Maintaining a Constant Charging Duration Independent of Battery Capacity for Battery Pack by Designing a Fast DC Charger

2019 ◽  
Vol 8 (3) ◽  
pp. 102-116
Author(s):  
Bassam Atieh ◽  
Mohammad Fouad Al-sammak
Keyword(s):  
Lower Cost ◽  
Lithium Ion ◽  
Power Source ◽  
Battery Pack ◽  
Simulation Environment ◽  
Electronic Components ◽  
Battery Capacity ◽  
Li Ion ◽  
Novel Strategy ◽  
Usage Flexibility

This article proposes a novel strategy for developing a new structure for a lithium-ion battery pack fast charger which aims to achieve fast DC charging, based on the topology of a boost converter. The proposed charger has been designed considering using fewer electronic components at lower cost. Varying initial charging percentage of the Li-ion cells has not been addressed in this article, an equal initial charging percentage of each Li-ion cell is assumed. Performance of the proposed structure of the charger has been tested using a simulation environment. This strategy has shown that this structure ensures scalability of this charger, while using the utility grid (220V, 50Hz) as a main power source for this charger has ensured practical usage flexibility. The results of this research are presented and discussed. These results have shown the outstanding performance and response of this charger.

Online Estimation of Lithium-Ion Battery Capacity Using Deep Convolutional Neural Networks

2018 ◽  
Author(s):  
Sheng Shen ◽  
M. K. Sadoughi ◽  
Xiangyi Chen ◽  
Mingyi Hong ◽  
Chao Hu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Lithium Ion ◽  
Relevance Vector Machine ◽  
Rechargeable Batteries ◽  
Li Ion Battery ◽  
Deep Convolutional Neural Networks ◽  
Battery Capacity ◽  
Li Ion

Over the past two decades, safety and reliability of lithium-ion (Li-ion) rechargeable batteries have been receiving a considerable amount of attention from both industry and academia. To guarantee safe and reliable operation of a Li-ion battery pack and build failure resilience in the pack, battery management systems (BMSs) should possess the capability to monitor, in real time, the state of health (SOH) of the individual cells in the pack. This paper presents a deep learning method, named deep convolutional neural networks, for cell-level SOH assessment based on the capacity, voltage, and current measurements during a charge cycle. The unique features of deep convolutional neural networks include the local connectivity and shared weights, which enable the model to estimate battery capacity accurately using the measurements during charge. To our knowledge, this is the first attempt to apply deep learning to online SOH assessment of Li-ion battery. 10-year daily cycling data from implantable Li-ion cells are used to verify the performance of the proposed method. Compared with traditional machine learning methods such as relevance vector machine and shallow neural networks, the proposed method is demonstrated to produce higher accuracy and robustness in capacity estimation.

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 Reliability Analysis of Different Cell Configurations of Lithium ion battery Pack

2019 ◽  
Vol 18 (2) ◽  
pp. 49-56
Author(s):  
Md. Nahian Al Subri Ivan ◽  
Sujit Devnath ◽  
Rethwan Faiz ◽  
Kazi Firoz Ahmed
Keyword(s):  
Mathematical Model ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Battery Pack ◽  
Li Ion Battery ◽  
Series Configuration ◽  
Li Ion ◽  
Parallel Module ◽  
The Mathematical Model ◽  
Parallel Series

To infer and predict the reliability of the remaining useful life of a lithium-ion (Li-ion) battery is very significant in the sectors associated with power source proficiency. As an energy source of electric vehicles (EV), Li-ion battery is getting attention due to its lighter weight and capability of storing higher energy. Problems with the reliability arises while li-ion batteries of higher voltages are required. As in this case several li-ion cells areconnected in series and failure of one cell may cause the failure of the whole battery pack. In this paper, Firstly, the capacity degradation of li-ion cells after each cycle is observed and secondly with the help of MATLAB 2016 a mathematical model is developed using Weibull Probability Distribution and Exponential Distribution to find the reliability of different types of cell configurations of a non-redundant li-ion battery pack. The mathematical model shows that the parallel-series configuration of cells is more reliable than the series configuration of cells. The mathematical model also shows that if the discharge rate (C-rate) remains constant; there could be an optimum number for increasing the cells in the parallel module of a parallel-series onfiguration of cells of a non-redundant li-ion battery pack; after which only increasing the number of cells in parallel module doesn’t increase the reliability of the whole battery pack significantly. 

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 Charge and Discharge Behaviour of Li-Ion Batteries at Various Temperatures Containing LiCoO2 Nanostructured Cathode Produced by CCSO

2015 ◽  
Vol 15 (4) ◽  
pp. 301 ◽  
Author(s):  
Y.Y. Mamyrbayeva ◽  
R.E. Beissenov ◽  
M.A. Hobosyan ◽  
S.E. Kumekov ◽  
K.S. Martirosyan
Keyword(s):  
Lithium Ion Battery ◽  
Active Material ◽  
Lithium Ion ◽  
Synthetic Approach ◽  
Capacity Fade ◽  
Li Ion Batteries ◽  
Material Loss ◽  
Method Performance ◽  
Battery Capacity ◽  
Li Ion

<p>There are technical barriers for penetration market requesting rechargeable lithium-ion battery packs for portable devices that operate in extreme hot and cold environments. Many portable electronics are used in very cold (-40 °C) environments, and many medical devices need batteries that operate at high temperatures. Conventional Li-ion batteries start to suffer as the temperature drops below 0 °C and the internal impedance of the battery  increases. Battery capacity also reduced during the higher/lower temperatures. The present work describes the laboratory made lithium ion battery behaviour features at different operation temperatures. The pouch-type battery was prepared by exploiting LiCoO<sub>2</sub> cathode material synthesized by novel synthetic approach referred as Carbon Combustion Synthesis of Oxides (CCSO). The main goal of this paper focuses on evaluation of the efficiency of positive electrode produced by CCSO method. Performance studies of battery showed that the capacity fade of pouch type battery increases with increase in temperature. The experimental results demonstrate the dramatic effects on cell self-heating upon electrochemical performance. The study involves an extensive analysis of discharge and charge characteristics of battery at each temperature following 30 cycles. After 10 cycles, the battery cycled at RT and 45 °C showed, the capacity fade of 20% and 25% respectively. The discharge capacity for the battery cycled at 25 °C was found to be higher when compared with the battery cycled at 0 °C and 45 °C. The capacity of the battery also decreases when cycling at low temperatures. It was important time to charge the battery was only 2.5 hours to obtain identical nominal capacity under the charging protocol. The decrease capability of battery cycled at high temperature can be explained with secondary active material loss dominating the other losses.</p>

Capacity Loss Estimation For Li-Ion Batteries Based On A Semi-Empirical Model

2021 ◽  
Author(s):  
Mohammed Rabah ◽  
Eero Immonen ◽  
Sajad Shahsavari ◽  
Mohammad-Hashem Haghbayan ◽  
Kirill Murashko ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Empirical Model ◽  
Lithium Ion ◽  
Average Error ◽  
Li Ion Batteries ◽  
Capacity Loss ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Li Ion ◽  
Semi Empirical

Understanding battery capacity degradation is instrumental for designing modern electric vehicles. In this paper, a Semi-Empirical Model for predicting the Capacity Loss of Lithium-ion batteries during Cycling and Calendar Aging is developed. In order to redict the Capacity Loss with a high accuracy, battery operation data from different test conditions and different Lithium-ion batteries chemistries were obtained from literature for parameter optimization (fitting). The obtained models were then compared to experimental data for validation. Our results show that the average error between the estimated Capacity Loss and measured Capacity Loss is less than 1.5% during Cycling Aging, and less than 2% during Calendar Aging. An electric mining dumper, with simulated duty cycle data, is considered as an application example.

scholarly journals Derating Guidelines for Lithium-Ion Batteries

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3295 ◽  
Author(s):  
Yongquan Sun ◽  
Saurabh Saxena ◽  
Michael Pecht
Keyword(s):  
Lithium Ion ◽  
Stress Factors ◽  
Li Ion Batteries ◽  
Degradation Data ◽  
Capacity Loss ◽  
Battery Capacity ◽  
Operational Life ◽  
Battery Degradation ◽  
Li Ion

Derating is widely applied to electronic components and products to ensure or extend their operational life for the targeted application. However, there are currently no derating guidelines for Li-ion batteries. This paper presents derating methodology and guidelines for Li-ion batteries using temperature, discharge C-rate, charge C-rate, charge cut-off current, charge cut-off voltage, and state of charge (SOC) stress factors to reduce the rate of capacity loss and extend battery calendar life and cycle life. Experimental battery degradation data from our testing and the literature have been reviewed to demonstrate the role of stress factors in battery degradation and derating for two widely used Li-ion batteries: graphite/LiCoO2 (LCO) and graphite/LiFePO4 (LFP). Derating factors have been computed based on the battery capacity loss to quantitatively evaluate the derating effects of the stress factors and identify the significant factors for battery derating.

scholarly journals Impacts of Driving Conditions on EV Battery Pack Life Cycle

2020 ◽  
Vol 11 (1) ◽  
pp. 17 ◽  
Author(s):  
Huijun Liu ◽  
Fenfang Chen ◽  
Yuxiang Tong ◽  
Zihang Wang ◽  
Xiaoli Yu ◽  
...  
Keyword(s):  
Life Cycle ◽  
Test Procedure ◽  
Lithium Ion ◽  
Driving Cycle ◽  
Battery Pack ◽  
Battery Life ◽  
Ambient Temperatures ◽  
Battery Capacity ◽  
Driving Conditions ◽  
The Impact

The aging of lithium-ion batteries (LIBs) is a crucial issue and must be investigated. The aging rate of LIBs depends not only on the material and electrochemical performance but also on the working conditions. In order to assess the impact of vehicle driving conditions, including the driving cycle, ambient temperature, charging mode, and trip distance on the battery life cycle, this paper first establishes an electric vehicle (EV) energy flow model to solve the operating parameters of the battery pack while working. Then, a powertrain test is carried out to verify the simulation model. Based on the simulated data under different conditions, the battery capacity fade process is estimated by using a semi-empirical aging model. The mileage (Ф) traveled by the vehicle before the end of life (EOL) of the battery pack is then calculated and taken as the evaluation index. The results indicate that the Ф is higher when the vehicle drives the Japanese chassis dynamometer test cycle JC08 than in the New European Driving Cycle (NEDC) and the Federal Test Procedure (FTP-75). The Ф will be dramatically reduced at both low and high ambient temperatures. Fast charging can increase the Ф at low ambient temperatures, whereas long trip driving can always increase Ф to varying degrees.

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.

scholarly journals A Comparative Study of Charging Voltage Curve Analysis and State of Health Estimation of Lithium-ion Batteries in Electric Vehicle

2019 ◽  
Vol 2 (4) ◽  
pp. 263-275 ◽  
Author(s):  
Xuebing Han ◽  
Xuning Feng ◽  
Minggao Ouyang ◽  
Languang Lu ◽  
Jianqiu Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Electrode Materials ◽  
Lithium Ion ◽  
Curve Analysis ◽  
Constant Current ◽  
Battery Management ◽  
State Of Health ◽  
Voltage Curve ◽  
Battery Capacity ◽  
Li Ion

AbstractLithium-ion (Li-ion) cells degrade after repeated cycling and the cell capacity fades while its resistance increases. Degradation of Li-ion cells is caused by a variety of physical and chemical mechanisms and it is strongly influenced by factors including the electrode materials used, the working conditions and the battery temperature. At present, charging voltage curve analysis methods are widely used in studies of battery characteristics and the constant current charging voltage curves can be used to analyze battery aging mechanisms and estimate a battery’s state of health (SOH) via methods such as incremental capacity (IC) analysis. In this paper, a method to fit and analyze the charging voltage curve based on a neural network is proposed and is compared to the existing point counting method and the polynomial curve fitting method. The neuron parameters of the trained neural network model are used to analyze the battery capacity relative to the phase change reactions that occur inside the batteries. This method is suitable for different types of batteries and could be used in battery management systems for online battery modeling, analysis and diagnosis.

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