A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications

SummaryPath dependence degradation detection is getting lot of attention due to high demand of long lasting batteries for applications with sporadic usage such as electric vehicles. In this regard, this work presents a study of the degradation mechanism of NMC/Graphite cells cycled under different depth of discharges. Analysis will be based on the study of incremental capacity curves. The trends observed on experimental curves will be compared to simulations in order to exemplify the differences and highlight the path dependence of the degradation.

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“…They have been studied also for SoH estimation, like in NMC cells [18][19][20], LFP [21][22][23] and also composites NMC+LMO [23] cells.…”

Section: Incremental Capacity Curvesmentioning

confidence: 99%

Degradation Mechanism Detection for NMC Batteries based on Incremental Capacity Curves

Berecibar

Dubarry

Omar

et al. 2016

WEVJ

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“…Ref. [30] proposed a remaining energy prediction method through coupled prediction of future states, parameters, and output. The predictive-adaptive energy prediction method is compared with the direct calculation method and other energy prediction methods.…”

mentioning

confidence: 99%

On-line remaining energy prediction: A case study in embedded battery management system

2017

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“…The Coulomb counting SoC estimation technique has been used extensively in the literature [50,56,[58][59][60] and can be used to determine and update the battery cells SoC during simulations. It counts the charge transferred from or to the battery in order to calculate the remaining available capacity, expressed as the SoC [54].…”

Section: State Of Charge Estimationmentioning

confidence: 99%

Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion

et al. 2016

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Abstract:In this paper, advanced equivalent circuit models (ECMs) were developed to model large format and high energy nickel manganese cobalt (NMC) lithium-ion 20 Ah battery cells. Different temperatures conditions, cell characterization test (Normal and Advanced Tests), ECM topologies (1st and 2nd Order Thévenin model), state of charge (SoC) estimation techniques (Coulomb counting and extended Kalman filtering) and validation profiles (dynamic discharge pulse test (DDPT) and world harmonized light vehicle profiles) have been incorporated in the analysis. A concise state-of-the-art of different lithium-ion battery models existing in the academia and industry is presented providing information about model classification and information about electrical models. Moreover, an overview of the different steps and information needed to be able to create an ECM model is provided. A comparison between begin of life (BoL) and aged (95%, 90% state of health) ECM parameters (internal resistance (R o ), polarization resistance (R p ), activation resistance (R p2 ) and time constants (τ) is presented. By comparing the BoL to the aged parameters an overview of the behavior of the parameters is introduced and provides the appropriate platform for future research in electrical modeling of battery cells covering the ageing aspect. Based on the BoL parameters 1st and 2nd order models were developed for a range of temperatures (15˝C, 25˝C, 35˝C, 45˝C). The highest impact to the accuracy of the model (validation results) is the temperature condition that the model was developed. The 1st and 2nd order Thévenin models and the change from normal to advanced characterization datasets, while they affect the accuracy of the model they mostly help in dealing with high and low SoC linearity problems. The 2nd order Thévenin model with advanced characterization parameters and extended Kalman filtering SoC estimation technique is the most efficient and dynamically correct ECM model developed.

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A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications

Cited by 88 publications

References 56 publications

Degradation Mechanism Detection for NMC Batteries based on Incremental Capacity Curves

Degradation Mechanism Detection for NMC Batteries based on Incremental Capacity Curves

On-line remaining energy prediction: A case study in embedded battery management system

Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion

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