A Comparative Study between Physics, Electrical and Data Driven Lithium-Ion Battery Voltage Modeling Approaches

Yang, Lijun; Emadi, Ali; Groß, Oliver; Vidal, Carlos; Canova, Marcello; Panchal, Satyam; Kollmeyer, Phillip J.; Naguib, Mina; Khanum, Fauzia

doi:10.4271/2022-01-0700

Cited by 39 publications

(12 citation statements)

References 29 publications

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“…Existing battery models can be broadly classified into three categories: data-driven models, electrochemical models, and electrical models. A comparative study on the three different modelling approaches are conducted by Liang et al [6]. Data-driven models are typically black-box models, such as neural network models.…”

Section: A Review Of Battery Modelsmentioning

confidence: 99%

Adaptive Piecewise Equivalent Circuit Model With SOC/SOH Estimation Based on Extended Kalman Filter

Huang

Best

Knowles

et al. 2023

IEEE Trans. Energy Convers.

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Section: A Review Of Battery Modelsmentioning

confidence: 99%

Adaptive Piecewise Equivalent Circuit Model With SOC/SOH Estimation Based on Extended Kalman Filter

Huang

Best

Knowles

et al. 2023

IEEE Trans. Energy Convers.

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“…The technology and demand of power battery have had great development in the past decades. , Among many batteries, lithium-ion batteries (LIBs) have high energy density, low self-discharge rate, and long cycle life . However, lithium batteries are easy to fall into thermal runaway and fire accidents when exposed to abuse conditions or manufacturing defects. , Previous studies have shown that the optimum temperature range for LIBs is 15–35 °C .…”

Section: Introductionmentioning

confidence: 99%

Review on Thermal Management of Lithium-Ion Batteries for Electric Vehicles: Advances, Challenges, and Outlook

Jing

et al. 2023

Energy Fuels

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Due to strict regulations and the requirement to reduce greenhouse gas emissions, electric vehicles (BEVs) are a promising mode of transportation. The lithium battery is the most important power source for an electric vehicle, but its performance and life are greatly restricted by temperature. To ensure the safety of automobile operation and alleviate mileage anxiety, it is urgent to understand the current situation and predict the development and challenge of battery thermal management system. This work reviews the existing thermal management research in five areas, including cooling and heating methods, modeling optimization, control methods, and thermal management system integration for lithium batteries. Battery thermal management types include air-based, liquid-based, PCM-based, heatpipe-based, and direct cooling. Designing a better battery thermal management system not only needs to be optimized using algorithms on the model but also it uses intelligent algorithms for precise control to achieve safety and reduce energy consumption. This work also reviews the differences in thermal management systems between square and cylindrical batteries and summarizes the development trend of modularity in battery thermal management systems.

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“…As the most common electrochemical energy storage unit, LIBs are widely used in distributed electrical power storage stations, electric vehicles, and aviation power systems. However, with the massive construction and commissioning of LIB energy projects, the safety issues have attracted widespread attention in fields and are characterized by thermal runaway, including battery condition monitoring, 1,2 thermal management, [3][4][5] and safety management. [6][7][8] According to news reports, a LIB electrical power storage station in Arizona, USA, suffered a thermal runaway accident in 2019 due to the high temperature of a battery pack.…”

Section: Introductionmentioning

confidence: 99%

Thermal runaway warning of lithium‐ion batteries based on photoacoustic spectroscopy gas sensing technology

Liao

Zhang

Gan

et al. 2022

Intl J of Energy Research

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Summary Thermal runaway is the most dangerous failure faced by lithium‐ion batteries (LIBs). In this paper, ethylene (C2H4), methane (CH4), and carbon monoxide (CO) were selected as the characteristic gases, the cantilever‐enhanced photoacoustic spectrometer was adopted as the gas detector, and a thermal runaway early warning system for LIBs was built based on characteristic gas sensing technology. After a series of repeated experimental calibrations and tests, the effectiveness of the thermal runaway warning system for lithium nickel cobalt manganese oxide (NCM) LIBs was verified. The experimental results show that the average warning times of the thermal runaway warning system were 7.3 and 11.5 min, respectively, for NCM LIBs with a state of charge of 50% and 100%. The warning times of the established system can meet both the requirements of the Chinese national standard GB 38031‐2020 and the United Nations global technical regulation ECE/TRANS/180/Add.20. Thus, the thermal runaway early warning system proposed in this paper can gain more time for the fire management of the NCM LIB system and the safe evacuation of personnel.

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A Comparative Study between Physics, Electrical and Data Driven Lithium-Ion Battery Voltage Modeling Approaches

Cited by 39 publications

References 29 publications

Adaptive Piecewise Equivalent Circuit Model With SOC/SOH Estimation Based on Extended Kalman Filter

Adaptive Piecewise Equivalent Circuit Model With SOC/SOH Estimation Based on Extended Kalman Filter

Review on Thermal Management of Lithium-Ion Batteries for Electric Vehicles: Advances, Challenges, and Outlook

Thermal runaway warning of lithium‐ion batteries based on photoacoustic spectroscopy gas sensing technology

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