A power monitoring system for electric bicycle batteries

Abstract

The present invention discloses a power monitoring system for an electric bicycle battery, which relates to the technical field of battery power monitoring. The system comprises an extraction module for collecting real-time power data, charge and discharge current ripple signals and voltage transient signals of the electric bicycle battery, and extracting the dynamic characteristic spectrum of power consumption and the power change gradient through dynamic time domain analysis; a division module for dividing the riding process into multiple monitoring modules according to the power consumption inducement attributes, each module correspondingly comprising an operation-driven power consumption module or an environment-sensing power consumption module. The present invention can not only evaluate abnormal power consumption caused by operating habits, but also predict battery life risks caused by the environment, thereby improving the accuracy and comprehensiveness of battery power monitoring, and helping to extend battery life and ensure riding safety.

Description

Technical Field

[0001] The invention belongs to the technical field of battery power monitoring, and in particular is a power monitoring system for an electric bicycle battery. Background Art

[0002] In the field of electric bicycle battery power monitoring, traditional monitoring systems have at least the following flaws: First, they can only provide a quantitative value of the remaining power, and cannot distinguish and quantify the causes of power consumption, making it difficult to determine whether the power decay is due to user riding operations or external environmental factors. Second, they lack the ability to evaluate the correlation between abnormal power consumption behavior and battery life, and cannot provide early warning of the risk of battery life decay. For example, in a low-temperature environment, frequent rapid acceleration by users can cause the battery power to drop rapidly. Traditional systems cannot distinguish the contribution of operational factors and environmental factors in this process, nor can they predict the impact of such behavior on battery life. This may cause users to misjudge the battery life, and long-term use will accelerate battery aging. Summary of the Invention

[0003] The purpose of the present invention is to provide an electric bicycle battery power monitoring system, which solves the technical problem that the existing traditional electric bicycle battery power monitoring only relies on single power data and ignores the comprehensive influence of operation and environment.

[0004] A power monitoring system for an electric bicycle battery, comprising:

[0005] The extraction module collects real-time power data, charge and discharge current ripple signals, and voltage transient signals of the electric bicycle battery, and extracts the dynamic characteristic spectrum of power consumption and power change gradient through dynamic time domain analysis;

[0006] Divide the riding process into multiple monitoring modules according to the power consumption inducement attributes. Each module includes an operation-driven power consumption module or an environment-sensing power consumption module.

[0007] The analysis and evaluation module analyzes the dynamic characteristic spectrum of power consumption and the temporal correlation between riding operations in the operation-driven power consumption module, calculates the operation impact coefficient, and evaluates the superposition effect of abnormal power consumption caused by operating habits;

[0008] Monitor the three-dimensional temperature field distribution and internal resistance transient response of the battery in the environmental sensing power consumption module, calculate the environmental sensitivity coefficient based on environmental parameters, and assess the risk of battery life degradation caused by environmental factors;

[0009] The construction module, based on the superposition effect of abnormal power consumption and the risk of battery life attenuation, integrates the operation energy consumption coupling coefficient and the environmental sensitivity coefficient to build a dynamic composite threshold for battery power abnormality;

[0010] The operation module, when the power abnormality threshold exceeds the limit, the module operates separately, and after evaluation and prediction, it is determined according to the strategy whether to remind or evaluate.

[0011] Furthermore, when the dynamic composite threshold of the abnormal battery power exceeds the set range, the operation-driven power consumption module is executed: the energy consumption fluctuation characteristic values ​​corresponding to each riding operation in the module are first extracted, and the initial operation optimization path is generated according to the dynamic weight sorting of the energy consumption fluctuation characteristics and intelligent guidance is implemented; after a preset riding period, the energy consumption fluctuation characteristic values ​​are extracted again and compared with the first extracted values ​​to determine whether the energy consumption optimization rate of the high-weight operation meets the preset standard; if not, the dynamic weight is recalculated and the operation optimization path is updated, and the extraction, comparison, and update steps are executed cyclically until the energy consumption optimization rate meets the standard or the riding ends.

[0012] Furthermore, during the loop execution process, each time the operation optimization path is updated, the high-energy consumption operation feature library in the historical riding data within the module is synchronously introduced for matching. If the matching degree between the current operation feature and the high-risk feature in the library exceeds the threshold, a forced intervention instruction is added to the generated operation optimization path to prioritize limiting the power output parameters of such high-risk operations.

[0013] Furthermore, when the dynamic composite threshold value of the abnormal battery power exceeds the set range, the environment-sensing power consumption module is executed:

[0014] Real-time tracking of the dynamic change curve of environmental parameters, dividing the parameter stable section and fluctuation section;

[0015] For the parameter stability segment, calculate the average synergistic influence of the environmental parameter combination within the segment and generate the basic environmental adaptation strategy;

[0016] For parameter fluctuations, capture the instantaneous values ​​of environmental parameters at the peak of fluctuations, calculate the instantaneous collaborative impact, and generate a temporary environmental adaptation strategy;

[0017] Compare the energy consumption control effects of the basic and temporary strategies. If the energy consumption reduction of the temporary strategy is better than that of the basic strategy, incorporate the control parameters of the temporary strategy into the basic strategy and update it.

[0018] Repeat the above tracking, division, calculation, comparison and update steps until the fluctuation range of the environmental parameters is less than the set value or the ride ends.

[0019] Furthermore, during the execution process, it also includes:

[0020] Each time the environmental adaptation strategy is generated or updated, the changes in the battery health status parameters are analyzed using the adaptive particle filter algorithm;

[0021] If the change exceeds the health fluctuation threshold, analyze the correlation between the environmental parameter control range in the strategy and the change in battery health status;

[0022] Adjust the control range of environmental parameters based on the correlation, reduce the control range of parameters that have a significant negative impact on battery health, and at the same time increase the control strength of other parameters to ensure energy consumption control effect;

[0023] The adjusted strategy and correlation data are recorded in the historical case library for the initial generation of strategies in similar environmental scenarios in the future.

[0024] Furthermore, when executing the environment-sensing power consumption module, after dividing the parameter into a stable segment and a fluctuating segment, the method further includes:

[0025] Real-time recording of the riding periods corresponding to the stable and fluctuating parameter periods, and the associated operation optimization paths generated by the operation-driven power consumption module during the same period;

[0026] Calculate the execution compliance rate of the operation optimization path within the stable period. If the compliance rate is lower than the threshold, add a compensation coefficient to the basic environment adaptation strategy to offset the impact of operation deviation by adjusting the battery output parameters;

[0027] For the fluctuation segment, the control parameters of the temporary environment adaptation strategy are synchronously pushed to the operation-driven module as a reference for updating its operation optimization path.

[0028] Furthermore, the cyclic update process of the environment sensing module also includes:

[0029] Each time the energy consumption control effects of the basic and temporary strategies are compared, the energy consumption optimization rate of the operation-driven module during the same period is extracted synchronously;

[0030] If the energy consumption reduction of the environmental strategy is negatively correlated with the optimization rate of the operational strategy, a collaborative intervention instruction is generated: adding operational restriction parameters to the environmental adaptation strategy and strengthening the corresponding guidance in the operational optimization path;

[0031] After the collaborative intervention is cyclically executed, the energy consumption improvement effects of the two modules are re-evaluated until the collaborative optimization rate of the two reaches the preset standard.

[0032] Furthermore, the riding process is divided into multiple monitoring modules according to the power consumption inducement attributes, specifically:

[0033] Preset operation feature library and environmental feature library. The operation feature library at least contains characteristic parameters of typical operations such as high-frequency start-stop and sudden acceleration. The environmental feature library at least contains environmental parameter thresholds of sudden changes in temperature and humidity and air pressure gradient.

[0034] Compare the matching degree of the current riding data with the feature library in real time. When the matching degree of the operation feature exceeds the first threshold, it is marked as an operation-driven power consumption module; when the matching degree of the environmental feature exceeds the second threshold, it is marked as an environment-sensing power consumption module;

[0035] If the matching degree of both types of features exceeds the corresponding threshold, the weight judgment mechanism is activated to calculate the contribution ratio of the operating characteristics and environmental characteristics to the current energy consumption. The type with the higher proportion is used as the main module type, and the other type is used as the associated module for collaborative monitoring.

[0036] Furthermore, when the analysis and evaluation module calculates the operation impact coefficient, the timing sequence of the riding operation is aligned with the energy consumption fluctuation range in the dynamic characteristic spectrum of power consumption, and the correlation strength between the operation and the energy consumption fluctuation is obtained through cross-correlation analysis. The correlation strength calculation includes the weight of the time difference between the operation start time and the starting point of the energy consumption fluctuation, and then combined with the operation frequency weighting to generate the operation impact coefficient.

[0037] Furthermore, when the analysis and evaluation module calculates the environmental sensitivity coefficient, a spatial gradient analysis is performed on the three-dimensional temperature field distribution to extract the characteristics of temperature fluctuations. Combined with the peak value of the transient response of the internal resistance, the weight of the influence of environmental parameters on the internal resistance change is obtained through multivariate regression. The parameter change amplitude is integrated to generate the environmental sensitivity coefficient, where the weight of the humidity parameter needs to be corrected by air pressure fluctuations.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention solves the problem of traditional electric bicycle battery power monitoring relying only on single power data and ignoring the combined influence of operation and environment. Through multi-module collaborative analysis and dynamic composite threshold construction, it can accurately identify power anomalies, evaluate abnormal power consumption caused by operating habits, and predict battery life risks caused by the environment, thereby improving the accuracy and comprehensiveness of battery power monitoring, helping to extend battery life and ensure riding safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Schematic diagram of an electric bicycle.

[0041] Figure 2 It is a schematic diagram of the system framework structure of the present invention. DETAILED DESCRIPTION

[0042] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] See also Figure 2 This application provides a battery monitoring system for electric bicycles, suitable for Figure 1 Electric bicycles, including:

[0044] The extraction module collects real-time power data, charge and discharge current ripple signals, and voltage transient signals of the electric bicycle battery, and extracts the dynamic characteristic spectrum of power consumption and power change gradient through dynamic time domain analysis;

[0045] Divide the riding process into multiple monitoring modules according to the power consumption inducement attributes. Each module includes an operation-driven power consumption module or an environment-sensing power consumption module.

[0046] The analysis and evaluation module analyzes the dynamic characteristic spectrum of power consumption and the temporal correlation between riding operations in the operation-driven power consumption module, calculates the operation impact coefficient, and evaluates the superposition effect of abnormal power consumption caused by operating habits;

[0047] Monitor the three-dimensional temperature field distribution and internal resistance transient response of the battery in the environmental sensing power consumption module, calculate the environmental sensitivity coefficient based on environmental parameters, and assess the risk of battery life degradation caused by environmental factors;

[0048] The construction module, based on the superposition effect of abnormal power consumption and the risk of battery life attenuation, integrates the operation energy consumption coupling coefficient and the environmental sensitivity coefficient to build a dynamic composite threshold for battery power abnormality;

[0049] The operation module, when the power abnormality threshold exceeds the limit, the module operates separately, and after evaluation and prediction, it is determined according to the strategy whether to remind or evaluate.

[0050] Among them, the data of the electric bicycle is obtained through sensors such as power sensors, transient voltage sensors, temperature sensors and high-frequency current sensors. This is existing technology and will not be elaborated on here.

[0051] Among them, the dynamic characteristic spectrum of power consumption is obtained by converting the curve of power consumption changing with time into frequency domain characteristics through Fourier transform, reflecting the intensity of power fluctuation at different frequencies. For example, the high-frequency fluctuation is enhanced during rapid acceleration.

[0052] Power change gradient: A sliding window algorithm is used to calculate the power difference within a unit time (such as 1 second). A positive value indicates charging (which rarely occurs), and a negative value indicates discharging. A larger absolute value indicates faster power consumption.

[0053] Charge and discharge current ripple signal: The high-frequency AC component contained in the current during battery charging and discharging is generated by the inverter switching action and can be collected by a high-frequency current sensor. Its amplitude and frequency changes reflect the stability of the electrochemical reaction inside the battery.

[0054] Voltage transient signal: The instantaneous fluctuation of battery voltage when the load changes suddenly (such as a sudden increase in motor power during rapid acceleration), usually lasting tens to hundreds of milliseconds, is captured by a high-speed voltage sampling module (sampling rate ≥ 1kHz).

[0055] Operation-driven power consumption module: A monitoring unit for power consumption caused by riding operations (such as sudden acceleration, sudden braking, and frequent starting and stopping). Each unit corresponds to the current / voltage change pattern of a specific operation.

[0056] The working principle of this application is as follows: first, the real-time power data of the electric bicycle battery, the charging and discharging current ripple signal (high-frequency fluctuations superimposed on the current), and the voltage transient signal (instantaneous change in voltage) are collected. Through dynamic time domain analysis (analyzing the change pattern of the signal over time), the dynamic characteristic spectrum of power consumption and the power change gradient are extracted to provide basic data for subsequent analysis. For example, during rapid acceleration, the current ripple signal fluctuation increases, the power change gradient is negative, and the absolute value becomes larger;

[0057] Secondly, the operation-driven power consumption module corresponds to riding operations, such as the acceleration operation module and the braking operation module; the environment-sensing power consumption module corresponds to different environmental conditions, such as high temperature environment module, low temperature environment module, and humid environment module, making power consumption analysis more targeted;

[0058] Next, we analyze the temporal correlation between the dynamic characteristic spectrum of power consumption and the riding operations in the operation-driven power consumption module (such as the change in the characteristic spectrum of power consumption when a sudden acceleration operation occurs), calculate the operation impact coefficient, and evaluate the superimposed effect of abnormal power consumption caused by operating habits (such as the superimposed additional power consumption caused by frequent sudden acceleration).

[0059] The three-dimensional temperature field distribution (temperature distribution at each point inside the battery) and internal resistance transient response (instantaneous change of internal resistance over time) of the battery in the environment-sensing power consumption module are monitored, and the environmental sensitivity coefficient is calculated in combination with environmental parameters (such as external temperature and humidity) to evaluate the risk of battery life degradation caused by environmental factors (such as increased internal resistance and shortened battery life in high temperature environments).

[0060] When the battery level exceeds the abnormal threshold, different modules are activated. The operation-driven module assesses whether operating habits need adjustment; the environmental sensing module assesses whether the environment is causing significant damage to the battery. After this assessment and prediction, a strategic decision is made regarding whether to issue a warning, such as reminding the user to reduce rapid acceleration, or to conduct further evaluation, such as checking whether the battery requires repair.

[0061] The innovation of this technical solution lies in that it solves the problem of traditional electric bicycle battery power monitoring relying only on single power data and ignoring the combined impact of operation and environment. Through multi-module collaborative analysis and dynamic composite threshold construction, it can accurately identify power anomalies, evaluate abnormal power consumption caused by operating habits, and predict battery life risks caused by the environment, thereby improving the accuracy and comprehensiveness of battery power monitoring, helping to extend battery life and ensure riding safety.

[0062] In some embodiments of the present application, when the dynamic composite threshold for abnormal battery charge on an electric bicycle exceeds a set range, simply issuing a reminder to the operation-driven power consumption module without monitoring the optimization results may result in the user's operating habits being difficult to effectively improve, high-energy consumption operations persisting, and the abnormal power consumption problem failing to be fundamentally resolved. For example, after receiving a reminder to reduce sudden acceleration, a user may continue to use their original high-energy-consuming riding style due to the lack of a specific optimization path or failure to monitor the optimization results, causing the abnormal battery state to persist.

[0063] In this regard, the present application further proposes that when the dynamic composite threshold of the abnormal battery power exceeds the set range, the operation-driven power consumption module is executed: the energy consumption fluctuation characteristic values ​​corresponding to each riding operation in the module are first extracted (including the start-stop power consumption increment ratio and the acceleration power transient peak value), and the initial operation optimization path is generated according to the dynamic weight sorting of the energy consumption fluctuation characteristics and intelligent guidance is implemented; after a preset riding period, the energy consumption fluctuation characteristic values ​​are extracted again and compared with the first extracted values ​​to determine whether the energy consumption optimization rate of the high-weight operation meets the preset standard; if not, the dynamic weight is recalculated and the operation optimization path is updated, and the extraction, comparison, and update steps are executed cyclically until the energy consumption optimization rate meets the standard or the riding ends.

[0064] Among them, the start-stop power consumption increment ratio is the ratio of the additional power consumed each time the vehicle is started to the power consumed under normal driving of the same distance. Frequent starting and stopping will significantly increase this ratio, reflecting the degree of additional power consumption caused by the start-stop operation.

[0065] Acceleration power transient peak: refers to the maximum value of the motor output power reached instantly during rapid acceleration. The larger the peak, the stronger the instantaneous impact on the battery and the more severe the power consumption.

[0066] The system then assigns dynamic weights to these energy consumption fluctuations based on their impact on total power consumption (for example, rapid acceleration is weighted higher than gentle acceleration). This weighted ranking generates an initial optimized path. Intelligent guidance is implemented through voice prompts and on-screen animations from the vehicle's system, for example, suggesting that users turn the throttle slowly when accelerating or anticipate road conditions to reduce frequent starting and stopping. This intelligent guidance ensures that these reminders are implemented.

[0067] Next, after a fixed distance or duration, the energy consumption fluctuation characteristic values ​​for each riding operation within the module are extracted again and carefully compared with the initially extracted values. The focus is on determining whether the energy consumption optimization rate of high-weighted operations (those with the greatest impact on energy consumption) meets the preset standard. This step clearly reflects the actual effectiveness of the initial guidance and confirms whether user operating habits are shifting toward lower energy consumption.

[0068] If the energy optimization rate doesn't meet the preset standard, the system re-analyzes the energy impact of each operation and adjusts its dynamic weighting (for example, if frequent starting and stopping, which originally had a low weight, is found to have a greater impact on energy consumption, its weighting will be increased). The optimized path is then updated accordingly, making guidance more targeted. The steps of extracting feature values, comparing optimization results, and updating the path are then repeated until the energy optimization rate reaches the standard or the ride ends.

[0069] The innovation of this technical solution lies in that after the first feature extraction and initial strategy generation, it enters a cyclic process of interval monitoring, optimization rate comparison, and dynamic path update, upgrading static optimization to dynamic adaptation, solving the problem of optimization failure due to changes in riding status after one-time guidance, and promoting the user's operating habits to continuously shift towards low energy consumption, which is conducive to alleviating abnormal battery power consumption, reducing high-load working time, and extending the endurance after a single charge. It can help reduce the loss caused by frequent deep discharge, thereby extending the battery life and improving the overall user experience of electric bicycles.

[0070] In some embodiments of the present application, during the cyclical optimization route update process, if the route is adjusted solely based on the energy consumption characteristics of the current ride, the user's long-standing high-risk operating habits may be overlooked. For example, if the user's repeated rapid acceleration is not specifically controlled, even multiple route updates will not be able to completely correct the abnormal power consumption.

[0071] In this regard, the present application further proposes that, during the cyclic execution process, each time the operation optimization path is updated, the high-energy consumption operation feature library in the historical riding data of the module is synchronously introduced for matching. If the matching degree between the current operation feature and the high-risk feature in the library exceeds the threshold, a forced intervention instruction is added to the generated operation optimization path to prioritize limiting the power output parameters of such high-risk operations.

[0072] Specifically, the high-energy-consumption operation feature library includes operation features that caused significant power anomalies during the user's past riding, such as:

[0073] Sudden acceleration lasting more than 3 seconds (the transient peak value of acceleration power exceeds 1.8 times the rated power); frequent starts and stops occurring more than 5 times within 1 minute (the incremental ratio of start-stop power consumption exceeds 1.6); sudden acceleration when driving at low speed (the power peak suddenly increases when the vehicle speed is less than 10km / h).

[0074] By calculating the similarity between the current operation characteristics (such as the acceleration power curve and start-stop frequency of this ride) and the high-risk characteristics in the library (using the cosine similarity algorithm, with a value range of 0-1), if the matching degree exceeds the threshold (such as 0.7), the current operation is judged to be high-risk.

[0075] If the matching degree between the current operation characteristics and the high-risk characteristics in the library exceeds the threshold, a mandatory intervention instruction is added to the generated operation optimization path to prioritize limiting the power output parameters of such high-risk operations. For example:

[0076] For sudden acceleration operations with excessive matching, the maximum motor power is forced to be limited to 1.2 times the rated power, and the throttle sensitivity is reduced (the proportional coefficient between throttle rotation angle and power output is reduced by 30%).

[0077] For high-risk operations involving frequent starts and stops, the intelligent start-stop assist mode is activated. When it detects that the user is about to start and stop frequently (such as three consecutive brake signals with an interval of less than 5 seconds), the motor idle time is automatically extended by 1-2 seconds to reduce the number of starts.

[0078] The forced intervention instructions are parallel to the original guidance suggestions. For example, when the screen shows that the risk of sudden acceleration is high and the power output has been limited, please accelerate smoothly while maintaining the voice guidance prompts.

[0079] After each matching and intervention, the energy consumption optimization rate of high-risk operations (such as the reduction in power peak during rapid acceleration and the reduction in the number of starts and stops) is recalculated. If the optimization rate exceeds the preset standard for two consecutive times (such as a reduction of more than 30%), the forced intervention of the operation will be temporarily lifted and only the guidance instructions will be retained. If the optimization rate still does not meet the standard, the intervention will be maintained or even strengthened (such as further increasing the power limit ratio to 1 times the rated power) until the end of the cycle or the ride is terminated.

[0080] The innovation of this technical solution lies in solving the problem of insufficient control over high-risk operations caused by relying solely on real-time data in cycle optimization. Through historical feature library matching, users' habitual high-energy-consuming operations can be accurately identified. Combined with mandatory intervention and guidance suggestions, it not only limits the impact of high-risk operations at the parameter level, but also guides users to gradually change their habits. This not only can more quickly alleviate abnormal battery power consumption and help reduce damage to the battery caused by long-term high loads, but also can form a long-term correction of user operating habits, which can help improve the energy consumption stability and safety of electric bicycles, making the optimization effect more lasting and reliable.

[0081] In some embodiments of the present application, when the dynamic composite threshold for abnormal battery charge exceeds a set range, using only a fixed environmental adaptation strategy for the environment-sensing power consumption module can be difficult to cope with dynamic changes in environmental parameters. For example, if the ambient temperature fluctuates significantly over a short period of time, the fixed strategy may not be able to adjust in time, resulting in poor battery energy consumption control and an inability to effectively alleviate the abnormal battery charge condition.

[0082] In this regard, the present application further proposes that when the dynamic composite threshold value of the abnormal battery power exceeds the set range, the environmental sensing power consumption module is executed: real-time tracking of the dynamic change curve of environmental parameters, such as temperature, humidity, and air pressure. Through the continuous collection of data by sensors, a curve of each parameter changing over time is drawn, and then the parameter stable segment and the fluctuating segment are divided according to the amplitude of the curve change. The parameter stable segment refers to the time period in which the environmental parameters change slightly within a certain period of time (such as the temperature change does not exceed ±2°C, and the humidity change does not exceed ±5%); the parameter fluctuating segment refers to the time period in which the environmental parameters change more drastically (such as the temperature changes by more than 5°C within 10 minutes).

[0083] For parameter stability periods, the average synergistic impact of environmental parameter combinations within that period is calculated. The average synergistic impact is an indicator that comprehensively considers the impact of multiple environmental parameters on battery energy consumption. For example, a high temperature and high humidity environment has a greater impact on battery energy consumption than a single high temperature or high humidity environment. Based on the calculated average synergistic impact, a basic environmental adaptation strategy is generated. For example, in an environment with stable and suitable temperature, the battery's normal charge and discharge parameters are maintained without making additional energy control adjustments.

[0084] For periods of parameter fluctuation, the instantaneous values ​​of environmental parameters at peak fluctuations are captured. This refers to the specific values ​​when the environmental parameters reach extreme values ​​(such as maximum temperature or minimum humidity). Based on these instantaneous values, the instantaneous synergistic impact is calculated. This metric reflects the impact of a dramatic change in environmental parameters on battery energy consumption. A temporary environmental adaptation strategy is generated based on the instantaneous synergistic impact. For example, at peak fluctuations due to a sudden rise in temperature, the battery's output power can be temporarily reduced to minimize the additional energy consumption caused by the high temperature.

[0085] Compare the energy consumption control effects of the basic and temporary environment adaptation strategies. This can be measured by the reduction in battery energy consumption over the same time period. If the temporary strategy achieves a greater reduction than the basic strategy, it indicates that the temporary strategy is more targeted at the current fluctuating environmental parameters. The control parameters of the temporary strategy (such as the power adjustment range and duration) are then incorporated into and updated in the basic strategy, enabling it to adapt to long-term changes in environmental parameters.

[0086] Repeat the above steps of tracking dynamic changes in environmental parameters, dividing stable segments into fluctuating segments, calculating synergistic influence, generating and comparing adaptation strategies, and updating basic strategies until the fluctuation range of environmental parameters is less than the set value (for example, temperature change does not exceed ±1°C, humidity change does not exceed ±3%), indicating that the environmental state is stable and no more frequent adjustments are required; or until the end of the ride, at which point the dynamic adaptation process ends.

[0087] The innovation of this technical solution lies in the dynamic adaptation method proposed in this solution. Its improvement lies in: dividing the environmental parameters into stable segments and fluctuating segments, and generating basic strategies and temporary strategies for different stages respectively, which is conducive to avoiding the lag of a single strategy when the parameters suddenly change. At the same time, by continuously comparing the effects of the two strategies and updating the basic strategy, it is helpful to solve the technical problem of optimization failure caused by continuous environmental changes after a one-time adjustment, ensuring that the strategy always matches the real-time environmental status.

[0088] This adjustment method not only maintains reasonable battery energy consumption when environmental parameters change dynamically, but also actively optimizes to reduce abnormal power loss caused by environmental factors, helping to mitigate the impact of extreme environments on battery performance. Compared to traditional modes, its response speed to environmental changes is significantly improved, and the battery's power stability in complex environments is significantly enhanced. In the long term, it also reduces battery degradation caused by environmental factors, further extending battery life and ensuring that e-bikes maintain stable and reliable endurance in diverse environments.

[0089] In some embodiments of the present application, in the prior art, environmental adaptation strategies often simply aim to reduce energy consumption, without fully considering the potential impact of strategy execution on battery health, and are prone to falling into the contradiction of "reduced energy consumption but increased battery damage". For example, in a low-temperature environment, in order to improve battery life, forcibly increasing the battery discharge current will reduce energy consumption fluctuations in the short term, but will accelerate the growth of lithium dendrites inside the battery, resulting in rapid capacity decay; and if the discharge parameters are excessively restricted, the energy consumption control effect will be sacrificed, affecting the riding experience. This one-way optimization mode cannot balance the relationship between energy consumption and battery health, and it is difficult to cope with the differentiated needs of different environmental scenarios.

[0090] In this regard, this application further proposes that during the implementation process, it also includes:

[0091] Each time an environmental adaptation strategy is generated or updated, an adaptive particle filter algorithm analyzes changes in battery health parameters. Battery health parameters include capacity decay rate, internal resistance growth rate, and remaining cycle life percentage. The adaptive particle filter algorithm accurately tracks parameter changes by dynamically adjusting the number of particles (for example, increasing the number of particles to 500 when health fluctuates significantly and reducing it to 200 when stable) to calculate the difference in health status before and after strategy execution.

[0092] If the change exceeds the health fluctuation threshold (e.g., a change in capacity decay rate greater than 0.8%), the current strategy may have an adverse impact on battery health. The correlation between the environmental parameter control range in the strategy and changes in battery health status needs to be analyzed. For example, in a high-temperature environment, a strategy that increases the cooling system power by 30% (control range) results in an increased battery temperature difference. If the correlation with the change in internal resistance growth rate is 0.7 (strong correlation) using the Pearson coefficient, then the control range is considered to have a significant impact on battery health.

[0093] Adjust the control range of environmental parameters based on the correlation, reduce the control range of parameters that have a significant negative impact on battery health, and at the same time increase the control strength of other parameters to ensure energy consumption control effect;

[0094] Adjust the control range of environmental parameters based on the degree of correlation: For parameters with a high degree of correlation (e.g., >0.6) (such as cooling system power), reduce the control range (e.g., from 30% to 20%); while simultaneously increasing the control strength of parameters with a low degree of correlation (e.g., <0.3) (such as charge and discharge cut-off voltage) (e.g., increasing the discharge cut-off voltage from 30V to 31V). This multi-parameter synergy ensures that energy consumption control effectiveness is not reduced. For example, after reducing cooling power, the energy loss can be compensated by optimizing voltage parameters to keep the overall energy consumption reduction within the expected range.

[0095] The adjusted strategy and correlation data (for example, "high temperature + cooling power adjustment range 20% + correlation 0.7") are recorded in the historical case library. The case library is categorized by environmental scenario (such as high temperature and high humidity, low temperature and dryness). When similar scenarios are encountered later, the control solution with the lowest correlation in the case library is directly used as the initial strategy, which helps reduce trial and error costs.

[0096] The improvement of this solution lies in: by introducing dynamic monitoring of battery health status and parameter correlation analysis, a two-way adjustment method for energy consumption control and health protection is established. This not only helps to ensure the strategy's efficient response to environmental changes, but also avoids irreversible damage to the battery. At the same time, it also enables rapid strategy iteration by leveraging a historical case library.

[0097] This overcomes the drawback of the existing technology's one-way optimization of environmental adaptation strategies. By dynamically monitoring battery health and adjusting parameter control ranges, it achieves a balance between energy consumption control and battery health. This not only avoids excessive battery damage caused by the strategy, ensuring stable performance over long-term use, but also ensures effective energy control in complex environments, improving battery life reliability. Furthermore, the use of a historical case library makes strategy generation more efficient, further enhancing the e-bike's adaptability to diverse environments and driving the development of intelligent and refined battery management.

[0098] In some embodiments of the present application, when implementing a dynamic adaptation strategy for an environment-sensing power consumption module, if only the stable and fluctuating phases of environmental parameters are independently processed, while ignoring the coordinated interaction with the operation-driven module, this may lead to a mismatch between the environmental adaptation strategy and the user's operating habits, affecting the overall energy consumption control effect. For example, if a user fails to follow the optimized operation path during the stable environmental phase and continues to accelerate rapidly, the energy consumption control effect will be significantly reduced if the basic environmental adaptation strategy is not adjusted accordingly. Furthermore, if the temporary strategy for the fluctuating environmental phase is not synchronized with the operation module, conflicts between operation and environmental control may occur (e.g., the operation module still allows high power output at high temperatures).

[0099] In this regard, the present application further proposes that when executing the environment-sensing power consumption module, after dividing the parameter into stable and fluctuating segments, the following steps are also included:

[0100] After dividing the parameters into stable and fluctuating periods, the corresponding riding periods of the two periods are recorded in real time (e.g., the stable period is 8:00-8:15, and the fluctuating period is 8:15-8:20). The operation optimization paths generated by the operation-driven power consumption module during the same period are associated with the timestamps (e.g., smooth acceleration guidance from 8:00-8:15, and reduced start-stop guidance from 8:15-8:20), thus establishing a temporal correlation matrix between environmental states and operational behaviors.

[0101] For the parameter stability period, calculate the execution compliance rate of the operation optimization path, that is, the matching degree between the user's actual operation (such as the accelerator handle rotation angle, start and stop frequency) and the guidance instruction. The calculation method is: For example, if the guidance instruction recommends "acceleration handlebar angle ≤ 60%", but 70% of the users' actual acceleration behaviors exceed 60%, the compliance rate is 30%. If the compliance rate is lower than the threshold (such as 50%), it means that the user's operation deviation is significant, and it is necessary to increase the compensation coefficient in the basic environment adaptation strategy: for example, by improving the battery output voltage stability (compensation coefficient 1.2) to offset the current fluctuation caused by sudden acceleration, or adjusting the energy recovery intensity (compensation coefficient 0.8) to reduce the energy loss caused by operation deviation, so as to ensure that the basic strategy can still maintain the expected energy consumption control effect under low user compliance rate;

[0102] During periods of parameter fluctuation, the control parameters of the temporary environmental adaptation strategy (such as limiting the battery's maximum output power to 80% of the rated value during high-temperature fluctuations and enabling enhanced cooling system mode) are simultaneously pushed to the operation-driven module. When updating the optimization path, the operation module uses these parameters as hard constraints. For example, during periods of high-temperature fluctuation, an operation guide is generated that requires the peak acceleration power to be ≤ 80% of the rated power. This prevents conflicts between operational behavior and environmental control and ensures that the energy consumption control goals of the temporary strategy are achieved through operational optimization.

[0103] The innovation of this technical solution lies in resolving the lack of synergy caused by the isolated processing of the environment-sensing module and the operation-driven module. By linking the operation optimization paths of the two time periods, dynamically compensating for operation deviations, and synchronously regulating parameters across modules, it facilitates the linkage of environmental adaptation and operation optimization. This not only improves the effectiveness of the environmental adaptation strategy in complex operation scenarios, but also ensures the consistency of the control direction of multiple modules, further reducing the risk of abnormal battery power consumption, extending battery life, and enhancing the integrity and intelligence of e-bike energy consumption management.

[0104] In some embodiments of the present application, during the cyclic update process of the environment-sensing module, if only the energy consumption control effects of the basic and temporary environment adaptation strategies are compared separately, while ignoring the correlation with the energy consumption optimization rate of the operation-driven module, a contradictory situation may occur in which the environmental strategy is effective but the operational strategy is ineffective, resulting in poor overall energy consumption improvement. For example, the environmental strategy may reduce some energy consumption by adjusting parameters, but the operational module may still have high energy consumption due to the user not following the instructions. The two effects offset each other, and the expected comprehensive energy saving goal cannot be achieved.

[0105] In this regard, the present application further proposes that the cyclic update process of the environment sensing module further includes:

[0106] Each time the energy consumption control effects of the basic and temporary environmental adaptation strategies are compared, the energy consumption optimization rate of the operation-driven module during the same period is extracted simultaneously. The energy consumption optimization rate is calculated by the difference between the user's actual operation energy consumption and the guided operation energy consumption, reflecting the actual effectiveness of the operation strategy. For example, if the environmental strategy reduces energy consumption by a certain percentage, but the operation optimization rate is negative (i.e., the actual operation energy consumption is higher than the guided energy consumption), it indicates that the two are negatively correlated, and the overall energy consumption improvement is offset;

[0107] If the energy consumption reduction of the environmental strategy is negatively correlated with the optimization rate of the operational strategy (for example, the environmental strategy reduces energy consumption by 5%, but the operational strategy increases energy consumption by 3% due to user violations), a coordinated intervention instruction is immediately generated: adding operational restriction parameters to the environmental adaptation strategy, such as reducing the maximum acceleration current from 15A to 12A, limiting the power output of high-energy-consuming operations at the hardware level; at the same time, strengthening corresponding guidance in the operational optimization path, such as increasing the voice warning frequency during sudden acceleration from once every 3 seconds to once every 1 second, and flashing a red warning icon on the screen simultaneously, to enhance users' awareness of operational standards through high-frequency prompts;

[0108] After a cyclical execution of the collaborative intervention, the energy efficiency improvements of both modules are re-evaluated. After each intervention, the energy efficiency reduction of the environmental strategy and the optimization rate of the operational strategy are monitored simultaneously, and the collaborative optimization rate of the two is calculated (i.e., the total energy efficiency reduction under the combined effects of the environmental and operational strategies). For example, after the collaborative intervention, the environmental strategy's energy efficiency decreases by 4%, the operational strategy's optimization rate increases to 3%, and the overall energy efficiency decreases by 7%. This is the current collaborative optimization rate. This process is repeated, with continuous adjustments to the operational constraint parameters and guidance intensity, until the collaborative optimization rate reaches the preset standard (e.g., a total energy efficiency reduction of ≥8%), ensuring a positive synergistic effect between the environmental and operational strategies.

[0109] The improvement of this application is that it solves the contradiction between energy consumption improvement caused by insufficient coordination between the environment-sensing module and the operation-driven module. By dynamically associating the energy consumption data of the two and implementing coordinated intervention, the environmental adaptation strategy and the operation optimization path form a mutually coordinated management and control system. This not only avoids the situation where the optimization effect of a single module is offset by another module, but also helps to improve the stability and effectiveness of the overall energy consumption improvement through two-way intervention. It can also help to cultivate users' low-energy operation habits in specific environments, enhance the coordination and intelligence level of electric bicycle energy consumption management, further extend the driving range, and reduce battery loss.

[0110] In some embodiments of this application, when the riding process is divided into monitoring modules based on power consumption factors, the lack of clear feature comparison standards and multi-factor comprehensive judgment logic can easily lead to module type confusion. For example, when riding simultaneously experiences frequent rapid acceleration (an operational characteristic) and low temperatures (an environmental characteristic), it is impossible to clearly identify the primary power consumption factor, resulting in insufficiently targeted control strategies and affecting energy optimization results.

[0111] In this regard, the present application further proposes to divide the riding process into multiple monitoring modules according to the power consumption inducement attributes, specifically:

[0112] Preset operation feature library and environmental feature library. The operation feature library at least contains characteristic parameters of typical operations such as high-frequency start-stop and sudden acceleration. The environmental feature library at least contains environmental parameter thresholds of sudden changes in temperature and humidity and air pressure gradient.

[0113] The operation signature library collects riding logs from different users (such as the frequency of accelerator and handlebar operation and the interval between starts and stops) to identify typical operations that significantly increase energy consumption (such as high-frequency starts and stops and sudden acceleration). Key parameters such as the current fluctuation amplitude and power change rate of these operations are extracted, and after multiple verifications, the characteristic threshold range is determined.

[0114] The operational signature library focuses on typical high-energy-consuming operations, such as frequent starts and stops and sudden acceleration, and defines their characteristic parameters. The characteristic parameter for frequent starts and stops is set as ≥6 starts and stops within 1 minute, with the peak current exceeding 1.3 times the rated current during each start. The characteristic parameter for sudden acceleration is a power jump from 20% to over 110% of the rated power within 1.5 seconds during acceleration. This accurately captures high-energy-consuming behaviors at the operational level.

[0115] The environmental characteristic library records the changes in battery energy consumption with environmental parameters through comparative experiments under different environmental conditions (high temperature, low temperature, high humidity, air pressure changes, etc.), and screens out the critical values ​​of environmental parameters that have a significant impact on energy consumption (such as the amplitude of temperature fluctuations, air pressure gradient thresholds, etc.), forming environmental characteristic parameter standards.

[0116] The environmental signature library sets specific parameter thresholds for environmental factors that significantly impact battery performance, such as sudden changes in temperature and humidity, and air pressure gradients. The threshold for sudden changes in temperature and humidity is a temperature change of ≥10°C or a relative humidity change of ≥25% within 15 minutes; the threshold for air pressure gradient is an hourly pressure change of ≥8hPa (corresponding to scenarios with rapid altitude changes). These thresholds define the range within which environmental factors significantly impact energy consumption.

[0117] It should be noted that the two types of feature libraries will be continuously updated and optimized as actual application data accumulates to ensure the accuracy and applicability of the feature parameters.

[0118] Compare the matching degree of the current riding data with the feature library in real time. When the matching degree of the operation feature exceeds the first threshold, it is marked as an operation-driven power consumption module; when the matching degree of the environmental feature exceeds the second threshold, it is marked as an environment-sensing power consumption module;

[0119] For operational characteristics, the vehicle controller collects throttle signals, brake signals, and current change curves in real time and compares them with parameters in the operational characteristic library. For example, the number of starts and stops within the current minute and the rate of power change during acceleration are calculated. If the matching degree with the characteristic parameters of high-frequency starts and stops or sudden acceleration exceeds a first threshold (e.g., 80%), the module is marked as an operation-driven power consumption module, indicating that the current energy consumption abnormality is mainly caused by operational behavior.

[0120] For environmental characteristics, real-time environmental data is collected through temperature, humidity, and pressure sensors and compared with thresholds in the environmental feature library. If the ambient temperature rises by 12°C within 15 minutes, and the degree of match with the temperature and humidity fluctuation characteristics exceeds a second threshold (e.g., 75%), the module is marked as an environmentally sensitive power consumption module, indicating that the current energy consumption anomaly is primarily affected by environmental factors.

[0121] If the matching degree of both features exceeds the corresponding threshold, the weighted judgment mechanism is activated, and the energy consumption attribution model is used to calculate the contribution of the operating characteristics and environmental characteristics to the current energy consumption. For example, by analyzing the additional energy consumption during rapid acceleration and the additional energy consumption in a high temperature environment, it is concluded that the contribution of the operating characteristics accounts for 65% and the contribution of the environmental characteristics accounts for 35%.

[0122] The type with the higher contribution is designated as the primary module type, while the other type is designated as an associated module for collaborative monitoring. For example, in the example above, the operational characteristics contribute more, so they are marked as the operation-driven primary module and the environment-sensing associated module. Subsequently, focusing on operational optimization while linking environmental strategies for collaborative management and control helps ensure that the primary factor is highlighted while also addressing secondary factors.

[0123] The innovation of this technical solution lies in establishing clear criteria for judgment by pre-setting an operational signature library containing parameters for high-frequency starts and stops and sudden acceleration, and an environmental signature library containing thresholds for rapid temperature and humidity fluctuations and air pressure gradients. After comparison, modules with operational or environmental signatures exceeding the threshold are marked as either operation-driven or environment-sensing modules, respectively. For cross-cutting scenarios, a weighted judgment mechanism calculates the energy consumption contribution of each factor, distinguishing between primary and associated modules and achieving precise classification of module types. This improvement enables the subsequent development of differentiated strategies for different modules (e.g., operation-driven strategies focus on habit correction, while environment-sensing strategies emphasize parameter adaptation), driving energy management from extensive overall regulation to refined module-based optimization, effectively improving the targeted and overall effectiveness of energy management.

[0124] In some embodiments of the present application, when calculating the operation impact coefficient, the analysis and evaluation module simply counts the relationship between the number of operations and total energy consumption, while ignoring the correlation between the timing of the operations and energy consumption fluctuations. This will lead to inaccurate coefficient calculations. For example, after a user performs a sudden acceleration operation, a significant fluctuation in power consumption may be delayed by several seconds. If this time difference is not taken into account, the impact of the operation on energy consumption will be misjudged, and the operation impact coefficient will not truly reflect the degree of correlation between the operation and energy consumption.

[0125] In this regard, the present application further proposes that when the analysis and evaluation module calculates the operation impact coefficient, it first aligns the timing sequence of the riding operation with the energy consumption fluctuation range in the dynamic power consumption characteristic spectrum. The timing sequence of the riding operation records the time and duration of each operation in chronological order (e.g., a sudden acceleration at 8:00:05 lasts for 2 seconds; a braking at 8:00:10 lasts for 1 second); the energy consumption fluctuation range in the dynamic power consumption characteristic spectrum marks the time period when the power level changes significantly (e.g., a rapid decrease in power level from 8:00:06 to 8:00:09). Through time axis alignment, the time range of the operation occurrence is matched with the time range of the energy consumption fluctuation, ensuring a clear and discernible correspondence between the two in the temporal dimension. The correlation strength between the operation and the energy consumption fluctuation is then determined through cross-correlation analysis. The correlation strength calculation includes a weighted time difference between the time when the operation is initiated and the starting point of the energy consumption fluctuation. The cross-correlation analysis calculates the similarity between the timing sequence of the operation and the energy consumption fluctuation curve, resulting in a correlation value (ranging from 0 to 1). A higher value indicates a closer correlation. At the same time, a time difference weighting is introduced: the smaller the time difference between the start of the operation and the onset of the energy consumption fluctuation (e.g., energy consumption fluctuation occurs within 0.5 seconds after the operation), the higher the weight (e.g., 0.9); the larger the time difference (e.g., fluctuation occurs more than 3 seconds after the operation), the lower the weight (e.g., 0.3). For example, if a rapid battery drop occurs within 1 second of a sudden acceleration operation, the time difference weighting is 0.7. Combined with the correlation coefficient of 0.8 obtained from the cross-correlation analysis, the correlation strength between this operation and the energy consumption fluctuation is 0.8 × 0.7 = 0.56. The operation influence coefficient is then weighted based on the operation frequency. The frequency of similar operations within a unit of time is counted (e.g., 8 sudden acceleration operations within 10 minutes), and higher weights are assigned to more frequent operations (e.g., if the frequency exceeds 5 / 10 minutes, the weighting coefficient is 1.2). The correlation strength of each operation is multiplied by its corresponding frequency weight, then accumulated and normalized (to a range of 0-1) to generate the operation influence coefficient. For example, the average value of the multiple correlation strengths of the sudden acceleration operation is 0.6, and the frequency weight is 1.2. The operation influence coefficient is calculated to be 0.6×1.2=0.72 (after normalization), indicating that the operation has a significant impact on the current energy consumption.

[0126] Specifically, the improvement of the present invention lies in that this calculation method solves the problem of ignoring timing correlation and time difference in traditional operation impact coefficient calculations. By ensuring the time correspondence between operations and energy consumption fluctuations through timing alignment, cross-correlation analysis combined with time difference weights accurately quantifies the strength of the correlation, and then introducing operation frequency weighting to reflect the cumulative impact of high-frequency operations, the generated operation impact coefficient can truly and comprehensively reflect the actual effect of operations on energy consumption. This provides an accurate basis for evaluating the superimposed effects of abnormal power consumption caused by operating habits, making subsequent operation optimization paths more targeted, effectively improving the control accuracy of operation-driven power consumption, helping to reduce the impact of abnormal power consumption on the battery and extend the driving range.

[0127] In some embodiments of the present application, when calculating the environmental sensitivity coefficient, if the analysis and evaluation module only considers environmental parameters such as temperature or humidity, and ignores the mutual influence between these parameters and their combined effect on the battery's internal resistance, the coefficient may not accurately reflect the true impact of the environment on energy consumption. For example, in a high-temperature and high-humidity environment, the effect of humidity on the battery's internal resistance may vary due to air pressure fluctuations. If the humidity weight is not corrected, the environmental sensitivity coefficient will be distorted, affecting the effectiveness of subsequent environmental adaptation strategies.

[0128] In this regard, the present application further proposes that when the analysis and evaluation module calculates the environmental sensitivity coefficient, it first performs a spatial gradient analysis on the three-dimensional temperature field distribution to extract the temperature change characteristics. The three-dimensional temperature field distribution is a three-dimensional temperature model constructed by collecting temperature data at each point from multiple temperature sensors inside the battery pack. The spatial gradient analysis calculates the temperature change rate between different locations (such as the ratio of the temperature difference between adjacent sensors to the distance). When the temperature gradient in a certain area exceeds a set threshold (such as 5°C / cm), it is determined to be a temperature change feature. This feature directly reflects the local energy consumption abnormality caused by uneven temperature distribution inside the battery. Combined with the internal resistance transient response peak, the weight of the environmental parameters on the internal resistance change is obtained through multivariate regression. The internal resistance transient response peak refers to the maximum value that the internal resistance of the battery reaches instantly when the environmental parameters change (such as a sudden temperature rise or a sudden change in humidity). This peak is positively correlated with the battery energy consumption (the larger the internal resistance, the higher the energy consumption). Environmental parameters such as temperature fluctuations, humidity changes, and pressure gradients are used as independent variables, and internal resistance changes are used as dependent variables. Multivariate regression analysis (e.g., least squares) is used to calculate the influence weights of each environmental parameter. For example, the weight of temperature fluctuations is 0.4, the weight of humidity changes is 0.3, and the weight of pressure gradient is 0.2. The remaining 0.1 is an error term. Higher weights indicate a more significant impact of each parameter on internal resistance changes. The magnitude of parameter changes is then integrated to generate an environmental sensitivity coefficient. The weight of the humidity parameter is corrected for pressure fluctuations. The parameter fluctuation magnitude refers to the degree of deviation between the actual value of the environmental parameter and the baseline value (e.g., a 10°C temperature change or a 30% humidity change). The sensitivity value of each parameter is obtained by multiplying the influence weight of each parameter by its corresponding fluctuation magnitude. Because the effect of humidity on battery internal resistance can be affected by pressure fluctuations (e.g., the actual effect of humidity is weakened in high pressure environments), the humidity weight is corrected using the pressure fluctuation coefficient (e.g., for every 10hPa change in pressure, the humidity weight correction factor is adjusted by ±0.05). For example, if the original humidity weight is 0.3 and the correction factor due to pressure fluctuation is 0.9, the corrected humidity weight is 0.3 × 0.9 = 0.27. Finally, all single parameter sensitivity values ​​are accumulated and normalized (range 0-1) to generate the environmental sensitivity coefficient.

[0129] Specifically, this calculation method solves the problem of ignoring the interaction between parameters and spatial distribution characteristics in traditional environmental sensitivity coefficient calculations. It captures local temperature anomalies through three-dimensional temperature field analysis, quantifies the impact of environmental parameters in combination with internal resistance transient response, and then corrects the humidity weight by air pressure. This allows the environmental sensitivity coefficient to comprehensively and accurately reflect the comprehensive effect of the environment on battery energy consumption, effectively improving the control accuracy of environmental-sensitive power consumption, helping to reduce damage to the battery caused by environmental factors and extend battery life.

[0130] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A battery power monitoring system for an electric bicycle, characterized in that: include: The extraction module collects real-time power data, charge and discharge current ripple signals, and voltage transient signals of the electric bicycle battery, and extracts the dynamic characteristic spectrum of power consumption and power change gradient through dynamic time domain analysis; Divide the riding process into multiple monitoring modules according to the power consumption inducement attributes. Each module includes an operation-driven power consumption module or an environment-sensing power consumption module. The analysis and evaluation module analyzes the dynamic characteristic spectrum of power consumption and the temporal correlation between riding operations in the operation-driven power consumption module, calculates the operation impact coefficient, and evaluates the superposition effect of abnormal power consumption caused by operating habits; Monitor the three-dimensional temperature field distribution and internal resistance transient response of the battery in the environmental sensing power consumption module, calculate the environmental sensitivity coefficient based on environmental parameters, and assess the risk of battery life degradation caused by environmental factors; The construction module, based on the superposition effect of abnormal power consumption and the risk of battery life attenuation, integrates the operation energy consumption coupling coefficient and the environmental sensitivity coefficient to build a dynamic composite threshold for battery power abnormality; The operation module, when the power abnormality threshold exceeds the limit, the module operates separately, and after evaluation and prediction, it is determined according to the strategy whether to remind or evaluate.

2. The electric bicycle battery power monitoring system according to claim 1, characterized in that: When the dynamic composite threshold of the abnormal battery power exceeds the set range, the operation-driven power consumption module is executed: the energy consumption fluctuation characteristic values ​​corresponding to each riding operation in the module are first extracted, and the initial operation optimization path is generated according to the dynamic weight sorting of the energy consumption fluctuation characteristics and intelligent guidance is implemented; after the preset riding period, the energy consumption fluctuation characteristic values ​​are extracted again and compared with the first extracted values ​​to determine whether the energy consumption optimization rate of the high-weight operation meets the preset standard; if not, the dynamic weight is recalculated and the operation optimization path is updated, and the extraction, comparison and update steps are executed repeatedly until the energy consumption optimization rate meets the standard or the riding ends.

3. The electric bicycle battery power monitoring system according to claim 2, characterized in that: During the loop execution process, each time the operation optimization path is updated, the high-energy consumption operation feature library in the historical riding data within the module is synchronously introduced for matching. If the matching degree between the current operation feature and the high-risk feature in the library exceeds the threshold, a forced intervention instruction is added to the generated operation optimization path to prioritize limiting the power output parameters of such high-risk operations.

4. The electric bicycle battery power monitoring system according to claim 1, characterized in that: When the dynamic composite threshold of abnormal battery power exceeds the set range, the following operations are executed on the environment-sensing power consumption module: Real-time tracking of the dynamic change curve of environmental parameters, dividing the parameter stable section and fluctuation section; For the parameter stability segment, calculate the average synergistic influence of the environmental parameter combination within the segment and generate the basic environmental adaptation strategy; For parameter fluctuations, capture the instantaneous values ​​of environmental parameters at the peak of fluctuations, calculate the instantaneous collaborative impact, and generate a temporary environmental adaptation strategy; Compare the energy consumption control effects of the basic and temporary strategies. If the energy consumption reduction of the temporary strategy is better than that of the basic strategy, incorporate the control parameters of the temporary strategy into the basic strategy and update it. Repeat the above tracking, division, calculation, comparison and update steps until the fluctuation range of the environmental parameters is less than the set value or the ride ends.

5. The electric bicycle battery power monitoring system according to claim 4, characterized in that: The execution process also includes: Each time the environmental adaptation strategy is generated or updated, the changes in the battery health status parameters are analyzed using the adaptive particle filter algorithm; If the change exceeds the health fluctuation threshold, analyze the correlation between the environmental parameter control range in the strategy and the change in battery health status; Adjust the control range of environmental parameters based on the correlation, reduce the control range of parameters that have a significant negative impact on battery health, and at the same time increase the control strength of other parameters to ensure energy consumption control effect; The adjusted strategy and correlation data are recorded in the historical case library for the initial generation of strategies in similar environmental scenarios in the future.

6. The electric bicycle battery power monitoring system according to claim 5, characterized in that: When executing the environment-sensing power consumption module, after dividing the parameter into stable and fluctuating segments, it also includes: Real-time recording of the riding periods corresponding to the stable and fluctuating parameter periods, and the associated operation optimization paths generated by the operation-driven power consumption module during the same period; Calculate the execution compliance rate of the operation optimization path within the stable period. If the compliance rate is lower than the threshold, add a compensation coefficient to the basic environment adaptation strategy to offset the impact of operation deviation by adjusting the battery output parameters; For the fluctuation segment, the control parameters of the temporary environment adaptation strategy are synchronously pushed to the operation-driven module as a reference for updating its operation optimization path.

7. The electric bicycle battery power monitoring system according to claim 6, characterized in that: The cyclic update process of the environmental sensing module also includes: Each time the energy consumption control effects of the basic and temporary strategies are compared, the energy consumption optimization rate of the operation-driven module during the same period is extracted synchronously; If the energy consumption reduction of the environmental strategy is negatively correlated with the optimization rate of the operational strategy, a collaborative intervention instruction is generated: adding operational restriction parameters to the environmental adaptation strategy and strengthening the corresponding guidance in the operational optimization path; After the collaborative intervention is cyclically executed, the energy consumption improvement effects of the two modules are re-evaluated until the collaborative optimization rate of the two reaches the preset standard.

8. The electric bicycle battery power monitoring system according to claim 1, characterized in that: The riding process is divided into multiple monitoring modules according to the power consumption inducement attributes, specifically: Preset operation feature library and environmental feature library. The operation feature library at least contains characteristic parameters of typical operations such as high-frequency start-stop and sudden acceleration. The environmental feature library at least contains environmental parameter thresholds of sudden changes in temperature and humidity and air pressure gradient. Compare the matching degree of the current riding data with the feature library in real time. When the matching degree of the operation feature exceeds the first threshold, it is marked as an operation-driven power consumption module; when the matching degree of the environmental feature exceeds the second threshold, it is marked as an environment-sensing power consumption module; If the matching degree of both types of features exceeds the corresponding threshold, the weight judgment mechanism is activated to calculate the contribution ratio of the operating characteristics and environmental characteristics to the current energy consumption. The type with the higher proportion is used as the main module type, and the other type is used as the associated module for collaborative monitoring.

9. The electric bicycle battery power monitoring system according to claim 1, characterized in that: When calculating the operation impact coefficient, the analysis and evaluation module aligns the timing sequence of the riding operation with the energy consumption fluctuation range in the dynamic characteristic spectrum of power consumption. The correlation strength between the operation and the energy consumption fluctuation is obtained through cross-correlation analysis. The correlation strength calculation includes the weight of the time difference between the operation start time and the starting point of the energy consumption fluctuation, and then combines the operation frequency weighting to generate the operation impact coefficient.

10. The electric bicycle battery power monitoring system according to claim 1, characterized in that: When calculating the environmental sensitivity coefficient, the analysis and evaluation module performs spatial gradient analysis on the three-dimensional temperature field distribution to extract the characteristics of temperature fluctuations. Combined with the peak value of the transient response of the internal resistance, the weight of the influence of environmental parameters on the internal resistance change is obtained through multivariate regression. The amplitude of the parameter change is integrated to generate the environmental sensitivity coefficient. The weight of the humidity parameter needs to be corrected by air pressure fluctuations.

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