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

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 multi-objective co-optimization problems often overlook trade-offs and scale differences between the objectives, resulting in misleading optimizations. To overcome these limitations, this study 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 PHEVs. The high-level Pareto knowledge assists in finding a best-compromise solution. The results of this work 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.

Centralized and Distributed Control Framework Under Homogeneous and Heterogeneous Platoon

“…The traffic oscillation, also called the slinky effect, causes traffic congestion, uncomfortable driving, traffic fatalities, and increased fuel consumption which is harmful to the environment. If string stability is satisfied then traffic oscillation can be reduced [14]- [18].…”

Modified Particle Swarm Optimization With Chaotic Attraction Strategy for Modular Design of Hybrid Powertrains

“…Powertrain optimization is critical for the success of hybrid vehicles [6]. Both online and offline optimization methods are being developed to minimize fuel consumption, emissions and cost [7], [8]. Model-based predictive control [9], [10], equivalent consumption minimization strategy [11], [12], and reinforcement learning [13]- [15] have been applied to online optimize energy flows of the vehicle.…”

Cooperative Adaptive Cruise Control Strategy Optimization for Electric Vehicles Based on SA-PSO With Model Predictive Control

“…There are two primary development directions of ADAS: one is to realize the interconnection from vehicle to vehicle (V2V), vehicle to infrastructure(V2I), and from infrastructure to vehicle (I2V); the other is to realize the adaptive cruise control (ACC). Cooperative adaptive cruise control (CACC) can be regarded as a combination of the V2V and ACC [1], [2].…”