Co-Estimation of SoC and SoP Using BiLSTM

Muh, Kumbayoni Lalu; Caliwag, Angela C.; Jeon, Il-Soo; Lim, Wansu

doi:10.7840/kics.2021.46.2.314

Cited by 2 publications

(1 citation statement)

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“…Data-driven methods have become popular in recent years due to their ability to estimate the SOC using only battery measurement data. Machine learning algorithms such as support vector machines (SVCs) [7], artificial neural networks [8], and fuzzy logic [9] have been used for SOC estimation. According to [10], there are three types of neural network methods: feed-forward neural networks, deep learning neural networks, and hybrid neural networks.…”

Section: Introductionmentioning

confidence: 99%

Smart Lithium-Ion Battery Monitoring in Electric Vehicles: An AI-Empowered Digital Twin Approach

Pooyandeh,

Sohn

2023

Mathematics

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This paper presents a transformative methodology that harnesses the power of digital twin (DT) technology for the advanced condition monitoring of lithium-ion batteries (LIBs) in electric vehicles (EVs). In contrast to conventional solutions, our approach eliminates the need to calibrate sensors or add additional hardware circuits. The digital replica works seamlessly alongside the embedded battery management system (BMS) in an EV, delivering real-time signals for monitoring. Our system is a significant step forward in ensuring the efficiency and sustainability of EVs, which play an essential role in reducing carbon emissions. A core innovation lies in the integration of the digital twin into the battery monitoring process, reshaping the landscape of energy storage and alternative power sources such as lithium-ion batteries. Our comprehensive system leverages a cloud-based IoT network and combines both physical and digital components to provide a holistic solution. The physical side encompasses offline modeling, where a long short-term memory (LSTM) algorithm trained with various learning rates (LRs) and optimized by three types of optimizers ensures precise state-of-charge (SOC) predictions. On the digital side, the digital twin takes center stage, enabling the real-time monitoring and prediction of battery activity. A particularly innovative aspect of our approach is the utilization of a time-series generative adversarial network (TS-GAN) to generate synthetic data that seamlessly complement the monitoring process. This pioneering use of a TS-GAN offers an effective solution to the challenge of limited real-time data availability, thus enhancing the system’s predictive capabilities. By seamlessly integrating these physical and digital elements, our system enables the precise analysis and prediction of battery behavior. This innovation—particularly the application of a TS-GAN for data generation—significantly contributes to optimizing battery performance, enhancing safety, and extending the longevity of lithium-ion batteries in EVs. Furthermore, the model developed in this research serves as a benchmark for future digital energy storage in lithium-ion batteries and comprehensive energy utilization. According to statistical tests, the model has a high level of precision. Its exceptional safety performance and reduced energy consumption offer promising prospects for sustainable and efficient energy solutions. This paper signifies a pivotal step towards realizing a cleaner and more sustainable future through advanced EV battery management.

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Section: Introductionmentioning

confidence: 99%

Smart Lithium-Ion Battery Monitoring in Electric Vehicles: An AI-Empowered Digital Twin Approach

Pooyandeh,

Sohn

2023

Mathematics

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A Linear Recursive State of Power Estimation for Fusion Model Component Analysis with Constant Sampling Time

Li¹,

Wang²,

Vilas³

et al. 2021

International Journal of Electrochemical Science

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The state of power of lithium-ion batteries, as the main product of choice for electric and hybrid electric vehicle energy storage systems, is one of the precise feedback control parameters for the battery management system. The proposed research establishes a method for the analysis of charging and discharging constitutive factors under the sampling time, realizes the online identification of parameters by building an adaptive forgetting factor recursive least-squares method based on the Thevenin model, and uses the online parameters to achieve an effective characterization of the power state under voltage and current limitations. The results demonstrate that the accuracy error of online parameter identification is less than 0.03 V. Combining the analysis of charging and discharging constitutive factors undersampling time with the fusion model of voltage and current limitation makes the power state estimation more reliable and accurate. The results demonstrate that the power state estimation error in the discharging state is less than 8%.

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Co-Estimation of SoC and SoP Using BiLSTM

Cited by 2 publications

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Smart Lithium-Ion Battery Monitoring in Electric Vehicles: An AI-Empowered Digital Twin Approach

Smart Lithium-Ion Battery Monitoring in Electric Vehicles: An AI-Empowered Digital Twin Approach

A Linear Recursive State of Power Estimation for Fusion Model Component Analysis with Constant Sampling Time

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