scholarly journals Electric Enabler

2017 ◽  
Vol 139 (12) ◽  
pp. 39-39
Author(s):  
John Kosowatz ◽  
Thomas Romer
Keyword(s):  
Production Process ◽  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Battery Pack ◽  
Automated System ◽  
Price Point ◽  
Battery Packs ◽  
Full Capacity ◽  
Safety Systems

This article explains how Tesla batteries are making electric vehicles (EVs) affordable for customers. Tesla’s battery revolution began when CEO Elon Musk declared that it would sell a mass-market EV for just $35,000. To produce battery packs cheaply enough to reach that price point, Tesla reengineered not only the production process, but also the factory in which the batteries are made. The Reno, Nev., Gigafactory is not yet operating at full capacity, but it is expected to produce 35 GW per year of lithium-ion batteries, about double the present-day global production. Tesla partnered with Panasonic to revamp the production process, and ended up redesigning the chemistry of the battery itself. The standard “18-650” cell format used thousands of less-expensive commodity cells, similar to lithium-ion batteries used in laptop computers. Tesla replaced individual safety systems built into each cell with an inexpensive fireproof system for the entire battery pack. Now, they have begun producing the new “2170” cell, which delivers higher density through an automated system developed with Panasonic to further reduce costs.

Lithium-Ion Battery Packs Formation With Improved Electrochemical Performance for Electric Vehicles: Experimental and Clustering Analysis

2019 ◽  
Vol 16 (2) ◽  
Author(s):  
Liu Yun ◽  
Jayne Sandoval ◽  
Jian Zhang ◽  
Liang Gao ◽  
Akhil Garg ◽  
...  
Keyword(s):  
Electrochemical Performance ◽  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Clustering Analysis ◽  
Lithium Ion ◽  
Internal Resistance ◽  
Battery Pack ◽  
Self Organizing Map ◽  
Battery Packs

With the increase of production of electrical vehicles (EVs) and battery packs, lithium ion batteries inconsistency problem has drawn much attention. Lithium ion battery imbalance phenomenon exists during three different stages of life cycle. First stage is premanufacturing of battery pack i.e., during the design, the cells of similar performance need to be clustered to improve the performance of pack. Second is during the use of battery pack in EVs, batteries equalization is necessary. In the third stage, clustering of spent lithium ion batteries for reuse is also an important problem because of the great recycling challenge of lithium batteries. In this work, several clustering and equalization methods are compared and summarized for different stages. The methods are divided into the traditional methods and intelligent methods. The work also proposes experimental combined clustering analysis for new lithium-ion battery packs formation with improved electrochemical performance for electric vehicles. Experiments were conducted by dismantling of pack and measurement of capacity, voltage, and internal resistance data. Clustering analysis based on self-organizing map (SOM) neural networks is then applied on the measured data to form clusters of battery packs. The validation results conclude that the battery packs formed from the clustering analysis have higher electrochemical performance than randomly selected ones. In addition, a comprehensive discussion was carried out.

scholarly journals SOH Estimation of Lithium-Ion Battery Pack Based on Integrated State Information from Cells

2020 ◽  
Vol 10 (19) ◽  
pp. 6637
Author(s):  
Xiaohong Wang ◽  
Wenhui Fan ◽  
Shixiang Li ◽  
Xinjun Li ◽  
Lizhi Wang
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Battery Pack ◽  
Accurate Estimation ◽  
The Novel ◽  
State Information ◽  
Battery Packs ◽  
New Energy ◽  
Interactive Relationship ◽  
Novel Method

Accompanied by the development of new energy resources, lithium-ion batteries have been used widely in various fields. Due to the significant influence of system performance, much attention has been paid to the accurate estimation and prediction about health status of lithium-ion batteries. In a battery pack, the structure connection causes sophisticated interaction between cells, or between the cells and the pack. Therefore, the degradation of any cell is the result of the deterioration of conjoint cells, and a rapid degradation speed for any individual cell can lead to the accelerated degradation of others beyond expectation, which is one of the primary reasons why the State of Health and life cannot be calculated precisely. To solve this problem, a novel method based on integrated state information from cells has been proposed to estimate status of packs, considering about the degradation effect that cells contribute to the corresponding pack. Using this method, the interactive relationship was described in the form of a neural network in order to mine the effect from the inter-degradation between cells. It was proven that the novel method had better performance than a method based only on the degradation indicators from battery packs.

scholarly journals System-Level Modeling and Thermal Simulations of Large Battery Packs for Electric Trucks

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4796
Author(s):  
Anandh Ramesh Ramesh Babu ◽  
Jelena Andric ◽  
Blago Minovski ◽  
Simone Sebben
Keyword(s):  
Electric Vehicles ◽  
Lithium Ion ◽  
Operating Conditions ◽  
Thermal Interface Material ◽  
Battery Pack ◽  
System Level ◽  
Modeling Framework ◽  
System Level Modeling ◽  
Battery Packs ◽  
Thermal Simulations

Electromobility has gained significance over recent years and the requirements on the performance and efficiency of electric vehicles are growing. Lithium-ion batteries are the primary source of energy in electric vehicles and their performance is highly dependent on the operating temperature. There is a compelling need to create a robust modeling framework to drive the design of vehicle batteries in the ever-competitive market. This paper presents a system-level modeling methodology for thermal simulations of large battery packs for electric trucks under real-world operating conditions. The battery pack was developed in GT-SUITE, where module-to-module discretization was performed to study the thermal behavior and temperature distribution within the pack. The heat generated from each module was estimated using Bernardi’s expression and the pack model was calibrated for thermal interface material properties under a heat-up test. The model evaluation was performed for four charging/discharging and cooling scenarios typical for truck operations. The results show that the model accurately predicts the average pack temperature, the outlet coolant temperature and the state of charge of the battery pack. The methodology developed can be integrated with the powertrain and passenger cabin cooling systems to study complete vehicle thermal management and/or analyze different battery design choices.

Investigation of the Temperature Distribution in the Lithium-Ion Battery for Pure Electric Vehicles

2012 ◽  
Vol 187 ◽  
pp. 324-328 ◽  
Author(s):  
Liang Zheng ◽  
Bin Wu ◽  
Guo Qing Xu
Keyword(s):  
Finite Element ◽  
Heat Conduction ◽  
Temperature Distribution ◽  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Heat Conduction Equation ◽  
Lithium Ion ◽  
Battery Pack ◽  
Excellent Performance ◽  
Finite Element Formulations

Lithium-ion batteries have more excellent performance than other types of batteries, thus it is widely used in electric vehicles. Using batteries as the main power supplier does inevitably generate a lot of heat which not only deteriorates the batteries, but endangers the safety of the vehicle. In this paper, the temperature distribution of the batteries in the pure electric vehicles was investigated to minimize the heat generated in the batteries. The finite element formulations of the 3-D heat conduction equation of the battery were established for both steady and transient states. Then the finite element simulations were developed to investigate and optimize the temperature distribution of the battery and the battery pack. A parametric study was completed such that the heat generated in the battery pack can be minimized.

scholarly journals Adopting a Conversion Design Approach to Maximize the Energy Density of Battery Packs in Electric Vehicles

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1939
Author(s):  
Erika Pierri ◽  
Valentina Cirillo ◽  
Thomas Vietor ◽  
Marco Sorrentino
Keyword(s):  
Energy Density ◽  
Electric Vehicles ◽  
Design Procedure ◽  
Lithium Ion ◽  
High Energy ◽  
Battery Pack ◽  
High Energy Density ◽  
Installation Space ◽  
Battery Electric Vehicles ◽  
Battery Packs

Innovative vehicle concepts have been developed in the past years in the automotive sector, including alternative drive systems such as hybrid and battery electric vehicles, so as to meet the environmental targets and cope with the increasingly stringent emissions regulations. The preferred hybridizing technology is lithium-ion battery, thanks to its high energy density. The optimal integration of battery packs in the vehicle is a challenging task when designing e-mobility concepts. Therefore, this work proposes a conceptual design procedure aimed at optimizing the sizing of hybrid and battery electric vehicles. Particularly, the influence of the cell type, physical disposition and arrangement of the electrical devices is accounted for within a conversion design framework. The optimization is focused on the trade-off between the battery pack capacity and weight. After introducing the main features of electric traction systems and their challenges compared to conventional ones, the relevant design properties of electric vehicles are analyzed. A detailed strategy, encompassing the selection of battery format and technology, battery pack design and final assessment of the proposed set-up, is presented and implemented in an exemplary application, assuming an existing commercial vehicle as the reference starting layout. Prismatic, cylindrical and pouch cells are configured aiming at achieving installed battery energy as close as possible to the reference one, while meeting the original installation space constraint. The best resulting configuration, which also guarantees similar peak power performance of the reference battery-pack, allows reducing the mass of the storage system down to 70% of its starting value.

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.

scholarly journals Reliability Modeling Method for Lithium-ion Battery Packs Considering the Dependency of Cell Degradations Based on a Regression Model and Copulas

Materials ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1054 ◽  
Author(s):  
Lizhi Wang ◽  
Yusheng Sun ◽  
Xiaohong Wang ◽  
Zhuo Wang ◽  
Xuejiao Zhao
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Electric Drive ◽  
Large Scale ◽  
Lithium Ion ◽  
Degradation Process ◽  
Battery Pack ◽  
Reliability Model ◽  
Dependent Relationship ◽  
Battery Packs

Lithium-ion batteries are widely used as basic power supplies and storage units for large-scale electric drive products such as electric vehicles. Their reliability is directly related to the life and safe operation of the electric drive products. In fact, the cells have a dependent relationship with the degradation process and they affect the degradation rate of the entire battery pack, thereby affecting its reliability. At present, most research focuses on the reliability of battery packs and assumes that their cells are independent of each other, which may cause the reliability of the evaluation to deviate greatly from the actual level. In order to accurately assess the reliability of lithium-ion batteries, it is necessary to build a reliability model considering the dependency among cells for the overall degradation of lithium-ion battery packs. Therefore, in this study, based on a lithium-ion battery degradation test, the Wiener process is used to analyze the reliability of four basic configurations of lithium-ion battery packs. According to the degradation data of the battery packs, the Copula function is used to quantitatively describe the dependent relationship in the degradation process of a single battery, and the quantitative dependent relationship is combined with the reliability model to form a new reliability model. Finally, taking the battery system of Tesla S as an example, a feasible optimization method for battery pack design is provided based on the model constructed in this work.

scholarly journals Advances of 2nd Life Applications for Lithium Ion Batteries from Electric Vehicles Based on Energy Demand

2021 ◽  
Vol 13 (10) ◽  
pp. 5726
Author(s):  
Aleksandra Wewer ◽  
Pinar Bilge ◽  
Franz Dietrich
Keyword(s):  
Life Cycle ◽  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Energy Demand ◽  
Lithium Ion ◽  
Life Cycles ◽  
Future Research ◽  
Research Areas ◽  
Study Results ◽  
The Impact

Electromobility is a new approach to the reduction of CO2 emissions and the deceleration of global warming. Its environmental impacts are often compared to traditional mobility solutions based on gasoline or diesel engines. The comparison pertains mostly to the single life cycle of a battery. The impact of multiple life cycles remains an important, and yet unanswered, question. The aim of this paper is to demonstrate advances of 2nd life applications for lithium ion batteries from electric vehicles based on their energy demand. Therefore, it highlights the limitations of a conventional life cycle analysis (LCA) and presents a supplementary method of analysis by providing the design and results of a meta study on the environmental impact of lithium ion batteries. The study focuses on energy demand, and investigates its total impact for different cases considering 2nd life applications such as (C1) material recycling, (C2) repurposing and (C3) reuse. Required reprocessing methods such as remanufacturing of batteries lie at the basis of these 2nd life applications. Batteries are used in their 2nd lives for stationary energy storage (C2, repurpose) and electric vehicles (C3, reuse). The study results confirm that both of these 2nd life applications require less energy than the recycling of batteries at the end of their first life and the production of new batteries. The paper concludes by identifying future research areas in order to generate precise forecasts for 2nd life applications and their industrial dissemination.

scholarly journals Effect of dynamic loads and vibrations on lithium-ion batteries

2021 ◽  
pp. 146134842110081
Author(s):  
Xia Hua ◽  
Alan Thomas
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Experimental Studies ◽  
Lithium Ion ◽  
Battery Pack ◽  
Dynamic Loads ◽  
Electrical Performance ◽  
Mechanical Degradation ◽  
Energy Storage Devices ◽  
Battery Cell

Lithium-ion batteries are being increasingly used as the main energy storage devices in modern mobile applications, including modern spacecrafts, satellites, and electric vehicles, in which consistent and severe vibrations exist. As the lithium-ion battery market share grows, so must our understanding of the effect of mechanical vibrations and shocks on the electrical performance and mechanical properties of such batteries. Only a few recent studies investigated the effect of vibrations on the degradation and fatigue of battery cell materials as well as the effect of vibrations on the battery pack structure. This review focused on the recent progress in determining the effect of dynamic loads and vibrations on lithium-ion batteries to advance the understanding of lithium-ion battery systems. Theoretical, computational, and experimental studies conducted in both academia and industry in the past few years are reviewed herein. Although the effect of dynamic loads and random vibrations on the mechanical behavior of battery pack structures has been investigated and the correlation between vibration and the battery cell electrical performance has been determined to support the development of more robust electrical systems, it is still necessary to clarify the mechanical degradation mechanisms that affect the electrical performance and safety of battery cells.

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