Hybrid Vehicle Power Distribution via Predictive SOC Trajectory
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Solution Overview
Problem
Current hybrid vehicle control systems are limited in optimizing fuel consumption due to insufficient prediction of future driving conditions and inefficient battery management, particularly in low-speed congestion sections where the engine must operate in low-efficiency regions to charge the battery.
Innovation Solution
A system and method that predict future driving conditions using GPS and ITS information, combined with a Markov driver model and stochastic dynamic programming, to optimize the state of charge (SOC) trajectory and power distribution between the engine and motor, allowing for optimal engine operation and EV mode utilization across a designated driving path.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the engine operates in low-speed congestion sections to charge the battery, then the battery SOC is maintained, but the engine operates in low-efficiency regions increasing fuel consumption
Solution Approach 1:
The system predicts future driving conditions (congestion sections, speed profiles) in advance and performs battery charging during high-speed sections before entering low-speed congestion sections. This preliminary charging action avoids the need to operate the engine in low-efficiency regions during congestion, thereby reducing overall fuel consumption while maintaining reliable SOC levels.
2Loss of energy
If the vehicle uses EV mode in low-speed congestion sections, then fuel consumption is reduced, but the battery may become energy depleted
Solution Approach 1:
The system performs preliminary battery charging during high-speed driving sections before the vehicle enters low-speed congestion sections where EV mode will be utilized. This ensures sufficient battery energy is available for EV mode operation during congestion without risking energy depletion, as the charging action occurs in advance when the engine operates more efficiently.
3Speed
If the controller optimizes based on instant driving conditions only, then the control response is fast, but the overall fuel efficiency across the driving path is suboptimal
Solution Approach 1:
The system performs preliminary prediction of the entire driving path conditions (including future congestion sections, speed profiles, and terrain) and pre-determines the optimal SOC trajectory and power distribution strategy. This allows the controller to maintain fast response to instant conditions while following a pre-optimized path that maximizes overall fuel efficiency across the entire driving journey.
Solution Approach 2:
The system dynamically adjusts the power distribution between engine and motor, and the SOC trajectory, based on real-time driving conditions while maintaining alignment with the pre-calculated optimal path. This dynamic adaptation allows the system to respond quickly to instantaneous conditions while preserving the overall fuel efficiency optimization achieved through predictive planning.
Data Source
AI summary
A method for controlling a hybrid vehicle is provided. The method includes setting a driving path of the vehicle based on an input destination and current position and predicting a future speed of the vehicle using information regarding the driving path, environmental information, and driving pattern information of a driver. An optimum power distribution map is derived including an optimum SOC trajectory and a power distribution ratio of the engine and the motor using the predicted future speed. Additionally, engine power and motor power is distributed using the optimum SOC trajectory and a power distribution ratio of the engine and the motor.