Li-ion batteries experience mechanical stress evolution due in part to Li intercalation into and de-intercalation out of the electrodes, ultimately resulting in performance degradation. In situ measurements of electrode stress can be used to analyze stress generation factors, verify mechanical deformation models, and validate degradation mechanisms. They can also be embedded in Li-ion battery management systems when stress sensors are either implanted in electrodes or attached on battery surfaces. This paper reviews in situ measurement methods of electrode stress based on optical principles, including digital image correlation, curvature measurement, and fiber optical sensors. Their experimental setups, principles, and applications are described and contrasted. This literature review summarizes the current status of these stress measurement methods for battery electrodes and discusses recent developments and trends.

In this paper,an efficient model structure composed of a second-order resistance-capacitance network and a simply analytical open circuit voltage versus state of charge(SOC) map is applied to characterize the voltage behavior of a lithium iron phosphate battery for electric vehicles(EVs).As a result,the overpotentials of the battery can be depicted using a second-order circuit network and the model parameterization can be realized under any battery loading profile,without a special characterization experiment.In order to ensure good robustness,extended Kalman filtering is adopted to recursively implement the calibration process.The linearization involved in the calibration algorithm is realized through recurrent derivatives in a recursive form.Validation results show that the recursively calibrated battery model can accurately delineate the battery voltage behavior under two different transient power operating conditions.A comparison with a first-order model indicates that the recursively calibrated second-order model has a comparable accuracy in a major part of the battery SOC range and a better performance when the SOC is relatively low.

The overheat abuse experiment of a 12S1P 37 Ah prismatic Lithium-ion battery module in a nominal energy of 1.65 kWh is conducted in this work. The cell behaviors and characterization in the process of thermal runaway propagation is investigated, including the gas eruption, the fire ejection, the flame combustion, the audio features, and the heat transfer, respectively. In the experiment, the central cell is heated on both sides until the pole temperature moves beyond 300 °C, the thermal runaway undergoes about 43 min and propagates from the central to both sides in the module, and all 12 cells burn. Results show that the first three runaway cells spout gas at first, and, then, emit sound with close amplitudes, frequencies, and energies, about 200 s earlier than the fire ejection. Then, the characteristic of the internal short circuit is the temperature rate zone of 1.0 K/s with time greater than 20 s. Moreover, the proposed thermal propagation coefficient is used to assess the thermal propagation capabilities of the runaway cells on their adjacent cells, and this explains the runaway sequence. It is anticipated that the experimental results can provide the deep understanding, thermal runaway warning, and evaluation method for the module safety design.

Because of their simplicity and dynamic response, current pulse series are often used to extract parameters for equivalent electrical circuit modeling of Li-ion batteries. These models are then applied for performance simulation, state estimation, and thermal analysis in electric vehicles. However, these methods have two problems: The assumption of linear dependence of the matrix columns and negative parameters estimated from discrete-time equations and least-squares methods. In this paper, continuous-time equations are exploited to construct a linearly independent data matrix and parameterize the circuit model by the combination of non-negative least squares and genetic algorithm, which constrains the model parameters to be positive. Trigonometric functions are then developed to fit the parameter curves. The developed model parameterization methodology was applied and assessed by a standard driving cycle.