Increasing generalization capability of battery health estimation using continual learning

Cell Reports Physical Science

Published On 2023/12/20

Accurate and reliable estimation of battery health is crucial for predictive health management. We report a strategy to strengthen the accuracy and generalization of battery health estimation. The model can be initially built based on one battery and then continuously updated using unlabeled data and sparse limited labeled data collected in early stages of testing batteries in different scenarios, satisfying incremental improvement in practical applications. We generate our datasets from 55 commercial pouch and prismatic batteries aged for more than 116,000 cycles under various scenarios. Our model achieves a root mean-square error of 1.312% for the estimation of different dynamic current modes and rates and variable temperature conditions over the entire lifespan using partial charging data. Our model is interpreted by the post hoc strategy with unbiased hidden features, prevents catastrophic forgetting, and …

Journal

Cell Reports Physical Science

Published On

2023/12/20

Volume

4

Issue

12

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

Simona Onori

Simona Onori

Stanford University

Position

H-Index(all)

46

H-Index(since 2020)

34

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Energy Storage Systems

Electric and Hybrid Vehicles

Emission Mitigation Devices - Physics-based Modeling- Control&Optimization

University Profile Page

Yunhong Che

Yunhong Che

Chongqing University

Position

H-Index(all)

16

H-Index(since 2020)

16

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Energy Storage Systems

Transportation electrification

Prognostics and Health Management

Battery

University Profile Page

Other Articles from authors

Yunhong Che

Yunhong Che

Chongqing University

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 …

Simona Onori

Simona Onori

Stanford University

Battery management system for determining a health of a power source based on an impedance indicator

A method is provided. The method includes determining an open circuit voltage of a battery of a vehicle. The method also includes determining a voltage of a current provided to the battery of the vehicle. The method further includes determining a impedance indicator based on the voltage, the open circuit voltage, and the current provided to the battery of the vehicle. The method further includes determining a health of the battery based on the impedance indicator.

Yunhong Che

Yunhong Che

Chongqing University

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 …

Simona Onori

Simona Onori

Stanford University

arXiv preprint arXiv:2402.18859

Taking Second-life Batteries from Exhausted to Empowered using Experiments, Data Analysis, and Health Estimation

The reuse of retired electric vehicle (EV) batteries in electric grid energy storage emerges as a promising strategy to address environmental concerns and boost economic value. This study concentrates on devising health monitoring algorithms for retired batteries (BMS) deployed in grid storage applications. Over 15 months of testing, we compile, analyze, and publicly share a dataset of second-life (SL) batteries, implementing a cycling protocol simulating grid energy storage load profiles within a 3 V-4 V voltage window. Four machine learning-based health estimation models, relying on BMS features and initial capacity, are developed and compared, with the selected model achieving a Mean Absolute Percentage Error (MAPE) below 2.3% on test data. Additionally, an adaptive online health estimation algorithm is proposed by integrating a clustering-based method, limiting estimation errors during online deployment. These results constitute an initial proof of concept, showcasing the feasibility of repurposing retired batteries for second-life applications. Based on obtained data and representative power demand, these SL batteries exhibit the potential, under specific conditions, for over a decade of grid energy storage use.

Simona Onori

Simona Onori

Stanford University

Journal of Dynamic Systems, Measurement, and Control

Exergy Management Strategies for Hybrid Electric Ground Vehicles: A Dynamic Programming Solution

In this work, exergy management strategies (ExMSs) for hybrid electric ground vehicles (HEVs) are developed. The main advantage of using the exergetic framework is the possibility of pursuing unconventional optimization goals that are inaccessible to the standard energy management strategy (EMS). For instance, in military applications, the critical goal of preventing thermal imaging detection from adversary units does not seem achievable with the conventional EMS. On the other hand, the exergy-based framework can be adopted to reduce the vehicle thermal emissions through the minimization of exergy terms related to heat exchange. Moreover, the overall efficiency of the vehicle can be increased through the minimization of the exergy destruction, a quantity that is not quantifiable by energy-based methods. In this paper, the exergetic model of a series hybrid electric military truck and the exergetic model of the …

Yunhong Che

Yunhong Che

Chongqing University

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 …

Simona Onori

Simona Onori

Stanford University

IEEE Transactions on Transportation Electrification

An experimentally validated electro-thermal EV battery pack model incorporating cycle-life aging and cell-to-cell variations

Lithium-ion batteries are used in a wide variety of applications. To meet the power and energy demands of these applications battery packs are composed of hundreds to thousands of cells. The electrical and thermal interactions between cells introduce additional complexity in the pack dynamics. To capture these effects, a battery pack model composed of 192 cells based on a first-generation (2012) Nissan Leaf battery pack is developed in MATLAB/Simulink/Simscape. With this model, we simulate the electrical dynamics (using a first-order equivalent-circuit model), the thermal dynamics (using a first-order lumped-parameter thermal model), and the aging dynamics (using a semi-empirical severity factor-based model) of every cell in the pack and we also create a pack thermal model that explicitly captures the heat exchange between the modules, and the cells contained within, during operation. The models are …

Simona Onori

Simona Onori

Stanford University

arXiv preprint arXiv:2404.10022

COBRAPRO: A MATLAB toolbox for Physics-based Battery Modeling and Co-simulation Parameter Optimization

COBRAPRO is a new open-source physics-based battery modeling software with the capability to conduct closed-loop parameter optimization using experimental data. Physics-based battery models require systematic parameter calibration to accurately predict battery behavior across different usage scenarios. While parameter calibration is essential to predict the dynamic behavior of batteries, many existing open-source DFN tools lack parameter identification features. COBRAPRO addresses this gap by featuring an embedded parameter optimization routines that optimizes the model parameters by minimizing the error between the simulated and experimentally observed current-voltage data. With COBRAPRO, users can non-invasively identify unknown battery model parameters for any given battery chemistry.

Yunhong Che

Yunhong Che

Chongqing University

Reliability Engineering & System Safety

Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection

Predictive health assessment is of vital importance for smarter battery management to ensure optimal and safe operations and thus make the most use of battery life. This paper proposes a general framework for battery aging prognostics in order to provide the predictions of battery knee, lifetime, state of health degradation, and aging rate variations, as well as the assessment of battery health. Early information is used to predict knee slope and other life-related information via deep multi-task learning, where the convolutional-long-short-term memory-bayesian neural network is proposed. The structure is also used for online state of health and degradation rate predictions for the detection of accelerating aging. The two probabilistic predicted boundaries identify the accelerating aging regions for battery health assessment. To avoid wrong and premature alarms, the empirical model is used for data preprocessing and …

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.

Simona Onori

Simona Onori

Stanford University

npj Computational Materials

Accelerating the transition to cobalt-free batteries: a hybrid model for LiFePO4/graphite chemistry

The increased adoption of lithium-iron-phosphate batteries, in response to the need to reduce the battery manufacturing process’s dependence on scarce minerals and create a resilient and ethical supply chain, comes with many challenges. The design of an effective and high-performing battery management system (BMS) for such technology is one of those challenges. In this work, a physics-based model describing the two-phase transition operation of an iron-phosphate positive electrode—in a graphite anode battery—is integrated with a machine-learning model to capture the hysteresis and path-dependent behavior during transient operation. The machine-learning component of the proposed “hybrid” model is built upon the knowledge of the electrochemical internal states of the battery during charge and discharge operation over several driving profiles. The hybrid model is experimentally validated over 15 h of …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Electric vehicle battery charging strategy

As a key enabler for transportation electrification and a contributor toward the net-zero carbon future, battery plays a pivotal role in determining the energy management performance of electric vehicles. Technical challenges facing the development of advanced automotive battery charging arise from various contradictory objectives, immeasurable internal states, and hard constraints. This chapter presents a critical introduction to the state-of-the-art charging strategies for the electric vehicle battery and their key enabling technologies. Specifically, battery charging solutions for electric vehicles are first classified and discussed. Then, the battery models on which these solutions rest are stated, the related charging frameworks are summarized, and the advantages and drawbacks of the adopted technologies are discussed. Suggestions for overcoming the limitations of the discussed charging strategies are proposed …

Remus Teodorescu

Remus Teodorescu

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 …

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Cell Reports Physical Science

Single-crystalline Mg3Sb2-xBix-based thermoelectric materials

N-type Mg3Sb2-xBix-based materials have become promising candidates for thermoelectric cooling and power generation. Most recently, Mg3Sb2-xBix single crystals have been successfully grown; these demonstrate superior thermoelectric performance and are essential to understanding electron and phonon transport. In this perspective, recent progress on single-crystalline Mg3Sb2-xBix-based thermoelectric materials is reviewed. Firstly, methods to grow Mg3Sb2-xBix single crystals are briefly introduced. Then, a comparison of electrical properties between the single-crystalline samples and polycrystalline counterparts is discussed. Afterward, theoretically calculated and experimentally identified point defects, along with their impact on the transport properties, are summarized. Moreover, the structural and thermoelectric anisotropy in Mg3Sb2-xBix single crystals is discussed. Finally, the perspective concludes …

Hong Ye

Hong Ye

University of Science and Technology of China

Cell Reports Physical Science

Bionic metamaterial for multispectral-compatible camouflage of solar spectrum and infrared in the background of vegetation

Hyperspectral and thermal infrared detectors have rendered single-band camouflage ineffective against the vegetative background commonly used to conceal military equipment, thus threatening the concealment efficacy. Here, to address this challenge, we draw inspiration from natural leaves and develop a bionic metamaterial with a three-layer structure for multispectral-compatible camouflage. This metamaterial exhibits similar solar spectral reflection characteristics to natural leaves and low infrared emissivity, countering both hyperspectral and infrared detections in vegetation backgrounds. Experimental results demonstrate a 94.7% similarity in solar spectral reflectance between the bionic metamaterial and natural leaves. Furthermore, the metamaterial's infrared emissivity is significantly lower (0.64 in 3–5 μm and 0.52 in 8–14 μm) compared to natural leaves (approximately 0.95 in both wavebands). Designed …

María de Jesús Gálvez-Vázquez

María de Jesús Gálvez-Vázquez

Universität Bern

Cell Reports Physical Science

Highly selective scalable electrosynthesis of 4-hydroxybenzo [e]-1, 2, 4-thiadiazine-1, 1-dioxides

4H-4-Hydroxybenzo[e]-1,2,4-thiadiazine-1,1-dioxides are based on a widely found structural motif for pharmaceutical applications, having an additional unique exocyclic N–O bond that is not accessible by conventional synthetic routes. Electrochemistry offers a sustainable tool for the direct synthesis of these heterocyclic structures containing this N-hydroxy modification. Here, we report a highly selective synthesis of 4H-4-hydroxybenzo[e]-1,2,4-thiadiazine-1,1-dioxides by direct reduction of widely available nitro arenes in almost quantitative yields. The electro-synthetic protocol is applied to more than 40 diverse examples, highlighting the versatility of this method. Furthermore, the technical relevance is demonstrated by two multi-gram-scale syntheses.

Qilong Cheng

Qilong Cheng

University of California, Berkeley

Cell Reports Physical Science

A dynamic wall design with tunable angular emissivity for all-season thermal regulation

Radiative cooling achieves passive cooling by emitting long-wavelength infrared radiation (LWIR) to outer space. Increasing attention has been paid to radiative cooling walls in the building envelope. However, its undesired cooling effect in the winter exacerbates the heating demand of buildings. Here, we report a scalable wall design with dynamic rotatable fins (FinWall) to achieve tunable angular LWIR emissivity on the wall surface, enabling all-season thermal regulation. Field tests demonstrate that the FinWall yields a ∼2.0°C temperature elevation under cold weather and a ∼3.1°C temperature drop under hot weather compared to conventional high LWIR emissivity walls. This translates to extra power savings of 37 W m−2 for heating and 53 W m−2 for cooling. Further building simulations indicate that a mid-rise apartment building equipped with FinWalls can save 24% (or 10%) annual energy versus the same …

Joonmyung Choi

Joonmyung Choi

Hanyang University

Cell Reports Physical Science

A computational mechanics model for producing molecular assembly using molecularly woven pantographs

The weave-based interlocking design has received considerable attention for preparing the patterned linkage of molecules via formation and dissociation of highly non-covalent bonds among molecules. Here, we design the mechanical behavior of a nanoscale pantograph structure in which tetraphenylethene derivatives are interlocked in the form of warp and weft strands in silico. The kinetics related to the width strain of the entire film are evaluated by quantifying the molecular-scale tilting deformation between the warp and weft strands following the inflow and outflow of methanol. The mechanical stiffness, structural durability, and deformation repeatability of the system caused by tightly interlocked molecular strands are investigated together. The cucurbituril hybrids present on the interface are successfully self-assembled into molecular bearings using the in-plane working stroke of the pantograph film.

Atin pramanik

Atin pramanik

University of St Andrews

Cell Reports Physical Science

Hard Carbon Anode for Lithium, Sodium and Potassium-Ion Batteries: Advancement and Future Perspective

Due to its overall performance, hard carbon (HC) is a promising anode for rechargeable lithium-, sodium-, and potassium-ion batteries (LIBs, NIBs, KIBs). The microcrystalline structure morphology of HCs facilitates the alkali metal -ion uptake and fast ion intercalation and deintercalation throughout the pores with low-potential intercalation properties. However, the large-scale industrial application of HCs is still lagging because of the first-cycle reversible capacity, which results in low initial Coulombic efficiency (ICE) and voltage hysteresis. This review focuses on the fundamental mechanism of HCs as alkali metal-ion batteries, with the current issues being discussed. This includes the formation of solid electrolyte interphase during the first cycle with low ICE, safety concerns, and improved performances, which are vital for practical applicability. The current state-of-the-art of HC anodes is discussed here with recent …

Marcos A. Reyes-Martinez

Marcos A. Reyes-Martinez

Princeton University

Cell Reports Physical Science

Topology as a limiting factor for mechanical properties in disordered networks

Disordered networks are ubiquitous in both natural and synthetic systems, with mechanical properties that span from significantly compliant to extremely rigid. While the significance of network topology in determining their overall mechanical properties has been established, the coupling between network topology and intrinsic material properties to control elasticity and fracture is not well understood. Here, we show that although the topology of two-dimensional disordered networks defines the occurrence of local network bond rupture events, it is the material properties of the constituent material that dictate the energy required to cause failure. Our results reveal opposite trends between the stiffness and fracture properties that depend on the constituent material, which is linked to how topology and materials couple to enhance either the local stiffness or extensibility of the network. We apply this understanding to …