Vehicle Speed Control Optimizer for Fuel Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle speed control systems struggle to optimize energy efficiency without prior knowledge of road grade variations or pre-planned routes, relying heavily on a priori data collection and analysis, which is impractical for diverse fleets and regions.
Innovation Solution
A vehicle apparatus that adjusts its powertrain speed using an optimizer based on a value function and transition probability model, collecting road grade data during routine driving to generate control policies that optimize fuel efficiency, allowing for real-time adaptation to current driving conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If GPS-based maps and advance routing are used to determine speed offsets, then energy consumption is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the essential function of predicting road grade variations from complex GPS map systems and implements it through a simplified sensor-based approach. Road grade sensors directly measure the actual grade variations without requiring external map data, routing algorithms, or computational processing of geographic information.
Solution Approach 2:
The vehicle's own sensors and onboard systems are used to characterize the driving conditions and generate control policies, eliminating the need for external GPS map services, remote data communications, and pre-loaded map data. The system serves itself by collecting and processing its own operational data.
2Productivity
If GPS navigation devices and map data are used, then real-time speed offsets can be determined, but cost and data communication requirements increase
Solution Approach 1:
The patent replaces expensive, permanent GPS map systems and navigation devices with simpler, transient sensor measurements. Instead of relying on costly map data and communication infrastructure, the system uses inexpensive road grade sensors that provide real-time measurements without requiring ongoing data subscriptions or complex hardware.
Solution Approach 2:
The patent replaces the electronic/GPS-based system with a mechanical sensor approach. Road grade sensors physically measure the incline directly, substituting the need for GPS satellite communication, map data processing, and computational routing algorithms with straightforward mechanical measurement.
3Productivity
If a priori data collection and analysis are performed, then control policies can be pre-loaded, but adaptability to diverse fleets and regions decreases
Solution Approach 1:
The patent implements dynamic adaptation by continuously collecting road grade data during routine driving and updating control policies in real-time. Instead of static pre-loaded policies, the system dynamically adjusts to current driving conditions, making it adaptable to diverse vehicles and regions without requiring pre-planning or region-specific configuration.
Solution Approach 2:
The system incorporates feedback loops where road grade sensor data is continuously collected, analyzed, and used to update control policies. This feedback mechanism enables the system to learn from actual driving conditions and adapt to diverse fleets and regions, eliminating the need for a priori data collection and pre-loading.
Data Source
AI summary
Vehicle apparatus adjusts a vehicle powertrain of the vehicle in response to a speed setpoint. An optimizer selects a control policy to periodically generate speed adjustments for applying to the speed setpoint to operate at increased efficiency. The control policy is based on a value function providing an optimized solution for a cost model and a transition probability model. The transition probability model corresponds to a driving state defined according to a plurality of dimensions including a time-of-day dimension and a geographic region dimension. The transition probability model and the control policy have inputs based on road grade and speed. The optimizer collects road grade data during routine driving of the vehicle to construct a observed transition probability model and uses divergence between the observed transition probability model and a set of predetermined transition probability models to identify a control policy for use during the routine driving.