Predictive AECMS by Utilization of Intelligent Transportation Systems for Hybrid Electric Vehicle Powertrain Control

Abstract

Information obtainable from intelligent transportation systems (ITS) provides the possibility of improving safety and efficiency of vehicles at different levels. In particular, such information also has the potential to be utilized for the prediction of driving conditions and traffic flow, which allows Hybrid Electric Vehicles (HEVs) to run their powertrain components in corresponding optimum operating regions. This paper proposes to improve the performance of one of the most promising realtime powertrain control strategies, called adaptive equivalent consumption minimization strategy (AECMS), using predicted driving conditions. In this paper, three real-time powertrain control strategies are proposed for HEVs, each of which introduces an adjustment factor for the cost of using electrical energy (equivalent factor) in AECMS. These factors are proportional to the predicted energy requirements of the vehicle, regenerative braking energy, and the cost of battery charging and discharging in a finite time window. Simulation results using detailed vehicle powertrain models illustrate that the proposed control strategies improve the performance of AECMS in terms of fuel economy, number of engine transients (ON/OFF), and charge sustainability of the battery.