Control co-design and life cycle assessment of battery energy storage systems

Liu, Zheng

Permalink

https://hdl.handle.net/2142/129475 Copy

Description

Title

Control co-design and life cycle assessment of battery energy storage systems

Author(s)

Liu, Zheng

Issue Date

2025-05-02

Director of Research (if dissertation) or Advisor (if thesis)

Wang, Pingfeng

Doctoral Committee Chair(s)

Wang, Pingfeng

Committee Member(s)

• Li, Yumeng
• Allison, James T.
• Miljkovic, Nenad

Department of Study

Industrial&Enterprise Sys Eng

Discipline

Systems & Entrepreneurial Engr

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Energy storage
• Manufacturing
• Optimization
• Life cycle assessment
• Simulation
• Surrogate modeling

Abstract

This dissertation addresses critical challenges in battery energy storage systems through comprehensive studies on control co-design and life cycle assessment. Experimental analyses on cylindrical and pouch battery cells employing various cooling methods were used to develop high-fidelity finite element models for battery thermal management systems. Gaussian Process-based surrogate models were implemented to reduce the computational demands from finite element simulations, significantly improving optimization efficiency. A control co-design approach was introduced to optimize both plant and control design parameters simultaneously, lowering cooling system energy consumption. Considering uncertainties in real-world scenarios, a novel reliability-based design optimization framework integrated with control co-design was proposed, achieving a 90% reduction in cooling system energy consumption while ensuring robust cooling performance. A generative artificial intelligence framework was developed to address unique geometric constraints in electric vehicle battery packs, optimizing battery cell layouts efficiently under complicated configurations. Additionally, a multi-fidelity physics-informed convolutional neural network was proposed to accurately estimate battery temperature distributions, significantly reducing computational costs and accelerating optimization. The dissertation further explored manufacturing and recycling for batteries, optimized the silicon anode structure, and considered the uncertainties in manufacturing and usage. The environmental impact of hydrometallurgical and direct recycling processes for prominent cathode active materials was conducted through the life cycle assessment, which identified the optimal recycling processes that reduce environmental impact by over 50%. Additionally, a comparative analysis highlighted the environmental advantages of reducing cobalt usage in the cathode. Furthermore, an evaluation of solid-state battery manufacturing via tape casting revealed environmentally favorable electrolytes and their corresponding thickness, emphasizing sustainable battery manufacturing practices.

Graduation Semester

2025-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/129475

Copyright and License Information

Copyright 2025 Zheng Liu

Owning Collections