Vehicle Propulsion Controller Forecasting Driving Events
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Solution Overview
Problem
Current vehicle propulsion systems operate reactively and fail to optimize performance attributes despite the availability of rich data sources from vehicle connectivity and onboard sensors, leading to suboptimal energy consumption and emissions.
Innovation Solution
A vehicle propulsion system with a controller that forecasts upcoming driving events and adjusts engine output torque to preempt driver inputs, optimizing performance parameters by utilizing data from sensors and infrastructure, enabling proactive engine management strategies such as 'sweet spots' operation, aggressive deceleration, start-stop, and smart transmission shifts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the propulsion system operates reactively based on driver input, then the system responds to actual driving demands, but performance attributes such as fuel efficiency and emissions are not optimized
Solution Approach 1:
The system performs preliminary actions by forecasting upcoming driving events (such as traffic lights, stop signs, congestion) using sensor data and connectivity information before the driver actually encounters them. This allows the propulsion system to proactively adjust torque delivery, shift timing, and engine operation to optimal settings in advance, rather than reacting passively to driver inputs. For example, the system can prepare for an upcoming stop by gradually reducing torque or coasting, improving fuel efficiency without compromising the driver's ability to respond to actual conditions.
2Use of energy by moving object
If the system preemptively adjusts torque based on forecasted events, then fuel efficiency improves, but the system complexity increases due to additional sensors and control algorithms
Solution Approach 1:
The controller is designed to perform multiple functions: it not only controls engine torque and transmission shifting for propulsion, but also forecasts upcoming driving events by processing data from various sensors (cameras, radar, LIDAR) and connectivity systems. This multi-functional approach consolidates what could be separate systems into a single intelligent controller, reducing overall system complexity while enabling proactive optimization of energy usage based on predicted driving conditions.
3Productivity
If the system uses rich data sources from connectivity and sensors, then optimization opportunities increase, but data processing requirements and computational load increase
Solution Approach 1:
The system extracts only the most relevant features and information from the rich data sources provided by connectivity and sensors. Rather than processing all available data in full detail, the controller identifies and focuses on key indicators of upcoming driving events (such as detected traffic lights, recognized stop signs, or identified congestion patterns) to make proactive adjustments. This selective extraction approach enables optimization capability while managing computational load by processing only the essential data needed for forecast accuracy.
Data Source
AI summary
A vehicle engine system includes a combustion engine configured to provide a propulsion torque to satisfy a propulsion demand in response to a driver input. The engine system also includes a controller programmed to receive data indicative of at least one upcoming driving event, and issue a command to impart a predetermined velocity profile based on the upcoming driving event. The predetermined velocity profile is arranged to optimize a performance attribute of the combustion engine and preempt the driver input.