Model Predictive Control-based Strategies for Energy-efficient Thermal Management of Electric Vehicles
Creators: name: Chen, Youyi
Publisher: name: My University
Year: 2024
Subjects: subject: Electric vehicle thermal management
subject: HVAC control
subject: Battery thermal management
subject: Model predictive control
subject: Koopman operator.
subject: Mechanical Sciences and Engineering

description: As increasingly strict vehicle emission regulations are published to tackle the environmental challenges of greenhouse gas emissions, vehicle electrification has become inevitable. This is because vehicles powered solely by internal combustion engines are finding it difficult to meet these requirements. As a result, automobile manufacturers are making more BEVs for their zero tailpipe emissions, and BEVs are gaining acceptance among customers. Despite the increasing acceptance by vehicle customers, range anxiety is still one of the concerns for the potential customers of EVs. To survive in the competitive EV market, it is critical for EV products to have outstanding driving ranges. Besides increasing the battery size at a higher cost, saving energy from the EV TMS is an effective alternative to improve driving range due to the high energy consumption of TMS actuators.This dissertation focuses on improving the energy efficiency of the EV TMS using optimal control methods. To achieve this goal, three MPC-based strategies have been developed, targeting three research focuses: 1) optimal control for the HVAC system considering the reheating process; 2) optimal control for both the HVAC system and the battery TMS; and 3) data-driven optimal control for both the HVAC system and the battery, mitigating prediction errors led by physic-based model assumptions.For the optimal control for the HVAC system with the reheating process, this dissertation proposes a real-time LTV-MPC-based approach using a physics-based control-oriented model. The control-oriented model is developed based on an assumption of a quasi-steady ideal vapor-compression cycle for the refrigerant system and a lumped-mass assumption for the vehicle cabin. Based on the control-oriented model, a real-time LTV-MPC algorithm is developed and evaluated through high-fidelity MATLAB/Simulink simulations. The LTV-MPC accelerates optimal control calculation because of its linear structure and the utilization of a QP solver. Compared with an NMPC benchmark, the proposed LTV-MPC shows 72.4% less computation time with similar temperature regulation and energy consumption.For the optimal control for the integrated TMS, consisting of the HVAC system and the battery TMS, this dissertation proposes an LTV-MPC-based approach using a physics-based control-oriented model. Similar to the modeling of the HVAC system, this model includes both a refrigerant circuit and a battery coolant circuit, which are modeled based on the quasi-steady ideal vapor-compression cycle assumption and the lumped-mass assumption. To accommodate the more complex system, an approximated value function is developed, allowing for a shorter prediction horizon and faster computation. Compared to an NMPC benchmark, the proposed LTV-MPC with the approximated value function shows 10,000 times faster calculation with similar temperature regulation and energy consumption by reducing the prediction horizon from 500 seconds to 5 seconds.For the data-driven optimal control for the integrated TMS, this dissertation proposes a Koopman MPC-based strategy. Thirteen Koopman prediction models are devised using an EDMD structure with data collected from high-fidelity MATLAB/Simulink simulations. The Koopman models are selected in consideration of two performance metrics: AICc values and open-loop simulation errors. The proposed Koopman MPC is evaluated in co-simulations with a high-fidelity MATLAB/Simulink plant model. Compared to a baseline NMPC, the Koopman MPC controller shows improved temperature regulation performance, 6.5% less energy, and a reduced computational time (from 247 milliseconds to 54 milliseconds).This dissertation comprehensively explores the optimal control of the EV TMS with physics-based and data-driven methodologies. Through this exploration, it underscores the importance of energy-efficient control for the EV TMS and advances the application of MPC in the domain of EV TMS.