Hybrid Engine Pull-Down Inhibition via Driving History
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Hybrid electric vehicles experience excessive engine starts and stops due to abrupt changes in power demands, leading to reduced comfort and fuel economy, as existing control strategies fail to effectively inhibit engine pull-down based on driving habits.
Innovation Solution
A control strategy that uses a moving average of past steering wheel angle or accelerator pedal position data to inhibit engine pull-down, preventing unnecessary engine shutdowns by maintaining the engine on when the weighted average of driving conditions exceeds a threshold, even if current conditions suggest engine shutdown.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the engine is stopped during minimal power demand to conserve fuel, then fuel efficiency is improved, but excessive stops and starts reduce comfort and drivability
Solution Approach 1:
The control processor calculates a moving average of driving conditions (steering wheel angle, accelerator pedal position) before making the engine stop/start decision. This preliminary analysis of past driving patterns allows the system to predict future power demands and inhibit engine pull-down when conditions suggest the driver will soon require power, thereby reducing unnecessary engine starts while maintaining fuel efficiency benefits during sustained low-demand periods.
2Ease of operation
If filters or algorithms are used to learn driving habits and reduce engine start/stop frequency, then comfort is improved, but the control complexity increases
Solution Approach 1:
The system continuously monitors driving conditions (steering wheel angle from sensor, accelerator pedal position from sensor) and uses this feedback to dynamically adjust engine control decisions. The moving average calculation provides a simplified feedback mechanism that captures driving patterns without requiring complex machine learning algorithms, thereby improving comfort through adaptive control while limiting complexity growth.
Data Source
AI summary
A hybrid vehicle includes an engine and a motor that are both capable of powering the wheels. While a vehicle is being driven, the vehicle's driving condition data is monitored. The driving condition data can include steering wheel angle or position, accelerator pedal position, driver torque or power demands, or road grade or incline. The vehicle includes a controller with a specific control scheme to receive the driving condition data, and subject the data to a moving average or a weighted moving average. Based on the averaged driving condition data, the engine is inhibited from stopping under certain conditions to reduce the frequency of the engine turning on and off.