Ressearch Interests: Control, Dynamics, Vehicle Systems, Energy Systems, Model Predictive Control
Power and thermal management of electrified vehicles
Optimal control problems for nonlinear dynamic systems result in nonconvex formulations, which entail large computational complexity. To reduce computational complexity for real-time implementation, linearization techniques are often applied to produce a linear system that approximates the nonlinear dynamics, with a first-order Taylor series expansion as the most common approach. However, Taylor series linearization typically has large error when the states are far from a fixed linearization point. On the other hand, lifting linearization methods provide a global linearization that can approximate nonlinear dynamics more accurately by using augmented states to capture nonlinear terms, as shown in Fig. 1.
Given nonlinear dynamics, the proposed method approximates these as linear dynamics with an augmented state and noncausal term. Although the approximated dynamics are noncausal, the formulation is linear and can be used for prediction in linear model predictive control (MPC) with additional constraints as shown in Fig. 2. Figure 3 and Table 1 show numerical simulation results of nonlinear MPC and linear MPC with several linearization methods. The proposed method performs similar to nonlinear MPC at a fraction of the computation time.
Research by Seho Park, Ph.D. Student:
Most of the energy consumed by a hybrid electric vehicle (HEV) comes from fuel. The battery is used to improve efficiency of both propulsion and auxiliary loads like air-conditioning. The lifespan of the battery has a major impact on the cradle-to-grave sustainability and cost of the vehicle. Fig. 1 shows how one type of battery degradation, called SEI growth rate, varies as a function of current, state-of-charge (SOC), and temperature. However, most strategies for power management of HEVs focus on maximizing fuel economy without explicitly considering the impacts of control decisions on battery health. Therefore, this research develops a framework for integrated power and thermal management of HEVs that explicitly manages tradeoffs between fuel economy and battery degradation.
Fig. 2 compares controllers with and without degradation management over a simulated drive cycle. The former allows explicit tradeoffs between battery degradation and fuel economy to be made in maximizing the overall sustainability of the vehicle. Fig. 3 shows that also considering battery thermal management in the controller allows the battery degradation to be decreased by 21% at the cost of 0.23 mpg fuel consumption. Thus, integrating thermal management into the controller can greatly improve battery lifespan with a small impact on fuel economy.