## 1 Introduction  
Driven by the ongoing electrification of transportation [1], advances in intelligent sensing, control algorithms, and vehicle-to-everything (V2X) communication have positioned automated vehicles (AVs) as a cornerstone of intelligent transportation systems. Unlike human drivers, AVs respond swiftly and operate with consistent logic, thereby reducing behavioral uncertainty and improving both safety and traffic efficiency [2, 3, 4, 5]. These capabilities make AVs well-suited for mitigating traffic congestion [6, 7], enhancing road safety [8, 9], and supporting sustainable mobility [10, 11]. However, large-scale deployment remains limited by the absence of robust, interpretable, and real-time control strategies capable of handling complex scenarios such as sudden cut-ins, unpredictable pedestrians, sensor noise, and abrupt traffic fluctuations. As a core function in longitudinal automation, adaptive cruise control (ACC) is responsible for maintaining appropriate speed and inter-vehicle distance to ensure safety, comfort, and energy efficiency. Early ACC systems based on PID and rule-based control offered low computational costs [12, 13], but their static nature limited adaptability to diverse traffic conditions. Model predictive control (MPC) has emerged as a mainstream solution, enabling multi-objective optimization over finite horizons while respecting system constraints [14; 15; 16]. Several extensions to MPC have been proposed, such as lead vehicle acceleration modeling [17], Bayesian prediction [18], and Gaussian process-based uncertainty modeling [19]. In addition, hybrid frameworks incorporating fuzzy logic [20; 21], adaptive weight adjustment [22], and multi-objective optimization [23; 24; 25] have shown improved performance under dynamic traffic conditions. Other strategies include variable headway models [26; 27], driving style-aware control [28], and optimization-based mode switching [29], often deployed within hierarchical structures [30]. Despite recent advances, many MPC-based methods still rely on fixed or manually tuned parameters, limiting their adaptability to changing traffic contexts. Moreover, few offer a unified mechanism to dynamically balance safety, comfort, and energy efficiency, an issue particularly critical for electric vehicles (EVs), where real-time energy management is essential. The increasing electrification of transportation has further intensified the demand for energy-aware control strategies. Although several approaches attempt to improve efficiency by incorporating motor energy models into the MPC cost function [31], applying genetic algorithms for speed planning [32], or adopting embedded-oriented multi-mode optimization [33], many of these strategies overlook real-time responsiveness and hardware constraints. Learning-based strategies have recently gained traction for their ability to handle complex and uncertain environments. Some integrate driving style recognition with LQR control [34], while others adopt deep reinforcement learning (DRL) to optimize energy usage and responsiveness under dense traffic [35]. Neural networks have also been employed for deceleration prediction [36], and distributionally robust MPC frameworks have been developed to improve robustness in lane-changing scenarios [37]. However, these methods typically function as black boxes and impose high computational burdens, limiting their applicability in embedded, safety-critical systems. In summary, although the ACC field has seen significant advances, three major challenges remain unresolved: (1) fixed control weights limit responsiveness in dynamic environments; (2) black-box AI models lack interpretability and demand high computational resources; (3) there is a lack of a unified framework for quantitatively evaluating safety, comfort, and energy efficiency. To address these challenges, we propose a dynamic weight model predictive control (DWMPC) strategy tailored for the longitudinal control of battery electric vehicles.