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Research Papers

A Computationally Efficient Approach for Optimizing Lithium-Ion Battery Charging

[+] Author and Article Information
Ji Liu

Department of Mechanical
and Nuclear Engineering,
Pennsylvania State University,
University Park, PA 16802
e-mail: jxl1081@psu.edu

Guang Li

School of Engineering
and Material Sciences,
Queen Mary University of London,
Mile End Road,
London E1 4NS, UK
e-mail: g.li@qmul.ac.uk

Hosam K. Fathy

Department of Mechanical
and Nuclear Engineering,
Pennsylvania State University,
University Park, PA 16802
e-mail: hkf2@psu.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received May 6, 2015; final manuscript received November 17, 2015; published online December 23, 2015. Assoc. Editor: Beshah Ayalew.

J. Dyn. Sys., Meas., Control 138(2), 021009 (Dec 23, 2015) (8 pages) Paper No: DS-15-1207; doi: 10.1115/1.4032066 History: Received May 06, 2015; Revised November 17, 2015

This paper presents a framework for optimizing lithium-ion battery charging, subject to side reaction constraints. Such health-conscious control can improve battery performance significantly, while avoiding damage phenomena, such as lithium plating. Battery trajectory optimization problems are computationally challenging because the problems are often nonlinear, nonconvex, and high-order. We address this challenge by exploiting: (i) time-scale separation, (ii) orthogonal projection-based model reformulation, (iii) the differential flatness of solid-phase diffusion dynamics, and (iv) pseudospectral trajectory optimization. The above tools exist individually in the literature. For example, the literature examines battery model reformulation and the pseudospectral optimization of battery charging. However, this paper is the first to combine these four tools into a unified framework for battery management and also the first work to exploit differential flatness in battery trajectory optimization. A simulation study reveals that the proposed framework can be five times more computationally efficient than pseudospectral optimization alone.

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References

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Figures

Grahic Jump Location
Fig. 1

Simulation results for problem (23) applying the flatness-based GPM for two current upper limits: Imax=5A (solid lines) and Imax=2A (dashed lines). The initial SOC in (a) 0.7 and (b)0.4.

Grahic Jump Location
Fig. 2

Simulation results for problem (42) applying the flatness-based GPM for two current upper limits: Imax=5A (solid lines) and Imax=2A (dashed lines): (a) results with initial SOC as 0.7 and voltage upper bound as 3.7 V and (b) results with initial SOC as 0.4 and voltage upper bound as 4.0 V

Grahic Jump Location
Fig. 3

Computational time versus the number of collocation points

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