Hybrid Powertrain Torque Split Adaptation
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Solution Overview
Problem
Hybrid vehicles face inefficiencies in energy management due to alternating operating points that deplete the traction battery quickly, leading to inefficient fuel consumption and battery usage, especially during steady-state conditions.
Innovation Solution
A hybrid vehicle powertrain system that adapts torque splits between the engine and electric machine based on historical data and battery state of charge, adjusting torque commands to optimize efficiency by learning from previous drive cycles and maintaining steady-state conditions for prolonged periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the powertrain frequently alternates between different operating points, then the vehicle can adapt to varying driver demands and road conditions, but the traction battery depletes quickly and fuel consumption increases
Solution Approach 1:
The controller stores historical data about the duration the powertrain has been at each operating point from previous drive cycles. This preliminary information is used to predict future operating patterns and proactively adjust torque splits to prevent excessive battery depletion before it occurs, rather than reacting after depletion has happened.
Solution Approach 2:
The torque split between the engine and electric machine is made dynamic and adaptive rather than fixed. The controller continuously adjusts the torque split based on the duration at the current operating point and historical patterns, allowing the system to optimize energy usage in real-time while maintaining adaptability to varying driving conditions.
2Use of energy by moving object
If the controller uses adaptive torque splitting based on historical drive cycle data, then fuel economy is improved and battery life is extended, but the control system complexity increases
Solution Approach 1:
The controller implements a feedback mechanism that monitors the duration the powertrain has been at each operating point and uses this information to adjust torque splits. The system continuously compares actual operating conditions with historical data and modifies engine and electric machine torque commands accordingly, creating a closed-loop control system that improves fuel economy through adaptive learning.
Solution Approach 2:
The control system performs self-learning by automatically storing and analyzing historical drive cycle data without requiring external intervention. The controller uses its own operational history to optimize future performance, making the system progressively more efficient over time while maintaining a relatively simple overall architecture.
Data Source
AI summary
A hybrid vehicle includes a powertrain having an engine, an electric machine, and a disconnect clutch configured to selectively couple the engine and the electric machine. The vehicle further includes a controller programmed to, for a given operating point defined by a powertrain speed and a driver-demanded torque, command a torque split between the engine and the electric machine that depends on a duration of time of the powertrain having been at the given operating point in a previous drive cycle such that for consecutive drive cycles, as the durations of time decrease, torque commanded to the engine decreases and torque commanded to the electric machine increases.