Battery management method and device, electronic equipment and storage medium

Abstract

This invention relates to the field of smart lock technology and discloses a battery management method, device, electronic device, and storage medium. The battery management method proposed in this invention includes: collecting multi-dimensional time-series data of the battery and packaging the multi-dimensional time-series data according to a preset period to form structured data; when the smart lock is in a first state, based on the collected runtime time-series data under several preset operating conditions, fitting and generating a standard discharge health baseline, the standard discharge health baseline including at least an initial attenuation coefficient; generating a dynamic aging attenuation model based on the initial attenuation coefficient and the structured data; calculating the target evaluation parameters of the battery based on the dynamic aging attenuation model and the acquired real-time no-load voltage of the battery, and determining the target management strategy of the battery according to the target evaluation parameters. This effectively solves the problem of premature alarms or unannounced power outages.

Description

Technical Field

[0001] This invention relates to the field of smart lock technology, and more specifically to battery management methods, devices, electronic devices, and storage media. Background Technology

[0002] With the rapid development of the smart home industry, smart door locks have become the mainstream access control devices in both residential and commercial scenarios due to their high security and ease of operation. However, most smart door locks are battery powered, and battery life management directly affects the reliability of the device and the user experience.

[0003] Currently, battery management technology in the industry is still at the stage of static parameter matching and passive result reminders. Due to the significant differences in discharge characteristics of different individual batteries and under different usage conditions, static parameters are difficult to adapt to the actual aging process, often leading to misjudgments such as premature alarms or no warning before the battery is depleted. Summary of the Invention

[0004] This invention provides a battery management method, device, electronic device, and storage medium to solve the problem of misjudgment that may lead to premature alarms or no warning before the battery is depleted.

[0005] In a first aspect, the present invention provides a battery management method applied to a smart lock, the method comprising: Collect multi-dimensional time-series data of the battery and package the multi-dimensional time-series data according to a preset period to form structured data; When the smart lock is in the first state, a standard discharge health baseline is generated based on the collected runtime sequence data under several preset working conditions. The standard discharge health baseline includes at least the initial attenuation coefficient. The first state is the state when the smart lock is powered on for the first time or after the battery is replaced. The preset working conditions include at least the static standby working condition, the wireless wake-up working condition, and the unlocking discharge working condition. A dynamic aging degradation model is generated based on the initial attenuation coefficient and structured data. Based on the dynamic aging degradation model and the real-time no-load voltage of the battery, the target evaluation parameters of the battery are calculated, and the target management strategy of the battery is determined according to the target evaluation parameters.

[0006] In this embodiment of the method, on the one hand, by collecting multi-dimensional time-series data and fitting a standard discharge health baseline including an initial attenuation coefficient when the smart lock is in its first state, and combining this with a dynamic aging attenuation model, the method can adapt to the actual aging trajectory of the battery in real time, effectively solving the problem of misjudgment such as premature alarms or power outages without warning. On the other hand, by collecting several target evaluation parameters under preset operating conditions of the smart lock, the method calculates the target evaluation parameters and determines the target management strategy based on these parameters. This ensures operational reliability while optimizing the battery's operating state, delaying aging attenuation, and thus extending the overall battery life.

[0007] In one alternative implementation, the structured data includes at least the measured voltage values; Based on the initial attenuation coefficient and structured data, a dynamic aging attenuation model is generated, including: Based on the initial decay coefficient, determine the first decay coefficient at the previous update time; Based on the measured voltage value and the first attenuation coefficient in the structured data within a preset time period, the second attenuation coefficient at the current update time is determined; A dynamic aging decay model is generated based on the second decay coefficient.

[0008] In this embodiment of the method, the second attenuation coefficient at the current update time is derived based on the first attenuation coefficient at the previous update time and combined with the current measured voltage value, so as to realize the nonlinear aging trajectory of the battery under actual complex working conditions in real time, and solve the problem that static parameters cannot reflect the true health status of the battery.

[0009] In one alternative implementation, the structured data further includes at least: the update time interval and the initial battery open-circuit voltage; Based on the measured voltage value and the first attenuation coefficient in the structured data within a preset time period, the second attenuation coefficient at the current update time is determined, including: The fitted voltage value is determined based on the first attenuation coefficient, the update time interval, and the initial battery open-circuit voltage. The second attenuation coefficient is calculated based on the difference between the fitted voltage value and the measured voltage value, the preset learning rate, and the first attenuation coefficient.

[0010] In this embodiment of the method, by calculating the fitted voltage value and comparing the fitted voltage value with the measured voltage value, the deviation between theory and reality can be accurately quantified, so that the update of the attenuation coefficient is based on the real error feedback, thereby improving the accuracy of the subsequently constructed dynamic aging attenuation model.

[0011] In one optional implementation, the target evaluation parameter includes at least the aging degradation ratio; Based on the dynamic aging degradation model and the obtained real-time no-load voltage of the battery, the target evaluation parameters of the battery are calculated, including: The aging degradation ratio is calculated based on the difference between the initial battery open-circuit voltage and the real-time open-circuit voltage, as well as the difference between the initial battery open-circuit voltage and the preset battery cut-off discharge voltage.

[0012] In this embodiment of the method, the difference between the initial battery open-circuit voltage and the real-time open-circuit voltage, as well as the difference between the initial battery open-circuit voltage and the preset battery cut-off discharge voltage, are normalized during calculation. This effectively reduces interference generated under dynamic operating conditions and ensures the authenticity and reliability of the calculated aging degradation ratio.

[0013] In one alternative implementation, the target evaluation parameter also includes remaining range; Based on the dynamic aging degradation model and the obtained real-time no-load voltage of the battery, the target evaluation parameters of the battery are calculated, including: The remaining range is calculated based on the difference between the real-time no-load voltage and the battery cut-off discharge voltage, as well as the second attenuation coefficient at the current update time.

[0014] In this embodiment of the method, by selecting the difference between the real-time no-load voltage and the battery cut-off discharge voltage as the basis for calculation, the phenomenon of falsely low voltage caused by voltage drop due to battery internal resistance is effectively avoided under instantaneous high current conditions such as unlocking and communication of smart door locks, thereby improving the accuracy of the calculated remaining battery life.

[0015] In one optional implementation, a target management strategy for the battery is determined based on target evaluation parameters, including: When the remaining battery life is lower than the first preset threshold, a battery life reminder warning will be triggered; If the aging and degradation rate exceeds the second preset threshold, a battery replacement warning will be triggered.

[0016] In this embodiment of the method, by judging the remaining battery life and the aging degradation ratio, an early and accurate warning can be issued before the battery is truly exhausted or severely aged and fails. This effectively avoids the safety hazard of the smart lock being unable to open due to a sudden power outage of the battery, and at the same time prevents safety risks such as leakage and bulging caused by excessive battery aging, greatly improving the security of the smart lock.

[0017] In one alternative implementation, the method further includes: When the smart lock is in the second state, the actual battery usage data is compared with the target evaluation parameters, and the second attenuation coefficient and / or preset learning rate are adjusted according to the comparison results. The second state is the state in which the smart lock runs continuously for a preset number of days.

[0018] In this embodiment of the method, the second attenuation coefficient and the preset learning rate are dynamically adjusted according to the comparison results, so that the dynamic aging attenuation model can be adaptively optimized, the accuracy of the dynamic aging attenuation model can be calibrated periodically, the cumulative error caused by long-term operation can be eliminated, and the evaluation results can always be consistent with the real physical state of the battery.

[0019] In a second aspect, the present invention provides a battery management device, the device comprising: The packaging module is used to collect multi-dimensional time-series data of the battery and package the multi-dimensional time-series data according to a preset period to form structured data; The first generation module is used to fit and generate a standard discharge health baseline based on several collected runtime sequence data under preset working conditions when the smart lock is in the first state. The standard discharge health baseline includes at least the initial attenuation coefficient. The first state is the state when the smart lock is powered on for the first time or after the battery is replaced. The preset working conditions include at least the static standby working condition, the wireless wake-up working condition, and the unlocking discharge working condition. The second generation module is used to generate a dynamic aging decay model based on the initial decay coefficient and structured data. The calculation module is used to calculate the target evaluation parameters of the battery based on the dynamic aging degradation model and the real-time no-load voltage of the battery, and to determine the target management strategy of the battery based on the target evaluation parameters.

[0020] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the battery management method of the first aspect or any corresponding embodiment described above.

[0021] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the battery management method of the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of a first type of battery management method according to an embodiment of the present invention; Figure 2This is a schematic diagram of a second process of the battery management method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the business process for the battery life of a smart lock according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a third process of the battery management method according to an embodiment of the present invention; Figure 5 This is a structural block diagram of a battery management device according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0026] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0027] With the rapid development of the smart home industry, smart locks have become the mainstream access control device in both residential and commercial scenarios due to their high security and ease of operation. Currently, the vast majority of smart locks are battery-powered, and battery life management directly affects the reliability of the device and the user experience.

[0028] Current mainstream smart lock battery management solutions employ a fixed voltage threshold alert mechanism. This involves collecting battery voltage data and triggering a low battery warning when the voltage falls below a preset threshold. Some improved solutions use software to periodically wake the device to update battery data or accumulate power consumption based on unlocking events, thereby optimizing battery life estimation.

[0029] However, overall, battery management technology in the industry is still at the stage of static parameter matching and passive result reminders. It has not been dynamically adapted to the battery aging pattern and users' personalized usage habits, nor has it formed a complete closed loop from data collection and status modeling to proactive maintenance. As a result, it is difficult to solve common industry problems such as inaccurate battery life prediction and sudden power outages from the root.

[0030] Current smart lock battery management methods have the following main defects and shortcomings: 1. Fixed voltage threshold has a high false alarm rate and cannot identify latent aging. Lithium batteries experience capacity decay and increased internal resistance with each charge-discharge cycle, resulting in a significant decrease in actual usable capacity at the same voltage. Fixed voltage judgment cannot identify latent battery aging, frequently causing malfunctions such as "power on display, sudden crash, and power outage during unlocking," seriously affecting equipment safety.

[0031] 2. The general static algorithm has poor adaptability and large battery life estimation error. Different users have great differences in unlocking frequency, number of peripheral device wake-up times, Bluetooth / WiFi dwell time, and ambient temperature. The general static power algorithm cannot adapt to personalized usage habits, resulting in the remaining battery life estimation error generally exceeding 20%, which has limited reference value.

[0032] 3. The solution only provides passive low battery reminders and lacks the ability to predict and proactively maintain power. Traditional solutions only trigger simple reminders before the battery is completely depleted. They do not have the ability to predict the decline trend or have corresponding proactive power-saving maintenance strategies. Users are prone to unexpected problems such as power outages when leaving home or door lock jams, resulting in a poor user experience.

[0033] 4. High-frequency monitoring solutions increase power consumption and exacerbate battery wear. Some improved solutions generate additional power consumption by monitoring in real time and waking up frequently to update the battery level, which accelerates battery discharge and fails to fundamentally optimize the battery life management effect.

[0034] 5. Lack of full life cycle assessment and insufficient operation and maintenance planning. The current solution can only estimate the short-term range after a single charge and cannot assess the overall remaining life of the battery. Users cannot plan the battery replacement time in advance, and it is difficult to achieve full life cycle status traceability of the battery.

[0035] Based on the above, according to an embodiment of the present invention, a battery management method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0036] This embodiment provides a battery management method that can be used in the aforementioned smart lock. Figure 1This is a schematic diagram of a first embodiment of the battery management method according to the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Collect multi-dimensional time-series data of the battery and package the multi-dimensional time-series data according to a preset period to form structured data.

[0037] Here, multidimensional time-series data refers to multiple data points that are continuously collected at different time points and can comprehensively reflect the health status of the battery. These data include at least battery terminal voltage, discharge current, cell operating temperature, and cumulative charge-discharge cycle count. This method does not limit the specific content of multidimensional time-series data.

[0038] The preset period refers to the time set in advance for packaging multi-dimensional time series data, such as 1 hour, 2 hours, etc. In this case, the method does not limit the preset period.

[0039] It should be noted that multidimensional time-series data can be sampled using a preset sampling frequency and a set number of consecutive samples. The preset sampling frequency can be any suitable value between 5 seconds / sample and 30 seconds / sample, and the number of consecutive samples can be any suitable value between 3 and 5. This method does not impose specific limitations on the preset sampling frequency and the number of consecutive samples.

[0040] For example, four types of data—voltage, current, temperature, and cycle count—of the battery are collected at fixed intervals of 10 seconds each. Three data points are read consecutively for each collection, and the median value is taken as the valid value for that sampling point to filter out transient voltage fluctuations. After accumulating valid sampling points for one hour, the arithmetic mean of all valid values ​​is calculated, forming a set of structured data records stored locally.

[0041] Furthermore, during the sampling process, extreme abnormal data such as voltage drop exceeding 0.5V, current exceeding twice the rated load, and temperature exceeding the battery's nominal operating range can be directly eliminated by setting a general abnormal threshold.

[0042] Step S102: When the smart lock is in the first state, a standard discharge health baseline is generated by fitting based on several collected runtime sequence data under preset working conditions. The standard discharge health baseline includes at least the initial attenuation coefficient. The first state is the state of the smart lock when it is powered on for the first time or after the battery is replaced. The preset working conditions include at least the static standby working condition, the wireless wake-up working condition, and the unlocking discharge working condition.

[0043] Here, the first state refers to the state when the smart lock is powered on for the first time or after a battery replacement. In this state, the smart lock's battery is either new or in an unknown state. Since there is no historical battery data, a cold start procedure needs to be performed to establish a standard discharge health baseline.

[0044] The preset operating conditions are pre-set operating states to fully acquire the battery's discharge characteristics under different usage scenarios, including at least static standby, wireless wake-up, and unlocked discharge conditions.

[0045] Among them, the static standby mode is the state in which the smart lock battery is in the lowest power consumption maintenance state when the smart lock is not operated. It corresponds to the battery light-load discharge scenario and reflects the battery's basic self-discharge and standby power consumption.

[0046] The wireless wake-up mode is triggered when the Bluetooth or WiFi module is woken up to communicate, corresponding to a battery discharge scenario, reflecting the additional power consumption when the communication function is enabled.

[0047] The unlocking and discharging condition is when the motor drives the lock tongue to perform the unlocking action, which corresponds to the battery heavy load instantaneous discharge scenario and reflects the battery's voltage drop and recovery characteristics under peak load.

[0048] The data collection ratio for each working condition can be set according to requirements; this method does not impose any restrictions on it.

[0049] The dimensions of the collected runtime timing data can be the same as or different from those of the multi-dimensional timing data. At a minimum, it includes battery terminal voltage, discharge current, cell operating temperature, and cumulative charge-discharge cycle count. Here, this method does not impose any restrictions on the parameters for collecting runtime timing data.

[0050] In this step, the standard discharge health baseline is generated by fitting runtime sequence data collected during the cold start phase, and is a benchmark model that specifically characterizes the standard discharge characteristics of the battery used.

[0051] The specific formula for the standard discharge health baseline of the benchmark model is as follows:

[0052] in, This is the initial open-circuit voltage of the battery under no-load conditions. This is the initial aging degradation coefficient. Standby time This is the standard terminal voltage.

[0053] The initial open-circuit voltage of the battery is the open-circuit voltage of a brand new battery, and it can be obtained in any suitable way.

[0054] Furthermore, the initial aging degradation coefficient and the open-circuit voltage of a brand-new battery can be permanently stored as a benchmark for all subsequent range estimations and aging determinations.

[0055] Step S103: Generate a dynamic aging degradation model based on the initial attenuation coefficient and structured data.

[0056] The initial degradation coefficient is the magnitude of voltage drop of a brand-new battery per unit time (hour), which can be obtained by fitting the collected runtime sequence data through univariate linear regression.

[0057] It should be noted that after a standard discharge health baseline is generated, the smart lock transitions from the cold start phase to the normal operation phase.

[0058] In some implementations, when the smart lock is in normal operation, a dynamic aging degradation model is formed by continuously correcting the current degradation coefficient through incremental updates, so as to reflect the current voltage degradation characteristics of the battery in real time.

[0059] Step S104: Based on the dynamic aging degradation model and the obtained real-time no-load voltage of the battery, calculate the target evaluation parameters of the battery, and determine the target management strategy of the battery according to the target evaluation parameters.

[0060] Here, the real-time no-load voltage refers to the battery's terminal voltage under no-load / very light-load conditions, and it is the core input for range estimation.

[0061] Real-time no-load voltage can be obtained by any suitable method.

[0062] In some implementations, a static rebound sampling method can be used. Taking advantage of the fact that smart locks are in standby mode most of the time, after each high-load operation such as unlocking or Bluetooth wake-up, a delay of several seconds (such as 30 seconds or 40 seconds) is made to allow the voltage to rebound and stabilize. At least one voltage is collected as an unloaded reference. At the same time, the average static standby voltage packaged every hour is combined to output the final real-time unloaded voltage.

[0063] In some implementations, the real-time no-load voltage can be obtained using the load voltage drop compensation method. Specifically, it can be obtained using the no-load voltage calculation formula, which is shown below:

[0064] in, This refers to the real-time terminal voltage of the battery when it is under load (such as motor operation or communication module operation). R is the real-time discharge current of the battery at the current moment, and R is the equivalent internal resistance of the battery at the current moment.

[0065] It should be noted that this can be obtained based on the structured data collected. and R is estimated synchronously using a dynamic aging degradation model.

[0066] In some implementations, the target evaluation parameters are battery evaluation indicators calculated based on a dynamic aging degradation model and real-time no-load voltage, including at least the aging degradation ratio and remaining range.

[0067] In some implementations, the goal management strategy is determined based on goal evaluation parameters.

[0068] In this embodiment of the method, on the one hand, by collecting multi-dimensional time-series data and fitting a standard discharge health baseline including an initial attenuation coefficient when the smart lock is in its first state, and combining this with a dynamic aging attenuation model, the method can adapt to the actual aging trajectory of the battery in real time, effectively solving the problem of misjudgment such as premature alarms or power outages without warning. On the other hand, by collecting several target evaluation parameters under preset operating conditions of the smart lock, the method calculates the target evaluation parameters and determines the target management strategy based on these parameters. This ensures operational reliability while optimizing the battery's operating state, delaying aging attenuation, and thus extending the overall battery life.

[0069] This embodiment provides a battery management method that can be used in the aforementioned smart lock. Figure 2 This is a schematic diagram of a second process of the battery management method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Collect multi-dimensional time-series data of the battery, and package the multi-dimensional time-series data according to a preset period to form structured data. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0070] Step S202: With the smart lock in its first state, based on collected runtime sequence data under several preset operating conditions, a standard discharge health baseline is fitted and generated. The standard discharge health baseline includes at least the initial attenuation coefficient. The first state refers to the smart lock's first power-on or power-on state after battery replacement. The preset operating conditions include at least static standby, wireless wake-up, and unlocking discharge conditions. For details, please refer to [link to details]. Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0071] Step S203: Based on the initial attenuation coefficient and structured data, generate a dynamic aging attenuation model.

[0072] Specifically, the structured data includes at least the measured voltage value; step S203 above includes: Step S2031: Based on the initial attenuation coefficient, determine the first attenuation coefficient at the previous update time.

[0073] Here, the previous update time refers to the time point before the current time when the decay coefficient incremental update was performed. For example, if an incremental update is performed once per hour during normal operation, the previous update time is the hour closest to the current time. That is, if the current time is 3:05 PM, then the previous update time is 2:00 PM.

[0074] The first attenuation coefficient refers to the battery voltage attenuation coefficient calculated and stored at the previous update time.

[0075] In some implementations, if it is the first update of the aging attenuation coefficient after entering normal operation, the initial attenuation coefficient stored in the cold start phase is directly read as the first attenuation coefficient. In some implementations, if it is not the first update, the first decay coefficient obtained in the previous update is read.

[0076] Step S2032: Based on the measured voltage value and the first attenuation coefficient in the structured data within a preset time period, determine the second attenuation coefficient at the current update time.

[0077] Here, the preset time is the pre-defined storage time for structured data.

[0078] In this step, by default, several groups (such as 200 groups, 300 groups, etc.) of valid packaged data from the most recent 7 days are stored. When new data comes in, the oldest data is automatically removed to control storage usage.

[0079] The structured data stores the measured voltage value and the first attenuation coefficient.

[0080] In some optional implementations, the structured data further includes at least: update time interval and initial battery open-circuit voltage; step S2032 above includes: Step a1: Determine the fitted voltage value based on the first attenuation coefficient, the update time interval, and the initial battery open-circuit voltage.

[0081] Here, the update time interval is the length of time between two adjacent attenuation coefficient updates, such as 1 hour, 2 hours, etc. The specific time interval can be set according to the requirements. This method does not limit it.

[0082] The fitted voltage value refers to the theoretically predicted voltage value at the current moment, calculated using the aging decay function.

[0083] Specifically, the aging degradation function is as follows:

[0084] in, This is the initial open-circuit voltage of the battery under no-load conditions. The first decay coefficient at the previous update time. For the update interval, To fit the voltage value.

[0085] It should be noted that the fitted voltage value represents the theoretical voltage value that the battery should reach at the current moment if it continues to decay at the aging rate assessed in the previous round. Therefore, it can be used to determine whether the actual aging rate has changed.

[0086] Step a2: Based on the difference between the fitted voltage value and the measured voltage value, the preset learning rate, and the first attenuation coefficient, the second attenuation coefficient is calculated.

[0087] When the data is updated, only the attenuation coefficient is updated. The second attenuation coefficient can be calculated according to the update formula.

[0088] The updated formula is shown below:

[0089] in, Here, 'a' represents the first decay coefficient at the previous update time, and 'a' represents the preset learning rate. This is the measured voltage value. To fit the voltage value, Standby time This is the second decay coefficient at the current update time.

[0090] It should be noted that standby time refers to the cumulative standby time from the start of operation of the smart lock (or battery), which can be obtained from the collected runtime sequence data. The preset learning rate can be any suitable value within the range of 0.01 to 0.1, such as 0.05, 0.06, etc., and this method does not impose any restrictions on it.

[0091] To further explain, the voltage deviation per unit time (i.e. The first attenuation coefficient is corrected by using a method that accumulates standby time. The longer the cumulative standby time, the more sufficient the data accumulation, and the more stable the magnitude of a single correction becomes.

[0092] In this embodiment of the method, by calculating the fitted voltage value and comparing the fitted voltage value with the measured voltage value, the deviation between theory and reality can be accurately quantified, so that the update of the attenuation coefficient is based on the real error feedback, thereby improving the accuracy of the subsequently constructed dynamic aging attenuation model.

[0093] Step S2033: Generate a dynamic aging decay model based on the second decay coefficient.

[0094] In this step, based on the calculated second attenuation coefficient, the complete dynamic aging attenuation model expression is formed as shown below.

[0095]

[0096] in, This is the initial open-circuit voltage of the battery under no-load conditions. Standby time This is the second decay coefficient at the current update time.

[0097] It should be noted that this model is represented in a parametric manner, requiring only the storage of one dynamic parameter k. n This allows for a complete description of the entire aging curve without the need to store all curve data points. When the range estimation module or other modules need to query the theoretical voltage at any given time, they can simply substitute the t-value for that time into this expression to calculate the voltage.

[0098] To further explain, the aging curve is updated after each incremental update to obtain the second decay coefficient. Since it is essentially a linear decay line that changes with time, it can be fully characterized by the second decay coefficient alone.

[0099] Furthermore, when calculating the theoretical voltage and remaining capacity at any given time, the corresponding values ​​can be directly calculated by substituting them into the above formula, without needing to store the complete curve data. If the door lock has a display screen that requires visualization, a simplified curve can be generated by calculating the voltage values ​​at 3-5 time points and connecting them; the core data is still calculated using the formula.

[0100] In this embodiment of the method, the second attenuation coefficient at the current update time is derived based on the first attenuation coefficient at the previous update time and combined with the current measured voltage value, so as to realize the nonlinear aging trajectory of the battery under actual complex working conditions in real time, and solve the problem that static parameters cannot reflect the true health status of the battery.

[0101] Step S204: Based on the dynamic aging degradation model and the obtained real-time no-load voltage of the battery, calculate the target evaluation parameters of the battery, and determine the target management strategy of the battery according to the target evaluation parameters.

[0102] In some optional implementations, the target evaluation parameter includes at least the aging degradation ratio; step S204 above includes: Step b1: Calculate the aging degradation ratio based on the difference between the initial battery open-circuit voltage and the real-time open-circuit voltage, and the difference between the initial battery open-circuit voltage and the preset battery cut-off discharge voltage.

[0103] Specifically, the aging degradation ratio can be calculated using the aging degradation calculation formula, which is shown below:

[0104] in, This is the real-time no-load voltage. This is the initial open-circuit voltage of the battery under no-load conditions. This is the battery's cutoff discharge voltage. This represents the percentage of aging and degradation.

[0105] Here, real-time no-load voltage refers to the currently measured no-load terminal voltage of the battery, which can be obtained in any suitable way.

[0106] In some implementations, when the smart lock is in the cold start phase, after the first power-on, it can be left to stand still for 30 minutes (without any unlocking, wake-up or other load operations) until the voltage is completely stable. Then, the battery terminal voltage is collected multiple times (e.g., 5 times, 4 times, etc.) and the average value is taken as the measured value of the no-load open circuit voltage of the new battery.

[0107] In some implementations, the nominal open-circuit voltage can be preset according to the battery specifications and then corrected using actual measurement data.

[0108] The battery's cut-off discharge voltage can be preset to a default value according to the battery's manufacturer's specifications and can be automatically matched according to the battery type. For example, 1.0V for alkaline dry cell batteries, 3.0V for lithium-ion batteries, and 2.5V for lithium iron phosphate batteries.

[0109] It should be noted that the battery cutoff discharge voltage is a baseline parameter, which is fixed by default with the battery type and is only adjusted when changing the battery type or calibrating the model.

[0110] In this embodiment of the method, the difference between the initial battery open-circuit voltage and the real-time open-circuit voltage, as well as the difference between the initial battery open-circuit voltage and the preset battery cut-off discharge voltage, are normalized during calculation. This effectively reduces interference generated under dynamic operating conditions and ensures the authenticity and reliability of the calculated aging degradation ratio.

[0111] In some optional implementations, the target evaluation parameter also includes remaining range; step S204 above includes: Step c1: Calculate the remaining range based on the difference between the real-time no-load voltage and the battery cut-off discharge voltage, as well as the second attenuation coefficient at the current update time.

[0112] Here, remaining battery life refers to the remaining usable time estimated based on the current battery state and aging rate. The unit can be days, hours, or other units of time measurement.

[0113] In this step, the remaining battery life can be calculated using the remaining battery life calculation formula, which is shown below:

[0114] in, This is the second decay coefficient at the current update time. This is the real-time no-load voltage. This is the battery's cutoff discharge voltage. This refers to the remaining number of days for continued spaceflight.

[0115] In other implementations, the remaining battery life can be calculated using variations of the above formula; however, this method is not limited to these variations.

[0116] For example, a user has been using a smart lock for about four months. The smart lock has completed a cold start and is tracking battery aging through hourly incremental updates. 30 seconds after a certain unlocking operation, the real-time no-load voltage Vreal = 5.0V is obtained using the static rebound sampling method.

[0117] During the cold start phase, the cutoff discharge voltage Vmin = 4.2V is preset according to the battery specifications (obtained by connecting four 1.0V alkaline batteries in series). Furthermore, the second attenuation coefficient k was calculated and stored in the latest incremental update (2:00 PM). n =0.0013825V / h.

[0118] Calculate the current remaining available voltage range: Vreal Vmin=5.0 4.2 = 0.8V.

[0119] Convert the second attenuation coefficient into daily attenuation: k n ×24=0.0013825×24=0.03318V / day.

[0120] The remaining space travel time is calculated as follows: T = (Vreal) Vmin) / (k n ×24)=0.8 / 0.03318≈24.1 days.

[0121] In this embodiment of the method, by selecting the difference between the real-time no-load voltage and the battery cut-off discharge voltage as the basis for calculation, the phenomenon of falsely low voltage caused by voltage drop due to battery internal resistance is effectively avoided under instantaneous high current conditions such as unlocking and communication of smart door locks, thereby improving the accuracy of the calculated remaining battery life.

[0122] In some optional implementations, step S204 above further includes: Step d1: Trigger a battery life reminder warning when the remaining battery life is below a first preset threshold. Step d2: Trigger a battery replacement warning when the battery degradation rate exceeds a second preset threshold.

[0123] Here, the first preset threshold refers to the remaining critical value of the regular battery life reminder, such as 30 days, 20 days, 1560 hours, etc. The specific value can be set according to needs, and this method does not limit it.

[0124] The second preset threshold refers to the critical value of the aging and degradation ratio that triggers the battery replacement warning, such as 80%, 70%, 65%, etc. The specific value can be set according to the needs, and this method does not limit it.

[0125] Battery replacement warnings and battery life reminders can be triggered in any suitable way, such as controlling LED flashing or voice announcements.

[0126] In some implementations, after calculating the remaining battery life, the remaining battery life is compared with a first preset threshold (e.g., 30 days). If the remaining battery life is ≤30 days, a battery life reminder warning is triggered. This is achieved by pushing a "Battery life is low, it is recommended to prepare to replace the battery" reminder message to the user's terminal via a local LED indicator (e.g., switching to a slow yellow flashing light), voice broadcast (if voice function is available), or Bluetooth / Wi-Fi. This warning continues to trigger until the user replaces the battery.

[0127] In some implementations, after calculating the aging degradation ratio, the ratio is compared with a second preset threshold (e.g., 80%). If the aging degradation ratio is ≥80%, a battery replacement warning is triggered. This is achieved through a local LED indicator (e.g., switching to a fast red flashing light), voice announcement, or by pushing a warning message to the user's terminal stating "The battery is severely aged; please replace it immediately." This warning determines whether the battery is no longer suitable for continued use based on its health status. Even if the remaining battery life is not less than 30 days (e.g., although the voltage is still acceptable, the internal resistance has significantly increased, and the discharge platform has collapsed), a replacement warning is still triggered.

[0128] It should be noted that the specifics can be as follows: Figure 3 As shown, Figure 3 A schematic diagram of the business process for extending the battery life of smart locks.

[0129] In this embodiment of the method, by judging the remaining battery life and the aging degradation ratio, an early and accurate warning can be issued before the battery is truly exhausted or severely aged and fails. This effectively avoids the safety hazard of the smart lock being unable to open due to a sudden power outage of the battery, and at the same time prevents safety risks such as leakage and bulging caused by excessive battery aging, greatly improving the security of the smart lock.

[0130] In some optional implementations, the above method further includes: Step e1: When the smart lock is in the second state, compare the actual battery usage data with the target evaluation parameters, and adjust the second attenuation coefficient and / or the preset learning rate according to the comparison results. The second state is the state in which the smart lock runs continuously for a preset number of days.

[0131] Here, actual usage data refers to the actual battery life from the start of use to the current moment, that is, the actual number of days the battery has been in the device from when it is installed until it is replaced or completely depleted. Actual usage data can be calculated by recording the battery installation timestamp and end timestamp.

[0132] In some implementations, the actual usage data and the remaining range in the target evaluation parameters can be compared and an error rate can be calculated. The error rate can be compared with a preset error threshold (such as 10%, 9%, etc.), and the second attenuation coefficient and / or preset learning rate can be adjusted according to the comparison results.

[0133] For example, when the second state is detected (the smart lock has been running continuously for 7 days), the actual battery life from installation to the present is first obtained. Then, the actual usage data is compared with the estimated remaining battery life within this period, and the error percentage is calculated. If the error is within 10%, the dynamic aging degradation model is considered to be accurate and no adjustment is performed; if the error exceeds 10%, the parameter correction process is triggered.

[0134] In some implementations, if the actual usage data is less than the remaining battery life, the learning rate can be increased appropriately, and the second attenuation coefficient can be adjusted based on the remaining battery life.

[0135] In some implementations, if the actual usage data is not less than the remaining battery life, the learning rate can be appropriately reduced, and the second attenuation coefficient can be adjusted based on the remaining battery life.

[0136] It should be noted that the error can be estimated using the error calculation formula (|T). The error rate is calculated as Tactual| / Tactual×100%.

[0137] In some implementations, when abnormal states such as sudden drop in battery voltage or overcurrent are detected, non-essential loads such as panel backlight and voice broadcast can be automatically turned off, while prioritizing power supply to the core unlocking function to extend battery life in extreme scenarios.

[0138] The specific judgment methods are as follows: Voltage drop judgment: if the voltage drop between two adjacent samples (e.g., 10-second interval) exceeds a preset voltage threshold (e.g., 0.5V), and this condition is met for several consecutive sampling cycles (e.g., 2), it is judged as abnormal. Simultaneously, the unlocking event signal is linked; the instantaneous voltage drop during the unlocking action is considered normal and is not judged as abnormal. Overcurrent judgment: if the real-time discharge current exceeds a preset multiple (e.g., 2 times) of the battery's rated maximum discharge current, and the duration exceeds a preset time threshold (e.g., 100ms), it is judged as an overcurrent anomaly. Instantaneous unlocking high currents with a duration less than the duration threshold (e.g., 500ms) are considered normal and are excluded. In other words, only unexpected and continuous abnormalities will trigger active power-saving protection; instantaneous fluctuations in normal usage scenarios will not trigger it falsely.

[0139] In this embodiment of the method, the second attenuation coefficient and the preset learning rate are dynamically adjusted according to the comparison results, so that the dynamic aging attenuation model can be adaptively optimized, the accuracy of the dynamic aging attenuation model can be calibrated periodically, the cumulative error caused by long-term operation can be eliminated, and the evaluation results can always be consistent with the real physical state of the battery.

[0140] In some implementations, the specific battery management methods are as follows: 1) When the smart lock is powered on for the first time or when a new battery is replaced, the dynamic aging and degradation model is initialized; 2) Collect several sets of multi-condition running sequence data, preprocess them to complete cold start training, and generate a standard discharge health baseline; 3) Baseline parameters are fixed and stored, and the smart lock enters the normal operation stage; 4) Collect multi-dimensional time-series data of the battery at fixed intervals, and complete filtering, anomaly removal and packaging processing; 5) Update the second decay coefficient using an incremental iteration method to generate a dynamic aging decay model; 6) Calculate the aging and decay ratio and the remaining space allowance based on the dynamic aging and decay model, and trigger corresponding graded early warnings; 7) When a battery malfunction is detected, the active power-saving protection strategy is activated; 8) After the cycle ends, the actual usage data is sent back, the parameters of the dynamic aging and decay model are corrected, and the next cycle begins.

[0141] It should be noted that the specifics can be as follows: Figure 4 As shown, Figure 4 This is a schematic diagram of the third battery management method.

[0142] The beneficial effects of this method are as follows: 1) Significantly improves the accuracy of range estimation and solves the pain point of false charge misjudgment. By correcting voltage error through a dedicated baseline and dynamic aging curve, the range estimation error can be stably controlled within ±5% at room temperature, accurately identifying hidden battery aging and false charge status, and avoiding the fault of "showing power but suddenly losing power" from the root.

[0143] 2) Personalized adaptive adaptation eliminates the impact of differences in usage habits. The parameters of the dynamic aging and decay model iterate dynamically with the user's usage habits, automatically adapting to different unlocking frequencies, peripheral usage intensity and ambient temperature. No manual parameter configuration is required, solving the problem of poor adaptability of general algorithms.

[0144] 3) Proactive prediction and maintenance improve equipment reliability. The battery life depletion point can be predicted in advance. With the help of graded warning and active power saving strategies, users can plan charging / battery swapping time in advance, which greatly reduces the probability of sudden power outages and door lock jamming.

[0145] 4) Extremely low computing power and power consumption overhead, with no additional hardware costs. The entire solution adopts a lightweight algorithm, with a single iteration calculation of less than 100 clock cycles. Ordinary 8-bit / 32-bit microcontroller units can run without pressure, without the need to upgrade hardware, and will not significantly increase the standby power consumption of the device. It is compatible with all types of smart locks.

[0146] 5) Closed-loop self-optimization, continuous improvement in accuracy: The model is corrected by reverse correction of actual usage data. The longer the equipment is used and the richer the data accumulation, the higher the estimation accuracy. No manual calibration or cloud maintenance is required.

[0147] 6) Covering the entire lifecycle management, optimizing user experience, while supporting short-term battery life estimation and long-term aging trend assessment, it can realize the traceability of the battery's status throughout the entire lifecycle, allowing users to clearly understand the battery's health status and significantly improve the user experience.

[0148] This embodiment also provides a battery management device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0149] This embodiment provides a battery management device, such as... Figure 5 As shown, it includes: The packaging module 501 is used to collect multi-dimensional time-series data of the battery and package the multi-dimensional time-series data according to a preset period to form structured data.

[0150] The first generation module 502 is used to generate a standard discharge health baseline based on several collected runtime sequence data under preset working conditions when the smart lock is in the first state. The standard discharge health baseline includes at least the initial attenuation coefficient. The first state is the state when the smart lock is powered on for the first time or after the battery is replaced. The preset working conditions include at least the static standby working condition, the wireless wake-up working condition, and the unlocking discharge working condition.

[0151] The second generation module 503 is used to generate a dynamic aging decay model based on the initial decay coefficient and structured data.

[0152] The calculation module 504 is used to calculate the target evaluation parameters of the battery based on the dynamic aging degradation model and the real-time no-load voltage of the battery, and to determine the target management strategy of the battery based on the target evaluation parameters.

[0153] In some optional implementations, the structured data includes at least the measured voltage value; the second generation module 503 includes: The first determination submodule is used to determine the first attenuation coefficient at the previous update time based on the initial attenuation coefficient.

[0154] The second determining submodule is used to determine the second attenuation coefficient at the current update time based on the measured voltage value and the first attenuation coefficient in the structured data within a preset time period.

[0155] The generation submodule is used to generate a dynamic aging decay model based on the second decay coefficient.

[0156] In some optional implementations, the structured data may include at least: update time interval and initial battery open-circuit voltage; The second determination submodule includes: The determination unit is used to determine the fitted voltage value based on the first attenuation coefficient, the update time interval, and the initial battery open-circuit voltage.

[0157] The calculation unit is used to calculate the second attenuation coefficient based on the difference between the fitted voltage value and the measured voltage value, the preset learning rate, and the first attenuation coefficient.

[0158] In some optional implementations, the target evaluation parameter includes at least the aging degradation ratio; the calculation module 504 includes: The first calculation submodule is used to calculate the aging degradation ratio based on the difference between the initial battery open-circuit voltage and the real-time open-circuit voltage, and the difference between the initial battery open-circuit voltage and the preset battery cut-off discharge voltage.

[0159] In some optional implementations, the target evaluation parameter also includes remaining range; the calculation module 504 includes: The second calculation submodule is used to calculate the remaining range based on the difference between the real-time no-load voltage and the battery cut-off discharge voltage, as well as the second attenuation coefficient at the current update time.

[0160] In some alternative implementations, the computing module 504 further includes: The first triggering submodule is used to trigger a battery life reminder warning when the remaining battery life is lower than a first preset threshold.

[0161] The second trigger submodule is used to trigger a battery replacement warning when the aging degradation ratio is higher than the second preset threshold.

[0162] In some alternative embodiments, the apparatus further includes: The comparison module is used to compare the actual battery usage data with the target evaluation parameters when the smart lock is in the second state. Based on the comparison results, the second attenuation coefficient and / or the preset learning rate are adjusted. The second state is the state in which the smart lock runs continuously for a preset number of days.

[0163] The battery management device provided in this embodiment of the invention can execute the battery management method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units described above are the same as in the corresponding embodiments described above, and will not be repeated here.

[0164] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0165] The following is a detailed reference. Figure 6 This diagram illustrates a suitable structural design for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of the electronic device. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0166] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0167] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in the battery management method of the embodiments of the present invention.

[0168] Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0169] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the battery management method shown in the above embodiments is implemented.

[0170] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0171] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A battery management method, characterized in that, Applied to smart locks, the method includes: Collect multi-dimensional time-series data of the battery, and package the multi-dimensional time-series data according to a preset period to form structured data; When the smart lock is in the first state, a standard discharge health baseline is generated by fitting based on several collected runtime sequence data under preset working conditions. The standard discharge health baseline includes at least an initial attenuation coefficient. The first state is the state when the smart lock is powered on for the first time or after the battery is replaced. The preset working conditions include at least a static standby working condition, a wireless wake-up working condition, and an unlocking discharge working condition. Based on the initial attenuation coefficient and the structured data, a dynamic aging attenuation model is generated; Based on the dynamic aging degradation model and the obtained real-time no-load voltage of the battery, the target evaluation parameters of the battery are calculated, and the target management strategy of the battery is determined according to the target evaluation parameters.

2. The method according to claim 1, characterized in that, The structured data includes at least the measured voltage values; The step of generating a dynamic aging degradation model based on the initial attenuation coefficient and the structured data includes: Based on the initial decay coefficient, determine the first decay coefficient at the previous update time; Based on the measured voltage value in the structured data within a preset time period and the first attenuation coefficient, the second attenuation coefficient at the current update time is determined; The dynamic aging degradation model is generated based on the second attenuation coefficient.

3. The method according to claim 2, characterized in that, The structured data also includes at least: update time interval and initial battery open-circuit voltage; The step of determining the second attenuation coefficient at the current update time based on the measured voltage value in the structured data within a preset time period and the first attenuation coefficient includes: Based on the first attenuation coefficient, the update time interval, and the initial battery open-circuit voltage, a fitted voltage value is determined; The second attenuation coefficient is calculated based on the difference between the fitted voltage value and the measured voltage value, the preset learning rate, and the first attenuation coefficient.

4. The method according to claim 3, characterized in that, The target evaluation parameters include at least the aging degradation ratio; The calculation of the target evaluation parameters of the battery based on the dynamic aging degradation model and the obtained real-time no-load voltage of the battery includes: The aging degradation ratio is calculated based on the difference between the initial battery open-circuit voltage and the real-time open-circuit voltage, and the difference between the initial battery open-circuit voltage and the preset battery cut-off discharge voltage.

5. The method according to claim 4, characterized in that, The target evaluation parameters also include remaining battery range; The calculation of the target evaluation parameters of the battery based on the dynamic aging degradation model and the obtained real-time no-load voltage of the battery includes: The remaining range is calculated based on the difference between the real-time no-load voltage and the battery cut-off discharge voltage, as well as the second attenuation coefficient at the current update time.

6. The method according to claim 5, characterized in that, The step of determining the target management strategy for the battery based on the target evaluation parameters includes: If the remaining battery life is lower than a first preset threshold, a battery life reminder warning will be triggered. If the aging degradation ratio exceeds a second preset threshold, a battery replacement warning is triggered.

7. The method according to claim 3, characterized in that, The method further includes: When the smart lock is in the second state, the actual usage data of the battery is compared with the target evaluation parameters, and the second attenuation coefficient and / or the preset learning rate are adjusted according to the comparison results. The second state is the state in which the smart lock runs continuously for a preset number of days.

8. A battery management device, characterized in that, The device includes: The packaging module is used to collect multi-dimensional time-series data of the battery and package the multi-dimensional time-series data according to a preset period to form structured data; The first generation module is used to fit and generate a standard discharge health baseline based on several collected runtime sequence data under preset working conditions when the smart lock is in the first state. The standard discharge health baseline includes at least an initial attenuation coefficient. The first state is the state when the smart lock is powered on for the first time or after the battery is replaced. The preset working conditions include at least a static standby working condition, a wireless wake-up working condition, and an unlocking discharge working condition. The second generation module is used to generate a dynamic aging decay model based on the initial decay coefficient and the structured data. The calculation module is used to calculate the target evaluation parameters of the battery based on the dynamic aging degradation model and the real-time no-load voltage of the battery, and to determine the target management strategy of the battery based on the target evaluation parameters.

9. An electronic device, characterized in that, include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the battery management method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the battery management method according to any one of claims 1 to 7.

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