2026-09-23T18:36:08Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/129475 2025-10-20 col_2142_5131 col_2142_16359 com_2142_5130 com_2142_16358 com_2142_234

application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms The student, Zheng Liu, accepted the attached license on 2025-05-02 at 11:28. The student, Zheng Liu, submitted this Dissertation for approval on 2025-05-02 at 12:59. This Dissertation was approved for publication on 2025-05-02 at 13:44. DSpace SAF Submission Ingestion Package generated from Vireo submission #22179 on 2025-10-19 at 18:19:47 Control co-design and life cycle assessment of battery energy storage systems Liu, Zheng 2025-05-02 Wang, Pingfeng Wang, Pingfeng Li, Yumeng Allison, James T. Miljkovic, Nenad Energy storage Manufacturing Optimization Life cycle assessment Simulation Surrogate modeling eng 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. 2025-05 Thesis https://hdl.handle.net/2142/129475 Copyright 2025 Zheng Liu Industrial&Enterprise Sys Eng Systems & Entrepreneurial Engr University of Illinois Urbana-Champaign Ph.D. Dissertation