Hybrid Vehicle Controller Using Look-Ahead Data for Energy Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Hybrid electric vehicles face inefficiencies in adapting to changing operating conditions due to a lack of consideration for future driving scenarios, leading to suboptimal fuel economy, drivability, and durability.
Innovation Solution
A method of controlling hybrid vehicles that utilizes data from active sensing systems and telematics to predict future operating conditions, allowing for adaptive energy management strategies by determining optimal engine and motor/generator parameters and commanding powertrain operating strategies based on this data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the controller uses only current operating conditions to determine powertrain mode, then the control system is simple and responsive, but fuel economy and energy efficiency are suboptimal
Solution Approach 1:
The controller receives and processes look-ahead data about future driving conditions (traffic patterns, road gradients, weather) in advance, allowing it to proactively determine optimal powertrain operating modes before the vehicle actually encounters those conditions. This preliminary analysis enables better fuel economy by anticipating upcoming events rather than merely reacting to current conditions.
Solution Approach 2:
The control system dynamically adjusts powertrain operating modes based on varying look-ahead time horizons and different future condition scenarios. The controller can extend or reduce the look-ahead window depending on traffic conditions, road topology, and vehicle state, making the control strategy adaptive rather than static.
2Use of energy by moving object
If the controller extends the look ahead time to improve energy management, then fuel economy improves, but the system becomes less responsive to immediate driving conditions
Solution Approach 1:
The controller dynamically adjusts the look-ahead time horizon based on current driving conditions, traffic patterns, and vehicle state. When immediate responsiveness is critical, the look-ahead window is reduced; when energy optimization is the priority and conditions are stable, the window is extended. This dynamic adjustment resolves the trade-off between energy efficiency and response speed.
Solution Approach 2:
The controller periodically updates and recalculates the optimal powertrain mode based on new look-ahead data as it becomes available, rather than using a single fixed look-ahead determination. This periodic recalculation allows the system to maintain energy efficiency while staying responsive to changing conditions.
3Use of energy by moving object
If the controller frequently changes powertrain operating modes to optimize for varying conditions, then fuel economy improves, but drivability and system reliability may deteriorate
Solution Approach 1:
The controller uses look-ahead data to anticipate future conditions and smoothly transition between powertrain modes in advance, rather than making abrupt changes when conditions suddenly change. This proactive approach improves fuel economy while maintaining drivability by avoiding frequent, sudden mode transitions that could affect vehicle performance and reliability.
Data Source
AI summary
A method of controlling a hybrid vehicle having a hybrid powertrain with an engine and a motor/generator includes receiving data indicative of anticipated future vehicle operating conditions, and determining via a controller optimal operating parameters for the engine and for the motor/generator based at least partially on the data. A controller then commands a powertrain operating strategy for the engine and the motor/generator based on the determined optimal operating parameters. The data received can be from active onboard sensing systems and from vehicle telematics systems.