Hybrid Powertrain Predictive Control for ICE-Motor Transitions
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Solution Overview
Problem
Hybrid vehicles face challenges in efficiently managing transitions between internal combustion engines and electric motors due to varying driving behaviors and conditions, leading to inefficient energy usage and performance.
Innovation Solution
A hybrid powertrain control system that predicts future powertrain demands by analyzing driving profiles, including location, road conditions, torque demand, and driver behavior, to optimize the transition between ICE and electric motors for better energy efficiency and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the control system uses real-time driving data only, then the responsiveness to current driver demands is improved, but the ability to predict future powertrain requirements deteriorates
Solution Approach 1:
The system performs preliminary analysis of driving patterns and predicts future powertrain requirements before they actually occur. By analyzing historical driving data and current context, the system anticipates upcoming power demands, allowing it to prepare optimal control strategies in advance rather than merely reacting to current conditions.
2Measurement precision
If the control system analyzes extensive driving profile data including historical behavior, then the accuracy of powertrain optimization is improved, but the computational complexity and processing time deteriorates
Solution Approach 1:
The system segments the driving profile analysis into distinct components: short-term recent driving behavior, long-term historical patterns, contextual factors (location, road conditions), and vehicle state data. This segmentation allows the complex analysis to be broken down into manageable processing stages, reducing computational burden while maintaining comprehensive analysis accuracy.
3Use of energy by moving object
If the system predicts driving profiles for longer future time periods, then the ability to optimize energy usage in advance is improved, but the accuracy of prediction deteriorates due to increasing uncertainty
Solution Approach 1:
The system dynamically adjusts the prediction time horizon based on confidence levels and changing driving conditions. As uncertainties increase over longer time periods, the system adaptively refines its prediction window and continuously updates predictions as new data becomes available, maintaining optimal energy management while accounting for increasing prediction uncertainty.
Data Source
AI summary
A method of controlling a hybrid powertrain in a vehicle, includes determining a first driving profile over a first time period, determining a second driving profile for a second time period, where the second time period includes at least some future time, determining a powertrain control instruction based at least in part on the second driving profile, and controlling the powertrain as a function of the powertrain control instruction.