Thermal Management in Plug-In Hybrid Electric Vehicles: A Real-Time Nonlinear Model Predictive Control Implementation

Abstract

Abstract-A real-time nonlinear model predictive control (NMPC) for the thermal management (TM) of the electrical components cooling circuit in a Plug-In Hybrid Electric Vehicle (PHEV) is presented. The electrical components are highly temperature-sensitive and therefore working out of the ranges recommended by the manufacturer can lead to their premature aging or even failure. Consequently, the goals for an accurate and efficient TM are two: to keep the main component, the Li-ion battery, within optimal working temperatures, and to consume the minimum possible electrical energy through the cooling circuit actuators. This multi-objective requirement is formulated as a finite-horizon optimal control problem (OCP) that includes a multi-objective cost function, several constraints and a prediction model especially suitable for optimization. The associated NMPC is performed on real-time by the optimization package MUSCOD-II and is validated in three different repeatable test-drives driven with a PHEV. Starting from identical conditions, each cycle is driven once being the cooling circuit controlled with NMPC and once with a conventional approach based on a finite-state machine. Compared to the conventional strategy, the NMPC proposed here results in a more accurate and healthier temperature performance, and at the same time, leads to reductions in the electrical consumption up to 8%.Index Terms-nonlinear model predictive control (NMPC), thermal management, plug-in hybrid electric vehicles (PHEV), Li-ion battery cooling.

Critical review towards thermal management systems of lithium-ion batteries in electric vehicle with its electronic control unit and assessment tools

“…105 Thus to accomplish smart controlled thermal management solution, the thermal controller has to be designed with control strategies like state estimator, observer, adaptive control and model predictive control. 106 These strategies along with the promotion of efficient operation also eliminate the requirement of physical sensors which reduces the cost of the thermal management system. The action of the thermal controller begins with temperature as an input control variable to find the optimal cooling temperature of air or liquid or volume of PCM required.…”

Real-Time Energy Management Strategy of Multi-Wheel Electric Drive Vehicles With Load Power Prediction Function

“…Under nonlinear model predictive control (NMPC) schemes, the multi-step disturbance variable in the prediction time domain is generally assumed to be a fixed value. However, because the actual disturbance variable will change continuously, applying simple setting processing will reduce the control accuracy [16]. Accordingly, the proposed method applies non-stationary load power combination forecasting method to predict the future multi-step load power change to be used as the NMPC reference disturbance variable signal.…”

Non-Linear Model Predictive Control of Cabin Temperature and Air Quality in Fully Electric Vehicles

“…Nonlinear model predictive control was used in [11] for vehicle cabin temperature control with evaporator temperature setpoint and blower speed as controlled variables. NMPC applied on Plug-in Hybrid Electric Vehicle (PHEV) thermal management (battery, charger and power electronics cooling) was successfully tested in a Software in the Loop (SIL) simulation [12] and then tested in the real environment [13]. Model predictive on-off control for cabin heating in ICE vehicles was developed and implemented in [14].…”