Multiobjective Co-Optimization of Cooperative Adaptive Cruise Control and Energy Management Strategy for PHEVs

He, Yinglong; Zhou, Quan; Makridis, Michail; Mattas, Konstantinos; Li, Ji; Williams, H. Leverne; Xu, Hongming · 2020 · OpenAlex-citations

DOI: 10.1109/tte.2020.2974588

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Summary

This paper addresses the challenge of co-optimizing Cooperative Adaptive Cruise Control (CACC) and Energy Management Strategies (EMS) for Plug-in Hybrid Electric Vehicles (PHEVs). The authors identify that previous approaches often rely on weighted-sum methods to combine multiple objectives—such as energy efficiency, tracking safety, and ride comfort—into a single objective function. This approach frequently fails to account for scale differences and trade-offs between objectives, leading to misleading optimizations where one metric (e.g., fuel economy) is over-optimized at the expense of others (e.g., safety). To resolve this, the study proposes a Pareto-based framework that explicitly handles the multi-objective nature of the problem, allowing for the identification of best-compromise solutions that balance conflicting goals. The methodology involves developing an integrated control framework that couples longitudinal driving dynamics with hybrid powertrain dynamics. The CACC system regulates vehicle speed and spacing based on leading vehicle data and safety constraints, while the EMS utilizes a Charge Depleting-Charge Sustaining (CD-CS) strategy to manage power split between the internal combustion engine and electric motor. The authors formulate a multi-objective optimization problem aiming to minimize energy consumption, tracking error, and jerk (for comfort). They solve this problem using a Pareto optimization method and compare the results against a traditional weighted-sum benchmark. Validation is conducted using real-world driving data, simulating car-following scenarios to assess the performance of the proposed framework against baseline schemes. The results demonstrate that the Pareto-based approach effectively balances the competing objectives. The study finds that energy consumption and ride comfort targets are harmonious, whereas both conflict with the safety target. Specifically, the Pareto optimum solution reduced energy consumption by 7.57% and tracking error by 68.94% compared to the baseline, while simultaneously satisfying ride comfort requirements. In contrast, the weighted-sum method failed to optimally scale the objective functions, leading to suboptimal trade-offs. Additionally, sensitivity analysis revealed that vehicle reaction time significantly impacts tracking safety but has a trivial effect on energy savings. The significance of this work lies in its demonstration that Pareto-based optimization provides a superior method for co-optimizing automated driving systems and energy management in PHEVs. By capturing the trade-offs and scale differences between objectives, the framework allows decision-makers to select solutions that accurately reflect their preferences without compromising critical safety or comfort metrics. This approach offers a more robust solution for the integration of electrification, automation, and connectivity in modern vehicles, addressing the limitations of single-objective or poorly scaled multi-objective methods prevalent in prior research.

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• eco driving