In-vehicle interconnection and mutual switching system and method thereof

Abstract

The invention provides an in-vehicle interconnection and mutual switching system and a method thereof, relates to the technical field of vehicle intelligence technology, electric vehicle energy management and block chain, in particular to the in-vehicle interconnection and mutual switching system and the method thereof, and aims to utilize a vehicle-mounted battery of an electric vehicle as a distributed energy storage resource. Intelligent balancing of the power grid load is realized through a block chain power grid load monitoring module, a battery state and demand prediction module, a load balancing strategy processing module, a user interaction module, an intelligent scheduling module and the like. The system collects power grid load data in real time, predicts the power grid demand in combination with the battery state, formulates and executes a load balancing strategy, effectively relieves the load pressure in the peak period of the power grid, and improves the energy utilization efficiency. Meanwhile, a user can flexibly adjust a battery use strategy through an interactive interface, and intelligent scheduling and optimization are achieved.

Description

Technical Field

[0001] The present invention relates to the fields of automobile intelligent technology, electric vehicle energy management and blockchain technology, and in particular to an in-vehicle interconnection and mutual investment system and method thereof. Background Art

[0002] With the rapid development of automobile technology, in-vehicle interconnection and mutual investment systems have gradually become an important part of modern automobile intelligence. Existing in-vehicle interconnection and mutual investment systems mainly rely on vehicle networks, wireless communication technologies and smart device interfaces to achieve information sharing and interaction between various components and systems inside the vehicle and smart devices carried by passengers. These systems usually include entertainment systems, navigation systems, vehicle status monitoring systems, and interconnection functions between passengers' personal devices and vehicles.

[0003] For new energy vehicles such as electric vehicles, the existing system fails to fully utilize the energy storage capacity of on-board batteries to achieve effective mutual investment between vehicle energy and the power grid or other vehicles to alleviate the load pressure on the power grid or improve energy utilization efficiency. The problem of power grid load fluctuation is becoming increasingly prominent, especially during peak hours. As a distributed energy storage unit, the on-board batteries of electric vehicles will significantly increase the load on the power grid during charging, especially during peak electricity consumption hours. This not only increases the load pressure on the power grid, but may also cause risks such as power grid instability, voltage fluctuations and even power outages, posing a threat to the safe and stable operation of the power system.

[0004] The existing in-vehicle interconnection and mutual investment system mainly focuses on data exchange and sharing between various systems inside the vehicle, as well as the interconnection and mutual investment between the vehicle and external intelligent devices, but it is still insufficient in solving the impact of electric vehicles on the grid load. At the same time, traditional grid load management methods mainly rely on the dispatch of large energy storage facilities and power plants, but these methods have problems such as slow response speed, poor flexibility, and high investment costs. As a potential distributed energy storage resource, electric vehicle onboard batteries have the advantages of large quantity, wide distribution, and rapid response. If they can be effectively used, they will play an important role in alleviating the load pressure of the grid during peak hours.

[0005] Therefore, in view of the impact of electric vehicle on-board batteries on the grid load during peak hours, we disclose an in-vehicle interconnection and mutual investment system and method thereof, aiming to solve the above technical problems. Summary of the invention

[0006] In order to achieve the above purpose, the technical solution adopted by the present invention is: An in-vehicle interconnection and mutual projection system, comprising: The blockchain power grid load monitoring module is used to collect and store power grid load data in real time and accurately; The battery status and demand forecasting module is used to receive real-time load data from the blockchain power grid load monitoring module and conduct comprehensive analysis and forecasting. The forecast results will be passed as input to the load balancing strategy processing module to formulate appropriate load balancing strategies; The load balancing strategy processing module is used to receive the prediction results from the battery status and demand prediction module, and calculate the optimal load balancing strategy through complex algorithms based on the actual situation of the current power grid load; The user interaction and intelligent scheduling module is responsible for interacting with users, displaying information such as grid load, battery status, and load balancing strategy, and automatically or manually adjusting the battery charging and discharging behavior according to the output of the load balancing strategy processing module.

[0007] Preferably, the blockchain power grid load monitoring module includes a load monitoring unit and a blockchain synchronization unit. The load monitoring unit monitors the load data of the power grid in real time and collects real-time load information of the power grid for analyzing load fluctuations. The blockchain synchronization unit synchronizes the power grid load data to the blockchain to ensure the immutability and transparency of the load data. Preferably, the battery status and demand forecasting module includes a battery status monitoring unit and a demand forecasting unit. The battery status monitoring unit obtains the charging and discharging status of the vehicle battery in real time, and is used to monitor the battery power, health status and charging and discharging capacity; the demand forecasting unit uses historical data and machine learning algorithms to predict grid demand, introduces a short-term load forecasting algorithm, and accurately predicts the grid load in the future, providing a basis for battery scheduling.

[0008] Preferably, the load balancing strategy processing module includes a load balancing strategy unit and a strategy execution unit. The load balancing strategy unit formulates a balancing strategy according to the grid load and the battery status, and is used to dynamically adjust the battery charging and discharging strategy to balance the grid load; the strategy execution unit executes the formulated load balancing strategy, and is used for the battery to feed power to the grid during peak hours and charge from the grid during off-peak hours, thereby smoothing out grid load fluctuations.

[0009] Preferably, the user interaction and intelligent scheduling module includes a user scheduling interface unit and an intelligent scheduling unit. The user scheduling interface unit provides a user operation interface, displays the grid load and battery status, and allows the user to adjust the battery usage strategy according to the grid load conditions; the intelligent scheduling unit automatically adjusts the battery charging and discharging according to the grid load, and adopts a reinforcement learning algorithm to achieve self-learning and optimization of the battery charging and discharging strategy.

[0010] Preferably, the load monitoring unit adopts a distributed sensor network, deploys high-precision sensors at key nodes of the power grid, monitors the current, voltage and frequency parameters of the power grid in real time, and aggregates sensor data on edge computing devices to reduce the amount of data transmission and improve data processing speed.

[0011] Preferably, edge computing processing is adopted in the demand forecasting unit. Before the data enters the forecasting model, the sensor data is first cleaned, converted and standardized to ensure that the forecasting model can be trained and analyzed based on high-quality data, and edge computing equipment is used for real-time data analysis. A deep neural network machine learning model is used to perform real-time load forecasting to improve the accuracy and timeliness of the forecast.

[0012] Preferably, in the demand forecasting unit, forecasting model training is adopted, and the forecasting model is trained and updated on the edge computing device. The model will be continuously adjusted and optimized according to real-time data, and the performance of the forecasting model will be evaluated through cross-validation and real-time feedback, and the model parameters will be continuously optimized to improve the accuracy of the forecast.

[0013] An in-vehicle interconnection and mutual investment method is applied to the in-vehicle interconnection and mutual investment system mentioned above, and the method comprises the following steps: Step S1: deploy load monitoring units, install high-precision sensors at grid nodes using a distributed sensor network, and monitor parameters such as current, voltage, and frequency in real time; Step S2: The load monitoring unit collects grid load data in real time and uploads the data to the blockchain through the blockchain synchronization unit; Step S3: The battery status monitoring unit acquires and processes the charging and discharging status, power, health status and charging and discharging capacity information of the vehicle battery in real time; Step S4: The demand forecasting unit receives the real-time load data from the blockchain power grid load monitoring module and the data from the battery status monitoring unit; Step S5: On the edge computing device, the sensor data is cleaned, converted, and standardized to improve the data quality; Step S6: Use the deep neural network model to perform real-time load forecasting, adjust and optimize the forecasting model according to real-time data, and improve the forecasting accuracy; Step S7: The load balancing strategy processing module receives the prediction result of the demand prediction unit and combines it with the actual situation of the current power grid load; Step S8: Calculate the optimal load balancing strategy through complex algorithms and dynamically adjust the battery charging and discharging strategy; Step S9: the strategy execution unit automatically adjusts the charging and discharging behavior of the battery according to the output of the load balancing strategy processing module; Step S10: The user interaction and intelligent scheduling module displays the grid load, battery status and load balancing strategy information through the user scheduling interface unit, allowing the user to make manual adjustments as needed; Step S11: The intelligent dispatching unit automatically adjusts the battery charging and discharging strategy according to the grid load, and continuously optimizes the dispatching strategy through a reinforcement learning algorithm; Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention achieves grid load balance by dynamically adjusting the battery charging and discharging strategy, effectively alleviating grid load pressure during peak hours. It makes full use of the energy storage capacity of electric vehicle onboard batteries, realizes effective mutual investment between vehicle energy and the grid or other vehicles, and improves energy utilization efficiency.

[0014] 2. Use blockchain technology to record the process and results of each strategy adjustment, ensure the authenticity and immutability of the data, and enhance the security and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the system flow of the present invention; Figure 2 It is a schematic diagram of the steps of the method of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0017] In the in-vehicle interconnection and mutual investment system, especially in the application scenario of load balancing of electric vehicle on-board batteries during peak hours, the existing in-vehicle interconnection and mutual investment system mainly focuses on data exchange and sharing between various systems inside the vehicle, as well as interconnection and mutual investment between the vehicle and external smart devices, but it is still insufficient in solving the impact of electric vehicles on the grid load.

[0018] Based on the above background technology, the present invention proposes an in-vehicle interconnection and mutual investment system and method, which are used for load balancing of electric vehicle on-board batteries during peak hours, aiming to achieve effective balancing of electric vehicle on-board batteries for grid load during peak hours through innovative system architecture and algorithm design, while improving the interconnection and mutual investment efficiency and user experience between various systems in the vehicle. Through real-time monitoring and short-term load forecasting, data support is provided for battery scheduling, and dynamic balancing of grid load is achieved through intelligent adjustment of battery charging and discharging strategies. User participation and system self-optimization are achieved through user interfaces and intelligent scheduling algorithms.

[0019] The present invention is further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0020] Reference Figure 1 , is an embodiment of the present invention, which provides an in-vehicle interconnection and mutual investment system, including: The blockchain power grid load monitoring module is mainly used to collect and store power grid load data in real time and accurately, ensuring the transparency, immutability and traceability of the data. Through blockchain technology, these load data can be shared and verified by all participants, including power grid operators, electric vehicle owners, and energy management systems.

[0021] The data collected by the blockchain power grid load monitoring module is the basis for subsequent modules to make decisions and optimize. It transmits real-time load information to the battery status and demand forecasting module to predict future load trends; at the same time, it also provides the actual situation of the current power grid load to the load balancing strategy processing module to guide the formulation of battery charging and discharging strategies.

[0022] The battery status and demand forecasting module is responsible for monitoring the real-time status of the electric vehicle's onboard battery, including power, health, and charging and discharging capabilities, and combines historical data and machine learning algorithms to predict future grid demand and battery demand. This forecast helps plan battery charging and discharging plans in advance to match grid load changes.

[0023] The battery status and demand forecasting module receives the real-time load data from the blockchain power grid load monitoring module and conducts a comprehensive analysis in combination with the data from the battery status monitoring unit. The forecast results will be passed as input to the load balancing strategy processing module in order to formulate a suitable load balancing strategy.

[0024] The load balancing strategy processing module is the core of the system. It dynamically formulates and adjusts the battery charging and discharging strategy based on the real-time situation of the grid load and the predicted results of the battery status. The goal of the strategy is to smooth the load fluctuation of the grid as much as possible while ensuring the normal use needs of electric vehicle owners.

[0025] It receives the prediction results from the battery status and demand prediction module, and combines the actual situation of the current grid load to calculate the optimal load balancing strategy through complex algorithms. Then, the strategy will be passed to the user interaction and intelligent scheduling module for execution.

[0026] User interaction and intelligent scheduling module, which is responsible for interacting with users, displaying information such as grid load, battery status, and load balancing strategy, and automatically or manually adjusting the battery charging and discharging behavior according to the output of the strategy processing module. In addition, it also has the function of intelligent scheduling, which can continuously optimize the scheduling strategy based on algorithms such as reinforcement learning. It is a bridge between the system and the user, providing an intuitive operation interface through the user scheduling interface unit, allowing users to understand the system status and participate in the scheduling process. At the same time, the intelligent scheduling unit automatically performs battery charging and discharging operations according to the strategy formulated by the strategy processing module, and interacts with the user to obtain more information or adjust the strategy when necessary.

[0027] These four modules are closely connected through data flow and control flow, and together form a closed-loop load balancing system. The blockchain power grid load monitoring module provides real-time and accurate data foundation; the battery status and demand forecasting module performs forecast analysis based on these data; the load balancing strategy processing module formulates strategies based on the forecast results; finally, the user interaction and intelligent scheduling module executes strategies and interacts with users. The whole process realizes seamless connection from data collection, forecast analysis, strategy formulation to execution and scheduling, effectively solving the problem of the impact of electric vehicle on-board batteries on power grid load during peak hours.

[0028] In this embodiment, the blockchain power grid load monitoring module includes a load monitoring unit and a blockchain synchronization unit. The load monitoring unit monitors the load data of the power grid in real time and collects real-time load information of the power grid for analyzing load fluctuations. The blockchain synchronization unit synchronizes the power grid load data to the blockchain to ensure the immutability and transparency of the load data. In this embodiment, the battery status and demand forecasting module includes a battery status monitoring unit and a demand forecasting unit. The battery status monitoring unit obtains the charging and discharging status of the vehicle battery in real time, and is used to monitor the battery power, health status and charging and discharging capacity; the demand forecasting unit uses historical data and machine learning algorithms to predict grid demand, introduces a short-term load forecasting algorithm, and accurately predicts the grid load in the future, providing a basis for battery scheduling.

[0029] In this embodiment, the load balancing strategy processing module includes a load balancing strategy unit and a strategy execution unit. The load balancing strategy unit formulates a balancing strategy according to the grid load and the battery status, and is used to dynamically adjust the battery charging and discharging strategy to balance the grid load; the strategy execution unit executes the formulated load balancing strategy, and is used for the battery to feed power to the grid during peak hours and charge from the grid during off-peak hours, thereby smoothing grid load fluctuations.

[0030] In this embodiment, the user interaction and intelligent scheduling module includes a user scheduling interface unit and an intelligent scheduling unit. The user scheduling interface unit provides a user operation interface, displays the grid load and battery status, and allows the user to adjust the battery usage strategy according to the grid load conditions; the intelligent scheduling unit automatically adjusts the battery charging and discharging according to the grid load, and adopts a reinforcement learning algorithm to achieve self-learning and optimization of the battery charging and discharging strategy.

[0031] In this embodiment, the load monitoring unit adopts a distributed sensor network, deploys high-precision sensors at power grid nodes, monitors power grid current, voltage and frequency parameters in real time, and aggregates sensor data on edge computing devices to reduce data transmission volume and improve data processing speed.

[0032] In this embodiment, edge computing processing is further adopted in the demand forecasting unit. Before the data enters the forecasting model, the sensor data is first cleaned, converted and standardized to ensure that the forecasting model can be trained and analyzed based on high-quality data. Edge computing devices are used for real-time data analysis, and deep neural network machine learning models are used to perform real-time load forecasting to improve the accuracy and timeliness of the forecast.

[0033] In this embodiment, the demand forecasting unit adopts forecasting model training, and the forecasting model is trained and updated on the edge computing device. The model is continuously adjusted and optimized according to real-time data, and the performance of the forecasting model is evaluated through cross-validation and real-time feedback, and the model parameters are continuously optimized to improve the accuracy of the forecast.

[0034] See also Figure 2 In combination with the above-mentioned in-vehicle interconnection and mutual investment system, this embodiment also proposes a method of the system, and the specific steps are as follows: Step S1: deploy load monitoring units, install high-precision sensors at grid nodes using a distributed sensor network, and monitor parameters such as current, voltage, and frequency in real time; Step S2: The load monitoring unit collects grid load data in real time and uploads the data to the blockchain through the blockchain synchronization unit.

[0035] Step S3: The battery status monitoring unit acquires and processes information such as the charging and discharging status, power level, health status, and charging and discharging capacity of the vehicle battery in real time.

[0036] Step S4: The demand forecasting unit receives real-time load data from the blockchain power grid load monitoring module and data from the battery status monitoring unit.

[0037] Step S5: On the edge computing device, clean, convert and standardize the sensor data to improve data quality.

[0038] Step S6: Use the deep neural network model to perform real-time load forecasting, adjust and optimize the forecasting model according to real-time data, and improve the forecasting accuracy.

[0039] Step S7: The load balancing strategy processing module receives the prediction result of the demand prediction unit and combines it with the actual situation of the current power grid load.

[0040] Step S8: Calculate the optimal load balancing strategy through complex algorithms and dynamically adjust the battery charging and discharging strategy.

[0041] Step S9: the strategy execution unit automatically adjusts the charging and discharging behavior of the battery according to the output of the load balancing strategy processing module.

[0042] Step S10: The user interaction and intelligent scheduling module displays information such as grid load, battery status, and load balancing strategy through a user scheduling interface unit, allowing the user to make manual adjustments as needed.

[0043] Step S11: The intelligent dispatching unit automatically adjusts the battery charging and discharging strategy according to the grid load, and continuously optimizes the dispatching strategy through a reinforcement learning algorithm.

[0044] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An in-vehicle interconnection and mutual projection system, characterized in that: include: The blockchain power grid load monitoring module is used to collect and store power grid load data in real time; The battery status and demand prediction module is used to receive real-time load data from the blockchain power grid load monitoring module and conduct comprehensive analysis and prediction. The prediction results will be passed as input to the load balancing strategy processing module; The load balancing strategy processing module is used to receive the prediction results from the battery status and demand prediction module, and calculate the optimal load balancing strategy through complex algorithms based on the actual situation of the current power grid load; The user interaction and intelligent scheduling module is responsible for interacting with the user, displaying the grid load, battery status and load balancing strategy information, and automatically or manually adjusting the battery charging and discharging behavior according to the output of the load balancing strategy processing module.

2. The in-vehicle interconnection and mutual investment system according to claim 1, characterized in that: The blockchain power grid load monitoring module includes a load monitoring unit and a blockchain synchronization unit. The load monitoring unit monitors the load data of the power grid in real time, collects real-time load information of the power grid, and is used to analyze load fluctuations; the blockchain synchronization unit synchronizes the power grid load data to the blockchain to ensure the immutability and transparency of the load data.

3. The in-vehicle interconnection and mutual investment system according to claim 1, characterized in that: The battery status and demand forecasting module includes a battery status monitoring unit and a demand forecasting unit. The battery status monitoring unit obtains the charging and discharging status of the vehicle battery in real time, and is used to monitor the battery power, health status and charging and discharging capacity; the demand forecasting unit uses historical data and machine learning algorithms to predict grid demand, and uses a short-term load forecasting algorithm to accurately predict the grid load in the future, providing a basis for battery scheduling.

4. The in-vehicle interconnection and mutual investment system according to claim 1, characterized in that: The load balancing strategy processing module includes a load balancing strategy unit and a strategy execution unit. The load balancing strategy unit formulates a balancing strategy according to the grid load and the battery status, and is used to dynamically adjust the battery charging and discharging strategy to balance the grid load; The strategy execution unit executes the formulated load balancing strategy, which is used for the battery to feed power to the power grid during peak hours and to charge from the power grid during off-peak hours, thereby smoothing out power grid load fluctuations.

5. The in-vehicle interconnection and mutual investment system according to claim 1, characterized in that: The user interaction and intelligent scheduling module includes a user scheduling interface unit and an intelligent scheduling unit. The user scheduling interface unit provides a user operation interface, displays the grid load and battery status, and allows the user to adjust the battery usage strategy according to the grid load condition; the intelligent scheduling unit automatically adjusts the battery charging and discharging according to the grid load, and adopts a reinforcement learning algorithm to realize self-learning and optimization of the battery charging and discharging strategy.

6. The in-vehicle interconnection and mutual investment system according to claim 2, characterized in that: The load monitoring unit adopts a distributed sensor network, deploys high-precision sensors at power grid nodes, monitors power grid current, voltage and frequency parameters in real time, and aggregates sensor data on edge computing devices to reduce data transmission volume and improve data processing speed.

7. The in-vehicle interconnection and mutual investment system according to claim 3, characterized in that: Edge computing processing is used in the demand forecasting unit. Before the data enters the forecasting model, the sensor data is first cleaned, converted and standardized to ensure that the forecasting model can be trained and analyzed based on high-quality data. Edge computing devices are used for real-time data analysis, and a deep neural network machine learning model is used to perform real-time load forecasting to improve the accuracy and timeliness of the forecast.

8. The in-vehicle interconnection and mutual investment system according to claim 7, characterized in that: In the demand forecasting unit, forecasting model training is adopted. The forecasting model is trained and updated on the edge computing device. The model will be continuously adjusted and optimized according to real-time data. The performance of the forecasting model is evaluated through cross-validation and real-time feedback, and the model parameters are continuously optimized to improve the accuracy of the forecast.

9. A method for in-vehicle interconnection and mutual investment, characterized in that: Applied to an in-vehicle interconnection and mutual investment system as described in any one of claims 1 to 8, the method comprises the following steps: Step S1: deploy load monitoring units, install high-precision sensors at grid nodes using a distributed sensor network, and monitor parameters such as current, voltage, and frequency in real time; Step S2: The load monitoring unit collects grid load data in real time and uploads the data to the blockchain through the blockchain synchronization unit; Step S3: The battery status monitoring unit acquires and processes the charging and discharging status, power, health status and charging and discharging capacity information of the vehicle battery in real time; Step S4: The demand forecasting unit receives the real-time load data from the blockchain power grid load monitoring module and the data from the battery status monitoring unit; Step S5: On the edge computing device, the sensor data is cleaned, converted, and standardized to improve the data quality; Step S6: Use the deep neural network model to perform real-time load forecasting, adjust and optimize the forecasting model according to real-time data, and improve the forecasting accuracy; Step S7: The load balancing strategy processing module receives the prediction result of the demand prediction unit and combines it with the actual situation of the current power grid load; Step S8: Calculate the optimal load balancing strategy through complex algorithms and dynamically adjust the battery charging and discharging strategy; Step S9: the strategy execution unit automatically adjusts the charging and discharging behavior of the battery according to the output of the load balancing strategy processing module; Step S10: The user interaction and intelligent scheduling module displays the grid load, battery status and load balancing strategy information through the user scheduling interface unit, allowing the user to make manual adjustments as needed; Step S11: The intelligent dispatching unit automatically adjusts the battery charging and discharging strategy according to the grid load, and continuously optimizes the dispatching strategy through a reinforcement learning algorithm.

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