Search Results

Design and optimization of a plug-in hybrid electric vehicle (PHEV) control strategy is typically based on a backward-looking (BWD) powertrain model, which ensures a high computational efficiency by neglecting the powertrain dynamics. However, the control strategy developed for BWD model may considerably underperform when applied to a forward-looking (FWD) powertrain model, which includes a dynamic driver model, powertrain dynamics, and corresponding low-level controls. This paper deals with bond-graph based modelling and energy balance analysis of BWD and FWD powertrain models for a P2 parallel PHEV-type city bus equipped with a 12-speed automated manual transmission. The powertrain consists of a motor/generator (M/G) machine supplied by the lithium-ion battery and placed at the transmission input shaft, and an internal combustion engine which can be disconnected from the rest of the powertrain by a main clutch placed between the engine and M/G machine.

In order to reduce air and noise pollution in urban environments, low emission zones (LEZ) are being introduced in many cities worldwide. This paper deals with design of a LEZ-anticipating control strategy for a Plug-in Hybrid Electric Vehicle (PHEV) given in a P2-type parallel powertrain configuration. A control-oriented backward-looking model of the PHEV powertrain is used as a design basis. The core control strategy is based on combining a rule-based (RB) controller including an explicit battery state-of-charge (SoC) controller and an equivalent consumption minimization strategy (ECMS), and it is superimposed by generating an optimal SoC reference trajectory aimed at enabling pure electric driving through forthcoming LEZs and minimizing the overall fuel consumption. The optimal SoC reference trajectory is generated by minimizing its length over travelled distance.

Automatic transmission (AT) upshift control performance in terms of shift duration and comfort can be improved during the inertia phase by coordinating the off-going clutch together with oncoming clutch and engine torque. The performance improvement is highest in low gear shifts (i.e., for high ratio steps), which are typically performed with open torque converter. In this paper, a discrete-time, linear quadratic regulation (LQR) is applied during the upshift inertia phase, as it provides an optimal multi-input/multi-output control action with respect to the prescribed cost function. The LQR law is based on a reduced-order drivetrain model, which is applicable to actual transmissions characterized by a limited number of available state measurements. The reduced-order model includes the linearized torque converter model. The shift duration is ensured by precise tracking of a linear-like oncoming clutch slip speed reference profile.