Hybrid Vehicle Energy Management via Map Link Pre-screening
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Solution Overview
Problem
Conventional hybrid vehicles lack optimal energy management due to the absence of environmental and terrain information, leading to sub-optimal usage of energy storage components, resulting in reduced efficiency and shortened lifespan, with existing GPS-based systems facing challenges in accurately matching location data with map links and being computationally intensive.
Innovation Solution
A system and method that uses a computer to identify a vehicle's location, access a map database, pre-screen links based on distance, and determine if the vehicle is following a previously mapped link, allowing for efficient data upload and optimization of energy storage component usage by leveraging historical data to adjust operating parameters based on route-specific power demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional GPS-based location matching is used to identify map links, then location data can be captured, but the system becomes computationally intensive and accuracy is reduced due to GPS tolerances
Solution Approach 1:
The system pre-screens map links to identify those within a given bounds of the current GPS location before performing detailed distance calculations. This preliminary filtering step reduces the number of links requiring computationally intensive processing, thereby reducing overall computational burden while maintaining matching accuracy.
Solution Approach 2:
The location matching process is segmented into multiple stages: first identifying links within geographic bounds, then calculating distances only to those specific links, and finally determining trajectory following. This segmentation divides the complex matching problem into manageable steps, reducing computational complexity at each stage.
2Adaptability or versatility
If the ESC state of charge is maintained near the midpoint to handle charging and discharging events, then the system can react to power demands, but regenerative capture opportunities are stopped short and battery life is reduced due to excessive stress
Solution Approach 1:
The system uses historical data to predict future power demands and regenerative opportunities along the current route. By anticipating upcoming charging and discharging events, the control system can proactively adjust the ESC state of charge to optimal levels before events occur, rather than reactively maintaining midpoint charge. This allows the battery to operate at higher state of charge when regenerative capture is expected, maximizing energy recovery while avoiding excessive stress.
Solution Approach 2:
The system continuously monitors actual power demands and compares them with historical predictions, using this feedback to refine future state of charge control strategies. This closed-loop approach enables the system to adapt to actual operating conditions while optimizing battery usage patterns to extend life.
3Productivity
If historical data is uploaded to a database for future trips, then system efficiency can be optimized, but GPS trajectory misalignment with map links causes data loss
Solution Approach 1:
The system pre-screens map links to identify those within a given bounds of the GPS location before attempting to match trajectories. This preliminary identification creates a buffer zone that accommodates GPS accuracy tolerances, ensuring that historical data is associated with the correct map links even when GPS trajectories do not perfectly align with stored routes.
Solution Approach 2:
The data upload process is segmented to first identify candidate links within geographic bounds, then associate power data with those specific links. This segmentation ensures that historical data is systematically matched to appropriate map segments, preventing data loss due to GPS tolerances.
4Reliability
If the battery is oversized to ensure stress limits are not exceeded, then reliability is improved, but system cost increases
Solution Approach 1:
The system dynamically adjusts the ESC state of charge based on predicted power demands and regenerative opportunities along the current route. By optimizing charge levels in real-time rather than using conservative fixed thresholds, the system ensures stress limits are not exceeded while allowing the battery to operate at higher state of charge when conditions permit, thereby reducing the required battery capacity for the same reliability level.
Data Source
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AI summary
A system for operating an electric or hybrid-electric vehicle includes a computer programmed to identify a location of a vehicle (212), access a map and identify a plurality of links therein (210), pre-screen the plurality of links to identify if any of the plurality of links is within a given bounds of the current location (214), and if one or more possible links are identified (222), then match the current location of the vehicle to one of the identified links (250), and upload power data for the vehicle corresponding to the matched location into a database (268).