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Technical Brief

Reusability Assessment of Lithium-Ion Laptop Batteries Based on Consumers Actual Usage Behavior

[+] Author and Article Information
Mostafa Sabbaghi

Industrial and Systems Engineering Department,
University at Buffalo-SUNY,
Buffalo, NY 14260
e-mail: mostafas@buffalo.edu

Behzad Esmaeilian

Department of Industrial and Systems Engineering,
Northern Illinois University
DeKalb, IL 60115
e-mail: besmaeilian@niu.edu

Ardeshir Raihanian Mashhadi

Department of Mechanical and Aerospace Engineering,
University at Buffalo-SUNY,
Buffalo, NY 14260
e-mail: ardeshir@buffalo.edu

Willie Cade

PC Rebuilders & Recyclers, Inc.,
Chicago, IL 60651
e-mail: willie@pcrr.com

Sara Behdad

Industrial and Systems Engineering Department,
University at Buffalo-SUNY,
Buffalo, NY 14260;
Department of Mechanical and Aerospace Engineering,
University at Buffalo-SUNY,
Buffalo, NY 14260
e-mail: sarabehd@buffalo.edu

Contributed by the Design for Manufacturing Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 10, 2015; final manuscript received September 2, 2015; published online October 16, 2015. Assoc. Editor: Gul O. Kremer.

J. Mech. Des 137(12), 124501 (Oct 16, 2015) (6 pages) Paper No: MD-15-1083; doi: 10.1115/1.4031654 History: Received February 10, 2015; Revised September 02, 2015

In this paper, a data set of Lithium-ion (Li-ion) laptop batteries has been studied with the aim of investigating the potential reusability of laptop batteries. This type of rechargeable batteries is popular due to their energy efficiency and high reliability. Therefore, understanding the life cycle of these batteries and improving the recycling process is becoming increasingly important. The reusability assessment is linked to the consumer behavior and degradation process simultaneously through monitoring the performance of batteries over their life cycle. After capturing the utilization behavior, the stability time of batteries is approximately derived. The stability time represents the interval that a battery works normally without any significant drop in performance. Consequently, the Reusability Likelihood of batteries is quantified using the number of cycles that the battery can be charged with the aim of facilitating future remarketing and recovery opportunities.

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References

Yang, Q. , Yu, S. , and Jiang, D. , 2014, “ A Modular Method of Developing an Eco-Product Family Considering the Reusability and Recyclability of Customer Products,” J. Cleaner Prod., 64, pp. 254–265. [CrossRef]
Zhang, Q. , Tse, P. W.-T. , Wan, X. , and Xu, G. , 2015, “ Remaining Useful Life Estimation for Mechanical Systems Based on Similarity of Phase Space Trajectory,” Expert Syst. Appl., 42(5), pp. 2353–2360. [CrossRef]
Mashhadi, A. R. , Behdad, S. , and Esmaeilian, B. , 2015, “ Uncertainty Management in Remanufacturing Decisions: A Consideration of Uncertainties in Market Demand, Quantity and Quality of Returns,” ASCE-ASME J. Risk Uncertainty Eng. Syst., Part B: Mech. Eng., 1(2), p. 021007.
Si, X.-S. , Wang, W. , Hu, C.-H. , Zhou, D.-H. , and Pecht, M. G. , 2012, “ Remaining Useful Life Estimation Based on a Nonlinear Diffusion Degradation Process,” Reliab. IEEE Trans., 61(1), pp. 50–67. [CrossRef]
Huang, J. , Esmaeilian, B. , and Behdad, S. , 2015, “ Multi-Purpose Disassembly Sequence Planning,” ASME Paper No. DETC2015-46906.
Behdad, S. , Joseph, A. T. , and Thurston, D. , 2013, “ Systems Simulation of Design for End-of-Life Recovery Under Uncertainty,” ASME Paper No. DETC2013-12639.
Kwak, M. , Kim, H. , and Thurston, D. , 2012, “ Formulating Second-Hand Market Value as a Function of Product Specifications, Age, and Conditions,” ASME J. Mech. Des., 134(3), p. 032001. [CrossRef]
Behdad, S. , Williams, A. S. , and Thurston, D. , 2012, “ End-of-Life Decision Making With Uncertain Product Return Quantity,” ASME J. Mech. Des., 134(10), p. 100902. [CrossRef]
Mashhadi, A. R. , Esmaeilian, B. , and Behdad, S. , 2015, “ Modeling Consumer Decisions on Returning End-of-Use Products Considering Design Features and Consumer Interactions: An Agent Based Simulation Approach,” ASME Paper No. DETC2015-46864.
Sabbaghi, M. , Esmaeilian, B. , and Mashhadi, A. , 2015, “ An Investigation of Used Electronics Return Flows: A Data-Driven Approach to Capture and Predict Consumers Storage and Utilization Behavior,” Waste Manage., 36, pp. 305–315. [CrossRef]
Tröltzsch, U. , Kanoun, O. , and Tränkler, H.-R. , 2006, “ Characterizing Aging Effects of Lithium Ion Batteries by Impedance Spectroscopy,” Electrochim. Acta, 51(8–9), pp. 1664–1672. [CrossRef]
Han, X. , Ouyang, M. , Lu, L. , and Li, J. , 2014, “ A Comparative Study of Commercial Lithium Ion Battery Cycle Life in Electric Vehicle: Capacity Loss Estimation,” J. Power Sources, 268, pp. 658–669. [CrossRef]

Figures

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Fig. 5

The graphical representation of A(200, 300) for a battery that has been used for 1 year

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Fig. 4

The degradation trend in the energy capacity of batteries across the used cycles

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Fig. 3

The degradation of type A batteries performance based on the number of used cycles. Here, capacity % equals [FCC/design capacity] × 100.

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Fig. 2

The 95% confidence interval plots of the used cycles for the first year

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Fig. 1

The distribution of used cycles of all batteries: (a) The histograms of used cycles by different classes of consumers through the first year (b), second year (c), and third year (d) of batteries usage

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Fig. 6

Estimation of reusability likelihood for batteries that have been used for 1, 2, and 3 years

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Fig. 7

The age-based expected profit of remanufacturing, refurbishing, and material recovery options based on Ω for a 1, 2, and 3 years old Li-ion battery

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Fig. 8

The optimal profit function and prices behavior as one-way functions of Ω, κ, ERM, with baseline values Ω = 300 cycles, κ = 0.1 $/cycle, ERM = 2 cycle/$, ERF = 4 cycle/$, ρ = 5$, rA = 10, w1 = 0.1, w2 = 0.1, w3 = 0.1. (a) The unit adjustment cost of remanufacturing, (b) the price elasticity demand for remanufactured LiBs, (c) the price elasticity demand for refurbished LiBs, and (d) the fixed material recovery.

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