Electric Commercial Vehicle Charging Strategy for Energy Cost and Resale
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Solution Overview
Problem
There is a need for an efficient and cost-effective energy management system for electric commercial vehicles that optimizes energy usage when parked or charging, considering variable pricing and energy resale opportunities based on factors like time of day and grid capacity.
Innovation Solution
The system obtains grid data and vehicle data to generate a strategy for charging and energy resale, selecting optimal charging locations and times based on energy purchase and resale prices, including carbon penalties and offsets, and adjusts the strategy in response to changes in data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the electric vehicle charges at any charging location without optimization, then the vehicle can be recharged, but the energy cost is not minimized and resale opportunities are not maximized
Solution Approach 1:
The energy management system performs preliminary actions by obtaining grid data and vehicle data in advance, generating a charging strategy before the actual charging occurs. This includes predicting energy prices, identifying optimal charging locations, and determining the best charging times based on mission requirements, thereby minimizing energy costs through proactive planning rather than reactive charging decisions
Solution Approach 2:
The energy management system enables self-service by autonomously generating charging strategies without requiring manual intervention. The system automatically processes grid data, vehicle data, and mission information to determine optimal charging locations and times, then executes the strategy independently, reducing operational complexity while improving energy cost efficiency
2Productivity
If the system generates an optimized charging strategy considering multiple factors, then energy costs are minimized and resale opportunities are maximized, but the processing complexity and data requirements increase
Solution Approach 1:
The energy management system applies segmentation by dividing the charging strategy generation into distinct functional modules: obtaining grid data, obtaining vehicle data, generating the strategy, and executing the strategy. Each module processes specific data types and performs dedicated functions, which simplifies the overall complex task of optimizing charging decisions while maintaining high energy management efficiency
Solution Approach 2:
The system utilizes parameter changes by dynamically adjusting charging decisions based on varying grid data (energy prices, resale opportunities) and vehicle data (energy levels, mission requirements). The strategy generation process analyzes multiple parameters and their changes over time to determine optimal charging locations and times, improving energy management efficiency through adaptive parameter optimization
3Speed
If the vehicle charges at high energy purchase price times, then the charging speed can be faster, but the energy cost increases
Solution Approach 1:
The energy management system applies dynamics by making charging decisions flexible and adaptive rather than static. The system dynamically adjusts charging speed and timing based on real-time grid data (energy prices, resale opportunities) and vehicle requirements, allowing the vehicle to charge faster when prices are low and slower or not charge when prices are high, thereby optimizing the balance between charging speed and energy cost
Data Source
AI summary
An electric vehicle energy management method and system includes obtaining, from a grid interface, grid data indicative of an energy purchase price, an energy re-sale price, and a plurality of charging locations. The data is obtained from a vehicle management controller, including vehicle data indicative of mission information, an energy requirement associated with the mission, and an estimated charging time based on the energy requirement and a stored energy amount stored by the electric vehicle. Based on the grid data and the vehicle data, a strategy for charging and energy re-sale is generated, including selecting at least one charging location of the plurality of charging locations, and selecting at least one charging time for charging the electric vehicle at the selected charging location.