EV Route Energy Prediction With Adaptive In-Drive Updating
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Solution Overview
Problem
Existing energy consumption predictions for electric vehicles before a trip are often imprecise due to various factors, leading to range anxiety and suboptimal trip planning.
Innovation Solution
A system and method for adaptive in-drive updating of energy consumption predictions, using a controller with a processor and memory to adjust pre-drive predictions based on actual energy consumption data from completed route segments, incorporating modification factors and machine learning models to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-drive energy consumption prediction is used for trip planning, then users can plan trips and alleviate range anxiety, but the prediction accuracy is imprecise due to various factors
Solution Approach 1:
The system implements feedback by continuously monitoring actual energy consumption during the drive and using this information to update and refine the energy consumption prediction model. The controller compares predicted versus actual consumption values and adjusts future predictions based on the deviation, creating a closed-loop system that improves accuracy over time.
Solution Approach 2:
The system performs preliminary action by establishing an initial energy consumption prediction before the drive using historical data and route information. This pre-drive prediction serves as a baseline that is subsequently refined during the actual drive, allowing the system to proactively provide trip planning information while maintaining the capability for real-time adjustments.
2Measurement precision
If in-drive updating is implemented to improve prediction accuracy, then better trip optimization is achieved, but the system complexity increases
Solution Approach 1:
The route is divided into multiple segments, and energy consumption is tracked and updated segment by segment. This segmentation allows the system to manage complexity by processing information in manageable units rather than attempting to calculate entire route consumption at once, enabling incremental updates without overwhelming computational requirements.
Solution Approach 2:
The system transitions from a static pre-drive prediction to a dynamic in-drive updating mechanism. The prediction model adapts in real-time as the vehicle progresses through the route, adjusting consumption estimates based on actual performance data. This dynamic approach maintains accuracy while managing complexity through continuous, incremental adjustments rather than complete recalculation.
Data Source
AI summary
A system for adaptive in-drive updating, for a vehicle travelling on a route, includes a controller adapted to obtain a pre-drive energy consumption prediction for the route, via an energy consumption predictor. An in-drive updating module is selectively executable by the controller at a timepoint during the route at which a completed portion of the route has been traversed and a remaining portion remains untraversed. The controller is adapted to obtain an actual energy consumption for segments in the completed portion of the route. The controller is adapted to obtain at least one modification factor based on a comparison of the actual energy consumption and the pre-drive energy consumption prediction for the segments in the completed portion of the route. The pre-drive energy consumption prediction for the remaining portion of the route is adjusted based on the modification factor.