Enterprise AI Analysis
A Fast Heuristic Search Approach for Energy-Optimal Profile Routing for Electric Vehicles
This paper introduces a novel A*-based approach for energy-optimal profile search for electric vehicles (EVs) in large-scale road networks. It addresses the challenge of planning optimal paths when the initial energy level is unknown, a common scenario in multi-leg EV routing. Unlike existing label-correcting methods that rely on complex profile-merging procedures, our method employs a label-setting approach with efficient pruning rules based on multi-objective A* search. This avoids the overhead of explicit profile merging. Experimental results on real-world road networks demonstrate that our energy profile A* search achieves performance comparable to energy-optimal A* with a known initial energy level, outperforming existing profile search methods in speed and simplicity. The approach also provides a practical framework for addressing energy-optimal profile queries in large road networks efficiently.
Executive Impact: Key Performance Indicators
Our innovative approach delivers tangible improvements in efficiency and operational performance for EV fleet management.
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Speedup vs. Dijkstra
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Performance vs. A* (known SoC)
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Profile Merging Overhead Eliminated
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Nodes Expanded (Forward Profile Search)
Deep Analysis & Enterprise Applications
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2.14x
Faster than Dijkstra for complex EV routing
Energy-Optimal Profile Search Process
Initialize Open and State Lists
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Extract Node with Smallest F-value
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Check Dominance & Prune
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Expand Node to Successors
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Adjust Profiles & Apply Constraints
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Add Non-Dominated Nodes to Open
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Return Optimal Profiles
| Feature | Proposed Pr-A* Approach | Traditional Label-Correcting Methods |
|---|---|---|
| Profile Merging | Avoided with dominance pruning | Explicit and complex procedures |
| Computational Complexity | Comparable to A* (known SoC) | Significantly slower due to profile merging |
| Ease of Implementation | Simpler label-setting, less complex rules | Requires specialized procedures and tricks |
| Handling Negative Costs | Efficiently with consistent heuristics | Requires reweighting or vertex re-exploration |
| Scalability | Fast on large road networks | Limited by profile complexity |
Real-World Impact: Nissan Leaf Fleet Optimization
Our algorithm was tested on real-world road networks using data from the 9th DIMACS Implementation Challenge, simulating a Nissan Leaf with an 85 kWh battery carrying four passengers. This setup allowed us to validate the practical applicability and performance benefits of our approach.
- Network Scale: Evaluated on maps with over 14 million states and 34 million edges, demonstrating robust performance on large-scale infrastructure.
- Energy Model: Utilized a realistic energy consumption model accounting for road gradient, vehicle mass, and speed profiles, ensuring accuracy.
- Performance Gains: Consistently achieved runtimes approximately twice as fast as Dijkstra and comparable to A* with known initial SoC, validating its efficiency for practical use.
- Profile Flexibility: Successfully computed minimum-energy paths for all possible initial energy levels (0-85 kWh), crucial for multi-leg trips and uncertain SoC scenarios.
Advanced ROI Calculator: Project Your Savings
Estimate the potential energy and cost savings for your enterprise by optimizing EV routing with our AI solution.
Estimated Annual Savings
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Estimated Annual Hours Reclaimed
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Implementation Roadmap: Your Path to Enhanced Efficiency
A clear, phased approach to integrate energy-optimal routing into your operations, ensuring smooth adoption and measurable results.
Phase 1: Initial Integration & Data Mapping
(2-4 Weeks)
Connect existing fleet management systems, map road network data, and integrate EV specifications.
Phase 2: Custom Heuristic Development & Tuning
(4-6 Weeks)
Adapt energy models and heuristic functions to specific vehicle types and operational patterns.
Phase 3: Pilot Deployment & Performance Validation
(6-8 Weeks)
Deploy on a subset of the fleet, gather real-world data, and fine-tune parameters for optimal energy savings.
Phase 4: Full-Scale Rollout & Continuous Optimization
(Ongoing)
Expand to the entire fleet, implement monitoring tools, and continuously optimize routing algorithms based on new data.
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