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In-Situ Capacity and Resistance Estimation Algorithm Development for Lithium-Ion Batteries Used in Electrified Vehicles

Varia, Adhyarth C
http://rave.ohiolink.edu/etdc/view?acc_num=osu1408665208

Abstract Details

2014, Master of Science, Ohio State University, Mechanical Engineering.
Battery life, cost and weight are some of the most important factors considered while designing battery packs for electrified vehicles. These factors directly affect the appeal of electric vehicles in the market. While, performance, cost and weight can be evaluated at the production and design stage, battery life is a dynamic parameter influenced by a multitude of factors and is hard to accurately predict, often leading to conservative designs with oversized and more expensive battery packs. Expensive batteries and complex, multi-factor aging phenomena ideally would require continuous tracking of the battery state of health. Battery capacity and internal resistance are commonly used to quantify battery state of health, as these metrics translate directly into range and power at the user level. While resistance growth is relatively easy to estimate in a vehicle, capacity fade requires measurements typically done at the laboratory level and conditions never encountered in a vehicle. This thesis aims to develop an algorithm capable of tracking in situ these two parameters throughout the life of battery. By far the most challenging aspects of battery state of health estimation is to only use information available in the vehicle during its normal use, and furthermore, suitable with available on-board computing resources for real-time implementation. To that effect, the `needs and wants’ of an ideal in situ capacity estimator were clearly defined at the beginning of this work and algorithms that satisfy all the constraints were developed, tested and validated. This work leverages the experimental results of an aging campaign conducted in out laboratories on a total of 17 cells aged under a variety of realistic operating conditions. A sensitivity analysis of the output of the algorithm was then carried out to assess accuracy of the algorithms in the presence of parameter variations and sensor errors. Next, the separate capacity and resistance estimation algorithms were integrated into a single framework to estimate both parameters simultaneously and hence tracking both metrics required to assess the state of health. Finally, tools for post-processing the raw capacity and resistance estimation results were developed to deal with data drops and other issues. In summary, a computationally inexpensive algorithm that could be imbedded on a micro-processor on board an electric or hybrid vehicle was developed to accurately track capacity and internal resistance during normal vehicle operations. This algorithm produces a data rich stream of estimates (1 per charge depleting driving event) which can then be fed into a state of health estimator to make remaining useful life predictions. Having such an in situ tool provides feedback to continuously refine life prediction in light of possibly changing usage conditions in actual vehicles in operation. The capabilities of the novel algorithms developed and validated in this thesis led to the filing of a provisional US patent (Application Number: 62/010, 671) in June 2014 to protect the intellectual property resulting from work done in this project.
Yann Guezennec, PhD (Advisor)
Giorgio Rizzoni, PhD (Committee Member)
192 p.
Automotive Engineering; Energy; Mechanical Engineering
Capacity estimation, State of health estimation, Lithium-ion batteries, Hybrid electric vehicles, Resistance estimation, State of charge estimation, Battery management systems, Battery aging

Recommended Citations

Citations

  • Varia, A. C. (2014). In-Situ Capacity and Resistance Estimation Algorithm Development for Lithium-Ion Batteries Used in Electrified Vehicles [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1408665208

    APA Style (7th edition)

  • Varia, Adhyarth. In-Situ Capacity and Resistance Estimation Algorithm Development for Lithium-Ion Batteries Used in Electrified Vehicles. 2014. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1408665208.

    MLA Style (8th edition)

  • Varia, Adhyarth. "In-Situ Capacity and Resistance Estimation Algorithm Development for Lithium-Ion Batteries Used in Electrified Vehicles." Master's thesis, Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1408665208

    Chicago Manual of Style (17th edition)

osu1408665208
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This open access ETD is published by The Ohio State University and OhioLINK.