Lithium-ion battery diagnosis is critical for ensuring optimal performance and mitigating safety risks; however, challenges remain due to overlapping electrochemical processes and dynamic operating conditions. This study introduces the dynamic impedance spectra to enable a comprehensive diagnosis of LIBs’ behaviors with active battery charging. For starters, the voltage drifts caused by the state of charge (SOC) shifts and polarization effects are eliminated by applying a moving average filter. The time-resolved impedance spectra and the corresponding equivalent circuit model (ECM) parameters are extracted and continuously updated to decouple and monitor electrochemical processes across timescales. Kramers-Kronig (K-K) validation confirms the dynamic impedance measurement validity, with residuals below 0.5%. The analysis of ECM parameter evolution provides in-situ insights into the electrochemical dynamics and material properties during the battery charging process.

The presence of DC current bias during the operation of energy storage systems renders traditional SOC estimation methods based on static electrochemical impedance measurements unsuitable. Considering the real-time variation of battery SOC during charging and discharging processes, rapid in-situ impedance acquisition becomes critical. Moreover, DC current under operational conditions exacerbates the difficulty of impedance extraction and introduces nonlinear disturbances to the impedance response. To address these challenges, this paper proposes a fast dynamic electrochemical impedance spectroscopy (DEIS) measurement framework based on a discrete interval binary sequence. By applying specialized time-domain processing, accurate extraction of DEIS under load conditions is achieved. Furthermore, the DEIS features are analyzed through distribution of relaxation times and equivalent circuit modeling to elucidate the kinetics characteristics of the battery under charging and discharging conditions. Based on the analysis results, characteristic parameters are selected to establish the correlation between component magnitudes and battery SOC, with a maximum mean absolute error is 2.41 %.

There is an imbalance in the retired battery data, primarily because the majority of the batteries are still in usable condition, leading to a severely skewed data distribution. This imbalance can significantly impact the performance of deep learning models, causing the classification results to be biased toward the majority class. To address the above problems, we propose a novel method for screening retired lithium-ion batteries based on the Feature Tokeniser-transformer (FT-transformer) and the synthetic minority oversampling technique (SMOTE). First, time series and internal resistance features are extracted based on partial charging voltage-SOC curves and direct current pulses. Considering the imbalance of the data distribution, some samples are added using SMOTE to balance the sample distribution. Then, the FT-transformer is used for retired battery multiclassification. The proposed method has been validated on our laboratory's self-collected and MIT public datasets, demonstrating higher accuracy and stronger stability.

The accurate instantaneous energy consumption estimation for electric buses (EBs) is helpful for drivers to schedule and use EBs more reasonably, which is also essential for the realization of electrification, intelligence, and cyber-physical integration of public transportation systems. In this paper, real-world driving data collected from ten electric buses in Beijing city are obtained, and the influencing factors of energy consumption from multiple sources such as driving-related, vehicle status, and external environment are extracted. With the extracted key influencing factors, we propose a new multi-model fusion method to estimate the 1 Hz energy consumption of electric buses. The results demonstrate that the proposed fusion model has a mean absolute percentage error of 6.21%, which is superior to the single model. Finally, we use the control variable method for feature importance analysis, which provides valuable insights into the factors that affect the energy consumption of EBs in real-world scenarios.

The lithium-ion battery needs to be heated to restore the charging/discharging performance under a low-temperature environment. The Alternating Current (AC) heating technique can heat the battery quickly and uniformly, and has advantages in terms of energy consumption, efficiency, and additional components. This paper presents a systematical review of the state-of-art AC heating technique for lithium-ion batteries. The heating effect of various AC waveforms under low-temperature environments is presented. Then, the factors affecting the AC heating effect are analyzed, including frequency, amplitude, ambient temperature, and current waveforms. Moreover, the implementations of AC heaters are summarized and compared. Finally, future trends in AC heating techniques are discussed.

A reasonable configuration of battery energy storage system (BESS) in distribution networks, especially those penetrated by photovoltaic (PV) systems, helps to improve the voltage stability and reduce the configuration and operation cost. In order to solve the sizing and placement problem of BESS in distribution network penetrated by PV systems, this paper proposes an integrated planning strategy for placement and sizing of BESS based on the Amartya Sen index. Inspired by the concept of Amartya Sen poverty index, the Amartya Sen index is adopted to describe the static voltage stability of distribution network in this paper. Considering three aspects of system static voltage stability, network loss, and BESS investment cost, a unified planning model for BESS placement and sizing is constructed with the goal of minimizing the Amartya Sen index and the economic cost. The model is solved based on power flow calculation and multi-objective particle swarm optimization algorithm (MOPSO). The case study conducted in the IEEE 33-node system validates the proposed method, which can effectively improve the static voltage stability of distribution network with reduced investment cost of BESS.

Electrochemical impedance spectroscopy (EIS) can provide fruitful information for Lithium-ion (Li-ion) battery modeling and diagnosis, yet EIS measurement is time-consuming with low-frequency signal injection. By stacking a group of broadband signals, pseudorandom sequence (PRS) makes it possible to obtain the battery EIS in a few seconds at the expense of measurement accuracy and signal-to-noise ratio (SNR). Thus, this article focuses on developing a highly effective signal processing procedure to extract useful information from the PRS for accurate EIS measurement. To enhance the ability of the data cleaning procedure, a three-dimensional cloud is first reconstructed for each impedance by integrating its power spectrum (PS). The impedance with lower PS can be easily removed through a statistical based multiple selection mechanism, which enables the extraction of the EIS without altering the original measurement. Experimental results on a 3000 mAh Li-ion battery prove the effectiveness of the proposed method.

During the last 4-5 years the plug in electrical vehicle market has known a tremendous growth, only in 2017 307400 units were sold, representing an increase of 39%. The battery management system and the battery charger are two of the most demanding systems in terms of required computational power added to this type of vehicles. Also, features related to driving automation, safety and comfort are becoming more complex, therefore a higher level of integration is necessary. The objective of this paper is to integrate an intelligent wireless battery management system into the same system on chip as the battery charger and to prove that a single IC can parallelize and coordinate all tasks conventionally carried out by multiple dedicated processors. The proposed solution exhibits higher integration, improved balancing, increased reliability and extended range.

An optimized state of charge (SOC) estimation method is critical for energy control strategy in hybrid energy system. For an embedded system, the executed algorithm should be less time consuming and also robust on measurement noise from sensors. Moreover, the estimation method should also be insensitive to initial SOC for the purpose of avoiding battery relaxing time in real application. The proposed method in this paper combines adaptive unscented Kalman filter (AUKF) and multivariate adaptive regression splines (MARS) to meet the above demands of embedded hybrid energy system. Samples which consist of battery current, terminal voltage and temperature are used to for MARS model training. The effectiveness and robustness of the proposed method is validated by experimental test. Also, the proposed method is compared with least squares support vector machine (LSSVM) based method in estimated accuracy and time consumption. Experiment results indicate that the proposed method is less time consuming as well as good accuracy is guaranteed.