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Date of Award


Document Type

Campus Access Dissertation

Degree Name

Doctor of Philosophy in Mechanical Engineering-Engineering Mechanics (PhD)

Administrative Home Department

Department of Mechanical Engineering-Engineering Mechanics

Advisor 1

Bo Chen

Committee Member 1

Lucia Gauchia

Committee Member 2

John E. Beard

Committee Member 3

Stephen A. Hackney


The goal of this research is to study the battery aging pattern for the application of hybrid electric vehicles (HEV) and advanced control algorithm to improve the performance of HEV energy management controller by maximizing fuel efficiency and minimizing battery aging speed at the same time. To achieve the combined goals, the tasks of this research can be laid out as follows.

The first part studies the HEV model provided by Autonomie software and the electrochemical battery model to be built and integrated with the whole vehicle model. The battery model integrated is an averaged single particle model with the battery thermal aging features added. The battery aging will be quantified as the increasing of SEI layer and decreasing of battery capacity. The battery model was able to simulate the aging performance under different temperature, charge current, SOC and other operational conditions. The simulation results of the vehicle following certain driving cycles and the simulation results of battery voltage output are presented.

The second part investigates the feasibility of the entire system to be running in a real-time hardware-in-the-loop system. The vehicle model together with the electrochemical battery model is built and loaded to the dSPACE simulator. The hybrid controller model is built and loaded to the dSPACE MicroAutoBox. The hybrid controller and dSPACE simulator communicate in real-time with vehicle components information coming from plant model and the control signals coming from the MicroAutoBox. The vehicle model together with the battery model is able to be running in Simulator with the battery model simulated correctly and providing battery aging features in real-time.

The third part of the research looks into the application of nonlinear model predictive control (NMPC) in the hybrid controller. To meet the goal of minimizing fuel consumption and battery aging speed, the nonlinear model predictive control without concern of battery aging is first studied. The predictive model is built to predict the dynamic performance of battery pack, the E-motors, the engine and the vehicle powertrain key part – planetary gear set. A cost function is built to provide the best control performance for our case. The performance of the NMPC is compared with the rule-based controller. And the performance of NMPC with different weighting factors is compared and analyzed.

Following the previous part, the NMPC with the concern of battery aging is also studied and simulated using the vehicle and battery model built and integrated into the first part. By changing the cost function of the NMPC, the battery aging performance is greatly improved compared with that of the previous part. The studied NMPC is able to maintain the fuel economy at similar or even better level compared with the NMPC without battery aging concern.

The last part of the research studies the modeling of a single shaft parallel hybrid electric vehicle built from the dSPACE Automotive Simulation Model (ASM) and the AutoLion-ST battery simulation software. Both commercial software packages provide solid physics-based modeling of HEV components such as E-motor, the lithium-ion battery pack, the engine etc., the entire vehicle model is built using these individual models to study the battery performance under different environmental and operational conditions.