AI Fleet Electrification Platform for Charging Point Placement
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Solution Overview
Problem
Current electric vehicle energy distribution systems face challenges such as vehicle downtime due to charge depletion, inflexible charging solutions, and high capital costs associated with transitioning to electric fleets, which hinder the transition from fuel-based to electric vehicles and exacerbate environmental concerns.
Innovation Solution
A system utilizing a processor and memory to optimize the placement and capacity of wireless charging points and energy storage devices based on vehicle positional and energy consumption data, minimizing the number of charging points while maximizing energy storage capacity, using low storage capacity, rapid recharge, high cycle life electric energy storage devices like ultracapacitors, and integrating a range extender for supplemental power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the energy storage capacity (battery size) of vehicles is increased to mitigate running out of charge, then the reliability is improved, but the charging time increases, thereby increasing vehicle downtime and capital cost
Solution Approach 1:
The system performs preliminary routing of electric vehicles to charging stations during periods when vehicles are not in vocational use. This advance scheduling ensures vehicles are charged before they are needed, preventing charge depletion during active service and eliminating the need to increase battery capacity to cover extended operational periods.
Solution Approach 2:
The energy distribution system dynamically adjusts routing and charging schedules based on real-time vehicle positions, charge levels, and vocational usage patterns. This dynamic optimization allows the system to adapt to changing conditions, maximizing the utilization of existing energy storage capacity without requiring oversized batteries.
2Loss of time
If charging stations are positioned along routes used by vehicles during vocational use, then the loss of time is reduced, but the system becomes inflexible and difficult to scale
Solution Approach 1:
The system pre-calculates optimal routing paths that incorporate charging stops based on predicted vehicle usage patterns and charge requirements. This preliminary routing allows vehicles to be directed to charging stations in advance, minimizing impact on vocational operations while maintaining route flexibility.
Solution Approach 2:
The system continuously monitors vehicle positions, charge levels, and routing efficiency, using this feedback to dynamically adjust charging station placement and routing recommendations. This feedback loop enables the system to adapt to changing operational patterns while maintaining optimal performance.
3Reliability
If a network of charging stations is provisioned to support electric vehicle fleets, then the reliability is improved, but the capital investment required becomes prohibitive
Solution Approach 1:
The system performs preliminary analysis of vehicle routing patterns and charge requirements to identify the minimum necessary charging infrastructure. By understanding vehicle usage patterns in advance, the system can provision charging stations only where and when needed, avoiding unnecessary capital investment while maintaining reliability.
Solution Approach 2:
The system optimizes charging station parameters such as power output, operating hours, and location based on actual vehicle usage data. This parameter optimization ensures that the charging infrastructure is sized and configured to meet actual demand, reducing capital costs while maintaining system reliability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for flexible, cost-effective electrification of vehicle fleets by reducing vehicle downtime, optimizing energy storage, and lowering capital expenditures, enabling predictable and incremental transition to electric vehicles while minimizing environmental impact.
Implementation Method 1
a wireless charging point configured to wirelessly charge the one or more electric energy storage devices
Data Source
AI summary
An artificial intelligence platform for electrification of a fleet of vehicles and an energy distribution system are described. The energy distribution system comprises a number of electric energy storage devices associated with vehicles in the fleet, as well as charging points. The configuration of the electric energy storage devices and the charging points is determined using an artificial intelligence platform and vehicle positional and energy consumption information. The platform allows for the iterative implementation of the process of electrification in a simple and predictable manner, given specific constraints, and provides energy distribution systems for electrical vocational vehicles in a flexible and cost-effective manner.