Energy control for Plug-In HEV with ultracapacitors lithium-ion batteries storage system for FIA Alternative Energy Cup Race

2010 ◽  
Author(s):  
Ferdinando Luigi Mapelli ◽  
Davide Tarsitano
Keyword(s):  
Lithium Ion Batteries ◽  
Alternative Energy ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Control

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 Nanomaterials for lithium-ion batteries and hydrogen energy

2017 ◽  
Vol 89 (8) ◽  
pp. 1185-1194 ◽  
Author(s):  
Irina A. Stenina ◽  
Andrey B. Yaroslavtsev
Keyword(s):  
Fuel Cells ◽  
Lithium Ion Batteries ◽  
Alternative Energy ◽  
Electrode Materials ◽  
Lithium Iron Phosphate ◽  
Electronic Conductivity ◽  
Lithium Ion ◽  
Iron Phosphate ◽  
Inorganic Nanoparticles ◽  
Hydrogen Energy

Abstract Development of alternative energy sources is one of the main trends of modern energy technology. Lithium-ion batteries and fuel cells are the most important among them. The increase in the energy and power density is the essential aspect which determined their future development. We provide a brief review of the state of developments in the field of nanosize electrode materials and electrolytes for lithium-ion batteries and hydrogen energy. The presence of relatively inexpensive and abundant elements, safety and low volume change during the lithium intercalation/deintercalation processes enables the application of lithium iron phosphate and lithium titanate as electrode materials for lithium-ion batteries. At the same time, they exhibit low ionic and electronic conductivity. To overcome this problem the following main approaches have been applied: use of nanosize materials, including nanocomposites, and heterovalent doping. Their impact in the property change is analyzed and discussed. Hybrid membranes containing inorganic nanoparticles enable a significant progress in the fuel cell development. Different approaches to their preparation, the reasons for ion conductivity and selectivity change, as well as the prospects for their application in low-temperature fuel cells are discussed. This review may provide some useful guidelines for development of advanced materials for lithium ion batteries and fuel cells.

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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