Hybrid Powertrain Torque Selection Optimizing System Loss
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing hybrid vehicle powertrain systems do not optimize power flow across all propulsion components, primarily focusing on engine operation while neglecting mechanical and electrical losses and battery usage factors when determining preferred operating points.
Innovation Solution
A method that considers the entire powertrain system, including energy storage system utilization, to determine operating points by calculating total system losses and applying energy storage system power utilization costs to identify the preferred operating point corresponding to minimum total system loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If only engine operation is optimized when determining preferred operating points, then engine efficiency is improved, but total system loss increases due to neglecting mechanical and electrical losses and battery usage factors
Solution Approach 1:
The patent merges the optimization of multiple subsystems (engine, transmission, motors, battery) into a unified system-level optimization framework. By combining the determination of preferred operating points for all propulsion system components into a single integrated process that minimizes total system loss, the patent resolves the contradiction between reducing energy loss and managing system complexity.
Solution Approach 2:
The patent creates a universal optimization method that simultaneously determines preferred operating points for multiple different components (engine, transmission, motors, battery) using a single systematic approach. This multi-functional optimization framework considers various loss mechanisms and battery usage factors together, achieving comprehensive system efficiency without requiring separate optimization processes for each component.
2Productivity
If battery power is selected based solely on road-load power requirements, then power delivery is simplified, but battery utilization efficiency deteriorates due to lack of consideration for battery usage factors
Solution Approach 1:
The patent implements feedback mechanisms where battery state information (state of charge, temperature, usage factors) is continuously monitored and fed back into the operating point determination process. This feedback loop allows the system to adjust battery power selection and preferred operating points based on real-time battery conditions, improving both power delivery efficiency and battery utilization reliability.
Solution Approach 2:
The patent performs preliminary determination of preferred operating points for the battery based on predicted usage factors and system requirements before actual power delivery occurs. By pre-calculating optimal battery operating conditions considering various usage factors, the system prepares the battery to deliver power more efficiently and reliably, rather than reacting to power demands in isolation.
3Loss of energy
If comprehensive system optimization is implemented considering all components and factors, then total system efficiency is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the comprehensive system optimization into distinct modular components, each handling specific aspects (engine optimization, transmission optimization, motor optimization, battery optimization). By dividing the overall optimization problem into manageable segments that can be processed separately and then integrated, the patent reduces computational complexity while maintaining system-wide efficiency improvements.
Solution Approach 2:
The patent transforms the complex multi-variable optimization problem into a more manageable form by changing parameters and using lookup tables with pre-calculated preferred operating points. Instead of performing real-time complex calculations for all system parameters, the system uses pre-determined optimization results stored in accessible formats, significantly reducing computational complexity while preserving the benefits of comprehensive system optimization.
Data Source
AI summary
A preferred input torque for a hybrid powertrain is determined within a solution space of feasible input torques in accordance with a plurality of powertrain system constraints that results in a minimum overall powertrain system loss. System power losses and battery utilization costs are calculated at feasible input torques and a solution for the input torque corresponding to the minimum total powertrain system loss is converged upon to determine the preferred input torque.