scholarly journals Study on Gas-Generating Property of Lithium-Ion Batteries

2021 ◽  
Vol 35 (5) ◽  
pp. 1-8
Author(s):  
Joon-Hyuk Lee ◽  
Sung-Ho Hong ◽  
Heung-Su Lee ◽  
Moon-Woo Park
Keyword(s):  
Lithium Ion Batteries ◽  
Storage System ◽  
Lithium Ion ◽  
Hydrogen Sensor ◽  
Energy Storage System ◽  
Thermal Runaway ◽  
Fire Propagation ◽  
Before And After ◽  
Gas Measurement ◽  
Combustible Gases

A main cause of fires and explosions in lithium-ion batteries is the generation of combustible gases by them, and when a large number of batteries are densely packed, like in an Energy Storage System, there is a high risk of thermal runaway and fire propagation. Currently, many studies are being conducted worldwide to predict and prevent the generation of combustible gases, and thermal runaway in lithium-ion batteries, but they are still in progress. Therefore, in this study, we analyzed the gases generated before and after thermal runaway in lithium ion batteries, to prepare a basis for reducing the risk of thermal runaway. We aimed to establish the basis for prevention by early detection in the event of thermal runaway, by understanding the type and characteristics of the generated gases. For the experiment, lithium ion batteries were classified in terms of appearance (cylindrical, prismatic, pouch type), and cathode materials (NCM, NCA, LFP). The gases generated was measured against time. An FT-IR analyzer was used for gas measurement, and a separate hydrogen sensor was installed in the chamber to analyze changes in the types of gas, and measure the mass of the lithium ion battery over time. In the experiment, CO2 and CO were generated the most during thermal runaway in all lithium-ion batteries. Thereafter, CO2 increased, and CO decreased in the prismatic and pouch types, and both CO2 and CO increased in the cylindrical type. HF (a toxic gas), and H2 having a wide explosive range, were also generated, and the concentrations of these gases were inversely proportional to each other.

UL 9540A Installation Level Tests with Outdoor Lithium-ion Energy Storage System Mockups

2021 ◽  
Author(s):  
Adam Barowy ◽  
Alex Klieger ◽  
Jack Regan ◽  
Mark McKinnon
Keyword(s):  
Energy Storage ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Storage System ◽  
Fire Service ◽  
Thermal Runaway ◽  
Systematic Evaluation ◽  
Review Section ◽  
Storage Container ◽  
Gas Measurement

This report covers results of experiments conducted to obtain data on the fire and deflagration hazards from thermal runaway and its propagation through energy storage systems (ESS). The UL 9540A test standard provides a systematic evaluation of thermal runaway and propagation in energy storage system at cell, module, unit, and installation levels. The data from this testing may be used to design fire and explosion protection systems needed for safe siting and installation of ESS. In addition to temperature, pressure, and gas measurement instruments installed inside of the container, fire service portable gas monitors were placed at locations inside and outside the storage container during the experiments to assess their ability to detect products of thermal runaway and inform fire service size-up decisions. Review section 2.2.3 Fire Service Size-up Equipment to learn more. This research demonstrates a clear need for responding firefighters to have early access to data from instrumentation installed within an ESS, particularly gas measurement instrumentation, available through a monitoring panel. Additionally, it highlights the importance of communication between responding firefighters and personnel responsible for management of the ESS, who can aid in complete evaluation of system data to develop a more clear picture of system status and potential hazards.

scholarly journals Design of a Test Platform for the Determination of Lithium-Ion Batteries State of Health

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Jules-Adrien Capitaine ◽  
Qing Wang
Keyword(s):  
Lithium Ion Batteries ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Storage System ◽  
State Of Health ◽  
Battery Cell ◽  
Test Platform ◽  
Battery Energy ◽  
Novel Design

This paper presents a novel design for a test platform to determine the state of health (SOH) of lithium-ion batteries (LIBs). The SOH is a key parameter of a battery energy storage system and its estimation remains a challenging issue. The batteries that have been tested are 18,650 Li-ion cells as they are the most commonly used batteries on the market. The test platform design is detailed from the building of the charging and discharging circuitry to the software. Data acquired from the testing circuitry are stored and displayed in LabVIEW to obtain the charging and discharging curves. The resulting graphs are compared to the outcome predicted by the battery datasheets, to verify that the platform delivers coherent values. The SOH of the battery is then calculated using a Coulomb counting method in LabVIEW. The batteries will be discharged through various types of resistive circuits, and the differences in the resulting curves will be discussed. A single battery cell will also be tested over 30 cycles and the decrease in the SOH will be clearly identified.

scholarly journals Accuracy improvement of SOC estimation in lithium-ion batteries by ANFIS vs ANN modeling of nonlinear cell characteristics

2021 ◽  
Author(s):  
Mohammad Hassan Amir Jamlouie
Keyword(s):  
Energy Storage ◽  
Lithium Ion Batteries ◽  
Intelligent System ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Storage System ◽  
Training Data ◽  
Accurate Estimation ◽  
Consumption Pattern ◽  
Load Prediction

Over the last century, the energy storage industry has continued to evolve and adapt to changing energy requirements. To run an efficient energy storage system two points must be considered. Firstly, precise load forecasting to determine energy consumption pattern. Secondly, is the correct estimation of state of charge (SOC). In this project there is a model introduced to predict the load consumption based on ANN implemented by MATLAB. The Designed intelligent system introduced for load prediction according to the hypothetical training data related to two years daily based load consumption of a residential area. For another obstacle which is accurate estimation of SOC, two separate models are provided based on ANN and ANFIS for Lithium-ion batteries as an energy storage system. There are several researches in this regard but in this project the author makes an effort to introduce the most efficient based on the MSE of each performance and as a result the method by ANN is found more accurate.

scholarly journals Design of a Test Platform for the Determination of Lithium-Ion Batteries State-of-Health

2018 ◽  
Author(s):  
Jules-Adrien Capitaine ◽  
Qing Wang
Keyword(s):  
Lithium Ion Batteries ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Storage System ◽  
State Of Health ◽  
Battery Cell ◽  
Test Platform ◽  
Battery Energy ◽  
Novel Design

This paper presents a novel design for a test platform to determine the State of Health (SOH) of lithium-ion batteries. The SOH is a key parameter of a battery energy storage system and its estimation remains a challenging issue. The batteries that have been tested are 18650 li-ion cells as they are the most commonly used batteries on the market. The test platform design is detailed from the building of the charging and discharging circuitry to the software. Data acquired from the testing circuitry is stored and displayed in LabView to obtain charging and discharging curves. The resulting graphs are compared to the outcome predicted by the battery datasheets, to verify the platform delivers coherent values. The SOH of the battery is then calculated using a Coulomb Counting method in LabView. The batteries will be discharged through various types of resistive circuits, and the differences in the resulting curves will be discussed. A single battery cell will also be tested over 30 cycles and the decrease in the SOH will be clearly pointed out.

scholarly journals Economic analysis for centralized battery energy storage system with reused battery from EV in Australia

2021 ◽  
Vol 300 ◽  
pp. 01003
Author(s):  
Yunfan Meng
Keyword(s):  
Energy Storage ◽  
Lithium Ion Batteries ◽  
Economic Model ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Storage System ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Development Potential ◽  
Battery Energy

With battery energy storage technology development, the centralized battery energy storage system (CBESS) has a broad prospect in developing electricity. In the meantime, the retired lithium-ion batteries from electric vehicles (EV) offer a new option for battery energy storage systems (BESS). This paper studies the centralized reused battery energy storage system (CRBESS) in South Australia by replacing the new lithium-ion batteries with lithium-ion second-life batteries (SLB) and evaluating the economic benefits with economic indicators as net present value (NPV), discounted payback period (DPBP), Internal rate of return (IRR) to depict a comprehensive understanding of the development potential of the CRBESS with the lithium-ion SLB as the energy storage system. This paper proposes a calculation method of frequency control ancillary services (FCAS) revenue referring to market share rate (MSR) when building the economic model. Moreover, the residual value of lithium-ion batteries is considered. This paper uses the economic model to calculate the profitability and development potential of CRBESS. From an economic perspective, the superiority and feasibility of CRBESS compared with CBESS were analyzed.

scholarly journals Accuracy improvement of SOC estimation in lithium-ion batteries by ANFIS vs ANN modeling of nonlinear cell characteristics

2021 ◽  
Author(s):  
Mohammad Hassan Amir Jamlouie
Keyword(s):  
Energy Storage ◽  
Lithium Ion Batteries ◽  
Intelligent System ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Storage System ◽  
Training Data ◽  
Accurate Estimation ◽  
Consumption Pattern ◽  
Load Prediction

Over the last century, the energy storage industry has continued to evolve and adapt to changing energy requirements. To run an efficient energy storage system two points must be considered. Firstly, precise load forecasting to determine energy consumption pattern. Secondly, is the correct estimation of state of charge (SOC). In this project there is a model introduced to predict the load consumption based on ANN implemented by MATLAB. The Designed intelligent system introduced for load prediction according to the hypothetical training data related to two years daily based load consumption of a residential area. For another obstacle which is accurate estimation of SOC, two separate models are provided based on ANN and ANFIS for Lithium-ion batteries as an energy storage system. There are several researches in this regard but in this project the author makes an effort to introduce the most efficient based on the MSE of each performance and as a result the method by ANN is found more accurate.

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