Self-Learning Hybrid Vehicle Control System for State-of-Charge Optimization
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Solution Overview
Problem
Hybrid vehicles face inefficiencies due to the inability to predict and prepare for future power demands, leading to suboptimal battery or ultracapacitor state-of-charge management, especially during high power demand situations like steep inclines or city-type driving.
Innovation Solution
A self-learning assisted hybrid vehicle system that uses a self-learning controls unit to predict future driving conditions and optimize power source utilization by managing the state-of-charge of the battery or ultracapacitor based on historical data, GPS information, and other inputs to anticipate and prepare for upcoming power demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the battery or ultracapacitor state-of-charge is kept low to maximize regenerative braking energy absorption, then fuel efficiency is improved in city-type driving, but performance during high power demand events deteriorates
Solution Approach 1:
The system performs preliminary action by predicting future high power demand events using GPS location data, historical driving patterns, and route information. When such events are anticipated, the control system proactively adjusts the battery state-of-charge to optimal levels before the demand occurs, ensuring both fuel efficiency during normal operation and sufficient power availability when needed.
Solution Approach 2:
The system implements feedback by continuously monitoring current driving conditions, battery state-of-charge, and comparing them against predicted future demands. The control algorithm dynamically adjusts power distribution and charging strategies based on this feedback loop, optimizing the balance between fuel efficiency and performance readiness in real-time.
2Reliability
If the battery state-of-charge is raised quickly to prepare for high power demand, then performance is improved, but fuel efficiency deteriorates due to the time required for charging
Solution Approach 1:
The system anticipates high power demand events before they occur by analyzing GPS location, historical driving data, and route characteristics. It begins charging the battery in advance during periods of lower power demand, so that when high power events occur, the battery is already optimally charged without requiring rapid, inefficient charging at the last moment.
3Power
If the fuel cell system operates at high load to meet power demands, then sufficient power is provided, but system efficiency deteriorates and thermal management demands increase
Solution Approach 1:
The system predicts upcoming high power demand events and proactively adjusts the fuel cell operating point before the demand occurs. By charging the battery in advance during lower demand periods, the fuel cell can operate at more efficient, lower load points while still meeting peak power demands through coordinated battery assistance.
Solution Approach 2:
The control system dynamically changes operating parameters of the fuel cell system based on predicted future conditions. It adjusts the fuel cell current and voltage operating points in advance to optimize efficiency, transitioning between different operating modes based on anticipated power demands and battery state-of-charge levels.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances fuel efficiency, reduces emissions, and optimizes performance by ensuring the battery or ultracapacitor is at the appropriate state-of-charge for different driving conditions, improving energy management and reducing the need for costly cooling systems.
Implementation Method 1
A hydrogen fuel cell is an electro-chemical device that includes an anode and a cathode with an electrolyte therebetween
Implementation Method 2
A first operating mode includes driving with the electric motor or motors powered by the battery alone
Implementation Method 3
In a fourth operating mode, the hybrid vehicle is slowed down by utilizing regenerative braking, which enables charging of the DC battery or ultracapacitor
Data Source
AI summary
A self-learning assisted hybrid vehicle system that includes a main power source for providing power to the vehicle, a supplemental power source for providing supplemental power for providing power to the vehicle and an electric motor or other mechanical system for driving the vehicle. The system also includes a self-learning controls unit that receives and stores information from a plurality of inputs associated with the vehicle. The self-learning controls unit uses the information to make predictions about future driving conditions of the vehicle to efficiently utilize the power sources of the hybrid vehicle.