Actively temperature controlled health-aware fast charging method for lithium-ion battery using nonlinear model predictive control

Yin, Yilin; Choe, Song-Yul

doi:10.1016/j.apenergy.2020.115232

Cited by 71 publications

(26 citation statements)

References 46 publications

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“…Therefore, the optimal battery temperature is a result of the opposite temperature dependence of Li plating and SEI growth. As one aging mechanism becomes dominant, the optimal temperature may shift accordingly to mitigate the corresponding capacity loss [74,75].…”

Section: Temperature Dependence Of Aging Mechanismsmentioning

confidence: 99%

A review of thermal physics and management inside lithium-ion batteries for high energy density and fast charging

Zeng

Chalise

Lubner

et al. 2021

Energy Storage Materials

133

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Traditionally it has been assumed that battery thermal management systems should be designed to maintain the battery temperature around room temperature. That is not always true as Lithium-ion battery (LIB) R&D is pivoting towards the development of high energy density and fast charging batteries. Therefore, it is necessary to have a comprehensive review of thermal considerations for LIBs targeted for high energy density and fast charging, i.e., the optimal thermal condition, thermal physics (heat transport and generation) inside the battery, and thermal management strategies. As the energy density and charge rate increases, the optimal battery temperature can shift to be higher than room temperature. To improve the temperature uniformity and avoid excessive internal temperature rise, heat transfer inside the battery needs to be enhanced, and reducing the thermal contact resistance between the electrodes and separator can significantly increase the effective thermal conductivity of batteries. In the first part of the review various challenges and latest developments related to thermal transport and properties of LIBs are discussed. In the second part of the review various sources of heat generation inside LIBs and various approaches to minimizing battery heat generation are summarized. The importance of heat of mixing due to ion diffusion during fast charging is also highlighted. Finally, a summary of latest advancement on smart control of internal temperature of LIBs is discussed as depending on the ambient temperature and the optimal temperature; the battery heat needs to be retained or dissipated to elevate or avoid temperature rise. Lithium-ion battery; Optimal battery temperature; High energy density; Fast charging; Battery thermal management

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Section: Temperature Dependence Of Aging Mechanismsmentioning

confidence: 99%

A review of thermal physics and management inside lithium-ion batteries for high energy density and fast charging

Zeng

Chalise

Lubner

et al. 2021

Energy Storage Materials

133

View full text Add to dashboard Cite

show abstract

“…[45,63,93,[154][155][156][157][158] Choe has applied this reduced order model to investigate degradation with the intent of mitigating aging and Li plating due to fast charging. [140,[150][151][152][159][160][161][162][163] This type of approach could be utilized in a battery management system or charge controller to avoid Li plating completely, or to allow for a quantifiable amount of Li plating that could be electrochemically removed later.…”

Section: Plating Modelingmentioning

confidence: 99%

Lithium Plating Detection Methods in Lithium-Ion Batteries

2020

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Lithium-ion batteries provide a low-cost, long cycle-life and high energy density solution to the expediting energy requirement of the automotive industry. There is a growing need of fast charging batteries for commercial application. However, large charging currents may cause Lithium plating which describes the deposition of metallic Lithium at the anode surface. It takes place at conditions of high currents and/or low temperatures because of kinetic limitations. The main reason behind plating is the slow solid-state diffusion of Lithium ions inside the active material. If the anode surface potential falls below 0 V versus Li/Li+, the formation of metallic Lithium is thermodynamically feasible. To avoid or reduce the amount Lithium plating, it is essential to detect its onset during a charging event. Determination of accurate Lithium plating curve is crucial in estimating the boundary conditions for battery operation without compromising life and safety. There are various data analysis methods involved in deriving the Lithium plating curve: anode potential using a three-electrode cell, variation of relaxation voltage after charging (dV/dt), variation of accumulated charge with voltage (dQ/dV) and coulombic efficiency of charge/discharge with %SOC (state of charge) are more commonly employed techniques. In addition to these methods, estimation of its occurrence is typically underpinned by electrochemical models where the negative (anode) electrode potential is expressed by a set of partial differential equations based on the electrochemical and physical properties of the battery components such as the electrodes and electrolyte. The present paper reviews the common test methods and analysis that are currently utilized in Lithium plating determination. Knowledge gaps are identified, and recommendations are made for the future development in the determination and verification of Lithium plating curve in terms of modelling and analysis.

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“…Model predictive control (MPC) appears to be the most used optimization-based methodology for charging of Liion batteries [25][26][27][28][29]. MPC is selected due to its robustness and adaptability.…”

Section: Introductionmentioning

confidence: 99%

“…[25] has proposed MPC strategies based on pseudo-two-dimension (P2D) electrochemical model, while the works in [26][27][28][29] have suggested the use of reduced-order electrochemical models to simplify model complexity. For MPC configurations, most of the research in this area utilizes general MPC with the exception of [28] which is based on the sensitivitybased MPC and [29] which adopts nonlinear MPC. Using these approaches, the best control sequence can be analytically obtained by solving quadratic programming or Diophantine equations.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Charging Algorithm for Lithium-Ion Batteries Based on Enumeration-Based Model Predictive Control

et al. 2020

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Lithium-ion (Li-ion) batteries play a substantial role in energy storage solutions for modernday technologies such as hand-held consumer electronics, aerospace, electric vehicles, and renewable energy systems. For Li-ion batteries, designing a high-quality battery charging algorithm is essential since it has significant influences on the performance and lifetime of Li-ion batteries. The objectives of a highperformance charger include high charging efficiency, short charging time, and long cycle life. In this paper, a model predictive control based charging algorithm is proposed, the presented technique aims to simultaneously reduce the charging time, and the temperature rise during charging. In this study, the coulomb counting method is utilized to calculate the future state-of-charge and an artificial neural network trained by experimental data is also applied to predict the future temperature rise. Comparing with the widely employed constant current-constant voltage charging method, the proposed charging technique can improve the charging time and the average temperature rise by 1.2 % and 4.13 %, respectively.

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Actively temperature controlled health-aware fast charging method for lithium-ion battery using nonlinear model predictive control

Cited by 71 publications

References 46 publications

A review of thermal physics and management inside lithium-ion batteries for high energy density and fast charging

A review of thermal physics and management inside lithium-ion batteries for high energy density and fast charging

Lithium Plating Detection Methods in Lithium-Ion Batteries

A Novel Charging Algorithm for Lithium-Ion Batteries Based on Enumeration-Based Model Predictive Control

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