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Analysis of battery management system issues in electric vehicles

V Karkuzhali 1 , P Rangarajan 2 , V Tamilselvi 3 and P Kavitha 1

Published under licence by IOP Publishing Ltd IOP Conference Series: Materials Science and Engineering , Volume 994 , International Conference on Recent Developments in Robotics, Embedded and Internet of Things (ICRDREIOT2020) 16- 17 October 2020, Tamil Nadu, India Citation V Karkuzhali et al 2020 IOP Conf. Ser.: Mater. Sci. Eng. 994 012013 DOI 10.1088/1757-899X/994/1/012013

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1 Assistant Professor Department of EEE, R. M. D Engineering College, Chennai, Tamilnadu, India

2 Professor Department of EEE, R. M. D Engineering College, Chennai, Tamilnadu, India

3 H. O. D Department of EEE, R. M. D Engineering College, Chennai, Tamilnadu, India

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Battery technology has dramatically advanced over a decade and many high performance batteries are being developed. Electric vehicles (EV) require high power batteries with suitable battery management systems (BMS) for safe and reliable operations. Intention of this paper is to discuss about the batteries used in electric vehicles and the key issues of battery management systems and to compare the Lithium ion (Li-ion) battery & Nickel metal hydride battery in terms of aging and effect of temperature using their state of charge (SOC) and open circuit voltage (OCV).

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State-of-Health Prediction for Li-ion Batteries for Efficient Battery Management System Using Hybrid Machine Learning Model

  • Original Article
  • Published: 19 June 2023
  • Volume 19 , pages 585–600, ( 2024 )

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battery management research papers

  • Varatharaj Myilsamy 1 ,
  • Sudhakar Sengan   ORCID: orcid.org/0000-0003-4901-1432 2 ,
  • Roobaea Alroobaea 3 &
  • Majed Alsafyani 3  

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Since Lithium-ion (Li-ion) batteries are frequently used for real-time applications, evaluating their State of Health (SoH) is crucial to guarantee their effectiveness and safety. Model-based methods with SoH prediction are helpful. However, the issues with battery modelling have led to a greater dependence on machine learning (ML). As a significant step in assessing the effectiveness of ML techniques, data preprocessing has also drawn much attention. In this work, a new preprocessing method using relative State of Charge (SoC) is proposed; further, this paper describes a hybrid learning model (HLM) that combines auto-regressive integrated moving average (ARIMA), gated recurrent unit (GRU) and convolutional neural network (CNN). Data: proposed HLM uses time-series and SoC domain data; the ARIMA + GRU algorithm trains the time-series data, while CNN trains the SoC domain data. Both outputs are mean averaged to get the final output prediction. The proposed HLM is evaluated for root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) using the National Aeronautics and Space Administration (NASA’s) randomized battery usage data set (RBUDS). The results indicate that the recommended HLM is more accurate and has a smaller error margin than existing ML models.

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The researchers would like to acknowledge the Deanship of Scientific Research at Taif University for funding this work.

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Department of Electrical and Electronics Engineering, V.S.B College of Engineering Technical Campus, Coimbatore, Tamil Nadu, 642109, India

Varatharaj Myilsamy

Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, 627152, India

Sudhakar Sengan

Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia

Roobaea Alroobaea & Majed Alsafyani

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VM: Writing-Review & Editing ; SS: Investigation, Writing-Original Draft, Conceptualization, Methodology , Formal analysis; RA: Validation, Software, Data Curation; MA: Supervision, Funding.

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Myilsamy, V., Sengan, S., Alroobaea, R. et al. State-of-Health Prediction for Li-ion Batteries for Efficient Battery Management System Using Hybrid Machine Learning Model. J. Electr. Eng. Technol. 19 , 585–600 (2024). https://doi.org/10.1007/s42835-023-01564-2

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Received : 30 April 2022

Revised : 19 May 2023

Accepted : 12 June 2023

Published : 19 June 2023

Issue Date : January 2024

DOI : https://doi.org/10.1007/s42835-023-01564-2

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battery management research papers

Researchers develop game-changing EV battery material that can reduce charging times to just 6 minutes

E xperts from South Korea’s Pohang University of Science and Technology claim they have developed a battery anode that can help deliver a six-minute recharge for electric vehicles . 

It’s part of an ongoing slew of innovations from researchers around the world geared to make EVs a reliable and affordable conveyance, no matter where you live. 

Pohang’s breakthrough involves replacing the typical graphite anode with a manganese ferrite one, which was found to store one-and-a-half more lithium ions than the researchers expected, according to an article on the science by Freethink. 

In common lithium-ion batteries (which power most EVs), ions are stored and move back and forth from the cathode to the anode as the battery charges and discharges, according to the U.S. Energy Department. 

“We have offered a new understanding on how to overcome the electrochemical limitations of conventional anode materials,” Professor Won Bae Kim, who led the research, said in a Pohang report published by EurekAlert.  

The translation: The tech could eliminate the need to park your EV for lengthy periods of time as it charges , bringing the power-up time closer to the typical gas station stop. 

Depending on the voltage (typically 120-900 or more), some EVs gain less than 10 miles of range during an hour’s charge, though higher voltages and faster charge times are becoming more common. And, tech breakthroughs that improve battery performance are frequent, which is important news in the effort to curb air pollution . 

Gas-guzzling vehicles made about 7.7 billion tons of dirty air last year, according to data collector Statista. Cars and vans were the largest polluters of that group. 

A big perk to the Pohang solution is that it doesn’t increase the size and weight of battery packs. On average, they weigh 1,000 pounds, according to an article posted on LinkedIn. In Pohang, the experts increased charge capacity through their nanometer-thick manganese ferrite sheets (a sheet of paper is about 100,000 nanometers, for reference), which are used as the anode.

It’s part of the “groundbreaking technique,” which includes reactions, synthesizing, and complex chemistry that results in greater capacity, as described by researchers in the EurekAlert report. 

But manganese ferrite might not dethrone the common graphite anodes immediately. Freethink reported that they are used because of “cost, life cycle, stability, and availability” —  all solid perks. 

Research on other anode materials, including silicon , is ongoing. 

“Ultimately, we will need high-capacity, fast-charging batteries in our EVs, and manganese ferrite could be the key to getting there,” Freethink wrote . 

Researchers develop game-changing EV battery material that can reduce charging times to just 6 minutes first appeared on The Cool Down .

“Ultimately, we will need high-capacity, fast-charging batteries in our EVs.

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