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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D: Energy Storage and Application".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 27112

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Special Issue Editor

Dr. Simone Barcellona

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Guest Editor

Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
Interests: circuits and systems; theory and applications; power electronic converters and energy storage systems
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Special Issue Information

Dear Colleagues,

Batteries, among the various energy storage systems, are electrochemical storage devices that have always been attractive for both stationary and mobile applications. The former includes applications such as in uninterruptable power systems and storage systems integrated with renewable energy sources. The latter includes applications ranging from small mobile applications (such as smartphones, notebooks, tablets, etc.) to larger ones (such as electric vehicles and railway traction systems). Different kinds of technology have been developed through the years (lead–acid, nickel–cadmium, nickel–metal hydride, lithium ion, etc.), and other novel technologies (metal–air, quasi-solid state battery, all-solid state battery, etc.) are still under study. The most important features that these devices aim to have are high power, energy density, and efficiency in addition to long lifecycle. In particular, the latter can be increased by developing novel technologies in the construction of the batteries themselves and/or in controlling them to operate in their optimal working conditions. To achieve this, the modelling of batteries and the estimation of their parameters becomes a very important challenge. Battery modelling can be related to different aspects such as chemical, electrical, thermal, or aging factors or a combination of these. In turn, for almost all these aspects, the modelling can be based on physical, mathematical, or circuital approaches with different levels of accuracy and complexity. Parameter estimation can be divided into online, offline, and analytical approaches. According to these models and estimation techniques, it is possible to study, analyse, and predict the behaviour of single battery cells or whole battery packs with different aims. On the one hand, battery models can be used for analyses of the batteries themselves to improve their efficiency and lifecycle, to build battery management systems, or for sizing battery packs. On the other hand, the same models can be used to analyse the behaviour of entire systems in which the battery is one part. In the literature, it is possible to find many articles on these topics, although they often appear as being disconnected from each other. Furthermore, novel technologies and applications need to be more deeply studied.

In the light of above, the aim of this Special Issue is to collect both original research works as well as review articles on battery chemical, electric, thermal, and aging models, integrated battery models and their composition, battery parameter estimation methods, and novel applications and technologies of batteries.

Dr. Simone Barcellona
Guest Editor

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Keywords

energy storage systems
battery technologies
battery applications
battery modelling (chemical, electrical, thermal, aging models)
parameter estimation techniques

Published Papers (14 papers)

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Research

Jump to: Review

22 pages, 18104 KiB

Open AccessArticle

Battery State of Health Estimation Using the Sliding Interacting Multiple Model Strategy

by Richard Bustos, Stephen Andrew Gadsden, Mohammad Biglarbegian, Mohammad AlShabi and Shohel Mahmud

Energies 2024, 17(2), 536; https://doi.org/10.3390/en17020536 - 22 Jan 2024

Cited by 1 | Viewed by 650

Abstract

Due to their nonlinear behavior and the harsh environments to which batteries are subjected, they require a robust battery monitoring system (BMS) that accurately estimates their state of charge (SOC) and state of health (SOH) to ensure each battery’s safe operation. In this study, the interacting multiple model (IMM) algorithm is implemented in conjunction with an estimation strategy to accurately estimate the SOH and SOC of batteries under cycling conditions. The IMM allows for an adaptive mechanism to account for the decaying battery capacity while the battery is in use. The proposed strategy utilizes the sliding innovation filter (SIF) to estimate the SOC while the IMM serves as a process to update the parameter values of the battery model as the battery ages. The performance of the proposed strategy was tested using the well-known B005 battery dataset available at NASA’s Prognostic Data Repository. This strategy partitions the experimental dataset to build a database of different SOH models of the battery, allowing the IMM to select the most accurate representation of the battery’s current conditions while in operation, thus determining the current SOH of the battery. Future work in the area of battery retirement is also considered. Full article

(This article belongs to the Special Issue Battery Modelling, Applications, and Technology)

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14 pages, 4187 KiB

Open AccessArticle

State of Health Estimation Procedure for Lithium-Ion Batteries Using Partial Discharge Data and Support Vector Regression

by Emil Petkovski, Iacopo Marri, Loredana Cristaldi and Marco Faifer

Energies 2024, 17(1), 206; https://doi.org/10.3390/en17010206 - 30 Dec 2023

Viewed by 948

Abstract

Battery aging is a complex phenomenon, and precise state of health (SoH) monitoring is essential for effective battery management. This paper presents a data-driven method for SoH estimation based on support vector regression (SVR), utilizing features built from both full and partial discharge capacity curves, as well as battery temperature data. It provides an in-depth discussion of the novel features constructed from different voltage intervals. Moreover, three combinations of features were analyzed, demonstrating how their efficacy changes across different voltage ranges. Successful results were obtained using the full discharge capacity curves, built from the full interval of 2 to 3.4 V and achieving a mean R² value of 0.962 for the test set, thus showcasing the adequacy of the selected SVR strategy. Finally, the features constructed from the full voltage range were compared with ones built from 10 small voltage ranges. Similar success was observed, evidenced by a mean R² value ranging between 0.939 and 0.973 across different voltage ranges. This indicates the practical applicability of the developed models in real-world scenarios. The tuning and evaluation of the proposed models were carried out using a substantial dataset created by Toyota, consisting of 124 lithium iron phosphate batteries. Full article

(This article belongs to the Special Issue Battery Modelling, Applications, and Technology)

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14 pages, 10377 KiB

Open AccessArticle

Load Capacity of Nickel–Metal Hydride Battery and Proton-Exchange-Membrane Fuel Cells in the Fuel-Cell-Hybrid-Electric-Vehicle Powertrain

by Ireneusz Pielecha, Filip Szwajca and Kinga Skobiej

Energies 2023, 16(22), 7657; https://doi.org/10.3390/en16227657 - 19 Nov 2023

Viewed by 735

Abstract

This article investigates the impact of loading on the hybrid powertrain of the FCAT-30 model, equipped with a proton-exchange-membrane fuel cell (PEMFC) and a nickel–metal hydride (NiMH) battery. This study involves analyzing structural component performance based on voltage and current measurements of the fuel cell, battery, and powertrain. Tests conducted under different load conditions reveal significant differences in battery current and fuel-cell voltage, highlighting the crucial role of the battery in the powertrain. External loading induces cyclic operation of the fuel cell, generating peak power. The energy balance analysis demonstrates that, under no-load conditions, the vehicle consumes 37.3% of its energy from the fuel cell, with a total energy consumption of 3597 J. Under load, the energy from the battery is significantly utilized, resulting in a constant fuel-cell share of approximately 19%, regardless of the vehicle’s load. This study concludes that the battery predominantly drives the powertrain, with the fuel cell acting as a secondary energy source. These findings provide valuable insights into the power distribution and energy balance in the hybrid powertrain. Using a load driving profile reduced the fuel-cell-stack energy contribution by 6.85% relative to driving without an external load. Full article

(This article belongs to the Special Issue Battery Modelling, Applications, and Technology)

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17 pages, 31437 KiB

Open AccessArticle

3D Heterogeneous Model for Electrodes in Lithium-Ion Batteries to Study Interfacial Detachment of Active Material Particles and Carbon-Binder Domain

by Mohammadali Mirsalehian, Bahareh Vossoughi, Jörg Kaiser and Stefan Pischinger

Energies 2023, 16(21), 7391; https://doi.org/10.3390/en16217391 - 1 Nov 2023

Viewed by 1099

Abstract

Mechanics plays a crucial role in the performance and lifespan of lithium-ion battery (LIB) cells. Thus, it is important to address the interplay between electrochemistry and mechanics in LIBs, especially when aiming to enhance the energy density of electrodes. Accordingly, this work introduces a framework for a fully coupled electro-chemo-mechanical heterogeneous 3D model that allows resolving the inhomogeneities accompanied by electrochemical and mechanical responses of LIB electrodes during operation. The model is employed to numerically study the mechanical degradation of a nickel manganese cobalt (NMC) cathode electrode, assembled in a half-cell, upon cycling. As opposed to previous works, a virtual morphology for a high-energy electrode with low porosity is developed in this study, which comprises distinct domains of active material (AM) particles, the carbon-binder domain (CBD), and the pore domain to resemble real commercial electrodes. It is observed that the mechanical strain mismatch between irregularly and randomly positioned AM particles and the CBD might lead to local contact detachment. This interfacial gap, in combination with the diminishing contact strength over cell cycling, continuously deteriorates the electrode performance upon cycling by impedance rise and capacity drop. In agreement with previous experimental reports, the presented simulation results exhibit that the contact loss mostly takes place in the regions closer to the separator. Eventually, the resulting gradual capacity drop and change in impedance spectrum over cycling, as the consequence of interfacial gap formation, are discussed and indicated. Full article

(This article belongs to the Special Issue Battery Modelling, Applications, and Technology)

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14 pages, 1045 KiB

Open AccessArticle

Data Augmentation and Feature Selection for the Prediction of the State of Charge of Lithium-Ion Batteries Using Artificial Neural Networks

by Sebastian Pohlmann, Ali Mashayekh, Manuel Kuder, Antje Neve and Thomas Weyh

Energies 2023, 16(18), 6750; https://doi.org/10.3390/en16186750 - 21 Sep 2023

Cited by 1 | Viewed by 816

Abstract

Lithium-ion batteries are a key technology for the electrification of the transport sector and the corresponding move to renewable energy. It is vital to determine the condition of lithium-ion batteries at all times to optimize their operation. Because of the various loading conditions these batteries are subjected to and the complex structure of the electrochemical systems, it is not possible to directly measure their condition, including their state of charge. Instead, battery models are used to emulate their behavior. Data-driven models have become of increasing interest because they demonstrate high levels of accuracy with less development time; however, they are highly dependent on their database. To overcome this problem, in this paper, the use of a data augmentation method to improve the training of artificial neural networks is analyzed. A linear regression model, as well as a multilayer perceptron and a convolutional neural network, are trained with different amounts of artificial data to estimate the state of charge of a battery cell. All models are tested on real data to examine the applicability of the models in a real application. The lowest test error is obtained for the convolutional neural network, with a mean absolute error of 0.27%. The results highlight the potential of data-driven models and the potential to improve the training of these models using artificial data. Full article

(This article belongs to the Special Issue Battery Modelling, Applications, and Technology)

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24 pages, 2224 KiB

Open AccessArticle

A Model-Aware Comprehensive Tool for Battery Energy Storage System Sizing

by Matteo Spiller, Giuliano Rancilio, Filippo Bovera, Giacomo Gorni, Stefano Mandelli, Federico Bresciani and Marco Merlo

Energies 2023, 16(18), 6546; https://doi.org/10.3390/en16186546 - 12 Sep 2023

Cited by 2 | Viewed by 1322

Abstract

This paper presents a parametric procedure to size a hybrid system consisting of renewable generation (wind turbines and photovoltaic panels) and Battery Energy Storage Systems (BESS). To cope with the increasing installation of grid-scale BESS, an innovative, fast and flexible procedure for evaluating an efficient size for this asset has been developed. The tool exploits a high-fidelity empirical model to assess stand-alone BESS or hybrid power plants under different service stacking configurations. The economic performance has been evaluated considering the revenue stacking that occurs when participating in up to four distinct energy markets and the degradation of the BESS performances due to both cycle- and calendar-aging. The parametric nature of the tool enables the investigation of a wide range of system parameters, including novel BESS control logic, market prices, and energy production. The presented outcomes detail the techno-economic performances of a hybrid system over a 20-year scenario, proposing a sensitivity analysis of both technical and economic parameters. The case study results highlight the necessity of steering BESS investment towards the coupling of RES and accurate planning of the service stacking. Indeed, the implementation of a storage system in an energy district improves the internal rate of return of the project by up to 10% in the best-case scenario. Moreover, accurate service stacking has shown a boost in revenues by up to 44% with the same degradation. Full article

(This article belongs to the Special Issue Battery Modelling, Applications, and Technology)

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21 pages, 5861 KiB

Open AccessArticle

HPPC Test Methodology Using LFP Battery Cell Identification Tests as an Example

by Tadeusz Białoń, Roman Niestrój, Wojciech Skarka and Wojciech Korski

Energies 2023, 16(17), 6239; https://doi.org/10.3390/en16176239 - 28 Aug 2023

Cited by 4 | Viewed by 6565

Abstract

The aim of this research was to create an accurate simulation model of a lithium-ion battery cell, which will be used in the design process of the traction battery of a fully electric load-hull-dump vehicle. Discharge characteristics tests were used to estimate the actual cell capacity, and hybrid pulse power characterization (HPPC) tests were used to identify the Thevenin equivalent circuit parameters. A detailed description is provided of the methods used to develop the HPPC test results. Particular emphasis was placed on the applied filtration and optimization techniques as well as the assessment of the quality and the applicability of the acquired measurement data. As a result, a simulation model of the battery cell was created. The article gives the full set of parameter values needed to build a fully functional simulation model. Finally, a charge-depleting cycle test was performed to verify the created simulation model. Full article

(This article belongs to the Special Issue Battery Modelling, Applications, and Technology)

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15 pages, 5189 KiB

Open AccessArticle

Degradation Prediction and Cost Optimization of Second-Life Battery Used for Energy Arbitrage and Peak-Shaving in an Electric Grid

by Rongheng Li, Ali Hassan, Nishad Gupte, Wencong Su and Xuan Zhou

Energies 2023, 16(17), 6200; https://doi.org/10.3390/en16176200 - 26 Aug 2023

Cited by 2 | Viewed by 1383

Abstract

With the development of the electric vehicle industry, the number of batteries that are retired from vehicles is increasing rapidly, which raises critical environmental and waste issues. Second-life batteries recycled from automobiles have eighty percent of the capacity, which is a potential solution for the electricity grid application. To utilize the second-life batteries efficiently, an accurate estimation of their performance becomes a crucial portion of the optimization of cost-effectiveness. Nonetheless, few works focus on the modeling of the applications of second-life batteries. In this work, a general methodology is presented for the performance modeling and degradation prediction of second-life batteries applied in electric grid systems. The proposed method couples an electrochemical model of the battery performance, a state of health estimation method, and a revenue maximization algorithm for the application in the electric grid. The degradation of the battery is predicted under distinct charging and discharging rates. The results show that the degradation of the batteries can be slowed down, which is achieved by connecting numbers of batteries together in parallel to provide the same amount of required power. Many works aim for optimization of the operation of fresh Battery Energy Storage Systems (BESS). However, few works focus on the second-life battery applications. In this work, we present a trade-off between the revenue of the second-life battery and the service life while utilizing the battery for distinct operational strategies, i.e., arbitrage and peak shaving against Michigan’s DTE electricity utility’s Dynamic Peak Pricing (DPP) and Time of Use (TOU) tariffs. Results from case studies show that arbitrage against the TOU tariff in summer is the best choice due to its longer battery service life under the same power requirement. With the number of retired batteries set to increase over the next 10 years, this will give insight to the retired battery owners/procurers on how to increase the profitability, while making a circular economy of EV batteries more sustainable. Full article

(This article belongs to the Special Issue Battery Modelling, Applications, and Technology)

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16 pages, 1704 KiB

Open AccessArticle

Evaluation of the Power Generation Impact for the Mobility of Battery Electric Vehicles

by Javier Rey and Lázaro V. Cremades

Energies 2023, 16(13), 5006; https://doi.org/10.3390/en16135006 - 28 Jun 2023

Cited by 2 | Viewed by 1268

Abstract

European institutions have decided to ban the sale of Internal Combustion Vehicles (ICEVs) in the EU from 2035. This opens a possible scenario in which, in the not-too-distant future, all vehicles circulating in Europe are likely to be Battery Electric Vehicles (BEVs). The Spanish vehicle fleet is one of the oldest and has the lowest percentage of BEVs in Europe. The aim of this study is to evaluate the hypothetical scenario in which the current mobility of ICEVs is transformed into BEVs, in the geographical area of the province of Barcelona and in Spain in general. The daily electricity consumption, the required installation capacity of wind and solar photovoltaic energies, and the potential reduction of NO_x and particulate matter (PM) emissions are estimated. The daily emission reduction would be about 314 tons of NO_x and 17 tons of PM in Spain. However, the estimated investment required in Spain to generate the additional electricity from renewable sources would be enormous (over EUR 25.4 billion), representing, for example, 5.5% of the total national budget in 2022. Full article

(This article belongs to the Special Issue Battery Modelling, Applications, and Technology)

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16 pages, 6903 KiB

Open AccessFeature PaperArticle

Calendar Aging Effect on the Open Circuit Voltage of Lithium-Ion Battery

by Simone Barcellona, Lorenzo Codecasa, Silvia Colnago and Luigi Piegari

Energies 2023, 16(13), 4869; https://doi.org/10.3390/en16134869 - 22 Jun 2023

Cited by 1 | Viewed by 1490

Abstract

In recent years, lithium-ion batteries (LiBs) have gained a lot of importance due to the increasing use of renewable energy sources and electric vehicles. To ensure that batteries work properly and limit their degradation, the battery management system needs accurate battery models capable of precisely predicting their parameters. Among them, the state of charge (SOC) estimation is one of the most important, as it enables the prediction of the battery’s available energy and prevents it from operating beyond its safety limits. A common method for SOC estimation involves utilizing the relationship between the state of charge and the open circuit voltage (OCV). On the other hand, the latter changes with battery aging. In a previous work, the authors studied a simple function to model the OCV curve, which was expressed as a function of the absolute state of discharge, q, instead of SOC. They also analyzed how the parameters of such a curve changed with the cycle aging. In the present work, a similar analysis was carried out considering the calendar aging effect. Three different LiB cells were stored at three different SOC levels (low, medium, and high levels) for around 1000 days, and an analysis of the change in the OCV-q curve model parameters with the calendar aging was performed. Full article

(This article belongs to the Special Issue Battery Modelling, Applications, and Technology)

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13 pages, 1936 KiB

Open AccessArticle

Comparing Machine Learning Strategies for SoH Estimation of Lithium-Ion Batteries Using a Feature-Based Approach

by Iacopo Marri, Emil Petkovski, Loredana Cristaldi and Marco Faifer

Energies 2023, 16(11), 4423; https://doi.org/10.3390/en16114423 - 30 May 2023

Cited by 3 | Viewed by 1827

Abstract

Lithium-ion batteries play a vital role in many systems and applications, making them the most commonly used battery energy storage systems. Optimizing their usage requires accurate state-of-health (SoH) estimation, which provides insight into the performance level of the battery and improves the precision of other diagnostic measures, such as state of charge. In this paper, the classical machine learning (ML) strategies of multiple linear and polynomial regression, support vector regression (SVR), and random forest are compared for the task of battery SoH estimation. These ML strategies were selected because they represent a good compromise between light computational effort, applicability, and accuracy of results. The best results were produced using SVR, followed closely by multiple linear regression. This paper also discusses the feature selection process based on the partial charging time between different voltage intervals and shows the linear dependence of these features with capacity reduction. The feature selection, parameter tuning, and performance evaluation of all models were completed using a dataset from the Prognostics Center of Excellence at NASA, considering three batteries in the dataset. Full article

(This article belongs to the Special Issue Battery Modelling, Applications, and Technology)

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19 pages, 3364 KiB

Open AccessArticle

Cloud-Based Deep Learning for Co-Estimation of Battery State of Charge and State of Health

by Dapai Shi, Jingyuan Zhao, Zhenghong Wang, Heng Zhao, Chika Eze, Junbin Wang, Yubo Lian and Andrew F. Burke

Energies 2023, 16(9), 3855; https://doi.org/10.3390/en16093855 - 30 Apr 2023

Cited by 20 | Viewed by 2381

Abstract

Rechargeable lithium-ion batteries are currently the most viable option for energy storage systems in electric vehicle (EV) applications due to their high specific energy, falling costs, and acceptable cycle life. However, accurately predicting the parameters of complex, nonlinear battery systems remains challenging, given diverse aging mechanisms, cell-to-cell variations, and dynamic operating conditions. The states and parameters of batteries are becoming increasingly important in ubiquitous application scenarios, yet our ability to predict cell performance under realistic conditions remains limited. To address the challenge of modelling and predicting the evolution of multiphysics and multiscale battery systems, this study proposes a cloud-based AI-enhanced framework. The framework aims to achieve practical success in the co-estimation of the state of charge (SOC) and state of health (SOH) during the system’s operational lifetime. Self-supervised transformer neural networks offer new opportunities to learn representations of observational data with multiple levels of abstraction and attention mechanisms. Coupling the cloud-edge computing framework with the versatility of deep learning can leverage the predictive ability of exploiting long-range spatio-temporal dependencies across multiple scales. Full article

(This article belongs to the Special Issue Battery Modelling, Applications, and Technology)

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13 pages, 718 KiB

Open AccessArticle

Simple Loss Model of Battery Cables for Fast Transient Thermal Simulation

by Emanuele Fedele, Luigi Pio Di Noia and Renato Rizzo

Energies 2023, 16(7), 2963; https://doi.org/10.3390/en16072963 - 23 Mar 2023

Cited by 1 | Viewed by 1066

Abstract

In electric vehicles, currents with high-frequency ripples flow in the power cabling system due to the switching operation of power converters. Inside the cables, a strong coupling between the thermal and electromagnetic phenomena exists, since the temperature and Alternating Current (AC) density distributions in the strands affect each other. Due to the different time scales of magnetic and heat flow problems, the computational cost of Finite Element Method (FEM) numeric solvers can be excessive. This paper derives a simple analytical model to calculate the total losses of a multi-stranded cable carrying a Direct Current (DC) affected by a high-frequency ripple. The expression of the equivalent AC cable resistance at a generic frequency and temperature is derived from the general treatment of multi-stranded multi-layer windings. When employed to predict the temperature evolution in the cable, the analytical model prevents the use of complex FEM models in which multiple heat flow and magnetic simulations have to be run iteratively. The results obtained for the heating curve of a 35 mm² stranded cable show that the derived model matches the output of the coupled FEM simulation with an error below 1%, whereas the simple DC loss model of the cable gives an error of 2.4%. While yielding high accuracy, the proposed model significantly reduces the computational burden of the thermal simulation by a factor of four with respect to the complete FEM routine. Full article

(This article belongs to the Special Issue Battery Modelling, Applications, and Technology)

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Review

Jump to: Research

21 pages, 1505 KiB

Open AccessReview

Cloud-Based Artificial Intelligence Framework for Battery Management System

by Dapai Shi, Jingyuan Zhao, Chika Eze, Zhenghong Wang, Junbin Wang, Yubo Lian and Andrew F. Burke

Energies 2023, 16(11), 4403; https://doi.org/10.3390/en16114403 - 30 May 2023

Cited by 20 | Viewed by 4027

Abstract

As the popularity of electric vehicles (EVs) and smart grids continues to rise, so does the demand for batteries. Within the landscape of battery-powered energy storage systems, the battery management system (BMS) is crucial. It provides key functions such as battery state estimation (including state of charge, state of health, battery safety, and thermal management) as well as cell balancing. Its primary role is to ensure safe battery operation. However, due to the limited memory and computational capacity of onboard chips, achieving this goal is challenging, as both theory and practical evidence suggest. Given the immense amount of battery data produced over its operational life, the scientific community is increasingly turning to cloud computing for data storage and analysis. This cloud-based digital solution presents a more flexible and efficient alternative to traditional methods that often require significant hardware investments. The integration of machine learning is becoming an essential tool for extracting patterns and insights from vast amounts of observational data. As a result, the future points towards the development of a cloud-based artificial intelligence (AI)-enhanced BMS. This will notably improve the predictive and modeling capacity for long-range connections across various timescales, by combining the strength of physical process models with the versatility of machine learning techniques. Full article

(This article belongs to the Special Issue Battery Modelling, Applications, and Technology)

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