Integrated Approximate Dynamic Programming and Equivalent Consumption Minimization Strategy for Eco-Driving in a Connected and Automated Vehicle

TL;DR

This paper presents a novel integrated approach combining approximate dynamic programming and equivalent consumption minimization for eco-driving in connected and automated hybrid vehicles, improving energy efficiency and real-time applicability.

Contribution

It introduces a new DP-ECMS method with a receding horizon implementation based on ADP principles, enabling efficient energy management for CAVs.

Findings

Validated vehicle energy consumption model with experimental data

Demonstrated improved real-time performance over traditional DP

Benchmarking shows computational efficiency of the proposed method

Abstract

This paper focuses on the velocity planning and energy management problems for Connected and Automated Vehicles (CAVs) with hybrid electric powertrains. The eco-driving problem is formulated in the spatial domain as a nonlinear dynamic optimization problem, in which information about the upcoming speed limits and road topography is assumed to be known a priori. To solve this problem, a novel Dynamic Programming (DP) based optimization method is proposed, in which a causal Equivalent Consumption Minimization Strategy (ECMS) is embedded. The underlying vehicle model to predict energy consumption over real-world routes is validated using experimental data. Further, a multi-layer hierarchical control architecture is proposed as a pathway to real-time implementation in a vehicle. The DP-ECMS algorithm is introduced for a long-horizon optimization problem, and then adapted for a receding horizon implementation using principles in Approximate Dynamic Programming (ADP). This computationally economical alternative to the traditional DP solution is then benchmarked and evaluated.