Telematics System Optimizing EV Charging with Renewable Energy Forecasts
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Solution Overview
Problem
Electric vehicle charging schedules do not effectively maximize the use of renewable energy sources, leading to inefficiencies and increased carbon footprints, as they often rely on non-renewable energy sources.
Innovation Solution
A telematics system that communicates with utility companies to acquire and analyze renewable energy mixture data, forecasting future energy sources and synchronizing vehicle charging schedules to optimize the use of renewable energy by scheduling recharging events during times when renewable energy is most available.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If vehicle charging schedules are optimized to maximize renewable energy use, then the ratio of renewable energy used increases, but the system complexity increases due to forecasting and scheduling requirements
Solution Approach 1:
A telematics unit acts as an intermediary between the vehicle charging system and utility company energy mixture data. The telematics unit receives, stores, and processes forecasts of renewable energy mixture data, then uses this information to optimize charging schedules. This intermediary component manages the complexity of data handling and scheduling algorithms, making the system feasible for implementation.
Solution Approach 2:
The system acquires and stores forecasts of renewable energy mixture data in advance of actual charging events. By having this forecasting information available beforehand, the system can plan optimal charging schedules that maximize renewable energy usage without requiring complex real-time decision-making, thereby reducing operational complexity.
2Object-generated harmful factors
If charging events are scheduled during peak renewable energy production periods, then carbon emissions are reduced, but flexibility in charging time windows is limited
Solution Approach 1:
The charging schedule is dynamically adjusted based on the forecasted renewable energy mixture data. The system identifies time windows with highest renewable energy availability and schedules charging events during those periods. This dynamic scheduling approach reduces carbon emissions by aligning charging with clean energy production while still providing flexibility to adapt to varying forecast conditions and vehicle availability.
Solution Approach 2:
The system changes the timing parameter of charging events to align with periods of peak renewable energy production. By varying the charge start time, duration, and rate based on forecasted energy mixture conditions, the system optimizes environmental performance while maintaining adaptability to different vehicle usage patterns and owner requirements.
3Measurement precision
If real-time monitoring of energy mixture data is implemented, then charging optimization accuracy improves, but data acquisition and processing requirements increase
Solution Approach 1:
Instead of implementing real-time monitoring, the system uses pre-acquired forecasts of renewable energy mixture data that are stored in the telematics unit. This preliminary data acquisition approach provides sufficiently accurate information for optimization without the continuous data streaming and processing requirements of real-time monitoring, thereby reducing overall data handling requirements while maintaining practical optimization accuracy.
Data Source
AI summary
Methods and systems for maximizing the proportion of renewable energy relative to the total energy used during the charging of electrically powered vehicles are described. The methods and systems contemplate the acquisition, by a TSP, of data pertaining to the renewable energy mixture, i.e. the ratio of energy produced from renewable sources to energy produced from non-renewable sources, of the energy provided by one or more utility companies. Renewable energy mixture forecasts and information pertaining to charging and use of one or more vehicles are used to generate schedules for charging one or more electrically powered vehicles such that the use of renewable energy in vehicle charging is optimized.