This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
A Model Reference Adaptive Controller for an Electric Motor Thermal Management System in Autonomous Vehicles
Journal Article
14-12-01-0001
ISSN: 2691-3747, e-ISSN: 2691-3755
Sector:
Topic:
Citation:
Shoai Naini, S., Miller, R., Rizoo, D., and Wagner, J., "A Model Reference Adaptive Controller for an Electric Motor Thermal Management System in Autonomous Vehicles," SAE Int. J. Elec. Veh. 12(1):3-16, 2023, https://doi.org/10.4271/14-12-01-0001.
Language:
English
Abstract:
Technological advancements and growth in electric motors and battery packs enable
vehicle propulsion electrifications, which minimize the need for fossil fuel
consumption. The mobility shift to electric motors creates a demand for an
efficient electric motor thermal management system that can accommodate heat
dissipation needs with minimum power requirements and noise generation. This
study proposes an intelligent hybrid cooling system that includes a
gravity-aided passive cooling solution coupled with a smart supplementary liquid
cooling system. The active cooling system contains a radiator, heat sink,
variable frequency drive, alternating current (AC) fan, direct current (DC)
pump, and real-time controller. A complete nonlinear mathematical model is
developed using a lumped parameter approach to estimate the optimum fan and pump
operations at each control interval. Four different control strategies,
including nonlinear model predictive controller, classical proportional-integral
(PI) control, sliding mode control (SMC), and stateflow (SF), are developed, and
their performance is compared. The experimental results demonstrate that the
nonlinear model predictive control (NMPC) method is the most effective strategy,
which reduces the cooling system fan power consumption by 73% for only a 5%
increase in the pump power usage compared to classical PI control for a specific
60-minute driving cycle.