Implementation of online battery state-of-power and state-of-function estimation in electric vehicle applications

Juang, Larry W.; Kollmeyer, Phillip J.; Jahns, T.M.; Lorenz, Robert D.

doi:10.1109/ecce.2012.6342591

Cited by 23 publications

(6 citation statements)

References 9 publications

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“…Various quantitative definitions for SOP and SOF exist in literature (e.g. [33][34][35]), which are all associated with the battery's power capabilities. In this paper, we define SOP as the available source or sink power over a short period of .…”

Section: State Definitionsmentioning

confidence: 99%

A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states

Nejad

Gladwin

Stone

2016

Journal of Power Sources

402

164

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Section: State Definitionsmentioning

confidence: 99%

A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states

Nejad

Gladwin

Stone

2016

Journal of Power Sources

402

164

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“…SOP indicates the ability of the battery to withstand the charge and discharge power. Some literatures are also called SOF, or peak power . Compared with the above state estimation, there is less concern about SOE.…”

Section: Model‐based Bms Applicationmentioning

confidence: 99%

A review on battery management system from the modeling efforts to its multiapplication and integration

2019

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Summary Progress in battery technology accelerates the transition of battery management system (BMS) from a mere monitoring unit to a multifunction integrated one. It is necessary to establish a battery model for the implementation of BMS's effective control. With more comprehensive and faster battery model, it would be accurate and effective to reflect the behavior of the battery level to the vehicle. On this basis, to ensure battery safety, power, and durability, some key technologies based on the model are advanced, such as battery state estimation, energy equalization, thermal management, and fault diagnosis. Besides, the communication of interactions between BMS and vehicle controllers, motor controllers, etc is an essential consideration for optimizing driving and improving vehicle performance. As concluded, a synergistic and collaborative BMS is the foundation for green‐energy vehicles to be intelligent, electric, networked, and shared. Thus, this paper reviews the research and development (R&D) of multiphysics model simulation and multifunction integrated BMS technology. In addition, summary of the relevant research and state‐of‐the‐art technology is dedicated to improving the synergy and coordination of BMS and to promote the innovation and optimization of new energy vehicle technology.

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“…Alexander Farmann and Dirk Uwe Sauer wrote a detailed summary of the SOF estimation strategy, and the SOF estimation strategy has so far been considered as mainly including methods based on the model and one based on the feature variables [1]. At present, SOF research has been primarily concentrated on two methods: one is to use the equivalent circuit and battery mechanism for battery modeling, and then obtain the variables directly related to the battery SOF, such as voltage, capacity, and other related parameters, to calculate SOF [2][3][4][5][6][7][8][9][10], but the calibration process is often expensive and has a long cycle; the other is to analyze the variables closely related to SOF, which is used in the estimation equation of SOF adopting data statistical methods, such as the neural network and Kalman filter, to acquire the estimation equation of SOF and then conduct the estimation of SOF -however, this needs a large amount of data and a lot of preparatory work. The fuzzy inference system, based on fuzzy mathematics, is an advanced intelligent system which uses fuzzy rules to describe knowledge and experience and make decisions.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Prediction of Power Lithium Ion Battery State of Function Based on the Fuzzy c-Means Clustering Algorithm

Wang

Yang

Gan

et al. 2019

WEVJ

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Following the widespread and large-scale application of power lithium ion battery, State of Function (SOF) estimation technology of power lithium ion batteries has gained an increasing amount of attention from both scientists and engineers. During the lifetime of the power lithium ion battery, SOF reflects the maximum instantaneous output power of the battery. When discarded, it is able to show the degree of performance degradation of the power battery when also taken as a performance evaluation parameter. In this paper, the variables closely related to SOF have been selected to conduct the fuzzy inference system, which is optimized by the fuzzy c-means clustering algorithm, to estimate the SOF of the power lithium ion battery, whose relations can be proved by experimental data. Our simulation results and experimental results demonstrate the feasibility and advantages of the estimation strategy.

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Implementation of online battery state-of-power and state-of-function estimation in electric vehicle applications

Cited by 23 publications

References 9 publications

A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states

A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states

A review on battery management system from the modeling efforts to its multiapplication and integration

Fuzzy Prediction of Power Lithium Ion Battery State of Function Based on the Fuzzy c-Means Clustering Algorithm

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