Description:The present invention relates to energy management control techniques for fuel cell-based electric vehicles (FCEVs) utilizing artificial intelligence (AI). More specifically, the invention describes a system and method for controlling the energy consumption and production of FCEVs using AI algorithms to optimize the performance and efficiency of the vehicle's fuel cell system.
Background of the invention:
The battery power and its management system are major components of electric vehicles (EVs), and developing a self-reconfigurable, flexible, and reliable battery management system (BMS) is a challenging issue for EVs. The limited dataset available for building accurate battery models in BMS vehicles has led to the utilization of Cyber Physical Systems (CPS) as a multi-function control method, which is widely used in smart homes, cloud computation, and big data centers.
Fuel cell-based electric vehicles (FCEVs) are becoming increasingly popular due to their potential to reduce greenhouse gas emissions and improve air quality. FCEVs use hydrogen fuel cells to generate electricity to power the vehicle's motor, emitting only water vapor as a byproduct.
While FCEVs have the potential to be an environmentally friendly alternative to traditional gasoline-powered vehicles, they face several challenges related to their energy management control systems. The efficiency of the fuel cell system is highly dependent on how it is managed, and any inefficiencies can result in reduced performance, increased operating costs, and decreased range.
To address these challenges, researchers have been exploring the use of artificial intelligence (AI) to optimize the energy management control of FCEVs. AI algorithms can analyze data on the vehicle's operating conditions, driving behavior, and external factors such as weather and traffic patterns to make real-time adjustments to the fuel cell system.
Various AI techniques, such as machine learning and neural networks, have been applied to FCEVs to optimize energy consumption and production, reduce fuel consumption, and improve performance. AI-based energy management control systems can adjust various parameters such as the fuel cell output, battery charge/discharge rate, and regenerative braking to ensure optimal performance and energy efficiency.
Overall, the use of AI in FCEVs has the potential to improve the efficiency, performance, and overall viability of these vehicles, making them a more attractive alternative to traditional gasoline-powered vehicles.
In addition to the benefits of reducing greenhouse gas emissions and improving air quality, FCEVs have other advantages over traditional vehicles, such as faster refueling times, longer range, and quieter operation. However, the technology behind FCEVs is still relatively new and requires further development to make them more practical and affordable for widespread adoption.
One of the key challenges of FCEVs is their energy management control system. The efficiency of the fuel cell system is highly dependent on how it is managed, and any inefficiencies can result in reduced performance, increased operating costs, and decreased range. In a fuel cell system, hydrogen gas is supplied to the anode, while oxygen is supplied to the cathode. The reaction between the two generates electricity, which powers the vehicle's motor. However, the rate of this reaction can be affected by various factors such as the temperature, humidity, and pressure of the fuel cell.

AI-enabled energy management control techniques can help address these challenges by optimizing the performance and efficiency of the fuel cell system. These techniques involve the use of various sensors and data acquisition systems to gather information on the vehicle's operating conditions and energy consumption patterns. This information is then processed by an AI-based control system that uses machine learning algorithms to predict future energy demands and optimize the operation of the fuel cell system accordingly.
One example of an AI-enabled energy management control system for FCEVs is the use of neural networks. Neural networks are a type of machine learning algorithm that can analyze large amounts of data and identify patterns to make predictions. In the case of FCEVs, a neural network can be used to predict the vehicle's energy demand based on factors such as the driving behavior, road conditions, and weather patterns. This information can then be used to adjust various parameters of the fuel cell system, such as the fuel cell output, to ensure optimal performance and efficiency.
Another example of an AI-enabled energy management control system for FCEVs is the use of reinforcement learning. Reinforcement learning is a type of machine learning algorithm that can learn to make decisions based on trial and error. In the case of FCEVs, a reinforcement learning algorithm can learn from the vehicle's past performance and adjust the energy management control system accordingly to improve future performance.
Prior art in the field of AI-enabled energy management control techniques for fuel cell-based electric vehicles includes research and development efforts from various industries and academic institutions.
One example of prior art is a patent filed by Honda Motor Co. Ltd. titled "Fuel Cell Vehicle Control Apparatus and Control Method." This patent describes an energy management control system for FCEVs that uses a neural network to predict the vehicle's energy demand based on various factors such as the driving behavior, road conditions, and weather patterns. The neural network is then used to adjust various parameters of the fuel cell system to ensure optimal performance and efficiency.
Another example of prior art is a patent filed by Toyota Motor Corporation titled "Control Device for Fuel Cell Vehicle." This patent describes an energy management control system for FCEVs that uses a reinforcement learning algorithm to optimize the operation of the fuel cell system. The reinforcement learning algorithm is trained using data from the vehicle's past performance and adjusts the fuel cell system in real-time to improve future performance.
In addition to patents, academic research has also contributed to the development of AI-enabled energy management control techniques for FCEVs. For example, researchers at the University of Michigan have developed a neural network-based energy management control system for FCEVs that can optimize the use of both the fuel cell and battery systems to improve energy efficiency.
Other researchers have explored the use of fuzzy logic-based control systems for FCEVs. Fuzzy logic is a type of mathematical logic that can handle imprecise or uncertain data. A fuzzy logic-based control system for FCEVs can adjust various parameters of the fuel cell system based on inputs such as the vehicle's speed, acceleration, and battery state-of-charge to ensure optimal performance and efficiency.
Another area of prior art in the field of AI-enabled energy management control techniques for FCEVs is the development of control systems that can optimize the performance of the fuel cell system under different operating conditions.
For example, researchers at the University of California, Riverside, have developed an energy management control system for FCEVs that can optimize the fuel cell system's performance under extreme temperatures. The control system uses a neural network to predict the energy demand of the vehicle based on inputs such as the driving behavior and the temperature of the fuel cell system. The neural network is then used to adjust various parameters of the fuel cell system to ensure optimal performance and efficiency under extreme temperature conditions.
Other researchers have focused on developing energy management control systems for FCEVs that can optimize the use of renewable energy sources. For example, researchers at the University of California, Irvine, have developed a control system that can optimize the use of solar energy to power FCEVs. The control system uses a reinforcement learning algorithm to adjust the fuel cell system's operation based on the available solar energy and the vehicle's energy demand.

In addition to research and development efforts, various companies have also begun to incorporate AI-enabled energy management control techniques into their FCEV products. For example, Hyundai Motor Company's Nexo FCEV uses an AI-enabled energy management control system that can optimize the fuel cell system's performance based on factors such as the driving behavior and the temperature of the fuel cell system.
Overall, the field of AI-enabled energy management control techniques for FCEVs is a rapidly evolving area of research and development. The prior art in this field provides valuable insights and inspiration for further research and development efforts to improve the performance and efficiency of FCEVs.
Summary of the proposed invention:
The proposed invention offers several advantages, including improved efficiency, reduced emissions, customizability, commercial potential, and real-time adjustments. However, there are also some limitations, including potential cost, reliability, complexity, training requirements, and compatibility issues.
Brief Description of the proposed invention:
The proposed invention is an AI-enabled energy management control system for fuel cell-based electric vehicles (FCEVs) that is designed to optimize the vehicle's energy usage and improve its overall performance and efficiency. The system uses a combination of machine learning algorithms and fuzzy logic-based control systems to adjust the parameters of the fuel cell and battery systems in real-time to meet the energy demands of the vehicle.
The energy management control system consists of several components, including a data acquisition system, a machine learning module, and a fuzzy logic-based control system. The data acquisition system collects data on various inputs such as the vehicle's speed, acceleration, and battery state-of-charge, as well as environmental factors such as temperature and humidity. The machine learning module uses this data to train a neural network to predict the vehicle's energy demand based on various inputs.
The neural network is then used to adjust the parameters of the fuel cell and battery systems in real-time to ensure optimal performance and efficiency. For example, if the vehicle's energy demand is high, the control system may adjust the fuel cell system to provide more power, or it may switch to the battery system if the fuel cell system is operating at a lower efficiency level.
In addition to the machine learning module, the energy management control system also incorporates a fuzzy logic-based control system to adjust the parameters of the fuel cell and battery systems based on imprecise or uncertain data. For example, if the vehicle is driving on a rough road surface, the fuzzy logic-based control system may adjust the fuel cell and battery systems to compensate for the increased energy demands caused by the rough terrain.
The energy management control system also includes a feedback loop that continuously monitors the vehicle's performance and adjusts the parameters of the fuel cell and battery systems in real-time to ensure optimal performance and efficiency. For example, if the vehicle's battery state-of-charge is low, the control system may adjust the fuel cell system to charge the battery or switch to the battery system if it is operating at a higher efficiency level.
The proposed invention also includes a user interface that allows the driver to monitor the vehicle's performance and energy usage in real-time. The user interface displays information such as the vehicle's energy usage, battery state-of-charge, and estimated range, as well as recommendations for optimizing the vehicle's energy usage based on driving behavior and environmental conditions.
The AI-enabled energy management control system for FCEVs offers several advantages over conventional energy management systems. By adjusting the parameters of the fuel cell and battery systems in real-time based on the vehicle's energy demands, the system can improve the vehicle's performance and efficiency, resulting in longer range and reduced energy consumption. Additionally, the system's ability to adjust to uncertain or imprecise data using fuzzy logic-based control systems can help to ensure optimal performance under a variety of driving conditions.
The AI-enabled energy management control system for FCEVs also has potential applications in other areas of transportation, such as hybrid electric vehicles and plug-in hybrid electric vehicles. By optimizing the energy usage of these vehicles, the system can help to reduce energy consumption and emissions, making them more sustainable and environmentally friendly.
The energy management control system can also be customized for different types of FCEVs, depending on their specific energy requirements and operating conditions. For example, the system can be optimized for FCEVs that are used in urban areas with frequent stop-and-go traffic, or for FCEVs that are used in rural areas with long-distance travel.
The proposed invention also has potential for commercialization, as it can be integrated into existing FCEV products or developed as a standalone product. The energy management control system can be marketed to FCEV manufacturers as a value-added feature, improving the performance and efficiency of their products and making them more competitive in the market. Additionally, the system can be sold to fleet operators, such as transportation companies or government agencies, who are looking to reduce their carbon footprint and improve the sustainability of their operations.
In terms of implementation, the AI-enabled energy management control system for FCEVs can be integrated into the vehicle's onboard computer system. The system can communicate with the vehicle's sensors and control systems, adjusting the fuel cell and battery systems in real-time based on the vehicle's energy demands. The user interface can be integrated into the vehicle's dashboard or center console, allowing the driver to monitor the vehicle's performance and energy usage.
To ensure the reliability and accuracy of the energy management control system, extensive testing and validation will be required. The system can be tested under a variety of driving conditions, such as different speeds, terrain, and weather conditions, to ensure that it performs optimally under all conditions. Additionally, the system's neural network and fuzzy logic-based control systems can be trained and tested using simulated data before being deployed in the vehicle.
In conclusion, the proposed invention of an AI-enabled energy management control system for FCEVs offers a promising solution for improving the performance and efficiency of these vehicles. By combining machine learning algorithms and fuzzy logic-based control systems, the system can optimize the fuel cell and battery systems to meet the vehicle's energy demands in real-time. The system's user interface also allows drivers to monitor the vehicle's performance and energy usage, providing recommendations for optimizing energy usage and improving the overall performance and efficiency of the vehicle. Certain benefits of the proposed invention mentioned below
Improved efficiency: The AI-enabled energy management control system can optimize the use of energy in fuel cell-based electric vehicles, leading to improved efficiency and extended range.
Reduced emissions: By optimizing energy usage, the system can help reduce emissions, making fuel cell-based electric vehicles more environmentally friendly.
Customizable: The energy management control system can be customized for different types of fuel cell-based electric vehicles, depending on their specific energy requirements and operating conditions.
Commercial potential: The energy management control system has potential for commercialization, making it a valuable addition to the field of sustainable transportation.
Real-time adjustments: The system can adjust fuel cell and battery systems in real-time based on the vehicle's energy demands, allowing for optimal performance under all driving conditions.
Limitations:
Cost: The implementation of an AI-enabled energy management control system may increase the cost of fuel cell-based electric vehicles, potentially limiting their adoption by consumers.
Reliability: The energy management control system must be extensively tested and validated to ensure its reliability and accuracy, which may require significant time and resources.
Complexity: The system's neural network and fuzzy logic-based control systems may be complex, requiring specialized expertise to develop and implement.
Training requirements: The system's neural network and fuzzy logic-based control systems require training and optimization before being deployed in the vehicle, potentially requiring additional time and resources.
Compatibility: The energy management control system may not be compatible with all types of fuel cell-based electric vehicles, limiting its potential applications.
, Claims:1. A system for managing the energy usage of a fuel cell-based electric vehicle, comprising a neural network-based control system and a fuzzy logic-based control system in communication with the vehicle's sensors and control systems.
2. The system of claim 1, wherein the neural network-based control system and fuzzy logic-based control system work in conjunction to optimize the use of energy in real-time based on the vehicle's energy demands.
3. The system of claim 1, further comprising a user interface that allows the driver to monitor the vehicle's performance and energy usage, providing recommendations for optimizing energy usage and improving overall performance and efficiency.
4. The system of claim 1, wherein the system is customizable for different types of fuel cell-based electric vehicles, depending on their specific energy requirements and operating conditions.
5. The system of claim 1, wherein the system adjusts the fuel cell and battery systems in real-time to optimize performance and extend the range of the vehicle.
6. The system of claim 1, wherein the system reduces emissions by optimizing energy usage, making fuel cell-based electric vehicles more environmentally friendly.
7. A method of managing the energy usage of a fuel cell-based electric vehicle, comprising using a neural network-based control system and a fuzzy logic-based control system to optimize the use of energy in real-time based on the vehicle's energy demands.
8. The method of claim 7, further comprising providing a user interface that allows the driver to monitor the vehicle's performance and energy usage and providing recommendations for optimizing energy usage and improving overall performance and efficiency.
9. The method of claim 7, wherein the system adjusts the fuel cell and battery systems in real-time to optimize performance and extend the range of the vehicle.
10. The method of claim 7, wherein the system reduces emissions by optimizing energy usage, making fuel cell-based electric vehicles more environmentally friendly.