Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List

Full text release has been delayed at the author's request until December 16, 2024

ETD Abstract Container

Abstract Header

On the Identification of Favorable Data Profile for Lithium-Ion Battery Aging Assessment with Consideration of Usage Patterns in Electric Vehicles

Huang, Meng
http://rave.ohiolink.edu/etdc/view?acc_num=osu15748487783319

Abstract Details

2019, Doctor of Philosophy, Ohio State University, Mechanical Engineering.
Tremendous amount of research attention has been paid to the lithium-ion battery as it becomes the technology of choice for powertrain electrification due to its increasing power and energy densities as well as decreasing cost. Despite recent progress, however, battery aging still remains as the key challenge that prevents its wider applications in energy storage. While research in battery aging has been dominated by improving modeling precision and increasing estimation algorithm efficiency, the importance of data implemented for aging assessment has long been overlooked. In other words, there has been no definite answers to which is the favorable (both sensitive and practically existing) data profile for battery aging assessment and how much data from such profile is sufficient to assess battery degradation. Additionally, presently reported research has mostly focused on a certain battery operating condition without taking into account the impacts from various different usage patterns in practical electric vehicles, thus losing credibility in adapting to varying operating scenarios. To address these two critical issues, this study is proposed to identify the favorable data profile for lithium-ion battery aging assessment with consideration of usage patterns in electric vehicles. Both model-based and in-situ experimental approaches have been employed to identify the favorable data profile for aging assessment. For the model-based approach, the electrolyte enhanced single particle model (ESPM) which has been simplified from the porous model while still retain parameters with physical meanings has been selected and parameterized at the beginning of life. The 1C CCCV charging with sufficiently wide SOC range is determined as the favorable data profile through open-loop identification based on aging characterization. Improved EKF for full state estimation of both electrodes is designed to perform the close-loop identification of favorable data profile for aging assessment. Despite the discovery of unequal contributions of data from different SOC sections toward battery aging estimation, the 1C CCCV charging with wide SOC range under fast measurement update rate still proves to be the favorable data profile for aging assessment. The in-situ experimental approach ICA has been employed and the magnitude of the dQ/dV peak in the lowest SOC range was identified as the optimal health feature to characterized cell capacity fade. And the SOC range below 25% with initial value lower than 10% from 1C CCCV charging is identified as the favorable data profile for aging assessment based on the optimal health feature. In addition, the experimental aging data of multiple cells aged under different load profiles have been analyzed through four stress factors, i.e. cut-off SOC, charging C-rate, operating mode, temperature, which jointly characterize the typical usage patterns in electric vehicles. Impact analysis of usage patterns provide useful information for BMS design on charging and thermal control to optimized the battery performance with extended life.
Mrinal Kumar (Advisor)
Ann Co (Committee Member)
Lei Cao (Committee Member)
Ran Dai (Committee Member)
Automotive Engineering; Mechanical Engineering
Lithium-ion battery, aging characterization, optimal data profile, usage pattern, electrochemical modeling, state estimation, incremental capacity analysis, degradation propagation, capacity fade, cyclable lithium

Recommended Citations

Citations

  • Huang, M. (2019). On the Identification of Favorable Data Profile for Lithium-Ion Battery Aging Assessment with Consideration of Usage Patterns in Electric Vehicles [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu15748487783319

    APA Style (7th edition)

  • Huang, Meng. On the Identification of Favorable Data Profile for Lithium-Ion Battery Aging Assessment with Consideration of Usage Patterns in Electric Vehicles. 2019. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu15748487783319.

    MLA Style (8th edition)

  • Huang, Meng. "On the Identification of Favorable Data Profile for Lithium-Ion Battery Aging Assessment with Consideration of Usage Patterns in Electric Vehicles." Doctoral dissertation, Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu15748487783319

    Chicago Manual of Style (17th edition)

osu15748487783319
© 2019, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.