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 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.

Development of a Two-Dimensional Thermal Model for Li-Ion Battery Pack With Experimental Validation

2019 ◽  
Vol 12 (2) ◽  
Author(s):  
Haoting Wang ◽  
Ning Liu ◽  
Lin Ma
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Experimental Validation ◽  
Computational Cost ◽  
Battery Pack ◽  
Two Dimensional ◽  
Li Ion Battery ◽  
New Approach ◽  
Battery Packs ◽  
Li Ion

Abstract This paper reports the development of a two-dimensional two states (2D2S) model for the analysis of thermal behaviors of Li-ion battery packs and its experimental validation. This development was motivated by the need to fill a niche in our current modeling capabilities: the need to analyze 2D temperature (T) distributions in large-scale battery packs in real time. Past models were predominately developed to either provide detailed T information with high computational cost or provide real-time analysis but only 1D lumped T information. However, the capability to model 2D T field in real time is desirable in many applications ranging from the optimal design of cooling strategies to onboard monitoring and control. Therefore, this work developed a new approach to provide this desired capability. The key innovations in our new approach involved modeling the whole battery pack as a complete thermal-fluid network and at the same time calculating only two states (surface and core T) for each cell. Modeling the whole pack as a complete network captured the interactions between cells and enabled the accurate resolution of the 2D T distribution. Limiting the calculation to only the surface and core T controlled the computational cost at a manageable level and rendered the model suitable for packs at large scale with many cells.

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.

scholarly journals A Review of State of Health Estimation of Energy Storage Systems: Challenges and Possible Solutions for Futuristic Applications of Li-Ion Battery Packs in Electric Vehicles

2019 ◽  
Vol 16 (4) ◽  
Author(s):  
Sudipta Bijoy Sarmah ◽  
Pankaj Kalita ◽  
Akhil Garg ◽  
Xiao-dong Niu ◽  
Xing-Wei Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Time Estimation ◽  
State Of Health ◽  
Li Ion Battery ◽  
Super Capacitor ◽  
Advantages And Disadvantages ◽  
Battery Packs ◽  
Li Ion

Lithium-ion (Li-ion) battery pack is vital for storage of energy produced from different sources and has been extensively used for various applications such as electric vehicles (EVs), watches, cookers, etc. For an efficient real-time monitoring and fault diagnosis of battery operated systems, it is important to have a quantified information on the state-of-health (SoH) of batteries. This paper conducts comprehensive literature studies on advancement, challenges, concerns, and futuristic aspects of models and methods for SoH estimation of batteries. Based on the studies, the methods and models for SoH estimation have been summarized systematically with their advantages and disadvantages in tabular format. The prime emphasis of this review was attributed toward the development of a hybridized method which computes SoH of batteries accurately in real-time and takes self-discharge into its account. At the end, the summary of research findings and the future directions of research such as nondestructive tests (NDT) for real-time estimation of battery SoH, finding residual SoH for the recycled batteries from battery packs, integration of mechanical aspects of battery with temperature, easy assembling–dissembling of battery packs, and hybridization of battery packs with photovoltaic and super capacitor are discussed.

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.

scholarly journals Characterization of the Compressive Load on a Lithium-Ion Battery for Electric Vehicle Application

Machines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 71
Author(s):  
Seyed Saeed Madani ◽  
Erik Schaltz ◽  
Søren Knudsen Kær
Keyword(s):  
Electrochemical Impedance Spectroscopy ◽  
Impedance Spectroscopy ◽  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Large Scale ◽  
Electrochemical Impedance ◽  
Applied Pressure ◽  
Lithium Ion ◽  
Battery Cells

Lithium-ion batteries are being implemented in different large-scale applications, including aerospace and electric vehicles. For these utilizations, it is essential to improve battery cells with a great life cycle because a battery substitute is costly. For their implementation in real applications, lithium-ion battery cells undergo extension during the course of discharging and charging. To avoid disconnection among battery pack ingredients and deformity during cycling, compacting force is exerted to battery packs in electric vehicles. This research used a mechanical design feature that can address these issues. This investigation exhibits a comprehensive description of the experimental setup that can be used for battery testing under pressure to consider lithium-ion batteries’ safety, which could be employed in electrified transportation. Besides, this investigation strives to demonstrate how exterior force affects a lithium-ion battery cell’s performance and behavior corresponding to static exterior force by monitoring the applied pressure at the dissimilar state of charge. Electrochemical impedance spectroscopy was used as the primary technique for this research. It was concluded that the profiles of the achieved spectrums from the experiments seem entirely dissimilar in comparison with the cases without external pressure. By employing electrochemical impedance spectroscopy, it was noticed that the pure ohmic resistance, which is related to ion transport resistance of the separator, could substantially result in the corresponding resistance increase.

A Methodological Approach for Supporting the Thermal Design of Li-Ion Battery for Customized Electric Vehicles

2014 ◽  
Author(s):  
Daniele Landi ◽  
Paolo Cicconi ◽  
Michele Germani
Keyword(s):  
Thermal Behavior ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Combustion Engine ◽  
Thermal Design ◽  
Battery Pack ◽  
Scale Production ◽  
Li Ion Battery ◽  
Suitable Alternative ◽  
Li Ion

An important issue in the mechanical industry is the reduction of the time to market, in order to meet quickly the customer needs. This goal is very important for SMEs that produce small lots of customized products. In the context of greenhouse gas emissions reduction, vehicles powered by electric motors seem to be the most suitable alternative to the traditional internal combustion engine vehicles. The market of customized electric vehicles is a niche market suitable for SMEs. Nowadays, the energy storage system of an electric vehicle powertrain consists of several Li-ion cells arranged in a container called battery pack. Particularly, the battery unit is considered as the most critical component in electric vehicle, because it impacts on performance and life cycle cost. Currently, the design of a battery pack mostly depends on the related market size. A longer design time is expected in the case of a large scale production. While a small customized production requires more agility and velocity in the design process. The proposed research focuses on a design methodology to support the designer in the evaluation of the battery thermal behavior. This work has been applied in the context of a customized small production. As test case, an urban electric light commercial vehicle has been analyzed. The designed battery layout has been evaluated and simulated using virtual prototyping tools. A cooling configuration has been analyzed and then prototyped in a physical vehicle. The virtual thermal behavior of a Li-ion battery has been validated at the test bench. The real operational conditions have been analyzed reproducing several ECE-15 driving cycles and many acceleration runs at different load values. Thermocouples have measured the temperature values during the physical experiments, in order to validate the analytical thermal profile evaluated with the proposed design approach.

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. 

Internal Resistance Based Assessment Model for the Degradation of Li-Ion Battery Pack

2020 ◽  
Author(s):  
Iffandya Popy Wulandari ◽  
Min-Chun Pan
Keyword(s):  
Internal Resistance ◽  
Remaining Useful Life ◽  
Assessment Model ◽  
Battery Pack ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Battery Management System ◽  
Li Ion Battery ◽  
Battery Packs ◽  
Li Ion

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.

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