A Multiobjective MPC Approach for Autonomously Driven Electric Vehicles

TL;DR

This paper introduces a multiobjective model predictive control algorithm for electric vehicles that adapts objectives in real-time, balancing conflicting goals like arrival time and energy use, with a focus on real-time implementation.

Contribution

The paper presents a novel real-time multiobjective MPC algorithm that combines various control concepts and exploits problem symmetries for efficient computation in electric vehicle control.

Findings

Effective real-time control of electric vehicle dynamics.

Balancing energy consumption and arrival time objectives.

Reduced computational complexity through symmetry exploitation.

Abstract

We present a new algorithm for model predictive control of non-linear systems with respect to multiple, conflicting objectives. The idea is to provide a possibility to change the objective in real-time, e.g.~as a reaction to changes in the environment or the system state itself. The algorithm utilises elements from various well-established concepts, namely multiobjective optimal control, economic as well as explicit model predictive control and motion planning with motion primitives. In order to realise real-time applicability, we split the computation into an online and an offline phase and we utilise symmetries in the open-loop optimal control problem to reduce the number of multiobjective optimal control problems that need to be solved in the offline phase. The results are illustrated using the example of an electric vehicle where the longitudinal dynamics are controlled with respect to the concurrent objectives arrival time and energy consumption.