A Brief Review of Post-Lithium-Ion Batteries

Кулова, Т. Л.

doi:10.20964/2020.08.22

Cited by 122 publications

(43 citation statements)

References 55 publications

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“…At present, new energy vehicles, aerospace, and many other emerging energy strategies are developing rapidly 1,2 . Lithium‐ion battery management systems (BMS) and real‐time reliable detection systems have been regarded as the core technology of new energy systems 3,4 . Lithium‐ion batteries' number of cycles, cost, reliability, and safety problems are becoming increasingly prominent.…”

Section: Introductionmentioning

confidence: 99%

“…1,2 Lithium-ion battery management systems (BMS) and real-time reliable detection systems have been regarded as the core technology of new energy systems. 3,4 Lithium-ion batteries' number of cycles, cost, reliability, and safety problems are becoming increasingly prominent. The demand for technological breakthrough and theoretical innovation of high-quality lithium-ion battery is rapidly expanding.…”

Section: Introductionmentioning

confidence: 99%

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Collaborative state estimation of lithium‐ion battery based on multi‐time scale low‐pass filter forgetting factor recursive least squares ‐ double extended Kalman filtering algorithm

Long

Wang

Cao

et al. 2022

Circuit Theory & Apps

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For the lithium battery management system and real-time safety monitoring, two issues are of great significance, namely, the ability to accurately update the model parameters in real time and to accurately estimate the state of charge and health. In this context, this thesis adopts the second-order RC equivalent circuit model and the forgetting factor recursive least squares -double extended Kalman filtering (FFRLS-DEKF) algorithm with multi-time scales and low-pass filter. Forgetting factor recursive least squares is applied to conduct online parameter identification, and the traditional double extended Kalman filtering algorithm is optimized to evaluate the state of charge and model parameters in the micro-scale and macro-scale. In this way, the error caused by two different characteristics is reduced, and a low-pass filter is added to optimize the fluctuation problem of the estimated value of the model parameters. According to the experiment results, the maximum error between the model simulation value and the actual value of the terminal voltage is 0.0459 V. If the initial value of the state of charge deviates from the actual value, the maximum errors under BBDST and HPPC conditions record 0.0235 and 0.0048, respectively, the forgetting factor recursive least squares -double extended Kalman filtering algorithm with multi-time scales and low-pass filter is able to track the true value within 40 s. Furthermore, the lithium-ion battery state of health both reaches 98% under the two conditions. In summary, the experimental analysis shows that the algorithm helps reduce the influence of initial values on the results, thereby reducing error accumulation and improving the robustness.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Collaborative state estimation of lithium‐ion battery based on multi‐time scale low‐pass filter forgetting factor recursive least squares ‐ double extended Kalman filtering algorithm

Long

Wang

Cao

et al. 2022

Circuit Theory & Apps

View full text Add to dashboard Cite

show abstract

“…Lithium-ion batteries are widely used and developed in new energy sectors because it has strong charge retention capacity and longer cycle life. 4 Lithiumion batteries are a multi-scale and multi-physical field system. 5 The lithium-ion battery charge and discharge process contains charge transfer, lithium ionic diffusion, electromigration, solid-phase diffusion of lithium ions, solid-phase conductive, and liquid phase conductive from the ionic electromagnets, electrode, and wires caused by solid-phase diffusion, solid-phase conductive, and liquid phase conductive.…”

Section: Introductionmentioning

confidence: 99%

An adaptive spherical square‐root double unscented Kalman filtering algorithm for estimating state‐of‐charge of lithium‐ion batteries

Jia

Wang

Qiao

et al. 2022

Intl J of Energy Research

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Accurate estimation of the charged state of lithium-ion batteries is very important for the application and development of lithium-ion batteries, it is directly related to whether or not the lithium-ion battery is safe and reliable during use. Based on the traditional unscented Kalman filter algorithm (UKF), the square root of the state variable error covariance matrix is used for iterative calculation variables in this paper, and the method solves the problem that the traditional (UKF) algorithm may make the covariance matrix negative when measuring SOC; two spherical Unscented transformations (UT) are selected to replace the one traditional Unscented transformation, it avoids the complexity of the process of adjusting parameters, and it also changes shortcomings of filtering divergence caused by the uncertainty of system noise and observation noise; the adaptive covariance matching of noise is introduced, which can automatically update and transfer the noise covariance matrix, and it also makes the noise closer to the real situation. The improved algorithm is called the adaptive spherical square-root double unscented Kalman filter algorithm (ASSR-DUKF). The experimental results show that the improved ASSR-DUKF algorithm can better estimate the SOC of the lithium-ion battery through the second-order RC equivalent circuit. The mean absolute errors of HPPC, BBDST, and DST are 0.53%, 0.39%, and 1.08%; the root mean square errors under three working conditions are 0.70%, 0.62%, and 1.22%; the convergence times under the three working conditions are 60, 110, and 74 s. It can be seen that the ASSR-DUKF algorithm has higher Estimate accuracy, better convergence, and better robustness. K E Y W O R D Sadaptive spherical square-root double unscented Kalman filter, battery management system, Lithium-ion battery, second-order RC model, state of charge

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“…It has a huge energy storage resource. 1 kg of lithium is capable of storing 3860 A h. [ 35 ] Compared with other batteries, lithium‐ion batteries have a high working voltage, up to 3.0–3.7 V, and an energy density of about 220 Wh kg −1 . [ 36 ] With high energy density and power density as well as long cycle life, [ 37 ] lithium‐ion battery has been studied ever since the 1980s [ 38 ] and been the most mature technology in the industry.…”

Section: Introductionmentioning

confidence: 99%

The Strategy of Achieving Flexibility in Materials and Configuration of Flexible Lithium‐Ion Batteries

Song

Liu

et al. 2021

Energy Tech

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In recent years, with the development of flexible intelligent wearable devices, flexible batteries have attracted widespread attention in energy storage systems. To satisfy the power supply as well as the integrity of flexibility for the flexible intelligent wearable devices, the development of batteries with both excellent electrochemical performance and wearable flexibility has attracted a lot attention. This review is focused on the discussion of how to realize battery flexibility from the perspective of flexible lithium‐ion batteries as one of the most ideal and promising flexible power sources. Also, the development of flexible lithium‐ion batteries in recent years and the methods to achieve the flexibility are reviewed from the aspects of battery materials and electrode structures, to provide new ideas for research about flexible lithium‐ion battery with a high energy density in the future.

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A Brief Review of Post-Lithium-Ion Batteries

Cited by 122 publications

References 55 publications

Collaborative state estimation of lithium‐ion battery based on multi‐time scale low‐pass filter forgetting factor recursive least squares ‐ double extended Kalman filtering algorithm

Collaborative state estimation of lithium‐ion battery based on multi‐time scale low‐pass filter forgetting factor recursive least squares ‐ double extended Kalman filtering algorithm

An adaptive spherical square‐root double unscented Kalman filtering algorithm for estimating state‐of‐charge of lithium‐ion batteries

The Strategy of Achieving Flexibility in Materials and Configuration of Flexible Lithium‐Ion Batteries

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