Multiobjective co-optimization of cooperative adaptive cruise control and energy management strategy for PHEVs

Research output: Contribution to journalArticlepeer-review


  • Yinglong He
  • Michail Makridis
  • Konstantinos Mattas
  • Huw Williams
  • Hongming Xu


Electrification, automation, and connectivity in the automotive and transport industries are gathering momentum, but there are escalating concerns over their need for co-optimization to improve energy efficiency, traffic safety, and ride comfort. Previous approaches to these multiobjective co-optimization problems often overlook tradeoffs and scale differences between the objectives, resulting in misleading optimizations. To overcome these limitations, this article proposes a Pareto-based framework that demonstrably optimizes the system parameters of the cooperative adaptive cruise control (CACC) and the energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEVs). The high-level Pareto knowledge assists in finding a best compromise solution. The results of this article suggest that the energy and the comfort targets are harmonious, but both conflict with the safety target. Validation using real-world driving data shows that the Pareto optimum for CACC and EMS systems, relative to the baseline, can reduce energy consumption (by 7.57%) and tracking error (by 68.94%) while simultaneously satisfying ride comfort needs. In contrast to the weighted-sum method, the proposed Pareto method can optimally balance and scale the multiple-objective functions. In addition, sensitivity analysis proves that the vehicle reaction time impacts significantly on tracking safety, but its effect on energy saving is trivial.


Original languageEnglish
Article number9000888
Pages (from-to)346 - 355
Number of pages10
JournalIEEE Transactions on Transportation Electrification
Issue number1
Early online date17 Feb 2020
Publication statusPublished - Mar 2020


  • cooperative adaptive cruise control (CACC), energy consumption, energy management strategy (EMS), multiobjective co-optimization, tracking safety