article · Applied System Innovation
Energy-aware Adaptive Cruise Control (Eco-ACC) has become an essential approach for enhancing the energy efficiency of electric vehicles while ensuring safe and comfortable driving. This paper presents a systematic review, following the PRISMA methodology, of 60 recent studies published between 2021 and 2025. The review provides a structured analysis of control strategies, validation approaches, computational demands, and battery-related considerations in Eco-ACC systems. The results indicate that Model Predictive Control (MPC) remains the most widely adopted technique (41.7%), primarily due to its ability to handle system constraints and address multi-objective optimization problems. Reinforcement Learning (RL) approaches (33.3%) are increasingly explored for their capability to adapt to uncertain and dynamic driving conditions. In addition, hybrid MPC–AI methods (16.7%) show strong potential for balancing optimal control performance with real-time implementation requirements. A key observation is the clear imbalance in validation practices: more than 73% of the studies rely on simulation-based evaluation, whereas only 10% include real-world experiments, revealing a pronounced simulation-to-reality (Sim2Real) gap. Furthermore, two critical research gaps are identified. First, the computational energy paradox highlights the trade-off between improved control performance and increased computational cost. Second, battery-aware control remains insufficiently addressed, as most existing methods overlook long-term battery degradation effects. Based on these findings, this review proposes a deployment-oriented research framework that prioritizes hybrid control architectures, real-time feasibility, and robust validation strategies, including Hardware-in-the-Loop and field testing. The presented insights aim to support the development of practical and energy-efficient Eco-ACC systems suitable for real-world deployment in next-generation electric vehicles.
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DOI: 10.3390/asi9050096
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