Experimental verification of UAS based battery terminal voltage collapse detection on a simple embedded platform

Alkhawaja, Fares; Koirala, Koshish; Daffalla, Mirghani; Kassem, Mohamad; Tily, Muhammad Amin; Al-Rousan, Ghaith; Mukhopadhyay, Shayok

doi:10.1109/isma.2018.8330142

Cited by 2 publications

(4 citation statements)

References 8 publications

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“…When a Nussbaum function is input to the observer, it injects enough sinusoids into the highgain observer, satisfying the required persistence of excitation (PE) condition [18]. Therefore, our previous [2], [19]- [21] and the present work are theoretically and experimentally verified without explicitly mathematically imposing the PE condition. The above mentioned properties of a Nussbaum function result in accurate parameter estimation, even without mathematically imposing PE.…”

Section: Low Computation Timementioning

confidence: 85%

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Real Time Adaptive Estimation of Li-ion Battery Bank Parameters

Usman¹,

Mukhopadhyay²,

Rehman³

2022

Preprint

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This paper proposes an accurate and efficient Universal Adaptive Stabilizer (UAS) based online parameters estimation technique for a 400 V Li-ion battery bank. The battery open circuit voltage, parameters modeling the transient response, and series resistance are all estimated in a single real-time test.In contrast to earlier UAS based work on individual battery packs, this work does not require prior offline experimentation or any post-processing. Real time fast convergence of parameters' estimates with minimal experimental effort enables self-update of battery parameters in run-time. The proposed strategy is mathematically validated and its performance is demonstrated on a 400 V, 6.6 Ah Li-ion battery bank powering the induction motor driven prototype electric vehicle (EV) traction system.

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Section: Low Computation Timementioning

confidence: 85%

“…A High gain adaptive estimator for a Li-ion battery model, based on (1)-( 6), is described by ( 16)- (21).…”

Section: A Proposed Uas Based Battery Parameters Estimation Methodologymentioning

confidence: 99%

Real Time Adaptive Estimation of Li-ion Battery Bank Parameters

Usman¹,

Mukhopadhyay²,

Rehman³

2022

Preprint

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“…experience terminal voltage collapse). The work in [20] implements the algorithm from [1] on a microcontroller, and also considers simple derivative based battery terminal voltage collapse detection. As mentioned earlier in the paper, simple derivative‐based battery failure detection has grave drawbacks when it comes to noise interference; therefore, we narrow our comparison to make it with the approach proposed in [1].…”

Section: Resultsmentioning

confidence: 99%

“…As for a numerical comparison, we compare with the approaches proposed in [1, 20]. The approach in [1] develops a high gain adaptive observer coupled with a low‐pass filter, which is used to track internal states of the battery, and also to filter effects of noise or disturbance.…”

Section: Resultsmentioning

confidence: 99%

Supervised learning for early and accurate battery terminal voltage collapse detection

Obeid

Tariq

Mukhopadhyay

2020

IET Circuits, Devices & Systems

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Rechargeable batteries are critical components in many electrical systems nowadays. One has to ensure reliable diagnosis and assessment of the installed batteries for smooth and safe operations. Assessment of the remaining capacity of a battery is crucial diagnostic information. A battery management system (BMS) needs to reliably report the ability of the battery to supply power or the lack thereof. If the BMS fails to do so at an early stage, this may compromise the health of the entire electric system. When a battery nears a region where the battery state-of-charge (SOC) is low, there is a risk of an abrupt drop in the terminal voltage. An early detection of such a region is crucial; otherwise, the BMS may not have enough time to react. To address this issue, our work provides a novel supervised learning approach towards an early detection of Li-ion battery terminal voltage collapse. No knowledge of initial SOC or battery model parameters is required. This is particularly important as batteries lose their capacity to store charge over time. The efficacy of the proposed approach is demonstrated by an early and accurate detection of terminal voltage collapse over a set of discharge tests conducted using multiple batteries.

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Experimental verification of UAS based battery terminal voltage collapse detection on a simple embedded platform

Cited by 2 publications

References 8 publications

Real Time Adaptive Estimation of Li-ion Battery Bank Parameters

Real Time Adaptive Estimation of Li-ion Battery Bank Parameters

Supervised learning for early and accurate battery terminal voltage collapse detection

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