Distributed Eco-Driving Control of a Platoon of Electric Vehicles Through Riccati Recursion

Abstract

This paper presents a distributed optimization procedure for the cooperative eco-driving control problem of a platoon of electric vehicles subject to safety and travel time constraints. Individual optimal trajectories are generated for each platoon member to account for heterogeneous vehicles and for the road slope. By rearranging the problem variables, the Riccati recursion can be applied along the chain-like structure of the platoon and be used to solve the problem by repeatedly transmitting information up and down the platoon. Since each vehicle is only responsible for its own part of the computations, the proposed control strategy is privacy-preserving and could therefore be deployed by any group of vehicles to form a platoon spontaneously while driving. The energy efficiency of this control strategy is evaluated in numerical experiments for platoons of electric trucks with different masses and rated motor powers.

A Robust Model Predictive Control Framework for Ecological Adaptive Cruise Control Strategy of Electric Vehicles

“…where x(0) is the initial condition defined in (23e), and A, B u , and B c are stacked coefficient matrices of A, B u , and B c , respectively. They are readily obtained from iterating the dynamics in ( 19) from k = 0 to k = N. By repeating the recursive steps in (21) and substituting recursive steps in (19) to eliminate the x(k) terms, the stacked coefficient matrices C f , D f u , and D f c are obtained, and hence the corresponding condensed form of the signal response function of constraint ( 21) can be written after substituting the stacked vectors defined in (28) as…”

“…Nevertheless, this simplified model cannot accurately predict energy usage due to the ignorance of the powertrain characteristics [18], [19]. Alternatively, a battery electric powertrain model is usually taken into account by a quadratic model of the driving force and the velocity, which strikes a balance between modelling accuracy and convexity of the problem [20], [21]. In this context, [21] utilises a sequential quadratic programming method to efficiently solve the nonlinear optimisation problem by reorganising the problem variables.…”

“…Alternatively, a battery electric powertrain model is usually taken into account by a quadratic model of the driving force and the velocity, which strikes a balance between modelling accuracy and convexity of the problem [20], [21]. In this context, [21] utilises a sequential quadratic programming method to efficiently solve the nonlinear optimisation problem by reorganising the problem variables. Moreover, [22] presents a convex scheme for a signal-free autonomous vehicle intersection crossing problem This work has been submitted to the IEEE for possible publication.…”

“…Fig.11: Energy loss compositions breakdown by REACC (N=11,15,21) and CDFS schemes, for different prediction horizon lengths. Energy losses are categorised into four main types: tyre-rolling, air-drag, powertrain propulsion, and powertrain regeneration losses.…”

Centralized model‐predictive cooperative and adaptive cruise control of automated vehicle platoons in urban traffic environments

“…He et al proposed a Pareto-based framework combined with energy management system (EMS) to reduce the energy consumption and tracking error while fulfilling the requirements for driving comfort [17]. In both [18][19][20][21], a distributed model predictive control (DMPC) are proposed. Fuel consumption and formation control were included in the objective function and the passenger comfort and vehicle safety were considered in the system constraints.…”