Machine learning approach for full impedance spectrum study of Li-ion battery

Water quality is one of the most highly debated issues worldwide at the moment. Inadequate water supplies affect human health, hinder food production, and degrade the environment. Using contemporary technology to analyze pollution statistics can help solve pollution issues. One option is to take advantage of advancements in intelligent data processing to conduct hydrological parameter analysis. To perform conclusive water quality studies, a lot of data is necessary. Unfilled data (information gaps) in the long-term hydrological data set may be due to equipment faults, collection schedule delays, or the data collection officer’s absence. The lack of hydrological data skews its interpretation. Therefore, interpolation is used to recreate and fill missing hydrological data. From 2012 to 2017, the Klang River’s biochemical oxygen demand (BOD) in Selangor, Malaysia, was sampled. This study examined three methods of interpolation for their effectiveness using the MATLAB software: piecewise cubic hermite interpolating polynomial (PCHIP), cubic Spline data interpolation (Spline), and modified Akima partitioned cubic hermite interpolation (Makima). The accuracy is assessed using root mean square error (RMSE). All interpolation algorithms offer excellent results with low RMSE. However, PCHIP delivers the best match between interpolated and original data.

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“…Makima is a technique that evolved from Akima [30]. Originally, Akima was based on a cubic polynomial.…”

Section: Makima Interpolationmentioning

confidence: 99%

Spatial interpolation method comparison for physico-chemical parameters of river water in Klang River using MATLAB

et al. 2022

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“…Psuedo-random sequences were also employed in [6] with a focus on various implementation aspects in EV. Authors in [7] proposed using machine learning to reconstruct the entire impedance spectrum based on a few multi-sine signal injection measurements, which also reduced the execution time significantly. In [8], a very comprehensive analysis of battery ageing in different storage conditions was performed on long-term data.…”

Section: State-of-the-art Reviewmentioning

confidence: 99%

Capacity State-of-Health Estimation of Electric Vehicle Batteries Using Machine Learning and Impedance Measurements

et al. 2022

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With the increasing adoption of electric vehicles (EVs) by the general public, a lot of research is being conducted in Li-ion battery-related topics, where state-of-health (SoH) estimation has a prominent role. Accurate knowledge of this parameter is essential for efficient and safe EV operation. In this work, machine-learning techniques are applied to estimate this parameter in EV applications and in diverse scenarios. After thoroughly analysing cell ageing in different storage conditions, a novel approach based on impedance data is developed for SoH estimation. A fully-connected feed-forward neural network (FC-FNN) is employed to estimate the battery’s maximum available capacity from a small set of impedance measurements. The method was tested for estimation in long-term scenarios and for diverse degradation procedures with data from real EV batteries. High accuracy was obtained in all situations, with a mean absolute error as low as 0.9%. Thus, the proposed algorithm constitutes a powerful and viable solution for fast and accurate SoH estimation in real-world battery management systems.

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Machine learning approach for full impedance spectrum study of Li-ion battery

Cited by 2 publications

References 13 publications

Spatial interpolation method comparison for physico-chemical parameters of river water in Klang River using MATLAB

Spatial interpolation method comparison for physico-chemical parameters of river water in Klang River using MATLAB

Capacity State-of-Health Estimation of Electric Vehicle Batteries Using Machine Learning and Impedance Measurements

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