Battery Pack Energy Efficiency Optimization Methods and Systems

Abstract

This application provides a battery pack energy efficiency optimization method and system. The method includes identifying the structural information of a cycling device and performing structural matching with the device's battery pack to output battery adaptation parameters; acquiring cycling data and allocating device power according to the battery adaptation parameters to obtain an energy efficiency control threshold; identifying battery state data of the device's battery pack based on the battery adaptation parameters and calculating thermal safety boundaries with the energy efficiency control threshold to output degradation trend data; and performing battery maintenance analysis on the device's battery pack based on the degradation trend data to generate subsequent maintenance planning information. This invention can provide early warning of battery performance degradation or thermal runaway risks, improving the safety of battery use.

Description

Technical Field

[0001] This application relates to the field of battery pack technology, and in particular to battery pack energy efficiency optimization methods and systems. Background Technology

[0002] With the increasing popularity of electric cycling devices, battery packs, as core energy components, have become crucial for improving user experience and device reliability through energy efficiency optimization and safety management. Especially in high-frequency usage scenarios such as shared mobility and logistics delivery, the long-term performance degradation, thermal safety risks, and dynamic power distribution issues of battery packs are becoming increasingly prominent. Current technologies for battery energy efficiency optimization largely rely on static parameter matching or single riding data feedback, lacking a collaborative analysis of device structural differences, riding conditions, and battery thermal safety boundaries. Existing methods may allocate power solely based on battery capacity or a fixed load model, ignoring the dynamic impact of cycling device structure (such as vehicle weight and motor type) on the actual battery output, or failing to incorporate preventative maintenance based on battery degradation trends, leading to insufficient energy efficiency control precision, shortened battery life, and even the risk of thermal runaway. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this application provides a battery pack energy efficiency optimization method and system that can provide early warning of battery performance degradation or thermal runaway risks, thereby improving the safety of battery use.

[0004] This application provides a method for optimizing the energy efficiency of a battery pack, including:

[0005] Identify the structural information of the cycling device and match it with the device's battery pack to output battery adaptation parameters;

[0006] Acquire cycling data and perform device power allocation with the battery adaptation parameters to obtain energy efficiency control thresholds;

[0007] 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.

[0008] Based on the degradation trend data, battery maintenance analysis is performed on the device's battery pack to generate subsequent maintenance planning information.

[0009] Preferably, the step of identifying the structural information of the cycling device and performing structural matching with the device's battery pack to output battery adaptation parameters includes:

[0010] The frame space parameters and structural extension parameters in the structural information are analyzed to generate cavity size parameters and mechanism displacement information;

[0011] Based on the cavity size parameters, current path identification is performed on the device battery pack to generate current distribution parameters;

[0012] 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.

[0013] Integrate the current distribution parameters with the power supply activation data to output the battery adaptation parameters.

[0014] Preferably, the step of activating the power supply zone of the device battery pack based on the mechanism displacement information and generating power supply activation data includes:

[0015] 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;

[0016] 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;

[0017] The power supply path of the activation instruction set is optimized by the cell connection relationship to obtain energy distribution data;

[0018] Integrate the energy distribution data and the motion phase parameters to output the energy supply activation data.

[0019] Preferably, the step of acquiring cycling data and allocating device power with the battery adaptation parameters to obtain an energy efficiency control threshold includes:

[0020] Collect and integrate the speed data, slope angle data, and load pressure data of the cycling equipment to generate the cycling data;

[0021] The cycling data is used to calculate the exercise load and generate power demand data.

[0022] The power of the battery pack cells in the device is allocated by the battery adaptation parameters to generate a current quota set;

[0023] 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.

[0024] Preferably, the step of identifying the battery status data of the device 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:

[0025] Based on the battery adaptation parameters, the device battery pack is monitored for individual cell temperature and voltage fluctuations to obtain battery status data.

[0026] 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;

[0027] The power limit parameters are obtained by identifying the charge and discharge power of the protection boundary parameters.

[0028] 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.

[0029] Preferably, 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 to generate protection boundary parameters includes:

[0030] Extract the cell temperature gradient and voltage fluctuation rate from the battery state data, perform overheating correlation analysis, and generate overheating data;

[0031] 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.

[0032] 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;

[0033] 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.

[0034] Preferably, the step of performing battery maintenance analysis on the device battery pack based on the degradation trend data to generate subsequent maintenance planning information includes:

[0035] Extract the historical charge and discharge records of the device's battery pack from the cycling device;

[0036] The battery degradation pattern is obtained by pattern matching between the preset battery degradation type and the degradation trend data;

[0037] Based on the battery degradation pattern and the historical charge and discharge records, segmented fitting is performed to obtain battery degradation correlation information;

[0038] Based on the battery degradation correlation information, the output power gradient of the device battery pack is adjusted to generate subsequent maintenance planning information.

[0039] Preferably, the step of performing segmented fitting based on the battery degradation pattern and the historical charge-discharge records to obtain battery degradation correlation information includes:

[0040] Extract core degradation parameters, including capacity decay, internal resistance drift, and self-discharge anomalies, from the battery degradation mode.

[0041] 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;

[0042] 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.

[0043] The decay trajectory is reconstructed through the aforementioned relationship structure to generate segment reconstruction parameters for each segment;

[0044] Perform full-cycle fusion analysis on the segment reconstruction parameters of all segments and output the battery degradation correlation information.

[0045] The present invention also provides a battery pack energy efficiency optimization system, applied to the battery pack energy efficiency optimization method described in any one of the above, comprising:

[0046] 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.

[0047] The parsing module is used to acquire cycling data, perform device power allocation with the battery adaptation parameters, and obtain energy efficiency control thresholds.

[0048] 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.

[0049] 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.

[0050] The technical solution provided in this application may include the following beneficial effects:

[0051] This application addresses the problem of insufficient structural compatibility in traditional methods by identifying the structural information of cycling equipment and matching it with the battery pack to output accurate battery adaptation parameters. This makes power allocation more aligned with the actual needs of the equipment, thereby improving battery energy efficiency. Combining cycling data with battery adaptation parameters for equipment power allocation allows for adjustment of energy efficiency control thresholds, preventing over-discharge or redundant power supply, enhancing dynamic energy efficiency management capabilities, and improving battery energy utilization efficiency. Based on battery adaptation parameters, battery status data is identified and thermal safety boundaries are calculated using energy efficiency control thresholds. This allows for accurate output of degradation trend data, providing early warnings of battery performance degradation or thermal runaway risks, improving battery safety. Analysis of degradation trend data and the equipment's battery pack generates targeted subsequent maintenance planning information, enabling a shift from passive to proactive predictive maintenance, extending battery life and reducing maintenance costs. By comprehensively considering the cycling equipment structure, cycling data, and battery status, the energy efficiency optimization strategy becomes more adaptable, meeting the battery management needs of different cycling modes and improving user experience. Attached Figure Description

[0052] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments thereof in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments thereof.

[0053] Figure 1 This invention provides a flowchart of a battery pack energy efficiency optimization method;

[0054] Figure 2 This invention provides a structural diagram of a battery pack energy efficiency optimization system. Detailed Implementation

[0055] Preferred embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, 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.

[0056] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0057] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0058] Reference Figure 1 As shown, the present invention provides a method for optimizing the energy efficiency of a battery pack, comprising:

[0059] Step S1: Identify the structural information of the cycling equipment and perform structural matching with the battery pack of the cycling equipment to output battery adaptation parameters;

[0060] Step S2: Obtain riding data, allocate device power with battery adaptation parameters, and obtain energy efficiency control threshold;

[0061] Step S3: Based on the battery adaptation parameters, identify the battery status data of the device's battery pack, calculate the thermal safety boundary with the energy efficiency control threshold, and output the degradation trend data;

[0062] Step S4: Perform battery maintenance analysis on the degradation trend data and the equipment battery pack to generate subsequent maintenance planning information.

[0063] Based on the steps described above, the detailed process is as follows:

[0064] Step S1:

[0065] 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.

[0066] Step S2:

[0067] 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.

[0068] 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.

[0069] Step S3:

[0070] 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.

[0071] The thermal balance equation is dynamically solved: when the local temperature rise rate is greater than 5℃ / s and deviates from the safe current range defined by the threshold matrix, it is marked as a thermal risk unit. Electrochemical stress analysis is performed simultaneously: based on the change in the relaxation time constant of the voltage sequence, the lithium-ion concentration gradient is inferred; the temperature field data is superimposed to generate an electrode expansion stress distribution map. The final output decay trend data is a tensor structure, including the coordinates of thermal risk units, stress concentration area markers, and predicted capacity decay slope values.

[0072] Step S4:

[0073] The coordinates of thermal risk units and stress concentration areas in the degradation trend data are analyzed and matched with the physical structure of the battery pack: if the thermal risk unit is located in the heat dissipation dead corner at the edge of the hub cavity, it is marked as a forced air cooling priority area; if the stress concentration area coincides with the hinge of the folding mechanism, it is marked as a mechanical reinforcement target position. Degradation modes are classified based on the predicted capacity degradation slope: a slope < 0.1% / week indicates natural aging, generating a balanced maintenance plan; a slope ≥ 0.5% / week indicates abnormal degradation, triggering a cell cluster replacement protocol.

[0074] Based on equipment maintainability constraints (e.g., L6 scooter chassis opening size limit for tool diameter ≤ 8mm), spatial feasibility verification is performed on maintenance actions: the replacement protocol automatically adapts to the micro hot air gun desoldering process, and the reinforcement solution uses pre-formed support components for embedded installation. Geographic location data and service resource databases are accessed to match the inventory of the nearest maintenance site: if the site has the same model of battery cells and the tools are compatible, a battery swapping instruction with a time window is generated (including spare parts codes and operation work orders); if no inventory is available, an emergency derating scheme is activated, and an output power limitation coefficient (e.g., maximum current reduced to 60% of the original threshold) is added to the subsequent maintenance planning information. The final subsequent maintenance planning information is a structured instruction set, including a triplet of fault coordinates, action sequence, resource code, and derating coefficient.

[0075] This application provides a battery pack energy efficiency optimization method that identifies the structural information of the cycling device and matches it with the battery pack, outputting accurate battery adaptation parameters. This solves the problem of insufficient structural adaptability in traditional methods, making power allocation more in line with the actual needs of the device, thereby improving battery energy efficiency. Combining cycling data with battery adaptation parameters for device power allocation can adjust energy efficiency control thresholds, avoiding battery over-discharge or redundant power supply, enhancing dynamic energy efficiency management capabilities, and improving battery energy utilization efficiency. Based on battery adaptation parameters, battery state data is identified, and thermal safety boundary calculations are performed using energy efficiency control thresholds. This allows for accurate output of degradation trend data, providing early warnings of battery performance degradation or thermal runaway risks, improving battery safety. Analysis of degradation trend data and the device's battery pack generates targeted subsequent maintenance planning information, enabling a shift from passive maintenance to proactive predictive maintenance, extending battery life and reducing maintenance costs. By comprehensively considering the cycling device structure, cycling data, and battery state, the energy efficiency optimization strategy becomes more adaptable, meeting the battery management needs of different cycling modes and improving user experience.

[0076] In one embodiment, the structural information of the cycling device is identified, and a structural match is performed with the device's battery pack to output battery adaptation parameters, including:

[0077] Frame space parameters refer to the geometric boundary characteristics of the equipment shell, including the range of wheel cavity diameter, spoke gap depth, and motor shaft positioning tolerance for hub-type equipment, or the range of chassis thickness variation and hinge rotation angle for folding equipment. Structural extension parameters describe dynamic deformation capabilities, such as the extension and retraction range of the folding mechanism and the compression allowance range of the hub damping spring.

[0078] Using 3D geometric analysis technology, core parameters such as the tolerance range of the wheel cavity diameter and the maximum rotation angle range of the hinge are extracted. For hub devices, the effective volume characteristics of the wheel cavity are calculated, generating a vector of annular cavity size parameters; for folding devices, the relationship between the hinge pin coordinates and the connecting rod length is measured, outputting a matrix of mechanism displacement information. The cavity size parameters are stored as vectors to represent geometric boundary features, and the mechanism displacement information is recorded as a matrix to represent the mapping relationship between displacement and angle. The output data directly constrains the physical layout of the battery pack, providing spatial boundary basis for current path identification.

[0079] The system inputs the effective volume feature vector of the wheel cavity from the cavity size parameters and retrieves the cell topology diagram of the device's battery pack. It identifies the gap characteristics between the wheel cavity and the battery pack: dividing the main path and restricted paths based on the radial gap threshold. Based on the parallel topology of the cell clusters, it plans the optimal current path: the main path uses low-resistance conductors arranged tangentially along the wheel cavity, while the restricted path uses a radial jumper structure. It generates a current distribution parameter matrix: row vectors correspond to cell cluster identifiers, and column vectors contain path type identifiers, impedance characteristic values, and current-carrying capacity thresholds. For foldable devices, it verifies the impact of displacement deformation on the path: if the path length change exceeds a preset ratio, a dynamic impedance compensation coefficient is added. Finally, the current distribution parameters are integrated into the battery adaptation parameter set to drive the subsequent power supply zone activation logic.

[0080] The displacement-angle mapping relationship in the input mechanism displacement information is analyzed to interpret the deformation characteristics of the equipment: for folding equipment, the correlation between the hinge rotation angle and the chassis unfolded thickness is extracted; for hub-type equipment, the ratio of damper spring compression stroke to wheel cavity volume change is identified. The partitioned topology of the equipment's battery pack is invoked to establish mapping rules between displacement features and power supply domains: if the chassis unfolded thickness meets the condition when the folding angle reaches a preset threshold, full-domain power supply is activated; if the angle is below the threshold and the thickness is limited, only the core power supply domain is activated. A power supply activation rule matrix is ​​generated: the row vectors are power supply domain identifiers, and the column vectors contain the angle activation threshold, current carrying capacity upper limit, and interlock protocol identifier. For hub-type equipment, a vibration condition response mechanism is configured: when the damper spring compression rate exceeds the preset range, a buffer power supply mode is triggered. The power supply activation rules integrate a displacement protection mechanism to ensure no circuit interference risk during mechanism movement.

[0081] Input current distribution parameters and power supply activation rules are aligned using feature space: the cell cluster identifiers within the power supply domain are associated with current path characteristics to construct a mapping table covering the entire domain. Consistency of elements is verified: if the upper limit of current carrying capacity in the power supply activation rule is lower than the maximum current carrying capacity of the current distribution characteristics, it is automatically derating to the matching value; if the power supply domain boundary overlaps with the current path restricted area, an interlocking protocol is added. Output battery adaptation parameters are hierarchical: the current control layer inherits the path type and impedance characteristics of the current distribution characteristics; the power supply logic layer embeds the angle threshold and interlocking protocol of the power supply activation rule; the thermal management interface layer reserves a temperature sensor coordinate field. The final parameter set is encapsulated in a binary protocol and transmitted to the device's energy efficiency control module via a standard bus.

[0082] This embodiment generates battery adaptation parameters by analyzing the structural information of the cycling device, achieving precise matching between the battery pack and the device's physical structure, thereby improving space utilization and reducing the risk of mechanical interference. Current paths are identified based on cavity size parameters, optimizing the current distribution characteristics between cell clusters and effectively balancing internal resistance losses. The power supply zone status is activated using mechanism displacement information, dynamically adapting to the deformation conditions of folding or hub-type devices, avoiding power loss in ineffective power supply areas. Current distribution characteristics and power supply activation rules are integrated to form a unified control protocol, ensuring coordinated optimization of power allocation and thermal safety boundaries. Finally, targeted maintenance instructions are generated through correlation analysis of degradation trend data and device structure, extending the energy efficiency stability of the battery pack throughout its entire lifecycle.

[0083] In one embodiment, the power supply zone of the device's battery pack is activated based on the mechanism displacement information to generate power supply activation data, including:

[0084] Structural change information refers to the dynamic deformation characteristics of mechanical components, including the coordinate change trajectory of the hinge pin of the folding mechanism and the compression stroke curve of the hub damping spring. Extension range data describes the deformation limit boundaries, such as the folding angle range and the expansion / contraction rate of the wheel cavity volume. Mechanism displacement information provides real-time motion quantities, including the instantaneous value of the folding hinge rotation angle and the compression displacement of the hub damping spring. Through spatial coordinate system transformation technology, the pin trajectory in the structural change information is mapped to a three-dimensional vector (X / Y / Z axis displacement components), and the extension range data is converted into motion constraint boundaries (e.g., a Z-axis displacement upper limit of 10mm). Multi-axis motion analysis performs vector synthesis: inputting the X / Y axis displacement components and Z-axis rotation angle of the folding mechanism, the magnitude and direction angle of the synthesized motion vector are calculated; inputting the Y / Z axis compression displacement and radial deformation of the hub device, the radius of curvature of the motion trajectory is solved. Motion phase parameters are output as a matrix: row vectors are motion stage identifiers (e.g., folding 0-30° is stage 1), and column vectors contain [synthetic vector magnitude, direction angle, radius of curvature, constraint boundary state], fully characterizing the spatial motion features of the equipment deformation.

[0085] Input motion phase parameters and analyze the synthetic vector features of each motion stage: if the orientation angle of the folding device is greater than 45° and the modulus is greater than the preset threshold, it is determined to be in the unfolded state; if the radius of curvature of the hub device is less than the critical value, it is marked as high vibration dynamic.

[0086] The device's battery pack power supply partition topology map (including power supply domain coordinate boundaries and positive and negative pole positions) is invoked, and position mapping is performed: the coordinates of the power supply domain vertices are transformed to the device motion coordinate system, and the spatial correlation between each domain and the motion vector is calculated. Activation calculation generates instructions based on the mapping results: the expanded state activates the power supply of the entire domain (instruction code 0xFF), and the high-vibration dynamic state activates the power supply of the core domain (instruction code 0x0F) and masks the edge domains.

[0087] Synchronously construct cell connection relationships: Based on the series and parallel topology of cell clusters within the power supply domain, generate a connection relationship matrix (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 relationships are a topology table. Together, they constitute the core elements of dynamic control of power supply zones.

[0088] The system inputs a cell connection matrix and analyzes the series and parallel topology between cell clusters: identifying the positive and negative interconnection paths of cell clusters within parallel groups and marking high-current-carrying channels; locating voltage transition interfaces between series nodes and marking impedance-sensitive areas. It then calls the binary control code stream of the activation instruction set and maps it to the physical location of the power supply zone: if the instruction set requires activation of the core power supply domain, it extracts the corresponding cell cluster connection path; if the instruction contains an interlocking protocol, it disables current transmission on conflicting paths. Power supply path optimization is performed: low-resistance conductor cross-sectional area thresholds are configured for high-current channels, and voltage compensation coefficients are added to impedance-sensitive areas; when the equipment is under high vibration conditions, mechanical stress buffer gaps are added to suspended jumper paths. An energy distribution rule set is generated, including a path current-carrying capacity table, voltage compensation protocol, and dynamic adjustment rules (vibration level → buffer gap value). This rule set ensures that the power supply path maintains electrical stability under deformation conditions.

[0089] The current-carrying capacity table and voltage compensation protocol of the input energy distribution rule set are combined with the synthetic vector features (direction angle, radius of curvature) in the motion phase parameters. Rule-based motion coupling is executed: when the motion direction angle > 45°, the extended current-carrying mode is activated; when the radius of curvature < the critical value, the vibration resistance protocol is triggered. Multi-dimensional energy supply activation rules are constructed: Electrical control layer: inherits the current-carrying threshold and compensation coefficient of the energy distribution rule set. Mechanical response layer: embeds the deformation characteristics and vibration resistance rules of the motion phase parameters. Thermal management interface: reserves coordinate fields for temperature monitoring points.

[0090] The output power activation rules are in a binary tree structure, with each node storing the ID1 region and rule parameters, and leaf nodes bound to interlock protocol identifiers. The final rule set is transmitted via the device control bus to drive the battery pack power supply partitions to perform dynamic activation.

[0091] This embodiment generates motion phase parameters using displacement information from a multi-axis motion analysis mechanism, achieving matching between equipment deformation conditions and power supply zones. Based on these motion phase parameters, power supply zone activation calculations and position mapping are performed to ensure precise switching of the power supply domain under folding or vibration conditions, avoiding ineffective power supply losses. The electrical characteristics of the power supply path are optimized using cell connection relationships, and internal resistance distribution is balanced through low-resistance channel configuration and voltage compensation mechanisms to reduce Joule heating effects. Multi-dimensional power supply activation rules are constructed by integrating energy distribution rules and motion phase parameters, forming a mechanical-electrical collaborative control protocol to enhance system stability under complex operating conditions. Finally, through standardized rule encapsulation and bus transmission, plug-and-play adaptability for battery pack power supply control is achieved.

[0092] In one embodiment, acquiring cycling data and allocating device power according to battery adaptation parameters to obtain an energy efficiency control threshold includes:

[0093] Speed ​​data is sourced from the hub motor Hall sensor or positioning module, recording instantaneous linear velocity changes. Slope and tilt angle data are acquired via an inertial measurement unit, analyzing the pitch and roll components in the direction of travel. Load pressure data is collected by strain gauge arrays distributed on the pedals or seat, measuring the dynamic load distribution. Timestamp alignment technology is used to synchronize the three data streams to a unified time axis, eliminating sensor sampling delay bias. The raw signals are filtered to suppress high-frequency vibration noise: speed data is filtered to remove pulsation components caused by wheel diameter deviations; slope data is compensated for tilt fluctuations caused by device attitude oscillations; and load data is filtered to remove pressure distortions caused by localized pedal deformation. The generated riding data is a multi-dimensional vector sequence, with each frame containing a timestamp, rate of change of speed, effective slope angle, and net load value quadruple, forming the input benchmark for subsequent power demand calculations.

[0094] Input cycling data vector sequence and analyze the motion load characteristics: the rate of change of speed is mapped to the acceleration inertial force effect, the slope angle is converted into the gravitational component drag effect, and the load value is associated with the mass inertial effect. Establish power demand calculation relationships: the power demand under acceleration conditions is associated with the product of load mass and the square of acceleration; the power demand under climbing conditions is associated with the product of load, the sine of the slope, and the steady-state speed; and the power demand under constant speed conditions is coupled with the drag coefficient and rolling friction loss characteristics. Dynamically adjust the calculation coefficients, correct the drag model characteristics based on equipment structure identification, and adaptively determine the rolling friction coefficient characteristics based on road surface type identification. Output power demand data is a two-dimensional time-power matrix, with row vectors representing time series nodes and column vectors containing the total power demand value and individual power components, providing a load benchmark for battery cell power allocation.

[0095] The system analyzes the electrical characteristics of cell clusters by inputting current distribution characteristics and power activation rules from the battery adaptation parameters: current distribution characteristics define the path impedance characteristics and current carrying capacity thresholds of each cluster, and power activation rules specify the activation state of the power domain under different operating conditions. It then executes cell cluster power allocation decisions: based on the domain status identifier in the power activation rules, it filters the set of cell clusters included in the current active domain; based on the impedance characteristics of the current distribution characteristics, it calculates the power allocation weights between active clusters. A current quota feature set is generated as an electrical characteristic matrix: row identifiers correspond to cell cluster numbers, and column identifiers include real-time current quota values, voltage monitoring point association identifiers, and temperature association coefficients. The allocation process follows a dynamic adjustment mechanism: when the power activation rules mark high vibration conditions, a derating mechanism is applied to floating path clusters; when the current distribution characteristics mark high impedance areas, the quota value is limited to not exceeding the safe current carrying capacity limit. The current quota feature set integrates thermal protection interface features, providing an electrical constraint benchmark for subsequent energy efficiency allocation.

[0096] The system inputs a time-power matrix of power demand features and analyzes the total power demand value and individual power components at each time point. It then calls the cluster current quota matrix from the current quota feature set to obtain the cluster-level current carrying capacity and voltage boundaries. Finally, it performs energy efficiency allocation calculations: decomposing the total power demand to active cell clusters according to current quota weights, constraining the output power of a single cluster to not exceed the product of the quota value and the voltage upper limit; and dynamically prioritizing the individual power demands, allocating ramp-up power to high-capacity clusters and accelerating power to low-resistance clusters.

[0097] The output energy efficiency control threshold is a multi-dimensional set of control parameters: the time dimension inherits the time node sequence of the power demand feature set, the electrical cluster dimension maps the cluster identifier of the current quota feature set, and the parameter dimension stores the charge / discharge current threshold and energy recovery intensity. An adaptive calibration mechanism is embedded in the threshold generation process: when the peak power demand duration exceeds preset conditions, a temporary derating protocol is triggered; when the temperature correlation coefficient indicates a high-temperature risk, derating protection is enabled. The final energy efficiency control threshold is transmitted to the battery management system via the control bus to drive real-time charge / discharge control.

[0098] This embodiment achieves precise matching between power demand and battery pack electrical characteristics through dynamic coordination of cycling data and battery adaptation parameters, improving energy distribution efficiency. Power demand data is generated based on exercise load calculations, ensuring the accuracy of the physical mapping from cycling conditions to power demand. Battery adaptation parameters are used to allocate cell cluster power, optimizing the spatial distribution of current paths and reducing internal resistance losses. Through global energy efficiency allocation of power demand and current quotas, multi-dimensional control thresholds are generated to achieve thermal safety boundary control during charging and discharging. Ultimately, a closed-loop optimization chain is formed between cycling conditions, battery status, and control thresholds, enhancing system energy efficiency stability and lifecycle management capabilities.

[0099] In one embodiment, battery state data of the device's battery pack is identified based on battery adaptation parameters, and thermal safety boundary is calculated using energy efficiency control thresholds to output degradation trend data, including:

[0100] The system calls upon the thermal management interface layer information in the battery adapter parameters to locate the characteristic distribution of temperature sensors: the coordinates of monitoring points equidistantly deployed radially for hub-type devices, and the sensor location characteristics distributed in a grid on the chassis plane for foldable devices. It synchronously acquires cluster-level voltage data streams from the voltage sampling channels, with each cluster configured with an independent monitoring node. Temperature monitoring performs three-dimensional thermal field characteristic reconstruction: by inputting instantaneous temperature values ​​at each location and combining them with the thermal conduction characteristics of the device structure, it calculates the temperature gradient distribution characteristics within the cavity. Voltage fluctuation monitoring analyzes fluctuation amplitude characteristics: by calculating the fluctuation characteristics of the voltage sequence within a time window on a cluster basis, it identifies abnormal voltage drop cluster identifiers. Battery status data is output as a structured dataset: temperature field data is stored as a three-dimensional spatial distribution matrix, and voltage fluctuation data is recorded as a mapping relationship between cluster identifiers and fluctuation amplitudes, providing an input benchmark for thermal safety boundary analysis.

[0101] The system inputs a 3D temperature field distribution matrix from the battery state data and analyzes local hotspot characteristics: marking locations of abrupt temperature gradient changes and identifying high-temperature accumulation areas. It then utilizes the charge / discharge current threshold features from the energy efficiency control threshold matrix to establish thermal accumulation analysis information: the Joule heat generation is correlated with the product of real-time current values ​​and cluster internal resistance characteristics; heat dissipation efficiency is correlated with the convolution operation of temperature gradient and thermal conductivity characteristics. Thermal safety boundary analysis calculates critical thermal equilibrium characteristics: when the heat generation rate exceeds the heat dissipation efficiency, it is marked as a thermal risk unit. The protection boundary parameters are output as a multi-dimensional feature set: the spatial dimension includes the coordinates of the thermal risk unit, the electrical dimension includes the maximum allowable current adjustment coefficient, the temporal dimension includes the risk duration threshold, and the control dimension includes the graded response protocol identifier. This parameter set directly constrains subsequent charge / discharge power identification operations.

[0102] The system inputs a multi-dimensional feature set of protection boundary parameters to analyze the spatial distribution characteristics and electrical derating factors of thermal risk units. It then utilizes the voltage fluctuation mapping relationship from battery state data to identify the impedance abrupt changes in high-fluctuation clusters. The system performs charge / discharge power identification calculations: calculating the maximum allowable charging current based on the derating factor of the thermal risk units; and generating a discharge power attenuation curve by combining the voltage fluctuation amplitude and the thermal risk duration threshold. The output power limit feature set is a three-dimensional control matrix, with rows corresponding to cell cluster numbers, columns containing charging power limits, discharging power limits, and attenuation response times, and layers corresponding to time series nodes. An adaptive compensation mechanism is embedded in the identification process: a forced air-cooling compensation protocol is activated when thermal risk units are concentrated at the wheel cavity edge; and mechanical stress buffer compensation is added when high-fluctuation clusters are located in the folding hinge area.

[0103] The system analyzes the charge and discharge power limit matrix of the input power limit feature set to parse the cell cluster-level power constraint features; it then calls the graded response protocol identifier of the protection boundary parameters to obtain the thermal risk control strategy. It performs attenuation identification analysis: the charging power limit is mapped to the upper limit feature of the lithium-ion insertion rate, and the discharge power limit is converted into the electrode phase transition stress threshold feature; the thermal risk unit coordinates are associated with the electrode expansion coefficient characteristics to calculate the local active material stripping rate feature. The output attenuation trend data is a tensor structure, with the first dimension being the cell cluster identifier, the second dimension including the capacity attenuation slope feature, the internal resistance growth rate feature, and the thermal runaway risk index feature, and the third dimension being the time evolution sequence. The identification process integrates a lifetime calibration mechanism: when the graded response protocol marks a current outage record, a cycle lifetime reduction factor is superimposed; when forced air cooling compensation is activated, a lifetime compensation coefficient is injected. Finally, the data is synchronized to the maintenance analysis module.

[0104] This embodiment achieves precise matching between the device structure and the battery pack through battery adaptation parameters, improving energy utilization efficiency in space-constrained scenarios. Power demand characteristics are dynamically generated based on riding data, ensuring real-time adaptability of power allocation when operating conditions change. Thermal safety boundary analysis identifies local thermal risk units, generating protective boundary parameters to constrain the charging and discharging process, reducing the probability of thermal runaway. Dynamic optimization of cell cluster-level charging and discharging thresholds is achieved through power limiting feature sets, balancing internal resistance losses and extending cycle life. Combined with degradation identification technology, multi-dimensional degradation trend data is output, providing quantitative basis for maintenance decisions and enhancing the battery pack's full lifecycle management capabilities. Ultimately, a closed-loop optimization system of "structural matching - dynamic allocation - thermal protection - lifespan prediction" is formed, improving the overall energy efficiency performance of small intelligent mobility devices.

[0105] In one embodiment, based on battery state data and energy efficiency control thresholds, a thermal safety boundary analysis is performed on the device's battery pack to generate protection boundary parameters, including:

[0106] Individual temperature gradient data is derived from the three-dimensional temperature field distribution characteristics, calculating the temperature difference variation characteristics between adjacent monitoring points. Voltage fluctuation rate data is taken from the fluctuation amplitude analysis results of cluster-level voltage sequences, identifying the fluctuation characteristics of abnormal voltage drop clusters. Overheating correlation analysis performs spatial-electrical coupling mapping: the coordinates of temperature gradient abrupt change points are spatially correlated with the locations of voltage fluctuation clusters. If the spatial distance between a high gradient point and a high fluctuation cluster is less than a critical threshold, it is marked as a thermal-electrical coupling risk unit. Temporal evolution characteristic analysis tracks the temporal synchronization characteristics of the temperature change rate and voltage fluctuation rate of risk units. When the correlation coefficient between the two trends exceeds a preset value, it is determined to be an overheating evolution hotspot. Overheating data is generated as a structured feature set: the spatial dimension stores the coordinate locations of risk units, the electrical dimension records the coupling strength characteristics, and the temporal dimension marks the hotspot evolution stage identifiers, providing an input benchmark for regional load allocation.

[0107] The system inputs the coordinates and coupling strength characteristics of risk units from the overheating data; invokes the upper limit features of the charging and discharging current from the energy efficiency control threshold matrix; and extracts the heat dissipation structure features from the structural information. It then performs regional load allocation decisions: applying derating current threshold features to risk units marked by the overheating data; and transferring the thermal load of risk units to the near-end region of the heat dissipation path based on the distribution of high thermal conductivity areas in the heat dissipation structure features. The output is a zoned thermal load distribution matrix: time series nodes correspond to hierarchical identifiers, region identifiers correspond to row identifiers, and column identifiers include thermal load value features, current derating coefficient features, and heat dissipation path distance features. A dynamic balancing mechanism is embedded in the allocation process: when the energy efficiency control threshold marks high power demand, a load compensation protocol is enabled for non-risk areas; when the structural information marks heat dissipation blind zones, forced air-cooled virtual load transfer is triggered. This distribution matrix provides a spatial load distribution benchmark for heat conduction path analysis.

[0108] The system inputs the thermal load value characteristics and heat dissipation path distance characteristics of the partitioned thermal load distribution matrix; it then retrieves the heat dissipation structure characteristics from the structural information, including the thickness distribution characteristics of the carbon fiber thermal conductive layer in the hub cavity or the density distribution map of the aluminum heat dissipation fins in the folding device. It performs a heat conduction path analysis: based on the material thermal conductivity and geometric dimensions in the heat dissipation structure characteristics, it analyzes the heat conduction efficiency characteristics; it sorts the thermal load value characteristics according to the heat dissipation path distance characteristics and assigns them to high thermal conductivity regions for priority processing. The output local temperature rise feature set is a three-dimensional tensor structure: the time node sequence corresponds to the hierarchical identifier, the region identifier corresponds to the row identifier, and the column identifier includes temperature rise value characteristics, heat accumulation rate characteristics, and heat dissipation response delay characteristics. The analysis process integrates a path blockage detection mechanism: when the heat dissipation path distance characteristic exceeds a critical threshold, it is marked as a conduction blockage region, triggering a virtual heat dissipation channel activation protocol. This feature set provides a thermodynamic evolution benchmark for protection parameter derivation.

[0109] Input the temperature rise value and heat accumulation rate characteristics of the local temperature rise feature set; call the preset temperature rise critical point matrix, which includes the material phase change temperature threshold, electrolyte boiling point threshold, and diaphragm shrinkage temperature threshold. Perform protection parameter deduction operation: when the temperature rise value reaches the diaphragm shrinkage threshold, trigger the current derating protocol and forced air cooling protocol; when the heat accumulation rate exceeds the critical value, activate the current interruption protocol and aerogel isolation protocol.

[0110] The output protection boundary parameters are a four-dimensional control tensor: the spatial dimension records the coordinates of the risk area, the electrical dimension includes the maximum allowable current characteristic and voltage fluctuation tolerance characteristic, the time dimension defines the risk duration threshold characteristic, and the control dimension encodes the response protocol identifier. An adaptive learning mechanism is embedded in the simulation process: when the same area continuously triggers the response protocol, the current upper limit characteristic is permanently reduced; when the temperature rise value remains below the critical value, the historical derating constraint is released. The final parameters are synchronized to the equipment's energy efficiency allocation module via the control bus to drive real-time control.

[0111] This embodiment accurately identifies thermal-electric coupling risk units in the battery pack through overheat correlation analysis, enabling early warning of potential thermal runaway. Based on the heat dissipation structure characteristics, it dynamically allocates regional thermal loads, optimizing heat dissipation efficiency in high thermal conductivity areas and reducing local temperature rise. Heat conduction path analysis, combined with material thermal conductivity and geometric features, effectively improves the spatial transfer accuracy of thermal loads. A graded response mechanism driven by the temperature rise critical point achieves stepped protection from current derating to emergency current interruption, ensuring safety redundancy under extreme operating conditions.

[0112] In one embodiment, battery maintenance analysis is performed on degradation trend data and the device's battery pack to generate subsequent maintenance planning information, including:

[0113] The charging and discharging history log file in the storage medium of the cycling device is accessed, and the data records are indexed by the number of cycles: the start time stamp of a single cycle, the end time stamp, the voltage-current waveform sampling sequence during the charging stage, the capacity decay trajectory during the discharging stage, and the extreme value record of the ambient temperature.

[0114] Data parsing performs integrity checks: incomplete cyclic records caused by signal interruptions are removed, and sampling points lost due to memory overflow are supplemented. Historical charge / discharge records are generated as a multi-dimensional data cube: the first dimension is the cycle count index, the second dimension is the time series node, and the third dimension stores a quadruple data field of voltage, current, temperature, and capacity values. This record set provides a time evolution benchmark for attenuation pattern matching.

[0115] The preset battery degradation type library includes four basic modes: positive electrode active material loss characteristics are characterized by an earlier shift in the charging voltage plateau; lithium inventory degradation characteristics are characterized by an increased slope in the discharge voltage plateau; SEI film thickening characteristics are characterized by a rise in the charging end voltage; and electrolyte decomposition characteristics are characterized by a sudden increase in the internal resistance growth rate. By inputting a three-dimensional tensor of degradation trend data, the characteristic curves of capacity degradation slope and internal resistance growth rate for each cluster are extracted.

[0116] Perform pattern matching calculation: dynamically time-warp and align the feature curve with the feature template of the preset pattern, and calculate the waveform matching degree; when the matching degree exceeds the recognition threshold, mark the corresponding attenuation pattern identifier. Output battery attenuation pattern as a structured encoding set: the row vector is the cell cluster identifier, and the column vector contains the attenuation pattern type code, the pattern stage identifier, and the pattern confidence coefficient, providing a classification benchmark for piecewise fitting.

[0117] The system inputs a structured encoding set of battery degradation modes and parses the degradation mode type encoding and mode stage identifier for each cell cluster. It then calls upon a multi-dimensional data cube of historical charge and discharge records to extract the voltage-current waveform sequence and capacity degradation trajectory for the corresponding cluster. Finally, it performs a piecewise fitting operation: dividing the historical time axis according to the mode stage identifier, and calculating key indicators such as charging voltage plateau slope characteristics and discharge capacity retention rate characteristics within the segmented window.

[0118] The output battery degradation correlation information is a three-dimensional tensor structure: the degradation mode type corresponds to the hierarchical identifier, the cell cluster identifier corresponds to the row identifier, and the column identifier includes slope fitting value features, internal resistance growth coefficient features, and thermal correlation factor features. A dynamic calibration mechanism is embedded in the fitting process: when the extreme ambient temperature in historical records continuously exceeds the limit, a temperature compensation offset is added; when abnormal oscillations exist in the waveform sequence, an anti-interference filtering algorithm is activated. This correlation information provides a quantitative basis for power gradient adjustment.

[0119] Input the slope fitting value characteristics and internal resistance growth coefficient characteristics of the battery degradation correlation information; call the maintainability characteristics of the device battery pack, including the hub cavity opening size characteristics or the disassembly complexity characteristics of the folding mechanism. Perform power gradient adjustment operation: when the slope fitting value characteristics exceed the set threshold, a linear derating protocol for charging current is generated; when the internal resistance growth coefficient characteristics show a sudden increase, a step-by-step derating mechanism for discharge power is triggered.

[0120] Maintenance planning and integration: A battery swapping protocol is developed based on maintainability characteristics, generating a maintenance instruction set that includes cell cluster identifiers, action sequence characteristics, resource requirement characteristics, and execution time window characteristics. The output maintenance planning information is a structured tree diagram: the root node stores the device's global maintenance strategy characteristics, child nodes store cluster-level maintenance instruction characteristics, and leaf nodes bind resource coding characteristics and execution condition characteristics. The final maintenance planning information is synchronized to the maintenance service network via the cloud to drive resource scheduling and is then transmitted to the corresponding riding devices.

[0121] This embodiment accurately identifies battery degradation mode types and evolution stages by dynamically matching historical charge / discharge records with degradation trend data, improving the accuracy of aging diagnosis. Based on a segmented fitting technique for degradation modes, it quantifies and correlates the capacity degradation slope and internal resistance growth characteristics at the cell cluster level, providing a scientific basis for power control. An output power gradient adjustment mechanism dynamically adapts linear and step-wise derating of charge / discharge current, effectively delaying battery performance degradation. Maintenance planning information integrates maintainability characteristics and resource scheduling requirements.

[0122] In one embodiment, battery degradation correlation information is obtained by segmenting and fitting the battery degradation pattern with historical charge-discharge records, including:

[0123] The system inputs a structured encoding set of battery degradation modes and parses the degradation mode type code and mode stage identifier for each cell cluster. Capacity degradation parameters are extracted based on the capacity degradation slope characteristic curve corresponding to the mode stage identifier, calculating the characteristic value of the capacity loss rate per unit cycle. Internal resistance drift parameters are extracted based on the changing trend of the internal resistance growth rate characteristic curve, identifying the inflection point of sudden internal resistance increase and drift rate characteristics. Self-discharge anomaly parameters are obtained through dormant voltage retention rate characteristic analysis, comparing the voltage drop rate during the resting stage with a benchmark threshold, and marking abnormal self-discharge cluster identifiers.

[0124] The core attenuation parameters are output as a three-dimensional tensor structure: row identifiers correspond to cell cluster numbers, column identifiers store capacity loss rate characteristic values, internal resistance drift rate characteristic values, and self-discharge anomaly identifiers, and hierarchy identifiers are associated with attenuation mode type codes. This parameter set provides a quantitative benchmark for segmenting the charge and discharge stages.

[0125] Input a multi-dimensional data cube containing historical charge and discharge records, and call the preset charge / discharge cycle definition matrix: the charge / discharge cycle is divided into three stages: initial stage, stable stage, and decay stage, based on the capacity decay rate threshold. Perform cycle segmentation operation: map the cycle number index of the data cube to the preset cycle stage identifier. Stage parameter extraction operation: extract constant current charging duration and discharge voltage plateau retention rate features for the initial stage; extract the voltage rise value at the end of charging and internal resistance change gradient features for the stable stage; extract the cycle number of capacity drop points and self-discharge rate increment features for the decay stage.

[0126] The generated charge / discharge stage parameter set is a hierarchical data block structure: the first level identifies the stage type; the second level stores a quadruple array of voltage, current, temperature, and capacity values ​​for all cycles in that stage; and the third level labels the stage boundary features, including the start and end cycle numbers. This parameter set provides a segmented dataset for attenuation correlation analysis.

[0127] The system takes a four-dimensional tensor representing the relationship structure as input and analyzes related elements such as the charging characteristic-attenuation rate mapping coefficient and the internal resistance characteristic-drift rate weight. It then performs an attenuation trajectory reconstruction operation: reconstructing the initial capacity attenuation slope characteristics based on the mapping coefficient between constant current charging duration and capacity loss rate; reconstructing the median trajectory characteristics of internal resistance evolution during the stable period through the product of the internal resistance change gradient and the drift rate weight; and reconstructing the self-discharge deterioration index curve characteristics during the decay period based on the self-discharge anomaly matching degree and incremental characteristics.

[0128] The output segmented reconstruction parameters are time evolution tensors: the hierarchical identifier corresponds to the charge / discharge stage type, the row identifier corresponds to the cycle number sequence node, and the column identifier stores the reconstruction capacity value feature, reconstruction internal resistance value feature, and self-discharge risk index feature. The reconstruction process incorporates a stage boundary calibration mechanism: when the reconstruction parameters of adjacent stages jump beyond a preset threshold, a smooth transition function feature is added. This parameter set provides a segmented evolution benchmark for full-cycle fusion.

[0129] Input the segmented reconstruction parameters and time evolution tensors for each stage, aligning them to the time axis by the loop count index. Perform full-cycle fusion analysis: Generate an S-shaped capacity decay trajectory function based on the initial slope characteristics, stable median characteristics, and decay drop point characteristics; construct a step-by-step internal resistance growth model by fusing stable drift rate characteristics and decay mutation point characteristics; and output a probabilistic self-discharge risk cloud map based on the fusion of abnormal matching degree characteristics and risk index characteristics.

[0130] The output battery degradation correlation information is a three-dimensional prediction field structure: the first dimension, the identifier, corresponds to the cell cluster ID; the second dimension includes the storage capacity degradation trajectory function features, internal resistance growth model features, and self-discharge risk cloud map features; and the third dimension corresponds to the cycle number evolution sequence. The fusion process integrates a confidence verification mechanism: when the deviation between the segmented reconstruction parameters and historical records exceeds a preset threshold, manual review and feature marking are triggered. Finally, the information is synchronized to the maintenance decision module via the control bus.

[0131] This embodiment quantifies capacity decay, internal resistance drift, and self-discharge anomalies using core decay parameter extraction technology, achieving precise calibration of the battery aging process. Charge / discharge stage segmentation technology divides historical operating data according to a preset cycle, constructing a staged analysis framework of initial, stable, and decay phases. Correlation structure technology establishes a cross-stage mapping channel between charge / discharge parameters and decay parameters, solving the coupling problem between aging characteristics and operating conditions. Decay trajectory reconstruction technology generates segmented evolution parameters, enabling continuous trajectory reconstruction of the aging process. Full-cycle fusion analysis technology integrates segmented parameters to generate a three-dimensional prediction field, outputting a capacity decay trajectory function, an internal resistance growth model, and a self-discharge risk cloud map, providing quantitative basis for lifespan prediction and fault early warning.

[0132] Reference Figure 2 As shown, this application also provides a battery pack energy efficiency optimization system, applied to the battery pack energy efficiency optimization method of any of the above, comprising:

[0133] The identification module is used to identify the structural information of the cycling equipment and match the structure with the battery pack of the cycling equipment, and output battery adaptation parameters.

[0134] The parsing module is used to acquire riding data, perform device power allocation with battery adaptation parameters, and obtain energy efficiency control thresholds.

[0135] The processing module is used to identify the battery status data of the device's battery pack based on the battery adaptation parameters, calculate the thermal safety boundary with the energy efficiency control threshold, and output the degradation trend data.

[0136] The module is used to perform battery maintenance analysis on degradation trend data and equipment battery packs, and generate subsequent maintenance planning information.

[0137] This application provides a battery pack energy efficiency optimization system that identifies the structural information of cycling equipment and matches it with the battery pack, outputting accurate battery adaptation parameters. This solves the problem of insufficient structural adaptability in traditional methods, making power allocation more in line with the actual needs of the equipment, thereby improving battery energy efficiency. Combining cycling data with battery adaptation parameters for equipment power allocation can adjust energy efficiency control thresholds, avoiding battery over-discharge or redundant power supply, enhancing dynamic energy efficiency management capabilities, and improving battery energy utilization efficiency. Based on battery adaptation parameters, battery state data is identified, and thermal safety boundaries are calculated using energy efficiency control thresholds. This allows for accurate output of degradation trend data, providing early warnings of battery performance degradation or thermal runaway risks, improving battery safety. Analysis of degradation trend data and the equipment's battery pack generates targeted subsequent maintenance planning information, enabling a shift from passive maintenance to proactive predictive maintenance, extending battery life and reducing maintenance costs. By comprehensively considering the cycling equipment structure, cycling data, and battery state, the energy efficiency optimization strategy is more adaptable, meeting the battery management needs of different cycling modes and improving user experience.

[0138] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments concerning the apparatus in the above embodiments, and will not be elaborated further here.

[0139] The solution of this application has been described in detail above with reference to the accompanying drawings. In the above embodiments, the descriptions of each embodiment have different emphases; parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. Those skilled in the art should also understand that the actions and modules involved in the specification are not necessarily essential to this application. Furthermore, it is understood that the steps in the method of this application embodiment can be adjusted, combined, and deleted according to actual needs, and the modules in the device of this application embodiment can be combined, divided, and deleted according to actual needs.

[0140] Furthermore, the method according to this application can also be implemented as a computer program or computer program product, which includes computer program code instructions for performing some or all of the steps in the method described above.

[0141] Alternatively, this application may be implemented as a non-transitory machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) storing executable code (or computer program, or computer instruction code) thereon, which, when executed by a processor of an electronic device (or electronic device, server, etc.), causes the processor to perform part or all of the steps of the methods described above according to this application.

[0142] Those skilled in the art will also understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described in connection with the present application can be implemented as electronic hardware, computer software, or a combination of both.

[0143] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems and methods according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0144] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for optimizing energy efficiency of a battery pack, the method comprising: include: Identify the structural information of the cycling device and match it with the device's battery pack to output battery adaptation parameters; Obtain 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, perform battery maintenance analysis on the device battery pack to generate subsequent maintenance planning information; 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.

2. The battery pack energy efficiency optimization method of claim 1, wherein, 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.

3. The battery pack energy efficiency optimization method of claim 1, wherein, 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.

4. 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.

5. The battery pack energy efficiency optimization method according to claim 4, 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.

6. 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.

7. The battery pack energy efficiency optimization method according to claim 6, 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.

8. A battery pack energy efficiency optimization system, characterized in that, The battery pack energy efficiency optimization method applied to any one of claims 1-7 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. The construction module is used to perform battery maintenance analysis on the device battery pack based on the attenuation trend data and generate subsequent maintenance planning information; 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.

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