Towards better estimability of electrode-specific state of health: Decoding the cell expansion

Mohtat, Peyman; Lee, Suhak; Siegel, Jason B.; Stefanopoulou, Anna G.

doi:10.1016/j.jpowsour.2019.03.104

Cited by 62 publications

(60 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These changes are measured as a function of lithiation state using X-ray diffraction (XRD) method [29]. Using this data the volumetric strain of a unit lattice cell is calculated and a functional fit was found [10]. For the NMC, it has been shown that the volumetric strain is approximately linear with a maximum value of 1 -1.5%, which agrees with crystallography measurements [30].…”

Section: Half-cell Expansion Functionssupporting

confidence: 61%

“…The half-cell voltage functions identified previously are used to estimate the stoichiometric operating window of each electrode, which is defined as (x 0 , x 100 ) for graphite, and (y 0 , y 100 ) for NMC. The estimation is done by means of a non-linear least squares with the steps detailed in [10]. The identified parameters are reported in Table 2 and the voltage fit is depicted in Fig.…”

Section: Full Cell Stoichiometric Windowmentioning

confidence: 99%

“…In [9], the estimability of electrode parameters are studied with respect to limited operating windows. In [10] we have shown that we can greatly reduce the requirement of high depth of discharge by incorporating cell swelling data into the estimation. Note, however, that this method still relies on open circuit measurements.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates

Mohtat¹,

Lee²,

Sulzer³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Differential voltage analysis (DVA) is a conventional approach for estimating capacity degradation in batteries. During charging, a graphite electrode goes through several phase transitions observed as plateaus in the voltage response. The transitions between these plateaus emerge as observable peaks in the differential voltage. The DVA method utilizes these peaks for estimating cell degradation. Unfortunately, at higher C-rates (above C/2) the peaks flatten and become unobservable. In this work, we show that, unlike the differential voltage, the peaks in the 2nd derivative of the expansion with respect to capacity remain observable up to 1C and thus make possible diagnostic algorithms at these charging rates. To understand why that is the case, we have developed an electrochemical and expansion model suitable for model-based estimation. In particular, we demonstrate that the single particle modeling methodology is not able to capture the peak smoothing effect, therefore a multi-particle approach for the graphite electrode is needed. Additionally, model parameters are identified using experimental data from a graphite/NMC pouch cell. The proposed model produces an excellent fit for the observed electric and mechanical swelling response of the cells and could enable physics-based data-driven degradation studies at practical charging rates.

show abstract

Section: Half-cell Expansion Functionssupporting

confidence: 61%

Section: Full Cell Stoichiometric Windowmentioning

confidence: 99%

See 1 more Smart Citation

Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates

Mohtat¹,

Lee²,

Sulzer³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In conclusion, it is worth mentioning that the variability of the bulk force vs SOC relationship with temperature and humidity, and its variation with time over the battery life remain to be studied. This article assumed that the expansion with respect to each half-cell is invariant as the cell ages, but a loss in active material or cycleable lithium could cause the shape to change with respect to SOC as the battery ages according to Reference [27]. These studies will be considered by the authors for future work.…”

Section: Discussionmentioning

confidence: 99%

“…The two functions V OC (SOC) and F(SOC) involved in the output equations (6) and 7are shown as curves in Figure 3. 27 As seen in Figure 3, both these functions are nonlinear functions of SOC. However, V OC (SOC) can be represented entirely using a monotonic polynomial curve, while the bulk force F(SOC) is clearly a nonmonotonic function.…”

Section: Output Functionsmentioning

confidence: 97%

Hybrid nonlinear observer for battery state‐of‐charge estimation using nonmonotonic force measurements

et al. 2020

Self Cite

View full text Add to dashboard Cite

This article focuses on state‐of‐charge (SOC) estimation in a lithium‐ion battery, using measurements of terminal voltage and bulk force. A nonlinear observer designed using Lyapunov analysis relying on lower and upper bounds of the Jacobian of the nonlinear output function is utilized. Rigorous analysis shows that the proposed observer has feasible design solutions only in each piecewise monotonic region of the output functions and has no constant stabilizing observer gain when the entire SOC range is considered. The nonmonotonicity challenge is then addressed by designing a hybrid nonlinear observer that switches between several constant observer gains. The global stability of the switched system is guaranteed by ensuring overlap between regions and an adequate dwell time between switches. The performance of the observer is evaluated first through simulations using a high‐fidelity battery model and then through experiments. The performance of the nonlinear observer is compared with that of an extended Kalman Filter. Simulation results show that with no model uncertainty the nonlinear observer provides estimates with an RMS error of 1.1%, while the EKF performs better, providing an RMS error less than 1%. However, when model error is introduced into this nonmonotonic system, the EKF becomes unstable for even very small model errors in the output curves. The nonlinear observer, on the other hand, continues to perform very well, providing accurate estimates and never becoming unstable. The experimental results verify the observations from the simulation and the experimental EKF is found to become unstable due to model errors, while the hybrid nonlinear observer continues to work reliably.

show abstract

Piezoresistive Free‐standing Microfiber Strain Sensor for High‐resolution Battery Thickness Monitoring

Nazari

Bäuerle

Zimmermann³

et al. 2023

Advanced Materials

View full text Add to dashboard Cite

from green electric mobility to all sorts of daily-life personal electronic gadgets. Increasing the lifetime of the Li-ion battery is of utmost importance, not only to prohibit premature capacity fade but also to effectively reduce the hazardous waste stemming from these devices. During the battery operation, several inevitable electrochemical reactions occur between the electrodes and the electrolyte due to Li-ion de/intercalation. On the one hand, lattice expansion, and contractions during charging and discharging, known as breathing cause reversible volume expansion, in the long run leading to gradual structural aging. [1] On the other hand, parasitic reactions such as lithiumplating, gas generation, and solid electrolyte interphase growth, decrease capacity retention over time and cause irreversible expansions known as swelling, leading to lithium inventory loss. [2,3] These reactions, depending on the used electrode and electrolyte chemistry cause relative volume changes ranging from 1% up to 10%. [3] Thus, in a battery management system, real-time monitoring of the volume changes Highly sensitive microfiber strain sensors are promising for the detection of mechanical deformations in applications where limited space is available. In particular for in situ battery thickness monitoring where high resolution and low detection limit are key requirements. Herein, the realization of a highly sensitive strain sensor for in situ lithium-ion (Li-ion) battery thickness monitoring is presented. The compliant fiber-shaped sensor is fabricated by an upscalable wet-spinning method employing a composite of microspherical core-shell conductive particles embedded in an elastomer. The electrical resistance of the sensor changes under applied strain, exhibiting a high strain sensitivity and extremely low strain detection limit of 0.00005 with high durability of 10 000 cycles. To demonstrate the accuracy and ease of applicability of this sensor, the real-time thickness change of a Li-ion battery pouch cell is monitored during the charge and discharge cycles. This work introduces a promising approach with the least material complexity for soft microfiber strain gauges.

show abstract

Towards better estimability of electrode-specific state of health: Decoding the cell expansion

Cited by 62 publications

References 27 publications

Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates

Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates

Hybrid nonlinear observer for battery state‐of‐charge estimation using nonmonotonic force measurements

Piezoresistive Free‐standing Microfiber Strain Sensor for High‐resolution Battery Thickness Monitoring

Contact Info

Product

Resources

About