A physically meaningful equivalent circuit network model of a lithium-ion battery accounting for local electrochemical and thermal behaviour, variable double layer capacitance and degradation

Srbik, Marie-Therese von; Marinescu, Monica; Martinez-Botas, Ricardo; Offer, Gregory J.

doi:10.1016/j.jpowsour.2016.05.051

Cited by 62 publications

(37 citation statements)

References 33 publications

(25 reference statements)

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“…To achieve high estimation accuracy, a highly accurate model of the battery is needed. Moreover, the inconsistency of the battery's environment and performance leads to its poor universality [8]. In the data-driven method, computational burden is usually caused [9,10], which reduces the estimated efficiency of the SoH and causes a higher request for CPU performance.…”

Section: Introductionmentioning

confidence: 99%

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Kang

et al. 2019

Energies

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Efficient and accurate state of health (SoH) estimation is an important challenge for safe and efficient management of batteries. This paper proposes a fast and efficient online estimation method for lithium-ion batteries based on incremental capacity analysis (ICA), which can estimate SoH through the relationship between SoH and capacity differentiation over voltage (dQ/dV) at different states of charge (SoC). This method estimates SoH using arbitrary dQ/dV over a large range of charging processes, rather than just one or a limited number of incremental capacity peaks, and reduces the SoH estimation time greatly. Specifically, this method establishes a black box model based on fitting curves first, which has a smaller amount of calculation. Then, this paper analyzes the influence of different SoC ranges to obtain reasonable fitting curves. Additionally, the selection of a reasonable dV is taken into account to balance the efficiency and accuracy of the SoH estimation. Finally, experimental results validate the feasibility and accuracy of the method. The SoH estimation error is within 5% and the mean absolute error is 1.08%. The estimation time of this method is less than six minutes. Compared to traditional methods, this method is easier to obtain effective calculation samples and saves computation time.

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Section: Introductionmentioning

confidence: 99%

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Kang

et al. 2019

Energies

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“…A pseudo 2D electrochemical model for modelling electrochemical systems subject to realistic automotive operation situations was proposed [1]. The model was developed for a lithium ion battery.…”

Section: Introductionmentioning

confidence: 99%

“…It consists of complicated electrochemical phenomena, which were generally eliminated in online battery performance forecasters such as over potentials owing to mass transport restrictions and the 2 of 15 full current-over potential relation and variable double layer capacitance. The model was able to simulate battery cell behaviour under dynamic procedures [1].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Thermal Behaviour of a Lithium Titanate Oxide Battery

2019

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One of the reasonable possibilities to investigate the battery behaviour under various temperature and current conditions is the development of a model of the lithium-ion batteries and then by employing the simulation technique to anticipate their behaviour. This method not only can save time but also they can predict the behaviour of the batteries through simulation. In this investigation, a three-dimensional model is developed to simulate thermal and electrochemical behaviour of a 13Ah lithium-ion battery. In addition, the temperature dependency of the battery cell parameters was considered in the model in order to investigate the influence of temperature on various parameters such as heat generation during battery cell operation. Maccor automated test system and isothermal battery calorimeter were used as experimental setup to validate the thermal model, which was able to predict the heat generation rate and temperature at different positions of the battery. The three-dimensional temperature distributions which were achieved from the modelling and experiment were in well agreement with each other throughout the entire of discharge cycling at different environmental temperatures and discharge rates.

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“…Several attempts have been made by researchers, pursuing different modeling strategies such as electric, chemical, thermal and multi-domain [8]. Among electrical models, equivalent circuit models play a major role, especially if representing electrical parameters of the cell as functions of Lithium-ion concentration [9]. Although such models are generally one-dimensional, it is possible to find some notable exceptions such as multi-bunch methodologies allowing a more detailed description of the cell topology [10].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale, Electro-Thermal Model of NMC Battery Cell

Morganti

Longo

Tirovic

et al. 2019

IEEE Trans. Veh. Technol.

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Battery models often either fail to deliver a complete picture of the physical phenomena occurring in the cell or fail to minimize computational effort. So far, the demand for a detailed internal thermal model of battery cells with a reasonable computation time has remained unanswered. This paper addresses such question introducing a multi-domain model whose accuracy makes it suitable for thermal management system development, at a lower computational cost than competing models. The approach features an equivalent circuit parameter model with chemistrybased parameters coupled with an internal heat transfer model. The internal heat transfer model includes different sections of the cell, addressing the anisotropy and the temperature-dependence of physical properties. Material properties are partly based on manufacturer's data sheet, partly taken from literature. The development software platform enables a sensible reduction of computational effort with respect to traditional modeling techniques. Results were validated against an aggressive current at different temperatures and against current profiles obtained from two different drive cycles at different ambient temperature. The model proves to be very good in terms of accuracy.

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A physically meaningful equivalent circuit network model of a lithium-ion battery accounting for local electrochemical and thermal behaviour, variable double layer capacitance and degradation

Cited by 62 publications

References 33 publications

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Simulation of Thermal Behaviour of a Lithium Titanate Oxide Battery

Multi-Scale, Electro-Thermal Model of NMC Battery Cell

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