Energy autonomous long-term online vehicle-mounted TBOX management method and system

Abstract

The invention discloses an energy autonomous long-term online vehicle-mounted TBOX management method and system, which is applied to a vehicle-mounted TBOX, and comprises the following steps: collecting multi-source environment energy by adopting a multi-source environment energy collecting module, mixing and storing the multi-source environment energy by adopting a dynamic energy budget and management module, and storing the multi-source environment energy by adopting a dynamic energy budget and management module. And the task scheduling module is used for generating a task scheduling algorithm of the low-power-consumption service module based on the multi-source environment energy mixed and stored by the dynamic energy budget and management module, and the low-power-consumption service module comprises a plurality of low-power-consumption sensors. According to the invention, the problem of static power consumption caused by power supply depending on a vehicle-mounted storage battery is solved, and real, reliable and unlimited vehicle-mounted TBOX long-term online intelligent service is realized.

Description

Technical Field

[0001] This invention relates to the field of vehicle energy management technology, specifically to a method and system for autonomous, long-term online vehicle-mounted TBOX management. Background Technology

[0002] As vehicles become increasingly intelligent and connected, the in-vehicle T-Box (Total Vehicle Module Box) plays an increasingly crucial role as the core hub for communication between the vehicle and the outside world. Users have a stronger demand for vehicles to remain "always online," expecting advanced functions such as seamless welcome unlocking and locking, 24 / 7 continuous sentry mode, and real-time remote status monitoring. However, realizing these functions faces a fundamental contradiction: these services require the T-Box and related sensors to operate continuously or frequently, while traditional solutions rely on the vehicle's battery for power. This results in significant "static power consumption" when the vehicle is stationary (engine off and in sleep mode), a major cause of battery depletion and vehicle starting failures. Current industry solutions are mostly compromises with significant drawbacks. 1. The prevalence and severity of static power consumption problems (a) For conventional gasoline-powered vehicles: their low-voltage electrical system relies entirely on a lead-acid battery. The continuous operation of the TBOX and associated sensors after the vehicle is turned off can easily deplete the battery, leading to the vehicle's inability to start. This is a long-standing problem.

[0003] (b) For new energy vehicles: The static power consumption problem is more complex and severe. New energy vehicles are usually also equipped with a low-voltage battery to power all low-voltage controllers and sensors, including the TBOX. Although the vehicle is equipped with a high-voltage power battery, the high-voltage system is usually disconnected when the vehicle is in a dormant state and cannot replenish the low-voltage system. Therefore, once the battery is depleted due to static power consumption from the TBOX, the vehicle will be completely "disconnected" and may be unable to wake up the high-voltage system for charging or starting due to the paralysis of the low-voltage system, resulting in more prominent user experience and safety risks.

[0004] 2. General solutions are not applicable: In the field of IoT technology, ambient energy is used to power low-power MCUs and BLE modules. However, this type of solution is a general design and cannot meet the requirements of automotive-grade reliability, complex function scheduling, high-level safety certification, and harsh electromagnetic environments. Specifically: (a) The lack of strategies to deal with vehicles parked in dark or temperature-controlled environments for extended periods can lead to complete system failure.

[0005] (b) The energy management is simple and cannot support the coordination of multiple tasks with different power consumption requirements, such as "greeting + sentry + communication". (c) It is not deeply integrated with the vehicle's electronic and electrical architecture, making it impossible to achieve safe vehicle control. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides an energy-autonomous long-term online vehicle TBOX management method and system, which solves the static power consumption problem caused by relying on vehicle battery power supply, and realizes truly reliable and unrestricted long-term online intelligent service of vehicle TBOX.

[0007] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0008] According to a first aspect of this application, a method for managing an energy-autonomous long-term online vehicle-mounted TBOX is provided, applied to a vehicle-mounted TBOX, comprising: Multi-source environmental energy harvesting modules are used to collect multi-source environmental energy; A dynamic energy budgeting and management module is used for the mixing and storage of multi-source environmental energy. Based on the mixed and stored multi-source environmental energy of the dynamic energy budget and management module, a task scheduling algorithm for generating a low-power service module is adopted using an automotive-grade dual-mode control module. The low-power service module includes several low-power sensors.

[0009] In some embodiments of this application, based on the aforementioned scheme, the method for obtaining the mixed multi-source environmental energy in each cycle is as follows: The exponential smoothing method is used to obtain the mixed multi-source environmental energy within the current period. The calculation formula is as follows:

[0010] in, This is the predicted power from the previous cycle. This is the measured power from the previous cycle. It is the predicted power for the current cycle. It is a smoothing factor, with a value ranging from 0 to 1; A power and weather condition preset table is established, which is used to characterize the mapping relationship between photovoltaic power, thermoelectric power, radio frequency power and total power and weather conditions, respectively. Based on the power obtained from the mapping table and trend prediction, the mixed multi-source environmental energy in the current period is calculated using the following formula:

[0011] in, It is the final multi-source environmental energy mixed within the current cycle, for The power is obtained from weather forecasts. It's the confidence level.

[0012] In some embodiments of this application, based on the foregoing scheme, the multi-source environmental energy includes solar energy, thermal energy, and radio frequency energy, the period includes multiple time periods, and the method for obtaining the mixed multi-source environmental energy in each time period within the period is as follows: The photovoltaic prediction sub-model, thermoelectric prediction sub-model, and radio frequency prediction sub-model are used to predict the predicted photovoltaic input power, predicted thermoelectric input power, and predicted radio frequency input power for the current time period, respectively. The predicted photovoltaic input power, predicted thermoelectric input power, and predicted radio frequency input power for the current time period are weighted and summed to obtain the preliminary predicted total input power for the current time period. The initial predicted total input power for the current period is corrected using a correction factor to obtain the predicted total input power for the current period, so that the predicted total input power for the current period gradually approaches the actual total input power for the current period. Based on energy constraints, the stored energy for the current period is obtained from the stored energy of the previous period, the predicted total input power for the current period, and the planned power consumption. The calculation formula is as follows:

[0013] in, For time periods, For the energy stored in the previous period, This represents the predicted total input power for the current time period. The planned power consumption for the current time period. This is the correction factor.

[0014] In some embodiments of this application, based on the foregoing scheme, the step of using a photovoltaic prediction sub-model to predict the predicted photovoltaic input power for the current time period includes: Based on future weather data, solar altitude angle, and season, a relative illuminance coefficient is generated for each time period within the cycle. The maximum power of the photovoltaic unit under standard illumination is estimated using the MPPT algorithm based on an improved perturbation observation method. The photovoltaic input power is then predicted using the relative illuminance coefficient and the maximum power of the photovoltaic unit under standard illumination; and / or, The method of using a thermoelectric prediction sub-model to predict the predicted thermoelectric input power for the current time period includes: The temperature difference is obtained based on the predicted outside temperature and the predicted inside temperature. Based on the temperature difference and the characteristic constants of the thermoelectric element, the predicted photovoltaic input power is obtained; and / or, The predicted radio frequency input power for the current time period is predicted using a radio frequency prediction sub-model, including: Based on historical radio frequency energy acquisition data and geographical location information for the same period, a historical average radio frequency energy baseline is obtained; Based on a historical average RF energy baseline, obtain the predicted RF input power; and / or, The method for obtaining the correction factor is as follows: The actual total input power for the current time period is obtained. The photovoltaic prediction sub-model, thermoelectric prediction sub-model, and radio frequency prediction sub-model are used to predict the predicted photovoltaic input power, predicted thermoelectric input power, and predicted radio frequency input power for the current time period, respectively. The predicted photovoltaic input power, predicted thermoelectric input power, and predicted radio frequency input power for the current time period are weighted and summed to obtain the preliminary predicted total input power for the current time period. The actual total input power is compared with the preliminary predicted total input power to obtain the correction factor.

[0015] In some embodiments of this application, based on the foregoing scheme, the task scheduling algorithm includes: Based on the service priority strategy for the current time period, obtain the execution cost; Based on the storage energy and execution cost for the current period, a predicted working mode for the current period is generated, and the storage energy for the current period is allocated according to the predicted working mode.

[0016] In some embodiments of this application, based on the aforementioned scheme, the service priority strategy includes security and access assurance services, vehicle environment perception services, and data interaction services. Based on the service priority strategy for the current time period, the execution cost for the current time period is obtained, calculated using the following formula:

[0017] in, All are weights. For security and access assurance services, Provides services for vehicle environmental perception and data interaction. For data interaction services; and / or, Based on the current storage energy and execution cost, a predicted operating mode for the current period is generated, and the storage energy for the current period is allocated according to the predicted operating mode, including: The predicted operating modes are defined as normal mode, energy-saving mode, and extreme mode; The energy revenue for the current period is obtained by standardizing the stored energy and execution costs, then weighting and summing them. Determine the forecasting mode for the current time period. When energy income is greater than or equal to the first threshold, the prediction working mode for the current period is the normal mode, which includes security and access assurance services, vehicle environment perception services, and data interaction services. When energy income is less than a first threshold but greater than a second threshold, the prediction working mode for the current period is the energy-saving mode, which includes security and access assurance services and vehicle environmental perception services. When energy revenue is less than the second threshold, the forecasting mode for the current period is the extreme mode, which includes security and access assurance services. The first threshold is greater than the second threshold.

[0018] According to a second aspect of this application, an energy-autonomous long-term online vehicle-mounted TBOX management system is provided, comprising: Multi-source environmental energy harvesting module, used to harvest multi-source environmental energy; The dynamic energy budget and management module is electrically connected to the multi-source environmental energy harvesting module and is used to mix and store multi-source environmental energy using an automotive-grade power management chip. The low-power service module includes several low-power sensors; The automotive-grade dual-mode control module is electrically connected to both the dynamic energy budget and management module and the low-power service module. It is used to generate the task scheduling algorithm of the low-power service module based on the multi-source environmental energy mixed and stored by the dynamic energy budget and management module.

[0019] In some embodiments of this application, based on the foregoing scheme, the multi-source environmental energy harvesting module includes: Photovoltaic units are used to collect solar energy using photovoltaic thin films; The thermoelectric unit is used to collect heat energy generated by the temperature difference between the inside and outside of the vehicle by using a miniature thermoelectric generator array attached between the shell of the vehicle TBOX and the body sheet metal. The radio frequency unit is used to integrate a wideband microstrip antenna and a low-power rectifier circuit on the vehicle-mounted TBOX to harvest radio frequency energy from the environment; and / or, The dynamic energy budget and management module also includes a supercapacitor, which is used for mixing and storing multi-source environmental energy.

[0020] According to a third aspect of this application, a computer-readable storage medium is provided that stores a computer program thereon, the computer program including executable instructions that, when executed by a processor, implement the method described above.

[0021] According to a fourth aspect of this application, an electronic device is provided, comprising: One or more processors; A memory for storing executable instructions of the processor, which, when executed by the one or more processors, cause the one or more processors to implement the method described above.

[0022] The beneficial effects of this application are as follows: (1) The present application provides an energy autonomous long-term online vehicle TBOX management method and system, which is energy independent. After the vehicle goes into hibernation, it switches to independent power supply from the supercapacitor in the multi-source environmental energy (photovoltaic, thermoelectric, radio frequency and other types) collection and dynamic energy budget and management module. The high-power main processor and cellular communication module of the vehicle TBOX are physically powered off.

[0023] (2) The energy autonomous long-term online vehicle TBOX management method and system provided in this application is maintained by a long-term online core composed of a tightly coupled automotive-grade ultra-low power safety MCU and an on-board hardware safety chip.

[0024] (3) The energy autonomous long-term online vehicle TBOX management method and system provided in this application runs a task scheduling algorithm, which can obtain the stored energy and execution cost in real time, and dynamically decide the service to be executed and the intensity of the service according to the preset service priority strategy, so as to ensure the energy autonomous long-term online vehicle TBOX management.

[0025] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit this application. Attached Figure Description

[0026] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and are intended to explain the invention, but do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a flowchart of an energy autonomous long-term online vehicle-mounted TBOX management method according to the present invention; Figure 2 This is a control block diagram of an energy-autonomous long-term online vehicle-mounted TBOX management system according to the present invention; Figure 3 This is a schematic diagram of an energy-autonomous long-term online vehicle-mounted TBOX management system according to the present invention on a vehicle; Figure 4 This is a schematic diagram of the three-level prediction working mode of the present invention; Figure 5 This is a schematic diagram of an energy-autonomous long-term online vehicle-mounted TBOX management system according to the present invention; Figure 6 This is a schematic diagram of an electronic device according to the present invention. Detailed Implementation

[0027] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0028] It should be understood that the terms "comprising" and other similar expressions in the specification, claims, and accompanying drawings of this invention are intended to cover a non-exclusive inclusion, such as a process, method, system, or apparatus that includes a series of steps or units and is not limited to the listed steps or units. Furthermore, "first" and "second" are used to distinguish different objects and are not intended to describe a specific order.

[0029] According to the first aspect of this application, Figures 1-3 As shown, this embodiment provides a method for managing an energy-autonomous, long-term online vehicle-mounted TBOX, applied to a vehicle-mounted TBOX, including: Step S101: Collect multi-source environmental energy using a multi-source environmental energy harvesting module.

[0030] In some embodiments of this example, after the vehicle goes into hibernation, it switches to independent power supply from the supercapacitor in the multi-source environmental energy harvesting and dynamic energy budgeting and management module. The high-power main processor and cellular communication module of the vehicle TBOX are physically powered off. The multi-source environmental energy includes solar energy, thermal energy and radio frequency energy.

[0031] Specifically, the multi-source environmental energy harvesting module includes a photovoltaic unit, a thermoelectric unit, and a radio frequency unit. The photovoltaic unit uses photovoltaic thin films to collect solar energy; the thermoelectric unit uses a miniature thermoelectric generator array attached between the shell of the vehicle TBOX and the vehicle body sheet metal to collect heat energy generated by the temperature difference between the inside and outside of the vehicle; the radio frequency unit integrates a broadband microstrip antenna and a low-power rectifier circuit on the vehicle TBOX to collect radio frequency energy from the environment.

[0032] In this embodiment, the vehicle-mounted TBOX (Telematics BOX) is one of the core components of the vehicle intelligent connected system. It is mainly responsible for the connection and interaction between the vehicle and the external network. By integrating a variety of communication technologies and sensors, it realizes the collection, transmission and remote control of vehicle data.

[0033] Step S102: Use the dynamic energy budget and management module to mix and store multi-source environmental energy.

[0034] In some implementations of this embodiment, an automotive-grade power management chip is used to track, mix, and store the maximum power point of the three energy sources: solar, thermal, and radio frequency energy, and to calculate the system's energy state and predict future revenue in real time. Based on historical data and current weather data obtained from the network, a storage energy prediction table for a future period is generated, providing preliminary predictions of total input power, planned power consumption, and other data.

[0035] In some embodiments of this example, the method for obtaining the mixed multi-source environmental energy within each cycle is as follows: The exponential smoothing method is used to obtain the mixed multi-source environmental energy within the current period. The calculation formula is as follows:

[0036] in, This is the predicted power from the previous cycle. This is the measured power from the previous cycle. It is the predicted power for the current period (made in the previous period). It is a smoothing factor, with a value ranging from 0 to 1, and typically from 0.3 to 0.5; A power and weather condition preset table is established. The power and weather condition preset table is used to characterize the mapping relationship between photovoltaic power, thermoelectric power, radio frequency power and total power and weather conditions, as shown in the table below, which is the power and weather condition preset table for a certain vehicle. Table 1. Power and Weather Condition Preset Table for a Certain Vehicle

[0037] Based on the power obtained from the mapping table and trend prediction, the mixed multi-source environmental energy in the current period is calculated using the following formula:

[0038] in, It is the final multi-source environmental energy mixed within the current cycle, for The power is obtained from weather forecasts. It is the confidence level, calculated based on factors such as the reliability of weather forecasts and historical accuracy, and its value ranges from 0 to 1.

[0039] If the weather forecast is very reliable, then C is close to 1; if the weather forecast is unreliable (for example, due to poor network signal, only a rough estimate can be made), then C is close to 0, relying more on trend prediction.

[0040] In some embodiments of this example, the period includes multiple time periods, and the method for obtaining the mixed multi-source environmental energy within each time period of the period is as follows: The photovoltaic prediction sub-model, thermoelectric prediction sub-model, and radio frequency prediction sub-model are used to predict the predicted photovoltaic input power, predicted thermoelectric input power, and predicted radio frequency input power for the current time period, respectively. The predicted photovoltaic input power, predicted thermoelectric input power, and predicted radio frequency input power for the current time period are weighted and summed to obtain the preliminary predicted total input power for the current time period. The initial predicted total input power for the current period is corrected using a correction factor to obtain the predicted total input power for the current period, so that the predicted total input power gradually approaches the reality. Based on energy constraints, the stored energy for the current period is obtained by taking into account the stored energy of the previous period, the predicted total input power for the current period, and the planned power consumption.

[0041] In some implementations of this embodiment, a photovoltaic prediction sub-model is used to predict the predicted photovoltaic input power for the current time period. The method is as follows: Based on future weather data (sunny, cloudy, rainy), solar altitude angle (time, GPS location), and season, generate the relative illuminance coefficient every 15 minutes for the next 24 hours (each time period is 15 minutes). (0~1), the photovoltaic input power is predicted by the relative illuminance coefficient, and the calculation formula is:

[0042] in, It is the maximum power of the photovoltaic unit under standard illumination.

[0043] In some embodiments of this example, the maximum power of the photovoltaic unit under standard illumination is estimated using the MPPT (Maximum Power Point Tracking) algorithm based on the improved perturbation observation method, specifically as follows: Set status parameters, including search direction and step size; Initialize the state parameters, set the search direction to the voltage increase direction, and set the step size to a preset step size; The formula for obtaining the current power and power change is as follows:

[0044]

[0045] in, For the current power, The power at the previous moment, This is the current voltage of the photovoltaic branch. This is the current. If the absolute value of the power change is less than or equal to a preset threshold, the search direction and the step size remain unchanged; if the absolute value of the power change is greater than the preset threshold, the search direction and the step size remain unchanged until the absolute value of the power change is greater than the preset threshold three times in a row, then the search direction is the direction of decreasing voltage, and the step size is the product of a preset step size and a preset coefficient.

[0046] Repeat the process of setting the state parameters until the step size is the product of the preset step size and the preset coefficient, and repeat this process in a loop.

[0047] In some implementations of this embodiment, a thermoelectric prediction sub-model is used to predict the photovoltaic input power for the current time period. The method is as follows: Based on predicted outside ambient temperature Predicted interior temperature (Based on thermal inertia and solar radiation models), the temperature difference is obtained, and the calculation formula is as follows: ; Based on the temperature difference and the characteristic constants of the thermoelectric element, the predicted photovoltaic input power is obtained using the following formula:

[0048] in, is the characteristic constant of the thermoelectric element.

[0049] In some implementations of this embodiment, the predicted radio frequency input power for the current time period is predicted using a radio frequency prediction sub-model. The method is as follows: Based on historical radio frequency (RF) energy acquisition data from the same period and geographical location information (e.g., whether located in a city center or suburbs), a historical average RF energy baseline is obtained. ; Based on the historical average RF energy baseline, the predicted RF input power is obtained using the following formula: .

[0050] In some embodiments of this example, the formula for calculating the preliminary predicted total input power for the current time period is as follows: .

[0051] In some embodiments of this example, the method for obtaining the correction factor is as follows: The actual total input power for the current time period is obtained. The photovoltaic prediction sub-model, thermoelectric prediction sub-model, and radio frequency prediction sub-model are used to predict the predicted photovoltaic input power, predicted thermoelectric input power, and predicted radio frequency input power for the current time period, respectively. The predicted photovoltaic input power, predicted thermoelectric input power, and predicted radio frequency input power for the current time period are weighted and summed to obtain the preliminary predicted total input power for the current time period. The actual total input power is compared with the preliminary predicted total input power to obtain the correction factor.

[0052] Specifically, a correction factor is obtained by comparing the actual total input power of the most recent 15 minutes with the preliminary predicted total input power of the previous period. .use Slightly revise the initial predicted total input power value for the current period to gradually bring the initial predicted total input power closer to reality.

[0053] In some embodiments of this example, the formula for calculating the actual total input power in the current time period is:

[0054] in, This is the voltage of the supercapacitor. This represents the actual input power of the photovoltaic system. This represents the actual input power of the thermoelectric generator. This represents the actual input power at radio frequency.

[0055] The formula for calculating the planned power consumption for the current time period is:

[0056] in, This represents the load of the low-power service module during the current time period.

[0057] The formula for calculating the stored energy in the current period is:

[0058] in, It is a supercapacitor.

[0059] Will , , Store the data in a queue of length L (e.g., the past hour) for short-term trend analysis.

[0060] In some embodiments of this example, based on energy constraints, the stored energy for the current time period is obtained according to the stored energy, the predicted total input power, and the planned power consumption. The calculation formula is as follows:

[0061] in, The time period is 15 minutes, and the time period is 900 seconds.

[0062] In this embodiment, the energy constraints include hard constraints and soft constraints. The calculation formula for hard constraints is as follows:

[0063] in, This is the first threshold.

[0064] The formula for calculating soft constraints is:

[0065] in, This is the second threshold.

[0066] In some embodiments of this example, the formula for calculating the total stored energy in each cycle is as follows:

[0067] in, This represents the total number of time periods within the cycle.

[0068] Step S103: Using an automotive-grade dual-mode control module, based on the mixed and stored multi-source environmental energy of the dynamic energy budget and management module, a task scheduling algorithm for a low-power service module is generated, wherein the low-power service module includes several low-power sensors.

[0069] In some embodiments of this example, the automotive-grade dual-mode control module employs a low-power MCU, connected to the vehicle's existing hardware security chip. The MCU internally stores a task scheduling algorithm. It includes built-in P0 / P1 / P2 task lists and their respective power consumption models, executes tasks according to a budget table, and monitors real-time changes in task execution frequency and intensity.

[0070] In some embodiments of this example, the task scheduling algorithm for generating low-power service modules includes: Based on the service priority strategy for the current time period, obtain the execution cost; Based on the storage energy and execution cost for the current period, a predicted working mode for the current period is generated, and the storage energy for the current period is allocated according to the predicted working mode.

[0071] This embodiment provides an energy-autonomous long-term online vehicle TBOX management method. Energy independence: After the vehicle goes into hibernation, the system switches to collecting and storing multi-source environmental energy through the dynamic energy budget and management module, and generates a task scheduling algorithm for the low-power service module based on the multi-source environmental energy mixed and stored by the dynamic energy budget and management module, and performs low-power scheduling for the low-power service module.

[0072] In some implementations of this embodiment, such as Figure 4 As shown, the service priority policy includes security and access assurance services ( ), vehicle environment perception services and data interaction services ( ) and data interaction services ( Based on the service priority strategy for the current time period, the execution cost is obtained, and the calculation formula is as follows:

[0073] in, All are weights.

[0074] Specifically, P0 level (Security and Access Assurance Service): Maintains continuous monitoring of encrypted wireless signals (such as BLE, UWB) to achieve seamless connection and welcome unlocking / unlocking with authorized devices.

[0075] Level P1 (Vehicle Environmental Awareness Service): Dispatch low-power sensors (millimeter-wave radar for monitoring minute movements around the vehicle) to operate intermittently, enabling a basic, sustainable sentry mode. Its operating frequency and accuracy can be dynamically adjusted based on available energy.

[0076] Level P2 (Data Interaction Service): When there is a stable energy surplus, the main communication module is activated to synchronize vehicle status data or event logs with the cloud.

[0077] To address the instability of environmental energy, this embodiment defines a three-level prediction operating mode: Normal mode: When energy storage is sufficient and the ambient energy input is stable, all services of P0, P1, and P2 are executed according to the optimal parameters.

[0078] Energy-saving mode (ECO mode): When energy revenue decreases, P0 service is prioritized, the frequency and accuracy of P1 service are reduced, and P2 service is suspended.

[0079] CRITICAL Mode: In the event of severe energy shortage, only the most basic listening of P0 service is maintained, and a controlled emergency power borrowing protocol is activated to obtain the minimum necessary energy from the vehicle's main battery under strict safety and logic conditions to prevent system shutdown.

[0080] In some embodiments of this example, based on the stored energy and execution cost of the current time period, a predicted operating mode for the current time period is generated, and the stored energy for the current time period is allocated according to the predicted operating mode, including: The predicted operating modes are defined as normal mode, energy-saving mode, and extreme mode; The energy revenue for the current period is obtained by standardizing the stored energy and execution costs, then weighting and summing them. Determine the forecasting mode for the current time period. When energy income is greater than or equal to the first threshold, the prediction working mode for the current period is the normal mode, which includes security and access assurance services, vehicle environment perception services, and data interaction services. When energy income is less than a first threshold but greater than a second threshold, the prediction working mode for the current period is the energy-saving mode, which includes security and access assurance services and vehicle environmental perception services. When energy revenue is less than the second threshold, the forecasting mode for the current period is the extreme mode, which includes security and access assurance services. The first threshold is greater than the second threshold.

[0081] Specifically, based on the stored energy and execution cost of the current time period, a predicted working mode for the current time period is generated, and the stored energy for the current time period is allocated according to the predicted working mode. Within all 16 time periods, the fixed energy required to execute the P0 task is first deducted. Calculate deductions Afterwards, from Initially, relying solely on revenue forecasts The energy storage trajectory at the end of the time period. If this trajectory remains above... If some time is less than 1000, then enter NORMAL mode; if some time is less than 1000, then enter NORMAL mode. But higher If it is touched at any time, it will enter ECO mode; Then enter CRITICAL mode.

[0082] In some embodiments of this example, surplus energy is allocated according to a pattern. .

[0083] NORMAL mode: Distribute surplus energy in the order of P1 (high) and P2 to fill each time period as much as possible.

[0084] ECO mode: Reduce P1 intensity (from medium / high to low) and completely suspend P2.

[0085] CRITICAL Mode: P1 Pause. Calculate the minimum energy required to maintain P0 until the predicted revenue recovery. If the current energy storage is below this value, immediately trigger the "Controlled Emergency Power Borrowing Protocol" to borrow just enough energy from the main battery to bring the energy storage back above the safety line. Simultaneously, mark this state.

[0086] The specific calculation formula is as follows:

[0087]

[0088] in, For the first Energy allocation at each level, For the first The weight of each level, .

[0089] In some implementations of this embodiment, the supercapacitor is estimated using an extended Kalman filter.

[0090] In some embodiments of this example, when using the energy-autonomous long-term online vehicle-mounted TBOX management method provided in this example for vehicle-mounted TBOX management, it further includes: The total recovered energy for each cycle is calculated using the following formula: .

[0091] A minimum energy level is set to maintain basic operation; the calculation formula is as follows:

[0092] in, This refers to the energy required to maintain a P0-level task (security surveillance) over a future period. Assume the power consumption of a P0-level task is... ,but , For the budget period (e.g., 24 hours). If This indicates that even running only the P0 task may not be enough, and in this case, it is necessary to enter emergency mode.

[0093] According to the second aspect of this application, such as Figure 5 As shown in the figure, this embodiment provides an energy-autonomous long-term online vehicle-mounted TBOX management system, applied to a vehicle-mounted TBOX, including: Multi-source environmental energy harvesting module, used to harvest multi-source environmental energy; The dynamic energy budget and management module is electrically connected to the multi-source environmental energy harvesting module and is used to mix and store multi-source environmental energy using an automotive-grade power management chip. The low-power service module includes several low-power sensors; The automotive-grade dual-mode control module is electrically connected to both the dynamic energy budget and management module and the low-power service module. It is used to generate the task scheduling algorithm of the low-power service module based on the multi-source environmental energy mixed and stored by the dynamic energy budget and management module.

[0094] In some implementations of this embodiment, such as Figure 2 and Figure 3 As shown, the multi-source environmental energy harvesting module includes: Photovoltaic units are used to collect solar energy using photovoltaic thin films; The thermoelectric unit is used to collect heat energy generated by the temperature difference between the inside and outside of the vehicle by using a miniature thermoelectric generator array attached between the shell of the vehicle TBOX and the body sheet metal. The radio frequency unit is used to integrate a wideband microstrip antenna and a low-power rectifier circuit on the vehicle-mounted TBOX to collect radio frequency energy from the environment.

[0095] Specifically, the photovoltaic thin film is integrated into the sunshade area along the upper edge of the windshield or the lower layer of the sunroof glass.

[0096] The RF unit is a wideband microstrip antenna and low-power rectifier circuit integrated on the TBOX PCB, used to capture RF energy in the environment at frequency bands such as 900MHz / 1.8GHz / 2.4GHz.

[0097] In some implementations of this embodiment, the low-power service module includes BLE (Bluetooth Low Energy), UWB (Ultra-Wideband), NFC (Near Field Communication), cellular IoT, millimeter-wave radar, vibration sensor, GPS / BeiDou, and CAN interface.

[0098] In this embodiment, as Figures 2-4 As shown, the photovoltaic unit (photovoltaic thin film) transmits power to the automotive-grade PMIC via DC cable, converting solar energy into electrical energy; the thermoelectric unit (micro thermoelectric generator array) transmits power to the automotive-grade PMIC via DC output, converting thermoelectricity into electrical energy; and the radio frequency unit (wideband microstrip antenna and low-power rectifier circuit) transmits power to the automotive-grade PMIC via DC output, converting radio frequency signals into electrical energy.

[0099] Vehicle main battery (emergency power): Power is supplied to the automotive-grade PMIC via an emergency cable (emergency power supply to the main battery).

[0100] The dynamic energy budget and management module includes an automotive-grade power management chip (PMIC, Power Management Integrated Circuits), a security chip, and a supercapacitor. The automotive-grade power management chip draws power from the photovoltaic, thermoelectric, and radio frequency units; the supercapacitor stores energy and provides energy buffering; and the security chip provides hardware encryption and is electrically connected to the automotive-grade power management chip via VCC / GND to draw power.

[0101] The automotive-grade dual-mode control module includes an ultra-low power MCU (Micro Control Unit) and a main controller.

[0102] Low-power service modules include body control modules (BCM), BLE / UWB modules, millimeter-wave radar, cellular communication modules, and licensed devices, etc., but this embodiment does not limit them.

[0103] The security chip and ultra-low power MCU are electrically connected via I2C / SPI, while the millimeter-wave radar and ultra-low power MCU are electrically connected via CAN / LIN. The ultra-low power MCU is electrically connected to the automotive-grade power management chip via VCC / GND to obtain power. The ultra-low power MCU is electrically connected to the vehicle body BCM via CAN (Controller Area Network) and to the BLE / UWB module via SPI (Serial Peripheral Interface). The BLE / UWB module is electrically connected to the automotive-grade power management chip via VCC / GND to obtain power, and the BLE / UWB module is wirelessly connected to the authorized equipment. The main controller and the cellular communication module are electrically connected via UART (Universal Asynchronous Receiver / Transmitter), and the cellular communication module and authorized equipment are both wirelessly connected to the cloud via the cellular network.

[0104] Specifically, this embodiment corresponds one-to-one with the above method embodiments. The functions of each module have been described in detail in the corresponding method embodiments, so they will not be repeated here.

[0105] According to a third aspect of this application, this embodiment provides a computer-readable storage medium having a computer program stored thereon, the computer program including executable instructions that, when executed by a processor, implement the method described above.

[0106] The present invention can implement all or part of the processes in the above methods, or it can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or system capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0107] According to the fourth aspect of this application, such as Figure 6 As shown, an electronic device is provided, comprising: One or more processors; Memory is used to store executable instructions for the processor, which, when executed by one or more processors, cause one or more processors to implement the methods described above.

[0108] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and a bus connecting different system components (including memory and processor).

[0109] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of a computer system, connecting all parts of the computer system through various interfaces and lines.

[0110] Memory can be used to store computer programs and / or modules. The processor implements various functions of the computer system by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system and at least one application program required for a function (e.g., sound playback, image playback, etc.); the data storage area can store data created based on the use of the mobile phone (e.g., audio data, video data, etc.). Furthermore, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMedia Cards (SMC), Secure Digital (SD) cards, Flash Cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0111] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, servers, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and memory) containing computer-usable program code.

[0112] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), servers, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0116] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0117] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for energy-autonomous long-term online vehicle-mounted TBOX management, applied to vehicle-mounted TBOX, characterized in that, include: Multi-source environmental energy harvesting modules are used to collect multi-source environmental energy; A dynamic energy budgeting and management module is used for the mixing and storage of multi-source environmental energy. Based on the mixed and stored multi-source environmental energy of the dynamic energy budget and management module, a task scheduling algorithm for generating low-power service modules is adopted by the automotive-grade dual-mode control module. The low-power service modules include several low-power sensors.

2. The method according to claim 1, characterized in that, The method for obtaining the mixed multi-source environmental energy within each cycle is as follows: The exponential smoothing method is used to obtain the mixed multi-source environmental energy within the current period. The calculation formula is as follows: in, This is the predicted power from the previous cycle. This is the measured power from the previous cycle. It is the predicted power for the current cycle. It is a smoothing factor, with a value ranging from 0 to 1; A power and weather condition preset table is established, which is used to characterize the mapping relationship between photovoltaic power, thermoelectric power, radio frequency power and total power and weather conditions, respectively. Based on the power obtained from the mapping table and trend prediction, the mixed multi-source environmental energy in the current period is calculated using the following formula: in, It is the final multi-source environmental energy mixed within the current cycle. It is the power obtained from weather forecasts. It's the confidence level.

3. The method according to claim 1, characterized in that, The multi-source environmental energy includes solar energy, thermal energy, and radio frequency energy. The period includes multiple time periods. The method for obtaining the mixed multi-source environmental energy in each time period within the period is as follows: The photovoltaic prediction sub-model, thermoelectric prediction sub-model, and radio frequency prediction sub-model are used to predict the predicted photovoltaic input power, predicted thermoelectric input power, and predicted radio frequency input power for the current time period, respectively. The predicted photovoltaic input power, predicted thermoelectric input power, and predicted radio frequency input power for the current time period are weighted and summed to obtain the preliminary predicted total input power for the current time period. The initial predicted total input power for the current period is corrected using a correction factor to obtain the predicted total input power for the current period, so that the predicted total input power for the current period gradually approaches the actual total input power for the current period. Based on energy constraints, the stored energy for the current period is obtained from the stored energy of the previous period, the predicted total input power for the current period, and the planned power consumption. The calculation formula is as follows: in, For time periods, For the energy stored in the previous period, This represents the predicted total input power for the current time period. The planned power consumption for the current time period. This is the correction factor.

4. The method according to claim 3, characterized in that: The method of using a photovoltaic prediction sub-model to predict the predicted photovoltaic input power for the current time period includes: Based on future weather data, solar altitude angle, and season, a relative illuminance coefficient is generated for each time period within the cycle. The maximum power of the photovoltaic unit under standard illumination is estimated using the MPPT algorithm based on an improved perturbation observation method. The photovoltaic input power is then predicted using the relative illuminance coefficient and the maximum power of the photovoltaic unit under standard illumination; and / or, The method of using a thermoelectric prediction sub-model to predict the predicted thermoelectric input power for the current time period includes: The temperature difference is obtained based on the predicted outside temperature and the predicted inside temperature. Based on the temperature difference and the characteristic constants of the thermoelectric element, the predicted photovoltaic input power is obtained; and / or, The predicted radio frequency input power for the current time period is predicted using a radio frequency prediction sub-model, including: Based on historical radio frequency energy acquisition data and geographical location information for the same period, a historical average radio frequency energy baseline is obtained; Based on a historical average RF energy baseline, obtain the predicted RF input power; and / or, The method for obtaining the correction factor is as follows: The actual total input power for the current time period is obtained. The photovoltaic prediction sub-model, thermoelectric prediction sub-model, and radio frequency prediction sub-model are used to predict the predicted photovoltaic input power, predicted thermoelectric input power, and predicted radio frequency input power for the current time period, respectively. The predicted photovoltaic input power, predicted thermoelectric input power, and predicted radio frequency input power for the current time period are weighted and summed to obtain the preliminary predicted total input power for the current time period. The actual total input power is compared with the preliminary predicted total input power to obtain the correction factor.

5. The method according to claim 3, characterized in that, The task scheduling algorithm includes: Based on the service priority strategy for the current time period, obtain the execution cost; Based on the storage energy and execution cost for the current period, a predicted working mode for the current period is generated, and the storage energy for the current period is allocated according to the predicted working mode.

6. The method according to claim 5, characterized in that: The service priority strategy includes security and access assurance services, vehicle environment perception services, and data interaction services. Based on the service priority strategy for the current time period, the execution cost for the current time period is obtained, and the calculation formula is as follows: in, All are weights. For security and access assurance services, Provides services for vehicle environmental perception and data interaction. For data interaction services; and / or, Based on the current storage energy and execution cost, a predicted operating mode for the current period is generated, and the storage energy for the current period is allocated according to the predicted operating mode, including: The predicted operating modes are defined as normal mode, energy-saving mode, and extreme mode; The energy revenue for the current period is obtained by standardizing the stored energy and execution costs, then weighting and summing them. Determine the forecasting mode for the current time period. When energy income is greater than or equal to a first threshold, the prediction working mode for the current period is normal mode, which includes security and access assurance services, vehicle environment perception services, and data interaction services. When energy income is less than a first threshold but greater than a second threshold, the prediction working mode for the current period is the energy-saving mode, which includes security and access assurance services and vehicle environmental perception services. When energy revenue is less than the second threshold, the forecasting mode for the current period is the extreme mode, which includes security and access assurance services. The first threshold is greater than the second threshold.

7. A self-sustaining, long-term online vehicle-mounted TBOX management system, characterized in that, include: Multi-source environmental energy harvesting module, used to harvest multi-source environmental energy; The dynamic energy budget and management module is electrically connected to the multi-source environmental energy harvesting module and is used to mix and store multi-source environmental energy using an automotive-grade power management chip. The low-power service module includes several low-power sensors; The automotive-grade dual-mode control module is electrically connected to both the dynamic energy budget and management module and the low-power service module. It is used to generate the task scheduling algorithm of the low-power service module based on the multi-source environmental energy mixed and stored by the dynamic energy budget and management module.

8. The system according to claim 7, characterized in that, The multi-source environmental energy harvesting module includes: Photovoltaic units are used to collect solar energy using photovoltaic thin films; The thermoelectric unit is used to collect heat energy generated by the temperature difference between the inside and outside of the vehicle by using a miniature thermoelectric generator array attached between the shell of the vehicle TBOX and the body sheet metal. The radio frequency unit is used to integrate a wideband microstrip antenna and a low-power rectifier circuit on the vehicle-mounted TBOX to harvest radio frequency energy from the environment; and / or, The dynamic energy budget and management module also includes a supercapacitor, which is used for mixing and storing multi-source environmental energy.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program includes executable instructions that, when executed by a processor, implement the method of claim 8.

10. An electronic device, characterized in that, include: One or more processors; A memory for storing executable instructions of the processor, which, when executed by the one or more processors, cause the one or more processors to implement the method of claim 8.

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