Efficient ACC with Stop&Go maneuvers for hybrid vehicle with online sub-optimal energy management

Abdrakhmanov, Rustem; Adouane, Lounis · 2017 · Crossref

DOI: 10.1109/romoco.2017.8003886

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Summary

This paper addresses the challenge of improving fuel economy and passenger comfort in Hybrid Electric Vehicles (HEVs), specifically urban buses, by integrating Adaptive Cruise Control with Stop&Go (ACCwSG) maneuvers. The authors identify that frequent stops and starts in urban environments significantly increase fuel consumption. While existing ACC systems often prioritize tracking capability, leading to inefficient acceleration and braking, this work proposes a strategy that simultaneously optimizes the vehicle’s speed profile and power split strategy. The goal is to minimize fuel and electrical energy consumption while maintaining safe inter-vehicular distances and respecting dynamic constraints such as maximum acceleration and battery state-of-charge limits. The methodology employs a two-stage approach using a high-fidelity dynamical model of the BUSINOVA series-parallel hybrid bus developed in MATLAB/TruckMaker. First, an offline optimization is performed using Dynamic Programming (DP) to determine the global optimal speed profiles and power split strategies for various road profiles, vehicle weights, and velocity demands. This process minimizes a multi-criteria cost function comprising fuel mass and electric power consumption, subject to constraints on battery discharge rates and motor dynamics. Because DP is computationally intensive and unsuitable for real-time application, the authors develop an Optimal Profiles Database (OPD-DP) based on these offline results. Second, an online sub-optimal strategy utilizes this database alongside multi-dimensional interpolation techniques to generate real-time speed and torque set-points for the electric and hydraulic motors, adapting to current road conditions and driver inputs. Simulation results demonstrate the efficiency of the proposed ACCwSG strategy. The offline DP analysis reveals that energy consumption increases with road slope and vehicle weight, and the optimal power split shifts toward the electric motor during deceleration and low-speed acceleration, while the internal combustion engine contributes more during high-load uphill acceleration. The online implementation successfully generates sub-optimal trajectories that closely follow the offline optimal solutions. The system effectively manages the trade-off between database size and interpolation accuracy, ensuring smooth transitions between different parameter sets. The results confirm that the strategy reduces unnecessary acceleration and emergency braking, thereby improving fuel economy compared to conventional ACC systems that focus solely on distance tracking. The significance of this work lies in its practical application for heavy urban buses, which are subject to highly energy-consuming stop-and-go cycles. By combining offline global optimization with online sub-optimal control, the proposed method offers a computationally feasible solution for real-time energy management. The integration of predictive battery state-of-charge management also contributes to prolonging battery life by avoiding extreme charge levels. This approach provides a robust framework for enhancing the efficiency of hybrid public transportation vehicles, balancing fuel savings with passenger comfort and safety requirements.

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• eco driving