A new cell-to-cell fast balancing circuit for Lithium-Ion batteries in Electric Vehicles and Energy Storage System

2016 ◽  
Author(s):  
Van-Long Pham ◽  
Thanh-Tung Nguyen ◽  
Duc-Hung Tran ◽  
Van-Binh Vu ◽  
Woojin Choi
Keyword(s):  
Energy Storage ◽  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Storage System

Secondary Use of PHEV and EV Lithium-Ion Batteries in Stationary Applications as Energy Storage System

2012 ◽  
Vol 528 ◽  
pp. 202-205 ◽  
Author(s):  
Guang Jin Zhao ◽  
Wen Long Wu ◽  
Wu Bin Qiu ◽  
Shao Lin Liu ◽  
Gang Wang
Keyword(s):  
Energy Storage ◽  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Storage System ◽  
Li Ion Batteries ◽  
Secondary Use ◽  
Li Ion

This manuscript introduces and reviews the background, necessity, opportunities, and recent research progresses for investigating and applying the secondary use of plug-in hybrid electric vehicles (PHEVs) and electric vehicles (EVs) lithium-ion (Li-ion) batteries in stationary applications. And the motivation, objective, and plans of our PHEV/EV lithium-ion battery secondary-use program are also described in detail.

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 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.

Sign in / Sign up

Export Citation Format

Share Document