A Novel Active Equalization Topology for Series-Connected Lithium-ion Battery Packs

Ding, Xiaofeng; Zhang, Donghuai; Cheng, Jiawei; Wang, Binbin; Chai, Yameng; Zhao, Zhihui; Xiong, Rui; Luk, P.C.K.

doi:10.1109/tia.2020.3015820

Cited by 39 publications

(12 citation statements)

References 25 publications

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“…He goes on to improve the circuit to include a full bridge configuration of switches with a coupled inductor. Ding [29] presents a modularised version where cells are grouped into threes, boasting reduced equalisation time compared to the basic buck-boost topology.…”

Section: Buck-boostmentioning

confidence: 99%

Cell equalisation circuits: A review

Carter

Fan

Cao

2020

Journal of Power Sources

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Section: Buck-boostmentioning

confidence: 99%

Cell equalisation circuits: A review

Carter

Fan

Cao

2020

Journal of Power Sources

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“…UE to the massive consumption of fossil fuel and pressing concerns over carbon emissions, high-efficiency and environment-friendly electric vehicles (EVs) have become the development mainstream in transportation electrification [1,2]. In EVs, lithium-ion batteries have been widely deployed as main energy sources, with a form of pack consisting of tens to thousands of cells connected in series, parallel or mixed topologies [3,4]. However, with the increase of operation, battery performance degrades gradually, including capacity decline, resistance increase and inconsistency aggravation among cells [5].…”

Section: Introductionmentioning

confidence: 99%

A Flexible State-of-Health Prediction Scheme for Lithium-Ion Battery Packs With Long Short-Term Memory Network and Transfer Learning

Shu

Shen

et al. 2021

IEEE Trans. Transp. Electrific.

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The application of machine learning-based state of health (SOH) prediction is hindered by large demand of training data. To conquer this defect, a flexible and easy transferred SOH prediction scheme for lithium-ion battery packs is developed. Firstly, the charging duration for a predefined voltage range is hired as the health feature to quantify capacity degradation. Then, the long short-term memory (LSTM) network and transfer learning (TL) with fine-tuning strategy are incorporated to constitute the cell mean model (CMM) for SOH prediction with partial training data. Next, to evaluate the SOH inconsistencies among cells, the LSTM model is employed as the cell difference model (CDM), and the minimum estimation value of CDM is identified to determine pack SOH. The experimental results reveal that even when the first 360 cycle data, occupying only 40% in the whole 904 cycle data, are chosen and constituted to the dataset for model training, the obtained estimation algorithm can still predict SOH precisely with the error of less than 3%, thus remarkably reducing the training data amount and mitigating the computation burden during model training. In addition, the preferable validation results on different types of lithium-ion batteries further manifest the extendibility of the proposed strategy. Index Terms-Lithium-ion battery pack, long short-term memory (LSTM), transfer learning (TL), state of health (SOH).

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“…The OCV-based method leverages the correlation between OCV and SOC to forecast SOC [9]. Apparently, it can not be employed for real applications as the OCV can only be acquired by shelving the battery for a sufficiently long time, usually more than two hours [10].…”

Section: Introductionmentioning

confidence: 99%

State of charge prediction framework for lithium-ion batteries incorporating long short-term memory network and transfer learning

Liu

Shu

et al. 2021

Journal of Energy Storage

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This study investigates accurate state of charge estimation algorithms for lithium-ion batteries based on the long short-term memory recurrent neural network and transfer learning. The long short-term memory network with the five typical layer topology is firstly constructed to learn the dependency of state of charge on measured variables. The transfer learning algorithm with fine-tuning strategy is then exploited to regulate the parameters of fully connected layer and share the knowledge of other layers. By this manner, the information from the source data can be applied to predict state of charge of other batteries with less training data. Additionally, a rolling learning method is developed to update the model parameters when the battery capacity is degraded. The precision and robustness of the proposed framework are comprehensively validated through comparative analysis of multitudinous sets of hyperparameters and methods. The experimental results manifest that the developed framework highlights precise estimation capability of state of charge at different aging states and time-varying temperature conditions. In addition, the proposed algorithm is verified feasible when transferred to different batteries based on only 30% training data.

show abstract

A Novel Active Equalization Topology for Series-Connected Lithium-ion Battery Packs

Cited by 39 publications

References 25 publications

Cell equalisation circuits: A review

Cell equalisation circuits: A review

A Flexible State-of-Health Prediction Scheme for Lithium-Ion Battery Packs With Long Short-Term Memory Network and Transfer Learning

State of charge prediction framework for lithium-ion batteries incorporating long short-term memory network and transfer learning

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