Real time condition monitoring in Li-Ion batteries via battery impulse response

Banaei, A.; Fahimi, Babak

doi:10.1109/vppc.2010.5729203

Cited by 18 publications

(11 citation statements)

References 12 publications

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“…1. The existing of these resistances raises the measurement difficulty to accurately obtain the ohmic resistance value [37], [38]. The measurement error can further cause the SoH estimation error and this has been discussed in paper [39].…”

Section: (A)mentioning

confidence: 99%

Offline State-of-Health Estimation for High-Power Lithium-Ion Batteries Using Three-Point Impedance Extraction Method

Yuan

Dung

2017

IEEE Trans. Veh. Technol.

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This paper presents an offline state-of-health (SoH) estimation based on charge transfer resistance for high power lithium-ion batteries, such as lithium iron phosphate or LFP batteries. As shown in the experimental results, the charge transfer resistance has a great aging change with battery degradation and good abilities against the state-of-charge (SoC) drift and external resistance variation in the impedance parameter set of a single-time-constant equivalent circuit model (ECM) including ohmic resistance, charge transfer resistance, doublelayer capacitance, and time constant, for SoH estimation. A fast and efficient three-point (TP) impedance extraction method is also proposed in this paper for accurately extracting the charge transfer resistance in offline SoH estimation. The results of longterm cycling test demonstrate that the TP impedance extraction method can successfully indicate the SoH of LFP batteries with low estimation error of 6.1%.Index Terms-Lithium battery, state-of-health (SoH), impedance estimation, offline battery measurement, battery second-use.

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Section: (A)mentioning

confidence: 99%

Offline State-of-Health Estimation for High-Power Lithium-Ion Batteries Using Three-Point Impedance Extraction Method

Yuan

Dung

2017

IEEE Trans. Veh. Technol.

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“…There are several factors that affect battery performance, such as [10]: An important challenge in BMS development is also the ability to monitoring the battery SOH in real time. In fact, most of the present methods used for this purpose are time consuming, meaning they are not suitable for online applications [10], [11].…”

Section: Electric Vehiclesmentioning

confidence: 99%

A General Overview on Hybrid and Electric Vehicles

Melo¹

2017

neutroaterra

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The Index TermsHybrid and electric vehicles, batteries and fuel cells, energy management systems. IntroductionHEV and EV concepts were first introduced at the end of the XIX century. At that time the main efforts were made to improve the internal combustion engine (ICE) features and the autonomy of electric motor (EM) based vehicles. It should be noted that ICE development was in the beginning, while EM technology was in a much higher level: for instance, braking mode was already available, allowing recover the vehicle kinetic energy and storing it in batteries.That was a major contribution for HEV and EV efficiency and autonomy, which is still a fundamental issue for its development, particularly for the last one [1].In the 1920´s there was a huge evolution in the ICE -higher HEV and EV are bringing new engineering challenges, since several different domains (electric motors, power electronics, energy storage devices, control theory, automobile technology) must be integrated, in order to achieve (at least) drivability performances similar to conventional vehicles.

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“…In the case of plain discharge, the SoC changes can be basically tracked by the basis of the B term like in Eq. (5) as conducted in the Coulomb-counting method [1]. In the presented battery model, the B term provides information on the long-term state of the battery and requires initial parameters such as C b and U b0 .…”

Section: Determining the Initial Capacity Of C Bmentioning

confidence: 99%

Initial Electrical Parameter Validation in Lead-Acid Battery Model Used for State Estimation

Csomós¹,

Fodor²,

Kohlrusz³

2017

Hungarian Journal of Industry and Chemistry

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The paper presents a current impulse-based excitation method for lead-acid batteries in order to define the initial electrical parameters for model-based online estimators. The presented technique has the capability to track the SoC (State of Charge) of a battery, however, it is not intended to be used for online SoC estimations. The method is based on the battery’s electrical equivalent Randles’ model [1]. Load current impulse excitation was applied to the battery clamps during discharge while the voltage and current was logged. Based on the Randles’ model, a model function and a fit function were implemented and used by exponential regression based on the measured data. The diffusion-related non-linear characteristic of the battery was approximated by a capacitorlike linear voltage function for speed and simplicity. The initial capacitance of this bulk capacitor was estimated by linear regression on measurements recorded in the laboratory. Then, the RC parameters of the equivalent battery model were derived from exponential regression on transients during each current impulse cycle. The battery model with initial RC parameters is suitable for model-based online observers.

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Real time condition monitoring in Li-Ion batteries via battery impulse response

Cited by 18 publications

References 12 publications

Offline State-of-Health Estimation for High-Power Lithium-Ion Batteries Using Three-Point Impedance Extraction Method

Offline State-of-Health Estimation for High-Power Lithium-Ion Batteries Using Three-Point Impedance Extraction Method

A General Overview on Hybrid and Electric Vehicles

Initial Electrical Parameter Validation in Lead-Acid Battery Model Used for State Estimation

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