conductivity of the heat dissipation path, ensuring that heat accumulation does not exceed the critical value.

[0023] Step S3: The thermal conductivity path diagram in the battery adapter parameters is used to locate the coordinates of the temperature sensor placement points (e.g., deploying 6 NTC probes equidistantly along the radial direction for a hub-and-spoke device). Simultaneously, cluster-level voltage sequences from the voltage sampling chip, charging and discharging waveforms from the current Hall sensor, and three-dimensional thermal field data from the temperature sensor are acquired to generate the raw battery state dataset. Based on the current upper limit constraint of the energy efficiency control threshold matrix, a heat accumulation rate model is constructed: real-time current values ​​are input, and combined with the cell internal resistance distribution, the Joule heat generation is calculated; the temperature gradient distribution is input, and combined with the thermal conductivity of the carbon fiber in the thermal conduction path, the heat dissipation efficiency is calculated.

[0024] Solve the heat balance equation dynamically: when the local temperature rise rate > 5℃ / s and deviates from the safety current range defined by the threshold matrix, mark it as a thermal risk unit. Perform electrochemical stress analysis simultaneously: according to the relaxation time constant change of the voltage sequence, deduce the lithium ion concentration gradient; superimpose the temperature field data to generate the electrode expansion stress distribution map. Finally output the attenuation trend data as a tensor structure, including thermal risk unit coordinates, stress concentration area identification, and capacity attenuation slope prediction value.

[0025] Step S4: Analyze the thermal risk unit coordinates and stress concentration area identification in the attenuation trend data, and match the device battery pack physical structure: if the thermal risk unit is located at the edge of the hub cavity heat dissipation dead angle, mark it as a forced air cooling priority area; if the stress concentration area coincides with the hinge of the folding mechanism, mark it as a mechanical reinforcement target position. Based on the capacity attenuation slope prediction value, classify the attenuation mode: slope <0.1% / week for natural aging, generate an equalization maintenance plan; slope ≥0.5% / week for abnormal attenuation, trigger the battery cell cluster replacement protocol.

[0026] Combine the device maintainability constraints (such as L6 scooter chassis opening size limit tool diameter ≤8mm), perform spatial feasibility verification on maintenance actions: the replacement protocol automatically adapts to the micro hot air gun disassembly and welding process, and the reinforcement scheme uses pre-formed support embedded installation. Call the geographic location data and service resource database, match the nearest maintenance site inventory: if the site has the same type of battery cell cluster and the tool is compatible, generate a battery replacement instruction with a time window (including spare part code and operation order); if there is no inventory, start the emergency derating scheme, and add the output power limitation coefficient (such as the maximum current is reduced to 60% of the original threshold) in the subsequent maintenance planning information. The final subsequent maintenance planning information is a structured instruction set, including fault coordinates, action sequence, resource code, and derating coefficient triplets.

[0027] The battery pack energy efficiency optimization method provided by the application solves the problem of insufficient structural adaptability in the traditional method by identifying the structural information of the riding device and matching it with the battery pack, outputting accurate battery adaptation parameters, making the power distribution more in line with the actual needs of the device, thereby improving the battery energy efficiency utilization rate. Combining riding data with battery adaptation parameters for device power distribution can adjust the energy efficiency control threshold, avoid battery over-discharge or redundant power supply, enhance dynamic energy efficiency management capability, and improve the energy utilization efficiency of the battery. Based on the battery adaptation parameters, the battery state data is identified, and the thermal safety boundary calculation is performed in combination with the energy efficiency control threshold, which can accurately output the attenuation trend data, early warning of battery performance degradation or thermal runaway risk, and improve the safety of battery use. Through analysis of the attenuation trend data and the device battery pack, targeted subsequent maintenance planning information is generated, realizing the transition from passive maintenance to active predictive maintenance, prolonging the battery life and reducing maintenance costs. By considering the structure of the riding device, riding data and battery state, the energy efficiency optimization strategy is more adaptable, meeting the battery management needs of different riding modes and improving user experience.

[0028] In one embodiment, the structural information of the riding device is identified and matched with the device battery pack of the riding device, and the battery adaptation parameters are output, including: The frame space parameter refers to the geometric boundary characteristics of the device shell, including the wheel cavity diameter range of the hub device, the spoke gap depth range, the motor middle shaft positioning tolerance band, or the chassis thickness variation interval of the folding device, the hinge rotation angle range. The structural extension parameter describes the dynamic deformation capability, such as the telescopic stroke interval of the folding mechanism, the compression residual amount range of the hub shock spring.

[0029] Through three-dimensional geometric analysis technology, core parameters such as wheel cavity diameter tolerance range and hinge maximum rotation angle interval are extracted. For hub devices, the effective volume characteristics of the wheel cavity are calculated to generate a ring cavity size parameter vector; for folding devices, the relationship between hinge pin shaft coordinates and connecting rod length is measured to output mechanism displacement information matrix. The cavity size parameter stores the geometric boundary characteristics in the form of a vector, and the mechanism displacement information records the mapping relationship between displacement and angle in the form of a matrix. The output data directly constrain the physical layout of the battery pack, providing spatial boundary basis for current path identification.

[0030] Input the wheel cavity effective volume feature vector in the cavity size parameter, call the cell topology diagram of the device battery pack. Identify the wheel cavity and battery pack gap features: divide the main path and forbidden zone path according to the radial gap threshold. Based on the cell cluster parallel group topology relationship, plan the optimal current path: the main path arranges low resistance conductors along the tangential direction of the wheel cavity, and the forbidden zone path adopts the radial jumper structure. Generate the current distribution parameter matrix: the row vector corresponds to the cell cluster identifier, and the column vector contains the path type identifier, impedance characteristic value and current carrying capacity threshold. For foldable devices, check the influence of displacement deformation on the path: if the path length changes exceed the preset proportion, add a dynamic impedance compensation coefficient. The final current distribution parameter set is integrated into the battery adaptation parameter set to drive the subsequent energy supply partition activation logic.

[0031] Input the displacement-angle mapping relationship in the displacement information of the mechanism, analyze the device deformation characteristics: extract the correlation characteristics of hinge rotation angle and chassis unfolding thickness for foldable devices, and identify the shock spring compression stroke and wheel cavity volume change ratio for hub devices. Call the partition topology structure of the device battery pack, and establish the mapping rule between displacement characteristics and energy supply domain: if the chassis unfolding thickness meets the condition when the folding angle reaches the preset threshold, activate the global energy supply; if the angle is lower than the threshold and the thickness is limited, only the core energy supply domain is activated. Generate the energy supply activation rule matrix: the row vector is the energy supply domain identifier, and the column vector contains the angle activation threshold, current carrying capacity upper limit and interlocking protocol identifier. For hub devices, configure the vibration working condition response mechanism: when the shock spring compression rate exceeds the preset range, trigger the buffer energy supply mode. The energy supply activation rule integrates the displacement protection mechanism to ensure that there is no risk of circuit interference during mechanism movement.

[0032] Input the current distribution parameters and energy supply activation rules, perform feature space alignment: associate the cell cluster identifiers contained in the energy supply domain with the current path features, and construct a globally covered mapping relationship table. Check the consistency of elements: if the current carrying upper limit value in the energy supply activation rule is lower than the maximum current carrying capacity of the current distribution feature, automatically synchronize the de-rating to the matching value; if the energy supply domain boundary overlaps with the current path forbidden zone, add an interlocking protocol. The output battery adaptation parameters are in a hierarchical structure: the current control layer inherits the path type and impedance characteristics of the current distribution feature; the energy supply logic layer embeds the angle threshold and interlocking protocol of the energy supply activation rule; the thermal management interface layer reserves the temperature sensor coordinate field. The final parameter set is packaged in a binary protocol and transmitted to the device energy efficiency control module through a standard bus.

[0033] The embodiment generates battery adaptation parameters by analyzing the structural information of the riding equipment, realizes the precise matching of the battery pack and the physical structure of the equipment, thereby improving the space utilization and reducing the risk of mechanical interference. Based on the cavity size parameter, the current path is identified, the current distribution characteristics between the battery clusters are optimized, and the internal resistance loss is effectively balanced. The mechanism displacement information is used to activate the energy supply partition state, dynamically adapt to the deformation working condition of the folding or hub equipment, and avoid power loss in the invalid energy supply area. The current distribution characteristics and energy supply activation rules are integrated to form a unified control protocol, ensuring the collaborative optimization of power distribution and thermal safety boundary. Finally, through the correlation analysis of the attenuation trend data and the equipment structure, targeted maintenance instructions are generated to prolong the energy efficiency stability of the battery pack throughout its life cycle.

[0034] In one embodiment, based on the mechanism displacement information, the energy supply partition of the equipment battery pack is activated to generate energy supply activation data, including: The structural change information refers to the dynamic deformation characteristics of mechanical components, including the coordinate change trajectory of the folding mechanism hinge pin shaft and the compression stroke curve of the hub shock spring. The extension range data describes the deformation limit boundary, such as the folding angle range and the wheel cavity volume expansion rate. The mechanism displacement information provides real-time motion amount, including the instantaneous value of the folding hinge rotation angle and the compression displacement amount of the hub shock spring. Through space coordinate system conversion technology, the pin shaft trajectory in the structural change information is mapped to a three-dimensional vector (X / Y / Z axis displacement component), and the extension range data is converted to a motion constraint boundary (such as the upper limit of Z-axis displacement 10mm). Multi-axis motion analysis performs vector synthesis: input the X / Y-axis displacement components and Z-axis rotation angle of the folding mechanism, calculate the synthesized motion vector length and direction angle; input the Y / Z-axis compression displacement and radial deformation of the hub device, solve the motion trajectory curvature radius. The motion phase parameter is output in matrix: the row vector is the motion stage identifier (such as 0-30° folding as stage 1), and the column vector contains [vector length, direction angle, curvature radius, constraint boundary state], which fully characterizes the spatial motion characteristics of the equipment deformation.

[0035] Input the motion phase parameter to analyze the synthesized vector characteristics of each motion stage: if the folding device direction angle > 45° and the length > the preset threshold, it is determined as the unfolded state; if the curvature radius of the hub device < the critical value, it is marked as the high vibration state.

[0036] Call the energy supply partition topology of the equipment battery pack (including power supply domain coordinate boundary, positive and negative pole position), execute position mapping: convert the power supply domain vertex coordinates to the equipment motion coordinate system, calculate the spatial correlation of each domain and the motion vector. The activation calculation generates instructions based on the mapping results: unfolded state activates full-domain energy supply (instruction code 0xFF), high vibration state activates core domain energy supply (instruction code 0x0F) and shields the edge domain.

[0037] Synchronous construction of cell connection relationship: According to the series-parallel connection topology of the cell cluster in the power supply domain, a connection matrix is generated (row = cell cluster ID, column = [positive connection cluster ID, negative connection cluster ID, interlock group number]). The final activation instruction set is a binary control code stream, and the cell connection relationship is a topology relationship table, which together constitute the core elements of dynamic control of energy supply partition.

[0038] Input the cell connection relationship matrix, and analyze the series-parallel connection topology between cell clusters: identify the positive and negative interconnection paths of the cell clusters in the parallel group, and mark the high-current carrying channels; locate the voltage transition interface between series nodes, and mark the impedance sensitive area. Call the binary control code stream of the activation instruction set, and map it to the physical location of the energy supply partition: if the instruction set requires to activate the core energy supply domain, extract the corresponding cell cluster connection path; if the instruction contains an interlock protocol, disable the current transmission of the conflict path. Perform energy supply path optimization: configure a low-resistance conductor cross-sectional area threshold for high-current channels, and add a voltage compensation coefficient to the impedance sensitive area; when the device is in high-vibration working condition, add a mechanical stress buffer gap to the suspended jumper path. Generate energy distribution rule set: including path current carrying capacity table, voltage compensation protocol, dynamic adjustment rule (vibration level -> buffer gap value). This rule set ensures that the energy supply path maintains electrical stability under deformation conditions.

[0039] Input the current carrying capacity table and voltage compensation protocol of the energy distribution rule set, combined with the synthetic vector features (direction angle, curvature radius) in the motion phase parameters. Perform rule-motion coupling: when the motion direction angle > 45°, activate the extended current carrying mode; when the curvature radius < critical value, trigger the anti-vibration protocol. Build multi-dimensional energy supply activation rules: electrical control layer: inherit the current carrying threshold and compensation coefficient of the energy distribution rule set. Mechanical response layer: embed the deformation characteristics and anti-vibration rules of the motion phase parameters. Thermal management interface: reserve temperature monitoring point coordinate field.

[0040] The output energy supply activation rule is a binary tree structure, with node storage area ID1 and rule parameters, and leaf nodes bound to interlock protocol identifiers. The final rule set is transmitted through the device control bus to drive the battery pack energy supply partition to execute dynamic activation.

[0041] The embodiment generates motion phase parameters by multi-axis motion analysis mechanism displacement information, realizes the matching of device deformation working condition and energy supply partition. Based on the motion phase parameters, the energy supply partition activation calculation and position mapping are performed to ensure the accurate switching of the energy supply domain in the folding or vibration state and avoid invalid power loss. The electrical characteristics of the energy supply path are optimized by using the cell connection relationship, the internal resistance distribution is balanced through the low resistance channel configuration and voltage compensation mechanism, and the joule heat effect is reduced. The multi-dimensional energy supply activation rules are constructed by integrating the energy distribution rules and the motion phase parameters, the mechanical-electrical collaborative control protocol is formed, and the system stability under complex working conditions is enhanced. Finally, the plug-and-play adaptation ability of the battery pack energy supply control is realized through the standardization rule packaging and bus transmission.

[0042] In one embodiment, the riding data is obtained, the device power distribution is performed with the battery adaptation parameters, and the energy efficiency control threshold is obtained, including: The speed data is derived from the hub motor Hall sensor or positioning module, records the instantaneous linear speed change characteristics; the slope angle data is obtained by the inertial measurement unit, analyzes the pitch angle and lateral roll component of the device traveling direction; the load pressure data is collected by the strain gauge array distributed on the pedal or seat cushion, measures the dynamic load distribution state. The three types of data streams are synchronized to the unified time axis by using the timestamp alignment technology, eliminating the sensor sampling delay deviation. The original signal is processed by filtering to suppress high-frequency vibration noise: the speed data filters out the pulsation component caused by the wheel diameter deviation, the slope data compensates for the inclination fluctuation caused by the device attitude oscillation, and the load data eliminates the pressure distortion caused by the local deformation of the pedal. The riding data is generated as a multi-dimensional vector sequence, each frame of data packet contains a timestamp, a speed change rate, an effective slope angle, and a net load value four-tuple, forming an input reference for subsequent power demand calculation.

[0043] The input riding data vector sequence is analyzed to analyze the motion load characteristics: the speed change rate is mapped to the acceleration inertia force effect, the slope angle is converted to the gravity component resistance effect, and the load value is associated with the mass inertia effect. The power demand calculation relationship is established, the acceleration working condition power demand is associated with the product of the load mass and the acceleration square, the climbing working condition power demand is associated with the product of the load, the slope sine value and the steady speed, and the uniform speed working condition power demand is coupled with the wind resistance coefficient and the rolling friction loss characteristics. The calculation coefficient is dynamically adjusted, the wind resistance model characteristics are corrected according to the device structure identifier, and the rolling friction coefficient characteristics are adaptively adjusted according to the road type identifier. The output power demand data is a time-power two-dimensional matrix, the row vector is a time sequence node, and the column vector contains the total power demand value and the itemized power component, providing a load reference for cell cluster power distribution.

[0044] The current distribution characteristics in the input battery adaptation parameters and the energy supply activation rule are analyzed to analyze the electrical characteristics of the battery cell cluster. The current distribution characteristics define the path impedance characteristics and current carrying capacity threshold of each cluster, and the energy supply activation rule specifies the activation state of the energy supply domain under different working conditions. The battery cell cluster power distribution decision is executed: according to the domain state identifier in the energy supply activation rule, the battery cell cluster set contained in the current activated domain is screened; based on the impedance characteristics of the current distribution characteristics, the power distribution weight between the activated clusters is calculated. The current quota feature set is generated as an electrical characteristic matrix: the row identifier corresponds to the battery cell cluster number, and the column identifier contains the real-time current quota value, voltage monitoring point association identifier and temperature association coefficient. The distribution process follows a dynamic adjustment mechanism: when the energy supply activation rule marks a high vibration working condition, a derating mechanism is applied to the suspended path cluster; when the current distribution characteristics mark a high impedance area, the quota value is limited to not exceed the upper limit of the safe current carrying capacity. The current quota feature set integrates the thermal protection interface feature, providing an electrical constraint benchmark for subsequent energy efficiency distribution.

[0045] The time-power matrix of the input power demand feature set is analyzed to analyze the total power demand value and the sub-power component at each time; the battery cluster current quota matrix of the current quota feature set is called to obtain the cluster-level current carrying capacity and voltage boundary. The energy efficiency distribution calculation is executed: the total power demand is decomposed to the activated battery cell cluster according to the current quota weight, and the single cluster output power is constrained to not exceed the product of the quota value and the voltage upper limit; the dynamic priority scheduling is performed on the sub-power demand, and the ramping power is preferentially distributed to the high-capacity cluster, and the acceleration power is distributed to the low internal resistance cluster.

[0046] The energy efficiency control threshold is output as a multi-dimensional control parameter set: the time dimension inherits the time node sequence of the power demand feature set, the battery cluster dimension maps the cluster identifier of the current quota feature set, and the parameter dimension stores the charging and discharging current threshold and the energy recovery intensity. The threshold generation process embeds an adaptive calibration mechanism: when the power demand peak duration exceeds the preset condition, a temporary upgrade protocol is triggered; when the temperature association coefficient marks a high temperature risk, a derating protection is enabled. The final energy efficiency control threshold is transmitted to the battery management system through the control bus to drive real-time charging and discharging control.

[0047] The embodiment realizes accurate matching of power demand and battery pack electrical characteristics through dynamic cooperation of riding data and battery adaptation parameters, and improves energy distribution efficiency. The power demand data is generated based on the motion load calculation, ensuring the accuracy of the physical mapping from the riding condition to the power demand. The battery cell cluster power is distributed by using the battery adaptation parameters, the current path space distribution is optimized, and the internal resistance loss is reduced. Through global energy efficiency distribution of power demand and current quota, multi-dimensional control thresholds are generated to realize thermal safety boundary control in the charging and discharging process. Finally, a closed-loop optimization chain of riding condition-battery state-control threshold is formed, enhancing the system energy efficiency stability and life cycle management capability.

[0048] In one embodiment, based on the battery adaptation parameter, the battery state data of the device battery pack is identified, the thermal safety boundary calculation is performed with the energy efficiency control threshold, and the attenuation trend data is output, including: The thermal management interface layer information in the battery adaptation parameter is called to locate the temperature sensor feature position distribution: the monitoring point coordinates of the hub device are deployed along the radial equidistant, and the sensor position features of the folding device are distributed in the chassis plane grid. The cluster level voltage data stream of the voltage sampling channel is synchronously acquired, and each cluster is configured with an independent monitoring node. The temperature monitoring performs three-dimensional thermal field characteristic reconstruction: input the temperature instantaneous value of each position, combine the thermal conduction characteristics of the device structure, and calculate the temperature gradient distribution characteristics in the cavity. The voltage fluctuation monitoring analyzes the fluctuation amplitude characteristics: the fluctuation characteristics of the voltage sequence in the time window are calculated in units of clusters, and the abnormal voltage drop cluster identifier is identified. The battery state data output is a structured data set: the temperature field data is stored as a three-dimensional space distribution matrix, and the voltage fluctuation data is recorded as a mapping relationship between the cluster identifier and the fluctuation amplitude, providing input reference for thermal safety boundary analysis.

[0049] The three-dimensional temperature field distribution matrix in the input battery state data is analyzed to analyze the local hot spot area characteristics: the temperature gradient mutation position is marked, and the high temperature aggregation area is identified. The charge and discharge current threshold characteristics of the energy efficiency control threshold matrix are called to establish thermal accumulation analysis information: the product relationship of the real-time current value associated with the Joule heat generation and the internal resistance characteristics in the cluster, and the convolution operation of the temperature gradient associated with the heat dissipation efficiency and the heat conduction characteristics. The thermal safety boundary analysis calculates the thermal balance critical characteristics: when the heat generation rate exceeds the heat dissipation efficiency, it is marked as a thermal risk unit. The protection boundary parameter output is a multi-dimensional feature set: the spatial dimension includes the coordinate position of the thermal risk unit, the electrical dimension includes the maximum allowable current adjustment coefficient, the time dimension includes the risk duration threshold, and the control dimension includes the hierarchical response protocol identifier. This parameter set directly restricts the subsequent charge and discharge power identification operation.

[0050] The multi-dimensional feature set of the protection boundary parameter is input to analyze the spatial distribution characteristics of the thermal risk unit and the electrical derating coefficient. The voltage fluctuation mapping relationship in the battery state data is called to identify the impedance mutation characteristics of the high fluctuation cluster. The charge and discharge power identification calculation is performed: the maximum allowable charging current value is calculated based on the thermal risk unit derating coefficient; and a discharge power attenuation curve is generated in combination with the voltage fluctuation amplitude and the thermal risk duration threshold. The output power limitation feature set is a three-dimensional control matrix, the row identifier corresponds to the cell cluster number, the column identifier includes the charge power limit value, the discharge power limit value and the attenuation response time, and the layer identifier corresponds to the time sequence node. The identification process is embedded with an adaptive compensation mechanism: when the thermal risk unit is concentrated on the wheel cavity edge, the forced air cooling compensation protocol is activated; and when the high fluctuation cluster is located in the folding hinge area, the mechanical stress buffer compensation is added.

[0051] The input power limit feature set is a charge-discharge power limit matrix, and the power constraint characteristics of the cell cluster level are analyzed. The hierarchical response protocol identification of the protection boundary parameters is called to obtain the thermal risk control strategy. Perform attenuation identification analysis: the charge power limit is mapped to the upper limit of the lithium ion intercalation rate, and the discharge power limit is converted to the electrode phase transition stress threshold feature; the thermal risk unit coordinates are associated with the electrode expansion coefficient characteristics, and the local active material stripping rate characteristics are calculated. The output attenuation trend data is a tensor structure, the first dimension is the cell cluster identifier, the second dimension includes the capacity attenuation slope feature, the internal resistance growth rate feature, and the thermal runaway risk index feature, and the third dimension is the time evolution sequence. The identification process integrates the life calibration mechanism: when the hierarchical response protocol mark is interrupted, the cycle life reduction factor is superimposed; when the forced air cooling compensation is activated, the life compensation coefficient is injected. The final data is synchronized to the maintenance analysis module.

[0052] The embodiment realizes precise matching of device structure and battery pack through battery adaptation parameters, and improves energy utilization efficiency in space-limited scenarios. Based on the dynamic generation of power demand characteristics from riding data, the real-time adaptability of power distribution is ensured when the working conditions change. Local thermal risk units are identified using thermal safety boundary analysis, and protection boundary parameters are generated to constrain the charging and discharging process, reducing the probability of thermal runaway. Through the power limit feature set, the cell cluster level charging and discharging threshold is dynamically optimized, the internal resistance loss is balanced, and the cycle life is prolonged. Combined with the attenuation identification technology, multi-dimensional attenuation trend data is output, providing a quantitative basis for maintenance decision-making, and enhancing the whole life cycle management capability of the battery pack. Finally, a closed-loop optimization system of "structure matching-dynamic distribution-thermal protection-life prediction" is formed, improving the comprehensive energy efficiency performance of small intelligent travel devices.

[0053] In one embodiment, according to the battery state data and the energy efficiency control threshold, the device battery pack is subjected to thermal safety boundary analysis, and protection boundary parameters are generated, including: The monomer temperature gradient data is derived from the three-dimensional temperature field distribution characteristics, and the temperature difference change characteristics between adjacent monitoring points are calculated; the voltage fluctuation rate data is obtained from the fluctuation amplitude analysis results of the cluster level voltage sequence, and the fluctuation characteristics of abnormal pressure drop clusters are identified. The overheat correlation analysis performs spatial-electric coupling mapping: the temperature gradient mutation point coordinates are spatially associated with the voltage fluctuation cluster position, and if the spatial distance between the high gradient point and the high fluctuation cluster is less than the critical threshold, it is marked as a thermal-electric coupling risk unit. The time evolution characteristic analysis tracks the time synchronization features of the temperature change rate and the voltage fluctuation rate of the risk unit, and when the correlation coefficient of the two change trends exceeds the preset value, it is determined as an overheat evolution hot spot. The overheat data is generated as a structured feature set: the spatial dimension stores the risk unit coordinate position, the electrical dimension records the coupling strength feature, and the time dimension marks the hot spot evolution stage identifier, providing input reference for regional load distribution.

[0054] Risk unit coordinates and coupling strength features of input overheat data; call charge-discharge current upper limit features of efficiency control threshold matrix; extract heat dissipation structure features in structure information. Execute regional load distribution decision: apply derating current threshold features to risk units marked by overheat data; transfer thermal load of risk units to regions near heat dissipation path according to high thermal conductivity region distribution in heat dissipation structure features. Output partition thermal load distribution matrix: time sequence node corresponds to hierarchical identifier, region identifier corresponds to row identifier, column identifier contains thermal load value features, current derating coefficient features and heat dissipation path distance features. Embed dynamic balance mechanism in distribution process: enable load compensation protocol for non-risk regions when efficiency control threshold marks high power demand; trigger forced air cooling virtual load transfer when structure information marks heat dissipation blind area. This distribution matrix provides spatial load distribution benchmark for heat conduction path analysis.

[0055] Thermal load value features and heat dissipation path distance features of input partition thermal load distribution matrix; call heat dissipation structure features in structure information including hub cavity carbon fiber heat conduction layer thickness distribution characteristics or folding equipment aluminum heat dissipation fin density distribution map. Execute heat conduction path analysis operation: analyze heat conduction efficiency features according to material thermal conductivity characteristics and geometric size features in heat dissipation structure features; assign thermal load value features to high thermal conductivity regions for priority processing after sorting according to heat dissipation path distance features. Output local temperature rise feature set as a three-dimensional tensor structure: time node sequence corresponds to hierarchical identifier, region identifier corresponds to row identifier, column identifier contains temperature rise value features, thermal accumulation rate features and heat dissipation response delay features. Integrate path blockage detection mechanism in analysis process: mark as conduction blockage region when heat dissipation path distance features exceed critical threshold, trigger virtual heat dissipation channel activation protocol. This feature set provides thermodynamic evolution benchmark for protection parameter deduction.

[0056] Temperature rise value features and thermal accumulation rate features of input local temperature rise feature set; call preset temperature rise critical point matrix including material phase transition temperature threshold, electrolyte boiling point threshold and separator shrinkage temperature threshold. Execute protection parameter deduction operation: trigger current derating protocol and forced air cooling protocol when temperature rise value reaches separator shrinkage threshold; activate current interruption protocol and aerogel isolation protocol when thermal accumulation rate exceeds critical value.

[0057] Output protection boundary parameters as a four-dimensional control tensor: spatial dimension records risk region coordinate position, electrical dimension contains maximum allowable current features and voltage fluctuation tolerance features, time dimension defines risk duration threshold features, and control dimension encodes response protocol identifier. Embed adaptive learning mechanism in deduction process: permanently reduce current upper limit features when the same region continuously triggers response protocol; remove historical derating constraints when temperature rise value continuously falls below critical value. Final parameters are synchronized to device efficiency allocation module through control bus to drive real-time control.

[0058] The embodiment accurately identifies the thermal-electric coupling risk unit of the battery pack through overheating correlation analysis, realizes early warning of thermal runaway hidden danger. Based on the dynamic allocation of regional thermal load according to the heat dissipation structure characteristics, the heat dissipation efficiency of the high thermal conductivity region is optimized, and the local temperature rise amplitude is reduced. The thermal conduction path analysis combines the material thermal conductivity characteristics and the geometric structure characteristics, effectively improves the spatial transfer accuracy of the thermal load. The staged response mechanism driven by the temperature rise critical point realizes the step-by-step protection from current reduction to emergency flow interruption, and guarantees the safety redundancy under extreme working conditions.

[0059] In one embodiment, the battery maintenance analysis is performed on the attenuation trend data and the device battery pack to generate subsequent maintenance planning information, including: The charge and discharge history log file in the riding device storage medium is called, and the data records are indexed according to the cycle number: single cycle start timestamp, end timestamp, charge phase voltage-current waveform sampling sequence, discharge phase capacity attenuation trajectory, and environmental temperature extreme value record.

[0060] Data analysis performs integrity check: removes incomplete cycle records caused by signal interruption, and completes missing sampling points caused by memory overflow. The historical charge and discharge records are generated as a multi-dimensional data cube: the first dimension is the cycle number index, the second dimension is the time sequence node, and the third dimension stores the four-tuple data field of voltage value, current value, temperature value and capacity value. This record set provides a time evolution benchmark for attenuation mode matching.

[0061] The preset battery attenuation type library contains four basic modes: positive active material loss characteristic is characterized by forward shift of charge voltage platform, lithium inventory attenuation characteristic is characterized by increase of discharge voltage platform slope, SEI film thickening characteristic is characterized by voltage uplift at the end of charging, and electrolyte decomposition characteristic is characterized by sudden increase of internal resistance growth rate. The three-dimensional tensor of input attenuation trend data is extracted to extract the capacity attenuation slope and internal resistance growth rate characteristic curve of each cluster.

[0062] Perform mode matching calculation: align the characteristic curve with the preset mode characteristic template by dynamic time warping, calculate the waveform matching degree; when the matching degree exceeds the recognition threshold, mark the corresponding attenuation mode identifier. The output battery attenuation mode is a structured coding set: the row vector is the cell cluster identifier, and the column vector contains the attenuation mode type code, mode stage identifier and mode confidence coefficient, which provides a classification benchmark for segmented fitting.

[0063] Input the structured coding set of the battery attenuation mode, analyze the attenuation mode type code and mode stage identifier of each cell cluster; call the multi-dimensional data cube of the historical charge and discharge record, extract the voltage-current waveform sequence and capacity attenuation trajectory of the corresponding cluster. Perform segmented fitting operation: divide the historical time axis according to the mode stage identifier, and calculate the charge voltage platform slope characteristic, discharge capacity retention rate characteristic and other key indicators in the segmented window.

[0064] The output battery attenuation correlation information is a three-dimensional tensor structure: the attenuation mode type corresponds to the level identifier, the battery cell cluster identifier corresponds to the row identifier, and the column identifier contains the slope fitting value feature, the internal resistance growth coefficient feature, and the thermal correlation factor feature. The fitting process embeds a dynamic calibration mechanism: when the environmental temperature extreme value in the historical record continuously exceeds the standard, a temperature compensation offset is added; when the waveform sequence has abnormal oscillation, an anti-interference filtering algorithm is enabled. This correlation information provides a quantitative basis for power gradient adjustment.

[0065] The slope fitting value feature and the internal resistance growth coefficient feature of the input battery attenuation correlation information; the maintainability features of the device battery pack include hub cavity opening size characteristics or folding mechanism disassembly complexity characteristics. Perform power gradient adjustment operations: generate a charging current linear derating protocol when the slope fitting value feature exceeds the set threshold; trigger a discharge power step derating mechanism when the internal resistance growth coefficient feature has a sudden increase feature.

[0066] Maintenance planning integration operation: develop a battery replacement protocol based on maintainability features, generate a maintenance instruction set containing battery cell cluster identifiers, action sequence features, resource demand features, and execution time window features. Output subsequent maintenance planning information as a structured tree graph: the root node stores the device global maintenance strategy feature, the child node stores the cluster-level maintenance instruction feature, and the leaf node binds the resource code feature and the execution condition feature. The final maintenance planning information is synchronized to the maintenance service network driving resource scheduling through the cloud, and is transmitted to the corresponding riding device.

[0067] This embodiment accurately identifies the battery attenuation mode type and evolution stage through dynamic matching of historical charge and discharge records and attenuation trend data, improving the accuracy of aging diagnosis. Based on the segmented fitting technology of the attenuation mode, the correlation between the battery cell cluster level capacity attenuation slope and the internal resistance growth characteristics is quantified, providing a scientific basis for power control. The output power gradient adjustment mechanism realizes linear and step derating dynamic adaptation of charging and discharging current, effectively delaying battery performance degradation. The maintenance planning information integrates maintainability features and resource scheduling requirements.

[0068] In one embodiment, based on the battery attenuation mode and the historical charge and discharge record, the battery attenuation correlation information is obtained by segmented fitting, including: Input the structured code set of battery attenuation mode, parse the attenuation mode type code and mode stage identifier of each cell cluster. Capacity attenuation parameter extraction is based on the capacity attenuation slope characteristic curve corresponding to the mode stage identifier to calculate the capacity loss rate characteristic value per cycle; internal resistance drift parameter extraction is based on the change trend of the internal resistance growth rate characteristic curve to identify the internal resistance sudden increase inflection point position and drift rate characteristic; self-discharge abnormal parameter is obtained through the sleep voltage retention rate characteristic analysis, and the static stage voltage drop rate is compared with the reference threshold to mark the abnormal self-discharge cluster identifier.

[0069] The core attenuation parameter output is a three-dimensional tensor structure: the row identifier corresponds to the cell cluster number, the column identifier stores the capacity loss rate characteristic value, the internal resistance drift rate characteristic value, and the self-discharge abnormal identifier, and the level identifier is associated with the attenuation mode type code. This parameter set provides a quantitative reference for the charge and discharge stage segmentation.

[0070] Input the multi-dimensional data cube of historical charge and discharge records, and call the preset charge and discharge cycle period definition matrix: the charge and discharge cycle is divided into three stages of initial stage, stable stage and decline stage according to the capacity attenuation rate threshold. Perform cycle period segmentation operation: map the cycle number index of the data cube to the preset period stage identifier. Stage parameter extraction operation: the initial stage extracts the constant current charging time length feature and the discharge voltage platform retention rate feature, the stable stage extracts the charging end voltage lifting value feature and the internal resistance change gradient feature, and the decline stage extracts the capacity diving point cycle number feature and the self-discharge rate increment feature.

[0071] Generate the charge and discharge stage parameter group as a hierarchical data block structure: the first level identifier marks the stage type, the second level stores the voltage value, current value, temperature value and capacity value four-tuple data array of all cycles in the stage, and the third level marks the stage boundary feature including the start cycle number and the end cycle number. This parameter group provides segmented data set for attenuation correlation analysis.

[0072] Input the four-dimensional tensor of the correlation relationship structure, and parse the correlation elements such as the charge feature-attenuation rate mapping coefficient and the internal resistance feature-drift rate weight. Perform attenuation trajectory reconstruction operation: reconstruct the initial stage capacity attenuation slope characteristic based on the mapping coefficient of constant current charging time and capacity loss rate; reconstruct the stable stage internal resistance evolution median trajectory feature by the product of internal resistance change gradient and drift rate weight; reconstruct the decline stage self-discharge deterioration index curve feature according to the self-discharge abnormal matching degree and increment feature.

[0073] The output segment reconstruction parameter is a time evolution tensor: the hierarchical identifier corresponds to the charge and discharge stage type, the row identifier corresponds to the cycle number sequence node, and the column identifier stores the reconstruction capacity value feature, the reconstruction internal resistance value feature, and the self-discharge risk index feature. The reconstruction process embeds a stage boundary calibration mechanism: when the reconstruction parameters of adjacent stages jump by more than a preset threshold, a smooth transition function feature is added. This parameter set provides a segment evolution benchmark for full-cycle fusion.

[0074] The input segment reconstruction parameter time evolution tensor of each stage is aligned with the time axis according to the cycle number index. The full-cycle fusion analysis is performed: based on the initial period slope feature, the stable period median feature, and the decline period diving point feature, an S-shaped capacity attenuation trajectory function is generated; by fusing the stable period drift rate feature and the decline period mutation point feature, a stepwise internal resistance growth model is constructed; according to the abnormal matching degree feature and the risk index feature, a probabilistic self-discharge risk cloud map is output.

[0075] The output battery attenuation related information is a three-dimensional prediction field structure: the first dimension identifier corresponds to the cell cluster ID, the second dimension stores the capacity attenuation trajectory function feature, the internal resistance growth model feature, and the self-discharge risk cloud map feature, and the third dimension corresponds to the cycle number evolution sequence. The fusion process integrates a confidence check mechanism: when the segment reconstruction parameter deviates from the historical record by more than a preset threshold, an artificial review marker feature is triggered. The final information is synchronized to the maintenance decision module through the control bus.

[0076] The core decay parameter extraction technology quantifies the capacity attenuation, internal resistance drift, and self-discharge abnormality features, and realizes the precise calibration of the battery aging process. The charge and discharge stage segmentation technology divides the historical operation data according to the preset period, and constructs a stage analysis framework of the initial period, the stable period, and the decline period. The correlation structure technology breaks through the cross-stage mapping channel of the charge and discharge parameters and the decay parameters, and solves the coupling correlation problem of the aging features and the operating conditions. The decay trajectory reconstruction technology generates segment evolution parameters, and realizes the continuous trajectory restoration of the aging process. The full-cycle fusion analysis technology integrates the segment parameters to generate a three-dimensional prediction field, and outputs the capacity attenuation trajectory function, the internal resistance growth model, and the self-discharge risk cloud map, providing a quantitative basis for life prediction and fault warning.

[0077] Referring to Figure 2 The application also provides a battery pack energy efficiency optimization system, which is applied to the battery pack energy efficiency optimization method of any one of the above, and includes: An identification module is configured to identify the structural information of the riding device and perform structural matching with the device battery pack of the riding device, and output battery adaptation parameters; An analysis module is configured to obtain riding data, perform device power distribution with the battery adaptation parameters, and obtain an energy efficiency control threshold; The processing module is configured to identify battery state data of the device battery pack based on the battery adaptation parameter, perform thermal safety boundary calculation on the energy efficiency control threshold, and output the attenuation trend data. The construction module is configured to perform battery maintenance analysis on the attenuation trend data and the device battery pack, and generate subsequent maintenance planning information.

[0078] The battery pack energy efficiency optimization system provided in the application solves the problem of insufficient structural adaptation in the traditional method by identifying the structural information of the riding device and matching it with the battery pack to output accurate battery adaptation parameters, so that the power distribution is more in line with the actual needs of the device, thereby improving the battery energy efficiency. The combination of riding data and battery adaptation parameters for device power distribution can adjust the energy efficiency control threshold, avoid battery over-discharge or redundant power supply, enhance dynamic energy efficiency management capability, and improve the energy utilization efficiency of the battery. Based on the battery adaptation parameter, the battery state data is identified, and the thermal safety boundary calculation is performed in combination with the energy efficiency control threshold, so that the attenuation trend data can be accurately output, the battery performance degradation or thermal runaway risk can be early warned, and the safety of the battery use can be improved. Through analysis of the attenuation trend data and the device battery pack, targeted subsequent maintenance planning information is generated, realizing the transition from passive maintenance to active predictive maintenance, prolonging the battery life and reducing the maintenance cost. Considering the structure of the riding device, the riding data and the battery state, the energy efficiency optimization strategy is more adaptable, meeting the battery management needs in different riding modes and improving the user experience.

[0079] As to the apparatus in the above-mentioned embodiments, the specific manners in which various modules perform operations have been described in detail in the embodiments related to the apparatus in the above-mentioned embodiments, and thus will not be described in detail here.

[0080] The scheme of the application has been described in detail above with reference to the drawings. In the above-mentioned embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments. It should also be known by those skilled in the art that the actions and modules involved in the specification are not necessarily required by the application. In addition, it can be understood that the steps in the method embodiments of the application can be adjusted, combined and reduced in sequence according to actual needs, and the modules in the device embodiments of the application can be combined, divided and reduced according to actual needs.

[0081] In addition, the method according to the application can also be implemented as a computer program or computer program product, which includes computer program code instructions for executing part or all of the steps in the above-mentioned method of the application.

[0082] Alternatively, the application can also be implemented as a non-transitory machine readable storage medium (or computer readable storage medium, or machine readable storage medium) on which an executable code (or computer program, or computer instruction code) is stored, and when the executable code (or computer program, or computer instruction code) is executed by a processor of an electronic device (or electronic device, server, etc.), the processor is caused to perform part or all of the steps of the above method according to the application.

[0083] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the present disclosure can be implemented as electronic hardware, computer software, or combinations of both.

[0084] The flow diagrams and block diagrams in the drawings are presented to illustrate the architecture, functionality, and operations of possible implementations of systems and methods according to various embodiments of the present application. In this regard, each block in the flow diagrams and block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may be executed in the reverse order, depending on the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.

[0085] The embodiments of the present application have been described above, the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles, practical applications, or improvements to the technology in the market of the embodiments, or to enable other ordinary skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for optimizing the energy efficiency of a battery pack, characterized in that, include: Identify the structural information of the cycling device and match it with the device's battery pack to output battery adaptation parameters; Acquire cycling data and perform device power allocation with the battery adaptation parameters to obtain energy efficiency control thresholds; Based on the battery adaptation parameters, the battery status data of the device battery pack is identified, and thermal safety boundary is calculated with the energy efficiency control threshold to output degradation trend data. Based on the degradation trend data, battery maintenance analysis is performed on the device's battery pack to generate subsequent maintenance planning information.

2. The battery pack energy efficiency optimization method according to claim 1, characterized in that, The process involves identifying the structural information of the cycling device and matching it with the device's battery pack to output battery compatibility parameters, including: The frame space parameters and structural extension parameters in the structural information are analyzed to generate cavity size parameters and mechanism displacement information; Based on the cavity size parameters, current path identification is performed on the device battery pack to generate current distribution parameters; Based on the displacement information of the mechanism, the power supply zone of the device battery pack is activated to generate power supply activation data. Integrate the current distribution parameters with the power supply activation data to output the battery adaptation parameters.

3. The battery pack energy efficiency optimization method according to claim 2, characterized in that, The step of activating the power supply zone of the device battery pack based on the displacement information of the mechanism and generating power supply activation data includes: Extract the structural change information and extension range data of the structural extension parameters, and perform multi-axis motion analysis with the displacement information of the mechanism to output motion phase parameters; Based on the motion phase parameters, activation calculation and position mapping are performed on the power supply zone to generate an activation instruction set and cell connection relationship; The power supply path of the activation instruction set is optimized by the cell connection relationship to obtain energy distribution data; Integrate the energy distribution data and the motion phase parameters to output the energy supply activation data.

4. The battery pack energy efficiency optimization method according to claim 1, characterized in that, The process of acquiring cycling data, allocating device power based on the battery adaptation parameters, and obtaining an energy efficiency control threshold includes: Collect and integrate the speed data, slope angle data, and load pressure data of the cycling equipment to generate the cycling data; The cycling data is used to calculate the exercise load and generate power demand data. The power of the battery pack cells in the device is allocated by the battery adaptation parameters to generate a current quota set; Based on the power demand data and the current quota set, the device energy efficiency is allocated, and the energy efficiency control threshold is output.

5. The battery pack energy efficiency optimization method according to claim 1, characterized in that, The process of identifying the battery status data of the device's battery pack based on the battery adaptation parameters, calculating the thermal safety boundary with the energy efficiency control threshold, and outputting degradation trend data includes: Based on the battery adaptation parameters, the device battery pack is monitored for individual cell temperature and voltage fluctuations to obtain battery status data. Based on the battery status data and the energy efficiency control threshold, thermal safety boundary analysis is performed on the device battery pack to generate protection boundary parameters; The power limit parameters are obtained by identifying the charge and discharge power of the protection boundary parameters; The battery pack of the device is degraded based on the power limiting parameters and the protection boundary parameters, and the degraded trend data is output.

6. The battery pack energy efficiency optimization method according to claim 5, characterized in that, The step of performing thermal safety boundary analysis on the device battery pack based on the battery state data and the energy efficiency control threshold, and generating protection boundary parameters, includes: Extract the cell temperature gradient and voltage fluctuation rate from the battery state data, perform overheating correlation analysis, and generate overheating data; The energy efficiency control threshold and the structural information are used to perform regional load allocation on the overheating data to generate a zoned thermal load dataset. Based on the heat dissipation structure information of the structural information, the heat conduction path analysis is performed on the partitioned heat load dataset to obtain local temperature rise data; Based on the preset temperature rise critical point, the local temperature rise data is used to deduce the protection parameters, and the protection boundary parameters are output.

7. The battery pack energy efficiency optimization method according to claim 1, characterized in that, The step of performing battery maintenance analysis on the device battery pack based on the attenuation trend data and generating subsequent maintenance planning information includes: Extract the historical charge and discharge records of the device's battery pack from the cycling device; The battery degradation pattern is obtained by pattern matching between the preset battery degradation type and the degradation trend data; Based on the battery degradation pattern and the historical charge and discharge records, segmented fitting is performed to obtain battery degradation correlation information; Based on the battery degradation correlation information, the output power gradient of the device battery pack is adjusted to generate subsequent maintenance planning information, which is then transmitted to the riding device.

8. The battery pack energy efficiency optimization method according to claim 7, characterized in that, The step of segmenting and fitting the battery degradation pattern with the historical charge-discharge records to obtain battery degradation correlation information includes: Extract core degradation parameters, including capacity decay, internal resistance drift, and self-discharge anomalies, from the battery degradation mode. The historical charge and discharge records are segmented based on preset charge and discharge cycle data to obtain a set of charge and discharge stage parameters; The charging and discharging stage parameter group is correlated with the core attenuation parameter to form a correlation structure between the charging and discharging parameters and the attenuation parameter. The decay trajectory is reconstructed through the aforementioned relationship structure to generate segment reconstruction parameters for each segment; Perform full-cycle fusion analysis on the segment reconstruction parameters of all segments and output the battery degradation correlation information.

9. A battery pack energy efficiency optimization system, characterized in that, The battery pack energy efficiency optimization method applied to any one of claims 1-8 includes: The identification module is used to identify the structural information of the cycling device, perform structural matching with the device's battery pack, and output battery adaptation parameters. The parsing module is used to acquire cycling data, perform device power allocation with the battery adaptation parameters, and obtain energy efficiency control thresholds. The processing module is used to identify the battery status data of the device battery pack based on the battery adaptation parameters, calculate the thermal safety boundary with the energy efficiency control threshold, and output degradation trend data. A construction module is used to perform battery maintenance analysis on the device battery pack based on the degradation trend data and generate subsequent maintenance planning information.

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