Towards scalable monitoring and maintenance of rechargeable batteries

Mills, Aaron; Zambreno, Joseph

doi:10.1109/eit.2014.6871837

Cited by 3 publications

(6 citation statements)

References 9 publications

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“…The model we employ is identical to that described in [3], and cell data is collected in the same way. As depicted in Fig.…”

Section: Cell Modelmentioning

confidence: 99%

“…More detail appears in [3] and [6]. The ARX constants a 1 ,a 2 ,b 1 , and b 2 are extracted from cell cycling data; η i is the cell's Coulombic Efficiency (we use 1.0); ∆t is our simulation timestep (i.e.…”

Section: A State-space Equationsmentioning

confidence: 99%

“…This issue was not addressed in [3]. In [5] this was addressed explicitly by adding additional model states, at the expense of computational complexity.…”

Section: B Modeling Refinementsmentioning

confidence: 99%

“…BMS) which monitor and maintain rechargeable batteries [1,2]. As for determining SOC and SOH, a number of papers have been published which apply a Kalman Filter (KF) to rechargeable cells such as Liion or LiPo [3,4,5,6]. This method has been shown capable of very accurate cell SOC estimation in real time.…”

Section: Introductionmentioning

confidence: 99%

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Estimating state of charge and state of health of rechargable batteries on a per-cell basis

Mills

Zambreno

2015

2015 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES)

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Much of current research on State-of-Charge (SOC) and State-of-Health (SOH) tracking for rechargeable batteries such as Li-ion focuses primarily on analyzing single cells, or otherwise treat a set of series-connected cells as a homogeneous unit. Since no two cells have precisely the same properties, for applications involving large batteries this can severely limit the accuracy and utility of the approach. In this paper we develop an model-driven approach using a Dual Unscented Kalman Filter to allow a Battery Monitoring System (BMS) to monitor in real time both SOC and SOH of each cell in a battery. A BMS is an example of a Cyber-Physical System (CPS) which requires deep understanding of the behavior of the physical system (i.e., the battery) in order to obtain reliability in demanding applications. In particular, since the SOH of a cell changes extremely slowly compared to SOC, our dual filter operates on two timescales to improve SOH tracking. We show that the use of the Unscented Kalman Filter instead of the more common Extended Kalman Filter simplifies the development of the system model equations in the multiscale case. We also show how a single "average" cell model can be used to accurately estimate SOH for different cells and cells of different ages.

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“…The model we employ is identical to that described in [3], and cell data is collected in the same way. As depicted in Fig.…”

Section: Cell Modelmentioning

confidence: 99%

Section: A State-space Equationsmentioning

confidence: 99%

“…This issue was not addressed in [3]. In [5] this was addressed explicitly by adding additional model states, at the expense of computational complexity.…”

Section: B Modeling Refinementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating state of charge and state of health of rechargable batteries on a per-cell basis

Mills

Zambreno

2015

2015 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES)

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“…The following section details an extended case study on piecewise-affine Kalman Filtering for tracking cell voltages in large batteries. It is based on results appearing in [80] and [81].…”

Section: Battery Monitoringmentioning

confidence: 99%

Design and implementation of an FPGA-based piecewise affine Kalman Filter for Cyber-Physical Systems

Mills¹

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Toward Predictive Modeling of Solar Power Generation for Multiple Power Plants

Thonglek

Ichikawa

Takahashi

et al. 2023

IEICE Trans. Commun.

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Solar power is the most widely used renewable energy source, which reduces pollution consequences from using conventional fossil fuels. However, supplying stable power from solar power generation remains challenging because it is difficult to forecast power generation. Accurate prediction of solar power generation would allow effective control of the amount of electricity stored in batteries, leading in a stable supply of electricity. Although the number of power plants is increasing, building a solar power prediction model for a newly constructed power plant usually requires collecting a new training dataset for the new power plant, which takes time to collect a sufficient amount of data. This paper aims to develop a highly accurate solar power prediction model for multiple power plants available for both new and existing power plants. The proposed method trains the model on existing multiple power plants to generate a general prediction model, and then uses it for a new power plant while waiting for the data to be collected. In addition, the proposed method tunes the general prediction model on the newly collected dataset and improves the accuracy for the new power plant. We evaluated the proposed method on 55 power plants in Japan with the dataset collected for two and a half years. As a result, the pre-trained models of our proposed method significantly reduces the average RMSE of the baseline method by 73.19%. This indicates that the model can generalize over multiple power plants, and training using datasets from other power plants is effective in reducing the RMSE. Fine-tuning the pre-trained model further reduces the RMSE by 8.12%.

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Towards scalable monitoring and maintenance of rechargeable batteries

Cited by 3 publications

References 9 publications

Estimating state of charge and state of health of rechargable batteries on a per-cell basis

Estimating state of charge and state of health of rechargable batteries on a per-cell basis

Design and implementation of an FPGA-based piecewise affine Kalman Filter for Cyber-Physical Systems

Toward Predictive Modeling of Solar Power Generation for Multiple Power Plants

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