scholarly journals Innovative Modeling Approach for Li-Ion Battery Packs Considering Intrinsic Cell Unbalances and Packaging Elements

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 356 ◽  
Author(s):  
Sung-Tae Ko ◽  
Jaehyung Lee ◽  
Jung-Hoon Ahn ◽  
Byoung Kuk Lee

In this paper, an innovative modeling approach for Li-ion battery packs is proposed by considering intrinsic cell unbalances and packaging elements. The proposed modeling method shows that the accurate battery pack model can be achieved if the overall influences of intrinsic cell unbalances and packaging elements are taken account. Concurrently, the proposed method takes a practical model structure, resulting in the reduction of computational burden in a battery management system. Furthermore, because the proposed method utilizes cell information without a manufactured battery pack, it can be helpful to design optimal battery packs. The proposed method is verified through simulation and experimental results of the Li-ion battery pack along with the battery cycler. In three test profiles, the mean absolute percentage errors and root mean square errors of the proposed pack model do not exceed 0.5% and 0.07 V, respectively.

2020 ◽  
Author(s):  
Iffandya Popy Wulandari ◽  
Min-Chun Pan

Abstract As one pioneer means for energy storage, Li-ion battery packs have a complex and critical issue about degradation monitoring and remaining useful life estimation. It induces challenges on condition characterization of Li-ion battery packs such as internal resistance (IR). The IR is an essential parameter of a Li-ion battery pack, relating to the energy efficiency, power performance, degradation, and physical life of the li-ion battery pack. This study aims to obtain reliable IR through applying an evaluation test that acquires data such as voltage, current, and temperature provided by the battery management system (BMS). Additionally, this paper proposes an approach to predict the degradation of Li-ion battery pack using support vector regression (SVR) with RBF kernel. The modeling approach using the relationship between internal resistance, different SOC levels 20%–100%, and cycle at the beginning of life 1 cycle until cycle 500. The data-driven method is used here to achieve battery life prediction.based on internal resistance behavior in every period using supervised machine learning, SVR. Our experiment result shows that the internal resistance was increasing non-linear, approximately 0.24%, and it happened if the cycle rise until 500 cycles. Besides, using SVR algorithm, the quality of the fitting was evaluated using coefficient determination R2, and the score is 0.96. In the proposed modeling process of the battery pack, the value of MSE is 0.000035.


The green energy evolution initiated the use of electric and hybrid electric vehicles at present on roads. These vehicles extensively use different types of batteries and among them lithium ion batteries are prominent. The Li-ion battery pack constitutes number of Li-ion battery cells connected in series and parallel configuration. This battery bank needs a suitable battery management system for its efficient operation. This paper presents a novel battery management system to monitor and control the battery current, voltage, state of charge and most importantly the cell temperature. The detail BMS scheme for Li-ion battery pack is presented and simulation is carried out to validate its performance with a driving cycle of electric car.


2014 ◽  
Vol 663 ◽  
pp. 504-509
Author(s):  
Yushaizad Yusof ◽  
Mohd Faiz Md. Adnan ◽  
Ralf Guenther ◽  
Mohd Hairi Mohd Zaman ◽  
Ahmad Asrul Ibrahim ◽  
...  

This paper presents the charging process procedure of Li-ion Battery pack for electric vehicle, which is implemented based on constant current and constant voltage (CC-CV) mode. All the informations regarding battery voltage level, state of charge (SOC) during charging and discharging processes, and battery temperature, is displayed on computer via battery management system (BMS). During the charging process, the BMS monitors the voltage balancing in Li-ion battery pack, as well as the cells voltage in each modules. The voltage difference between the highest voltage cell and the lowest voltage cell is very small, which validates the voltage stability and balance in battery pack during the charging and discharging processes.


2019 ◽  
Author(s):  
Mehrdad Zandigohar ◽  
Nima Lotfi

Abstract Li-ion batteries have gained increased popularity in the past few decades as the main source in various mobile and stationary energy storage applications. Battery management system design, especially fault diagnosis, however, is still a challenge regarding Li-ion batteries. Traditional Li-ion BMSs rely on measurements from current, voltage, and temperature sensors sparsely located throughout the battery pack. Such a BMS is not capable of predicting battery behavior under various operating conditions; moreover, it cannot account for internal discrepancies among battery cells, incipient faults, the distributed nature of battery parameters and states, and the propagation effects inside a battery pack. Although majority of these effects have already been observed and reported, they are either studied in electrochemistry laboratories using in-situ techniques and detailed theoretical analysis or in practical manufacturing settings by engineers and technicians, which are typically considered proprietary information. The aim of this paper is to bridge the gap between these two domains. In other words, a detailed electrochemical/thermal simulation of a Li-ion battery cell under healthy and faulty conditions is performed to provide a better understanding of the exact spatial requirements for an efficient and reliable thermal management system for Li-ion batteries. The results of this study are specifically of great importance for battery fault detection and identification, mainly due to the recent advancements in distributed sensing technologies such as fiber optics.


Author(s):  
Guodong Fan ◽  
Ke Pan ◽  
Alexander Bartlett ◽  
Marcello Canova ◽  
Giorgio Rizzoni

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.


Author(s):  
Puspita Ningrum ◽  
Novie Ayub Windarko ◽  
Suhariningsih Suhariningsih

Abstract— Battery is one of the important components in the development of renewable energy technology. This paper presents a method for estimating the State of Charge (SoC) for a 4Ah Li-ion battery. State of Charge (SoC) is the status of the capacity in the battery in the form of a percentage which makes it easier to monitor the battery during use. Coulomb calculations are widely used, but this method still contains errors during integration. In this paper, SoC measurement using Open Circuit Voltage Compensation is used for the determination of the initial SoC, so that the initial SoC reading is more precise, because if the initial SoC reading only uses a voltage sensor, the initial SoC reading is less precise which affects the next n second SoC reading. In this paper, we present a battery management system design or commonly known as BMS (Battery Management System) which focuses on the monitoring function. BMS uses a voltage sensor in the form of a voltage divider circuit and an ACS 712 current sensor to send information about the battery condition to the microcontroller as the control center. Besides, BMS is equipped with a protection relay to protect the battery. The estimation results of the 12volt 4Ah Li-ion battery SoC with the actual reading show an error of less than 1%.Keywords—Battery Management System, Modified Coulomb Counting, State of Charge.


2021 ◽  
Vol 2089 (1) ◽  
pp. 012017
Author(s):  
Ramu Bhukya ◽  
Praveen Kumar Nalli ◽  
Kalyan Sagar Kadali ◽  
Mahendra Chand Bade

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.


Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2212
Author(s):  
Hien Vu ◽  
Donghwa Shin

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.


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