Hybrid Powertrain Control Using Route-Aware Dynamic Calibration
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Solution Overview
Problem
Existing hybrid vehicle powertrain control systems use a single calibration for all use cases, failing to optimize fuel economy, performance, emissions, and component life simultaneously.
Innovation Solution
A dynamic control system that includes an optimizer module to receive route and vehicle status information, using online learning and digital twins to manage power distribution between the engine and electric motor, adjusting strategies based on real-time data and historical patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a single calibration is used for all use cases, then the control system is simple and easy to implement, but it cannot optimize fuel economy, performance, emissions, and component life simultaneously
Solution Approach 1:
The patent segments the control system into multiple calibration sets, each optimized for specific use cases or operating conditions. Instead of using a single universal calibration, the system divides the control strategy into condition-specific calibrations that can be selected based on current vehicle status and route information, allowing optimization for fuel economy, performance, emissions, and component life in their respective domains
Solution Approach 2:
The patent implements dynamic calibration selection where the control system transitions from static single calibration to dynamic multi-calibration selection. The system dynamically chooses appropriate calibration sets based on real-time vehicle status information and route characteristics, enabling the control strategy to adapt to changing conditions and optimize multiple conflicting objectives simultaneously
2Device complexity
If preset predetermined rules are used for power source selection, then the control logic is simple, but it cannot arrive at optimal decisions regarding fuel economy, performance, emissions, and component life
Solution Approach 1:
The patent incorporates feedback mechanisms where the control system continuously monitors vehicle status information and route conditions, then uses this feedback to select appropriate calibration sets and adjust power source selection decisions. This closed-loop feedback enables the system to learn from historical data and improve decision-making regarding fuel economy, performance, emissions, and component life optimization
Solution Approach 2:
The patent changes the control parameters from fixed predetermined rules to dynamic parameter selection based on vehicle status and route information. The system adjusts calibration parameters such as power split targets, state of charge profiles, and range extender operation decisions based on real-time conditions, enabling optimal decisions across multiple performance dimensions
3Reliability
If dynamic control with online learning and digital twins is implemented, then fuel economy, performance, and component life are optimized, but the system complexity increases
Solution Approach 1:
The patent introduces digital twins as intermediary virtual models that simulate vehicle behavior and predict performance outcomes. These digital twins act as mediators between real-time sensor data and control decisions, allowing the system to evaluate multiple scenarios and select optimal strategies without directly complexifying the physical control hardware. The digital twins enable online learning and prediction while keeping the actual control system relatively simple
Data Source
AI summary
Methods and systems for a powertrain power management in a vehicle with an electric motor, and an engine are disclosed. The methods and systems involve a powertrain that is operatively coupled to the engine and the electric motor, and an optimizer module operatively coupled to the powertrain. The optimizer module receives an operator information to travel a route from a remote management module, receives current route information for the route from a mapping application in response to the operator information, measures current vehicle status information for the hybrid vehicle, and decides a power management strategy for the vehicle based on the current route information and the current vehicle status information.