All-temperature area battery application mechanism, performance, and strategies

Chen, Siqi; Wei, Xuezhe; Zhang, Guangxu; Wang, Xueyuan; Zhu, Jiangong; Feng, Xuning; Dai, Haifeng; Ouyang, Minggao

doi:10.1016/j.xinn.2023.100465

Cited by 30 publications

(5 citation statements)

References 237 publications

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“…When the element temperature exceeds the phase change equilibrium temperature, the thermal capacity of the PCM decreases sharply and allows excess heat to be dissipated outwards. Often used at high temperatures, a study in the early 2000s conducted by and al [70] used this calorific value for batteries at low temperatures. If the temperature of the battery pack is below the set point PCM phase change, the heat stored by the PCM will be released to the pack of batteries.…”

Section: Phase Change Materialsmentioning

confidence: 99%

Empowering the Future: Exploring the Construction and Characteristics of Lithium-Ion Batteries

Dan

2024

ACES

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Lithium element has attracted remarkable attraction for energy storage devices, over the past 30 years. Lithium is a light element and exhibits the low atomic number 3, just after hydrogen and helium in the periodic table. The lithium atom has a strong tendency to release one electron and constitute a positive charge, as Li + . Initially, lithium metal was employed as a negative electrode, which released electrons. However, it was observed that its structure changed after the repetition of charge-discharge cycles. To remedy this, the cathode mainly consisted of layer metal oxide and olive, e.g., cobalt oxide, LiFePO 4 , etc., along with some contents of lithium, while the anode was assembled by graphite and silicon, etc. Moreover, the electrolyte was prepared using the lithium salt in a suitable solvent to attain a greater concentration of lithium ions. Owing to the lithium ions' role, the battery's name was mentioned as a lithium-ion battery. Herein, the presented work describes the working and operational mechanism of the lithium-ion battery. Further, the lithium-ion batteries' general view and future prospects have also been elaborated.

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Section: Phase Change Materialsmentioning

confidence: 99%

Empowering the Future: Exploring the Construction and Characteristics of Lithium-Ion Batteries

Dan

2024

ACES

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“…The cell chemistry comprises Li x C 6 as the negative electrode, Li y Mn 2 O 4 as the positive electrode, and LiPF 6 in 2:1 EC: DMC as the electrolyte. The 1C-current density of the cell is 17.5 A/m 2 . The open circuit voltage (OCV) of the positive and negative electrode materials is approximated by Formulas ( 24) and ( 25), where θ i , i = p, n is the ratio of the lithium-ion concentration on the particle surface to the maximum lithium-ion concentration of the electrode, which is the SOC of the electrode.…”

Section: Data Preparationmentioning

confidence: 99%

“…Lithium-ion batteries (LIBs) are a highly promising energy storage technology due to their high energy density, low self-discharge property, nearly zero-memory effect, high open-circuit voltage, and long lifespan [1]. For better management of the battery system, establishing the battery model and studying the battery algorithm is of vital importance [2].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Single-Particle Model Using a Physics-Informed Neural Network Considering Electrolyte Dynamics for Lithium-Ion Batteries

Xue,

Jiang,

Zhu

et al. 2023

Batteries

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As power sources for electric vehicles, lithium-ion batteries (LIBs) have many advantages, such as high energy density and wide temperature range. In the algorithm design process for LIBs, various battery models with different model structures are needed, among which the electrochemical model is widely used due to its high accuracy. However, the electrochemical model is composed of multiple nonlinear partial differential equations (PDEs) that make the simulating process time-consuming. In this paper, a physics-informed neural network single-particle model (PINN SPM) is proposed to improve the accuracy of the single-particle model (SPM) under high C-rates, while ensuring high solving speed. In PINN SPM, an SPM-Net is designed to solve the distribution of lithium-ion concentration in the electrolyte. In the neural network learning process, a loss function is designed based on the physical constraints brought by the PDEs, which reduces the error of the neural network under dynamic working conditions. Finally, the PINN SPM proposed in this paper can achieve a maximum relative error of up to 1.2% compared with the high-fidelity data generated from the P2D model under various conditions. Additionally, the PINN SPM is 20.8% faster than traditional numerical solution methods with the same computational resources.

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“…Containing substantial active chemical materials and stored electrical energy, LIBs are susceptible to exceeding their normal operating temperature range under abusive conditions. 6 , 7 , 8 These conditions can arise from thermal, electrical, and mechanical abuse. 9 If the generated heat is not effectively dissipated, then it can initiate a sequence of self-sustaining exothermic reactions, eventually resulting in the catastrophic phenomenon known as thermal runaway (TR).…”

Section: Introductionmentioning

confidence: 99%

Advances and challenges in thermal runaway modeling of lithium-ion batteries

Wang,

Ping,

Kong

et al. 2024

The Innovation

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All-temperature area battery application mechanism, performance, and strategies

Cited by 30 publications

References 237 publications

Empowering the Future: Exploring the Construction and Characteristics of Lithium-Ion Batteries

Empowering the Future: Exploring the Construction and Characteristics of Lithium-Ion Batteries

An Enhanced Single-Particle Model Using a Physics-Informed Neural Network Considering Electrolyte Dynamics for Lithium-Ion Batteries

Advances and challenges in thermal runaway modeling of lithium-ion batteries

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