Dynamic Charging Rate Modulation for Autonomous EV Battery Life
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Solution Overview
Problem
Current charging strategies for autonomous electric vehicles in mass transit systems, such as airports, face challenges in balancing battery life and passenger capacity, often leading to either excessive battery wear or increased infrastructure costs due to inefficient charging rates.
Innovation Solution
A system and method that utilize a controller to modulate the charging rate of autonomous electric vehicles based on forecasted passenger loads, adjusting the rate proportionally or inversely with the duration before the vehicle's duty mode, using historical data, image analysis, or flight information to optimize battery charging and extend battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If high charging rate is used to recharge battery quickly, then charging time is reduced, but battery life is damaged
Solution Approach 1:
The charging rate is made dynamic rather than static. The system continuously adjusts the charging rate based on real-time conditions including forecasted passenger load, current battery state of charge, and time until next duty mode. This dynamic adjustment allows the system to optimize between charging speed and battery protection, applying high charging rates only when necessary and using lower rates when sufficient time is available.
Solution Approach 2:
The system performs preliminary forecasting of passenger load to determine when vehicles will be needed again. By knowing in advance when a vehicle will transition from idle to duty mode, the charging system can plan the charging schedule optimally, starting charging earlier at lower rates when time permits, thereby avoiding the need for aggressive high-rate charging that damages batteries.
2Quantity of substance
If bottle-feeding charging strategy is used to reduce battery quantity, then battery cost is reduced, but travel time increases due to longer stops
Solution Approach 1:
The system enables continuous operation of the fleet by implementing overlapping charging cycles. While some vehicles are in service carrying passengers, other vehicles are being recharged. The forecasted passenger load data allows the system to continuously rotate vehicles between duty and idle modes, ensuring that charging operations never interrupt the overall service continuity, thereby maintaining high productivity.
Solution Approach 2:
The system dynamically adjusts the number of vehicles in duty versus idle modes based on forecasted passenger demand. When passenger load is forecasted to be high, more vehicles are kept in duty mode and charging occurs during off-peak periods. When demand is lower, more vehicles can be charged simultaneously. This dynamic allocation optimizes both battery quantity reduction and maintenance of passenger throughput.
3Reliability
If point catenary recharge is used to extend battery life, then battery life is improved, but system cost increases significantly
Solution Approach 1:
The system extracts the intelligent control function from the physical charging infrastructure. Instead of requiring expensive specialized hardware like point catenary systems, the patent implements a software-based forecasting and control system that runs on standard charging equipment. The value is created through information processing (passenger load forecasting) rather than through complex physical infrastructure, thereby achieving battery life extension without increasing infrastructure costs.
Solution Approach 2:
The system enables charging stations to self-adjust their operation based on forecasted demand and battery state. The intelligent controller automatically determines optimal charging parameters and schedules without requiring complex manual intervention or specialized expensive equipment. This self-service capability allows standard infrastructure to perform optimally, eliminating the need for costly specialized point catenary systems.
4Productivity
If more vehicles are used to maintain passenger capacity, then passengers per hour is maintained, but fleet size and operational cost increase
Solution Approach 1:
The system uses preliminary forecasting of passenger load to proactively schedule vehicle availability. By predicting when high demand will occur, the system ensures vehicles are charged and ready in advance, rather than reacting after vehicles become unavailable. This allows the fleet to maintain service levels during peak periods without needing to permanently deploy additional vehicles, as the same vehicles can be efficiently rotated through charging cycles based on forecasted demand patterns.
Data Source
AI summary
A system and a method for managing a charging station allow charging a battery of a connected autonomous electric vehicle for carrying passengers in a controlled environment. A controller connected to the charging station determines and modulates a charging rate with which the charging station charges the battery based on a duration between a start of charging the vehicle and a forecasted time of start of duty mode of the vehicle. The duration is determined by the controller based on a forecasted passenger load as a function of time.