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Kolluri, Suryanarayana

Publications and source records attributed to Kolluri, Suryanarayana.

Revealing the Mechanism Behind Sudden Capacity Loss in Lithium Metal Batteries

Rechargeable Li-metal batteries (LMBs) are attractive energy storage candidates for electric vehicles (EVs) because they offer higher energy density than batteries built with intercalation electrodes. However, one of the main barriers to the commercial deployment of LMBs has been their relatively short cycle life. Re-designing the electrolyte system shows promise in achieving acceptable cycle life, but even so, the resulting cells display a challenging end-of-life (EOL) behavior: a sudden capacity loss. Herein, we report a new method for analyzing voltage profiles during cycling to distinguish between the capacity loss originating from the loss of cathode capacity vs growth in cell resistance. Further, this analysis reveals that sudden capacity loss was preceded by acceleration in the rate of growth of cell resistance, and cycling of multiple cells showed that this phenomenon is sensitive to the initial quantity of electrolyte in the cells. In contrast, the cathode capacity degraded at a constant rate independent of the electrolyte quantity. Combining this evidence with post-analysis of harvested electrolyte and electrodes, we conclude that neither the loss of active lithium nor the loss of active cathode material was the primary source of sudden capacity loss; instead, consumption and decomposition of electrolyte causes the drastic capacity loss at EOL.

25 ENERGY STORAGE↗

Techniques for controlling charging and/or discharging of batteries using a tanks-in-series model

In some embodiments, a battery management system is provided. The battery management system comprises a connector for electrically coupling a battery to the battery management system, at least one sensor configured to detect a battery state, a programmable chip configured to control at least one of charging and discharging of the battery, and a controller device. The controller device is configured to receive at least one battery state from the at least one sensor; provide the at least one battery state as input to a tanks-in-series model that represents the battery; and provide at least one output of the tanks-in-series model to the programmable chip for controlling at least one of charging and discharging of the battery.

25 ENERGY STORAGE↗

Systems and methods for direct estimation of battery parameters using only charge/discharge curves

Electrochemical models for the lithium-ion battery are useful in predicting and controlling its performance. The values of the parameters in these models are vital to their accuracy. However, not all parameters can be measured precisely, especially when destructive methods are prohibited. In some embodiments of the present disclosure, a parameter estimation approach is used to estimate the open circuit potential of the positive electrode (Up) using piecewise linear approximation together with all the other parameters of a single particle model. Up and 10 more parameters may be estimated from a single discharge curve without knowledge of the electrode chemistry using a technique such as a genetic algorithm. Different case studies were presented for estimating Up with different types of parameters of the battery model. The estimated parameters were then validated by comparing simulations at different discharge rates with experimental data.

Qi, Yanbo↗

A Tanks-in-Series Approach to Estimate Parameters for Lithium-Ion Battery Models

Advanced Battery Management Systems (BMS) play a vital role in monitoring, predicting, and controlling the performance of lithium-ion batteries. BMS employing sophisticated electrochemical models can help increase battery cycle life and minimize charging time. However, in order to realize the full potential of electrochemical model-based BMS, it is critical to ensure accurate predictions and proper model parameterization. The accuracy of the predictions of an electrochemical model is dependent on the accuracy of its parameters, the values of which might change with battery cycling and aging. Parameter estimation for an electrochemical model is generally challenging due to the nonlinear nature and computational complexity of the model equations. To this end, this work utilizes the recently proposed Tanks-in-Series model for Li-ion batteries (J.Electrochem. Soc., 167, 013534 (2020)) to perform parameter estimation. The Tanks-in-Series approach allows for substantially faster parameter estimation compared to the original pseudo two-dimensional (p2D) model. The objective of this work is thus to demonstrate the gain in computational efficiency from the Tanks-in-Series approach. A sensitivity analysis of model parameters is also performed to benchmark the fidelity of the Tanks-in-Series model.

25 ENERGY STORAGE↗