Modeling of voltage hysteresis and relaxation of HEV NiMH battery

Ota, Yutaka; Hashimoto, Y.

doi:10.1002/eej.21102

“…V and I are the output voltage and current. After being given an example of the current, voltage and SoC value, the parameter values can be searched using the following algorithm [10]- [12]: (1)…”

Section: Modeling Li-ion Battery Structurementioning

confidence: 99%

Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi

Zaini

¹

,

Harfina

²

,

Iswar

³

2021

AJEEET

0

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Measurement of electric charge on the battery in real-time cannot be separated from external noise and disturbances such as temperature and interference. An optimal State of Charge (SoC) estimator model is needed to make the estimation more accurate. To obtain the model, the battery was tested under room temperature conditions and at a temperature of 40oC to obtain a second-order RC model for the Li-Ion battery used. Based on the test data obtained, the data will be tested first using the Kalman Filter method to get an estimate of the State of Charge (SoC). Tests were carried out using MATLAB software. After the method was tested, the online SoC Estimator design began using the Raspberry Pi Single Board Computer (SBC). After that, the estimator will be tested first using data from offline measurements and then used in real-time (online) SoC estimation measurements. The Voc before the battery discharge test was 13.16 V and after the test, the measured Voc was 11.58 V. During the discharge the Voc was reduced by 1.58 V. While the discharge data from the battery manufacturer showed the reduced Voc during the discharge was 1.2V.

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“…V and I are the output voltage and current. After being given an example of the current, voltage and SoC value, the parameter values can be searched using the following algorithm [10]- [12]: (1)…”

Section: Modeling Li-ion Battery Structurementioning

confidence: 99%

Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi

Zaini

¹

,

Harfina

²

,

Iswar

³

2021

AJEEET

0

View full text Add to dashboard Cite

Measurement of electric charge on the battery in real-time cannot be separated from external noise and disturbances such as temperature and interference. An optimal State of Charge (SoC) estimator model is needed to make the estimation more accurate. To obtain the model, the battery was tested under room temperature conditions and at a temperature of 40oC to obtain a second-order RC model for the Li-Ion battery used. Based on the test data obtained, the data will be tested first using the Kalman Filter method to get an estimate of the State of Charge (SoC). Tests were carried out using MATLAB software. After the method was tested, the online SoC Estimator design began using the Raspberry Pi Single Board Computer (SBC). After that, the estimator will be tested first using data from offline measurements and then used in real-time (online) SoC estimation measurements. The Voc before the battery discharge test was 13.16 V and after the test, the measured Voc was 11.58 V. During the discharge the Voc was reduced by 1.58 V. While the discharge data from the battery manufacturer showed the reduced Voc during the discharge was 1.2V.

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“…As far as NiMH batteries are concerned, models first-principles equations [23], or the Nernst equation, including the entropy of reaction influence and an empirical expression to capture the salient features associated with voltage hysteresis [10,24] are adopted. A multilayer model for nickel active materials with significant deviations from Nernst model is proposed in [25], a circuit approach with an RC "hysteretic" branch, based on an improved Takacs model [26], is used in [27,28], where additional polynomial functions are employed to fit experimental data.…”

Section: An Example Of Hysteretic Behaviour Experimentally Measured Omentioning

confidence: 99%

Preisach modelling of lithium-iron-phosphate battery hysteresis

Baronti

¹

,

Femia

²

,

Saletti

³

et al. 2015

Journal of Energy Storage

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The hysteresis of the open-circuit voltage as a function of the state-of-charge in a 20 Ah lithium-iron-phosphate battery is investigated starting from pulsed-current experiments at a fixed temperature and ageing state, in order to derive a model that may reproduce well the battery behaviour. The hysteretic behaviour is modelled with the classical Preisach model used in magnetic materials. The paper shows that the Preisach model can successfully be applied to the lithium-ion battery hysteresis. First, the model is discretised by using the Everett function and identified by means of experiments, in which first-order reversal branches are measured. Then, the model is simulated and compared to some experimental data collected with different current profiles and to a one-state variable model previously used in the literature. The results show that the hysteresis is well reproduced with rms errors around 2%. The advantages of the Preisach-based method, when compared to other models, are the formal and repeatable identification procedure and the limited computational resources needed for the model simulation that makes it appropriate for the online implementation on low-complexity hardware platforms

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Modeling of voltage hysteresis and relaxation of HEV NiMH battery

Cited by 6 publications

References 7 publications

Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi

Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi

Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi

Preisach modelling of lithium-iron-phosphate battery hysteresis

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