Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Short Circuit

Yang, Ruixin; Xiong, Rui; Shen, Weixiang; Lin, Xinfan

doi:10.1016/j.eng.2020.08.015

Cited by 111 publications

(27 citation statements)

References 30 publications

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“…Once an ESC fault occurs, it may cause a dramatic increase in battery temperature, which would result in thermal runaway [143], [144]. For this, it is necessary to study the thermal behavior of batteries under ESC faults for battery safety management [145]. Li3V2(PO4)3 (LVP) and Li4Ti5O12 (LTO) were chosen as the cathode and the anode to build a full battery in [146] owing to their robust structures.…”

Section: ) Review Of Sos Estimationmentioning

confidence: 99%

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

et al. 2021

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Battery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial and lightweight machinery due to battery failure causes a substantial economic loss for the industry. Consequently, battery state estimation, management system, and estimation of the remaining useful life (RUL) have become a topic of interest for researchers. Considering this, appropriate battery data acquisition and proper information on available battery data sets may require. This review paper is mainly focused on three parts. The first one is battery data acquisitions with commercially and freely available Li-ion battery data set information. The second is the estimation of the states of battery with the battery management system. And third is battery RUL estimation. Various RUL prognostic methods applied for Li-ion batteries are classified, discussed, and reviewed based on their essential performance parameters. Information on commercially and publicly available data sets of many battery models under various conditions is also reviewed. Various battery states are reviewed considering advanced battery management systems. To that end, a comparative study of Li-ion battery RUL prediction is provided together with the investigation of various RUL prediction algorithms and mathematical modelling.INDEX TERMS Battery datasets, battery data repository, remaining useful life (RUL), battery management, li-ion battery, RUL prediction methods.

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Section: ) Review Of Sos Estimationmentioning

confidence: 99%

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

et al. 2021

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“…The input weight and bias in the ELM model are set randomly to reduce the time of parameter optimization ( Lipu et al., 2019 ). By supposing that n samples, i.e.,

, are collected, where

means the input data and

indicates the corresponding output, the output of ELM with

hidden layer neurons can be formulated as

where

represents the activation function and a typical format is shown in ( Equation 17 ),

and

are the weight vectors, and

means the bias of the i th hidden neuron ( Yang et al., 2021 ). By randomly initializing

and

, the optimal solution can be formulated in ( Equation 18 ):

where

denotes the Moore–Penrose generalized inverse of

, which can be presented as

…”

Section: Machine-learning-based Soh Predictionmentioning

confidence: 99%

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

et al. 2021

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Summary Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques. In this paper, the conception of SOH is defined, and the state-of-the-art prediction methods are classified based on their primary implementation procedure. As an essential step in ML-based SOH algorithms, the health feature extraction methods reported in the literature are comprehensively surveyed. Next, an exhausted comparison is conducted to elaborate the development of ML-based SOH prediction techniques. Not only their advantages and disadvantages of the application in SOH prediction are reviewed but also their accuracy and execution process are fully discussed. Finally, pivotal challenges and corresponding research directions are provided for more reliable and high-fidelity SOH prediction.

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“…However in most ML application, large volume of data is needed for initial training. In applications where a model is establish or readily identifiable, it is more convenient to use model-based strategies such as in the case of batteries where the model can provide additional insight into the internal dynamics of the system [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Interacting Multiple Model Strategy for Electric Vehicle Batteries State of Charge/Health/ Power Estimation

2021

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States estimation of lithium-ion batteries is an essential element of Battery Management Systems (BMS) to meet the safety and performance requirements of electric and hybrid vehicles. Accurate estimations of the battery's State of Charge (SoC), State of Health (SoH), and State of Power (SoP) are essential for safe and effective operation of the vehicle. They need to remain accurate despite the changing characteristics of the battery as it ages. This paper proposes an online adaptive strategy for high accuracy estimation of SoC, SoH and SoP to be implemented onboard of a BMS. A third-order equivalent circuit model structure is considered with its state vector augmented with two more variables for estimation including the internal resistance and SoC bias. An Interacting Multiple Model (IMM) strategy with a Smooth Variable Structure Filter (SVSF) is then employed to determine the SoC, internal resistance, and SoC bias of a battery. The IMM strategy results in the generation of a mode probability that is related to battery aging. This mode probability is then combined with an estimation of the battery's internal resistance to determine the SoH. The estimated internal resistance and the SoC are then used to determine the battery SoP which provides a complete estimation of the battery states of operation and condition. The efficacy of the proposed condition-monitoring strategy is tested and validated using experimental data obtained from accelerated aging tests conducted on Lithium Polymer automotive battery cells.

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Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Short Circuit

Cited by 111 publications

References 30 publications

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Interacting Multiple Model Strategy for Electric Vehicle Batteries State of Charge/Health/ Power Estimation

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