Investigation of Simultaneous Influences of Significant Charging Factors on Lithium‐Ion Batteries and Identifying Interaction Effects

Moralı, Uğur

doi:10.1002/ente.202001078

Cited by 5 publications

(4 citation statements)

References 42 publications

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“…More importantly, using XPS characterization, one can clearly see the evolution of SEI film. For example, Nojabaee et al used XPS to fully track the chemical and electrochemical evolution of SEI under static current conditions − and according to the electrode materials studied, the SEI formed in contact with LiTDI/glyme-based electrolytes is mainly composed of inorganic material. It is composed of LiF and Li 3 N without Li 2 CO 3 .…”

Section: Characterization Of Seimentioning

confidence: 99%

Review on Action Mechanism and Characterization of Natural/Artificial Solid Electrolyte Interphase of Lithium Battery: Latest Progress and Future Directions

et al. 2023

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At present, the basic structure and mechanism of solid electrolyte interface (SEI) have been studied based on different models. Although many researchers have observed the characteristics and properties of SEI through various characterization methods, it is still unable to explain its dynamic mechanism of action from the very small microscopic level. Among them, most researchers change the electrochemical performance of batteries by adding additives or material coating. Traditional research methods only state the performance of batteries from the appearance but lack detailed analysis of the electrochemical reaction of batteries during the research process. Based on the analysis of some models established by SEI, this study summarized the analysis of the research angle of SEI film through various characterization methods. The present traditional SEI and artificial SEI are introduced. Finally, the methods to improve the chemical performance of lithium battery and the challenges to solve the problems are summarized and prospected.

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Section: Characterization Of Seimentioning

confidence: 99%

Review on Action Mechanism and Characterization of Natural/Artificial Solid Electrolyte Interphase of Lithium Battery: Latest Progress and Future Directions

et al. 2023

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“…In order to summarize the relationship between temperature and δ, the average δ is used instead of the varying δ, and the data are recorded in Table 6. According to the relationship between the average value of δ and temperature in Table 6, a parabolic curve can be fitted, as shown in Figure 11, and the corresponding second-order polynomial mathematical relationship model can be obtained, as shown in Equation (9).…”

Section: %-75% Soc Intervalmentioning

confidence: 99%

“…[2][3][4][5][6] Accurate stateof-charge (SOC) estimation is an important factor to control the stable operation of batteries, which directly affects the life and safety of the battery. [7][8][9] At present, SOC estimation can be roughly divided into two categories: principle-based and model-based methods. [10,11] The modelbased SOC estimation method is widely used because of its high robustness and accuracy under variable battery operating conditions.…”

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confidence: 99%

Battery Modeling Considering Hysteresis Effect and Temperature Adaptability

et al. 2023

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With the increasing severity of energy crisis and the gradual increase of environmental awareness, countries around the world have devoted themselves to the new energy vehicle industry. [1] As the foundation and core component of electric vehicles, the power battery pack and its management system play a decisive role in the performance and development of electric vehicles. The power battery pack often uses lithium-ion batteries with higher energy density, lower self-discharge rate, and long cycle life. [2][3][4][5][6] Accurate stateof-charge (SOC) estimation is an important factor to control the stable operation of batteries, which directly affects the life and safety of the battery. [7][8][9] At present, SOC estimation can be roughly divided into two categories: principle-based and model-based methods. [10,11] The modelbased SOC estimation method is widely used because of its high robustness and accuracy under variable battery operating conditions. [12,13] Battery models, as an important part of model-based SOC estimation methods, commonly include electrochemical models, blackbox models and equivalent circuit models (ECMs). The electrochemical model describes the diffusion process and charge transfer process of lithium ions in the cell based on porous electrodes and concentrated solution theory and uses a set of coupled partial differential equations to achieve real-time estimation of SOC. [14][15][16] Ref.[17] designed a nonlinear observer with terminal-voltage feedback injection based on the electrochemical single-particle model, which improved the accuracy of SOC estimation. Although the electrochemical model can reflect the actual electrochemical reaction process inside the battery more accurately, the structure is complicated and not conducive to practical simulation and calculation. The blackbox model establishes an abstract mapping relationship between inputs and outputs, reflecting a generalized direct causal relationship between the factors involved. Real-time estimation of SOC can be achieved by taking real-time state quantities (e.g., voltage, current, etc.) during battery operation as inputs. [18][19][20][21][22] Ref. [23] proposed an improved feedforward-long short-term memory (FF-LSTM) modeling method to realize an accurate whole-life-cycle SOC prediction by effectively considering the current, voltage, and temperature variations. Although the calculation of the blackbox model is simple, the accuracy of the estimation depends to a large extent on the quality and quantity of the training data, and the training process may take a long time. From the perspective of external electrical characteristics, ECM uses circuit elements such as resistance, capacitance, and voltage source to form a circuit network to express the relationship between voltage and current in the cell. [24] It cleverly avoids the real structure and complex electrochemical reactions inside the battery and can estimate SOC in real-time by combining it with the filtering algorithm. [25][26][27][28][29] Ref. [30] proposed a novel feedback

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“…Lithium-ion batteries have been widely used in electric vehicles because of their advantages, such as low DOI: 10.1002/adts.202100602 self-discharge rates, long cycle lifetimes, high energy, and high power density. [6,7] During the operation of an electric vehicle, the battery discharges continuously, and the current amplitude increases rapidly, especially under special acceleration and climbing conditions. When the battery discharges, a large quantity of heat is generated and accumulated by the interactions between the chemical substances within.…”

Section: Introductionmentioning

confidence: 99%

Iterative Dynamic Programming Strategy for Electric Vehicle Battery Thermal Management Optimization

Ma¹,

2022

Advcd Theory and Sims

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Lithium‐ion batteries are extensively used in electric vehicles because of their superior performance in the general environment of global energy shortage. However, lithium‐ion batteries generate large amounts of heat as the battery packs are charged and discharged. If this accumulated heat is not dissipated timely, then the power supply system will fail, resulting in safety accidents. Therefore, a high‐efficiency and energy‐saving optimization strategy is required for both ensuring operating temperature of the battery and saving energy. Herein, a lumped thermal model is established on the basis of the heat generation characteristics of batteries and Newton's law of cooling, and its exactitude is proven by comparison with a liquid cooling system established using AMESim. In addition, to reduce computation time and enhance the real‐time capability, an iterative dynamic programming (IDP) method is proposed to determine the optimal values iteratively in a multidimensional search space, in view of the high nonlinearity and time‐variability of a battery thermal management system as well as the complexity of dynamic programming (DP). Thus, the results from co‐simulations performed in Matlab and AMESim verify that the proposed strategy has high cooling efficiency and can save considerable energy for cooling system compared with PID and DP.

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Investigation of Simultaneous Influences of Significant Charging Factors on Lithium‐Ion Batteries and Identifying Interaction Effects

Cited by 5 publications

References 42 publications

Review on Action Mechanism and Characterization of Natural/Artificial Solid Electrolyte Interphase of Lithium Battery: Latest Progress and Future Directions

Review on Action Mechanism and Characterization of Natural/Artificial Solid Electrolyte Interphase of Lithium Battery: Latest Progress and Future Directions

Battery Modeling Considering Hysteresis Effect and Temperature Adaptability

Iterative Dynamic Programming Strategy for Electric Vehicle Battery Thermal Management Optimization

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