Electrified Powertrain with Multiple Planetary Gears and Corresponding Energy Management Strategy

Modern hybrid electric vehicles (HEVs) like the fourth generation of Toyota Prius incorporate multiple planetary gears (PG) to interconnect various power components. Previous studies reported that increasing the number of planetary gears from one to two reduces energy consumption. However, these studies did not compare one PG and two PGs topologies at their optimal operation. Moreover, the size of the powertrain components are not the same and hence the source of reduction in energy consumption is not clear. This paper investigates the effect of the number of planetary gears on energy consumption under optimal operation of the powertrain components. The powertrains with one and two PGs are considered and an optimal simultaneous torque distribution and mode selection strategy is proposed. The proposed energy management strategy (EMS) optimally distributes torque demands amongst the power components whilst also controlling clutches (i.e., mode selection). Results show that increasing from one to two PGs reduces energy consumption by 4%

Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop

The process of developing control functions for embedded systems is resource-, time-, and data-intensive, often resulting in sub-optimal cost and solutions approaches. Reinforcement Learning (RL) has great potential for autonomously training agents to perform complex control tasks with minimal human intervention. Due to costly data generation and safety constraints, however, its application is mostly limited to purely simulated domains. To use RL effectively in embedded system function development, the generated agents must be able to handle real-world applications. In this context, this work focuses on accelerating the training process of RL agents by combining Transfer Learning (TL) and X-in-the-Loop (XiL) simulation. For the use case of transient exhaust gas re-circulation control for an internal combustion engine, use of a computationally cheap Model-in-the-Loop (MiL) simulation is made to select a suitable algorithm, fine-tune hyperparameters, and finally train candidate agents for the transfer. These pre-trained RL agents are then fine-tuned in a Hardware-in-the-Loop (HiL) system via TL. The transfer revealed the need for adjusting the reward parameters when advancing to real hardware. Further, the comparison between a purely HiL-trained and a transferred agent showed a reduction of training time by a factor of 5.9. The results emphasize the necessity to train RL agents with real hardware, and demonstrate that the maturity of the transferred policies affects both training time and performance, highlighting the strong synergies between TL and XiL simulation

Simultaneous control of NOx, soot and fuel economy of a dies engine with dual-loop EGR and VNT using economic MPC

This paper proposes a nonlinear Economic Model Predictive Control (eMPC) strategy for the airpath management of diesel engines with dual-loop Exhaust Gas Recirculation (EGR) and Variable Nozzle Turbocharger (VNT). The controller directly controls the VNT and dual-loop EGR valves to simultaneously minimise NOx, soot and pumping loss. The novelty of this management strategy is to use a merged controller to replace a conventional combination of supervisory and tracking controllers, as well as eliminating the offline determination of the intake manifold pressure and cylinder oxygen concentration set points. The eMPC computes the set points online based on the engine operating condition, state feedback and the weightings of each economic objective, namely NOx, soot and fuel economy. The eMPC is simulated with an experimentally validated EURO 6 2L four-cylinder engine model. The performance of the eMPC is compared to a production controller over the Worldwide harmonised Light vehicles Test Cycles (WLTC). Compared to the production controller, the eMPC demonstrates NOx and soot emission reductions as well as improved torque tracking and fuel economy