Optimal pulse-modulated Lithium-ion battery charging: Algorithms and simulation

2018 ◽  
Vol 15 ◽  
pp. 359-367 ◽  
Author(s):  
Huazhen Fang ◽  
Christopher Depcik ◽  
Vadim Lvovich
Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 227 ◽  
Author(s):  
Bongwoo Kwak ◽  
Myungbok Kim ◽  
Jonghoon Kim

In this paper, an add-on type pulse charger is proposed to shorten the charging time of a lithium ion battery. To evaluate the performance of the proposed pulse charge method, an add-on type pulse charger prototype is designed and implemented. Pulse charging is applied to 18650 cylindrical lithium ion battery packs with 10 series and 2 parallel structures. The proposed pulse charger is controlled by pulse duty, frequency and magnitude. Various experimental conditions are applied to optimize the charging parameters of the pulse charging technique. Battery charging data are analyzed according to the current magnitude and duty at 500 Hz and 1000 Hz and 2000 Hz frequency conditions. The proposed system is similar to the charging speed of the constant current method under new battery conditions. However, it was confirmed that as the battery performance is degraded, the charging speed due to pulse charging increases. Thus, in applications where battery charging/discharging occurs frequently, the proposed pulse charger has the advantage of fast charging in the long run over conventional constant current (CC) chargers.


Author(s):  
Ji Liu ◽  
Guang Li ◽  
Hosam K. Fathy

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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