Charging control method for consumer electronic device and related apparatus

Abstract

The embodiment of the application provides a charging control method for a consumer electronic device and related devices, and relates to the battery management technical field of the consumer electronic device. The method is to acquire a current battery operation state and external environment information; based on the current battery operation state and the external environment information, a comprehensive reward function is constructed; a preset charging strategy is adjusted according to the comprehensive reward function, and an adjusted charging strategy is obtained; the battery charging parameters are adjusted according to the adjusted charging strategy, and charging is performed according to the adjusted battery charging parameters, so that the overall performance of the charging process is improved, the adaptability of the system to external environment and working condition changes is enhanced, the battery life is prolonged, the energy utilization efficiency is improved, and a more intelligent, flexible and personalized charging experience is provided for users.

Description

Technical Field

[0001] This invention relates to the field of battery management technology for consumer electronic devices, and more specifically, to a charging control method and related apparatus for consumer electronic devices. Background Technology

[0002] As consumer electronics products continue to evolve towards higher performance and thinner designs, users are increasingly demanding longer battery life and faster charging efficiency. Meanwhile, lithium batteries, as the mainstream energy storage component, are significantly affected by the charging method. Improper charging methods can not only accelerate battery aging but also pose safety hazards and increase users' electricity costs.

[0003] Currently, most electronic devices use fixed rules or preset curves for charging control. For example, when the battery temperature exceeds a certain threshold (such as 45°C), the charging current is reduced; trickle charging mode is entered after the battery reaches 80% capacity; and devices that support fast charging protocols initiate high-voltage fast charging when the original charger is detected.

[0004] While existing methods can ensure basic safety and charging speed to a certain extent, they have significant limitations. Preventing overheating or overvoltage charging by setting fixed temperature or voltage limits is a passive response, unable to intervene proactively based on battery health trends. For example, continuing high-speed charging with the original strategy when battery capacity is continuously decreasing will further exacerbate the aging risk. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a charging control method and related apparatus for consumer electronic devices.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of the present invention are as follows: In a first aspect, the present invention provides a charging control method for consumer electronic devices, the method comprising: Obtain current battery operating status and external environment information; Based on the current battery operating status and the external environment information, a comprehensive reward function is constructed; The preset charging strategy is adjusted according to the comprehensive reward function to obtain the adjusted charging strategy; Adjust the battery charging parameters according to the adjusted charging strategy, and charge the battery according to the adjusted battery charging parameters.

[0007] Optionally, the comprehensive reward function includes at least one of a safety constraint term, a user experience adaptation term, and an economic incentive term; the current battery operating status includes: the current battery temperature, the current discharge current, and a historical full-charge capacity sequence; the external environment information includes: the current time, the peak-valley electricity price division rules of the region, and a historical sequence of the charger being plugged in after full charge. The step of constructing the comprehensive reward function based on the current battery operating state and the external environment information includes: The safety constraint term is constructed based on the current battery temperature and the historical full charge capacity sequence; And / or, construct the user experience adaptation item based on the current discharge current and the historical sequence of the charger being plugged in after full charge; And / or, construct the economic incentive item based on the current time and the peak-valley electricity price division rules of the region.

[0008] Optionally, the safety constraints include a capacity decay trend penalty and / or a high-temperature charging penalty; The step of constructing the safety constraint term based on the current battery temperature and the historical full-charge capacity sequence includes: Using the historical full-charge capacity sequence, the change in capacity difference is obtained, and based on the change in capacity difference, the capacity decay trend penalty term is derived; and / or, If the current battery temperature is greater than the preset battery safe operating temperature threshold, then the high-temperature charging penalty item is obtained using the current battery temperature, the preset battery safe operating temperature threshold, and the preset temperature penalty coefficient.

[0009] Optionally, the historical full charge capacity sequence includes the most recent N full charge capacities, where N is an integer greater than or equal to 3; The step of obtaining the change in capacity difference using the historical full-capacity sequence includes: Calculate the difference between the Nth full charge capacity and the (N-1th)th full charge capacity sequentially to obtain multiple capacity difference values; The change in capacity difference is obtained based on the multiple capacity differences.

[0010] Optionally, the user experience adaptation items include high-load usage reward items and / or mobile usage scenario matching reward items, and the step of constructing the user experience adaptation items based on the current discharge current and the historical sequence of the charger being plugged in after full charge includes: The high-load usage reward is obtained using the current discharge current and the preset current reward coefficient; and / or, If it is determined from the historical sequence of the time the charger is plugged in after full charge that the user is in a frequent mobile usage scenario, then a scenario matching flag item is generated; Based on the scene matching flag and the preset scene matching coefficient, the mobile usage scene matching reward item is obtained.

[0011] Optionally, the step of determining whether the user is in a frequent mobile usage scenario based on the historical sequence of the charger being plugged in after a full charge includes: If the historical sequence of the charger being plugged in after full charge shows a preset trend, it is determined that the user is in a frequent mobile usage scenario.

[0012] Optionally, the economic incentive includes a peak-valley electricity price response reward, and the step of constructing the economic incentive based on the current time and the peak-valley electricity price allocation rules of the region includes: Based on the current time and the peak-valley electricity price division rules of the region, determine the electricity stage to which the current time belongs and the normalized unit electricity price corresponding to the current time; If the electricity period is a low-valley electricity price period, then the peak-valley electricity price response reward item is obtained by using the normalized value of the unit electricity price and the preset electricity price response coefficient.

[0013] In a second aspect, the present invention provides a charging control device for consumer electronic devices, the device comprising: The acquisition module is used to acquire the current battery operating status and external environment information; The construction module is used to construct a comprehensive reward function based on the current battery operating state and the external environment information; The adjustment module is used to adjust the preset charging strategy according to the comprehensive reward function to obtain the adjusted charging strategy; adjust the battery charging parameters according to the adjusted charging strategy; and charge the battery according to the adjusted battery charging parameters.

[0014] Thirdly, the present invention provides a consumer electronic device, including a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor can execute the machine-executable instructions to implement the charging control method for a consumer electronic device described in the first aspect above.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the charging control method for a consumer electronic device as described in the first aspect above.

[0016] The charging control method and related apparatus for consumer electronic devices provided in this invention include: acquiring current battery operating status and external environment information; constructing a comprehensive reward function based on the current battery operating status and external environment information; adjusting a preset charging strategy according to the comprehensive reward function to obtain an adjusted charging strategy; adjusting battery charging parameters according to the adjusted charging strategy; and charging according to the adjusted battery charging parameters. Because this invention constructs a comprehensive reward function based on the current battery operating status and external environment information to optimize the battery charging strategy, it improves the overall performance of the charging process while ensuring battery operating safety. By dynamically adjusting charging parameters, it enhances the system's adaptability to changes in the external environment and operating conditions, extends battery life, improves energy utilization efficiency, and provides users with a more intelligent, flexible, and personalized charging experience.

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This figure shows a schematic block diagram of a consumer electronic device provided by an embodiment of the present invention; Figure 2 A schematic flowchart of a charging control method for consumer electronic devices provided by an embodiment of the present invention is shown. Figure 3 This diagram illustrates a functional block diagram of a charging control device for consumer electronic devices according to an embodiment of the present invention.

[0020] Icons: 100 - Consumer electronic device; 110 - Memory; 120 - Processor; 130 - Communication module; 200 - Charging control device; 201 - Acquisition module; 202 - Construction module; 203 - Adjustment module. Detailed Implementation

[0021] 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, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0023] It should be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0024] With the widespread adoption of consumer electronics and battery technology, users are increasingly demanding higher standards for battery lifespan, charging safety, energy efficiency, and user experience. Traditional battery charging management technologies primarily rely on fixed charging parameter configurations and preset charging strategies, such as constant current-constant voltage (CC-CV) charging modes or time / capacity threshold-based phased control methods. These methods are typically designed based on ideal operating conditions and do not fully consider the complex and ever-changing environmental factors and differences in user behavior during actual use.

[0025] Currently, battery management systems (BMS) in mainstream consumer electronics devices generally employ static rules for charging control. For example, when the battery temperature exceeds a certain fixed threshold (such as 45°C), the system reduces the charging current to prevent overheating; or it automatically stops charging once the battery reaches 100% charge to avoid the risk of overcharging. However, such strategies lack dynamic adaptability and cannot be optimized in a coordinated manner based on multi-dimensional scenarios such as electricity price fluctuations at different times, users' mobile work needs, and battery aging trends.

[0026] In particular, there are significant technical limitations in the following aspects: 1. Poor environmental adaptability: Existing charging algorithms struggle to sense and respond to changes in the external environment in real time. For example, prolonged connection to a charger in a high-temperature environment can cause the battery to remain under high stress, accelerating electrolyte decomposition and electrode material degradation, thereby significantly shortening battery cycle life. Although existing technologies have introduced temperature protection mechanisms, their response methods are mostly "switching" cut-off or current limiting, lacking the ability to predict temperature rise trends and provide gradual adjustment.

[0027] 2. Lack of Economic Efficiency: Currently, most consumer electronic devices do not incorporate electricity market price signals into their charging decision-making process. While peak-valley pricing mechanisms have been widely implemented on the grid side, with off-peak electricity prices potentially only 30%-50% of peak prices, users still tend to charge immediately, failing to leverage price differences to optimize electricity costs. This not only increases users' electricity expenses but also hinders peak shaving and valley filling of the grid load and the absorption of clean energy.

[0028] 3. Disjointed User Experience: Modern users frequently switch between various scenarios such as mobile work, commuting, and staying at home, placing higher demands on the flexibility and availability of charging. For example, in scenarios requiring frequent outings for work, users prefer devices to be efficiently recharged in a short time, rather than aiming for a full charge. However, existing systems lack the ability to learn and recognize usage patterns, and cannot dynamically adjust charging goals and rates based on user habits.

[0029] 4. Lack of long-term health maintenance mechanism: Battery capacity gradually decreases with increasing usage, but traditional BMS can only passively reflect the current capacity and cannot actively intervene to slow down the degradation process. Although some advanced solutions attempt to extend lifespan through periodic calibration or shallow charge / discharge strategies, these strategies are mostly set offline and lack online adaptive adjustment capabilities based on actual usage data.

[0030] In recent years, artificial intelligence technology, especially reinforcement learning (RL), has shown great potential in areas such as resource scheduling and intelligent control. Existing research has attempted to apply reinforcement learning to energy management in electric vehicles or to optimize the charging and discharging of energy storage systems. However, mature applications have not yet been seen in the small lithium-ion battery scenarios for consumer electronics. The main reason is that consumer devices have limited computing power, are power-sensitive, and require high-frequency real-time responses while ensuring safety. This places higher demands on the lightweight, stability, and interpretability of algorithms.

[0031] In summary, existing battery charging technologies for consumer electronic devices generally suffer from insufficient adaptability to various scenarios and difficulties in synergistically optimizing safety, economy, and user experience.

[0032] To overcome the shortcomings of the prior art, embodiments of the present invention provide a charging control method and related apparatus for consumer electronic devices, which will be described in detail below.

[0033] Please refer to Figure 1This is a block diagram of a consumer electronic device 100. The consumer electronic device 100 can be a portable electronic terminal such as a smartphone, tablet computer, thin and light laptop, smartwatch, or wireless earphone charging case. All of these devices have a built-in battery management system sensor array and operating system-level log interface, which can obtain the status and environmental information required by the embodiments of this invention in real time.

[0034] The consumer electronic device 100 includes a memory 110, a processor 120, and a communication module 130. The memory 110, processor 120, and communication module 130 are electrically connected to each other directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses or signal lines.

[0035] The memory 110 is used to store programs or data. The memory 110 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0036] The processor 120 is used to read / write data or programs stored in the memory 110 and to perform corresponding functions.

[0037] The communication module 130 is used to establish a communication connection between the consumer electronic device 100 and other communication terminals through the network, and to send and receive data through the network.

[0038] It should be understood that, Figure 1 The structure shown is only a schematic diagram of the consumer electronic device 100. The consumer electronic device 100 may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown. Figure 1 The components shown can be implemented using hardware, software, or a combination thereof.

[0039] Please refer to Figure 2 The charging control method for consumer electronic devices provided in this embodiment of the invention includes steps S101 to S104.

[0040] S101, obtain the current battery operating status and external environment information.

[0041] The current battery operating status includes the current battery temperature, current discharge current, and historical full charge capacity sequence.

[0042] External environmental information includes the current time, the peak and off-peak electricity pricing rules of the region (e.g., peak hours are 8:00–11:00 and 13:00–22:00, and off-peak hours are 23:00–7:00 the next day), and the historical sequence of the time the charger is plugged in after a full charge. This information is used to analyze whether users frequently unplug the charger immediately after the battery is fully charged or keep it connected for a long time, thereby inferring their usage habits.

[0043] The aforementioned data can be collected and cached in real time through modules such as built-in sensors, BMS, operating system event logs, and network-synchronized electricity price databases in consumer electronic devices. S102, based on the current battery operating status and external environment information, constructs a comprehensive reward function.

[0044] After obtaining the aforementioned state and environmental information, this embodiment of the invention constructs a multi-level, multi-objective comprehensive reward function based on a reinforcement learning framework to guide the optimization direction of the charging strategy. This comprehensive reward function includes at least one of the following sub-items: a safety constraint term, an economic incentive term, and a user experience adaptation term. The overall feedback signal is formed by summing these sub-items.

[0045] Among them, the safety constraint term represents the non-linear penalty that should be imposed on charging behavior that violates the safety boundary; the economic incentive term refers to the conversion of time-of-use electricity price (peak / flat / valley) into the revenue weight of charging decision, which is used to drive the battery to actively store energy during low electricity price periods, so as to achieve the dual goals of minimizing user electricity costs and flexibly adjusting grid load; the user experience adaptation term represents the scenario-based positive incentive corresponding to the user behavior pattern, which is used to dynamically increase the charging response priority and solve the power anxiety problem in the "plug and play" scenario.

[0046] The following sections will introduce the construction process of security constraints, economic incentives, and user experience adaptation items.

[0047] In this embodiment of the invention, safety constraints can be constructed based on the current battery temperature and historical full-charge capacity sequence. These safety constraints include a capacity decay trend penalty term and / or a high-temperature charging penalty term.

[0048] The construction process for the capacity decay trend penalty term can be as follows: using the historical full-charge capacity sequence, obtain the change in capacity difference, and based on the change in capacity difference, obtain the capacity decay trend penalty term.

[0049] Further, the historical full charge capacity sequence includes the most recent N full charge capacities, where N is an integer greater than or equal to 3. The implementation process of "obtaining the change amount of capacity difference using the historical full charge capacity sequence" can be as follows: calculate the difference between the Nth full charge capacity and the (N - 1)th full charge capacity in sequence to obtain multiple capacity differences; based on the multiple capacity differences, obtain the change amount of capacity difference.

[0050] Exemplarily, assume N is 3, Full_Capacity1 is the most recent full charge capacity, Full_Capacity2 is the second most recent full charge capacity, and Full_Capacity3 is the third most recent full charge capacity. Calculate the difference between adjacent full charge capacities in sequence: Delta_1 =Full_Capacity1 -Full_Capacity2 Delta_2 = Full_Capacity2 - Full_Capacity3 The capacity attenuation trend penalty term can be expressed as: R_capacity = -α*(Delta_1 - Delta_2) where α > 0, and α is the capacity attenuation weight coefficient, which is used to adjust the sensitivity of the charging strategy to the capacity attenuation trend. Its value range can be 0.1 to 0.5. Exemplarily, in the scenario of mobile office with a laptop, α can be 0.3. [[ID=&15]]

[0051] Delta_1 < Delta_2 indicates that the capacity attenuation rate is accelerating, which is an adverse trend and triggers the penalty mechanism.

[0052] If Delta1 ≥ Delta2, it indicates that the capacity difference decreases or tends to be stable. Then, according to the formula of the capacity attenuation trend penalty term above, the capacity attenuation trend penalty term approaches zero or even turns into a positive reward, encouraging the maintenance of healthy charging behavior.

[0053] For the high-temperature charging penalty term, its construction process can be as follows: if the current temperature of the battery is greater than the preset battery safe operating temperature threshold, then use the current temperature of the battery, the preset battery safe operating temperature threshold, and the preset temperature penalty coefficient to obtain the high-temperature charging penalty term.

[0054] When it is detected that the current temperature T_current of the battery exceeds the preset safe operating temperature threshold T_safe (usually set to 40°C), a non-linear temperature penalty is imposed to strengthen the suppression of extreme temperature rise states. For example, a penalty function in the form of a square is adopted: R_temp = -β * max(0, T_current - T_safe)^2 Where β>0, β is the temperature penalty coefficient, and its value can range from 0.3 to 1.0. For example, in a mobile office scenario using a laptop, β can be 0.5. By setting a high-temperature charging penalty, the charging rate is only slightly suppressed when the temperature is slightly exceeded, while the charging power is significantly reduced when the temperature is severely exceeded, thus avoiding the risk of thermal runaway.

[0055] Understandably, when the safety constraint includes a capacity decay trend penalty term, the safety constraint can be expressed as: R_safe = R_capacity.

[0056] When the safety constraint includes a high-temperature charging penalty, the safety constraint can be expressed as: R_safe = R_temp.

[0057] When the safety constraint includes a capacity decay trend penalty and a high-temperature charging penalty, the safety constraint can be expressed as: R_safe = R_capacity + R_temp.

[0058] For user experience adaptation items, embodiments of the present invention can construct user experience adaptation items based on the current discharge current and the historical sequence of charger plug-in duration after full charge. User experience adaptation items include high-load usage reward items and / or mobile usage scenario matching reward items.

[0059] The process for constructing a high-load usage reward item can be as follows: use the current discharge current and a preset current reward coefficient to obtain the high-load usage reward item.

[0060] In other words, the current discharge current C_current reflects the current load intensity of the device. If C_current is large (e.g., exceeding 2A or 3A), it indicates that the user is running high-performance applications (such as games, video editing, etc.), and the user has a higher demand for rapid power replenishment. Therefore, a positive reward mechanism is introduced: R_current = γ * max(0, C_current) Where γ>0, γ is the current reward coefficient, used to convert the current magnitude into a reward value, incentivizing the system to prioritize charging needs under high load scenarios. The value of γ can range from 0.1 to 0.3; for example, in a laptop mobile office scenario, γ can be 0.2.

[0061] The construction process for the mobile usage scenario matching reward item can be as follows: if it is determined that the user is in a frequent mobile usage scenario based on the historical sequence of the time the charger is plugged in after full charging, then a scenario matching flag item is generated; based on the scenario matching flag item and the preset scenario matching coefficient, the mobile usage scenario matching reward item is obtained.

[0062] Furthermore, the implementation process of "determining that the user is in a frequent mobile usage scenario based on the historical sequence of the charger being plugged in after a full charge" can be as follows: if the historical sequence of the charger being plugged in after a full charge shows a preset trend, then it is determined that the user is in a frequent mobile usage scenario.

[0063] In other words, by analyzing the historical sequence of charger plug-in duration after full charge, typical user patterns are identified. If the sequence shows a trend of "short-term connection, frequent plugging and unplugging" (e.g., average plug-in time less than 30 minutes), it is determined that the user is in a frequent mobile work or travel scenario, and a scenario matching flag, strategy_matches_scene=1, is generated; otherwise, it is set to 0. Based on this, the mobile usage scenario matching reward item is obtained: R_scene = ω * (strategy_matches_scene) Where ω>0, ω is the scene matching coefficient, and its value can range from 0.05 to 0.2. For example, in a mobile office scenario using a laptop, ω can be 0.1. This mechanism encourages the system to appropriately increase the charging speed or delay the trickle charging end time when a mobile scenario is detected, in order to enhance usability.

[0064] Understandably, when the user experience adaptation includes high-load usage rewards, the user experience adaptation can be represented as: R_experience = R_current.

[0065] When the user experience adaptation item includes mobile usage scenario matching reward items, the user experience adaptation item can be represented as: R_experience = R_scene.

[0066] When the user experience adaptation items include high-load usage reward items and mobile usage scenario matching reward items, the user experience adaptation items can be represented as: R_experience = R_current + R_scene.

[0067] For the economic incentive items, embodiments of the present invention can construct the economic incentive items based on the current time and the peak-valley electricity price allocation rules of the region. The economic incentive items include peak-valley electricity price response rewards.

[0068] The construction process for peak-valley electricity price response reward items can be as follows: Based on the peak-valley electricity price division rules of the current time and the region, determine the electricity phase to which the current time belongs and the normalized unit electricity price value corresponding to the current time; if the electricity phase is the off-peak electricity price period, then use the normalized unit electricity price value and the preset electricity price response coefficient to obtain the peak-valley electricity price response reward items.

[0069] In other words, the peak-valley electricity price response reward item R_price guides the system to actively charge during periods of lower electricity prices, reducing users' electricity costs and promoting grid load balancing.

[0070] The system determines the current electricity phase (peak, flat, or low) based on the peak-valley electricity pricing rules for the current region. It also obtains the normalized value p of the unit electricity price for the current time period, where p=0 represents the lowest price and p=1 represents the highest price.

[0071] If the current electricity price is during off-peak hours, a positive reward will be given: R_price = σ * (1 - electricity_price) Wherein, σ is the electricity price response coefficient, used to adjust the system's sensitivity to electricity price differences. Its value ranges from 0.05 to 0.2. For example, in a laptop mobile office scenario, σ can be 0.1. This design makes the system tend to complete the main charging tasks late at night or during off-peak electricity consumption periods, achieving the dual benefits of energy saving, cost reduction, and social emission reduction.

[0072] It should be noted that in the embodiments of the present invention, the values ​​of α, β, γ, ω and σ must meet the priority of the corresponding scenario. For example, in the mobile office scenario of a laptop (i.e. a business laptop that is frequently used outside), the priority of α, β, γ, ω and σ is β (security) > α (lifespan) > γ (instant performance) > ω (scenario adaptability) ≈ σ (economy).

[0073] In this embodiment of the invention, if the comprehensive reward function includes only the safety constraint term, then the comprehensive reward function can be expressed as R_total=R_safe.

[0074] If the total reward function only includes the user experience adaptation item, then the total reward function can be expressed as R_total = R_experience.

[0075] If the total reward function includes only the economic incentive term, then the total reward function can be expressed as R_total = R_price.

[0076] If the overall reward function includes two sub-items: a safety constraint term and a user experience adaptation term, then the overall reward function can be expressed as R_total = R_safe + R_experience.

[0077] If the comprehensive reward function includes two sub-terms: a safety constraint term and an economic incentive term, then the comprehensive reward function can be expressed as R_total = R_safe + R_price.

[0078] If the overall reward function includes two sub-items: user experience adaptation and economic incentive, then the overall reward function can be expressed as R_total = R_experience + R_price.

[0079] If the comprehensive reward function includes three sub-items: safety constraint, user experience adaptation, and economic incentive, then the comprehensive reward function can be expressed as R_total = R_safe + R_experience + R_price. S103, the preset charging strategy is adjusted according to the comprehensive reward function to obtain the adjusted charging strategy.

[0080] In this embodiment of the invention, a reinforcement learning algorithm based on Q-learning or Deep Deterministic Policy Gradient (DDPG) is used as the core decision engine. The initial charging strategy is set by expert experience (such as constant current-constant voltage charging curve, trickle maintenance strategy, etc.) and serves as the starting point of the policy network.

[0081] The current state is input into the policy network, which outputs a set of candidate actions, such as adjusting the target charging voltage, switching charging modes (fast charging / slow charging / pause), and activating the pre-cooling mechanism. After each action is executed, the system collects feedback results and calculates the corresponding R_total, which is then fed back to the learning model as a reward signal.

[0082] Through continuous iterative learning, the policy network gradually converges to the optimal policy that maximizes long-term cumulative rewards in complex and ever-changing environments. This process represents a fundamental leap from "fixed parameters" to "dynamic adaptation," solving the problem that traditional charging algorithms cannot cope with diverse usage scenarios.

[0083] For example, smartphones are typically equipped with 3000–5000mAh lithium-ion batteries. Their charging control needs to balance high-power fast charging (such as 65W PD / PPS protocol) and heat dissipation constraints. In this embodiment of the invention, a comprehensive reward function including a high-temperature charging penalty, a high-load usage reward, and a mobile usage scenario matching reward can be used to adjust the smartphone's preset charging strategy. That is, by integrating the discharge current, the historical sequence of the charger being plugged in after full charge, and the local temperature in real time, the current peak during high-temperature fast charging can be dynamically suppressed to alleviate thermal degradation in the scenario of charging and using at the same time.

[0084] Thin and light laptops are limited by their heat dissipation capabilities. In this embodiment of the invention, a comprehensive reward function is constructed by using historical full-charge capacity sequences to construct a capacity decay trend penalty term and a peak-valley electricity price response reward term to adjust the preset charging strategy of thin and light laptops. That is, the historical full-charge capacity sequences are used to identify the battery aging stage, and shallow charging protection is performed in the middle of the calendar life (such as after 12-18 months of use). Combined with peak-valley electricity price response, efficient charging is completed during the off-peak hours at night, taking into account both battery anxiety and battery health.

[0085] Tablet computers are more susceptible to localized temperature rises due to their high screen-to-body ratio and compact battery layout. This invention constructs a comprehensive reward function that includes a high-temperature charging penalty term. When the SoC and casing temperatures are detected to be too high, the charging voltage is reduced to decrease heat sources and improve user handheld comfort.

[0086] For micro energy storage devices such as smartwatches and wireless earphone charging cases, embodiments of the present invention can achieve charging parameter adjustment with low computing power overhead through a comprehensive reward function that includes safety constraints and user experience adaptation items, thereby meeting the resource constraints of their processors.

[0087] S104, adjust the battery charging parameters according to the adjusted charging strategy, and charge the battery according to the adjusted battery charging parameters.

[0088] Based on the optimized charging strategy output in step S103, an instruction is sent to the power management unit to dynamically adjust the actual charging parameters, including but not limited to: maximum allowable charging voltage, upper limit of charging current, and whether to enable pulse charging mode.

[0089] For example, during a nighttime charging process, if it is determined that the current period is a low-voltage period (R_price is high), the battery temperature is normal, and the historical capacity decay is slowing down (R_capacity is positive), then it is decided to start the high-speed charging mode to shorten the charging time. However, if it is detected that the user is traveling during the daytime high temperature environment (R_scene is triggered), but the battery already has 80% charge, then it is selected to suspend charging or limit the current to maintain the battery and prevent overheating and aging.

[0090] The entire control process operates in a closed-loop feedback manner, supporting millisecond-level dynamic response and ensuring coordinated optimization of safety, economy, and user experience under any operating condition.

[0091] The charging control method provided in this invention achieves the following beneficial effects by introducing a multi-dimensional dynamic reward mechanism and a reinforcement learning framework: 1. Significantly extends battery cycle life and calendar life, slows down capacity decay, and indirectly extends the replacement cycle of consumer electronics products; 2. Utilize the difference between peak and off-peak electricity prices to optimize charging timing, help users save on electricity costs, and at the same time assist the power grid in peak shaving and valley filling, reducing reliance on thermal power and carbon emissions; 3. It can autonomously identify typical scenarios such as mobile office and high-intensity use, dynamically match charging needs, and improve user experience; 4. It has a strong ability to suppress high temperature, high pressure and abnormal charging behavior, which improves the safety and reliability of battery use; 5. Supports cross-device and cross-scenario deployment, suitable for various application platforms such as smartphones, laptops, electric vehicles and home energy storage systems. To perform the corresponding steps in the above embodiments and various possible methods, an implementation of a charging control device 200 for a consumer electronic device is given below. Further, please refer to... Figure 3 , Figure 3 This is a functional block diagram of a charging control device 200 for a consumer electronic device provided in an embodiment of the present invention. It should be noted that the basic principle and technical effects of the charging control device 200 for a consumer electronic device provided in this embodiment are the same as those in the above embodiments. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content in the above embodiments. The charging control device 200 for a consumer electronic device includes: The acquisition module 201 is used to acquire the current battery operating status and external environment information.

[0092] Module 202 is used to construct a comprehensive reward function based on the current battery operating status and external environment information.

[0093] The adjustment module 203 is used to adjust the preset charging strategy according to the comprehensive reward function to obtain the adjusted charging strategy; adjust the battery charging parameters according to the adjusted charging strategy; and charge the battery according to the adjusted battery charging parameters.

[0094] Optionally, the above modules can be stored in the form of software or firmware. Figure 1 The memory 110 shown is either stored in or embedded in the operating system (OS) of the electronic device 100, and can be used by... Figure 1 The processor 120 executes the program. Meanwhile, the data and program code required to execute the above modules can be stored in the memory 110.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0096] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0097] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, electronic device 100, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0098] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A charging control method for a consumer electronic device, characterized by, The method includes: Obtain current battery operating status and external environment information; Based on the current battery operating status and the external environment information, a comprehensive reward function is constructed; The preset charging strategy is adjusted according to the comprehensive reward function to obtain the adjusted charging strategy; Adjust the battery charging parameters according to the adjusted charging strategy, and charge the battery according to the adjusted battery charging parameters.

2. The charging control method for consumer electronic devices as described in claim 1, characterized in that, The comprehensive reward function includes at least one of the following: a security constraint, a user experience adaptation, and an economic incentive. The current battery operating status includes: current battery temperature, current discharge current, and historical full charge capacity sequence. The external environment information includes: current time, peak and off-peak electricity pricing rules for the region, and historical sequence of charger plugged-in duration after full charge. Correspondingly, the step of constructing a comprehensive reward function based on the current battery operating state and the external environment information includes: The safety constraint term is constructed based on the current battery temperature and the historical full charge capacity sequence; And / or, construct the user experience adaptation item based on the current discharge current and the historical sequence of the charger being plugged in after full charge; And / or, construct the economic incentive item based on the current time and the peak-valley electricity price division rules of the region.

3. The charging control method for consumer electronic devices as described in claim 2, characterized in that, The safety constraints include a capacity decay trend penalty and / or a high-temperature charging penalty; The step of constructing the safety constraint term based on the current battery temperature and the historical full-charge capacity sequence includes: Using the historical full-charge capacity sequence, the change in capacity difference is obtained, and based on the change in capacity difference, the capacity decay trend penalty term is obtained; And / or, if the current temperature of the battery is greater than the preset safe operating temperature threshold of the battery, the high temperature charging penalty item is obtained by using the current temperature of the battery, the preset safe operating temperature threshold of the battery, and the preset temperature penalty coefficient.

4. The charging control method as described in claim 3, characterized in that, The historical full charge capacity sequence includes the most recent N full charge capacities, where N is an integer greater than or equal to 3; The step of obtaining the change in capacity difference using the historical full-capacity sequence includes: Calculate the difference between the Nth full charge capacity and the (N-1th)th full charge capacity sequentially to obtain multiple capacity difference values; The change in capacity difference is obtained based on the multiple capacity differences.

5. The charging control method as described in claim 2, characterized in that, The user experience adaptation items include high-load usage reward items and / or mobile usage scenario matching reward items. The step of constructing the user experience adaptation items based on the current discharge current and the historical sequence of the charger being plugged in after full charge includes: The high-load usage reward item is obtained by using the current discharge current and the preset current reward coefficient; And / or, if it is determined from the historical sequence of the charger being plugged in after full charge that the user is in a frequent mobile usage scenario, then a scenario matching flag item is generated; Based on the scene matching flag and the preset scene matching coefficient, the mobile usage scene matching reward item is obtained.

6. The charging control method as described in claim 5, characterized in that, The step of determining whether a user is in a frequent mobile usage scenario based on the historical sequence of charger plug-in duration after full charge includes: If the historical sequence of the charger being plugged in after full charge shows a preset trend, it is determined that the user is in a frequent mobile usage scenario.

7. The charging control method for consumer electronic devices as described in claim 2, characterized in that, The economic incentives include a peak-valley electricity price response reward. The step of constructing the economic incentives based on the current time and the peak-valley electricity price allocation rules of the region includes: Based on the current time and the peak-valley electricity price division rules of the region, determine the electricity stage to which the current time belongs and the normalized unit electricity price corresponding to the current time; If the electricity period is a low-valley electricity price period, then the peak-valley electricity price response reward item is obtained by using the normalized value of the unit electricity price and the preset electricity price response coefficient.

8. A charging control device for consumer electronic devices, characterized in that, The device includes: The acquisition module is used to acquire the current battery operating status and external environment information; The construction module is used to construct a comprehensive reward function based on the current battery operating state and the external environment information; The adjustment module is used to adjust the preset charging strategy according to the comprehensive reward function to obtain the adjusted charging strategy; adjust the battery charging parameters according to the adjusted charging strategy; and charge the battery according to the adjusted battery charging parameters.

9. A consumer electronic device, characterized in that, It includes a processor and a memory, the memory storing machine-executable instructions that can be executed by the processor to implement the charging control method for a consumer electronic device according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the charging control method for consumer electronic devices as described in any one of claims 1-7.

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