To effectively assess retired batteries from electric vehicles, it is indispensable to estimate the battery state of charge (SoC), as many aging characteristic parameters are sensitive to it. This study aims to develop a rapid SoC estimation approach based on the simplified electrochemical model and genetic algorithms without restricting the initial equilibrium state of batteries. Specifically, the full-order pseudo-two-dimensional (P2D) model is simplified with Padé approximation and further improved by introducing the initial state parameters from the perspective of overpotentials. Next, a novel fast SoC estimation scheme is proposed using the constrained optimization method with a current pulse of 60 s instead of recursive estimation with a time-consuming relaxation procedure from 1 to 3 hours in conventional methods. The parameter sensitivity analysis is conducted to determine the key variables to be optimized. In addition, the genetic algorithm is improved by applying a repair operator to handle the constraints caused by battery operating conditions. Finally, three test cycles are designed to diversify the battery initial states for experimental verification. The results indicate that the mean absolute deviations among the entire SoC range are no more than 4% with different battery initial states, which are reduced by 3.5% compared to the adaptive extended Kalman filter algorithm with an equivalent circuit model.

Online monitoring of Lithium-ion batteries (LIBs) internal temperature (IT) is a mandatory requirement to ensure their safety and longevity. In this article, the second-harmonic current (SHC) that inherently exists in single-phase dc/ac converters is utilized to estimate LIB IT for the first time. First, the SHC needs to be suppressed to the appropriate level as a disturbance signal for online impedance measurement. Then, the dual-channel digital lock-in amplifier is used to calculate LIBs impedance at the second-harmonic frequency (SHF). Moreover, the Pisarenko harmonic decomposition algorithm is adopted to estimate the actual SHF to ensure the accuracy of the impedance calculation. Three commercial 18 650 LIBs are tested at the experimental setup. A strong correlation between the imaginary impedance at SHF and IT is observed, which is nearly not affected by the battery's state of charge. Therefore, an online SHF impedance-based battery IT estimation method is developed. Finally, the validity of the proposed method is confirmed through a comparison between actual and online estimated temperatures.

Electrochemical impedance spectroscopy (EIS) is a widely employed non-invasive detection technique for battery state estimation and fault diagnosis. This article introduces a novel multi-sinusoidal pulse-width modulation (MSPWM) excitation for the purpose of online battery impedance spectroscopy identification. The introduced excitation is a unique form of broadband binary excitation that enables precise design of the amplitude and phase for every fundamental component, which can meet customized measurement requirements. Furthermore, the article proposes a method to inject the MSPWM excitation into cells and conduct online EIS measurements using reconfigurable battery systems (RBSs). Through modulation of the battery pack’s charging or discharging current during regular operations, without the need for an additional external programmable power supply, the MSPWM excitation can be injected into the RBS cells, facilitating impedance measurement without interrupting the normal operation of the system. To validate the proposed method, a DC RBS consisting of 16 lithium-ion battery cells was constructed. The experimental results show strong consistency between the EIS measurements conducted using the proposed method and those obtained from a commercial electrochemical workstation.

This article presents a hierarchical balancing architecture for the dc reconfigurable battery pack (RBP) with a modular design. The battery cells in the RBP are divided into several modules, and each module is equipped with a slave controller that is only responsible for a subset of cells. The system controller performs the closed-loop control and allocates the desired voltage steps into battery modules. The proposed architecture can significantly reduce communication and computation requirements for large-scale applications. Moreover, considering the second-harmonic current (SHC) widely existing in a single-phase dc/ac system, which may induce the aging of battery cells, the dc RBP is used to suppress the SHC for the first time without an additional central dc/dc converter. Due to the high output voltage quality and low-frequency operation of the dc RBP, the filter size can be significantly reduced. Experimental validation of the proposed control scheme is demonstrated through the utilization of a dc RBP comprising 48 3-Ah 18 650 lithium-ion cells. The experimental results demonstrate that the proposed method achieves effective battery cell balancing and SHC suppression.