Vehicle Powertrain Control Using Multiple Horizon Optimization
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Solution Overview
Problem
Existing vehicle dynamics and powertrain control systems face challenges in efficiently optimizing fuel consumption over an entire itinerary, particularly when incorporating route-related features like speed limits and traffic conditions, as these features are cumbersome to include in temporal domain problems.
Innovation Solution
The implementation of multiple horizon optimization, which involves performing long horizon optimization at the beginning or during a trip, determining an optimal value function, and using a rollout algorithm for short horizon optimization with updated route information from V2I/V2V modules or cloud-based services to adjust vehicle operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If long horizon optimization is performed for the entire itinerary, then fuel consumption is minimized, but computational complexity increases significantly
Solution Approach 1:
The patent divides the optimization problem into two segments: long horizon optimization for the entire itinerary to minimize fuel consumption, and short horizon optimization for real-time adjustments. This segmentation allows the system to maintain computational feasibility while achieving global optimization goals.
Solution Approach 2:
The system performs preliminary long horizon optimization to determine an optimal value function before executing the trip. This preliminary computation provides a foundation for subsequent real-time control decisions, reducing the computational burden during actual operation.
2Measurement precision
If route-related features like speed limits and traffic lights are incorporated, then optimization accuracy improves, but ease of operation deteriorates
Solution Approach 1:
The patent transforms the optimization problem from the temporal domain to the spatial domain. By formulating the problem in terms of spatial trajectory and incorporating route features like speed limits and traffic lights as spatial constraints, the system achieves better integration of route information while maintaining computational tractability.
3Adaptability or versatility
If real-time adjustments are made using short horizon optimization, then adaptability to changing conditions improves, but computational requirements increase
Solution Approach 1:
The system employs dynamic optimization where the optimization horizon adapts to changing conditions. Short horizon optimization is performed in real-time to adjust to changing traffic conditions, while leveraging the pre-computed optimal value function from long horizon optimization to reduce computational requirements.
Solution Approach 2:
The system uses feedback from the optimal value function obtained through long horizon optimization to guide real-time short horizon optimization decisions. This feedback mechanism enables adaptive control while avoiding redundant computations.
Data Source
AI summary
The use of multiple horizon optimization for vehicle dynamics and powertrain control of a vehicle is provided. Long horizon optimization for a trip of the vehicle is performed, and an optimal value function is determined. Data is received from powertrain and/or connectivity features from one or more of components of the vehicle. Short horizon optimization for the trip is performed using a rollout algorithm, the optimal value function, and the received data. The operation of the vehicle is adjusted using results of the short horizon optimization.