Toyota jidosha kabushiki kaisha (20240161554). CONTROLLER simplified abstract
Contents
- 1 CONTROLLER
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CONTROLLER - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does the controller calculate the power consumption of the auxiliary equipment accurately?
- 1.11 What are the learning results mentioned in the abstract, and how are they obtained?
- 1.12 Original Abstract Submitted
CONTROLLER
Organization Name
toyota jidosha kabushiki kaisha
Inventor(s)
Shingo Eto of Toyota-shi Aichi-ken (JP)
Noritaka Takuda of Toyota-shi Aichi-ken (JP)
CONTROLLER - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161554 titled 'CONTROLLER
Simplified Explanation
The controller is installed in the vehicle along with the motor for driving, the auxiliary equipment, and the battery that supplies power to the motor and the auxiliary equipment. After the vehicle's system starts up, the controller is programmed to (a) calculate the power consumption of the auxiliary equipment using the power output from the battery; (b) estimate the auxiliary equipment electricity cost using the power consumption of the auxiliary equipment, the estimated driving time, and the estimated driving distance; and (c) predict the driving electricity cost for the estimated driving distance using the auxiliary electricity cost and the driving learning electricity cost from the learning results.
- Controller installed in vehicle with motor, auxiliary equipment, and battery.
- Calculates power consumption of auxiliary equipment.
- Estimates auxiliary equipment electricity cost based on consumption, driving time, and distance.
- Predicts driving electricity cost using auxiliary electricity cost and driving learning results.
Potential Applications
This technology can be applied in electric vehicles to accurately estimate and predict electricity costs for driving, helping users plan their trips more efficiently and effectively.
Problems Solved
This technology solves the problem of uncertainty regarding electricity costs for driving electric vehicles, providing users with a clear understanding of the expenses involved in their journeys.
Benefits
The benefits of this technology include cost-effective trip planning, improved budgeting for electric vehicle owners, and increased awareness of the financial aspects of driving an electric vehicle.
Potential Commercial Applications
One potential commercial application of this technology is in the development of electric vehicle management systems that offer real-time cost estimations for drivers, enhancing the overall user experience and satisfaction.
Possible Prior Art
One possible prior art for this technology could be existing electric vehicle management systems that provide similar functionalities, although this specific approach to estimating and predicting electricity costs may be unique.
Unanswered Questions
How does the controller calculate the power consumption of the auxiliary equipment accurately?
The abstract does not provide specific details on the methodology or algorithms used by the controller to calculate the power consumption of the auxiliary equipment. Further information on this process would be beneficial for a deeper understanding of the technology.
What are the learning results mentioned in the abstract, and how are they obtained?
The abstract mentions using learning results to predict driving electricity costs, but it does not elaborate on what these learning results entail or how they are acquired. More information on this aspect would help clarify the predictive capabilities of the technology.
Original Abstract Submitted
the controller is installed in the vehicle along with the motor for driving, the auxiliary equipment, and the battery that supplies power to the motor and the auxiliary equipment. after the vehicle's system starts up, the controller is programmed to (a) calculate the power consumption of the auxiliary equipment using the power output from the battery; (b) estimate the auxiliary equipment electricity cost using the power consumption of the auxiliary equipment, the estimated driving time, and the estimated driving distance; and (c) predict the driving electricity cost for the estimated driving distance using the auxiliary electricity cost and the driving learning electricity cost from the learning results.
