scholarly journals Projecting the Price of Lithium-Ion NMC Battery Packs Using a Multifactor Learning Curve Model

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5276
Author(s):  
Xaviery N. Penisa ◽  
Michael T. Castro ◽  
Jethro Daniel A. Pascasio ◽  
Eugene A. Esparcia ◽  
Oliver Schmidt ◽  
...  
Keyword(s):  
Learning Curve ◽  
Lithium Ion ◽  
Energy Systems ◽  
Energy System ◽  
Battery Pack ◽  
Solar Photovoltaic ◽  
Li Ion Batteries ◽  
Curve Model ◽  
Battery Packs ◽  
Li Ion

Renewable energy (RE) utilization is expected to increase in the coming years due to its decreasing costs and the mounting socio-political pressure to decarbonize the world’s energy systems. On the other hand, lithium-ion (Li-ion) batteries are on track to hit the target 100 USD/kWh price in the next decade due to economy of scale and manufacturing process improvements, evident in the rise in Li-ion gigafactories. The forecast of RE and Li-ion technology costs is important for planning RE integration into existing energy systems. Previous cost predictions on Li-ion batteries were conducted using conventional learning curve models based on a single factor, such as either installed capacity or innovation activity. A two-stage learning curve model was recently investigated wherein mineral costs were taken as a factor for material cost to set the floor price, and material cost was a major factor for the battery pack price. However, these models resulted in the overestimation of future prices. In this work, the future prices of Li-ion nickel manganese cobalt oxide (NMC) battery packs - a battery chemistry of choice in the electric vehicle and stationary grid storage markets - were projected up to year 2025 using multi-factor learning curve models. Among the generated models, the two-factor learning curve model has the most realistic and statistically sound results having learning rates of 21.18% for battery demand and 3.0% for innovation. By year 2024, the projected price would fall below the 100 USD/kWh industry benchmark battery pack price, consistent with most market research predictions. Techno-economic case studies on the microgrid applications of the forecasted prices of Li-ion NMC batteries were conducted. Results showed that the decrease in future prices of Li-ion NMC batteries would make 2020 and 2023 the best years to start investing in an optimum (solar photovoltaic + wind + diesel generator + Li-ion NMC) and 100% RE (solar photovoltaic + wind + Li-ion NMC) off-grid energy system, respectively. A hybrid grid-tied (solar photovoltaic + grid + Li-ion NMC) configuration is the best grid-tied energy system under the current net metering policy, with 2020 being the best year to deploy the investment.

Self-Balancing by Design in Hybrid Electrochemical Battery Packs

2018 ◽  
Author(s):  
Nur Adilah Aljunid ◽  
Michelle A. K. Denlinger ◽  
Hosam K. Fathy
Keyword(s):  
Feedback Loop ◽  
Lithium Ion ◽  
Battery Pack ◽  
Hybrid Cells ◽  
Battery Cell ◽  
Battery Packs ◽  
Li Ion ◽  
Electrochemical Battery ◽  
Novel Concept ◽  
Physics Based Simulation

This paper explores the novel concept that a hybrid battery pack containing both lithium-ion (Li-ion) and vanadium redox flow (VRF) cells can self-balance automatically, by design. The proposed hybrid pack connects the Li-ion and VRF cells in parallel to form “hybrid cells”, then connects these hybrid cells into series strings. The basic idea is to exploit the recirculation and mixing of the VRF electrolytes to establish an internal feedback loop. This feedback loop attenuates state of charge (SOC) imbalances in both the VRF battery and the lithium-ion cells connected to it. This self-balancing occurs automatically, by design. This stands in sharp contrast to today’s battery pack balancing approaches, all of which require either (passive/active) power electronics or an external photovoltaic source to balance battery cell SOCs. The paper demonstrates this self-balancing property using a physics-based simulation of the proposed hybrid pack. To the best of the authors’ knowledge, this work represents the first report in the literature of self-balancing “by design” in electrochemical battery packs.

scholarly journals Designing of Lithium - Ion Battery Pack Rechargeable on a Hybrid System with Battery Management System (BMS) for DC Loads of Low Power Applications – A Prototype Model

2021 ◽  
Vol 2089 (1) ◽  
pp. 012017
Author(s):  
Ramu Bhukya ◽  
Praveen Kumar Nalli ◽  
Kalyan Sagar Kadali ◽  
Mahendra Chand Bade
Keyword(s):  
Lithium Ion Battery ◽  
Management System ◽  
Short Circuit ◽  
Lithium Ion ◽  
Battery Pack ◽  
Prototype Model ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Batteries ◽  
Li Ion

Abstract Now a days, Li-ion batteries are quite possibly the most exceptional battery-powered batteries; these are drawing in much consideration from recent many years. M Whittingham first proposed lithium-ion battery technology in the 1970s, using titanium sulphide for the cathode and lithium metal for the anode. Li-ion batteries are the force to be reckoned with for the advanced electronic upset in this cutting-edge versatile society, solely utilized in cell phones and PC computers. A battery is a Pack of cells organized in an arrangement/equal association so the voltage can be raised to the craving levels. Lithium-ion batteries, which are completely utilised in portable gadgets & electric vehicles, are the driving force behind the digital technological revolution in today’s mobile societies. In order to protect and maintain voltage and current of the battery with in safe limit Battery Management System (BMS) should be used. BMS provides thermal management to the battery, safeguarding it against over and under temperature and also during short circuit conditions. The battery pack is designed with series and parallel connected cells of 3.7v to produce 12v. The charging and releasing levels of the battery pack is indicated by interfacing the Arduino microcontroller. The entire equipment is placed in a fiber glass case (looks like aquarium) in order to protect the battery from external hazards to design an efficient Lithium-ion battery by using Battery Management System (BMS). We give the supply to the battery from solar panel and in the absence of this, from a regular AC supply.

scholarly journals Scheduled Pre-Heating of Li-Ion Battery Packs for Balanced Temperature and State-of-Charge Distribution

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2212
Author(s):  
Hien Vu ◽  
Donghwa Shin
Keyword(s):  
Thermal Characteristics ◽  
Lithium Ion ◽  
State Of Charge ◽  
Battery Pack ◽  
Li Ion Battery ◽  
Internal Heating ◽  
Heating Efficiency ◽  
Battery Packs ◽  
Unit Cells ◽  
Li Ion

Lithium-ion batteries exhibit significant performance degradation such as power/energy capacity loss and life cycle reduction in low-temperature conditions. Hence, the Li-ion battery pack is heated before usage to enhance its performance and lifetime. Recently, many internal heating methods have been proposed to provide fast and efficient pre-heating. However, the proposed methods only consider a combination of unit cells while the internal heating should be implemented for multiple groups within a battery pack. In this study, we investigated the possibility of timing control to simultaneously obtain balanced temperature and state of charge (SOC) between each cell by considering geometrical and thermal characteristics of the battery pack. The proposed method schedules the order and timing of the charge/discharge period for geometrical groups in a battery pack during internal pre-heating. We performed a pack-level simulation with realistic electro-thermal parameters of the unit battery cells by using the mutual pulse heating strategy for multi-layer geometry to acquire the highest heating efficiency. The simulation results for heating from −30 ∘ C to 10 ∘ C indicated that a balanced temperature-SOC status can be achieved via the proposed method. The temperature difference can be decreased to 0.38 ∘ C and 0.19% of the SOC difference in a heating range of 40 ∘ C with only a maximum SOC loss of 2.71% at the end of pre-heating.

Modeling of Multi-Cell Lithium-Ion Battery Packs for Electric Vehicles Considering Effects of Manufacturing Processes

2013 ◽  
Author(s):  
Chongye Wang ◽  
Yong Wang ◽  
Lin Li ◽  
Hua Shao ◽  
Changxu Wu
Keyword(s):  
Carbon Dioxide Emissions ◽  
Single Cells ◽  
Chemical Properties ◽  
Lithium Ion ◽  
Manufacturing Processes ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Battery Packs ◽  
Li Ion ◽  
Multiple Cells

Electric vehicle (EV) technologies have received great attention due to the potential contributions to relieving the energy dependence on petroleum and reducing carbon dioxide emissions. The advancement of EV technologies greatly relies on the development of battery technologies. Lithium-ion (Li-ion) batteries have recently become the main choice as the power source for major EV manufacturers. Previous research on EV Li-ion batteries is mainly focused on materials and chemical properties of single cells, while the effects of manufacturing processes on the performance of entire battery packs have almost been neglected. In practice, EV batteries are used in packs containing multiple cells, which may not be ideally manufactured. This research proposes a novel modeling method for analyzing the effects of manufacturing processes on the dynamics of EV Li-ion battery packs. The method will help engineers gain a deeper understanding of the roles of manufacturing processes in improving EV Li-ion battery performance.

scholarly journals Investigation of Individual Cells Replacement Concept in Lithium-Ion Battery Packs with Analysis on Economic Feasibility and Pack Design Requirements

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2263
Author(s):  
Manh-Kien Tran ◽  
Carlo Cunanan ◽  
Satyam Panchal ◽  
Roydon Fraser ◽  
Michael Fowler
Keyword(s):  
Negative Impact ◽  
Life Stage ◽  
Lithium Ion ◽  
Economic Feasibility ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Cell Replacement ◽  
Replacement Method ◽  
Battery Packs ◽  
Li Ion

The optimization of lithium-ion (Li-ion) battery pack usage has become essential due to the increasing demand for Li-ion batteries. Since degradation in Li-ion batteries is inevitable, there has been some effort recently on research to maximize the utilization of Li-ion battery cells in the pack. Some promising concepts include reconfigurable battery packs and cell replacement to limit the negative impact of early-degraded cells on the entire pack. This paper used a simulation framework, based on a cell voltage model and a degradation model, to study the feasibility and benefits of the cell replacement concept. The simulation conducted in MATLAB involves generating and varying Li-ion cells in the packs stochastically and simulating the life of the cells as well as the packs until they reach their end-of-life stage. It was found that the cell replacement method can increase the total number of cycles of the battery packs, effectively prolonging the lifespan of the packs. It is also determined that this approach can be more economically beneficial than the current approach of simple pack replacement. For the cell replacement concept to be practical, two main design criteria should be satisfied including individual cell monitoring and easy accessibility to cells at failure stage.

scholarly journals A Dynamic Battery State-of-Health Forecasting Model for Electric Trucks: Li-Ion Batteries Case-Study

2020 ◽  
Author(s):  
Matti Huotari ◽  
Shashank Arora ◽  
Avleen Malhi ◽  
Kary Främling
Keyword(s):  
Supervised Learning ◽  
Arima Model ◽  
Forecast Accuracy ◽  
Lithium Ion ◽  
Maintenance Cost ◽  
State Of Health ◽  
Li Ion Batteries ◽  
Data Set ◽  
Battery Packs ◽  
Li Ion

Abstract It is of extreme importance to monitor and manage the battery health to enhance the performance and decrease the maintenance cost of operating electric vehicles. This paper concerns the machine-learning-enabled state-of-health (SoH) prognosis for Li-ion batteries in electric trucks, where they are used as energy sources. The paper proposes methods to calculate SoH and cycle life for the battery packs. We propose autoregressive integrated modeling average (ARIMA) and supervised learning (bagging with decision tree as the base estimator; BAG) for forecasting the battery SoH in order to maximize the battery availability for forklift operations. As the use of data-driven methods for battery prognostics is increasing, we demonstrate the capabilities of ARIMA and under circumstances when there is little prior information available about the batteries. For this work, we had a unique data set of 31 lithium-ion battery packs from forklifts in commercial operations. On the one hand, results indicate that the developed ARIMA model provided relevant tools to analyze the data from several batteries. On the other hand, BAG model results suggest that the developed supervised learning model using decision trees as base estimator yields better forecast accuracy in the presence of large variation in data for one battery.

scholarly journals Calibration Optimization Methodology for Lithium-Ion Battery Pack Model for Electric Vehicles in Mining Applications

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3532 ◽  
Author(s):  
Majid Astaneh ◽  
Jelena Andric ◽  
Lennart Löfdahl ◽  
Dario Maggiolo ◽  
Peter Stopp ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Large Scale ◽  
Lithium Ion ◽  
Operating Conditions ◽  
Battery Pack ◽  
Li Ion Battery ◽  
Battery Packs ◽  
Li Ion ◽  
Optimization Methodology ◽  
Battery Cells

Large-scale introduction of electric vehicles (EVs) to the market sets outstanding requirements for battery performance to extend vehicle driving range, prolong battery service life, and reduce battery costs. There is a growing need to accurately and robustly model the performance of both individual cells and their aggregated behavior when integrated into battery packs. This paper presents a novel methodology for Lithium-ion (Li-ion) battery pack simulations under actual operating conditions of an electric mining vehicle. The validated electrochemical-thermal models of Li-ion battery cells are scaled up into battery modules to emulate cell-to-cell variations within the battery pack while considering the random variability of battery cells, as well as electrical topology and thermal management of the pack. The performance of the battery pack model is evaluated using transient experimental data for the pack operating conditions within the mining environment. The simulation results show that the relative root mean square error for the voltage prediction is 0.7–1.7% and for the battery pack temperature 2–12%. The proposed methodology is general and it can be applied to other battery chemistries and electric vehicle types to perform multi-objective optimization to predict the performance of large battery packs.

Electrochemical-Thermal Modeling of Li-Ion Battery Packs

2014 ◽  
Author(s):  
Guodong Fan ◽  
Ke Pan ◽  
Alexander Bartlett ◽  
Marcello Canova ◽  
Giorgio Rizzoni
Keyword(s):  
Cell Model ◽  
Lithium Ion ◽  
Battery Pack ◽  
Battery Management ◽  
Li Ion Battery ◽  
Thermal Boundary Conditions ◽  
Battery Packs ◽  
Li Ion ◽  
Electrical Connections ◽  
The Impact

Lithium-ion batteries for automotive applications are subject to aging with usage and environmental conditions, leading to the reduction of the performance, reliability and life span of the battery pack. To this extent, the ability of simulating the dynamic behavior of a battery pack using high-fidelity electrochemical and thermal models could provide very useful information for the design of Battery Management Systems (BMS). For instance such models could be used to predict the impact of cell-to-cell variations in the electrical and thermal properties on the overall performance of the pack, as well as on the propagation of degradation from one cell to another. This paper presents a method for fast simulation of an integrated electrochemical-thermal battery pack model based on first-principles. First, a coupled electrochemical and thermal model is developed for a single cell, based upon the data of a composite LiNi1/3Mn1/3Co1/3O2 – LiMn2O4 (LMO-NMC) Li-ion battery, and validated on experimental data. Then, the cell model is extended to a reconfigurable and parametric model of a complete battery pack. The proposed modeling approach is completely general and applicable to characterize any pack topology, varying electrical connections and thermal boundary conditions. Finally, simulation results are shown to illustrate the effects of parameter variability on the pack performance.

Overcharge protection of lithium-ion batteries with phenothiazine redox shuttles

2021 ◽  
Author(s):  
Susan A. Odom
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Li Ion Batteries ◽  
Phenothiazine Derivatives ◽  
Redox Shuttles ◽  
Li Ion ◽  
Overcharge Protection

Overcharge protection of Li-ion batteries with a variety of phenothiazine derivatives.

scholarly journals Parameter optimization and yield prediction of cathode coating separation process for direct recycling of end-of-life lithium-ion batteries

RSC Advances ◽  
2021 ◽  
Vol 11 (39) ◽  
pp. 24132-24136
Author(s):  
Liurui Li ◽  
Tairan Yang ◽  
Zheng Li
Keyword(s):  
Lithium Ion Batteries ◽  
End Of Life ◽  
Lithium Ion ◽  
Separation Process ◽  
Yield Prediction ◽  
Li Ion Batteries ◽  
Treatment Efficiency ◽  
Control Factors ◽  
Li Ion ◽  
Pre Treatment

The pre-treatment efficiency of the direct recycling strategy in recovering end-of-life Li-ion batteries is predicted with levels of control factors.

Sign in / Sign up

Export Citation Format

Share Document