Modeling and online parameter identification of Li-Polymer battery cells for SOC estimation

Rahimi-Eichi, Habiballah; Baronti, Federico; Chow, Mo-Yuen

doi:10.1109/isie.2012.6237284

Cited by 54 publications

(30 citation statements)

References 10 publications

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“…In our previous publications on online adaptive parameters identification and SOC estimation of different lithium ion batteries [11][12][13], using a Thevenin-based model we showed that the model parameters change under different conditions of SOC, C-rate, temperature and ageing. We also illustrated that ignoring the changes in the parameters with offline identification causes transient and steady-state errors in SOC estimation results.…”

Section: Introductionmentioning

confidence: 94%

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Sensitivity analysis of lithium-ion battery model to battery parameters

Rahimi-Eichi

Balagopal

Chow

et al. 2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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Different models have been proposed so far to represent the dynamic characteristics of batteries. These models contain a number of parameters and each of them represents an internal characteristic of the battery. Since the battery is an entity that works based on many electrochemical reactions, the battery parameters are subject to change due to different conditions of state of charge (SOC), C-rate, temperature and ageing. Referring to our previous work on online identification of the battery parameters, the change in the parameters even during one charging cycle is an experimental fact at least for many lithium-ion batteries. In this paper, the terminal voltage is used as the output to investigate the effect of changes in the parameters on the battery model. Therefore, we analyze the sensitivity of the model to the parameters and validate the analysis by comparing it with the simulation results. Since the output of the model is one of the main components in estimation of the state of charge (SOC), the sensitivity analysis determines the need to update each of the battery parameters in the SOC estimation structure.

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Section: Introductionmentioning

confidence: 94%

“…(11) . (12) In order to better analyze the system, the steady state value of the change in the response of the system to changes in the parameter α can be calculated by equation 13:…”

Section: Mathematical Analysismentioning

confidence: 99%

Sensitivity analysis of lithium-ion battery model to battery parameters

Rahimi-Eichi

Balagopal

Chow

et al. 2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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show abstract

“…In scientific papers there are many elaborations about Li-poly cells, not only including chemical processes [5,7], but also simplified models, which represents battery voltage from state of charge [3,13,17,18]. Author decided to choose simplified model without temperature compensations due to good cooling provided by airflow.…”

Section: Li-poly Batterymentioning

confidence: 99%

Modelling of unmanned aerial vehicle – tricopter

Zrebiec¹

2016

automat

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“…Electrochemical models are conventional models that use the equations governing the electrochemical phenomena (chemical reactions) that occur in a battery cell [25,26]. The implementation of these models requires extended knowledge about the battery chemical, mechanical and electrical properties and thus they are more appropriate for battery design optimization process than for battery SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

et al. 2018

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State of charge (SOC) is one of the most important parameters in battery management systems, as it indicates the available battery capacity at every moment. There are numerous battery model-based methods used for SOC estimation, the accuracy of which depends on the accuracy of the model considered to describe the battery dynamics. The SOC estimation method proposed in this paper is based on an Extended Kalman Filter (EKF) and nonlinear battery model which was parameterized using extended laboratory tests performed on several 13 Ah lithium titanate oxide (LTO)-based lithium-ion batteries. The developed SOC estimation algorithm was successfully verified for a step discharge profile at five different temperatures and considering various initial SOC initialization values, showing a maximum SOC estimation error of 1.16% and a maximum voltage estimation error of 44 mV. Furthermore, by carrying out a sensitivity analysis it was showed that the SOC and voltage estimation error are only slightly dependent on the variation of the battery model parameters with the SOC.

show abstract

Modeling and online parameter identification of Li-Polymer battery cells for SOC estimation

Cited by 54 publications

References 10 publications

Sensitivity analysis of lithium-ion battery model to battery parameters

Sensitivity analysis of lithium-ion battery model to battery parameters

Modelling of unmanned aerial vehicle – tricopter

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

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