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Towards a Smarter Battery Management System

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A special issue of Batteries (ISSN 2313-0105).

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 20994

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Prof. Dr. Chris Mi

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Guest Editor

Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA
Interests: wireless power transfer; battery management systems; power electronics; hybrid electric vehicles; electric machines
Special Issues, Collections and Topics in MDPI journals

Dr. Wei Gao

E-Mail Website
Guest Editor

Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA
Interests: battery management system; lithium-ion battery; electric vehicle battery

Special Issue Information

Dear Colleagues,

Lithium-ion batteries are widely used in electric vehicles (EV) and the energy storage industry due to their high-energy density and long cycle life. As their price decreases, lithium-ion batteries will continue to be used in the future. Battery management systems (BMS) are the key component to ensure the stable and reliable operation of battery systems. It monitors the battery operation data; estimates the battery state of charge (SOC) and state of health (SOH); conducts battery balance; manages thermal systems; and performs fault diagnosis, etc. BMS-related hardware and algorithm have developed rapidly in recent years. Therefore, this Special Issue aims to demonstrate the latest BMS-related technologies, such as SOC and SOH estimation algorithms, balance systems, wireless BMS, and second life battery applications, etc.

Potential topics include, but are not limited to:

Battery management system hardware and algorithms;
Battery modeling;
Battery parameter identification;
Battery state of charge (SOC) estimation;
Battery state of health (SOH) estimation;
Battery fault diagnostics;
Battery balance or equalization topology and method;
Battery thermal management;
Battery second life application;
Wireless BMSs.

Prof. Dr. Chris Mi
Dr. Wei Gao
Guest Editors

Manuscript Submission Information

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Keywords

lithium-ion battery
battery management system
battery modeling
battery SOC estimation
battery SOH estimation
battery parameter identification
battery balance
battery equalization
battery thermal management
battery thermal runaway
second life battery
battery recycling
wireless BMS

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Related Special Issue

Towards a Smarter Battery Management System: 2nd Edition in Batteries (2 articles)

Published Papers (11 papers)

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Research

10 pages, 14447 KiB

Open AccessArticle

Revealing a Correlation between Physical Parameters and Differential Voltage Analysis of a Commercial Li-Ion Battery Based on Fiber Optic Sensors

by Lucca Matuck, Marta S. Ferreira and Micael Nascimento

Batteries 2024, 10(8), 289; https://doi.org/10.3390/batteries10080289 - 13 Aug 2024

Viewed by 1157

Abstract

This work describes a specialized optical fiber hybrid sensing configuration conceived to monitor internal physical parameters (temperature and pressure) within Li-ion batteries (LiBs) and correlate them with electrochemical performance in operando. The batteries underwent thorough cycling tests under C/3 and C/5 operating rate conditions. Throughout the cycling tests, the optical fiber sensors revealed a compelling correlation between internal and external temperature behavior. Additionally, the application of differential voltage analysis derivative curves during battery operation unveiled insights into the relationship between pressure and temperature changes and the batteries’ electrochemical performance. This optical sensing approach contributes to an understanding of internal LiB dynamics, offering implications for optimizing their performance and safety across diverse applications. Full article

(This article belongs to the Special Issue Towards a Smarter Battery Management System)

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21 pages, 4792 KiB

Open AccessArticle

Estimation Procedure for the Degradation of a Lithium-Ion Battery Pack

by Natascia Andrenacci, Manlio Pasquali, Francesco Vellucci and Alberto Venanzoni

Batteries 2024, 10(7), 234; https://doi.org/10.3390/batteries10070234 - 28 Jun 2024

Viewed by 1003

Abstract

This paper proposes a test procedure for evaluating the degradation of cells in a battery pack. The test can be performed using only the charger’s converters and the battery management system (BMS) without requiring sophisticated instrumentation. The method circumvents the difficulties related to the evaluation of derivative quantities for estimating the state of health (SOH) using integral quantities in the evaluation. The method introduces a ‘degradation function’ that is calculated with respect to the reference performance of pristine cells. The procedure was applied to the JuiceRoll Race Edition system, an innovative electric vehicle (EV) DC charger with internal storage, made in ENEL X and used during the MotoE championship races. Using this procedure, the degradation of performance in individual groups of cells composing the battery pack was quantified in comparison to the reference group. The procedure helps identify modules that have aged too early or show reliability issues. The method is mature for field operational applications. Full article

(This article belongs to the Special Issue Towards a Smarter Battery Management System)

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20 pages, 10524 KiB

Open AccessArticle

Development of a Fast Running Equivalent Circuit Model with Thermal Predictions for Battery Management Applications

by Vijayakanthan Damodaran, Thiyagarajan Paramadayalan, Diwakar Natarajan, Ramesh Kumar C, P. Rajesh Kanna, Dawid Taler, Tomasz Sobota, Jan Taler, Magdalena Szymkiewicz and Mohammed Jalal Ahamed

Batteries 2024, 10(6), 215; https://doi.org/10.3390/batteries10060215 - 19 Jun 2024

Cited by 1 | Viewed by 1854

Abstract

Equivalent circuit modelling (ECM) is a powerful tool to study the dynamic and non-linear characteristics of Li-ion cells and is widely used for the development of the battery management system (BMS) of electric vehicles. The dynamic parameters described by the ECM are used by the BMS to estimate the battery state of charge (SOC), which is crucial for efficient charging/discharging, range calculations, and the overall safe operation of electric vehicles. Typically, the ECM approach represents the dynamic characteristics of the battery in a mathematical form with a limited number of unknown parameters. Then, the parameters are calculated from voltage and current information of the lithium-ion cell obtained from controlled experiments. In the current work, a faster and simplified first-order resistance–capacitance (RC) equivalent circuit model was developed for a commercial cylindrical cell (LGM50 21700). An analytical solution was developed for the equivalent circuit model incorporating SOC and temperature-dependent RC parameters. The solution to the RC circuit model was derived using multiple expressions for different components like open circuit voltage (OCV), instantaneous resistance (R₀), and diffusional parameters (R₁ and C₁) as a function of the SOC and operating temperature. The derived parameters were validated against the virtual HPPC test results of a validated physics-based electrochemical model for the voltage behavior. Using the developed RC circuit model, a polynomial expression is derived to estimate the temperature increase of the cell including both irreversible and reversible heat generation components. The temperature predicted by the proposed RC circuit model at different battery operating temperatures is in good agreement with the values obtained from the validated physics model. The developed method can find applications in (i) onboard energy management by the BMS and (ii) quicker evaluation of cell performance early in the product development cycle. Full article

(This article belongs to the Special Issue Towards a Smarter Battery Management System)

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25 pages, 9188 KiB

Open AccessArticle

Battery Modeling for Emulators in Vehicle Test Cell

by Chris Roberts, Simon Petrovich and Kambiz Ebrahimi

Batteries 2024, 10(6), 199; https://doi.org/10.3390/batteries10060199 - 6 Jun 2024

Viewed by 1068

Abstract

This paper investigates modeling techniques for the mathematical representation of HV (high-voltage) Li-ion batteries to be used in conjunction with battery emulators for the test cell environment. To enable the impact of the battery response to be assessed in conjunction with other electrified systems, battery emulators are used with advanced mathematical models describing the expected voltage output with respect to current load. This paper conducted research into different modeling types: electrochemical, thermal, and electronic equivalent circuit models (EECMs). EECMs were identified as the most suitable to be used in conjunction with emulation techniques. A foundation EECM was created in conjunction with a thermal part to simulate thermal dependency. Hybrid Pulse Power Characterization (HPPC) tests were conducted on an NMC Li-ion cell across a range of temperatures from −20 °C to 25 °C. Using parameter optimization techniques, the HPPC test data were used to identify the resistance, capacitance, and the open-circuit voltage of the cell across a range of state of charge bounds and across a temperature range of 0 °C to 25 °C. The foundation model was assessed using identified parameters on two current profiles derived from drive cycles across a temperature range of 0 °C to 10 °C. The FMU (Functional Mockup Unit) model format was determined as the required interface for an AVL battery emulator. Full article

(This article belongs to the Special Issue Towards a Smarter Battery Management System)

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13 pages, 4213 KiB

Open AccessArticle

On the Use of Randomly Selected Partial Charges to Predict Battery State-of-Health

by Søren B. Vilsen and Daniel-Ioan Stroe

Batteries 2024, 10(6), 193; https://doi.org/10.3390/batteries10060193 - 31 May 2024

Viewed by 1140

Abstract

As society becomes more reliant on Lithium-ion (Li-ion) batteries, state-of-health (SOH) estimation will need to become more accurate and reliable. Therefore, SOH modelling is in the process of shifting from using simple and continuous charge/discharge profiles to more dynamic profiles constructed to mimic real operation when ageing the Li-ion batteries. However, in most cases, when ageing the batteries, the same exact profile is just repeated until the battery reaches its end of life. Using data from batteries aged in this fashion to create a model, there is a very real possibility that the model will rely on the built-in repetitiveness of the profile. Therefore, this work will examine the dependence of the performance of a multiple linear regression on the number of charges used to train the model, and their location within the profile used to age the batteries. The investigation shows that it is possible to train models using randomly selected partial charges while still reaching errors as low as 0.5%. Furthermore, it shows that only one randomly sampled partial charge is needed to achieve errors smaller than 1%. Lastly, as the number of randomly sampled partial charges used to train the model increases, the dependence on particular partial charges tends to decrease. Full article

(This article belongs to the Special Issue Towards a Smarter Battery Management System)

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23 pages, 4117 KiB

Open AccessArticle

Cross-Stitch Networks for Joint State of Charge and State of Health Online Estimation of Lithium-Ion Batteries

by Jiaqi Yao, Steven Neupert and Julia Kowal

Batteries 2024, 10(6), 171; https://doi.org/10.3390/batteries10060171 - 22 May 2024

Viewed by 1178

Abstract

As a superior solution to the developing demand for energy storage, lithium-ion batteries play an important role in our daily lives. To ensure their safe and efficient usage, battery management systems (BMSs) are often integrated into the battery systems. Among other critical functionalities, BMSs provide information about the key states of the batteries under usage, including state of charge (SOC) and state of health (SOH). This paper proposes a data-driven approach for the joint online estimation of SOC and SOH utilizing multi-task learning (MTL) approaches, particularly highlighting cross-stitch units and cross-stitch networks. The proposed model is able to achieve an accurate estimation of SOC and SOH in online applications with optimized information sharing and multi-scale implementation. Comprehensive results on training and testing of the model are presented. Possible improvements for future work are also discussed in the paper. Full article

(This article belongs to the Special Issue Towards a Smarter Battery Management System)

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13 pages, 3087 KiB

Open AccessArticle

The Effect of Battery Configuration on Dendritic Growth: A Magnetic Resonance Microscopy Study on Symmetric Lithium Cells

by Rok Peklar, Urša Mikac and Igor Serša

Batteries 2024, 10(5), 165; https://doi.org/10.3390/batteries10050165 - 17 May 2024

Cited by 1 | Viewed by 1165

Abstract

The potential of metallic lithium to become the anode material for next-generation batteries is hampered by significant challenges, chief among which is dendrite growth during battery charging. These dendritic structures not only impair battery performance but also pose safety risks. Among the non-destructive analytical techniques in battery research, Magnetic Resonance Imaging (MRI) stands out as a promising tool. However, the direct imaging of lithium by ⁷Li MRI is limited by its low sensitivity and spatial resolution, making it a less effective way of imaging dendrite growth. Instead, a recently introduced indirect imaging approach which is based on ¹H MRI of the electrolyte was used in this study. This method was used to sequentially 3D image and thus monitor the charging process of lithium metal symmetric cells in three different electrical circuits, namely those composed of a single cell, four cells in parallel, and four cells in series. The measured sequential images allowed for the measurement of dendrite growth in each cell using volumetric analysis. The growth results confirmed the theoretical prediction that the growth across cells is uneven in a parallel circuit, and even in a series circuit. The methods presented in this study can also be applied to analyze many other dendrite-related issues in batteries. Full article

(This article belongs to the Special Issue Towards a Smarter Battery Management System)

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30 pages, 6948 KiB

Open AccessArticle

Integrating Life Cycle Principles in Home Energy Management Systems: Optimal Load PV–Battery–Electric Vehicle Scheduling

by Zaid A. Al Muala, Mohammad A. Bany Issa and Pastora M. Bello Bugallo

Batteries 2024, 10(4), 138; https://doi.org/10.3390/batteries10040138 - 19 Apr 2024

Cited by 3 | Viewed by 1624

Abstract

Energy management in the residential sector contributes to energy system dispatching and security with the optimal use of renewable energy systems (RES) and energy storage systems (ESSs) and by utilizing the main grid based on its state. This work focuses on optimal energy flow, ESS parameters, and energy consumption scheduling based on demand response (DR) programs. The primary goals of the work consist of minimizing electricity costs while simultaneously extending the lifetime of ESSs in conjunction with extracting maximum benefits throughout their operational lifespan and reducing CO₂ emissions. Effective ESS and photovoltaic (PV) energy usage prices are modeled and an efficient energy flow management algorithm is presented, which considers the life cycle of the ESSs including batteries, electrical vehicles (EVs) and the efficient use of the PV system while reducing the cost of energy consumption. In addition, an optimization technique is employed to obtain the optimal ESS parameters including the size and depth of discharge (DOD), considering the installation cost, levelized cost of storage (LCOS), winter and summer conditions, energy consumption profile, and energy prices. Finally, an optimization technique is applied to obtain the optimal energy consumption scheduling. The proposed system provides all of the possibilities of exchanging energy between EV, battery, PV system, grid, and home. The optimization problem is solved using the particle swarm optimization algorithm (PSO) in MATLAB with an interval time of one minute. The results show the effectiveness of the proposed system, presenting an actual cost reduction of 28.9% and 17.7% in summer and winter, respectively, compared to a base scenario. Similarly, the energy losses were reduced by 26.7% in winter and 22.3% in summer, and the EV battery lifetime was extended from 9.2 to 19.1 years in the winter scenario and from 10.4 to 17.7 years in the summer scenario. The integrated system provided a financial contribution during the operational lifetime of EUR 11,600 and 7900 in winter and summer scenarios, respectively. The CO₂ was reduced by 59.7% and 46.2% in summer and winter scenarios, respectively. Full article

(This article belongs to the Special Issue Towards a Smarter Battery Management System)

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18 pages, 5824 KiB

Open AccessArticle

Low-Computational Model to Predict Individual Temperatures of Cells within Battery Modules

by Ali Abbas, Nassim Rizoug, Rochdi Trigui, Eduardo Redondo-Iglesias and Serge Pelissier

Batteries 2024, 10(3), 98; https://doi.org/10.3390/batteries10030098 - 12 Mar 2024

Cited by 1 | Viewed by 2293

Abstract

Predicting the operating temperature of lithium-ion battery during different cycles is important when it comes to the safety and efficiency of electric vehicles. In this regard, it is vital to adopt a suitable modeling approach to analyze the thermal performance of a battery. In this paper, the temperature of lithium-ion NMC pouch battery has been investigated. A new formulation of lumped model based on the thermal resistance network is proposed. Unlike previous models that treated the battery as a single entity, the proposed model introduces a more detailed analysis by incorporating thermal interactions between individual cells and tabs within a single cell scenario, while also considering interactions between cells and insulators or gaps, located between the cells, within the module case. This enhancement allows for the precise prediction of temperature variations across different cells implemented within the battery module. In order to evaluate the accuracy of the prediction, a three-dimensional finite element model was adopted as a reference. The study was performed first on a single cell, then on modules composed of several cells connected in series, during different operating conditions. A comprehensive comparison between both models was conducted. The analysis focused on two main aspects, the accuracy of temperature predictions and the computational time required. Notably, the developed lumped model showed a significant capability to estimate cell temperatures within the modules. The thermal results revealed close agreement with the values predicted by the finite element model, while needing significantly lower computational time. For instance, while the finite element model took almost 21 h to predict the battery temperature during consecutive charge/discharge cycles of a 10-cell module, the developed lumped model predicted the temperature within seconds, with a maximum difference of 0.42 °C. Full article

(This article belongs to the Special Issue Towards a Smarter Battery Management System)

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16 pages, 2827 KiB

Open AccessArticle

A Novel Long Short-Term Memory Approach for Online State-of-Health Identification in Lithium-Ion Battery Cells

by Mike Kopp, Alexander Fill, Marco Ströbel and Kai Peter Birke

Batteries 2024, 10(3), 77; https://doi.org/10.3390/batteries10030077 - 26 Feb 2024

Viewed by 2669

Abstract

Revolutionary and cost-effective state estimation techniques are crucial for advancing lithium-ion battery technology, especially in mobile applications. Accurate prediction of battery state-of-health (SoH) enhances state-of-charge estimation while providing valuable insights into performance, second-life utility, and safety. While recent machine learning developments show promise in SoH estimation, this paper addresses two challenges. First, many existing approaches depend on predefined charge/discharge cycles with constant current/constant voltage profiles, which limits their suitability for real-world scenarios. Second, pure time series forecasting methods require prior knowledge of the battery’s lifespan in order to formulate predictions within the time series. Our novel hybrid approach overcomes these limitations by classifying the current aging state of the cell rather than tracking the SoH. This is accomplished by analyzing current pulses filtered from authentic drive cycles. Our innovative solution employs a Long Short-Term Memory-based neural network for SoH prediction based on residual capacity, making it well suited for online electric vehicle applications. By overcoming these challenges, our hybrid approach emerges as a reliable alternative for precise SoH estimation in electric vehicle batteries, marking a significant advancement in machine learning-based SoH estimation. Full article

(This article belongs to the Special Issue Towards a Smarter Battery Management System)

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13 pages, 13121 KiB

Open AccessArticle

Pretreatment of Lithium Ion Batteries for Safe Recycling with High-Temperature Discharging Approach

by Arpita Mondal, Yuhong Fu, Wei Gao and Chunting Chris Mi

Batteries 2024, 10(1), 37; https://doi.org/10.3390/batteries10010037 - 21 Jan 2024

Cited by 5 | Viewed by 4057

Abstract

The ongoing transition toward electric vehicles is a major factor in the exponential rise in demand for lithium-ion batteries (LIBs). There is a significant effort to recycle battery materials to support the mining industry in ensuring enough raw materials and avoiding supply disruptions, so that there will be enough raw materials to produce LIBs. Nevertheless, LIBs that have reached the end of their useful lives and are sent for recycling may still have some energy left in them, which could be dangerous during handling and processing. Therefore, it is important to conduct discharge pretreatment of LIBs before dismantling and crushing them, especially in cases where pyrometallurgical recycling is not used. Electrochemical discharge in conducting solutions has been commonly studied and implemented for this purpose, but its effectiveness has yet to be fully validated. Non-electrochemical discharge has also been researched as a potentially cleaner and more efficient discharge technology at the same time. This article presents a non-electrochemical discharge process by completely draining the energy from used batteries before recycling. A comprehensive investigation of the behavior of LIBs during discharge and the amount of energy remaining after fully discharging the battery at different temperatures is analyzed in this work. According to the experimental findings, completely discharging the battery at higher temperatures results in a reduced amount of residual energy in the battery. This outcome holds great importance in terms of safe and environmentally friendly recycling of used LIBs, emphasizing that safety and environmentally friendly recycling must go hand in hand with a cost-effective and sustainable solution. Full article

(This article belongs to the Special Issue Towards a Smarter Battery Management System)

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