A Simplified Historical-Information-Based SOC Prediction Method for Supercapacitors

Wang, Houlian; Zhou, Gongbo; Xu, Jing; Liu, Zhiqiang; Yan, Xiaodong; McCann, Julie A.

doi:10.1109/tie.2021.3130331

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…One of the distinguishing characteristics of LSTMs is their ability to capture long-term dependencies. This property is of particular relevance when it comes to modeling the dynamic behavior of the SOC over time [163,164]. The recurrent structure of LSTMs gives them a natural adaptability to temporal sequences.…”

Section: Long Short-term Memorymentioning

confidence: 99%

Review on Modeling and SOC/SOH Estimation of Batteries for Automotive Applications

Dini,

Colicelli,

Saponara

2024

Batteries

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Lithium-ion batteries have revolutionized the portable and stationary energy industry and are finding widespread application in sectors such as automotive, consumer electronics, renewable energy, and many others. However, their efficiency and longevity are closely tied to accurately measuring their SOC and state of health (SOH). The need for precise algorithms to estimate SOC and SOH has become increasingly critical in light of the widespread adoption of lithium-ion batteries in industrial and automotive applications. While the benefits of lithium-ion batteries are undeniable, the challenges related to their efficient and safe management cannot be overlooked. Accurate estimation of SOC and SOH is crucial for ensuring optimal battery management, maximizing battery lifespan, optimizing performance, and preventing sudden failures. Consequently, research and development of reliable algorithms for estimating SOC and SOH have become an area of growing interest for the scientific and industrial community. This review article aims to provide an in-depth analysis of the state-of-the-art in SOC and SOH estimation algorithms for lithium-ion batteries. The most recent and promising theoretical and practical techniques used to address the challenges of accurate SOC and SOH estimation will be examined and evaluated. Additionally, critical evaluation of different approaches will be highlighted: emphasizing the advantages, limitations, and potential areas for improvement. The goal is to provide a clear view of the current landscape and to identify possible future directions for research and development in this crucial field for technological innovation.

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Section: Long Short-term Memorymentioning

confidence: 99%

Review on Modeling and SOC/SOH Estimation of Batteries for Automotive Applications

Dini,

Colicelli,

Saponara

2024

Batteries

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show abstract

“…The SOC of the battery can directly reflect its sustainable power supply capacity and is essential for battery energy management [2]. To address this issue and alleviate "range anxiety", it is imperative to develop an accurate SOC prediction model [3]. Such a model would aid in formulating an appropriate battery energy management strategy, extending the battery's service life, and ensuring its safe operation [4].…”

Section: Introductionmentioning

confidence: 99%

State-of-Charge Prediction Model for Ni-Cd Batteries Considering Temperature and Noise

Chen

et al. 2023

Applied Sciences

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The accurate prediction of the state of charge (SOC) of Ni-Cd batteries is critical for developing battery management systems for high-speed trains. To address the challenges of the large floating charge voltage of Ni-Cd batteries and the vulnerability of a battery’s SOC to environmental factors such as temperature, this paper proposes an adaptive adjustment mechanism-based particle swarm optimization (APSO) generalized regression neural network (GRNN) model. The proposed model introduces the concept of the particle aggregation degree to quantify the convergence of the particle swarm optimization (PSO) algorithm. Furthermore, the speed weight of the particle swarm is adaptively adjusted using a comprehensive loss function to optimize the parameters of the GRNN model. To validate the proposed method, simulation experiments are conducted under test conditions using Ni-Cd batteries, and the prediction accuracies of various algorithms are compared. The experimental results demonstrate that the APSO-GRNN model significantly reduces the model’s prediction error. In addition, under the influence of different temperatures and noises, this method demonstrates strong robustness and high practical application value by accurately predicting the SOC, even with limited data samples.

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A neural-driven stochastic degradation model for state-of-health estimation of lithium-ion battery

Long,

Yuan,

Yin

et al. 2024

Journal of Energy Storage

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A Simplified Historical-Information-Based SOC Prediction Method for Supercapacitors

Cited by 6 publications

References 25 publications

Review on Modeling and SOC/SOH Estimation of Batteries for Automotive Applications

Review on Modeling and SOC/SOH Estimation of Batteries for Automotive Applications

State-of-Charge Prediction Model for Ni-Cd Batteries Considering Temperature and Noise

A neural-driven stochastic degradation model for state-of-health estimation of lithium-ion battery

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