Design of Modular Battery Management System with Point-to-point SoC Estimation Algorithm

Caliwag, Angela C.; Muh, Kumbayoni Lalu; Kang, So Hee; Park, Jeonghun; Lim, Wansu

doi:10.1109/icaiic48513.2020.9065224

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…The trend of using the modular BMS topology is gaining attention because of its advantages such as deployment flexibility and scalability [21][22][23]. Using modular BMS topology enhances the performance efficiency of batterybased applications because of its flexibility and scalability whereas it can be reconfigured to meet the system demand and can be easily modified to address evolving deployment requirements.…”

Section: B Motivation and Contributionmentioning

confidence: 99%

“…In battery-based applications, deployment flexibility, efficiency, and maintenance, fault tolerance is already a crucial concern. Furthermore, integrating a BMS for intelligent and state-of-the-art deployment is another crucial factor to consider to address the technological advancement; especially in this industry 4.0 era which is lacking [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”

Section: B Motivation and Contributionmentioning

confidence: 99%

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Design, Implementation, and Deployment of Modular Battery Management System for IIoT-Based Applications

2022

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This paper proposes design and implementation of a battery management system (BMS) for the industrial internet of things (IIoT) enabled applications. The hardware and software development of this BMS is briefly presented in this paper. In terms of hardware development, the presented BMS have modular topology and has 1) high fault tolerance and 2) has exceptionally flexible deployment owing to its topology having multiple local management units (LMUs) connected to a central management unit (CMU). This hardware design approach aims to address the overall design efficiency and cost trade-off of BMS deployment. The hardware design efficiency is tested using actual deployment. In terms of fault tolerance using 1 -3 LMUs at fault, the voltage monitoring accuracy is maintained for each LMUs. An average of 0.00017 V, 0.0008 V and 0.001 V voltage difference is yielded for 1, 2, and 3 modules at fault respectively. Additionally, core BMS sub-circuit is tested to verify hardware design efficiency such as the DC-to-DC converter which yielded 92.74%. Furthermore, the CMU is integrated with a wireless communication circuit that enables IIoTbased applications such as the emerging edge-based, and a plethora of intelligent deployment. In terms of software, the presented BMS aims to realize state-of-the-art processing through IIoT based approach. For software testing and verification, the BMS is deployed to an unmanned ground vehicle (UGV). The signal stability is tested for UGV based application at a 3500s deployment time whereas an average of 0.0010V voltage difference is yielded. This is verified using time markers which is further analyzed using softwarebased signal processing and acquisition simulation. Concisely, the proposed BMS aims to converge IIoT applications to its actual deployment. The proposed BMS is designed, implemented, and successfully deployed to test its viability both in the simulation platform and actual deployment.INDEX TERMS Battery management system, edge processing, industrial IoT, lithium-ion, modular architecture.

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Section: B Motivation and Contributionmentioning

confidence: 99%

Section: B Motivation and Contributionmentioning

confidence: 99%

Design, Implementation, and Deployment of Modular Battery Management System for IIoT-Based Applications

2022

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Enhanced SOC estimation of lithium ion batteries with RealTime data using machine learning algorithms

D.,

Babu,

et al. 2024

Sci Rep

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Accurately estimating Battery State of Charge (SOC) is essential for safe and optimal electric vehicle operation. This paper presents a comparative assessment of multiple machine learning regression algorithms including Support Vector Machine, Neural Network, Ensemble Method, and Gaussian Process Regression for modelling the complex relationship between real-time driving data and battery SOC. The models are trained and tested on extensive field data collected from diverse drivers across varying conditions. Statistical performance metrics evaluate the SOC prediction accuracy on the test set. Gaussian process regression demonstrates superior precision surpassing the other techniques with the lowest errors. Case studies analyse model competence in mimicking actual battery charge/discharge characteristics responding to changing drivers, temperatures, and drive cycles. The research provides a reliable data-driven framework leveraging advanced analytics for precise real-time SOC monitoring to enhance battery management.

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State of Charge (SoC) algorithm for Battery Management System (BMS)

Thobde

Somani

Gunale

2022

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Design of Modular Battery Management System with Point-to-point SoC Estimation Algorithm

Cited by 5 publications

References 8 publications

Design, Implementation, and Deployment of Modular Battery Management System for IIoT-Based Applications

Design, Implementation, and Deployment of Modular Battery Management System for IIoT-Based Applications

Enhanced SOC estimation of lithium ion batteries with RealTime data using machine learning algorithms

State of Charge (SoC) algorithm for Battery Management System (BMS)

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