Explicit multi-model predictive control of a waste heat Rankine based system for heavy duty trucks

Abstract

Abstract-This paper presents an explicit multi-model predictive controller (MMPC) for a waste heat recovery system (WHRS) mounted on a heavy duty truck engine. WHRS based on the Rankine cycle principle attracts a lot of interest in the heavy duty industry, over the past few years, to decrease the fuel consumption and reach the future pollutant emissions standards. Control issues have still to be faced before the integration of such a system into a vehicle. Model predictive controllers suits really well for our control purpose due to their ability to handle online optimal control problem. This paper focuses on the development of an explicit control law based on MMPC with aim to control the fluid temperature at the inlet of the expansion machine in order to increase the system performance and the reliability.

Real-Time Optimization of Organic Rankine Cycle Systems by Extremum-Seeking Control

“…Further efforts on better tuning PI-like strategies are reported in [11], where gain-scheduling and feed-forward are implemented to improve PI performance. In [12], an explicit multi-model predictive controller is used to regulate the superheating of an ORC mounted on a heavy duty truck. Multivariable predictive control strategies are also studied as reported in [13,14].…”

Recent developments of control strategies for organic Rankine cycle (ORC) systems

“…Compared with most of the other control techniques, MPC and improved MPC (multiple MPC, switching MPC) generally provides better control performance because they can deal with nonlinearities, constraints on process variables, disturbances and varying operating condition. (2) Most of the control strategies proposed for ORC systems depend on model, although some control oriented models have been built for ORC systems, for example, two state model (Peralez et al, 2015), (simplified) physical model (Quoilin et al, 2011;Zhang et al, 2012Zhang et al, , 2016a, LPV model (Zhang et al, 2016a), FOPTD (Grelet et al, 2015) and transfer functions (Hernandez et al, 2014). Modelling of ORC systems should be studied via system identification techniques or analyzing physical models.…”

“…Although nonlinear MPC strategy is effective in ORC control systems, it requires system identification or complex mathematical analysis. Grelet et al (2015) employed an explicit multiple MPC to control the fluid temperature at the inlet of the expander in an ORC-based waste heat recovery system mounted on a heavy duty truck engine. The fluid temperature at the inlet of the expansion machine is controlled by manipulating the working fluid mass flow rate entering the evaporator.…”

Data-driven multi-model control for a waste heat recovery system

“…Another approach to deal with nonlinear systems is the use of multiple linear controllers. That local controller can be combined with a weighting scheme as in [4] and [5] using a Bayesian estimator, or as [6] in which the control of a waste heat recovery system is addressed.…”