Efficient temperature control battery management method and system for small household electrical appliance

Abstract

The invention discloses an efficient temperature control battery management method and system for a small household electrical appliance, and the method comprises the steps: S1, collecting the real-time working parameters of a battery, and calculating the real-time thermal load of the battery; s2, according to the real-time thermal load of the battery, determining a corresponding heat dissipation demand through a preset temperature control strategy database; s3, activating the corresponding heat dissipation module according to the heat dissipation requirement; s4, the efficiency of the heat dissipation process is monitored in real time, and the battery temperature is maintained within the optimal working range; s5, utilizing an intelligent algorithm to automatically adjust a temperature control strategy according to a battery use state and an external environment, and performing heat dissipation efficiency and energy management; s6, when the working state of the battery is low in load, the working intensity of the heat dissipation module is automatically reduced, and energy consumption is reduced; and S7, regularly carrying out self-detection on the heat dissipation system, and timely finding and processing faults. According to the invention, the problem of heat dissipation in small household appliances can be effectively solved, especially the challenge of efficient heat dissipation caused by limited space.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature-controlled battery management, and in particular to a method and system for efficiently managing temperature-controlled batteries for small household appliances. Background Art

[0002] With the popularity of smart homes and portable electronic devices, the performance and functions of small household appliances are constantly improving, and the requirements for battery management and heat dissipation technology are also increasing. In these devices, the battery not only needs to provide long-term power support, but also needs to maintain a good operating temperature in a limited space to ensure stable operation of the device and battery life. However, existing battery management and heat dissipation technologies face many challenges, especially the difficulty of achieving effective heat dissipation in small devices, which is mainly due to the following defects:

[0003] Limited heat dissipation space: The size and design of small household appliances limit the size and form of the heat dissipation system. Traditional heat dissipation solutions such as large heat sinks and fans are difficult to apply, which leads to low heat dissipation efficiency and cannot meet the heat dissipation needs of high-performance appliances.

[0004] Insufficient heat dissipation efficiency: The heat dissipation materials and designs in existing technologies often cannot achieve efficient heat conduction and convection in a compact space, which limits the rapid dissipation of heat and increases the risk of battery overheating, thereby affecting device performance and battery safety.

[0005] Lack of intelligent temperature control: Traditional temperature control solutions mostly rely on simple thermal sensing devices and mechanical adjustment mechanisms, lack intelligent control, and are unable to dynamically adjust the heat dissipation strategy according to the real-time working status of the battery and the external environment, resulting in the heat dissipation response being not sensitive and accurate enough.

[0006] Poor energy management: In existing technologies, battery management systems often fail to effectively combine energy consumption and heat dissipation requirements, resulting in high energy consumption when high performance output is not required, increasing unnecessary energy waste and heat generation.

[0007] Maintenance and reliability issues: Existing cooling systems are difficult to maintain, especially inside compact equipment. Once a problem occurs in the cooling system, it may cause equipment overheating, performance degradation, or even damage. The system's self-detection and fault warning mechanisms are insufficient, making it difficult to detect and resolve problems in a timely manner.

[0008] Therefore, how to provide an efficient temperature-controlled battery management method and system for small household appliances is an urgent problem that needs to be solved by those skilled in the art. Summary of the invention

[0009] One purpose of the present invention is to propose an efficient temperature control battery management method and system for small household appliances. The present invention provides a set of efficient and intelligent temperature control and battery management solutions by combining the heat dissipation structure design of high thermal conductivity materials, compact heat dissipation mechanical structure, intelligent temperature control strategy, dynamically adjustable heat dissipation system, computer simulation and optimization technology, adaptive energy management and integrated design method. This solution is particularly suitable for small household appliances with limited space and difficult heat dissipation. It can optimize energy utilization efficiency, extend battery life, and reduce environmental impact while ensuring high performance of the equipment.

[0010] An efficient temperature-controlled battery management method for a small household appliance according to an embodiment of the present invention comprises the following steps:

[0011] S1. Collect the real-time working parameters of the battery and calculate the real-time thermal load of the battery according to the real-time working parameters of the battery;

[0012] S2. Determine the corresponding heat dissipation requirements based on the real-time thermal load of the battery through the preset temperature control strategy database;

[0013] S3. Activate the corresponding heat dissipation module according to the heat dissipation requirements;

[0014] S4, monitor the efficiency of the heat dissipation process in real time, adjust the working state of the heat dissipation module through feedback, and maintain the battery temperature within the optimal working range;

[0015] S5. Use intelligent algorithms to automatically adjust temperature control strategies according to battery usage status and external environment to manage heat dissipation efficiency and energy.

[0016] S6: When the battery is in low load state, the working intensity of the heat dissipation module is automatically reduced to reduce energy consumption;

[0017] S7. Regularly conduct self-inspection of the cooling system to promptly detect and handle faults to maintain the long-term stability of the cooling module system.

[0018] Optionally, the S1 specifically includes:

[0019] S11. Collect the battery voltage V, current I, surface temperature T, and ambient temperature T env and humidity H env As real-time working parameters;

[0020] S12, calculate the real-time power consumption P of the battery:

[0021]

[0022] Where V(t′) and I(t′) represent the instantaneous voltage and current at time t′, respectively, and the integration is from a certain initial time t0 to the current time t;

[0023] S13. Estimate the real-time thermal load Q of the battery based on power consumption and battery efficiency η:

[0024] Q = P × (1-η);

[0025] Here, η represents the energy conversion efficiency of the battery.

[0026] Optionally, the S2 specifically includes:

[0027] S21, establish a temperature control strategy database, which presets a series of heat dissipation demand parameters according to different battery working states and environmental conditions, including heat dissipation mode M and heat dissipation intensity threshold I threshold ;

[0028] S22, based on the collected real-time battery heat load Q and ambient temperature T env and humidity H env , use the mapping relationship in the database to determine the heat dissipation requirements:

[0029] M=f(Q,T env ,H env )=a·Q+b·T env +c·H env +d;

[0030] Where M is the numerical representation of the recommended cooling mode, and a specific cooling strategy can be selected based on this value. Q is the real-time thermal load of the battery, and T env is the ambient temperature, H env is the ambient humidity, a, b, c and d are coefficients pre-determined according to the actual heat dissipation requirements and the system, and the degree of influence of each factor on the selection of the heat dissipation mode is adjusted;

[0031] S23, using the required heat dissipation intensity I for the determined heat dissipation mode M required :

[0032] I required =g(Q,I threshold , T env )=k·(QQ base )+m·(T env -T base )+I threshold ;

[0033] Among them, I required is the required heat dissipation intensity, Q is the real-time heat load of the battery, and I threshold is the threshold of heat dissipation intensity, that is, the minimum heat load at which the heat dissipation intensity starts to increase under certain conditions, T env is the current ambient temperature, Q baseis the base heat load. When the heat load is lower than this value, the cooling system may work at the basic or minimum intensity level. base is the reference ambient temperature, which is used to adjust the effect of ambient temperature on the heat dissipation demand. k and m are coefficients based on the actual heat dissipation demand and system design to adjust the degree of influence of battery heat load and ambient temperature on the required heat dissipation intensity.

[0034] S24, determine the heat dissipation mode M and heat dissipation intensity I required After that, the system automatically adjusts the working status of the heat dissipation module.

[0035] Optionally, the S3 specifically includes:

[0036] S31. Select a corresponding heat dissipation mode M according to the determined heat dissipation requirement, including but not limited to natural heat dissipation, forced air cooling, and liquid cooling;

[0037] S32: For the natural cooling mode, no active cooling measures are taken, and the temperature control is maintained by relying on the natural cooling capacity of the electrical design;

[0038] S33, for forced air cooling mode, activate the built-in micro fan according to the required heat dissipation intensity I required Adjust the fan speed S fan :

[0039] S fan =f fan (I required ,T env )=v·I required +w·(T env -T ambient )+x;

[0040] Among them, S fan Indicates the fan speed, I required is the required heat dissipation intensity, T env is the ambient temperature, T ambient is the preset baseline ambient temperature, v, w, and x are coefficients adjusted according to fan performance and cooling requirements;

[0041] S34: For the liquid cooling mode, start the liquid cooling pump according to the required heat dissipation intensity I required and ambient temperature T env Adjust the pump flow rate F pump :

[0042] F pump =f pump (I required , T env )=y·I required +z·(T env -T coolant )+a1;

[0043] Among them, F pump Indicates the flow rate of the pump, I required is the required heat dissipation intensity, T env is the ambient temperature, T coolant is the baseline temperature of the coolant, y, z, and a1 are coefficients adjusted according to the cooling system performance and heat dissipation requirements;

[0044] S35. Dynamically adjust the heat dissipation strategy according to the actual working status of the heat dissipation module.

[0045] Optionally, the S4 specifically includes:

[0046] S41, real-time monitoring of battery temperature T battery And the working status of the cooling module, including the working mode M of the cooling module, the fan speed S fan Or the pump flow rate F pump ;

[0047] S42, calculating the battery temperature change rate ΔT / Δt, where ΔT represents the change in battery temperature, and Δt represents the change in time;

[0048] S43, adjusting the working state of the heat dissipation module with respect to the fan speed S according to the battery temperature change rate ΔT / Δt and the preset temperature control target fan :

[0049] S fan_new ≤S fan +α·(ΔT / Δt);

[0050] Among them, S fan_new is the adjusted fan speed, α is the coefficient adjusted according to the heat dissipation demand, which is used to adjust the influence of the temperature change rate on the fan speed;

[0051] For the pump flow rate F pump :

[0052] F pump_new =F pump +β·(ΔT / Δt);

[0053] Among them, F pump_new is the adjusted pump flow rate, β is the coefficient adjusted according to the heat dissipation demand, which is used to adjust the influence of the temperature change rate on the pump flow rate;

[0054] S44, according to the adjusted working state S of the heat dissipation module fan_new or F pump_new , re-evaluate heat dissipation efficiency and energy consumption to ensure that the battery temperature is maintained within the optimal operating range while optimizing the overall energy efficiency of the system.

[0055] Optionally, the S5 specifically includes:

[0056] S51. Use the support vector machine (SVM) algorithm to build a temperature control strategy model based on the battery usage status and external environment data:

[0057] Data train ={(x1,y1),(x2,y2),...,(x n ,y n )};

[0058] Among them, each sample (x i ,y i ) is given by the eigenvector x i and label y i Composition, feature vector x i The battery current I i , voltage V i ,temperature Ambient temperature and ambient humidity constitute;

[0059] S52, SVM algorithm learns the relationship between the battery's operating status and heat dissipation requirements through the training data set and constructs a decision boundary:

[0060] SVM model =Train(SVM,Data train );

[0061] Among them, SVM model Represents the trained SVM model, Data train Represents the dataset used for training, including the characteristics of the battery and environmental states and the corresponding heat dissipation requirement labels;

[0062] S53. Using SVM model Predict cooling requirements based on real-time collected battery and environmental status data:

[0063] M new , I required_new =Predict(SVM model ,I,V,TT battery ,T env ,H env );

[0064] Among them, M new and I required_new They represent the predicted heat dissipation mode and heat dissipation intensity respectively, and Predict represents the prediction function of the SVM model;

[0065] S54. Automatically adjust the working state of the heat dissipation module according to the prediction result of the support vector machine.

[0066] Optionally, the S6 specifically includes:

[0067] S61, monitor the working state of the battery in real time, determine the load level L of the battery according to the working state of the battery, and when the load level L is lower than the preset low load threshold L threshold , then the battery is judged to be in a low-load working state:

[0068] L=λ·I+μ·V+v·T battery ;

[0069] Among them, λ, μ and v are weight coefficients, which adjust the contribution of current, voltage and temperature to the load level;

[0070] S62: When the battery is in a low-load working state, the working intensity of the heat dissipation module is automatically reduced. required :

[0071] I required_new =max(I required -δ·(L threshold -L),I min );

[0072] Among them, I required_new is the adjusted heat dissipation intensity, I min is the minimum working intensity of the heat dissipation module, and δ is the adjustment coefficient, which is used to adjust the influence of load level difference on the reduction of heat dissipation intensity.

[0073] Optionally, the S7 specifically includes:

[0074] S71. Regularly perform self-tests on the cooling system and collect key performance parameters of the cooling module.

[0075] S72. Analyze the collected data and evaluate the current working condition of the cooling system:

[0076]

[0077] Among them, Q removed represents the actual amount of heat removed, Q generated represents the heat generated by the battery, η cooling Indicates heat dissipation efficiency;

[0078] S73: If the heat dissipation efficiency is detected to be lower than the preset minimum efficiency threshold η min , then the fault diagnosis program is automatically triggered, and the diagnostic program checks for physical damage, blockage of the cooling module or failure of the electronic control unit;

[0079] S74. Based on the fault diagnosis results, the system makes maintenance or replacement recommendations and issues warnings to the user or maintenance personnel to ensure the long-term stability of the cooling system and the safe operation of the battery.

[0080] An efficient temperature-controlled battery management system for small household appliances, comprising:

[0081] The sensor module collects the battery voltage V, current I, and temperature T in real time battery And the ambient temperature T env and humidity H env ;

[0082] A control unit is connected to the sensor module, receives and processes data collected by the sensor module, and includes a microprocessor and a built-in storage device, in which a temperature control strategy database and an artificial intelligence algorithm are pre-stored;

[0083] A heat dissipation module, which is controlled by a control unit and includes a micro fan, a heat pipe, and a liquid cooling system, and is used to adjust the heat dissipation intensity and mode according to the instructions of the control unit;

[0084] Communication interface, for data exchange between the system and external devices, supporting remote monitoring of fault diagnosis information and system status;

[0085] Power management module, which manages the battery charging and discharging process, reduces heat generation, and extends battery life while maintaining high performance of the device;

[0086] The artificial intelligence module is implemented inside the control unit to analyze battery usage status and environmental data, and dynamically adjust the temperature control strategy and the working status of the cooling module.

[0087] The beneficial effects of the present invention are:

[0088] (1) The present invention achieves efficient heat dissipation in small household appliances through efficient heat transfer design and compact heat dissipation system. In particular, it addresses the problem of difficulty in heat dissipation in limited space. It adopts high thermal conductivity materials and unique heat dissipation path design, as well as innovative application of micro fans and microchannel liquid cooling systems, which significantly improves the heat dissipation efficiency, effectively reduces the operating temperature of batteries and equipment, and ensures the stability of the equipment and the safety of the battery.

[0089] (2) The present invention uses an intelligent temperature control strategy and a dynamic thermal management system to automatically adjust the heat dissipation strategy according to the battery usage status and the external environment to achieve the optimal energy efficiency ratio and battery protection. The intelligent algorithm developed using electronic engineering and artificial intelligence technology can monitor and dynamically adjust the heat dissipation intensity in real time, thereby improving the response speed and accuracy of the heat dissipation system.

[0090] (3) The present invention improves the overall energy efficiency of the system and the battery life through advanced simulation and optimization techniques, as well as adaptive energy management strategies. Advanced thermal simulation and fluid dynamics analysis using computer science technology optimizes the heat dissipation design and reduces energy waste. At the same time, the charging and discharging process optimized by intelligent algorithms reduces heat generation and extends battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0092] Figure 1 This is an overall flow chart of an efficient temperature-controlled battery management method for a small household appliance proposed by the present invention;

[0093] Figure 2 A schematic diagram of a heat dissipation structure of a high thermal conductivity material of a small household appliance high-efficiency temperature control battery management method proposed by the present invention;

[0094] Figure 3 A flow chart of an automatic adjustment of heat dissipation strategy of a high-efficiency temperature-controlled battery management method for a small household appliance proposed by the present invention;

[0095] Figure 4 A framework diagram of a highly efficient temperature-controlled battery management method for a small household appliance proposed in the present invention. DETAILED DESCRIPTION

[0096] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0097] refer to Figure 1-Figure 4 , an efficient temperature-controlled battery management method for a small household appliance, comprising the following steps:

[0098] S1. Collect the real-time working parameters of the battery and calculate the real-time thermal load of the battery according to the real-time working parameters of the battery;

[0099] In this implementation, S1 specifically includes:

[0100] S11. Collect the battery voltage V, current I, surface temperature T, and ambient temperature T env and humidity H env As real-time working parameters;

[0101] S12, calculate the real-time power consumption P of the battery:

[0102]

[0103] Where V(t′) and I(t′) represent the instantaneous voltage and current at time t′, respectively, and the integration is from a certain initial time t0 to the current time t;

[0104] S13. Estimate the real-time thermal load Q of the battery based on power consumption and battery efficiency η:

[0105] Q = P × (1-η);

[0106] Here, η represents the energy conversion efficiency of the battery.

[0107] S2. Determine the corresponding heat dissipation requirements based on the real-time thermal load of the battery through the preset temperature control strategy database;

[0108] In this implementation, S2 specifically includes:

[0109] S21, establish a temperature control strategy database, which presets a series of heat dissipation demand parameters according to different battery working states and environmental conditions, including heat dissipation mode M and heat dissipation intensity threshold I threshold ;

[0110] S22, based on the collected real-time battery heat load Q and ambient temperature T env and humidity H env , use the mapping relationship in the database to determine the heat dissipation requirements:

[0111] M=f(Q,T env ,H env )=a·Q+b·T env +c·H env +d;

[0112] Where M is the numerical representation of the recommended cooling mode, and a specific cooling strategy can be selected based on this value. Q is the real-time thermal load of the battery, and T env is the ambient temperature, H env is the ambient humidity, a, b, c and d are coefficients pre-determined according to the actual heat dissipation requirements and the system, and the degree of influence of each factor on the selection of the heat dissipation mode is adjusted;

[0113] S23, using the required heat dissipation intensity I for the determined heat dissipation mode M required :

[0114] I required =g(Q,I threshold , T env )=k·(QQ base )+m·(T env -T base )+I threshold ;

[0115] Among them, Irequired is the required heat dissipation intensity, Q is the real-time heat load of the battery, and I threshold is the threshold of heat dissipation intensity, that is, the minimum heat load at which the heat dissipation intensity starts to increase under certain conditions, T env is the current ambient temperature, Q base is the base heat load. When the heat load is lower than this value, the cooling system may work at the basic or minimum intensity level. base is the reference ambient temperature, which is used to adjust the effect of ambient temperature on the heat dissipation demand. k and m are coefficients based on the actual heat dissipation demand and system design to adjust the degree of influence of battery heat load and ambient temperature on the required heat dissipation intensity.

[0116] S24, determine the heat dissipation mode M and heat dissipation intensity I required After that, the system automatically adjusts the working status of the heat dissipation module.

[0117] S3. Activate the corresponding heat dissipation module according to the heat dissipation requirements;

[0118] In this implementation, S3 specifically includes:

[0119] S31. Select a corresponding heat dissipation mode M according to the determined heat dissipation requirement, including but not limited to natural heat dissipation, forced air cooling, and liquid cooling;

[0120] S32: For the natural cooling mode, no active cooling measures are taken, and the temperature control is maintained by relying on the natural cooling capacity of the electrical design;

[0121] S33, for forced air cooling mode, activate the built-in micro fan according to the required heat dissipation intensity I required Adjust the fan speed S fan :

[0122] S fan =f fan (I required ,T env )=v·I required +w·(T env -T ambient )+x;

[0123] Among them, S fan Indicates the fan speed, I required is the required heat dissipation intensity, T env is the ambient temperature, T ambient is the preset baseline ambient temperature, v, w, and x are coefficients adjusted according to fan performance and cooling requirements;

[0124] S34: For the liquid cooling mode, start the liquid cooling pump according to the required heat dissipation intensity I required and ambient temperature T envAdjust the pump flow rate F pump :

[0125] F pump =f pump (I required , T env )=y·I required +z·(T env -T coolant )+a1;

[0126] Among them, F pump Indicates the flow rate of the pump, I required is the required heat dissipation intensity, T env is the ambient temperature, T coolant is the baseline temperature of the coolant, y, z, and a1 are coefficients adjusted according to the cooling system performance and heat dissipation requirements;

[0127] S35. Dynamically adjust the heat dissipation strategy according to the actual working status of the heat dissipation module.

[0128] S4, monitor the efficiency of the heat dissipation process in real time, adjust the working state of the heat dissipation module through feedback, and maintain the battery temperature within the optimal working range;

[0129] In this implementation, S4 specifically includes:

[0130] S41, real-time monitoring of battery temperature T battery And the working status of the cooling module, including the working mode M of the cooling module, the fan speed S fan Or the pump flow rate F pump ;

[0131] S42, calculating the battery temperature change rate ΔT / Δt, where ΔT represents the change in battery temperature, and Δt represents the change in time;

[0132] S43, adjusting the working state of the heat dissipation module with respect to the fan speed S according to the battery temperature change rate ΔT / Δt and the preset temperature control target fan :

[0133] S fan_new =S fan +α·(ΔT / Δt);

[0134] Among them, S fan_new is the adjusted fan speed, α is the coefficient adjusted according to the heat dissipation demand, which is used to adjust the influence of the temperature change rate on the fan speed;

[0135] For the pump flow rate F pump :

[0136] F pump_new =F pump+β·(ΔT / Δt);

[0137] Among them, F pump_new is the adjusted pump flow rate, β is the coefficient adjusted according to the heat dissipation demand, which is used to adjust the influence of the temperature change rate on the pump flow rate;

[0138] S44, according to the adjusted working state S of the heat dissipation module fan_new or F pump_new , re-evaluate heat dissipation efficiency and energy consumption to ensure that the battery temperature is maintained within the optimal operating range while optimizing the overall energy efficiency of the system.

[0139] S5. Use intelligent algorithms to automatically adjust temperature control strategies according to battery usage status and external environment to manage heat dissipation efficiency and energy.

[0140] In this implementation, S5 specifically includes:

[0141] S51. Use the support vector machine (SVM) algorithm to build a temperature control strategy model based on the battery usage status and external environment data:

[0142] Data train ={(x1,y1),(x2,y2),...,(x n ,y n )};

[0143] Among them, each sample (x i ,y i ) is given by the eigenvector x i and label y i Composition, feature vector x i The battery current I i , voltage V i ,temperature Ambient temperature and ambient humidity constitute;

[0144] S52, SVM algorithm learns the relationship between the battery's operating status and heat dissipation requirements through the training data set and constructs a decision boundary:

[0145] SVM model =Train(SVM,Data train );

[0146] Among them, SVM model Represents the trained SVM model, Data train Represents the dataset used for training, including the characteristics of the battery and environmental states and the corresponding heat dissipation requirement labels;

[0147] S53. Using SVM modelPredict cooling requirements based on real-time collected battery and environmental status data:

[0148] M new , I required_new =Predict(SVM model ,I,V,T battery , T env , H env );

[0149] Among them, M new and I required_new They represent the predicted heat dissipation mode and heat dissipation intensity respectively, and Predict represents the prediction function of the SVM model;

[0150] S54. Automatically adjust the working state of the heat dissipation module according to the prediction result of the support vector machine.

[0151] S6: When the battery is in low load state, the working intensity of the heat dissipation module is automatically reduced to reduce energy consumption;

[0152] In this implementation, S6 specifically includes:

[0153] S61, monitor the working state of the battery in real time, determine the load level L of the battery according to the working state of the battery, and when the load level L is lower than the preset low load threshold L threshold , then the battery is judged to be in a low-load working state:

[0154] L=λ·I+μ·V+v·T battery ;

[0155] Among them, λ, μ and v are weight coefficients, which adjust the contribution of current, voltage and temperature to the load level;

[0156] S62: When the battery is in a low-load working state, the working intensity of the heat dissipation module is automatically reduced. required :

[0157] I required_new =max(I required -δ·(L threshold -L),I min );

[0158] Among them, I required_new is the adjusted heat dissipation intensity, I min is the minimum working intensity of the heat dissipation module, and δ is the adjustment coefficient, which is used to adjust the influence of load level difference on the reduction of heat dissipation intensity.

[0159] S7. Regularly conduct self-inspection of the cooling system to promptly detect and handle faults to maintain the long-term stability of the cooling module system.

[0160] In this implementation, S7 specifically includes:

[0161] S71. Regularly perform self-tests on the cooling system and collect key performance parameters of the cooling module.

[0162] S72. Analyze the collected data and evaluate the current working condition of the cooling system:

[0163]

[0164] Among them, Q removed represents the actual amount of heat removed, Q generated represents the heat generated by the battery, η cooling Indicates heat dissipation efficiency;

[0165] S73: If the heat dissipation efficiency is detected to be lower than the preset minimum efficiency threshold η min , then the fault diagnosis program is automatically triggered, and the diagnostic program checks for physical damage, blockage of the cooling module or failure of the electronic control unit;

[0166] S74. Based on the fault diagnosis results, the system makes maintenance or replacement recommendations and issues warnings to the user or maintenance personnel to ensure the long-term stability of the cooling system and the safe operation of the battery.

[0167] An efficient temperature-controlled battery management system for small household appliances, characterized in that the system comprises:

[0168] The sensor module collects the battery voltage V, current I, and temperature T in real time battery And the ambient temperature T env and humidity H env ;

[0169] A control unit is connected to the sensor module, receives and processes data collected by the sensor module, and includes a microprocessor and a built-in storage device, in which a temperature control strategy database and an artificial intelligence algorithm are pre-stored;

[0170] A heat dissipation module, which is controlled by a control unit and includes a micro fan, a heat pipe, and a liquid cooling system, and is used to adjust the heat dissipation intensity and mode according to the instructions of the control unit;

[0171] Communication interface, for data exchange between the system and external devices, supporting remote monitoring of fault diagnosis information and system status;

[0172] Power management module, which manages the battery charging and discharging process, reduces heat generation, and extends battery life while maintaining high performance of the device;

[0173] The artificial intelligence module is implemented inside the control unit to analyze battery usage status and environmental data, and dynamically adjust the temperature control strategy and the working status of the cooling module.

[0174] Embodiment 1:

[0175] In a residential area located in a hot climate area, due to the high temperature and humid environment, the heat dissipation problem of household appliances, especially portable devices, becomes particularly important. Residents generally report that their portable devices such as smartphones, tablets and other small household appliances often suffer from performance degradation or even damage due to overheating after long-term use. In response to this problem, a local appliance manufacturer decided to apply the high-efficiency temperature-controlled battery management system for small household appliances proposed in the present invention in its new generation of products.

[0176] In the embodiment, the manufacturer first upgraded its product line and introduced the heat dissipation design and intelligent temperature control strategy proposed in the present invention. These products are designed to use a heat dissipation structure built with high thermal conductivity materials, equipped with a micro fan and a microchannel liquid cooling system. At the same time, each device is integrated with an intelligent temperature control algorithm that can automatically adjust the heat dissipation strategy according to the battery usage status and external environment.

[0177] Table 1: Comparison before and after application of the technology of the present invention

[0178]

[0179]

[0180] As can be seen from Table 1 above, in the embodiment, the manufacturer conducted a three-month field test on these new products. Test data shows that compared with similar products of traditional design, the heat dissipation efficiency of the equipment using the technology of the present invention in high temperature environment is improved by about 40%. Specifically, when the external ambient temperature reaches 35 degrees Celsius, the internal temperature of these devices is 4 to 6 degrees Celsius lower than that of traditional devices. In addition, the intelligent temperature control strategy effectively reduces the energy consumption of the equipment in standby or light load state, and the battery life is improved by about 20% compared with traditional devices.

[0181] On the other hand, the manufacturer's long-term stability test of the product has also achieved positive results. In the test of 1,000 hours of continuous operation, the equipment using the technology of the present invention showed high reliability, and the failure rate was about 30% lower than that of the traditional design equipment. While maintaining the optimal operating temperature of the equipment, the intelligent heat dissipation system also reduces the long-term loss caused by overheating.

[0182] In summary, the present invention not only solves the heat dissipation problem of small household appliances in high temperature environments, but also improves the battery efficiency and the overall stability of the device. These effects greatly improve the market competitiveness of the product and enhance the consumer experience. For manufacturers and users of small household appliances, the present invention provides a comprehensive, efficient and reliable solution.

[0183] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An efficient temperature-controlled battery management method for small household appliances, characterized in that: The following steps are involved: S1. Collect the real-time working parameters of the battery and calculate the real-time thermal load of the battery according to the real-time working parameters of the battery; S2. Determine the corresponding heat dissipation requirements based on the real-time thermal load of the battery through the preset temperature control strategy database; S3. Activate the corresponding heat dissipation module according to the heat dissipation requirements; S4, monitor the efficiency of the heat dissipation process in real time, adjust the working state of the heat dissipation module through feedback, and maintain the battery temperature within the optimal working range; S5. Use intelligent algorithms to automatically adjust temperature control strategies according to battery usage status and external environment to manage heat dissipation efficiency and energy. S6: When the battery is in low load state, the working intensity of the heat dissipation module is automatically reduced to reduce energy consumption; S7. Regularly conduct self-inspection of the cooling system to promptly detect and handle faults to maintain the long-term stability of the cooling module system.

2. The efficient temperature-controlled battery management method for a small household appliance according to claim 1, characterized in that: The S1 specifically includes: S11. Collect the battery voltage V, current I, surface temperature T, and ambient temperature T env and humidity H env As real-time working parameters; S12, calculate the real-time power consumption P of the battery: Where V(t′) and I(t′) represent the instantaneous voltage and current at time t′, respectively, and the integration is from a certain initial time t0 to the current time t; S13. Estimate the real-time thermal load Q of the battery based on power consumption and battery efficiency η: Q = P × (1-η); Here, η represents the energy conversion efficiency of the battery.

3. The efficient temperature-controlled battery management method for small household appliances according to claim 1, characterized in that: The S2 specifically includes: S21. Establish a temperature control strategy database, which presets a series of heat dissipation demand parameters according to different battery working states and environmental conditions, including heat dissipation mode M and heat dissipation intensity threshold I. threshold ; S22, based on the collected real-time battery heat load Q and ambient temperature T env and humidity H env , use the mapping relationship in the database to determine the heat dissipation requirements: M=f(W,T env ,H env )=a·Q+b·T env +c·H env +d; Where M is the numerical representation of the recommended cooling mode, and a specific cooling strategy can be selected based on this value. Q is the real-time thermal load of the battery, and T env is the ambient temperature, H env is the ambient humidity, a, b, c and d are coefficients pre-determined according to the actual heat dissipation requirements and the system, and the degree of influence of each factor on the selection of the heat dissipation mode is adjusted; S23, using the required heat dissipation intensity I for the determined heat dissipation mode M required : T required =g(Q,T threshold ,T env )=k·(Q-Q base )+m·(T env -T base) +I threshold ; Among them, I required is the required heat dissipation intensity, Q is the real-time heat load of the battery, and I threshold is the threshold of heat dissipation intensity, that is, the minimum heat load at which the heat dissipation intensity starts to increase under certain conditions, T env is the current ambient temperature, Q base is the base heat load. When the heat load is lower than this value, the cooling system may work at the basic or minimum intensity level. base is the reference ambient temperature, which is used to adjust the effect of ambient temperature on the heat dissipation demand. k and m are coefficients designed based on the actual heat dissipation demand and system design to adjust the degree of influence of battery heat load and ambient temperature on the required heat dissipation intensity. S24, determine the heat dissipation mode M and heat dissipation intensity I required After that, the system automatically adjusts the working status of the heat dissipation module.

4. The efficient temperature-controlled battery management method for a small household appliance according to claim 1, characterized in that: The S3 specifically includes: S31. Select a corresponding heat dissipation mode M according to the determined heat dissipation requirement, including but not limited to natural heat dissipation, forced air cooling, and liquid cooling; S32: For the natural cooling mode, no active cooling measures are taken, and the temperature control is maintained by relying on the natural cooling capacity of the electrical design; S33, for forced air cooling mode, activate the built-in micro fan according to the required heat dissipation intensity I required Adjust the fan speed S fan : S fan =f fan (I required ,T env )=v·I required +w·(T env -T ambient )+x; Among them, S fan Indicates the fan speed, I required is the required heat dissipation intensity, T env is the ambient temperature, T ambient is the preset baseline ambient temperature, v, w, and x are coefficients adjusted according to fan performance and cooling requirements; S34: For the liquid cooling mode, start the liquid cooling pump according to the required heat dissipation intensity I required and ambient temperature T env Adjust the pump flow rate F pump : f pump =f pump (I required ,T env )=y·I required +z·(T env -T coolant )+a1; Among them, F pump Indicates the flow rate of the pump, I required is the required heat dissipation intensity, T env is the ambient temperature, T coolant is the baseline temperature of the coolant, y, z, and a1 are coefficients adjusted according to the cooling system performance and heat dissipation requirements; S35. Dynamically adjust the heat dissipation strategy according to the actual working status of the heat dissipation module.

5. The efficient temperature-controlled battery management method for a small household appliance according to claim 1, characterized in that: The S4 specifically includes: S41, real-time monitoring of battery temperature T battery And the working status of the cooling module, including the working mode M of the cooling module, the fan speed S fan Or the pump flow rate F pump ; S42, calculating the battery temperature change rate ΔT / Δt, where ΔT represents the change in battery temperature, and Δt represents the change in time; S43, adjusting the working state of the heat dissipation module with respect to the fan speed S according to the battery temperature change rate ΔT / Δt and the preset temperature control target fan : S fan_new =S fan +α·(ΔT / Δt); Among them, S fan_new is the adjusted fan speed, α is the coefficient adjusted according to the heat dissipation demand, which is used to adjust the influence of the temperature change rate on the fan speed; For the pump flow rate F pump : F pump_new =F pump +β·(ΔT / Δt); Among them, F pump_new is the adjusted pump flow rate, β is the coefficient adjusted according to the heat dissipation demand, which is used to adjust the influence of the temperature change rate on the pump flow rate; S44, according to the adjusted working state of the heat dissipation module fan_new or F pump_new , re-evaluate heat dissipation efficiency and energy consumption to ensure that the battery temperature is maintained within the optimal operating range while optimizing the overall energy efficiency of the system.

6. The efficient temperature-controlled battery management method for a small household appliance according to claim 1, characterized in that: The S5 specifically includes: S51. Use the support vector machine (SVM) algorithm to build a temperature control strategy model based on the battery usage status and external environment data: Data train ={(x1,y1),(x2,y2),...,(x n ,y n )}; Among them, each sample (x i ,y i ) is given by the eigenvector x i and label y i Composition, feature vector x i The battery current I i , voltage V i ,temperature Ambient temperature and ambient humidity constitute; S52, SVM algorithm learns the relationship between the battery's operating status and heat dissipation requirements through the training data set and constructs a decision boundary: SVM model =Train(SVM,Data train ); Among them, SVM model Represents the trained SVM model, Data train Represents the dataset used for training, including the characteristics of the battery and environmental states and the corresponding heat dissipation requirement labels; S53. Using SVM model Predict cooling requirements based on real-time collected battery and environmental status data: M new ,I required_new =Predict(SVM model ,I,V,T battery ,T env ,H env ); Among them, M new and I required_new They represent the predicted heat dissipation mode and heat dissipation intensity respectively, and Predict represents the prediction function of the SVM model; S54. Automatically adjust the working state of the heat dissipation module according to the prediction result of the support vector machine.

7. The efficient temperature-controlled battery management method for a small household appliance according to claim 1, characterized in that: The S6 specifically includes: S61, monitor the working state of the battery in real time, determine the load level L of the battery according to the working state of the battery, and when the load level L is lower than the preset low load threshold L threshold , then the battery is judged to be in a low-load working state: L=λ·I+μ·V+v·T battery ; Among them, λ, μ and v are weight coefficients, which adjust the contribution of current, voltage and temperature to the load level; S62: When the battery is in a low-load working state, the working intensity of the heat dissipation module is automatically reduced. required : I required_new =max(I required -δ·(L threshold -L),I min ); Among them, I required_new is the adjusted heat dissipation intensity, I min is the minimum working intensity of the heat dissipation module, and δ is the adjustment coefficient, which is used to adjust the influence of load level difference on the reduction of heat dissipation intensity.

8. The efficient temperature-controlled battery management method for a small household appliance according to claim 1, characterized in that: The S7 specifically includes: S71. Regularly perform self-tests on the cooling system and collect key performance parameters of the cooling module. S72. Analyze the collected data and evaluate the current working condition of the cooling system: Among them, Q removed represents the actual amount of heat removed, Q generated represents the heat generated by the battery, η cooling Indicates heat dissipation efficiency; S73: If the heat dissipation efficiency is detected to be lower than the preset minimum efficiency threshold η min , then the fault diagnosis program is automatically triggered, and the diagnostic program checks for physical damage, blockage of the cooling module or failure of the electronic control unit; S74. Based on the fault diagnosis results, the system makes maintenance or replacement recommendations and issues warnings to the user or maintenance personnel to ensure the long-term stability of the cooling system and the safe operation of the battery.

9. An efficient temperature-controlled battery management system for small household appliances, characterized in that: The system includes: The sensor module collects the battery voltage V, current I, and temperature T in real time battery And the ambient temperature T env and humidity H env ; A control unit is connected to the sensor module, receives and processes data collected by the sensor module, and includes a microprocessor and a built-in storage device, in which a temperature control strategy database and an artificial intelligence algorithm are pre-stored; A heat dissipation module, which is controlled by a control unit and includes a micro fan, a heat pipe, and a liquid cooling system, and is used to adjust the heat dissipation intensity and mode according to the instructions of the control unit; Communication interface, for data exchange between the system and external devices, supporting remote monitoring of fault diagnosis information and system status; Power management module, which manages the battery charging and discharging process, reduces heat generation, and extends battery life while maintaining high performance of the device; The artificial intelligence module is implemented inside the control unit to analyze battery usage status and environmental data, and dynamically adjust the temperature control strategy and the working status of the cooling module.

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