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Abstract Over the past two decades, mesenchymal stem/stromal cell (MSC) therapy has made substantial strides, transitioning from experimental clinical applications to commercial products. MSC therapies hold considerable promise for treating refractory and critical conditions such as acute graft-versus-host disease, amyotrophic lateral sclerosis, and acute respiratory distress syndrome. Despite recent successes in clinical and commercial applications, MSC therapy still faces challenges when used as a commercial product. Current detection methods have limitations, leaving the dynamic biodistribution, persistence in injured tissues, and ultimate fate of MSCs in patients unclear. Clarifying the relationship between the pharmacokinetic characteristics of MSCs and their therapeutic effects is crucial for patient stratification and the formulation of precise therapeutic regimens. Moreover, the development of advanced imaging and tracking technologies is essential to address these clinical challenges. This review provides a comprehensive analysis of the kinetic properties, key regulatory molecules, different fates, and detection methods relevant to MSCs and discusses concerns in evaluating MSC druggability from the perspective of integrating pharmacokinetics and efficacy. A better understanding of these challenges could improve MSC clinical efficacy and speed up the introduction of MSC therapy products to the market.

Abstract Mesenchymal stromal/stem cells (MSC) have emerged as a promising therapeutic avenue for treating autoimmune diseases, eliciting considerable interest and discussion regarding their underlying mechanisms. This study revealed the distinctive ability of human umbilical cord MSC to aggregate within the lymph nodes of mice afflicted with autoimmune diseases, but this phenomenon was not observed in healthy mice. The specific distribution is driven by the heightened expression of the CCL21-CCR7 axis in mice with autoimmune diseases, facilitating the targeted homing of MSC to the lymph nodes. Within the lymph nodes, MSC exhibit a remarkable capacity to modulate Th17 cell function, exerting a pronounced anti-inflammatory effect. Transplanted MSC stimulates the secretion of L-amino-acid oxidase (LAAO), a response triggered by elevated levels of tumor necrosis factor-α (TNF-α) in mice with autoimmune diseases through the NF-κB pathway. The presence of LAAO is indispensable for the efficacy of MSC, as it significantly contributes to the inhibition of Th17 cells. Furthermore, LAAO-derived indole-3-pyruvic acid (I3P) serves as a potent suppressor of Th17 cells by activating the aryl hydrocarbon receptor (AHR) pathway. These findings advance our understanding of the global immunomodulatory effects exerted by MSC, providing valuable information for optimizing therapeutic outcomes.

Abstract Background Mesenchymal stem cells (MSCs) demonstrate a wide range of therapeutic capabilities in the treatment of inflammatory bowel disease (IBD). The intraperitoneal injection of MSCs has exhibited superior therapeutic efficacy on IBD than intravenous injection. Nevertheless, the precise in vivo distribution of MSCs and their biological consequences following intraperitoneal injection remain inadequately understood. Additional studies are required to explore the correlation between MSCs distribution and their biological effects. Methods First, the distribution of human umbilical cord MSCs (hUC-MSCs) and the numbers of Treg and Th17 cells in mesenteric lymph nodes (MLNs) were analyzed after intraperitoneal injection of hUC-MSCs. Subsequently, the investigation focused on the levels of transforming growth factor beta1 (TGF-β1), a key cytokine to the biology of both Treg and Th17 cells, in tissues of mice with colitis, particularly in MLNs. The study also delved into the impact of hUC-MSCs therapy on Treg cell counts in MLNs, as well as the consequence of TGFB1 knockdown hUC-MSCs on the differentiation of Treg cells and the treatment of IBD. Results The therapeutic effectiveness of intraperitoneally administered hUC-MSCs in the treatment of colitis was found to be significant, which was closely related to their quick migration to MLNs and secretion of TGF-β1. The abundance of hUC-MSCs in MLNs of colitis mice is much higher than that in other organs even the inflamed sites of colon. Intraperitoneal injection of hUC-MSCs led to a significant increase in the number of Treg cells and a decrease in Th17 cells especially in MLNs. Furthermore, the concentration of TGF-β1, the key cytokine for Treg differentiation, were also found to be significantly elevated in MLNs after hUC-MSCs treatment. Knockdown of TGFB1 in hUC-MSCs resulted in a noticeable reduction of Treg cells in MLNs and the eventually failure of hUC-MSCs therapy in colitis. Conclusions MLNs may be a critical site for the regulatory effect of hUC-MSCs on Treg/Th17 cells and the therapeutic effect on colitis. TGF-β1 derived from hUC-MSCs promotes local Treg differentiation in MLNs. This study will provide new ideas for the development of MSC-based therapeutic strategies in IBD patients.

Abstract Background Inevitably developed resistance of the third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) limited its clinical benefit on non-small cell lung cancer (NSCLC). Upfront combination therapy is promising to prevent this resistance. Compelling clinical evidence indicated the failure of third-generation EGFR TKIs combined with either immunotherapy or antiangiogenic agents. In comparison, combined treatment of third-generation EGFR TKIs and chemotherapy might be a favorable choice. Herein, we systematically analyzed and compared the effects of pemetrexed and a novel third-generation EGFR TKI aumolertinib combined in different sequences, subsequently revealed the potential mechanisms and proved the optimal combination schedule with clinical retrospective study. Methods Three combination schedules involving pemetrexed and aumolertinib in different sequences were developed. Their inhibition effects on cell proliferation and metastasis were firstly compared upon three human NSCLC cell lines in vitro, by cell counting kit-8, colony formation, wound healing and transwell assays respectively. Further evaluation in vivo was proceeded upon H1975 and HCC827 xenograft model. Gene and protein expression were detected by Q-PCR and western blot. Drug concentration was determined by LC–MS/MS. VEGF secretion was determined by ELISA. Tumor vessel was visualized by immunofluorescence. Lastly, a clinical retrospective study was raised with 65 patients’ data. Results The combination of pemetrexed and aumolertinib exhibited a sequence-dependent and EGFR mutant-dependent synergistic effect in vitro and in vivo. Only treatment with aumolertinib following pemetrexed (P-A) exhibited synergistic effect with stronger anti-tumor growth and anti-metastasis ability than monotherapy and also other combination sequences. This synergism could exclusively be observed in H1975 and HCC827 but not A549. Pathway analysis showed that P-A significantly enhanced the suppression of EGFR pathway. In addition, our results intriguingly found an obvious reduction of VEGF secretion and the accompanying normalization of the intratumor vessel, consequently increasing intratumoral accumulation of pemetrexed in P-A group. Finally, the clinical retrospective study verified the synergistic effect of P-A combination by significantly superior tumor response than aumolertinib monotherapy. Conclusion Aumolertinib-pemetrexed combined therapy is promising for EGFR mutant NSCLC but only in right administration sequence. P-A could become an advantageous combination strategy in clinical with synergistic inhibition of tumor growth and metastasis.

Vascular abnormality is a hallmark of most solid tumors and facilitates immune evasion. Targeting the abnormal metabolism of tumor endothelial cells (TECs) may provide an opportunity to improve the outcome of immunotherapy. Here, in comparison to vascular endothelial cells from adjacent peritumoral tissues in patients with colorectal cancer (CRC), TECs presented enhanced glycolysis with higher glyceraldehyde-3-phosphate dehydrogenase (GAPDH) expression. Then an unbiased screening identified that osimertinib could modify the GAPDH and thus inhibit its activity in TECs. Low-dose osimertinib treatment caused tumor regression with vascular normalization and increased infiltration of immune effector cells in tumor, which was due to the reduced secretion of lactate from TECs by osimertinib through the inhibition of GAPDH. Moreover, osimertinib and anti-PD-1 blockade synergistically retarded tumor growth. This study provides a potential strategy to enhance immunotherapy by targeting the abnormal metabolism of TECs.

The role of gut microbiota in the development of various tumors has been a rising topic of public interest, and in recent years, many studies have reported a close relationship between microbial groups and tumor development. Gut microbiota play a role in host metabolism, and the positive and negative alterations of these microbiota have an effect on tumor treatment. The microbiota directly promote, eliminate, and coordinate the efficacy of chemotherapy drugs and the toxicity of adjuvant drugs, and enhance the ability of patients to respond to tumors in adjuvant immunotherapy. In this review, we outline the significance of gut microbiota in tumor development, reveal its impacts on chemotherapy and immunotherapy, and discover various potential mechanisms whereby they influence tumor treatment. This review demonstrates the importance of intestinal microbiota-related research for clinical tumor treatment and provides additional strategy for clinical assistance in cancer treatment.

Frequent local recurrence and metastasis are generally involved in human liposarcoma, but the management is a challenge. There is an urgent need for improved effective therapy. In the present study, we reported that SBF-1, a steroidal glycoside, inhibited the growth of cultured highly malignant human liposarcoma SW872-S cells in vitro and in vivo. SBF-1 down-regulated the phosphorylation of protein kinase B (AKT) and thus reduced cell adhesion to fibronectin and laminin. Then we found that SBF-1 inhibited the expression of oxysterol binding protein (OSBP) in SW872-S cells, indicating that OSBP may be involved in malignant liposarcoma cell survival. Cancer cell growth and AKT phosphorylation were inhibited significantly upon knockdown of OSBP in SW872-S cells in vitro. Taken together, these results suggest that SBF-1 causes an apparent loss of OSBP function in SW872-S cells, resulting in growth inhibition. Based on our findings, OSBP serves as a potential therapeutic target for human liposarcoma. Keywords: SBF-1, Malignant liposarcoma, SW872-S

Background/Aims: Metastasis is the leading cause resulting in high mortality in triple negative breast cancer (TNBC) patients. Cancer cells are skilled at utilizing thioredoxin (Trx) system as an efficient antioxidant system to counteract oxidative damage, facilitating the occurrence of metastasis. Here, we identified an organosulfur compound named DATS isolated from garlic, that inhibits the expression of Trx-1 and the enzyme activity of Trx reductase in breast cancer cells. Methods: Tissue microarray of breast cancer patients and immunohistochemical method were used to analyze the role of Trx-1 in breast cancer metastasis. Spotaneous metastasis model and experimental metastasis model combined with HE staining, immunohistochemistry were used to verify in vivo anti-metastatic effect of DATS as well as its regulation on thioredoxin. Western blot, immunofluorescence, redox state assessment and detection of enzyme activity were employed to determine the effect of DATS on thioredoxin system. Trx-1 siRNA interference was used to investigate the conclusive evidence that Trx-1 was the target of DATS. Results: In agreement with reduced Trx-1 nuclear translocation from cytoplasm by DATS, the production of reduced form of Trx-1 was dramatically decreased. Furthermore, in vivo, DATS administration was observed to significantly suppress spontaneous and experimental metastasis in nude mice. Delivery of DATS also resulted in decreased expression of Trx-1 as the direct target, as well as expression of NF-κB and MMP2/9 in primary tumor and lung tissue. Notably, the effects of DATS on the expression of downstream metastasis-associated genes were mediated by Trx-1, as demonstrated by the combination use of DATS and Trx-1 siRNA. Conclusion: Collectively, this present study indicates that targeting Trx system with DATS may provide a promising strategy for treating metastasis of TNBC.

Abstract Intra-pulse modulation recognition under negative signal-to-noise ratio (SNR) environment is a research challenge. This article presents a robust algorithm for the recognition of 5 types of radar signals with large variation range in the signal parameters in low SNR using the combination of the Short-time Ramanujan Fourier transform (ST-RFT) and pseudo-Zernike moments invariant features. The ST-RFT provides the time-frequency distribution features for 5 modulations. The pseudo-Zernike moments provide invariance properties that are able to recognize different modulation schemes on different parameter variation conditions from the ST-RFT spectrograms. Simulation results demonstrate that the proposed algorithm achieves the probability of successful recognition (PSR) of over 90% when SNR is above -5 dB with large variation range in the signal parameters: carrier frequency (CF) for all considered signals, hop size (HS) for frequency shift keying (FSK) signals, and the time-bandwidth product for Linear Frequency Modulation (LFM) signals.

Migration and invasion are two crucial steps of tumor metastasis. Blockage of these steps may be an effective strategy to reduce the risk. The objective of the present study was to investigate the effects of diallyl trisulfide (DATS), a natural organosulfuric compound with most sulfur atoms found in garlic, on migration and invasion in triple negative breast cancer (TNBC) cells. Molecular mechanisms underlying the anticancer effects of DATS were further investigated.MDA-MB-231 cells and HS 578t breast cancer cells were treated with different concentrations of DATS. DATS obviously suppressed the migration and invasion of two cell lines and changed the morphological. Moreover, DATS inhibited the mRNA/protein/ enzymes activities of MMP2/9 via attenuating the NF-κB pathway. DATS also inhibited ERK/MAPK rather than p38 and JNK.DATS inhibits MMP2/9 activity and the metastasis of TNBC cells, and emerges as a potential anti-cancer agent. The inhibitory effects are associated with down-regulation of the transcriptional activities of NF-κB and ERK/MAPK signaling pathways.

Analyzing capacity degradation characteristics and accurately predicting the knee point of capacity are crucial for the safety management of lithium-ion batteries (LIBs). However, the degradation mechanism of LIBs is complex. A key but challenging problem is how to clarify the degradation mechanism and predict the knee point. According to the external characteristics such as capacity decline gradievnt and the peak value of increment capacity curve (IC curve), the capacity degradation can be divided into four stages, including initial decline stage, slow decline stage, transition stage and high-speed decline stage. The degradation mechanism of LIBs is compared from the longitudinal and horizontal aspects, respectively. Among them, the battery usage from the initial stage to the end of life (EOL) is longitudinal analysis. The battery under different conditions, such as charging and discharging, different discharge rate, different cathode material degradation mechanism is horizontal analysis. Moreover, a method based on neural network is proposed to predict the knee point. Two features are used to predict the capacity and cycle of the knee point, which are the gradient of the capacity degradation curve and the difference of the IC curve with the maximum correlation. The experimental results show that a two-dimensional surface can be obtained using only the first 100 cycles, which can provide a reference for the position of the knee point accurately prediction.

The fatigue performance of the asphalt mixture was the main focus of this study, with five typical factors—phase angle, cumulative dissipated energy, failure strain, failure stiffness modulus, and strain rate—identified as potential design indexes. The effect of asphalt content on the parameters under different gradation and stress ratios was tested. It was observed that the selected parameters exhibited varying levels of sensitivity and relevance to the fatigue behavior of asphalt mixtures under cyclic loads. By comparison, the strain rate proved sensitive to the asphalt content and independent of the other parameters, namely aggregate gradations and stress ratio, thus establishing the strain rate as a critical design index based on fatigue performance. On this basis, a design method based on the fatigue performance for the asphalt mixtures is herein proposed. It was confirmed that the asphalt mixture formulated using the proposed method exhibited enhanced fatigue endurance compared to those designed using the conventional method.

Among the methods of landslide treatment, anti-slide piles are an effective measure. The soil arching effect cannot be ignored in the design of anti-slide piles, which has a significant impact on the supporting. Based on the field investigation, this paper selects the Xinyan landslide in Yanbian area of Jilin Province and uses Flac-3D to build a numerical model to simulate its soil arching effect with different pile arrangements. The simulation results show that the pile arrangement has a significant influence on the soil arching effect. When arranging piles in parallel, the soil arches between the front and rear rows of piles are distributed parallel to each other. When arranging piles in quincunx-shape, the soil arches between the rear rows of piles are the same as when laying piles in parallel. The soil between the piles flows around once when the piles are laid out in parallel, while it flows around twice when the piles are laid out in a quincunx-shape. The latter has better support than the former.

Of major concern is the lack of correlation between the material design and structural function of asphalt pavement in China. The objective of this paper is to identify the layer in asphalt pavement where permanent deformation occurs most seriously and to propose a control index for that layer’s asphalt mixture. The permanent deformation of each layer was determined through the utilization of thickness measurements obtained from field cores. The results indicate that the reduction in thickness is more significant in the driving lane than in the ridge band and shoulder. This phenomenon can be attributed to the intensified densification and shearing deformation that arise from the combined impacts of recurrent axle loads and high temperatures. Compared to surface and base layers, the bearing layer is the primary area of concern for permanent deformation in asphalt pavement. Therefore, it is imperative to incorporate the ability of bearing-layer asphalt mixture to withstand permanent deformation as a crucial design parameter. The dynamic modulus of the bearing-layer asphalt mixture is significantly influenced by the type of asphalt, gradation, and asphalt content, compared to other design parameters. Based on the relationship established between dynamic modulus and dynamic stability, with creep rate as the intermediate term, a control standard was proposed to evaluate the permanent deformation of the bearing-layer asphalt mixture. This study can provide reasonable and effective guidance for prolonging pavement life and improving pavement performance.

The state-of-health (SOH) of lithium-ion batteries is one of the important core issues of battery management systems (BMS). After the battery reaches its end of life (EOL), its safety performance will deteriorate rapidly, which will be a huge threat to electric vehicles (EVs). Therefore, the accurate SOH prediction can ensure the safety and reliable operation of the battery, which is a critical and challenging issue. Accordingly, this paper proposes a novel SOH prediction method for lithium-ion batteries based on the long short-term memory (LSTM) neural network combined with attention mechanism (AM). First, moving average filter is applied to the lithium-ion battery capacity data for the purpose of reducing noise. Then, according to the battery capacity data of different datasets and different discharge rates, different weights are given to the LSTM hidden layer by AM to enhance the important information, so as to complete SOH prediction. Finally, the model is tested on new data and compared with the current data-driven prediction model. The experiment results show that the proposed SOH prediction method is more accurate, simple and robust. Furthermore, the SOH prediction method proposed in this paper is full of promising for practical EVs applications.

In this paper, the fractional-order modeling of multiple groups of lithium-ion batteries with different states is discussed referring to electrochemical impedance spectroscopy (EIS) analysis and iterative learning identification method. The structure and parameters of the presented fractional-order equivalent circuit model (FO-ECM) are determined by EIS from electrochemical test. Based on the working condition test, a P-type iterative learning algorithm is applied to optimize certain selected model parameters in FO-ECM affected by polarization effect. What’s more, considering the reliability of structure and adaptiveness of parameters in FO-ECM, a pre-tested nondestructive 1/f noise is superimposed to the input current, and the correlative information criterion (CIC) is proposed by means of multiple correlations of each parameter and confidence eigen-voltages from weighted co-expression network analysis method. The tested batteries with different state of health (SOH) can be successfully simulated by FO-ECM with rarely need of calibration when excluding polarization effect. Particularly, the small value of CICα indicates that the fractional-order α is constant over time for the purpose of SOH estimation. Meanwhile, the time-varying ohmic resistance R0 in FO-ECM can be regarded as a wind vane of SOH due to the large value of CICR0. The above analytically found parameter-state relations are highly consistent with the existing literature and empirical conclusions, which indicates the broad application prospects of this paper.

The extended Kalman filter (EKF) has a wide range of applications (especially in power battery management systems) with a rapidly increasing market share. It aims to minimize the symmetric loss function (mean square error) and it has high accuracy and efficiency in battery state estimation. This study deals with the second-order extended Kalman filter-based process and the measurement white noise estimation problem for nonlinear continuous-discrete systems. The design of the white noise filter and smoother were, firstly, converted into a linear estimation problem by the second-order Taylor series expansion approximation and the function that makes the second-order term approximately equivalent to the estimation error variance. Secondly, based on the projection formula of the Kalman filtering (KF) theory and the Lemma of expectation for quadratic and quartic product traces of random vectors, the second-order EKF was derived. Then, to generate white noise estimators in the forms of filtering and smoothing, we derived a recursive solution, using an innovation method. Finally, a numerical example is given to show the effectiveness of the proposed method.

The frequent occurrence of electric vehicle fire accidents reveals the safety hazards of batteries. When a battery fails, its symmetry is broken, which results in a rapid degradation of its safety performance and poses a great threat to electric vehicles. Therefore, accurate battery fault diagnoses and prognoses are the key to ensuring the safe and durable operation of electric vehicles. Thus, in this paper, we propose a new fault diagnosis and prognosis method for lithium-ion batteries based on a nonlinear autoregressive exogenous (NARX) neural network and boxplot for the first time. Firstly, experiments are conducted under different temperature conditions to guarantee the diversity of the data of lithium-ion batteries and then to ensure the accuracy of the fault diagnosis and prognosis at different working temperatures. Based on the collected voltage and current data, the NARX neural network is then used to accurately predict the future battery voltage. A boxplot is then used for the battery fault diagnosis and early warning based on the predicted voltage. Finally, the experimental results (in a new dataset) and a comparative study with a back propagation (BP) neural network not only validate the high precision, all-climate applicability, strong robustness and superiority of the proposed NARX model but also verify the fault diagnosis and early warning ability of the boxplot. In summary, the proposed fault diagnosis and prognosis approach is promising in real electric vehicle applications.

This paper provides guidance for the use of asphalt concrete materials through the study of the action mechanism of asphalt concrete materials and the specific test results. Fiber-asphalt concrete is selected as an example and different mixtures are used to conduct indoor tests. The results show that after 0.3% fiber is added to the asphalt concrete material, the dynamic stability of the material itself has been significantly improved, with an average increase of 20%. It can be found through the experiment that the addition of different mixtures has different effect on the overall performance of the asphalt concrete material and the performance of the concrete has been significantly improved with the addition of fiber. The enhancing effect of fiber material on the performance of dense-graded concrete material is the most significant.

Saline soil in seasonally frozen soil areas has caused tremendous damage to engineering and the ecological environment. The unfrozen water is the main factor affecting the properties of saline soil in seasonally frozen soil area and therefore needs to be studied. However, due to the high cost of laboratory measurement of the unfrozen water content, this study focuses on using an adaptive network fuzzy inference system (ANFIS) and a back propagation neural network (BPNN) to predict the unfrozen water content of saline soil in the Zhenlai area, Western Jilin. The data for the constructed model is obtained by nuclear magnetic resonance (NMR) testing. The initial water content (W0), salt content (S), and temperature (T) are used as input parameters for predicting the unfrozen water content (Wu). The results of the ANFIS and BPNN models are compared. The results show that although both methods are suitable for predicting the unfrozen water content of saline soil in the Zhenlai area, western Jilin, the prediction accuracy of the ANFIS model is higher.

Previously, we have shown that paraspeckle protein 1 (PSPC1), a protein component of paraspeckles that was involved in cisplatin-induced DNA damage response (DDR), probably functions at the G1/S checkpoint. In the current study, we further examined the role of PSPC1 in another DNA-damaging agent, methyl methanesulfonate (MMS)-induced DDR, in particular, focusing on MMS-induced apoptosis in HeLa cells. First, it was found that MMS treatment induced the expression of PSPC1. While MMS treatment alone can induce apoptosis, depletion of PSPC1 expression using siRNA significantly increased the level of apoptosis following MMS exposure. In contrast, overexpressing PSPC1 decreased the number of apoptotic cells. Interestingly, morphological observation revealed that many of the MMS-treated PSPC1-knockdown cells contained two or more nuclei, indicating the occurrence of mitotic catastrophe. Cell cycle analysis further showed that depletion of PSPC1 caused more cells entering the G2/M phase, a prerequisite of mitosis catastrophe. On the other hand, over-expressing PSPC1 led to more cells accumulating in the G1/S phase. Taken together, these observations suggest an important role for PSPC1 in MMS-induced DDR, and in particular, depletion of PSPC1 can enhance MMS-induced apoptosis through mitotic catastrophe.

This paper mainly investigates the sensitive characteristics of lithium-ion batteries so as to provide scientific basises for simplifying the design of the state estimator that adapt to various environments. Three lithium-ion batteries are chosen as the experimental samples. The samples were tested at various temperatures (−20 ∘ C, −10 ∘ C, 0 ∘ C , 10 ∘ C , 25 ∘ C) and various current rates (0.5C, 1C, 1.5C) using a battery test bench. A physical equivalent circuit model is developed to capture the dynamic characteristics of the batteries. The experimental results show that all battery parameters are time-varying and have different sensitivity to temperature, current rate and state of charge (SOC). The sensitivity of battery to temperature, current rate and SOC increases the difficulty in battery modeling because of the change of parameters. The further simulation experiments show that the model output has a higher sensitivity to the change of ohmic resistance than that of other parameters. Based on the experimental and simulation results obtained here, it is expected that the adaptive parameter state estimator design could be simplified in the near future.

Due to the low cost, small size, and ease of control, the switched-capacitor (SC) battery equalizers are promising among active balancing methods. However, it is difficult to achieve the full cell equalization for the SC equalizers due to the inevitable voltage drops across Metal-Oxide-Semiconductor Field Effect Transistor (MOSFET) switches. Moreover, when the voltage gap among cells is larger, the balancing efficiency is lower, while the balancing speed becomes slower as the voltage gap gets smaller. In order to soften these downsides, this paper proposes a cell-to-cell battery equalization topology with zero-current switching (ZCS) and zero-voltage gap (ZVG) among cells based on three-resonant-state SC converters. Based on the conventional inductor-capacitor (LC) converter, an additional resonant path is built to release the charge of the capacitor into the inductor in each switching cycle, which lays the foundations for obtaining ZVG among cells, improves the balancing efficiency at a large voltage gap, and increases the balancing speed at a small voltage gap. A four-lithium-ion-cell prototype is applied to validate the theoretical analysis. Experiment results demonstrate that the proposed topology has good equalization performances with fast equalization, ZCS, and ZVG among cells.

This paper presents an improved battery parameter estimation method based on typical operating scenarios in hybrid electric vehicles and pure electric vehicles. Compared with the conventional estimation methods, the proposed method takes both the constant-current charging and the dynamic driving scenarios into account, and two separate sets of model parameters are estimated through different parts of the pulse-rest test. The model parameters for the constant-charging scenario are estimated from the data in the pulse-charging periods, while the model parameters for the dynamic driving scenario are estimated from the data in the rest periods, and the length of the fitted dataset is determined by the spectrum analysis of the load current. In addition, the unsaturated phenomenon caused by the long-term resistor-capacitor (RC) network is analyzed, and the initial voltage expressions of the RC networks in the fitting functions are improved to ensure a higher model fidelity. Simulation and experiment results validated the feasibility of the developed estimation method.