Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery

Hu, Chao; Jain, Geetika; Zhang, Puqiang; Schmidt, Craig L.; Gomadam, Parthasarathy M.; Gorka, Tom

doi:10.1016/j.apenergy.2014.04.077

Cited by 234 publications

(112 citation statements)

References 18 publications

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“…A new data-processing method is proposed using load collectives to generate the input and output vectors of the required SVM training data set. Hu et al [81] proposed a data-driven classification method, i.e., the K-nearest neighbor (KNN) method, for battery capacity estimation. Again, only the on-board measurable signals, i.e., voltage and current, are needed.…”

Section: Soh Estimationmentioning

confidence: 99%

“…Similar to battery SOC estimation, data mining methods have also been applied for battery SOH estimation [77][78][79][80][81][82][83][84][85][86][87]. Nuhic et al [79] proposed a battery health diagnosis and prognosis method in an alternative power train using SVM, which relies only on on-board measurable data, e.g., battery terminal voltage and current and operating temperature.…”

Section: Soh Estimationmentioning

confidence: 99%

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A brief review on key technologies in the battery management system of electric vehicles

et al. 2018

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Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

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Section: Soh Estimationmentioning

confidence: 99%

Section: Soh Estimationmentioning

confidence: 99%

A brief review on key technologies in the battery management system of electric vehicles

et al. 2018

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Section: Time Intervals Extracted From Cc/cv Chargementioning

confidence: 99%

“…The fixed cutoff voltage and current cannot provide direct degradation information for capacity estimation. Hu et al [23] reported that total charge capacity can be divided into two parts, namely, CC charge capacity (CQcc) and CV (CQcv) charge capacity. The formulas of CQcc and CQcv are expressed as (1) (2) where t0 and tcc denote the beginning and ending time of the CC charge step, respectively; is the current variable; Icc is the value of the constant current, and tcv denotes the end time of the CV charge step.…”

Section: Time Intervals Extracted From Cc/cv Chargementioning

confidence: 99%

Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine

Zhang

Guo

2015

Energies

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Abstract:Prognostics is necessary to ensure the reliability and safety of lithium-ion batteries for hybrid electric vehicles or satellites. This process can be achieved by capacity estimation, which is a direct fading indicator for assessing the state of health of a battery. However, the capacity of a lithium-ion battery onboard is difficult to monitor. This paper presents a data-driven approach for online capacity estimation. First, six novel features are extracted from cyclic charge/discharge cycles and used as indirect health indicators. An adaptive multi-kernel relevance machine (MKRVM) based on accelerated particle swarm optimization algorithm is used to determine the optimal parameters of MKRVM and characterize the relationship between extracted features and battery capacity. The overall estimation process comprises offline and online stages. A supervised learning step in the offline stage is established for model verification to ensure the generalizability of MKRVM for online application. Cross-validation is further conducted to validate the performance of the proposed model. Experiment and comparison results show the effectiveness, accuracy, efficiency, and robustness of the proposed approach for online capacity estimation of lithium-ion batteries.

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“…Hu et al [63] proposed a data-driven methodology for estimating the capacity of Li-ion battery based on the charge voltage and current curves. In this methodology, five characteristic features of the charge curves are defined to indicate the capacity.…”

Section: Battery Applicationsmentioning

confidence: 99%

Prognostics: a literature review

Elattar

Elminir

Riad

2016

Complex Intell. Syst.

147

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Integrated systems health management (ISHM) is an enabling technology used to preserve safe and reliable operation of complex engineering systems. It also helps in reducing processing and operation time, manpower and cost, and increasing system availability and utility. ISHM includes various technologies ranging from design, analysis, build, and verify to operate and maintain. Prognostics is one of the most challenging and beneficial aspects of ISHM. Knowledge of the remaining useful life using prognostics can make a significant paradigm shift in ISHM. Researchers that have new interest in prognostics need to read hundreds of articles to have a complete picture about prognostics and its relation to other disciplines. Our contribution to solving this problem is by introducing the first comprehensive vision about prognostics as a part of ISHM in a single literature review paper. We focus on prognostics benefits, approaches, applications, and challenges. This paper can be considered as the starting point for studying prognostics and health management.

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Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery

Cited by 234 publications

References 18 publications

A brief review on key technologies in the battery management system of electric vehicles

A brief review on key technologies in the battery management system of electric vehicles

Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine

Prognostics: a literature review

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