Hybrid Vehicle Route Segment Energy Optimization
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Solution Overview
Problem
There is a need for a method and system to actively and dynamically adjust vehicle operations to improve overall energy efficiency, particularly for hybrid vehicles, as existing technologies do not effectively optimize energy use across various energy forms like fuel and electrical energy.
Innovation Solution
A system that predicts routes and adjusts engine start/stop thresholds based on stored energy efficiency data, using sensors and a processor to optimize powertrain parameters for hybrid vehicles, incorporating data on fuel and battery power usage to enhance energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the vehicle uses traditional energy management systems, then the control logic is simple, but the energy efficiency is not optimized
Solution Approach 1:
The system performs preliminary actions by storing energy efficiency data for multiple route segments in advance and predicting the vehicle's future route before traversal. This allows the control system to pre-load optimal control parameters for upcoming segments, enabling proactive optimization rather than reactive adjustment.
Solution Approach 2:
The system dynamically adapts control parameters based on predicted route segments. The engine start/stop threshold is not fixed but varies according to the characteristics of upcoming route segments (e.g., uphill segments may warrant keeping the engine running, while flat segments allow stop-start operation). This dynamic adaptation optimizes energy efficiency in real-time.
2Use of energy by moving object
If the system adjusts engine operations dynamically, then energy efficiency improves, but the control complexity increases
Solution Approach 1:
The route is divided into multiple discrete route segments, each with stored energy efficiency data and characteristics. The control system processes and adjusts parameters segment by segment rather than treating the entire route as a single unit. This segmentation allows manageable complexity while achieving comprehensive optimization across all segments.
Solution Approach 2:
The system incorporates feedback mechanisms where actual energy consumption data from traversed segments is stored and used to refine predictions and control decisions for future segments. This feedback loop enables continuous improvement of energy efficiency while adapting to actual vehicle performance and route conditions.
3Use of energy by moving object
If the vehicle optimizes for each route segment individually, then energy efficiency maximizes, but the processing time increases
Solution Approach 1:
Energy efficiency data and optimal control parameters are pre-calculated and stored for each route segment before the vehicle traverses them. When the vehicle approaches a segment, the system quickly retrieves pre-computed parameters rather than calculating them in real-time, significantly reducing processing time while maintaining optimization effectiveness.
Solution Approach 2:
The system focuses computational resources on predicting and optimizing only the relevant upcoming route segments rather than processing the entire route or all possible segments. This partial action approach concentrates processing effort where it is most needed, reducing overall processing time while achieving sufficient optimization.
Data Source
AI summary
A method/system for maintaining the energy efficiency history of predicted route segments in a database, providing adaptive feedback to hybrid vehicle controls, and displaying the energy efficiency history to the driver. As the vehicle travels on a route, the system stores energy efficiency information for route segments. The system may include sensors, a memory, and a processor to learn energy efficiency information based on how fuel and battery power are used over a route segment and/or route. The method/system may utilize energy efficiency information to adjust a start/stop engine threshold of a hybrid vehicle. When the processor predicts that the vehicle will travels on the predicted route segment again, the vehicle may optimize powertrain parameters such as the start/stop engine threshold during traveling on the predicted route segment and/or before reaching the predicted route segment.