EV Fleet Electrification Forecasting for Location-Based TCO
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Solution Overview
Problem
Transitioning from a vehicle fleet with internal combustion engines to electric vehicles poses complex financial challenges due to varying utility electricity rates, fuel costs, and potential vehicle-to-grid revenue, necessitating a system to determine the financial impact and timing of this transition.
Innovation Solution
A computerized system that provides insights and forecasts the total cost of ownership, feasibility analysis, and optimal charging infrastructure deployment for transitioning to an electric vehicle fleet, incorporating real-world driving dynamics, battery degradation, and utility infrastructure planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If a vehicle fleet transitions from internal combustion engines to electric vehicles, then total cost of ownership decreases due to lower fuel costs and maintenance, but capital expenditure increases and financial impact becomes complex to determine
Solution Approach 1:
The forecasting system segments the financial impact into multiple independent variables including capital expenditure, operational costs, fuel prices, electricity rates, and V2G revenue. Each variable can be adjusted separately in the scenario builder, allowing users to analyze specific financial aspects without being overwhelmed by the entire complex system.
Solution Approach 2:
The system introduces a computerized forecasting model as an intermediary between the fleet electrification decision and the financial outcome. This intermediary processes multiple input variables (fuel costs, electricity rates, vehicle specifications) and produces standardized financial projections, simplifying the complex relationship between electrification and financial impact.
2Adaptability or versatility
If utility electricity rates and fuel costs vary by location, then financial impact depends on site location, but this increases the complexity of determining financial impact
Solution Approach 1:
The scenario builder allows users to input location-specific parameters such as utility electricity rates, fuel costs, and V2G revenue potential for different geographic locations. The system then generates financially tailored forecasts for each location, capturing local quality differences without requiring a completely different analysis framework.
Solution Approach 2:
The forecasting system dynamically adjusts financial projections based on user-input variables that can change between scenarios. Users can modify electricity rates, fuel prices, and other location-dependent parameters to see how financial impact changes across different locations and time periods, making the system adaptable to varying conditions.
3Difficulty of detecting and measuring
If vehicle fleet electrification is pursued, then operational costs decrease, but capital expenditure increases
Solution Approach 1:
The system performs preliminary forecasting and scenario analysis before the actual fleet electrification takes place. By projecting future operational cost savings and capital requirements in advance, users can better plan and budget for the transition, understanding the full financial picture before committing to the electrification strategy.
Solution Approach 2:
The scenario builder provides feedback by allowing users to input actual or projected values for capital expenditure and operational costs, then generating updated financial projections. This feedback loop enables users to see how changes in capital spending affect long-term operational savings and vice versa, facilitating more informed decision-making.
Data Source
AI summary
A system and method for performing forecasts for entities planning to transition from a vehicle fleet with internal combustion engines (“ICE”) to an electric vehicle (“EV”) fleet is provided. The total cost of ownership is determined regarding the replacement fleet of EVs based on vehicle energy analytics regarding the existing fleet of ICE vehicles and the replacement fleet of EVs. Financial analytics regarding potential grants, credits and/or incentives for the replacement fleet of EVs could be factored into the determination. There is a user interface that displays a forecast regarding the total cost of ownership. In some cases, the user interface includes user-adjustable elements to change one or more parameters regarding the vehicle energy analytics and the financial analytics, such that adjustments to total cost of ownership are made in real-time based on adjustment to the user-adjustable elements.