CALCE is focused on the development of state of the art battery management systems (BMS) for single and multi-cell systems to provide the most accurate state of charge (SOC) and state of health (SOH) metrics. CALCE is working towards this goal through studies on fundamental process that degrade battery cells, battery testing methods, techniques for battery failure analysis, and advanced data processing techniques for implementation of battery prognostics and health management (PHM) solutions. PHM is an enabling discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk. By applying these methods, CALCE hopes to develop a BMS that not only assures safe usage, but also provides the best reliability and operational health information to the user. 


Lithium-ion batteries represent a major and expanding energy storage solution. Lithium ion batteries present more advantages, including high energy densities, cycling durability, no memory effect and low self-discharge over other available battery chemistries. At present, lithium-ion batteries are used in virtually all portable electronic devices including cell phones, laptops, and cameras. Demand for these batteries will continue to increase as the market for hybrid and electric vehicles takes over pure gasoline-powered cars. Other markets that require portable power supplies are also taking significant interest in lithium-ion batteries. These markets have begun utilizing lithium-ion batteries in military applications such as unmanned ground vehicles, aerospace applications for unmanned aerial vehicles and satellite hardware, medical applications such as implantable devices and portable health monitoring equipment, and in conjunction with renewable energy technologies such as solar panels and wind turbines.

Although lithium-ion batteries are reliable and popular as energy storage devices, the thermal and electrochemical instability of the electrodes and the flammability of the electrolyte make lithium-ion batteries prone to catastrophic failures. Failures involving fires and explosions have resulted in numerous accidents in the consumer electronics, automotive and airplane industry involving millions of batteries. Fig. 3 shows a damaged United Parcel Service (UPS) cargo plan fire in 2006, a failed battery pack in a Boeing 787 in 2013 and a Chevy Volt battery fire after side-impact collision test in 2012.

Fig. 3 Battery Failure (a) UPS cargo plane fire (b) lithium-ion battery pack in Boeing 787 (c) Chevy Volt battery fire three weeks after side-impact collision test.

The strides made over the past two decades of lithium ion battery commercialization have opened the door to many future opportunities. However, the technology surrounding lithium-ion batteries is still in its infancy and has room for improvement. This improvement can be realized through two major avenues:
  • The first is through the development of new battery materials. For example, it has been known for decades that the theoretical capacity of silicon to store lithium polymer batteries in today’s market. However, lithium insertion into silicon is accompanied by a large volume expansion resulting in large stresses and structural damage to the anode.
  • The second avenue for improvement is the optimization of battery performance and reliability. Apart from the battery materials, the research can be directly applied to battery management systems (BMSs), which are electronic systems that monitor battery parameters and govern current and voltage to assure safe and reliable operation.
CALCE is looking at different research directions as follows:
  • With respect to material science, CALCE’s degradation analysis identifies dominate mechanisms of failure and how these are influenced by usage conditions. This information can be used to help optimize cell design based on the expected field conditions.
  • Taking into account the significance of BMSs, CALCE has been generating data to test and validate algorithms that can be used in advanced battery management systems, particularly in the area of state of charge and state of health estimation.
CALCE proposed an advanced BMS with prognostic components. The novel features in the new BMS include SOC prediction, SOH prediction, PHM-based decision making: Fig. 4 illustrates a decision making example based on PHM, the SOH of the battery can be evaluated. We not only can know the SOH at the current cycle, but also can predict the SOH in a future cycle. This allows for better mission plan and maintenance arrangement. Since we know the maximum capacity of the battery, we can predict whether the battery will run out of power during the mission. Second, PHM provides state-of-life prediction, or the remaining useful cycles of the battery. In this example, the remaining useful cycle is 18 cycles. Based on this information, the maintenance arrangement and action can be scheduled in advance to ensure the availability of the system and save cost.
Fig. 4 Decision making based on battery PHM.
Safety incidents plague the lithium-ion battery industry, with widely publicized recalls and hazardous events regularly appearing in the news. Lithium-ion batteries are used as power sources for many types of systems; however, despite technological improvements in battery chemistry and design, safety is still a concern. When improperly fabricated or used, lithium-ion batteries are prone to escalating chemical reactions known as thermal runaway. CALCE is analyzing the root causes of battery thermal runaway. Based on the root cause analysis, strategies to improve battery safety and prevent thermal runaway failures are being recommended.
Although lithium-ion batteries are reliable and popular as energy storage devices, the thermal and electrochemical instability of the electrodes and the flammability of the electrolyte make lithium-ion batteries prone to catastrophic failures. Failures involving fires and explosions have resulted in numerous recalls in the consumer electronics industry involving millions of batteries. Lithium-ion batteries used in electric vehicles (EVs) are susceptible to the same types of failure. However, due to different life cycle load, the batteries used in EVs may experience additional failure modes and mechanisms. Additionally, EVs often use hundreds or thousands of high-energy batteries, and battery safety remains a major concern. Figure 1 shows the latest accident that electric vehicle battery caught fire in the charging station on April 26th, 2015 in Shenzhen, China. Figure 2 shows a Chevy Volt that was destroyed by a battery three weeks after a side-impact collision test that was conducted by the U.S. National Highway Traffic Safety Administration (NHTSA).
Figure 1. Wuzhoulong EV bus with LiFePO4 power batteries caught fire
in the charging station on Apr. 26th, 2015 in Shenzhen, China
Figure 2. Chevy Volt battery fire three weeks after side-impact test by the U.S. NHTSA
Figure 3. (a) UPS cargo plane fire (b) lithium-ion battery pack in Boeing 787
The transportation or use of lithium-ion batteries in aerospace applications have also resulted in catastrophic failure. Figure 3 (a) below shows a damaged United Parcel Service (UPS) cargo plane that was carrying a large number of lithium-ion batteries as cargo when a fire broke out. Figure 3 (b) below shows the failed battery pack in a Boeing 787. It was suspected that the problems originated from malfunctioning lithium-ion batteries: the batteries showed signs of short circuit and thermal runaway, which led to overheating and fire. Figure 4 shows major EV battery incidents since 2008.
Figure 4. Major EV battery fire accidents since 2008
One of the most significant cell components to ensure cell safety is the separator. Its main function is to prevent physical contact between the anode and cathode and at the same time facilitate ion transport in the cell. The challenging issue on designing a battery separator is how to balance between mechanical robustness and transport properties. Now, the separator has been designed with multi-layers and a shutdown feature. The idea behind it is that different multi-layers have different phase transition temperatures. The shutdown feature is expected to work when there is an increase in cell temperature. The lower melting component melts and fills the pores of one of the solid layers. Hence, the ion transport will stop and the current drain of the cell will become zero. Figure 5 shows how the shutdown separator functions. The related work can be found in [1]. Through root cause analysis for design becoming an effective way to improve battery safety.
Figure 5. How separator shutdown functions [1]


[1] C. J. Orendorff, "The role of separators in lithium-ion cell safety," Electrochemical Society Interface, vol. 21, pp. 61-65, 2012.


The strides made over the past two decades of lithium ion battery commercialization have opened the door to many future opportunities. However, the technology surrounding lithium-ion batteries is still in its infancy and has room for improvement. This improvement can be realized through either development of new battery materials or through optimization of battery performance and reliability. CALCE has its projects spread across each of the sections shown in the figure on the right. CALCE is majorly focused on the development of state of the art battery management systems (BMS) for single and multi-cell systems to provide the most accurate state of charge (SOC) and state of health (SOH) metrics. CALCE is working towards this goal through studies on fundamental process that degrade battery cells, battery testing methods, techniques for battery failure analysis, and advanced data processing techniques for implementation of battery prognostics and health management (PHM) solutions.

Exploration of Novel Accelerated Testing Conditions for Qualifying Li-ion Batteries

Life testing of Li-ion batteries is conducted to qualify a battery by assessing its capacity fade and power requirements for its targeted application. However, testing at normal operating conditions can be quite time-consuming. To identify highly accelerated testing methods, CALCE has been collaborating with a major consumer electronics manufacturer and 6 of the world’s largest battery manufacturers. While the effects of temperature on accelerated degradation of Li-ion battery performance have been studied in the literature, the combined effects of discharge C-rate with other factors still require deeper understanding. 
Increasing the charge/discharge C-rate reduces the time required to conduct a charge-discharge cycle and affects capacity fade over cycles. Of note is whether discharge C-rate affects battery capacity fade behavior, through ohmic heating and the resulting increase in battery temperature or whether the C-rate as its own unique degradation effects. Other parameters of interest include the effects of rest time after batteries are fully charged on long-term capacity fade and on degradation acceleration. Depth of discharge and charge cut-off current are additional related parameters which can affect the state of charge ranges during cycling and can be used for accelerating the battery degradation. 



Thermal Runaway Characterization of Li-ion Batteries

Recent Li-ion battery fire incidents in smartphones, hover boards, e-cigarettes, electric vehicles, and aircrafts highlight the limited understanding of battery thermal runaway failure mechanisms. Various studies have been conducted to investigate Li-ion thermal runaway failures but still the literature is inadequate for designing a safety system. CALCE is currently studying the effects of thermal and mechanical design parameters (e.g., venting) and operating conditions on Li-ion battery thermal runaway by performing a detailed electrical and thermal characterization of battery under catastrophic failure conditions to improve the safety mechanisms and thermal management of these batteries.
Commercial 18650 Li-ion cells are used for the experiments which have a charge cut-off voltage of 4.2 V and a discharge cut-off voltage of 2.5 V, specified by the manufacturer. The batteries are taken to specified SOC levels by charging and discharging the cell using an Arbin BT2000 Battery Tester. In order to understand battery thermal runaway behavior, heating wire is wrapped around the cell and supplied by DC power and Thermo couples are mounted near the two terminals of the cell to monitor the cell surface temperature. The cell voltage measurements are recorded during the entire testing.

Investigation of Tab Design and Failures in Cylindrical Li-ion Batteries

Lithium-ion (Li-ion) batteries have powered today’s portable and rechargeable products, and the cylindrical format is widely used in applications ranging from e-cigarettes to electric vehicles. The tabs in these batteries connect the electrodes (current collectors) to the external circuits. Li-ion battery failures such as fires and explosions can be caused by manufacture defects, especially associated with tab defects such as welding burrs and improper tab locations. The electrode tabs are the metallic strips that are welded onto the current collectors without active materials. When the battery is charged or discharged, the temperature around the electrode tabs is higher than other places inside the cell due to the high current density. CALCE is studying the effect of tab design and placement on battery reliability and safety.


Partial State of Charge (SOC) Cycling Effects on Capacity Fade of Lithium-ion Cells

In practical applications, batteries may undergo charge-discharge cycling only for partial SOC ranges as opposed to the full 0%–100% range. CALCE has been performing cycle life testing of commonly used graphite/LiCoO2 pouch cells in different SOC ranges (e.g., 0-100%, 20-80%, 20-100%) to understand the effects of different SOC ranges on battery capacity fade and to model the battery capacity fade as a function of mean SOC, ∆SOC and cycle count. The developed models can be used for battery health management in field applications as well as for accelerated testing during battery qualification.

SOC ranges during cycling affect degradation mechanisms such as SEI layer formation and crack generation in the electrode. For example, as we increase the upper limit of SOC (end of charge voltage) in cycling, the amount of lithium in the anode increases, resulting in anode lattice volume expansion and causing localized stress. Also continuous cycling with higher ∆SOC value increases the probability of crack generation in the anode due to cyclic fatigue. These cracks in the anode provide fresh sites for electrolyte reduction and SEI layer growth, causing loss of cycleable lithium and higher electronic resistance. The figure below shows the effect of lowering the ∆SOC on the battery degradation.

Ref: Saurabh Saxena, Christopher Hendricks and Michael Pecht, Cycle Life Testing and Modeling of Graphite/ LiCoO2 cells under different state of charge ranges, Journal of Power Sources, 327 (2016), pp.394-400, 2016.

Accelerated Degradation Models for C-rate Loading of Lithium-ion Batteries

Qualification testing for Li-ion batteries can be quite time consuming. To give a perspective, an example of battery cycle life testing with C-rate as one of the stress factors can be considered. The battery current is usually expressed in terms of C-rate; e.g. the battery current normalized to the rated capacity (C) of the battery. For a 1 Ah battery, a C-rate of 1C represents a 1 A current; C/2 (0.5C) rate represents a 0.5 A current. Thus, in order to test a battery at a C-rate of C/2, it will take approximately 4h to complete 1 cycle (not counting any resting period to cool the battery down). For Li-ion batteries that are expected to have cycle lives in the range of 1000 cycles, then nearly 6 months is required to test at a C-rate of C/2. Testing at a high C-rate is one approach to accelerated testing for Li-ion batteries.
To assess the reliability of a product quickly, accelerated reliability testing is conducted by increasing the loading (stress) conditions on the product. Acceleration models are used to extrapolate the testing results in accelerating stress variable and often in time as well. CALCE is currently developing accelerated degradation models which utilize both the historical degradation data and the basic physics behind the degradation of batteries. These models will be useful in reducing the time required for battery qualification to less than 200 cycles or 1 month.

Simplified Electrochemical Model and Parameter Estimation

As electrochemical models can accurately simulate battery behaviors with the entire scope of state of charge (SOC), they are very appealing in BMS. However, the large computational cost of solving partial differential equations limits their practical applications. On account of low computational cost, simplified electrochemical models are more suitable. A simplified electrochemical thermal coupling model with reduced and regrouped model parameters was established and a method for nondestructive parameter estimation for individual cells was developed based on excitation response analysis. The parameters were classified into two categories: inherent characteristic parameters and mechanistic parameters. Inherent characteristic parameters could be obtained by consulting manufacturers directly or measuring. Mechanistic parameters were obtained by the excitation response analysis. According to different response time of different processes in the developed model when a cell was applied with different current excitations, the corresponding parameters were then obtained by least square fit.

The potential application of this model is that it can be applied to estimate SOC of single cell. And the model parameters can also be used as features to assess battery health state. The remaining useful life of battery can be predicted by analyzing the variations of parameters at different aging stages based on the developed model. However, the large number of model parameters and the long period of testing time are currently the limits in terms of parameter estimation for battery pack. According to the contributions of model parameters on battery behavior simulation, the selection of key parameters can be a potential solution to reduce the number of estimated parameters, and an alternative short testing schedule can also reduce the estimation cost.

Ref: [1] Li J, Wang L, Lyu C, Wang H, Liu X. New method for parameter estimation of an electrochemical-thermal coupling model for LiCoO2 battery. Journal of Power Sources. 2016;307:220-30. [2] Li J, Wang L, Lyu C, Wang H, Lai Q. A method for SOC estimation based on simplified mechanistic model for LiFePO4 battery. Energy. 2016;114C:1266-76.

Electrochemical Characterization of Lithium-ion Batteries Using a Three-electrode System

Recently, Lithium-ion batteries have been used in many electronic devices including laptops, smartphones, electric vehicles (EVs) and are also being considered for military and space applications. Generally, (commercial) Li-ion batteries have two-electrode. Therefore, the battery voltage or impedance can be measured only across the negative and positive electrode. In order to understand the fundamental electrochemical characteristics and to utilize the Li-ion battery chemistry more efficiently and safely, it is of interest to study the performance of each electrode separately. A three-electrode system is able to interpret the electrochemical characteristics of the individual electrodes. A widely used commercial 18650 battery (LiFePO4/ graphite) was reconstructed into a three-electrode full cell. Based on the three-electrode cell, the voltage and impedance of not only the full cell but also the individual electrodes were monitored. Accordingly, the electrochemical behavior of the commercial cell was explained and the contribution of each electrode to the full cell was identified.


Dendrite Formation Mechanism

Lithium-ion batteries are commonly used in daily life. Concerns regarding lithium-ion battery safety are increasing with the widespread use of these cells in various applications. Among all the reported battery incidents, lithium dendrite formation causing internal short circuits was considered as the direct or indirect reason for battery failure. Dendrites can cause short-circuits, which can lead to catastrophic failures and even fires. Lithium dendrite is a metallic microstructure that forms on the negative electrode during the charging process. This is one possible reason for the internal short-circuits of lithium ion batteries. The lithium dendrite issue can occur in the lithium ion batteries when the battery is overcharged or charged at low temperatures. The dendrite growth is influenced by the applied current density.
In order to increase Li-ion battery safety, it is necessary to conduct research on lithium dendrite formation mechanism. For this purpose, an in-situ observation method was used to detect dendrite formation at various current densities and temperatures. The relationship between the applied current density and the dendrite growth rate is the research focus in the first step of study. In order to determine the dendrite growth rate, a symmetrical lithium cell (both the positive and negative electrodes were made of lithium metal) was charged at constant current. The developed in-situ testing method can be used for identification of dendrite formation inside cells. Battery safety operation boundary conditions can be determined using this work.

An Early Degradation Detection Method for Lithium-ion Batteries

Early detection of the potential degradation in the Lithium-ion batteries can avoid catastrophic failure and reduce the maintenance cost. In the conventional battery failure detection system, the sensors can only detect the external signals of the cell, such as the current, voltage and temperature. However, the cell’s health state cannot be directly reflected from these signals. Actually, the health state of a cell is also physically coupled with the distribution and changes in the material properties, such as the density and modulus. Thus, a sensor that can sense the inherent physical changes in the cell during operation is more desirable for early detection of the degradation in the cell. Ultrasonic inspection method is used to detect internal material defects in metallic, composite materials, etc. This method has the potential to reflect the internal state of the cells, and provide early fault indications of failures, such as of internal gas formation, material deposition, and internal density changes.
We are now conducting ultrasonic test for three-type of polymer lithium-ion batteries using a set of ultrasonic hardware provided by X-wave Innovations, Inc. This hardware contains of a thin transducer which is mounted on the cell surface using petroleum jelly, a pulser-receiver (model US-Key) with DAQ capability. This hardware has small volume and low power compared with commercial bulky ultrasonic probes and equipment. Ultrasonic signals are collected after every charge-discharge cycling test of the cells. The time of flight and amplitude of the reflected echo are extracted from the ultrasonic signals. Now it can be seen that there is a strong correlation between the signal features and the cell capacity. This results preliminary verify that the ultrasonic signals can provide very useful information to indicate the health state of the cell and the ultrasonic inspection method can be used for early degradation detection of the cell.

Battery Management System (BMS) Research

CALCE is dedicated to developing a BMS that not only assures safe usage, but also provides the most reliable performance and operational battery health information.

A BMS is an electronic device that manages a rechargeable battery in order to protect it from damage, prolong its life, maintain it in a healthy state, and provide the user with its operational status. A BMS consists of a number of sensors that measure the battery parameters (current, voltage, impedance, and temperature). The central unit of a BMS is comprised of a set of models and algorithms that estimate the battery SOC and SOH and then, based on the state estimation, make control strategies.
Another essential function of a BMS is cell balancing, which is vital for maximizing the usable battery capacity and lifetime. A typical BMS for electric vehicles (EVs) should contain the following functions: data acquisition, cell protection, charge/discharge control, SOC and SOH estimation, cell balancing, thermal management, and communication. The problem of state estimation must be considered in the context of the entire BMS. Certain applications may have restrictions on the types of data that can be collected. For example, a BMS in an EV can rely on frequent discharge data in order to make SOC estimations, whereas a BMS in a standby power supply must make state estimations offline due to infrequent use.
Therefore, the type of sensors available to a BMS must be considered when developing a state estimation algorithm. In order for these algorithms to be used effectively, they must interact with other subsystems of the BMS. If SOH monitoring is applied to individual cells in a multi-cell battery pack, then SOH can be used to determine when to perform cell balancing. If a voltage measurement largely disagrees with the modeled voltage in the SOC algorithm, then a fault condition could be triggered and the BMS should stop current flow through the battery. A high-level schematic that outlines some of the interactions between subsystems of a BMS is shown below.
A high level Schematic of a BMS
Real-time data processing algorithms are key components in BMSs. These algorithms evaluate inputs such as current, voltage, and temperature in order to estimate the remaining charge in a battery (the SOC), the amount of degradation that has occurred in a battery (the SOH), and the remaining time the battery can operate before it must be replaced (the remaining useful performance, or RUP). SOC is necessary to ensure that a battery can perform a given task before it requires a recharge while SOH and RUP are used for planning maintenance and battery replacement. As shown below, A state of the art filtering technique that CALCE has developed is being applied to estimate and predict battery SOH and RUP and A temperature-based model is being used to estimate battery SOC taking into account different ambient temperatures.


The CALCE Battery Research Group features state-of-the-art shared instrumentation facilities that support high-level scientific research and student training. Each Facility offers multiple research instruments and high-end tools, needed to support the diverse research activities within the Department. These facilities welcome users from the many universities, companies and federal laboratories, providing hubs for collaboration and innovation.

Electrical Characterization


Cadex C8000 Battery Test System

4 channel, 1.2V - 36V, 10A charge and discharge per channel,100W per channel on charge; 80W on discharge


Agilent 34970A Data Acquisition

3-slot mainframe, 96 matrix crosspoints, Measures and converts 11 different input signals, 60 channels input


INL Battery Testing System

2 Channel Impedance measuring device, multipoint impedance spectra in as little as 10 seconds, measures upto 50V


Neware BTS4000


4 channels, current control range of 0A to 3000A, voltage ranges of 0V to 110V, Accuracy of ∓0.05% FS


Arbin BT2000 Battery Test System

16 channel battery tester, tests up to 5V per channel, 0.02 to 0.1% FSR control


Arbin BT-I


16 channel battery tester, 1mA-10A, 10-20V per channel, 0.02 to 0.1% FSR control 




Environmental Chambers

MBraun Globebox

MBRAUN Glove Box Workstation

A Stainless steel design with Inert gas purification system having Large antechamber with sliding tray and PLC-M


Yamato DVS 402

Laboratory Constant Temperature Ovens with temperature ranging from Room temp +5°C to 260°C with accuracy of ∓1.0°C

Temperature Humidity Chamber

Thermo Temperature Humidity Chamber

Incubator- Temperature range: Ambient +5° to 75°C. 120V/300W, True laminar airflow for tight temperature uniformity


Mechanical Testing

A range of equipment is available to measure electrical, thermal, mechanical, and material properties critical to the performance and reliability of systems. Specific equipment includes:


TA's Dynamic Mechanical Analyzer

Texas Instrument's Rheometrics Solids Analyzer,generates 0.0001N-18N Force with force resolution of 0.00001N, Deformation range: ∓0.5 to 10,000 pm, Temperature Range:-150 to 600°C


Data Physics 1D Electrodynamic Shaker

Max Sinusoidal Force- :469Kgf, Max Random Force:261Kgf, Max Shock Force:994Kgf, Frequency Range: 5- 3000Hz

Temperature Humidity Chamber

Perkin-Elmer Differential Scanning Calorimeter

Furnace: 20 to 600°C; Maximum scan rate: 500°C/min; Sensitivity:0.1µW, Uses Nitrogen as shield gas and Helium as purge gas


6D Electrodynamic Shaker

Vibrotron 6D-Electrodynamic Shaker

Peak force of 4200N for sinusoidal vibrations, 3400N RMS force for random vibrations and 7100N force for classic shock, Frequency range:5- 4500Hz


DAC Torsion Tester

ElectroMechanical Test Machine with Torque range of 1Nm to 5.5kNm with frequency range of 2Hz- 30Hz capable of performing static, dynamic and fatigue testing applications


Mechanical Test System

Axial and torsional actuators; Temperature Range: -129° to 540°C, Digital controllers with high channel density, high capacity and superior configurability


Failure Analysis

Extensive experience and state-of-the-art equipment are used for failure analysis of batteries, electronic components, printed circuit boards, connectors, products and systems.

Optical Microscope

Zeiss AxioCam MRc5 Optical Microscopy

5 Megapixel (2584 x1936) resolution, 12 bit / 12 Mhz pixel clock, 36-bit color depth, Dynamic range of 1:1300 for optimal


Agilent Fourier Transform Infrared Spectroscopy

Multi-range FT-IR spectrometer capable of both rapid scan and step scan operation. Spectral range 11,000-400/cm.


Nikon Atomic Force Microscopy


3D topography measurement, vertical resolution typically 50 pm, characterization of the topography and electrical, chemical, magnetic and mechanical properties


Fischer X-ray Fluorescence Microscopy

Micro-focus tube, measurement chamber with a C-slot, 4-x aperture changer, 3 primary filters, Larger measuring distances possible (DCM, stroke 0-80 mm)


GE Phoenix Nanome X-ray Microscope

Combined 2D / 3D CT operation, Digital image chain and active temperature-stabilized digital detector with 30 fps, 180 kV / 15 W nanofocus tube, 200nm detail detectability


Scanning Electron Microscopy/ Energy Dispersive Spectroscopy

3 modes: High vacuum, Low vacuum and ESEM, Resolution range: 0.8nm- 3nm, Accelerating voltage: 200V – 30kV, Digital video recording, Vacuum: 6e- 4000Pa


Okis Scanning Acoustic Microscope

Digital Pulse Receiver, Optional second channel, Max velocity: 1500mm/s, Accuracy and repeatability: ∓0.5 micron


Thermo-mechanical Analyzer

Temperature Range: -150 to 1,000 °C, Precision: ∓1°C, Maximum Sample Size: 26mm x 1.0mm x 4.7mm, Measurement Precision: ∓0.1%


Prof. Michael Pecht

Director of CALCE                       


Dr. Michael Osterman

Director of the CALCE Consortium 


Saurabh Saxena

Ph.D. Candidate



Weiping Diao

Ph.D. Student 


Lingxi Kong

Ph.D. Student 

Bin Xu

Visiting Ph.D. Scholar

Neda Shafiei

Ph.D. Student




Past Members

Dr. Xingyan Yao Visiting Scholar
Dr. Yongquan Sun Visiting Scholar
Christopher Hendricks Ph.D. Student


CALCE Center for Advanced Life Cycle Engineering 1103 Engineering Lab Building
University of Maryland
College Park, MD 20742
Phone: 301-405-5323 | Fax: 301-314-9269

For more information, contact Prof. Michael Pecht (301-405-5323) or Dr. Michael Osterman (301-405-8023).

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