On-road PHEV power management with hierarchical strategies in vehicular networks

Abstract

In plug-in hybrid electric vehicles (PHEVs), the power management system coordinates powertrain operations to achieve high energy efficiency. Conventional PHEV power management systems work in either an online or offline mode. Most online systems are based on some pre-set power balancing strategies without utilizing the driving cycle or route information. Offline management strategies solved from historical driving cycles are not optimal for real specific driving routes. With the rapid development of vehicular networks and proliferation of smartphones, real-time traffic information can be collected by smartphones from a vehicular network so as to facilitate online PHEV power management. This paper proposes an on-road PHEV power management cyberphysical system (CPS) with 2-level hierarchical optimizations to minimize the fuel consumption of a trip. The high-level online stochastic optimization generates a battery energy budget for each road at runtime according to the traffic prediction and trip information. The low-level powertrain policies are solved offline from historical driving cycles. During driving, the high-level battery energy budgets and low-level policies are combined to get the optimal power decisions according to current driving states. Simulation results show that the proposed method significantly outperforms other three methods in fuel savings.

A Rule-Based Energy Management Strategy for a Plug-in Hybrid School Bus Based on a Controller Area Network Bus

“…The synthesized velocity trajectory is used to determine the battery's charge-depleting control that is formulated as a mixed-integer linear programming problem to minimize the total trip fuel consumption [6]. Jiang and Fei proposed an on-road PHEV energy management cyber-physical system (CPS) with 2-level hierarchical optimizations to minimize the fuel consumption of a trip [7]. Yu and Kuang presented a strategy that improves the fuel economy of power-split powertrain systems by optimizing the power demand distribution between the mechanical and electrical paths [8].…”