A Real-Time Robust Ecological-Adaptive Cruise Control Strategy for Battery Electric Vehicles

TL;DR

This paper presents a real-time, robust ecological-adaptive cruise control strategy for electric vehicles that improves energy efficiency and safety by handling disturbances and modeling inaccuracies through a novel convex optimization approach.

Contribution

It introduces a space-domain formulation with a convex optimal control problem solved by a semi-definite programming relaxation and sLMI techniques for real-time implementation.

Findings

The proposed REACC outperforms nominal MPC in speed constraint adherence.

It achieves better energy economy than existing time-domain RMPC schemes.

REACC effectively maintains safe distances compared to traditional strategies.

Abstract

This work addresses the ecological-adaptive cruise control problem for connected electric vehicles by a computationally efficient robust control strategy. The problem is formulated in the space-domain with a realistic description of the nonlinear electric powertrain model and motion dynamics to yield a convex optimal control problem (OCP). The OCP is approached by a novel robust model predictive control (RMPC) method handling various disturbances due to modelling mismatch and inaccurate leading vehicle information. The RMPC problem is solved by semi-definite programming relaxation and single linear matrix inequality (sLMI) techniques for further enhanced computational efficiency. The performance of the proposed real-time robust ecological-adaptive cruise control (REACC) method is evaluated using an experimentally collected driving cycle. Its robustness is verified by comparison with a nominal MPC which is shown to result in speed-limit constraint violations. The energy economy of the proposed method outperforms a state-of-the-art time-domain RMPC scheme, as a more precisely fitted convex powertrain model can be integrated into the space-domain scheme. The additional comparison with a traditional constant distance following strategy (CDFS) further verifies the effectiveness of the proposed REACC. Finally, it is verified that the REACC can be potentially implemented in real-time owing to the sLMI and resulting convex algorithm.