Smart battery technology for lifetime improvement

Batteries

Published On 2022/10/9

Applications of lithium-ion batteries are widespread, ranging from electric vehicles to energy storage systems. In spite of nearly meeting the target in terms of energy density and cost, enhanced safety, lifetime, and second-life applications, there remain challenges. As a result of the difference between the electric characteristics of the cells, the degradation process is accelerated for battery packs containing many cells. The development of new generation battery solutions for transportation and grid storage with improved performance is the goal of this paper, which introduces the novel concept of Smart Battery that brings together batteries with advanced power electronics and artificial intelligence (AI). The key feature is a bypass device attached to each cell that can insert relaxation time to individual cell operation with minimal effect on the load. An advanced AI-based performance optimizer is trained to recognize early signs of accelerated degradation modes and to decide upon the optimal insertion of relaxation time. The resulting pulsed current operation has been proven to extend lifetime by up to 80% in laboratory aging conditions. The Smart Battery unique architecture uses a digital twin to accelerate the training of performance optimizers and predict failures. The Smart Battery technology is a new technology currently at the proof-of-concept stage.

Journal

Batteries

Published On

2022/10/9

Volume

8

Issue

10

Page

169

Authors

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Position

Professor at

H-Index(all)

104

H-Index(since 2020)

72

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Power Electronics

Smart Batteries

AI

University Profile Page

Daniel Stroe

Daniel Stroe

Aalborg Universitet

Position

Head of Battery Storage Systems Research Programme at

H-Index(all)

49

H-Index(since 2020)

46

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Lithium-ion Batteries

Energy Storage

Electric Vehicles

Renewable Energy

Energy Management

University Profile Page

Xin Sui

Xin Sui

Aalborg Universitet

Position

H-Index(all)

14

H-Index(since 2020)

14

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Li-ion batteries

SOH estimation

RUL prediction

University Profile Page

Pallavi Bharadwaj

Pallavi Bharadwaj

Massachusetts Institute of Technology

Position

Postdoctoral Research Associate

H-Index(all)

10

H-Index(since 2020)

9

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Smart power electronics

Green energy optimization

Net zero transition

Søren B. Vilsen

Søren B. Vilsen

Aalborg Universitet

Position

Post Doc

H-Index(all)

9

H-Index(since 2020)

9

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Statistics

Forensic Genetics

Battery's

University Profile Page

Other Articles from authors

Daniel Stroe

Daniel Stroe

Aalborg Universitet

Data in Brief

Dataset of lithium-ion battery degradation based on a forklift mission profile for state-of-health estimation and lifetime prediction

Lithium-ion (Li-ion) batteries are becoming an increasingly integral part of modern society, through consumer electronics, stabilisation of the electric grid, and electric vehicles. However, Lithium-ion batteries degrade in effectiveness over time; a degradation which is extremely dependent on the usage of the battery. Therefore, to study how a battery cell degrades under dynamic conditions, a realistic load profile was constructed based on the operation of forklifts. This profile was used to age three Lithium-ion battery cells at 45, 40, and 35°C and the response of the cells was measured on a second-by-second basis. Periodically the ageing was halted to perform a reference test of the cells allowing for the tracking of their degradation.

Søren B. Vilsen

Søren B. Vilsen

Aalborg Universitet

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

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 build 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 build models using randomly selected partial charges while still reaching errors as low as 0.5%. Furthermore, it shows that only two randomly sampled partial charges are needed to achieve errors of less than 1%. Lastly, as the number of randomly sampled partial charges used to create the model increases, then the dependence on particular partial charges tends to decrease.

Daniel Stroe

Daniel Stroe

Aalborg Universitet

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

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 build 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 build models using randomly selected partial charges while still reaching errors as low as 0.5%. Furthermore, it shows that only two randomly sampled partial charges are needed to achieve errors of less than 1%. Lastly, as the number of randomly sampled partial charges used to create the model increases, then the dependence on particular partial charges tends to decrease.

Daniel Stroe

Daniel Stroe

Aalborg Universitet

Batteries

Lithium-Ion Supercapacitors and Batteries for Off-Grid PV Applications: Lifetime and Sizing

The intermittent nature of power generation from photovoltaics (PV) requires reliable energy storage solutions. Using the storage system outdoors exposes it to variable temperatures, affecting both its storage capacity and lifespan. Utilizing and optimizing energy storage considering climatic variations and new storage technologies is still a research gap. Therefore, this paper presents a modified sizing algorithm based on the Golden Section Search method, aimed at optimizing the number of cells in an energy storage unit, with a specific focus on the unique conditions of Denmark. The considered energy storage solutions are Lithium-ion capacitors (LiCs) and Lithium-ion batteries (LiBs), which are tested under different temperatures and C-rates rates. The algorithm aims to maximize the number of autonomy cycles—defined as periods during which the system operates independently of the grid, marked by intervals between two consecutive 0% State of Charge (SoC) occurrences. Testing scenarios include dynamic temperature and dynamic load, constant temperature at 25 °C, and constant load, considering irradiation and temperature effects and cell capacity fading over a decade. A comparative analysis reveals that, on average, the LiC storage is sized at 70–80% of the LiB storage across various scenarios. Notably, under a constant-temperature scenario, the degradation rate accelerates, particularly for LiBs. By leveraging the modified Golden Section Search algorithm, this study provides an efficient approach to the sizing problem, optimizing the number of cells and thus offering a potential solution for energy storage in off-grid PV systems.

Søren B. Vilsen

Søren B. Vilsen

Aalborg Universitet

Data in Brief

Dataset of lithium-ion battery degradation based on a forklift mission profile for state-of-health estimation and lifetime prediction

Lithium-ion (Li-ion) batteries are becoming an increasingly integral part of modern society, through consumer electronics, stabilisation of the electric grid, and electric vehicles. However, Lithium-ion batteries degrade in effectiveness over time; a degradation which is extremely dependent on the usage of the battery. Therefore, to study how a battery cell degrades under dynamic conditions, a realistic load profile was constructed based on the operation of forklifts. This profile was used to age three Lithium-ion battery cells at 45, 40, and 35°C and the response of the cells was measured on a second-by-second basis. Periodically the ageing was halted to perform a reference test of the cells allowing for the tracking of their degradation.

Pallavi Bharadwaj

Pallavi Bharadwaj

Massachusetts Institute of Technology

IEEE Transactions on Industrial Informatics

A Battery Digital Twin From Laboratory Data Using Wavelet Analysis and Neural Networks

Lithium-ion (Li-ion) batteries are the preferred choice for energy storage applications. Li-ion performances degrade with time and usage, leading to a decreased total charge capacity and to an increased internal resistance. In this article, the wavelet analysis is used to filter the voltage and current signals of the battery to estimate the internal complex impedance as a function of state of charge (SoC) and state of health (SoH). The collected data are then used to synthesize a battery digital twin (BDT). This BDT outputs a realistic voltage signal as a function of SoC and SoH inputs. The BDT is based on feedforward neural networks trained to simulate the complex internal impedance and the open-circuit voltage generator. The effectiveness of the proposed method is verified on the dataset from the prognostics data repository of NASA.

Daniel Stroe

Daniel Stroe

Aalborg Universitet

Journal of Energy Storage

Identification of the aging state of lithium-ion batteries via temporal convolution network and self-attention mechanism

Deep learning methods have been widely used for battery aging state estimation with either manual or automatic features, while the contribution of multi-source features is rarely considered. To solve this problem, a hybrid method is proposed to combine the manual and automatic features based on a temporal convolution network (TCN) and a self-attention mechanism (SA). Specifically, the local voltage, capacity, and incremental capacity are manually extracted as battery aging features. Then, for extracting automatic features, TCN employs dilated convolution to capture the capacity regeneration phenomenon during battery degradation. Considering the contribution of multi-source features, we use SA to fuse the obtained manual and automatic features. Finally, the available capacity and remaining useful life of the battery are predicted using a fully connected neural network on one dataset from our lab, the Oxford …

Xin Sui

Xin Sui

Aalborg Universitet

IEEE Transactions on Industry Applications

Small-Sample-Learning-Based Lithium-Ion Batteries Health Assessment: An Optimized Ensemble Framework

Machine Learning is widely studied in battery state of health (SOH) estimation due to its advantage in establishing the non-linear mapping between measurements and SOH. However, the requirement of a big dataset and the lack of robustness limit the practical application, especially in small sample learning. To tackle these challenges, an optimal ensemble framework called BaggELM (bagging extreme learning machine) is proposed for battery SOH estimation. Specifically, the required dataset is reduced by optimizing the input voltage and the hyperparameters of the BaggELM algorithm. Moreover, a statistical post-processing method is used to aggregate multiple ELMs, and the final estimate is determined by the maximum probability density value. As a result, the effects of random parameterization of ELM and the training data size on SOH estimation are suppressed, thus improving the robustness and accuracy of …

Daniel Stroe

Daniel Stroe

Aalborg Universitet

Battery state-of-health estimation using machine learning

Over the years, lithium–ion batteries have developed as a key enabling technology for the green transition. Although many of these batteries’ characteristics, such as energy density, power capability, and cost, have gradually improved, uncertainties remain concerning their performance over their lifetimes. Thus, to ensure reliable and efficient battery operation, the battery's available performance, known as its state of health (SOH), must be known at every moment. This chapter introduces the most common battery SOH estimation methods, from direct measurements to deep neural networks, discussing their key performance metrics, advantages, and drawbacks.

Daniel Stroe

Daniel Stroe

Aalborg Universitet

Advanced Energy Materials

Unravelling the Mechanism of Pulse Current Charging for Enhancing the Stability of Commercial LiNi0.5Mn0.3Co0.2O2/Graphite Lithium‐Ion Batteries

The key to advancing lithium‐ion battery (LIB) technology, particularly with respect to the optimization of cycling protocols, is to obtain comprehensive and in‐depth understanding of the dynamic electrochemical processes during battery operation. This work shows that pulse current (PC) charging substantially enhances the cycle stability of commercial LiNi0.5Mn0.3Co0.2O2 (NMC532)/graphite LIBs. Electrochemical diagnosis unveils that pulsed current effectively mitigates the rise of battery impedance and minimizes the loss of electrode materials. Operando and ex situ Raman and X‐ray absorption spectroscopy reveal the chemical and structural changes of the negative and positive electrode materials during PC and constant current (CC) charging. Specifically, Li‐ions are more uniformly intercalated into graphite and the Ni element of NMC532 achieves a higher energy state with less Ni─O bond length variation …

Daniel Stroe

Daniel Stroe

Aalborg Universitet

Thermal state monitoring of lithium-ion batteries: Progress, challenges, and opportunities

Transportation electrification is a promising solution to meet the ever-rising energy demand and realize sustainable development. Lithium-ion batteries, being the most predominant energy storage devices, directly affect the safety, comfort, driving range, and reliability of many electric mobilities. Nevertheless, thermal-related issues of batteries such as potential thermal runaway, performance degradation at low temperatures, and accelerated aging still hinder the wider adoption of electric mobilities. To ensure safe, efficient, and reliable operations of lithium-ion batteries, monitoring their thermal states is critical to safety protection, performance optimization, as well as prognostics, and health management. Given insufficient onboard temperature sensors and their inability to measure battery internal temperature, accurate and timely temperature estimation is of particular importance to thermal state monitoring. Toward …

Daniel Stroe

Daniel Stroe

Aalborg Universitet

State-of-energy balancing control with cascaded H-bridge for second-life batteries

Battery energy storage systems (BESS) are fre-quently utilized to support the power grid. Second-Life batteries (SLBs) are considered as a potential solution to reduce the cost of BESS in stationary applications. However, using SLBs has certain challenges due to the non-uniform State-of-Charge (SOC) and State-of-Health (SOH) among the battery modules. This study examines two control strategies for a cascaded H-bridge (CHB) topology used for BESS applications. The possibilities for flexible control of the BESS are enabled by employing a CHB topology. By utilizing the CHB topology, each battery module can be individually charged and discharged. Two battery energy management system (BEMS) techniques are proposed based on the batteries' State-of-Energy (SOE): 1) continuous and 2) discrete balancing techniques. The experimental tests demonstrate the advantages of the proposed solutions in terms of …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE Transactions on Industrial Electronics

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions using domain-adaptive multitask learning (MTL) with long-term regularization. First, features extracted from partial charging curves are utilized for short-term state of health predictions. Then, the long-term degradation trajectory is directly predicted by recursively using the predicted features within the multitask framework, enhancing the model integrity and lowering the complexity. Then, domain adaptation (DA) is adopted to reduce the discrepancies between different working conditions. Additionally, a long-term regularization is introduced to address the shortcoming that arises when the model is extrapolated recursively for future health predictions. Thus, the short …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE/ASME Transactions on Mechatronics

Online Sensorless Temperature Estimation of Lithium-Ion Batteries Through Electro-Thermal Coupling

Owing to the nonnegligible impacts of temperature on the safety, performance, and lifespan of lithium-ion batteries, it is essential to regulate battery temperature to an optimal range. Temperature monitoring plays a fundamental role in battery thermal management, yet it is still challenged by limited onboard temperature sensors, particularly in large-scale battery applications. As such, developing sensorless temperature estimation is of paramount importance to acquiring the temperature information of each cell in a battery system. This article proposes an estimation approach to obtain the cell temperature by taking advantage of the electrothermal coupling effect of batteries. An electrothermal coupled model, which captures the interactions between the electrical and the thermal dynamics, is established, parameterized, and experimentally validated. A closed-loop observer is then designed based on this coupled model …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries

The State of Health (SOH) estimation for automotive batteries is currently assessed with different techniques which may involve long testing procedure or require costly hardware to be implemented. This paper aims at contributing to this domain by exploiting the response of a lead-acid battery with respect to a short-term current profile using an Artificial Neural Network (ANN) classifier for SOH estimation. The method is applicable onboard the vehicle and no additional instrumentation is required on the retained vehicle. The design and validation of a SOH method with a short-term current profile using Artificial Intelligence (AI) in lead-acid batteries, which are commonly used in heavy-duty vehicles for cranking and cabin systems, are presented. The paper validates the considered approach with experimental data, which are representative of actual vehicle operations. In detail, the paper describes the retained …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

arXiv preprint arXiv:2402.07777

Novel Low-Complexity Model Development for Li-ion Cells Using Online Impedance Measurement

Modeling of Li-ion cells is used in battery management systems (BMS) to determine key states such as state-of-charge (SoC), state-of-health (SoH), etc. Accurate models are also useful in developing a cell-level digital-twin that can be used for protection and diagnostics in the BMS. In this paper, a low-complexity model development is proposed based on the equivalent circuit model (ECM) of the Li-ion cells. The proposed approach uses online impedance measurement at discrete frequencies to derive the ECM that matches closely with the results from the electro-impedance spectroscopy (EIS). The proposed method is suitable to be implemented in a microcontroller with low-computational power, typically used in BMS. Practical design guidelines are proposed to ensure fast and accurate model development. Using the proposed method to enhance the functions of a typical automotive BMS is described. Experimental validation is performed using large prismatic cells and small-capacity cylindrical cells. Root-mean-square error (RMSE) of less than 3\% is observed for a wide variation of operating conditions.

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE Transactions on Industrial Informatics

A Battery Digital Twin From Laboratory Data Using Wavelet Analysis and Neural Networks

Lithium-ion (Li-ion) batteries are the preferred choice for energy storage applications. Li-ion performances degrade with time and usage, leading to a decreased total charge capacity and to an increased internal resistance. In this article, the wavelet analysis is used to filter the voltage and current signals of the battery to estimate the internal complex impedance as a function of state of charge (SoC) and state of health (SoH). The collected data are then used to synthesize a battery digital twin (BDT). This BDT outputs a realistic voltage signal as a function of SoC and SoH inputs. The BDT is based on feedforward neural networks trained to simulate the complex internal impedance and the open-circuit voltage generator. The effectiveness of the proposed method is verified on the dataset from the prognostics data repository of NASA.

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE Transactions on Industry Applications

Small-Sample-Learning-Based Lithium-Ion Batteries Health Assessment: An Optimized Ensemble Framework

Machine Learning is widely studied in battery state of health (SOH) estimation due to its advantage in establishing the non-linear mapping between measurements and SOH. However, the requirement of a big dataset and the lack of robustness limit the practical application, especially in small sample learning. To tackle these challenges, an optimal ensemble framework called BaggELM (bagging extreme learning machine) is proposed for battery SOH estimation. Specifically, the required dataset is reduced by optimizing the input voltage and the hyperparameters of the BaggELM algorithm. Moreover, a statistical post-processing method is used to aggregate multiple ELMs, and the final estimate is determined by the maximum probability density value. As a result, the effects of random parameterization of ELM and the training data size on SOH estimation are suppressed, thus improving the robustness and accuracy of …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Intelligent Cell Balancing Control for Lithium-Ion Battery Packs

This study introduces a balancing control strategy that employs an Artificial Neural Network (ANN) to ensure State of Charge (SOC) balance across lithium-ion (Li-ion) battery packs, consistent with the framework of smart battery packs. The model targets a battery pack consisting of cells with diverse characteristics, reflecting real-world heterogeneous conditions. A fundamental aspect of this approach is the ability to bypass individual cells optimally. This key feature stops current flow to and from the cell, allowing it to rest and cool off while avoiding charging or discharging cycles. The implementation of ANN enables adaptive and dynamic management of SOC, which is essential for optimizing performance and extending the lifespan of battery packs. The results demonstrate the effectiveness of the proposed ANN-based balancing strategy in SOC balancing, demonstrating its potential as a critical solution in enhancing battery management systems for electric vehicles.

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Grid Impedance Shaping for Grid-Forming Inverters: A Soft Actor-Critic Deep Reinforcement Learning Algorithm

This paper proposed an advanced method for adjusting grid impedance in grid-forming inverters, utilizing the Soft Actor-Critic Deep Reinforcement Learning (SAC-DRL) algorithm. The approach contains a flexible strategy for controlling virtual impedance, supported by an equivalent grid impedance estimator. This facilitates accurate modifications of virtual impedance based on the grid’s X/R ratio and the converter’s power capacity, aiming to optimize power flow and maintain grid stability. A unique feature of this methodology is the division of virtual reactance into two segments: one adhering to standard control protocols and the other designated for precision enhancement via the SAC-DRL method. This strategy introduces a layer of intelligence to the system, strengthening its resilience against fluctuations in grid impedance. Experimental validations, executed on a laboratory setup, verify the robustness of this approach, highlighting its potential to significantly improve intelligent power grid management practices.

Other articles from Batteries journal

Wenxue Liu

Wenxue Liu

Chongqing University

Batteries

A Novel Differentiated Control Strategy for an Energy Storage System That Minimizes Battery Aging Cost Based on Multiple Health Features

In large-capacity energy storage systems, instructions are decomposed typically using an equalized power distribution strategy, where clusters/modules operate at the same power and durations. When dispatching shifts from stable single conditions to intricate coupled conditions, this distribution strategy inevitably results in increased inconsistency and hastened system aging. This paper presents a novel differentiated power distribution strategy comprising three control variables: the rotation status, and the operating boundaries for both depth of discharge (DOD) and C-rates (C) within a control period. The proposed strategy integrates an aging cost prediction model developed to express the mapping relationship between these control variables and aging costs. Additionally, it incorporates the multi-colony particle swarm optimization (Mc-PSO) algorithm into the optimization model to minimize aging costs. The aging cost prediction model consists of three functions: predicting health features (HFs) based on the cumulative charge/discharge throughput quantity and operating boundaries, characterizing HFs as comprehensive scores, and calculating aging costs using both comprehensive scores and residual equipment value. Further, we elaborated on the engineering application process for the proposed control strategy. In the simulation scenarios, this strategy prolonged the service life by 14.62%, reduced the overall aging cost by 6.61%, and improved module consistency by 21.98%, compared with the traditional equalized distribution strategy. In summary, the proposed strategy proves effective in elongating service life, reducing overall aging …

Jinsheng Xiao

Jinsheng Xiao

Wuhan University of Technology

Batteries

Thermal Performance Analysis of a Prismatic Lithium-Ion Battery Module under Overheating Conditions

Thermal runaway (TR) of lithium-ion batteries has always been a topic of concern, and the safety of batteries is closely related to the operating temperature. An overheated battery can significantly impact the surrounding batteries, increasing the risk of fire and explosion. To improve the safety of battery modules and prevent TR, we focus on the characteristics of temperature distribution and thermal spread of battery modules under overheating conditions. The heat transfer characteristics of battery modules under different battery thermal management systems (BTMSs) are assessed. In addition, the effects of abnormal heat generation rate, abnormal heat generation location, and ambient temperature on the temperature distribution and thermal spread of battery modules are also studied. The results indicate that the BTMS consisting of flat heat pipes (FHPs) and bottom and side liquid cooling plates can effectively suppress thermal spread and improve the safety of the battery module.

Paul Arevalo Cordero

Paul Arevalo Cordero

Universidad de Jaén

Batteries

Enhancing Virtual Inertia Control in Microgrids: A Novel Frequency Response Model Based on Storage Systems

The integration of renewable resources in isolated systems can produce instability in the electrical grid due to its intermintency. In today’s microgrids, which lack synchronous generation, physical inertia is substituted for inertia emulation. To date, the most effective approach remains the frequency derivative control technique. Nevertheless, within this method, the ability to provide virtual drooping is often disregarded in its design, potentially leading to inadequate development in systems featuring high renewable penetration and low damping. To address this issue, this paper introduces an innovative design and analysis of virtual inertia control to simultaneously mimic droop and inertia characteristics in microgrids. The dynamic frequency response without and with renewable energy sources penetration is comparatively analyzed by simulation. The proposed virtual inertia control employs a derivative technique to measure the rate of change of frequency slope during inertia emulation. Sensitivity mapping is conducted to scrutinize its impact on dynamic frequency response. Finally, the physical battery storage system of the University of Cuenca microgrid is used as a case study under operating conditions.

Tamás Kerekes

Tamás Kerekes

Aalborg Universitet

Batteries

Lithium-Ion Supercapacitors and Batteries for Off-Grid PV Applications: Lifetime and Sizing

The intermittent nature of power generation from photovoltaics (PV) requires reliable energy storage solutions. Using the storage system outdoors exposes it to variable temperatures, affecting both its storage capacity and lifespan. Utilizing and optimizing energy storage considering climatic variations and new storage technologies is still a research gap. Therefore, this paper presents a modified sizing algorithm based on the Golden Section Search method, aimed at optimizing the number of cells in an energy storage unit, with a specific focus on the unique conditions of Denmark. The considered energy storage solutions are Lithium-ion capacitors (LiCs) and Lithium-ion batteries (LiBs), which are tested under different temperatures and C-rates rates. The algorithm aims to maximize the number of autonomy cycles—defined as periods during which the system operates independently of the grid, marked by intervals between two consecutive 0% State of Charge (SoC) occurrences. Testing scenarios include dynamic temperature and dynamic load, constant temperature at 25 °C, and constant load, considering irradiation and temperature effects and cell capacity fading over a decade. A comparative analysis reveals that, on average, the LiC storage is sized at 70–80% of the LiB storage across various scenarios. Notably, under a constant-temperature scenario, the degradation rate accelerates, particularly for LiBs. By leveraging the modified Golden Section Search algorithm, this study provides an efficient approach to the sizing problem, optimizing the number of cells and thus offering a potential solution for energy storage in off-grid PV systems.

Daniel Ramos Louzada

Daniel Ramos Louzada

Pontifícia Universidade Católica do Rio de Janeiro

Batteries

Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries

Rapid technological changes and disruptive innovations have resulted in a significant shift in people’s behavior and requirements. Electronic gadgets, including smartphones, notebooks, and other devices, are indispensable to everyday routines. Consequently, the demand for high-capacity batteries has surged, which has enabled extended device autonomy. An alternative approach to address this demand is battery swapping, which can potentially extend the battery life of electronic devices. Although battery sharing in electric vehicles has been well studied, smartphone applications still need to be explored. Crucially, assessing the batteries’ state of health (SoH) presents a challenge, necessitating consensus on the best estimation methods to develop effective battery swap strategies. This paper proposes a model for estimating the SoH curve of lithium-ion batteries using the state of charge curve. The model was designed for smartphone battery swap applications utilizing Gated Recurrent Unit (GRU) neural networks. To validate the model, a system was developed to conduct destructive tests on batteries and study their behavior over their lifetimes. The results demonstrated the high precision of the model in estimating the SoH of batteries under various charge and discharge parameters. The proposed approach exhibits low computational complexity, low cost, and easily measurable input parameters, making it an attractive solution for smartphone battery swap applications.

Constantina Lekakou

Constantina Lekakou

University of Surrey

Batteries

DFT Simulations Investigating the Trapping of Sulfides by 1T-LixMoS2 and 1T-LixMoS2/Graphene Hybrid Cathodes in Li-S Batteries

The aim of this study is to investigate new materials that can be employed as cathode hosts in Li-S batteries, which would be able to overcome the effect of the shuttling of soluble polysulfides and maximize the battery capacity and energy density. Density functional theory (DFT) simulations are used to determine the adsorption energy of lithium sulfides in two types of cathode hosts: lithiated 1T-MoS2 (1T-LixMoS2) and hybrid 1T-LixMoS2/graphene. Initial simulations of lithiated 1T-MoS2 structures led to the selection of an optimized 1T-Li0.75MoS2 structure, which was utilized for the formation of an optimized 1T-Li0.75MoS2 bilayer and a hybrid 1T-Li0.75MoS2/graphene bilayer structure. It was found that all sulfides exhibited super-high adsorption energies in the interlayer inside the 1T-Li0.75MoS2 bilayer and very good adsorption energy values in the interlayer inside the hybrid 1T-Li0.75MoS2/graphene bilayer. The placement of sulfides outside each type of bilayer, over the 1T-Li0.75MoS2 surface, yielded good adsorption energies in the range of −2 to −3.8 eV, which are higher than those over a 1T-MoS2 substrate.

Pascal Venet

Pascal Venet

Université Claude Bernard Lyon 1

Batteries

Aging in First and Second Life of G/LFP 18650 Cells: Diagnosis and Evolution of the State of Health of the Cell and the Negative Electrode under Cycling

Second-life applications for lithium-ion batteries offer the industry opportunities to defer recycling costs, enhance economic value, and reduce environmental impacts. An accurate prognosis of the remaining useful life (RUL) is essential for ensuring effective second-life operation. Diagnosis is a necessary step for the establishment of a reliable prognosis, based on the aging modes involved in a cell. This paper introduces a method for characterizing specific aging phenomenon in Graphite/Lithium Iron Phosphate (G/LFP) cells. This method aims to identify aging related to the loss of active material at the negative electrode (LAMNE). The identification and tracking of the state of health (SoH) are based on Incremental Capacity Analysis (ICA) and Differential Voltage Analysis (DVA) peak-tracking techniques. The remaining capacity of the electrode is thus evaluated based on these diagnostic results, using a model derived from half-cell electrode characterization. The method is used on a G/LFP cell in the format 18650, with a nominal capacity of 1.1 Ah, aged from its pristine state to 40% of state of health.

Marco Giorgetti

Marco Giorgetti

Università degli Studi di Bologna

Batteries

Aging Mechanism of Mn-based Prussian Blue Cathode Material by Synchrotron 2D X-ray Fluorescence

The aging mechanism of 10% and 30% nickel-substituted manganese hexacyanoferrate cathode material in aqueous zinc-ion batteries has been explored through the advanced synchrotron-based two-dimensional X-ray fluorescence technique. Thanks to the two-dimension modality, not only were the metal concentration dynamics throughout the entire electrodes followed during the aging process, but their spatial distribution was also revealed, suggesting the route of the material transformation. The dissolution of Mn and Ni, as well as the penetration of Zn inside the framework were detected, while the Mn aggregations were found outside the hexacyanoferrate framework. Additionally, the possibility of conducting X-ray absorption spectroscopy measurements on the regions of interest made it possible to explore the chemical state of each metal, and furthermore, synchrotron-based powder X-ray diffraction demonstrated the gradual structural modification in 30% Ni-containing sample series in terms of the different phase formation.

Nataly Carolina Rosero-Navarro

Nataly Carolina Rosero-Navarro

Hokkaido University

Batteries

Carbon-Free Cathode Materials Based on Titanium Compounds for Zn-Oxygen Aqueous Batteries

The impact of global warming has required the development of efficient new types of batteries. One of the most promising is Zn-O2 batteries because they provide the second biggest theoretical energy density, with relevant safety and a cycle of life long enough to be fitted for massive use. However, their industrial use is hindered by a series of obstacles, such as a fast reduction in the energy density after the initial charge and discharge cycles and a limited cathode efficiency or an elevated overpotential between discharge and charge. This work is focused on the synthesis of titanium compounds as catalyzers for the cathode of a Zn-O2 aqueous battery and their characterization. The results have shown a surface area of 350 m2/g after the elimination of the organic templates during heat treatment at 500 °C in air. Different thermal treatments were performed, tuning different parameters, such as intermediate treatment at 500 °C or the atmosphere used and the final temperature. Surface areas remain high for samples without an intermediate temperature step of 500 °C. Raman spectroscopy studies confirmed the nitridation of samples. SEM and XRD showed macro–meso-porosity and the presence of nitrogen, and the electrochemical evaluation confirmed the catalytic properties of this material in oxygen reaction reduction (ORR)/oxygen evolution reaction (OER) analysis and Zn-O2 battery tests.

Thang Phan Nguyen

Thang Phan Nguyen

Gachon University

Batteries

Film Thickness Effect in Restructuring NiO into LiNiO2 Anode for Highly Stable Lithium-Ion Batteries

The long-term stability of energy-storage devices for green energy has received significant attention. Lithium-ion batteries (LIBs) based on materials such as metal oxides, Si, Sb, and Sn have shown superior energy density and stability owing to their intrinsic properties and the support of conductive carbon, graphene, or graphene oxides. Abnormal capacities have been recorded for some transition metal oxides, such as NiO, Fe2O3, and MnO/Mn3O4. Recently, the restructuring of NiO into LiNiO2 anode materials has yielded an ultrastable anode for LIBs. Herein, the effect of the thin film thickness on the restructuring of the NiO anode was investigated. Different electrode thicknesses required different numbers of cycles for restructuring, resulting in significant changes in the reconstituted cells. NiO thicknesses greater than 39 μm reduced the capacity to 570 mAh g−1. The results revealed the limitation of the layered thickness owing to the low diffusion efficiency of Li ions in the thick layers, resulting in non-uniformity of the restructured LiNiO2. The NiO anode with a thickness of approximately 20 μm required only 220 cycles to be restructured at 0.5 A g−1, while maintaining a high-rate performance for over 500 cycles at 1.0 A g−1, and a high capacity of 1000 mAh g−1.

Reza Shahbazian-Yassar

Reza Shahbazian-Yassar

University of Illinois at Chicago

Batteries

High-Entropy Materials for Lithium Batteries

High-entropy materials (HEMs) constitute a revolutionary class of materials that have garnered significant attention in the field of materials science, exhibiting extraordinary properties in the realm of energy storage. These equimolar multielemental compounds have demonstrated increased charge capacities, enhanced ionic conductivities, and a prolonged cycle life, attributed to their structural stability. In the anode, transitioning from the traditional graphite (372 mAh g−1) to an HEM anode can increase capacity and enhance cycling stability. For cathodes, lithium iron phosphate (LFP) and nickel manganese cobalt (NMC) can be replaced with new cathodes made from HEMs, leading to greater energy storage. HEMs play a significant role in electrolytes, where they can be utilized as solid electrolytes, such as in ceramics and polymers, or as new high-entropy liquid electrolytes, resulting in longer cycling life, higher ionic conductivities, and stability over wide temperature ranges. The incorporation of HEMs in metal–air batteries offers methods to mitigate the formation of unwanted byproducts, such as Zn(OH)4 and Li2CO3, when used with atmospheric air, resulting in improved cycling life and electrochemical stability. This review examines the basic characteristics of HEMs, with a focus on the various applications of HEMs for use as different components in lithium-ion batteries. The electrochemical performance of these materials is examined, highlighting improvements such as specific capacity, stability, and a longer cycle life. The utilization of HEMs in new anodes, cathodes, separators, and electrolytes offers a promising path towards future energy …

Carmelo Mineo

Carmelo Mineo

Università degli Studi di Palermo

Batteries

Electric Vehicle Battery Disassembly Using Interfacing Toolbox for Robotic Arms

This paper showcases the integration of the Interfacing Toolbox for Robotic Arms (ITRA) with our newly developed hybrid Visual Servoing (VS) methods to automate the disassembly of electric vehicle batteries, thereby advancing sustainability and fostering a circular economy. ITRA enhances collaboration between industrial robotic arms, server computers, sensors, and actuators, meeting the intricate demands of robotic disassembly, including the essential real-time tracking of components and robotic arms. We demonstrate the effectiveness of our hybrid VS approach, combined with ITRA, in the context of Electric Vehicle (EV) battery disassembly across two robotic testbeds. The first employs a KUKA KR10 robot for precision tasks, while the second utilizes a KUKA KR500 for operations needing higher payload capacity. Conducted in T1 (Manual Reduced Velocity) mode, our experiments underscore a swift communication protocol that links low-level and high-level control systems, thus enabling rapid object detection and tracking. This allows for the efficient completion of disassembly tasks, such as removing the EV battery’s top case in 27 s and disassembling a stack of modules in 32 s. The demonstrated success of our framework highlights its extensive applicability in robotic manufacturing sectors that demand precision and adaptability, including medical robotics, extreme environments, aerospace, and construction.

Maria Fatima Ludovico de Almeida

Maria Fatima Ludovico de Almeida

Pontifícia Universidade Católica do Rio de Janeiro

Batteries

Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries

Rapid technological changes and disruptive innovations have resulted in a significant shift in people’s behavior and requirements. Electronic gadgets, including smartphones, notebooks, and other devices, are indispensable to everyday routines. Consequently, the demand for high-capacity batteries has surged, which has enabled extended device autonomy. An alternative approach to address this demand is battery swapping, which can potentially extend the battery life of electronic devices. Although battery sharing in electric vehicles has been well studied, smartphone applications still need to be explored. Crucially, assessing the batteries’ state of health (SoH) presents a challenge, necessitating consensus on the best estimation methods to develop effective battery swap strategies. This paper proposes a model for estimating the SoH curve of lithium-ion batteries using the state of charge curve. The model was designed for smartphone battery swap applications utilizing Gated Recurrent Unit (GRU) neural networks. To validate the model, a system was developed to conduct destructive tests on batteries and study their behavior over their lifetimes. The results demonstrated the high precision of the model in estimating the SoH of batteries under various charge and discharge parameters. The proposed approach exhibits low computational complexity, low cost, and easily measurable input parameters, making it an attractive solution for smartphone battery swap applications.

Varun Shreyas

Varun Shreyas

University of Louisville

Batteries

Functionalization of Cathode–Electrolyte Interface with Ionic Liquids for High-Performance Quasi-Solid-State Lithium–Sulfur Batteries: A Low-Sulfur Loading Study

We introduce a quasi-solid-state electrolyte lithium-sulfur (Li–S) battery (QSSEB) based on a novel Li-argyrodite solid-state electrolyte (SSE), Super P–Sulfur cathode, and Li-anode. The cathode was prepared using a water-based carboxymethyl cellulose (CMC) solution and styrene butadiene rubber (SBR) as the binder while Li6PS5F0. 5Cl0. 5 SSE was synthesized using a solvent-based process, via the introduction of LiF into the argyrodite crystal structure, which enhances both the ionic conductivity and interface-stabilizing properties of the SSE. Ionic liquids (IL) were prepared using lithium bis (trifluoromethyl sulfonyl) imide (LiTFSI) as the salt, with pre-mixed pyrrolidinium bis (trifluoromethyl sulfonyl) imide (PYR) as solvent and 1, 3-dioxolane (DOL) as diluent, and they were used to wet the SSE–electrode interfaces. The effect of IL dilution, the co-solvent amount, the LiTFSI concentration, the C rate at which the batteries are tested and the effect of the introduction of SSE in the cathode, were systematically studied and optimized to develop a QSSEB with higher capacity retention and cyclability. Interfacial reactions occurring at the cathode–SSE interface during cycling were also investigated using electrochemical impedance spectroscopy, cyclic voltammetry, and X-ray photoelectron spectroscopy supported by ab initio molecular dynamics simulations. This work offers a new insight into the intimate interfacial contacts between the SSE and carbon–sulfur cathodes, which are critical for improving the electrochemical performance of quasi-solidstate lithium–sulfur batteries.

ARPITA MONDAL

ARPITA MONDAL

Indian Institute of Technology Kharagpur

Batteries

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

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 …

Daniel Stroe

Daniel Stroe

Aalborg Universitet

Batteries

Lithium-Ion Supercapacitors and Batteries for Off-Grid PV Applications: Lifetime and Sizing

The intermittent nature of power generation from photovoltaics (PV) requires reliable energy storage solutions. Using the storage system outdoors exposes it to variable temperatures, affecting both its storage capacity and lifespan. Utilizing and optimizing energy storage considering climatic variations and new storage technologies is still a research gap. Therefore, this paper presents a modified sizing algorithm based on the Golden Section Search method, aimed at optimizing the number of cells in an energy storage unit, with a specific focus on the unique conditions of Denmark. The considered energy storage solutions are Lithium-ion capacitors (LiCs) and Lithium-ion batteries (LiBs), which are tested under different temperatures and C-rates rates. The algorithm aims to maximize the number of autonomy cycles—defined as periods during which the system operates independently of the grid, marked by intervals between two consecutive 0% State of Charge (SoC) occurrences. Testing scenarios include dynamic temperature and dynamic load, constant temperature at 25 °C, and constant load, considering irradiation and temperature effects and cell capacity fading over a decade. A comparative analysis reveals that, on average, the LiC storage is sized at 70–80% of the LiB storage across various scenarios. Notably, under a constant-temperature scenario, the degradation rate accelerates, particularly for LiBs. By leveraging the modified Golden Section Search algorithm, this study provides an efficient approach to the sizing problem, optimizing the number of cells and thus offering a potential solution for energy storage in off-grid PV systems.

James Ed Darnbrough

James Ed Darnbrough

University of Oxford

Batteries

Lithium Metal under Static and Dynamic Mechanical Loading

Macro-scale mechanical testing and finite element analysis of lithium metal in compression have been shown to suggest methods and parameters for producing thin lithium anodes. Consideration of engineering and geometrically corrected stress experiments shows that the increasing contact area dominates the stress increase observed during the compression, not strain hardening, of lithium. Under static loading, the lithium metal stress relaxes, which means there is a speed of deformation (engineering strainrate limit of 6.4×10−5 s−1) where there is no increase in stress during compression. Constant displacement tests show that stress relaxation depends on the initial applied stress and the amount of athermal plastic work within the material. The finite element analysis shows that barrelling during compression and the requirement for high applied stresses to compress lithium with a small height-to-width ratio are friction and geometric effects, respectively. The outcomes of this work are discussed in relation to the diminishing returns of stack pressure, the difficulty in closing voids, and potential methods for designing and producing sub-micron lithium anodes.

DINH MINH CHAU

DINH MINH CHAU

Changwon National University

Batteries

A Low-Cost and High-Efficiency Active Cell-Balancing Circuit for the Reuse of EV Batteries

In this paper, a high-efficiency and low-cost active cell-to-cell balancing circuit for the reuse of electric vehicle (EV) batteries is proposed. In the proposed method, a battery string is divided into two legs to transfer the charge from each cell in one leg to that in the other and a bidirectional CLLC resonant converter is used to transfer energy between the selected cells. Thanks to the proposed structure, the number of bidirectional switches and gate drivers can be reduced by half compared to the conventional direct cell-to-cell topologies, thereby achieving lower cost for the system. The CLLC converter is used to transfer the charge, and it is designed to work at resonant frequencies to achieve zero-voltage zero-current switching (ZVZCS) for all the switches and diodes. Consequently, the system’s efficiency can be enhanced, and hence, the fuel economy of the system can also be improved significantly. To verify the performance of the proposed active cell-balancing system, a prototype is implemented for balancing the three EV battery modules that contain twelve lithium-ion batteries from xEV. The maximum efficiency achieved for the charge transfer is 89.4%, and the balancing efficiency is 96.3%.

Shumaila Babar

Shumaila Babar

University of Surrey

Batteries

DFT Simulations Investigating the Trapping of Sulfides by 1T-LixMoS2 and 1T-LixMoS2/Graphene Hybrid Cathodes in Li-S Batteries

The aim of this study is to investigate new materials that can be employed as cathode hosts in Li-S batteries, which would be able to overcome the effect of the shuttling of soluble polysulfides and maximize the battery capacity and energy density. Density functional theory (DFT) simulations are used to determine the adsorption energy of lithium sulfides in two types of cathode hosts: lithiated 1T-MoS2 (1T-LixMoS2) and hybrid 1T-LixMoS2/graphene. Initial simulations of lithiated 1T-MoS2 structures led to the selection of an optimized 1T-Li0.75MoS2 structure, which was utilized for the formation of an optimized 1T-Li0.75MoS2 bilayer and a hybrid 1T-Li0.75MoS2/graphene bilayer structure. It was found that all sulfides exhibited super-high adsorption energies in the interlayer inside the 1T-Li0.75MoS2 bilayer and very good adsorption energy values in the interlayer inside the hybrid 1T-Li0.75MoS2/graphene bilayer. The placement of sulfides outside each type of bilayer, over the 1T-Li0.75MoS2 surface, yielded good adsorption energies in the range of −2 to −3.8 eV, which are higher than those over a 1T-MoS2 substrate.

Santosh Kumar Behara

Santosh Kumar Behara

Indian Institute of Technology Madras

Batteries

An Investigation into the Viability of Battery Technologies for Electric Buses in the UK

This study explores the feasibility of integrating battery technology into electric buses, addressing the imperative to reduce carbon emissions within the transport sector. A comprehensive review and analysis of diverse literature sources establish the present and prospective landscape of battery electric buses within the public transportation domain. Existing battery technology and infrastructure constraints hinder the comprehensive deployment of electric buses across all routes currently served by internal combustion engine counterparts. However, forward-looking insights indicate a promising trajectory with the potential for substantial advancements in battery technology coupled with significant investments in charging infrastructure. Such developments hold promise for electric buses to fulfill a considerable portion of a nation’s public transit requirements. Significant findings emphasize that electric buses showcase considerably lower emissions than fossil-fuel-driven counterparts, especially when operated with zero-carbon electricity sources, thereby significantly mitigating the perils of climate change.