Projects

Sponsored Research Projects

• "Collaborative Research: CPS: Medium: On-Line Control and Soft-Sensing for Thermal Food Processing Based on a Reduced-Order Modeling Approach," 12/2023 - 11/2026, Lee, C.W. (Lead PI) and Zikanov, O. (Co-PI) at the UMD in collaboration with Jeong, S. (PI) and Dolan, K. (Co-PI) at Michigan State University (MSU), Total Award: $1,195,652 (UMD Share: $695,652), USDA-NIFA (via NSF Cyber-Physical Systems Program).

• “MRI: Track 1 Acquisition of an Accelerating Rate Calorimeter System for Multidisciplinary Research, Education and Outreach,” 2023-26, Lee. C.W. (Co-PI) with Chen, L. (PI), Zhou, X., Kim, Y., and Wang, M. (Co-PIs), NSF.

• “Development of a Lithium Ion Battery Separator Aging Model,” 2019-22, Lee, C.W. (PI) with Ayoub, G. (Co-PI), UM-Ford Alliance, Ford Motor Company.

• "Reduced-Order Modeling of Battery Thermal Management Systems," 2018-19, Lee, C.W. (PI) with Zikanov, O. (Co-PI), General Motors.

• “Fast Simulation of Performance Degradation in Lithium-Ion Batteries Based on Physics-Based Models," 2014, Lee, C.W. (PI) with Xi, Z. and Kim, T. (Co-PIs), IAVS Seed Funding, University of Michigan – Dearborn.

• “Online Diagnosis for Degradation of Battery Cell Capacity and Power Capability Considering Temperature and Cell-to-Cell Variability,” 2013-14, Lee, C.W. (Co-PI) with Xi, Z. (PI), UM-Ford Alliance, Ford Motor Company. 

• “MRI: Acquisition of Instruments for the Research of the Control and Diagnosis of Plug-In Hybrid Electric Vehicles,” 2011-2012, Lee. C.W. (Senior Personnel) with Kim, T. (PI), Shen, J., Murphey, Y., Jung, D., Mi, C., and Shulze, R. (Co-PIs), National Science Foundation (NSF).

• "Optimization of the Honing Process," 2010-2011, Lee, C.W. (PI), Detroit Diesel Corporation. 

• “Support the Development of Materials for Solid Oxide Fuel Cells,” 2010-2011, Lee, C.W. (PI), Samchun Pure Chemical Co., Ltd. 

• “Stochastic Dynamic Optimization of the Machining Process,” 2008 - 2009, Lee, C.W. (PI), HP-CEEP, University of Michigan – Dearborn. 

Alter-and-Excite (A&E) Approach for Reduced-Order Modeling

Deriving a computationally-efficient counterpart of a CFD model, i.e., reduced-order modeling (ROM), has attracted much interest for various time-critical applications where a high-fidelity model simulation is desired at a minimal computational cost. Despite the surging interest in ROMs, there still exist major hindrances to their industry-wide adoption. Many existing techniques rely on access to the discretization operator of the CFD model, which is often unavailable for general-purpose CFD softwares suitable for practical applications. The performance of data-driven ROMs can also vary widely depending on how training data are selected. Note many of the data-driven ROM techniques originate from conventional system identification and data compression techniques for an actual system. With an actual system, while its excitation conditions can be manipulated to improve identification, its physical parameters such as input source location should remain intact. Our team has been implementing a new concept of altering the digital twin, i.e., CFD Model, of the target system for improved identification. In the proposed approach, the CFD model is altered and excited as prescribed by the Krylov subspace method so that a physics-based ROM (equipped with superior generalizability) can be built using response data only. This alter-and-excite (A&E) approach can potentially overcome the limitations of both conventional and data-driven approaches by obviating the need for the CFD model operator and judicious choice of training data. 

Reduced-Order Modeling of Li-Ion Batteries

Multirate Estimation for Discrete Manufacturing Processes

The overall goal of this research is to add a new dimension to the information horizon for monitoring and controlling the machining process.  Existing approaches tend to rely on only one out of two data streams that are generated by the in-process sensors and postprocess inspection in batch production.  The proposed research is one of the first attempts to systematically utilize the multirate data flows from a series of discrete processes for estimating immeasurable variables in real-time.  Our validation experiments showed that the part quality and tool condition can be accurately estimated even with a small number of trivial sensors if the available data from post-cycle inspections in typical batch production are actively fed into the observer. 

We completed validation experiments on real-time estimation of surface roughness and wheel wear in the grinding process. As an example, Fig. 1 shows the performance of the multirate scheme in estimating coefficient R0 in the output equation for the surface roughness, Ra, as well as in predicting Ra before each grinding cycle ends. The proposed multirate scheme based on multirate measurement of surface roughness and grinding power is compared with a single-rate scheme based on measurement of grinding power P only. The two estimates of R0 are shown in Fig. 1(a), and the prediction in Fig. 1(b) refers to an a priori estimate of surface roughness at the end of each grinding cycle before an a posteriori update takes place based on measurement of the actual surface roughness. The coefficient R0 in the output equation for the surface roughness cannot be estimated without feedback of the surface roughness.  In contrast, R0 was updated at the end of each cycle with the multirate measurement setting as shown in Fig. 1(a), leading to a good agreement between the measured surface roughness and the prediction at the end of each cycle in Fig. 1(b).