Non-destructive analysis of degradation mechanisms can be very beneficial for the prognostics and health management (PHM) study of lithium-ion batteries. In this paper, a type of graphite/LiCoO₂ battery was cycle aged at high ambient temperature, then 25 parameters of the multi-physics model were identified. Nine key parameters degraded with the cycle life, and they were treated as indicators of battery degradation. Accordingly, the degradation mechanism was discussed by using the multi-physics model and key parameters, and the reasons for capacity fade and the internal resistance increase were analyzed in detail. All evidence indicates that the formation reaction of the solid electrolyte interface (SEI) film is the main cause of battery degradation at high ambient temperature.

Better performance consistency of regrouped batteries retired from electric vehicles can guarantee the residual value maximized, which greatly improves the second-use application economy of retired batteries. This paper develops a fast identification approach for micro-health parameters characterizing negative electrode material and electrolyte in LiFePO₄ batteries on the basis of a simplified pseudo two-dimensional model by using Padé approximation is developed. First, as the basis for accurately identifying micro-health parameters, the liquid-phase and solid-phase diffusion processes of pseudo two-dimensional model are simplified based on Padé approximation, especially according to enhanced boundary conditions of liquid-phase diffusion. Second, the reduced pseudo two-dimensional model with the lumped parameter is proposed, the target parameters characterizing negative electrode material (ε_n, D_s,n) and electrolyte (D_e, C_e) are grouped with other unknown but fixed parameters, which ensures that no matter whether the target parameters can be achieved, the corresponding varying traces is able to be effectively and independently monitored by lumped parameters. Third, the fast identification method for target micro-health parameters is developed based on the sensitivity of target parameters to constant-current charging voltage, which shortens the parameter identification time in comparison to that obtained by other approaches. Finally, the identification accuracy of the lumped micro-health parameters is verified under 1 C constant-current charging condition.

Generation from solar is inherently variable. Through a strategic combination of excessive capacity expansion (i.e., overbuilding) and battery storage, the variable solar generation can be cost-effectively firmed up, in that, it is able to meet the required generation target with absolute certainty. Firming up solar generation implies additional cost, which can be quantified through the firm kWh premium. This paper proposes a new model for the optimization of firm kWh premium through either a mixed-integer linear program or a bilinear program, depending on whether a generic or detailed battery model is used. The (bi)linear-program formulation greatly reduces the complexity of the original iterative approach. Additionally, since the firm kWh premium is a function of photovoltaic and battery prices, we show how future price change can affect the economics of firm power delivery and whether true grid parity can be eventually achieved. Lastly, the sensitivity of the firm kWh premium to photovoltaic modeling uncertainty and inter-annual solar resource uncertainty is analyzed.

Electrochemical impedance spectroscopy (EIS) can access intrinsic information on various physical and chemical processes within batteries and thus has become popular for battery state estimation and fault diagnosis. Impedance spectrum measurements based on power converters with battery management systems facilitate the practical deployment of this method. However, existing excitation signals have shortcomings, such as low measurement accuracy or difficulty in being implemented to converters. In view of this, this article proposes a random low-frequency-modulated multisine (RLMMS) excitation, which is free of higher-order harmonics and exhibits a relatively low frequency of level switching. The RLMMS excitation is applied to measure the impedance spectrum of individual cells in series-connected battery packs based on a dual-active bridge (DAB) converter. The experimental results demonstrate that this excitation is easy to generate and exhibits superior measurement accuracy compared to existing excitation signals across different battery types, even under varying temperature conditions.

Lithium-ion batteries have become critical components to industries as automotive, military, space, medical and consumer electronics. Electrochemical impedance spectroscopy (EIS) is a well-known technique employed on various electrochemical cells including lithium-ion cells, which is practical in estimating battery state of health, as ohmic resistance, formation of solid electrolyte interphase (SEI) film, activation on solid and liquid interface and diffusion process in the battery can be reflected in the Nyquist curve of EIS. However, the long acquisition time of standard EIS makes it unsuitable for rapid on-line impedance measurements. This paper presents a time domain measuring technique based on Fast Fourier Transformation (FFT). Time domain excitation, such as a multi-sine excitation, is applied to the measured system. The excitation and response of the system are acquired simultaneously. Through FFT, acquired signals are transformed from time domain to frequency domain, thus the impedance in frequency domain is calculated. The technique is verified on both lithium-ion battery equivalent circuit and real lithium-ion battery. The resulting impedance data show that proposed technique has good concordance with standard EIS and greatly reduceds the acquisition time compared with standard EIS.

Timely identification of early internal short circuit faults, commonly referred to as micro short circuits (MSCs), is essential yet poses significant challenges for the safe and reliable operation of lithium-ion battery (LIB) energy storage systems. This paper introduces an innovative diagnostic method for early internal short circuits in LIB packs, utilizing dynamic time warping (DTW) applied to incremental capacity (IC). Initially, the terminal voltages of all cells within the LIB pack are ordered at any moment to determine the median terminal voltage, which is then used to generate the median IC curve. This curve acts as a reference benchmark that represents the condition of healthy cells in the pack. Subsequently, the DTW algorithm is utilized to measure the similarity between each cell’s IC curve and the median IC curve. Cells exhibiting similarity scores that exceed a specified threshold are identified as having MSC faults. Lastly, for the cells diagnosed with MSC conditions, a method for estimating short-circuit resistance (SR) based on variations in maximum charging voltage is devised to quantitatively evaluate the severity and evolution of the MSC. Experimental findings reveal that the proposed method effectively identifies MSC cells in the LIB pack and estimates their SRs without the necessity of a battery model, thereby affirming the method’s validity.