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Economic Potential for Hybrid Electric Vehicles in Urban Signal-free Intersections with Decentralized MPC

Kai Tang, Weijie Wang, Xiao Pan, Boli Chen, Simos A. Evangelou

Abstract

The development of electric and connected vehicles as well as automated driving technologies are key towards the smart city, with convenient urban mobility and high energy economy performance. However, the global rise in electricity price provokes renewed interest on CAVs with hybrid electric powertrains rather than considering battery electric powertrains. This paper provides a decentralized coordination strategy for a group of connected and autonomous vehicles (CAVs) with a series hybrid electric (sHEV) powertrain at signal-free intersections. The problem is formulated as a convex form with suitable relaxation and approximation of the powertrain model and solved by decentralized model predictive control, which is able to ensure a rapid search and unique solution in real time. Numerical examples validate the effectiveness of the proposed methods concerning physical and safety constraints. By utilizing the petrol fuel and battery charging prices over the last year, the performance of the proposed approach is evaluated against the optimal results produced by two benchmark solutions, conventional vehicles (CVs) and battery electric vehicles (BEVs). The comparison results show that the traveling cost of sHEVs approaches and even under some circumstances reaches the same level as for BEVs, which indicates the importance of hybridization, particularly under the current rising electricity price situation.

Economic Potential for Hybrid Electric Vehicles in Urban Signal-free Intersections with Decentralized MPC

Abstract

The development of electric and connected vehicles as well as automated driving technologies are key towards the smart city, with convenient urban mobility and high energy economy performance. However, the global rise in electricity price provokes renewed interest on CAVs with hybrid electric powertrains rather than considering battery electric powertrains. This paper provides a decentralized coordination strategy for a group of connected and autonomous vehicles (CAVs) with a series hybrid electric (sHEV) powertrain at signal-free intersections. The problem is formulated as a convex form with suitable relaxation and approximation of the powertrain model and solved by decentralized model predictive control, which is able to ensure a rapid search and unique solution in real time. Numerical examples validate the effectiveness of the proposed methods concerning physical and safety constraints. By utilizing the petrol fuel and battery charging prices over the last year, the performance of the proposed approach is evaluated against the optimal results produced by two benchmark solutions, conventional vehicles (CVs) and battery electric vehicles (BEVs). The comparison results show that the traveling cost of sHEVs approaches and even under some circumstances reaches the same level as for BEVs, which indicates the importance of hybridization, particularly under the current rising electricity price situation.
Paper Structure (7 sections, 26 equations, 6 figures, 1 table)

This paper contains 7 sections, 26 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Problem Statement
  3. Convex Modeling of Intersection Crossing Problem
  4. Convex Modeling of Series Hybrid Electric Vehicle
  5. Decentralized Autonomous Intersection Control Framework
  6. Numerical Result
  7. Conclusions

Figures (6)

  • Figure 1: The schematic of a decentralized signal-free intersection model with CAVs and a coordinator.
  • Figure 2: Powertrain configuration for sHEVs.
  • Figure 3: Linear regression function \ref{['eq:SOC_approximate_timeDomain']} of the derivative of the battery SOC, $\frac{d}{dt}\text{SOC}_i$, with respect to the SS output power, $P_{SS,i}$, with R-square of 99.89%.
  • Figure 4: The optimal traveled distance trajectories of 20 CAVs obtained by solving the convex DMPC \ref{['eq:decentralized']} at an arrival rate of 700 veh/h per lane and an average traveled time of approximately 12s.
  • Figure 5: The trade-offs between average price and average travel time for optimization problems with three different vehicle types (i.e CVs, sHEVs, and BEVs) in each case at an arrival rate of 700 veh/h per lane. The average prices are calculated by the price datasets of Oct. 2021 (top) and Oct. 2022 (bottom) ukelectricityukgasoline.
  • ...and 1 more figures