Bidirectional Trailer Charging Control for In-Transit EV Power Flow
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Solution Overview
Problem
Existing systems for electrified vehicles lack efficient methods for prioritizing and managing in-flight bidirectional energy transfers between vehicles and charging trailers, particularly based on real-time conditions such as trajectory, weather, traffic, and user itinerary, which can lead to suboptimal battery charging during travel.
Innovation Solution
A bidirectional energy transfer system with a control module that uses real-time prognostic information, battery prognostic data, and environmental factors to create an energy transfer prioritization strategy, assigning priority ranking scores to ensure optimal energy distribution between electrified vehicles and charging trailers, and adjusting these scores dynamically during travel to achieve full charge near the destination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If in-flight bidirectional energy transfers are implemented between electrified vehicles and charging trailers, then energy availability and convenience during travel are improved, but system complexity and control difficulty increase
Solution Approach 1:
A control module serves as an intermediary between the electrified vehicle and charging trailer, managing the bidirectional energy transfer process. This centralized control mechanism coordinates power flow, monitors system status, and automates charging decisions, thereby reducing operational complexity for users while enabling convenient in-flight charging.
Solution Approach 2:
The system implements automated energy management where the control module independently monitors battery status, trajectory, weather conditions, and traffic information to autonomously determine optimal charging timing and power allocation, eliminating the need for manual user intervention while maintaining system simplicity.
2Productivity
If real-time prognostic information and multiple environmental factors are considered for energy transfer prioritization, then charging optimization and energy efficiency are improved, but information processing complexity and computational requirements increase
Solution Approach 1:
The control module pre-processes and stores trajectory data, weather forecasts, and traffic patterns before energy transfer events occur. By preparing this prognostic information in advance, the system reduces real-time computational burden while maintaining optimized charging decisions based on comprehensive environmental factors.
Solution Approach 2:
The system continuously monitors real-time battery status, energy transfer rates, and environmental conditions, using this feedback to dynamically adjust charging priorities and power allocation. This closed-loop control optimizes charging efficiency while managing computational complexity through iterative refinement rather than exhaustive calculation.
3Reliability
If dynamic priority ranking scores are assigned and adjusted during in-flight energy transfer events, then energy distribution optimization is improved, but control algorithm complexity increases
Solution Approach 1:
The control module implements dynamic priority ranking that automatically adjusts based on real-time battery state of charge, remaining travel time, and environmental conditions. This dynamic adaptation ensures reliable energy distribution throughout the journey without requiring complex static algorithms, as the system evolves its control strategy alongside changing operational conditions.
Solution Approach 2:
The system optimizes energy transfer by changing key parameters such as charge rate, power allocation, and priority scores based on monitored conditions. By adjusting these parameters dynamically rather than using fixed control algorithms, the system achieves reliable energy distribution while maintaining manageable computational complexity through parameter-based control.
4Ease of operation
If energy transfers are optimized to achieve full charge near destination based on trajectory and user itinerary, then user convenience and travel reliability are improved, but real-time monitoring and control requirements increase
Solution Approach 1:
The control module uses pre-loaded user itinerary and trajectory information to plan energy transfer events in advance, targeting full charge arrival at destinations. This preliminary planning reduces the need for complex real-time automation while maintaining high user convenience, as the system executes pre-determined charging strategies based on known future conditions.
Solution Approach 2:
The system autonomously monitors battery status and environmental factors, automatically adjusting energy transfer parameters to ensure optimal charge levels arrive at destinations. This self-service automation handles complex real-time control internally while presenting a simple, convenient interface to users who do not need to manually manage charging operations.
Data Source
AI summary
Systems and methods are provided for coordinating and controlling power flow during in-flight bidirectional energy transfer events between an electrified vehicle, one or more charging trailers, and optionally, one or more electrified recreational vehicles. The systems and methods may prioritize energy transfers between each connected energy unit based on various parameters, including but not limited to in-transit travel logistics, environmental information, time of day, etc. Charge energy may be transferred to the appropriate power source to meet customer needs with varying levels of priority according to an energy transfer prioritization control strategy that is derived from the various inputs that are considered.