Predictive Route Segmentation for Hybrid Vehicle Energy Management
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Solution Overview
Problem
Hybrid electric vehicle systems face challenges in minimizing fuel consumption and emissions while maintaining drivability, as existing energy management control strategies often fail to optimize energy use efficiently across varying driving conditions and route characteristics.
Innovation Solution
Implementing a powertrain control system that uses predictive route segmentation based on powertrain operating mode, acceleration, and road grade transitions to optimize battery state of charge, thereby scheduling battery SOC setpoints along a route to minimize fuel consumption through path forecasting and real-time energy management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If predictive route segmentation is implemented to optimize battery SOC scheduling, then fuel consumption is reduced, but computational complexity increases
Solution Approach 1:
The route is divided into multiple segments based on predicted powertrain operating mode transitions, acceleration transitions, or road grade transitions. This segmentation allows the controller to optimize battery SOC scheduling for each segment independently, reducing overall fuel consumption while managing computational complexity through divide-and-conquer approach
Solution Approach 2:
The controller performs predictive route segmentation and SOC scheduling in advance based on forecasted driving conditions and route characteristics. By pre-calculating optimal SOC setpoints for upcoming segments, the system reduces real-time computational burden while achieving fuel efficiency improvements
2Loss of energy
If battery SOC is optimized for fuel economy, then fuel consumption decreases, but drivability may be compromised
Solution Approach 1:
The controller dynamically adjusts battery SOC setpoints based on real-time driving conditions, predicted route characteristics, and powertrain operating modes. This dynamic optimization allows the system to balance fuel economy with drivability requirements by adapting SOC targets to actual driving needs rather than using fixed optimization strategies
Solution Approach 2:
The system changes SOC scheduling parameters based on predicted powertrain operating mode transitions, acceleration transitions, and road grade transitions. By adjusting SOC targets according to these parameter changes, the controller achieves fuel efficiency while maintaining appropriate battery charge levels for drivability
Data Source
AI summary
A vehicle engine, electric machine and battery are operated, in certain examples, such that a predetermined route is segmented based on varying criteria to determine target battery state of charge at the segment endpoints along the route. The endpoints are a superposition of endpoints defined by predicted powertrain operating mode transitions, predicted vehicle acceleration transitions or predicted road grade transitions along the route.