Vehicle Engine Pull-Down Control via Predictive Fuel Analysis
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Solution Overview
Problem
In hybrid electric vehicles and conventional vehicles with stop/start features, there is a challenge in determining when to shut down and restart the engine efficiently to maximize fuel savings, as existing systems lack predictive capabilities to balance engine shutdown duration with restart fuel consumption based on dynamic driving conditions.
Innovation Solution
A vehicle system that includes a controller and wireless transceiver, which predicts fuel savings and engine restart fuel usage by analyzing attribute data such as route patterns and power demands, selectively shutting down the engine only when predicted fuel savings exceed restart fuel consumption, and inhibiting shutdown if savings are not justified.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the engine is shut down to save fuel during low power demand, then fuel consumption is reduced, but the fuel required to restart the engine increases overall consumption
Solution Approach 1:
The controller performs preliminary analysis of attribute data (route patterns, traffic conditions, power demands) to predict future engine operation requirements before making the shutdown decision. This allows the system to anticipate whether the engine will need to restart soon, preventing shutdowns that would result in net fuel loss.
Solution Approach 2:
The shutdown decision is made dynamic rather than static, adapting to varying driving conditions, route characteristics, and power demand patterns. The controller continuously evaluates changing conditions to determine the optimal shutdown timing, ensuring decisions are optimized for each specific scenario rather than applying a fixed rule.
2Device complexity
If the engine is shut down based on simple duration thresholds, then control logic is simplified, but fuel savings are not optimized for varying driving conditions
Solution Approach 1:
Attribute data including route patterns, traffic conditions, and power demands are collected and analyzed in advance of the shutdown decision. This preliminary information gathering enables the controller to make informed decisions that optimize fuel savings while accounting for specific driving conditions, rather than relying on simple duration thresholds.
Solution Approach 2:
The control system evaluates multiple varying parameters (route characteristics, traffic conditions, power demand patterns, predicted engine off duration) rather than relying on a single parameter like duration alone. By considering changes in these multiple parameters, the system achieves optimized fuel efficiency without requiring excessively complex control logic.
3Speed
If the engine restarts at high speed to match predicted vehicle speed, then responsiveness to driving demands is improved, but fuel consumption during pull-up increases
Solution Approach 1:
The target engine speed parameter is dynamically adjusted based on predicted vehicle motion patterns and power demands along the route. Rather than always restarting at maximum speed, the controller selects an optimal restart speed that balances responsiveness requirements with fuel consumption considerations, changing the speed parameter according to specific driving conditions.
Solution Approach 2:
The engine restart strategy is made dynamic, with the target speed varying based on real-time evaluation of attribute data and predicted driving conditions. The system adapts the restart speed parameter to match actual vehicle needs, avoiding unnecessary high-speed restarts that would consume excessive fuel while maintaining adequate responsiveness.
Data Source
AI summary
A vehicle includes an engine, a wireless transceiver that receives attribute data, and a controller. The controller selectively shuts down the engine responsive to such requests based on whether a predicted fuel savings associated with a predicted engine off duration is greater than a predicted fuel usage to restart the engine.