Research on the Reversible and Irreversible Heat Generation of LiNi1−x−yCoxMnyO2-Based Lithium-Ion Batteries

Zhang, Qiaoping; Wei, Fanglin; Zhang, Peng; Dong, Ruize; Li, Jiaxin; Li, Pengzhao; Jia, Qi; Liu, Yanxia; Mao, Jing; Shao, Guosheng

doi:10.1007/s10694-022-01220-7

Cited by 10 publications

(2 citation statements)

References 51 publications

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“…There always is heat generation during the battery operation, which is divided to reversible and irreversible heat. The reversible heat is generated by the entropy change of the electrochemical reaction in electrodes, while irreversible heat is mainly the ohmic resistance heat, polarization heat and the side reactions heat [83]. Those thermal characteristics are also charged with battery degradation.…”

Section: B Analysis-based Methodsmentioning

confidence: 99%

A Review of Lithium-Ion Battery Capacity Estimation Methods for Onboard Battery Management Systems: Recent Progress and Perspectives

et al. 2022

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With the widespread use of Lithium-ion (Li-ion) batteries in Electric Vehicles (EVs), Hybrid EVs and Renewable Energy Systems (RESs), much attention has been given to Battery Management System (BMSs). By monitoring the terminal voltage, current and temperature, BMS can evaluate the status of the Li-ion batteries and manage the operation of cells in a battery pack, which is fundamental for the high efficiency operation of EVs and smart grids. Battery capacity estimation is one of the key functions in the BMS, and battery capacity indicates the maximum storage capability of a battery which is essential for the battery State-of-Charge (SOC) estimation and lifespan management. This paper mainly focusses on a review of capacity estimation methods for BMS in EVs and RES and provides practical and feasible advice for capacity estimation with onboard BMSs. In this work, the mechanisms of Li-ion batteries capacity degradation are analyzed first, and then the recent processes for capacity estimation in BMSs are reviewed, including the direct measurement method, analysis-based method, SOC-based method and data-driven method. After a comprehensive review and comparison, the future prospective of onboard capacity estimation is also discussed. This paper aims to help design and choose a suitable capacity estimation method for BMS application, which can benefit the lifespan management of Li-ion batteries in EVs and RESs.

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Section: B Analysis-based Methodsmentioning

confidence: 99%

A Review of Lithium-Ion Battery Capacity Estimation Methods for Onboard Battery Management Systems: Recent Progress and Perspectives

et al. 2022

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“…z E-mail: maojing@zzu.edu.cn From a practical standpoint, the blended cathode shows better electrochemical performance for synergetic effects, [31][32][33] herein, the soft pack cells with LiCoO 2 /LiMn 2 O 4 blended cathode and graphite anode were selected. Based on our previous research, 34 the heat generation and temperature rise characteristics of the cells that experienced a single overcharge were studied comprehensively. Series of experiments were conducted to research the effect of cutoff voltages (COV) (4.5-4.9 V), temperatures (10, 25, 40 °C) and Crates (0.1, 1, 2, 3 C) on the overcharged cells, and the changes of key parameters such as capacity decay and internal resistance change of the overcharged cell were also analyzed.…”

Section: List Of Symbols Q Totalmentioning

confidence: 99%

Heat Generation and Temperature Rise Characteristics of Single Overcharged Lithium-Ion Batteries

Zhang¹,

Li²,

Liu³

et al. 2022

J. Electrochem. Soc.

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It’s critical to quantitatively investigate the thermal characteristics of single overcharged lithium-ion batteries to realize security alert before thermal runaway occurs. In this work, various (LiCoO2 + LiMn2O4)/graphite soft pack cells overcharged under different cut-off voltages, temperatures and C-rates are tested electrochemically to calculate the heat generation rate and distinguish the dominating heat resource. The results show that overcharged cells with higher cut-off voltage, overcharge temperature and the lower overcharge C-rate exhibit higher heat generation and temperature rise rate as well as poorer state of healthy. Among nonexplosive tested cells, the cell overcharged to 4.8 V at 0.1 C rate and 40°C exhibits the highest heat generation and temperature rise rates of 9.17 W·L-1 and 4.60 °C·h-1 during 1 C charging at 25°C. For overcharged cells, lithium plating, increased resistance and gas generation are observed, which are the reason for the accelerated total heat generation rate compared to baseline cells. Comparing with reversible heat, the irreversible heat resulting from diffusion overpotential and the sum of ohmic and charge transfer overpotential is dominating for overcharged cells working under high current. It’s recommended to comprehensively monitor the temperature change of each cell of battery pack.

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Data‐driven battery capacity estimation using support vector regression and model bagging under fast‐charging conditions

Wang,

Luo,

Cao

et al. 2024

Can J Chem Eng

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Lithium‐ion batteries offer significant advantages in terms of their high energy and power density and efficiency, but capacity degradation remains a major issue during their usage. Accurately estimating the remaining capacity is crucial for ensuring safe operations, leading to the development of precise capacity estimation models. Data‐driven models have emerged as a promising approach for capacity estimation. However, existing models predominantly focus on constant current charging conditions, limiting their applicability in real‐world scenarios where fast‐charging conditions are commonly employed. The primary objective of this work is to develop a more versatile machine learning model (i.e., support vector regression [SVR]) capable of estimating battery capacity under fast‐charging conditions, with broader applicability across various work conditions. Genetic algorithm and cross‐validation techniques are employed to simultaneously optimize feature extraction hyperparameters and SVR hyperparameters. A model bagging method is further implemented to address prediction challenges under unknown fast‐charging conditions. The effectiveness of the developed model is validated on a cycling dataset of lithium‐ion batteries under different two‐stage fast‐charging conditions.

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Research on the Reversible and Irreversible Heat Generation of LiNi1−x−yCoxMnyO2-Based Lithium-Ion Batteries

Cited by 10 publications

References 51 publications

A Review of Lithium-Ion Battery Capacity Estimation Methods for Onboard Battery Management Systems: Recent Progress and Perspectives

A Review of Lithium-Ion Battery Capacity Estimation Methods for Onboard Battery Management Systems: Recent Progress and Perspectives

Heat Generation and Temperature Rise Characteristics of Single Overcharged Lithium-Ion Batteries

Data‐driven battery capacity estimation using support vector regression and model bagging under fast‐charging conditions

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