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Real-Time Energy-Optimal Path Planning for Electric Vehicles

Saman Ahmadi, Guido Tack, Daniel Harabor, Philip Kilby, Mahdi Jalili

Abstract

The rapid adoption of electric vehicles (EVs) in modern transport systems has made energy-aware routing a critical task in their successful integration, especially within large-scale networks. In cases where an EV's remaining energy is limited and charging locations are not easily accessible, some destinations may only be reachable through an energy-optimal path: a route that consumes less energy than all other alternatives. The feasibility of such energy-efficient paths depends heavily on the accuracy of the energy model used for planning, and thus failing to account for vehicle dynamics can lead to inaccurate energy estimates, rendering some planned routes infeasible in reality. This paper explores the impact of vehicle dynamics on energy-optimal path planning for EVs. We develop an accurate energy model that incorporates key vehicle dynamics parameters into energy calculations, thereby reducing the risk of planning infeasible paths under battery constraints. The paper also introduces two novel online reweighting functions that allow for a faster, pre-processing free, pathfinding in the presence of negative energy costs resulting from regenerative braking, making them ideal for real-time applications. Through extensive experimentation on real-world transport networks, we demonstrate that our approach considerably enhances energy-optimal pathfinding for EVs in both computational efficiency and energy estimation accuracy.

Real-Time Energy-Optimal Path Planning for Electric Vehicles

Abstract

The rapid adoption of electric vehicles (EVs) in modern transport systems has made energy-aware routing a critical task in their successful integration, especially within large-scale networks. In cases where an EV's remaining energy is limited and charging locations are not easily accessible, some destinations may only be reachable through an energy-optimal path: a route that consumes less energy than all other alternatives. The feasibility of such energy-efficient paths depends heavily on the accuracy of the energy model used for planning, and thus failing to account for vehicle dynamics can lead to inaccurate energy estimates, rendering some planned routes infeasible in reality. This paper explores the impact of vehicle dynamics on energy-optimal path planning for EVs. We develop an accurate energy model that incorporates key vehicle dynamics parameters into energy calculations, thereby reducing the risk of planning infeasible paths under battery constraints. The paper also introduces two novel online reweighting functions that allow for a faster, pre-processing free, pathfinding in the presence of negative energy costs resulting from regenerative braking, making them ideal for real-time applications. Through extensive experimentation on real-world transport networks, we demonstrate that our approach considerably enhances energy-optimal pathfinding for EVs in both computational efficiency and energy estimation accuracy.

Paper Structure

This paper contains 21 sections, 7 equations, 7 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Case Study and Motivation
  3. Energy-optimal Pathfinding
  4. Problem Formulation and Background
  5. Energy Constraints and Adjustments
  6. Negative Weights and Graph Reweighting
  7. Related Work and Existing Solutions
  8. Vehicle Dynamics and Energy Efficiency
  9. pathfinding with online reduced costs
  10. Model-based Reduced Cost
  11. Model-independent Reduced Cost
  12. Energy-optimal Search with Reduced Costs
  13. Experimental Analysis
  14. Parametric Comparison of Approaches
  15. Algorithmic Performance
  16. ...and 6 more sections

Figures (7)

  • Figure 1: [Coloured] Path, energy and elevation changes for a sample energy-efficient round-trip in Calgary.
  • Figure 2: The WLTP cycle and its four speed profiles ciuffo2015development.
  • Figure 3: Average energy efficiency for the Nissan Leaf over WLTP in different scenarios using synthetic data.
  • Figure 4: EV block diagram in ADVISOR, simultaneous backward and forward simulations
  • Figure 5: Differences in the energy-optimum paths resulted by adding the parameters of vehicle dynamics. In every city, routes only based on gradient in left and routes with gradient, mass and driving patterns consideration in right.
  • ...and 2 more figures

Theorems & Definitions (1)

  • proof