Abstract
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 language | English |
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Article number | 9000888 |
Pages (from-to) | 346 - 355 |
Number of pages | 10 |
Journal | IEEE Transactions on Transportation Electrification |
Volume | 6 |
Issue number | 1 |
Early online date | 17 Feb 2020 |
DOIs | |
Publication status | Published - Mar 2020 |
Keywords
- cooperative adaptive cruise control (CACC)
- energy consumption
- energy management strategy (EMS)
- multiobjective co-optimization
- tracking safety