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

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.

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Electric Enabler

Mechanical Engineering ◽

10.1115/1.2017-dec-6 ◽

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.

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

Journal of Electrochemical Energy Conversion and Storage ◽

10.1115/1.4042093 ◽

2019 ◽

Vol 16 (2) ◽

Cited By ~ 9

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.

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A Novel Method for Judging Health/Degradation of Lithium-Ion Batteries

MRS Proceedings ◽

10.1557/opl.2014.671 ◽

2014 ◽

Vol 1681 ◽

Cited By ~ 1

Author(s):

Futao Kaneko ◽

Takahiro Kawakami ◽

Akira Baba ◽

Kazunari Shinbo ◽

Keizo Kato ◽

...

Keyword(s):

Lithium Ion Batteries ◽

Real Time ◽

Lithium Ion ◽

Effective Area ◽

Differential Capacitance ◽

The State ◽

The Novel ◽

Voltage Range ◽

The Real ◽

Novel Method

ABSTRACTA novel and fundamental method was reported to judge states of lithium ion batteries (LIBs) using the capacitance and the voltage of the cells that were estimated from the real-time currents and voltage characteristics of the cells. We measured the differential capacitance, that is, dQ/dV or delta Q/ delta V that is equal to the currents (I) divided by differential voltages (dV/dt) calculated from the current and the voltage characteristics of the cell during the charging/ discharging, where Q is the charge that flows through the cell, V is the voltage of the cell and t is time. It is thought that the capacitance decrease with the degradation of the cell because the effective area of the electrodes is decreasing due to formation of undesirable compounds. The differential capacitance in some specific voltage range for the LIBs was approximately directly proportional to the state of the degradation of the cell. Therefore, it is concluded that the novel method is very useful to judge the state of the LIBs.

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

Materials ◽

10.3390/ma12071054 ◽

2019 ◽

Vol 12 (7) ◽

pp. 1054 ◽

Cited By ~ 6

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.

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Synthesis and Electrochemical Characteristics of the Novel FeS2/VGCF Material for Lithium-Ion Batteries

Journal of Inorganic Materials ◽

10.3724/sp.j.1077.2013.13076 ◽

2013 ◽

Vol 28 (12) ◽

pp. 1291-1295 ◽

Cited By ~ 1

Author(s):

Ling LIU ◽

Zhong-Zhi YUAN ◽

Cai-Xia QIU ◽

Si-Jie Cheng ◽

Jin-Cheng LIU

Keyword(s):

Lithium Ion Batteries ◽

Lithium Ion ◽

Electrochemical Characteristics ◽

The Novel

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Effect of dynamic loads and vibrations on lithium-ion batteries

Journal of Low Frequency Noise Vibration and Active Control ◽

10.1177/14613484211008112 ◽

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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Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

Sustainability ◽

10.3390/su13094648 ◽

2021 ◽

Vol 13 (9) ◽

pp. 4648

Author(s):

Rana Muhammad Adnan ◽

Kulwinder Singh Parmar ◽

Salim Heddam ◽

Shamsuddin Shahid ◽

Ozgur Kisi

Keyword(s):

Clustering Algorithm ◽

Suspended Sediments ◽

Ecological Impacts ◽

Accurate Estimation ◽

Data Sets ◽

The Novel ◽

Neuro Fuzzy ◽

Adaptive Regression ◽

Novel Method ◽

Model Training

The accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs using antecedent discharge and sediment information. The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARS–KM). The proposed method’s efficacy is established by comparing its performance with the adaptive neuro-fuzzy system (ANFIS), MARS, and M5 tree (M5Tree) models in predicting SSs at two stations situated on the Yangtze River of China, according to the three assessment measurements, RMSE, MAE, and NSE. Two modeling scenarios are employed; data are divided into 50–50% for model training and testing in the first scenario, and the training and test data sets are swapped in the second scenario. In Guangyuan Station, the MARS–KM showed a performance improvement compared to ANFIS, MARS, and M5Tree methods in term of RMSE by 39%, 30%, and 18% in the first scenario and by 24%, 22%, and 8% in the second scenario, respectively, while the improvement in RMSE of ANFIS, MARS, and M5Tree was 34%, 26%, and 27% in the first scenario and 7%, 16%, and 6% in the second scenario, respectively, at Beibei Station. Additionally, the MARS–KM models provided much more satisfactory estimates using only discharge values as inputs.

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Self-Balancing by Design in Hybrid Electrochemical Battery Packs

Volume 2: Control and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems ◽

10.1115/dscc2018-9106 ◽

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.

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

10.32920/ryerson.14652222.v1 ◽

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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