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Topic

Optimal Energy Management of Electrified Propulsion Systems with Data-driven Li-ion Battery and PEM Fuel Cell Performance and Degradation Predictions

Department of Mechanical Engineering

Date & location

• Tuesday, February 4, 2025
• 1:30 P.M.
• Engineering Office Wing, Room 430

Examining Committee

Supervisory Committee

• Dr. Zuomin Dong, Department of Mechanical Engineering, University of Victoria (Supervisor)
• Dr. Yang Shi, Department of Mechanical Engineering, UVic (Member)
• Dr. Daniela Constantinescu, Department of Mechanical Engineering, UVic (Member)
• Dr. Aaron Gulliver, Department of Electrical and Computer Engineering, UVic (Outside Member)

External Examiner

• Dr. Jason Jiacheng Wang, School of Mechatronic Systems Engineering, Simon Fraser University

Chair of Oral Examination

• Dr. Reuven Gordon, Department of Electrical and Computer Engineering, UVic

Abstract

Electrified propulsion systems for vehicles and marine vessels, including engine-battery hybrid, fuel cell-battery hybrid, and battery electric propulsion systems, present clean propulsion solutions for improving performance, energy efficiency, emissions and lifecycle costs (LCC). Battery energy storage systems (BESSs) are essential in these systems, storing and delivering electrical energy to complement or replace the onboard energy converter.

With the rapid development of Lithium-ion (Li-ion) battery technology, battery electric vehicles (BEVs) are becoming increasingly popular for personal transportation. Fuel cell electric vehicles (FCEVs), powered by a proton exchange membrane fuel cell (PEMFC) system and a BESS supplement, offer a zero-emission propulsion solution for heavy-duty applications, overcoming some limitations of BEVs. Liquefied natural gas (LNG), a low-cost cleaner fuel, is a viable replacement for diesel in compression ignition (CI) engines for heavy-duty engine-battery hybrid electric propulsions to reduce fuel consumption, air pollutants and GHG emissions.

However, BESSs and PEMFC systems suffer relatively short service lives and high replacement costs. A better understanding and modelling of their degradation patterns and corresponding optimal system design and energy management are vital to extending their service lives to reduce the vehicle's LCCs. Ultracapacitors (UCs), known for their high-power density and insensitivity to operating temperatures, if properly designed and controlled, can be combined with the BESS to form hybrid energy storage systems (HESS) to extend battery life, system performance and energy efficiency.

The performance of BESSs and PEMFCs depends on their degradation levels. Usage patterns and temperature conditions influence their degradation rate. This study collected performance and degradation data for Li-ion batteries and PEMFCs under various usage conditions to develop advanced battery and PEMFC performance, degradation, and thermal models. These models aid in optimizing the hybrid electric propulsion and HESS design and energy management across different operating scenarios. The research also introduced new methods for dynamically updating the BESS and PEMFC degradation models using real-time operational data to improve the optimal energy management of electrified vehicles and marine vessels.

This work developed new methods for generating integrated optimal system design and energy management strategy using nested global optimizations to satisfy system design requirements and achieve maximum energy efficiency and minimum life cycle costs. Dynamic programming (DP) was used to search for the optimal energy management solutions for each system design, and a simulation-based, top-level global optimization was formulated to identify the best system design solution and solved using a very efficient metamodel global optimization algorithm.

Methods for generating real-time optimal energy management and control for the hybrid electric propulsion system and HESS, segment by segment, based on an extended model prediction control (MPC), were introduced. New methods for using real-time operation data to dynamically update the Li-ion battery degradation model using identified equivalent total charge/discharge cycle number and the PEMFC degradation model using identified actual active area were introduced. Jointly considering the measured vehicle speed and benchmark test cycle, and the BESS and PEMFC operating data and updated degradation models using the Extended Kalman filter (EKF), these approaches provided improved real-time optimal control for the hybrid propulsion system and HESS.

Case study 1 involves a fuel cell electric ferry ship powered by a PEMFC system and BESS. The approach minimizes LCC by balancing system performance, fuel economy, and degradations of the PEMFC and BESS. An optimal EMS is developed for the ship based on real-time operational data by introducing accurate performance and degradation models. This approach significantly reduces LCC, promoting clean ship propulsion technologies.

Case study 2 focuses on a global optimal propulsion system design for a LNG-fueled hybrid electric ferry ship. The system addresses the increased CO2 equivalent emissions due to methane leakage from LNG engines and the high costs associated with BESS replacements. The optimal integration of the LNG engine, BESS, and EMS is achieved using DP, while an EKF models real-time changes in ship propulsion power. MPC is used to develop an optimal control strategy that optimizes fuel consumption, BESS degradation, and emissions. This case highlights the advantages of global optimization and real-time control.

Case study 3 introduces a new approach to optimizing the design and EMS of a battery UC HESS. The approach improves Li-ion battery operation under high current charge/discharge, mitigates low-temperature impact, and significantly extends battery life. The combination of battery and UC improves overall performance, while adding an active UC-based battery thermal management strategy (TMS) in the optimal EMS reduces the LCC of the BEVs. Updating the battery performance and degradation models in real-time continuously enabled more precise optimal control through MPC.
These case studies demonstrate the feasibility, advantages and benefits of the newly introduced integrated modelling, design and control optimization methods.