Adaptive cruise control for electric vehicles using hybrid-mode MPC.

Sharkawy, AE; Ali, AM; Asfoor, MS; Yacoub, MI · 2026 · PubMed Central

DOI: 10.1038/s41598-026-51908-x

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Summary

This paper addresses the challenge of robustly formulating and integrating Advanced Driver Assistance Systems (ADAS) for electric vehicles (EVs), specifically focusing on Adaptive Cruise Control (ACC). The authors identify that accurate modeling of EV drivelines and the computational complexity of control systems are significant barriers to effective ADAS implementation. To mitigate these issues, the study proposes a novel, single-platform methodology that combines high-fidelity model parameter tuning with online optimization of control layers. The core contribution is a hybrid-mode Model Predictive Control (MPC) framework designed to handle the complex dynamics of EV powertrains while maintaining computational efficiency. The methodology employs a decentralized control structure with three distinct operational modes: cruising, spacing, and braking. These modes are managed by a unified prediction model that utilizes real-time measurements from an on-board LiDAR sensor to estimate driving situations and adaptively select reference signals and cost-function weights. The vehicle dynamics are modeled using a backward longitudinal dynamics approach, incorporating aerodynamic, rolling, grade, and inertia resistances, alongside a semi-empirical tire model and a first-order Thevenin battery model coupled with in-wheel permanent magnet DC motors. A LabVIEW-based graphical user interface was developed to facilitate simultaneous tuning of model parameters and ACC settings. The system was validated through experimental testing on a real EV using a chassis dynamometer, where an emulated lead vehicle was detected by the LiDAR sensor to simulate various disruptive driving scenarios. The experimental results demonstrate the efficacy of the hybrid-mode MPC in maintaining speed tracing and precise spacing. The system achieved a tracking accuracy of 98% in cruise control mode and 87.8% in spacing control mode. In braking mode, which operated under coasting-only constraints, the tracking accuracy was 55.0%. The unified prediction model successfully facilitated look-ahead estimation and smooth swapping between control modes based on inter-vehicle distance and relative speed thresholds. The study confirms that the proposed architecture can effectively handle nonlinearities and complex driveline modeling challenges without the excessive computational burden associated with more complex nonlinear MPC formulations or offline-only dynamic programming approaches. The significance of this work lies in its provision of a unified, computationally efficient solution for EV ACC systems. By integrating accurate parameter estimation with a switched MPC framework, the approach mitigates the technical difficulties often encountered in modeling sophisticated EV architectures. The results suggest that this method offers a robust alternative to traditional control schemes, enhancing safety and performance in electromobility applications. The study highlights the potential for such adaptive, model-based control techniques to improve road safety and passenger security by reducing human error and optimizing vehicle response in hazardous situations.

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• adaptive driving beam