Particulate Filter Regeneration Route Selection
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Solution Overview
Problem
Existing methods for particulate filter regeneration in vehicles are inefficient due to premature terminations during urban driving conditions, leading to increased fuel consumption and reduced regeneration efficiency, as they fail to account for real-time driver behavior and environmental factors.
Innovation Solution
A system that dynamically selects and updates routes based on particulate filter loading, past driving history, and real-time traffic and environmental conditions, using a non-homogeneous state transition matrix to estimate driver state of mind and adjust regeneration efficiency, thereby optimizing particulate filter regeneration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If passive regeneration is used during urban driving conditions, then fuel consumption is reduced, but regeneration efficiency deteriorates due to premature terminations
Solution Approach 1:
The system dynamically adjusts the regeneration strategy by transitioning between passive and active regeneration modes based on real-time monitoring of driving conditions, PF loading, and predicted driving behavior. The controller continuously adapts the regeneration approach to maintain efficiency while ensuring complete regeneration when necessary.
Solution Approach 2:
The system implements a feedback mechanism by monitoring driving conditions, PF loading levels, and driver behavior patterns, then using this information to predict future driving scenarios and adjust the regeneration strategy accordingly. This closed-loop control ensures that regeneration is initiated or terminated based on actual system state and predicted outcomes.
2Reliability
If active regeneration is used to ensure complete regeneration, then regeneration efficiency is improved, but fuel consumption increases
Solution Approach 1:
The system dynamically selects between passive and active regeneration modes based on real-time conditions. Passive regeneration is used when driving conditions are favorable and PF loading is moderate, while active regeneration is triggered only when necessary to ensure complete regeneration, thereby optimizing the balance between efficiency and fuel consumption.
Solution Approach 2:
The system predicts future driving scenarios based on current driving behavior patterns and environmental conditions. By anticipating whether future conditions will be favorable for passive regeneration, the system can make informed decisions about whether to initiate active regeneration now or wait for more favorable conditions, thereby avoiding unnecessary fuel consumption.
3Reliability
If driver-specific information is used to predict regeneration phases, then regeneration efficiency is improved, but system complexity increases
Solution Approach 1:
The system utilizes readily available data from existing vehicle sensors and onboard systems (engine control unit, navigation system, weather services) to predict driving behavior and environmental conditions. By leveraging data that the vehicle already collects for other purposes, the system avoids the need for additional specialized sensors or complex hardware infrastructure.
Solution Approach 2:
The controller integrates multiple functions including driving behavior analysis, environmental condition monitoring, PF loading assessment, and regeneration control into a single unified system. This multi-functional approach consolidates complexity into one control unit rather than requiring separate systems for each function.
4Ease of operation
If route selection is based solely on driver preferences, then ease of operation is improved, but regeneration efficiency deteriorates
Solution Approach 1:
The system provides feedback to the driver by presenting multiple route options with their respective regeneration benefits. The driver can then make an informed decision based on their preferences and the system's recommendations, achieving a balance between convenience and regeneration efficiency.
Solution Approach 2:
The route recommendation system dynamically adjusts suggestions based on current PF loading levels and predicted driving conditions. When regeneration is urgently needed, the system prioritizes routes that will facilitate complete regeneration, while normally it respects driver preferences more heavily.
Data Source
AI summary
Methods and systems are provided for selecting a first travel route for a vehicle from a database based on particulate filter regeneration requirements and an inferred initial driver state of mind. In one example, the initial driver state of mind may be selected based on a past driver history, and during travel along the first travel route, the driver state of mind may be updated based on the driver interactions with traffic. The route selection may also be updated based on the updated driver state of mind.