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ZLDI-8 is an A disintegrin and metalloproteinase domain 17 (ADAM17) inhibitor that suppresses the shedding of Notch1 to the Notch1 intracellular domain (NICD). In previous studies, we found that ZLDI-8 was able to sensitize HCC to sorafenib, but the mechanism of action remains unclear. The sensitizing effects of ZLDI-8 were tested both in vitro and in vivo. EMT-related factors, sorafenib sensitivity-related proteins and ECM-related gene expression were assessed using immunohistochemistry, RTPCR and Western blotting. Knockdown assays were conducted to determine the relationship between the Notch and Integrin pathways. CoIP assays, nuclear and cytoplasmic fractionation and immunofluorescence colocalization were applied to explore the interaction between the Notch and Integrin pathways. Appropriate statistical analysis methods were used to assess the significance of the experimental results and to ensure the scientific validity and reliability of the experimental design. We found that ECM- and EMT-related proteins were downregulated after ZLDI-8 treatment (P

Abstract The exploration of electromagnetic vibration sources is very important for the vibroacoustic reduction of a squirrel‐cage induction motor. However, the influence of high‐order electromagnetic force on the electromagnetic vibration of induction motor has not been studied before. The influence of high‐order electromagnetic force on motor vibration is investigated in detail. Firstly, the Maxwell stress tensor method is applied to calculate the electromagnetic force. Next, the force transfer function is introduced to describe the phenomenon that the high‐order electromagnetic force induces the low‐mode motor vibration. Then, the high‐order forces in the air gap of the induction motor are analysed in detail and their effects on motor vibration are simulated. Finally, an experiment is conducted to verify the correctness of the analytical scheme.

Lithium titanium oxide (LTO) batteries offer superior performance compared to graphite-based anodes in terms of rapid charge/discharge capability and chemical stability, making them promising candidates for fast-charging and power-assist vehicle applications. However, commonly used battery models often struggle to accurately describe the current–voltage characteristics of LTO batteries, particularly before the charge/discharge cutoff conditions. In this work, a novel electrical model based on the solid-phase diffusion equation is proposed to capture the unique electrochemical phenomena arising from the diffusion mismatch between the positive and negative electrodes in high-power LTO batteries. The robustness of the proposed model is evaluated under various loading conditions, including constant current and dynamic current tests, and the results are compared against experimental data. The experimental results for LTO batteries exhibit remarkable alignment with the model estimation, demonstrating a maximum voltage error below 3%.

Abstract Four‐switch Buck‐Boost (FSBB) converter usually adopts multi‐mode control methods in applications requiring both voltage step‐up and step‐down. Model predictive current control (MPCC) can improve the dynamic performance and simplify multi‐mode switching control, but parameter mismatch will deteriorate its control performances. To this end, the theoretical steady‐state error of MPCC caused by parameter mismatch is derived; the influence of parameter mismatch on the dynamic and steady‐state performances is analyzed. Further, an improved MPCC strategy with the parameter disturbance observer (PDO) and dynamic adjustment block for the FSBB converter is presented. The PDO is developed to correct the prediction model and eliminate steady‐state errors due to parameter mismatch. The dynamic adjustment block related to the tracking error can further avoid the dynamic performance degradation. Additionally, a load disturbance observer (LDO) is designed to improve the dynamic response to the unknown load variation. Then, multi‐mode operation of the FSBB converter is realized with the proposed MPCC, which determines the operating mode according to the prediction results without extra mode detection. Finally, simulation and experimental results demonstrate that the proposed strategy can improve the dynamic and steady‐state performances of MPCC with parameter mismatch and achieve multi‐mode operation with high control performances.

This study establishes for the first time a P2D-coupled non-ideal double-layer capacitor model (P2D-CNIC), which can be used for mechanism analysis under high-frequency periodic signal excitation. The novelty of this work is the consideration of the generally neglected electric double-layer capacitance and its dispersion effects, especially the capacitance of the solid electrolyte interface (SEI) film. The dispersion effect of the model is verified by a periodic current excitation signal and the corresponding phase change in the voltage response. Under sinusoidal alternating current (AC) excitation, a comparative analysis was conducted between the traditional P2D model, the traditional P2D model coupled with the ideal double-layer capacitor (P2D-CIC), and the proposed P2D-CNIC mechanism model. Furthermore, three models were evaluated under periodic short-circuit pulse discharge conditions to verify the accuracy and reliability of P2D-CNIC. The simulation results are used to analyze the dominant order of faradaic and non-Faraday processes under sinusoidal AC excitation, thereby providing insights into the internal mechanism analysis of lithium batteries under high-frequency cycling conditions.

Precise land use land cover (LULC) data are essential for understanding the landscape structure and spatial pattern of land use/cover in the Yellow River Basin (YRB) to regulate scientific and rational territorial spatial planning and support sustainable development. However, differences in the multiple sets of LULC products in portraying the land composition of the YRB limit our understanding of the land cover composition in this region. To address this issue, this study chose five sets of open and high spatiotemporal LULC data in 2020, namely, CLCD, LSV10, ESRI10, CLC_FCS30, and Globeland30, to evaluate the accuracy and consistency of classification in the YRB. Our results show that: (1) The LULC composition of the YRB in 2020 was mapped consistently by the five datasets. Grasslands, croplands, and woodlands constitute the major LULC types, accounting for 96% of the total area of the study area. (2) The correlation coefficients of the LULC types of any two of the five datasets ranged from 0.926 to 0.998, showing high land compositional consistency. However, among the five datasets, there were considerable differences in the areas of a single LULC type. (3) The classification consistencies of croplands, woodlands, grasslands, and water bodies were higher than 60% in any two datasets. The spatial consistencies of grasslands, croplands, and woodlands were higher than those of other LULC types. An area with better consistency can reach more than 50% of the average area of the corresponding land types, but grasslands were mixed with other LULC types in ESRI10 and GLC_FCS30. (4) According to the accuracy assessments, LSV10 data have the highest overall classification accuracy, 79.32%, and the classification accuracy of major land types is also higher than 70%; GLC_FCS30 data have the lowest overall accuracy, 70.14%. Based on these results, LSV10 can more accurately demonstrate LULC than the other four datasets. This study can be used as a reference for selecting land cover data, and it also highlights that the necessary assessments of consistency and accuracy are essential when conducting land use/cover change studies in a specific region.

Accurate estimation of lithium-ion battery state of health (SOH) can effectively improve the operational safety of electric vehicles and optimize the battery operation strategy. However, previous SOH estimation algorithms developed based on high-precision laboratory data have ignored the discrepancies between field and laboratory data, leading to difficulties in field application. Therefore, aiming to bridge the gap between the lab-developed models and the field operational data, this paper presents a feature engineering-based SOH estimation method with downgraded laboratory battery data, applicable to real vehicles under different operating conditions. Firstly, a data processing pipeline is proposed to downgrade laboratory data to operational fleet-level data. The six key features are extracted on the partial ranges to capture the battery’s aging state. Finally, three machine learning (ML) algorithms for easy online deployment are employed for SOH assessment. The results show that the hybrid feature set performs well and has high accuracy in SOH estimation for downgraded data, with a minimum root mean square error (RMSE) of 0.36%. Only three mechanism features derived from the incremental capacity curve can still provide a proper assessment, with a minimum RMSE of 0.44%. Voltage-based features can assist in evaluating battery state, improving accuracy by up to 20%.

Chemical drug design based on the biochemical characteristics of cancer cells has become an important strategy for discovering new anti-tumour drugs to improve tumour targeting effects and reduce off-target toxicities. Colchicine is one of the most prominent and historically microtubule-targeting drugs, but its clinical applications are hindered by notorious adverse effects. In this study, we presented a novel tumour-specific conjugate 9 that consists of deacetylcolchicine (Deac), biotin, and a cleavable disulphide linker. 9 was found to exhibit potent anti-tumour activity and exerted higher selectivity between tumour and nontarget cells than Deac. The targeting moiety biotin might enhance the transport capability and selectivity of 9 to tumour cells via biotin receptor-mediated endocytosis. The tubulin polymerisation activity of 9 (with DTT) was close to the parent drug Deac. These preliminary results suggested that 9 is a high potency and reduced toxicity antitumor agent and worthy of further investigation.

The identification of knee points in lithium-ion (Li-ion) batteries is crucial for predicting the battery life, designing battery products, and managing battery health. Knee points (KPs) refer to the transition points in the aging speed and aging trajectory of Li-ion batteries. KPs can be identified using a wealth of aging data and various regression-based methods. However, KP identification relies on a large amount of aging data, which is exceedingly time-consuming and resource-intensive. To overcome this issue, we propose a novel method based on KP characteristics to identify the KPs and critical aging speed. Firstly, we extract the main aging trajectory using curve-fitting techniques. Secondly, we calculate the aging speed at each cycle to identify the KPs. We then explore the relationship between the KPs and cycle life and develop a knee point identification algorithm. The correlation coefficient between the KPs and cycle life provides a valuable indicator of the critical aging speed, enabling accurate identification of KPs. To validate our approach, we apply it to the Li(NiCoMn)O2, LiFePO4, and LiCoO2 cell datasets. Our results demonstrate a strong correlation between the KPs and cycle life for these battery types. By employing our proposed method, KPs can be identified for battery life prediction, product design, and health management. Moreover, we summarize a critical degradation speed of −0.03%/cycle can serve as an empirical threshold for warning against capacity diving and KPs. The statistical transition speed threshold can eliminate the dependence on extensive aging data throughout the entire battery’s lifecycle for identifying capacity knee points.

Motor faults not only damage the motor body but also affect the entire production system. When the motor runs in a steady state, the characteristic frequency of the fault current is close to the fundamental frequency, so it is difficult to effectively extract the fault current components, such as the broken rotor bar. In this paper, according to the characteristics of electromagnetic force and vibration, when the rotor eccentricity and the broken bar occur, the vibration signal is used to analyze and diagnose the fault. Firstly, the frequency, order, and amplitude characteristics of electromagnetic force under rotor eccentricity and broken bar fault are analyzed. Then, the fault vibration acceleration value collected by a one-dimensional dilated convolution pair is extracted, and the SeLU activation function and residual connection are introduced to solve the problem of gradient disappearance and network degradation, and the fault motor model is established by combining average ensemble learning and SoftMax multi-classifier. Finally, experiments of normal rotor eccentricity and broken bar faults are carried out on 4-pole asynchronous motors. The experimental results show that the accuracy of the proposed method for motor fault detection can reach 99%, which meets the requirements of fault motor detection and is helpful for further application.

The state of health (SOH) evaluation and remaining useful life (RUL) prediction for lithium-ion batteries (LIBs) are crucial for health management. This paper proposes a novel sequence-to-sequence (Seq2Seq) prediction method for LIB capacity degradation based on the gated recurrent unit (GRU) neural network with the attention mechanism. An improved particle swarm optimization (IPSO) algorithm is developed for automatic hyperparameter search of the Seq2Seq model, which speeds up parameter convergence and avoids getting stuck in local optima. Before model training, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm decomposes the capacity degradation sequences. And the intrinsic mode function (IMF) components with the highest correlation are employed to reconstruct the sequences, reducing the influence of noise in the original data. A real-cycle-life data set under fixed operating conditions is employed to validate the superiority and effectiveness of the method. The comparison results demonstrate that the proposed model outperforms traditional GRU and RNN models. The predicted mean absolute percent error (MAPE) in SOH evaluation and RUL prediction can be as low as 0.76% and 0.24%, respectively.

A large proportion of electric vehicle accidents are attributed to lithium-ion battery failure recently, which demands the time-efficient diagnosis and safety warning in advance of severe fault occurrence to ensure reliable operation of electric vehicles. However, serious battery system faults are often not caused by easily-observed cell state inconsistency, but derived from a certain cell failure with precursory signals untended, or occasional abuse, thus eventually thermal runaway. In this paper, a signal-based fault diagnosis method is presented, including signal analysis to eliminate the impact of state inconsistency on time-series feature extraction, feature fusion, and dimensionality reduction by manifold learning, with clustering-based outlier detection to identify abnormal signal features. The challenges in threshold determination of fused features can be effectively resolved by supplementary correction to largely reduce the amount of false alarms. Compared with the judgments from actual battery management systems, and other signal-based methods with single features, earlier detections can be achieved with robustness, verified by real-world pre-fault operation data of electric vehicles that suffered thermal runaway.

A series of 1-aryl-5-(4-arylpiperazine-1-carbonyl)-1H-tetrazols as microtubule destabilizers were designed, synthesised and evaluated for anticancer activity. Based on bioisosterism, we introduced the tetrazole moiety containing the hydrogen-bond acceptors as B-ring of XRP44X analogues. The key intermediates ethyl 1-aryl-1H-tetrazole-5-carboxylates 10 can be simply and efficiently prepared via a microwave-assisted continuous operation process. Among the compounds synthesised, compound 6–31 showed noteworthy potency against SGC-7901, A549 and HeLa cell lines. In mechanism studies, compound 6–31 inhibited tubulin polymerisation and disorganised microtubule in SGC-7901 cells by binding to tubulin. Moreover, compound 6–31 arrested SGC-7901cells in G2/M phase. This study provided a new perspective for development of antitumor agents that target tubulin.

This article addresses a bidirectional low power loss series-parallel partial-power modular converter (SPPC) suitable for series-connected high voltage large power battery energy storage system (BESS). A specific capacitor is placed on the top of the series battery packs, which voltage can be adjusted by the SPPC to compensate for the voltage fluctuation of the battery packs and the DC power line. The power loss and cost of the SPPC are relatively low since its rated power is reduced to about 22%–48% of the full-power converter. A 3 level distributed control method is proposed to achieve the fast transient response of the DC power line current and deal with the series inconsistency of battery packs during discharging and charging, further improving the total energy utilization efficiency (EUE) of the BESS. The critical design considerations for SPPC-BESS are analyzed. A bidirectional 800W phase-shift full-bridge (PSFB) converter hardware prototype along with the Gallium Nitride (GaN) devices is provided to verify the high efficiency of DC-DC sub-module. Then a simulation model of a 6.6kW SPPC-BESS with 3 sub-modules to verify the feasibility of the control method. Theoretical analysis proves that the total power loss of the SPPC is lower than the existing types of modular converters. The EUE of the SPPC-BESS is 1.7% higher than the conventional BESS when the average maximum available capacities of batteries attenuate to 80% of the initial maximum available capacity.

An improved method for the remaining useful life (RUL) prognostic of Lithium-ion batteries with Li(NiMnCo)O2 cathode using improved unscented particle filter (UPF) is proposed with respect to capacity diving in later capacity degradation curve. Key points of this paper are: (1) An appropriate empirical model for the situation as the most contributive work, is put forward as an alternative to the widely used UPF models, and the prediction performance is respectively verified by least square fitting and the improved UPF; (2) Systematic noise in Gamma distribution is attempted in state space equations of the proposed method, so as to avoid potential shape shifting of the prediction curve after sampling the particles with Gaussian noise, for model parameters could get zero-crossed; (3) With training data preprocessed considering the capacity recovery phenomenon concisely, the residual error and root mean square error of fitting could get further reduced, as a supplement to traditional treatments like smoothing, thus relieving the sensitivity of data-driven methods to data by enhancing quality. Validations are implemented by applying the proposed method to the battery data by conducting cycle aging tests under different working conditions, where improved approximation and prediction performance can be obtained.

The resistance of the internal short-circuit (ISC) has a potential evolution trend accompanied by an increasing safety risk. Thus, an accurate online resistance estimation for the ISC is crucial for evaluating its safety risk and taking staged handling measures. Since the ISC battery mainly presents abnormal stage of charge (SOC) depletion behaviors, the SOC estimation processes based on state observers and battery models will act an important basis of the ISC resistance estimation problem. However, as it will be exhibited in this paper, when directly using the measured voltage of the ISC battery as the output variable of the state observer, the battery model error will limit the SOC estimation accuracy and further lead to very inaccurate or even divergent ISC resistance estimation results for large-format batteries, which present quite slight SOC depletion behaviors at the ISC state. To this end, this paper proposes a novel SOC and ISC resistance co-estimation method which combines a reconstruction method of the model-predicted voltage of the ISC battery. Experimental validations are carried out with a 37 Ah battery, results show that the proposed method which uses the reconstructed model-predicted voltage (RMPV) as the output variable of the state observer only present maximum estimation errors of 39.96 Ω and 2.00 Ω for the ISC resistances of 100 Ω and 10 Ω, respectively.

Hybrid energy storage systems (HESSs) have become an effective solution for smoothing the active power variations of photovoltaic (PV). In order to reduce the required capacities and costs of the HESS, a coordinated control scheme is developed to mitigate the power variations of a PV plant by using the HESS and the active power curtailment (APC) of PV. Furthermore, a multi-objective optimization model is established to dispatch the output power of batteries and supercapacitors, considering the overall losses and the state-of-charge (SOC) deviation of the supercapacitor. Based on the proposed smoothing strategy, an allocation model is developed to optimize the energy and power capacities of the HESS with the aim of maximizing the annual net income of the PV and HESS plant. The numerous simulations are carried out to verify the effectiveness of the proposed smoothing and allocation methods by using the real data of a PV plant. In addition, we also discuss the impacts of the different dispatching strategies of the HESS, grid requirements of power variations, and solution methods on the HESS allocation results.

The existence of the consistency degradation of the battery pack hinders the accurate estimation of pack capacity and cell capacity in the battery pack. The paper focuses on the capacity estimation of cells in the serial battery pack. The shape invariance of the charging voltage curve is discussed and used as the theoretical foundation of cell capacity difference identification. The matching relationship between two voltage curves is obtained based on the dynamic time warping algorithm. Then the capacity difference identification algorithm to calculate the capacity difference between the two cells is proposed. Based on the algorithm, a three-step capacity estimation method is established. The proposed method can only use the previous charging curve of one cell in the pack and the current charging data of the battery pack to rapidly estimate the capacity of each cell in the battery pack. A 16 serial LiFePO4 battery pack is employed to verify the method. The result shows the estimation error of cell capacities is less than 3% rated capacity. With this method, the cell capacities in the pack can be rapidly and accurately estimated, providing a foundation for the consistency analysis and equalization of the battery pack.

Nowadays, the lithium-ion battery (LIB) has been widely used as an energy source for electric vehicles or the auxiliary power supply for rail transit. However, the sensitivity of LIB to temperature greatly limits its application condition. Due to internal impedance and entropy change, temperature of LIB varies during charging and discharging. This variation conversely affects the electrical characteristics of batteries, and then the heat generation rate. In order to accurately model this coupled thermoelectric process of the lithium-ion battery, a co-simulation method based on coupled thermoelectric model is developed in this paper, which combines equivalent circuit model (ECM) and Computational Fluid Dynamics (CFD) software. For purpose of verifying the feasibility and accuracy of the proposed method, Experiments are performed at different temperatures and C-rates conditions, using a large-format pouch cell applied in rail transit projects. The comparison indicates that this method joined with temperature correction has higher accuracy than the traditional thermal simulation without temperature correction and the simulated result shows good agreement with measurements.

Within the context of sustainable development and a low-carbon economy, electric vehicles (EVs) are regarded as a promising alternative to engine vehicles. Since the increase of charging EVs brings new challenges to charging stations and distribution utility in terms of economy and reliability, EV charging should be coordinated to form a friendly and proper load. This paper proposes a novel approach for pricing of charging service fees in a public charging station based on prospect theory. This behavioral economics-based pricing mechanism will guide EV users to coordinated charging spontaneously. By introducing prospect theory, a model that reflects the EV owner’s response to price is established first, considering the price factor and the state-of-charge (SOC) of batteries. Meanwhile, the quantitative relationship between the utility value and the charging price or SOC is analyzed in detail. The EV owner’s response mechanism is used in modeling the charging load after pricing optimization. Accordingly, by using the particle swarm optimization algorithm, pricing optimization is performed to achieve multiple objectives such as minimizing the peak-to-valley ratio and electricity costs of the charging station. Through case studies, the determined time-of-use charging prices by pricing optimization is validated to be effective in coordinating EV users’ behavior, and benefiting both the station operator and power systems.

An LLC resonant converter has the advantages of simple structure and soft switching. It can enable bidirectional power transmission, but it is difficult to realize a normalized gain greater than one under backward mode (backward step-up mode). Cascaded dc/dc converters or topological changes can solve this problem, but additional switches and components are required and losses are added. Without changing the LLC resonant converter’s basic topology, this paper proposes a variable duty-cycle control strategy of primary side switches for backward step-up mode. Using variable duty-cycle control, the LC resonant tank can be charged, and then the backward step-up mode can be realized. Soft switching characteristics of some primary side switches and all secondary side switches are guaranteed. In this study, the working principle of an LLC resonant converter under bidirectional control strategy was analyzed, and the backward step-up control was analyzed in detail. The voltage gain and the boundary of continuous conduction mode (CCM) and discontinuous conduction mode (DCM) were derived. A synchronous rectification method related to the backward step-up control is proposed. The control strategy was verified by experiments.

In the rapid promotion of China’s electricity spot market, a large number of electricity retailers and large consumers participate in power trading, of which medium- and long-term power trading accounts for a large proportion. In the electricity spot market, the previous medium- and long-term transactions need to be closely combined with the current spot market transaction settlement rules. This paper analyzes the trading strategy of large retailers in the power market. In order to effectively reduce the total electricity cost, it is necessary to optimize the medium- and long-term transactions based on three aspects: electricity quantity and benchmark price decisions of medium- and long-term contracts, the daily electricity decomposition method in the day-ahead (DA) market, and the daily load curve decomposition strategy. According to load history characteristics that are extracted by the X12 method, daily electricity is decomposed from the medium- and long-term electricity quantity in the DA market. This paper introduces three methods of decomposing the daily load curve and proves that the particle swarm algorithm is the best method for effectively minimizing the cost in the DA market. Through analyzing the total electricity cost change pattern, we prove that the basic component of decision making is the relative relationship between the electricity price of medium- and long-term contracts and the equivalent kWh price of medium- and long-term electricity in the DA market, which is determined by the decomposition daily curve method. If the equivalent kilowatt-hour price obtained by the decomposition method in the DA market is greater than the electricity price of medium- and long-term contracts, the larger the electrical energy of medium- and long-term contracts, the lower the costs. Based on the above principles, electricity retailers can carry out planning for medium- and long-term transactions, as well as the decomposition and declaration of the daily electricity quantities and daily load curves.

Battery life is of critical importance for the reliable and economical operation of electric vehicles (EVs). Normal aging accounts for more than 80% of the battery available cycle range. Accurate and robust battery life models of normal aging are essential for battery health management systems and life evaluation before accelerated aging. Capacity recovery, test errors and accelerated aging all affect life model building during normal aging. Therefore, this paper proposes an improved life model based on wavelet transform (WT) signal processing to accurately predict the decline trend of the battery in the normal aging stage. In this paper, the capacity recovery, test noise and capacity diving in the aging trend are effectively removed by wavelet transform. We obtained an optimized life model through the analysis of the model structure and the analysis of the parameter sensitivity of the life model. The particle swarm algorithm (PSO) is employed to identify the parameters of the empirical models with the normal aging data extracted by the WT. Through verification, it is found that the modified cycle life model proposed in this paper can accurately predict the normal aging trajectory of batteries under different discharge rates and temperatures. The prediction error of the improved life model for normal aging is 1.09%.

Due to the development of China’s electricity spot market, the peak-shifting operation modes of energy storage devices (ESD) are not able to adapt to real-time fluctuating electricity prices. The settlement mode of the spot market aggravates the negative impact of deviation assessments on the cost of electricity retailers. This article introduces the settlement rules of China’s power spot market. According to the electricity cost settlement process and the assessment methods, this paper proposes a comprehensive electricity cost optimization algorithm that optimizes day-ahead market (DA) electricity cost, real-time market (RT) electricity cost and deviation assessment through ESD control. According to the trial electricity price data of the power trading center in Guangdong province (China), many typical load curves and different deviation assessment policies, the algorithm calculates DA electricity cost, RT electricity cost and deviation assessment cost by utilizing a comprehensive electricity cost optimization algorithm. Compared with the original electricity cost and optimization cost, this method is proven to effectively save overall electricity costs under the spot market settlement system. Based on three different initial investment prices of ESD, this paper analyzes the economics of the ESD system and proves that ESD investment can be recovered within 5 years. Considering the small amounts of operating data in China’s power spot market, the algorithm generates random data according to characteristics of these data. Then, this paper verifies that the comprehensive electricity cost optimization algorithm remains reliable under random circumstances.

To relieve the peak operating power of the electric grid for an electric bus fast-charging station, this paper proposes to install a stationary energy storage system and introduces an optimization problem for obtaining the optimal sizes of an energy buffer. The charging power demands of the fast-charging station are uncertain due to arrival time of the electric bus and returned state of charge of the onboard energy storage system can be affected by actual traffic conditions, ambient temperature and other factors. The introduced optimization is formulated as a stochastic program, where the power matching equality of the total charging demands of connected electric buses is described as a chance constraint by denoting a satisfaction probability, then a stochastic supremum for the operating power of the electric grid is defined by actual data and the problem finally can be solved by convex programming. A case study for an existing electric bus fast-charging station in Beijing, China was utilized to verify the optimization method. The result shows that the operation capacity cost and electricity cost of the electric grid can be decreased significantly by installing a 325 kWh energy storage system in the case of a 99% satisfaction probability.

To enhance the operational reliability and safety of electric vehicles (EVs), big data platforms for EV supervision are rapidly developing, which makes a large quantity of battery data available for fault diagnosis. Since fault types related to lithium-ion batteries play a dominant role, a comprehensive fault diagnosis method is proposed in this paper, in pursuit of an accurate early fault diagnosis method based on voltage signals from battery cells. The proposed method for battery fault diagnosis mainly includes three parts: variational mode decomposition in the signal analysis part to separate the inconsistency of cell states, critical representative signal feature extraction by using a generalized dimensionless indicator construction formula and effective anomaly detection by sparsity-based clustering. The signal features of the majority of signal-based battery fault detection studies are found to be particular cases with a specific set of parameter values of the proposed indicator construction formula. With the sensitivity and stability balanced by appropriate moving-window size selection, the proposed signal-based method is validated to be capable of earlier anomaly detection, false-alarm reduction, and anomalous performance identification, compared with traditional approaches, based on actual pre-fault operating data from three different situations.

This paper introduces an optimal sizing method for a catenary-free tram, in which both on-board energy storage systems and charging infrastructures are considered. To quantitatively analyze the trade-off between available charging time and economic operation, a daily cost function containing a whole life-time cost of energy storage and an expense of energy supplies is formulated for the optimal sizing problem. A mixed particle swarm optimization algorithm is utilized to find optimal solutions for three schemes: (1) ultracapacitors storage systems with fast-charging at each station; (2) battery storage systems with slow-charging at starting and final stations; (3) battery storage systems with fast-swapping at swapping station. A case study on an existing catenary-free tramline in China is applied to verify the effectiveness of the proposed method. Results show that a daily-cost reduction over 30% and a weight reduction over 40% can be achieved by scheme 2, and a cost saving of 34.23% and a weight reduction of 32.46% can be obtained by scheme 3.

With the rapid growth of renewable energy and the DC fast charge pile of the electric vehicle, their inherent volatility and randomness increase a power system’s unbalance of instantaneous power. The need for power grid frequency regulation is increasing. The energy storage system (ESS) can be used to assist the thermal power unit so that a better frequency regulation result is obtained without changing the original operating mode of the unit. In this paper, a set of different charging/discharging control strategies of the lithium titanate battery (LTO) is proposed, which are chosen according to the interval of the State of energy (SOE) to improve the utilization rate of the ESS. Finally, the cost-benefit model of the ESS participating in automatic generation control ancillary service is established. Case analysis proves that after a 1.75 MWh ESS is configured for a 600 MW thermal power unit, Kp and D is increased from 1.42 to 6.38 and 2857 to 6895 MW. The net daily income is increased from 20,284 yuan to 199,900 yuan with a repayment period of 93 days. The results show that the control strategies and the energy configuration method can improve the performance and economic return of the system.

With global conventional energy depletion, as well as environmental pollution, utilizing renewable energy for power supply is the only way for human beings to survive. Currently, distributed generation incorporated into a distribution network has become the new trend, with the advantages of controllability, flexibility and tremendous potential. However, the fluctuation of distributed energy resources (DERs) is still the main concern for accurate deployment. Thus, a battery energy storage system (BESS) has to be involved to mitigate the bad effects of DERs’ integration. In this paper, optimal scheduling strategies for BESS operation have been proposed, to assist with consuming the renewable energy, reduce the active power loss, alleviate the voltage fluctuation and minimize the electricity cost. Besides, the electric vehicles (EVs) considered as the auxiliary technique are also introduced to attenuate the DERs’ influence. Moreover, both day-ahead and real-time operation scheduling strategies were presented under the consideration with the constraints of BESS and the EVs’ operation, and the optimization was tackled by a fuzzy mathematical method and an improved particle swarm optimization (IPSO) algorithm. Furthermore, the test system for the proposed strategies is a real distribution network with renewable energy integration. After simulation, the proposed scheduling strategies have been verified to be extremely effective for the enhancement of the distribution network characteristics.

Currently, electric taxis have been deployed in many cities of China. However, the charging unbalance in both temporal and spatial scale has become a rising problem, which leads to low charging efficiency or charging congestion in different stations or time periods. This paper presents a multi-objective coordinated charging strategy for electric taxis in the temporal and spatial scale. That is, the objectives are maximizing the utilization efficiency of charging facilities, minimizing the load unbalance of the regional power system and minimizing the customers’ cost. Besides, the basic configuration of a charging station and operation rules of electric taxis would be the constraints. To tackle this multi-objective optimizing problems, a fuzzy mathematical method has been utilized to transfer the multi-objective optimization to a single optimization issue, and furthermore, the Improved Particle Swarm Optimization (IPSO) Algorithm has been used to solve the optimization problem. Moreover, simulation cases are carried out, Case 1 is the original charging procedure, and Cases 2 and 3 are the temporal and spatial scale optimized separately, followed with Case 4, the combined coordinated charging. The simulation shows the significant improvement in charging facilities efficiency and users’ benefits, as well as the better dispatching of electric taxis’ charging loads.

Fast capacity estimation is a key enabling technique for second-life of lithium-ion batteries due to the hard work involved in determining the capacity of a large number of used electric vehicle (EV) batteries. This paper tries to make three contributions to the existing literature through a robust and advanced algorithm: (1) a three layer back propagation artificial neural network (BP ANN) model is developed to estimate the battery capacity. The model employs internal resistance expressing the battery’s kinetics as the model input, which can realize fast capacity estimation; (2) an estimation error model is established to investigate the relationship between the robustness coefficient and regression coefficient. It is revealed that commonly used ANN capacity estimation algorithm is flawed in providing robustness of parameter measurement uncertainties; (3) the law of large numbers is used as the basis for a proposed robust estimation approach, which optimally balances the relationship between estimation accuracy and disturbance rejection. An optimal range of the threshold for robustness coefficient is also discussed and proposed. Experimental results demonstrate the efficacy and the robustness of the BP ANN model together with the proposed identification approach, which can provide an important basis for large scale applications of second-life of batteries.

As an inhibitor of microtubule assembly, combretastatin A-4 (CA-4)-induced biological responses in tumor cells have been well known, but the corresponding changes in nano-biophysical properties were not investigated given the lack of an ideal tool. Using AFM technique, we investigated the alteration of nano-biophysical properties when CA-4-treated tumor cells underwent the different biological processes, including cell cycle arrest, apoptosis and autophagy. We found that CA-4-resistant cells were rougher with the presence of characteristic "ridges", indicating that the development of "ridge" structure may be a determinant of the sensitivity of cells to CA-4 compounds. CA-4 induced G2/M arrest and apoptosis in sensitive cells but triggered anti-apoptotic autophagy in resistant cells. CA-4 treatment caused an increase in stiffness in both sensitive and resistant cells. However, these cells exhibited different changes in cell surface roughness. CA-4 decreased Ra and Rq values in sensitive cells but increased these values in resistant cells. The reorganization of F-actin might contribute to the different changes of nano-biophysical properties in CA-4-sensitive and-resistant cells. Our results suggest that cellular nano-biophysical properties, such as "ridges", roughness and stiffness, could be applied as potential biomarkers for evaluating CA-4 compounds, and knowledge regarding how biological alterations cause changes in cellular nano-biophysical properties is helpful to develop a new high-resolution screening tool for anti-tumor agents.

A series of N,4-diaryl-1,3-thiazole-2-amines containing three aromatic rings with an amino linker were designed and synthesized as tubulin inhibitors and evaluated for their antiproliferative activity in three human cancer cell lines. Most of the target compounds displayed moderate antiproliferative activity, and N-(2,4-dimethoxyphenyl)-4-(4-methoxyphenyl)-1,3-thiazol-2-amine (10s) was determined to be the most potent compound. Tubulin polymerization and immunostaining experiments revealed that 10s potently inhibited tubulin polymerization and disrupted tubulin microtubule dynamics in a manner similar to CA-4. Moreover, 10s effectively induced SGC-7901 cell cycle arrest at the G2/M phase in both concentration- and time-dependent manners. The molecular docking results revealed that 10s could bind to the colchicine binding site of tubulin.

With the pervasiveness of electric vehicles and an increased demand for fast charging, stationary high-power fast-charging is becoming more widespread, especially for the purpose of serving pure electric buses (PEBs) with large-capacity onboard batteries. This has resulted in a huge distribution capacity demand. However, the distribution capacity is limited, and in some urban areas the cost of expanding the electric network capacity is very high. In this paper, three battery energy storage system (BESS) integration methods—the AC bus, each charging pile, or DC bus—are considered for the suppression of the distribution capacity demand according to the proposed charging topologies of a PEB fast-charging station. On the basis of linear programming theory, an evaluation model was established that consider the influencing factors of the configuration: basic electricity fee, electricity cost, cost of the energy storage system, costs of transformer and converter equipment, and electric energy loss. Then, a case simulation is presented using realistic operation data, and an economic comparison of the three configurations is provided. An analysis of the impacts of each influence factor in the case study is discussed to verify the case results. The numerical results indicate that the appropriate BESS configuration can significantly reduce the distribution demand and stationary cost synchronously.

We completed a new and efficient synthesis of D-chalcose (I) and the first synthesis of its C-3 epimer (I′) in nine steps with overall yields of 23% and 24%, respectively. The key steps in the sequence were the formation of the stereocenter on C3 via Grignard reaction, the introduction of the stereogenic center on C2 by Sharpless asymmetric dihydroxylation, the protection of the C1 and C2 hydroxy groups with tert-butyldimethylsilyl trifluoromethanesulfonate (TBSOTf), and the selective cleavage of the primary OTBS ether using catalytic DL-10-camphorsulfonic acid (CSA) in MeOH.

Purpose: Polarization voltage of the lithium-ion battery is an important parameter that has direct influence on battery performance. The paper aims to analyze the impedance characteristics of the lithium-ion battery based on EIS data. Design/methodology/approach: The effects of currents, initial SOC of the battery on charge polarization voltage are investigated, which is approximately linear function of charge current. The change of charge polarization voltage is also analyzed with the gradient analytical method in the SOC domain. The charge polarization model with two RC networks is presented, and parts of model parameters like Ohmic resistance and charge transfer impedance are estimated by both EIS method and battery constant current testing method. Findings: This paper reveals that the Ohmic resistance accounts for much contribution to battery total polarization compared to charge transfer impedance. Practical implications: Experimental results demonstrate the efficacy of the model with the proposed identification method, which provides the foundation for battery charging optimization. Originality/value: The paper analyzed the impedance characteristics of the lithium-ion battery based on EIS data, presented a charge polarization model with two RC networks, and estimated parameters like Ohmic resistance and charge transfer impedance.

In our continuing structure-activity relationship study of a new class of erythromycin A (EM-A) derivatives with antiproliferative activity, a new series of de(N-methyl) EM-A dimers jointed by a four-atom linker, -CH2CH = CHCH2-, were prepared and their antiproliferative activity against three human tumor cell lines was evaluated by MTT assay. The most active EM-A dimer, compound 1b, that carrying C6 methoxyl groups was further investigated and showed potent antiproliferative activity in six other human tumor cell lines. Flow cytometry analysis of 1b treated HeLa and MCF-7 cells indicated that the four-atom EM-A dimers induced the SubG1 phase cell cycle arrest and cell apoptosis, in time- and dose-dependent manners. Further experiments including morphologic observation, DNA agarose gel electrophoresis, mitochondrial potential alternation and western blot analysis revealed that the antiproliferative mechanism may involve the induction of apoptosis in activating the mitochondrial pathway, and regulation of apoptotic proteins.

A robust extended Kalman filter (EKF) is proposed as a method for estimation of the state of charge (SOC) of lithium-ion batteries used in hybrid electric vehicles (HEVs). An equivalent circuit model of the battery, including its electromotive force (EMF) hysteresis characteristics and polarization characteristics is used. The effect of the robust EKF gain coefficient on SOC estimation is analyzed, and an optimized gain coefficient is determined to restrain battery terminal voltage from fluctuating. Experimental and simulation results are presented. SOC estimates using the standard EKF are compared with the proposed robust EKF algorithm to demonstrate the accuracy and precision of the latter for SOC estimation.

Combretastatin A-4 (CA-4), its analogues and their excellent antitumoral and antivascular activities, have attracted considerable interest of medicinal chemists. In this article, a docking simulation was used to identify molecules having the same binding mode as the lead compound, and 3D-QSAR models had been built by using CoMFA based on docking. As a result, these studies indicated that the QSAR models were statistically significant with high predictabilities (CoMFA model, q2 = 0.786, r2 = 0.988). Our models may offer help to better comprehend the structure-activity relationships for this class of compounds and also facilitate the design of novel inhibitors with good chemical diversity.

In order to reduce the recharging time of electric vehicles, the charging power and voltage are becoming higher, which has led to a huge distribution capacity demand and load fluctuation, especially in pure electric buses (PEBs) with large onboard batteries. Based on one actual direct current (DC) fast-charging station, a two-step strategy for the suppression of the peak charging power was developed in this paper, which combined charging optimization and a battery energy storage system (BESS) configuration. A novel charging strategy was proposed, with the PEBs fast-charging during operating hours and normal charging at night, based on a new charging topology. Then, a charging sequence optimization model was established, according to the operation characteristics analysis of the DC fast-charging station. The particle swarm optimization (PSO) algorithm is applied to optimize the charging sequence, which is disordered at present. Linear programming is used to configure the battery energy storage system in order to further decrease the peak charging power and satisfy the distribution capacity constraint. The two-step strategy was simulated by the dataset from the real station. The results show that the distribution capacity demand, charging load fluctuation, electricity cost, and size of the BESS were significantly decreased.

With the fast development of the electric vehicle industry, the reuse of second-life batteries in vehicles are becoming more attractive, however, both the state-of-charge (SOC) inconsistency and the capacity inconsistency of second-life batteries have limits in their utilization. This paper focuses on the second-life batteries applied battery energy storage system (BESS) based on modular multilevel converter (MMC). By analyzing the power flow characteristics among all sources within the MMC-BESS, a three-level SOC equilibrium control strategy aiming to battery capacity inconsistency is proposed to balance the energy of batteries, which includes SOC balance among three-phase legs, SOC balance between the upper and lower arms of each phase, and SOC balance of submodules within each arm. In battery charging and discharging control, by introducing power regulations based on battery capacity proportion of three-phase legs, capacity deviation between the upper and lower’s arm, and the capacity coefficient of the submodule into the SOC feedback control loop, SOC balance of all battery modules is accomplished, thus effectively improving the energy utilization of second-life battery energy storage system. Finally, the effectiveness and feasibility of the proposed methods are verified by results obtained from simulations and the experimental platform.

Fast charging stations enable the high-powered rapid recharging of electric vehicles. However, these stations also face challenges due to power fluctuations, high peak loads, and low load factors, affecting the reliable and economic operation of charging stations and distribution networks. This paper introduces a battery energy storage system (BESS) for charging load control, which is a more user-friendly approach and is more robust to perturbations. With the goals of peak-shaving, total electricity cost reduction, and minimization of variation in the state-of-charge (SOC) range, a BESS-based bi-level optimization strategy for the charging load regulation of fast charging stations is proposed in this paper. At the first level, a day-ahead optimization strategy generates the optimal planned load curve and the deviation band to be used as a reference for ensuring multiple control objectives through linear programming, and even for avoiding control failure caused by insufficient BESS energy. Based on this day-ahead optimal plan, at a second level, real-time rolling optimization converts the control process to a multistage decision-making problem. The predictive control-based real-time rolling optimization strategy in the proposed model was used to achieve the above control objectives and maintain battery life. Finally, through a horizontal comparison of two control approaches in each case study, and a longitudinal comparison of the control robustness against different degrees of load disturbances in three cases, the results indicated that the proposed control strategy was able to significantly improve the charging load characteristics, even with large disturbances. Meanwhile, the proposed approach ensures the least amount of variation in the range of battery SOC and reduces the total electricity cost, which will be of a considerable benefit to station operators.

A flexible group battery energy storage system (FGBESS) based on cascaded multilevel converters is attractive for renewable power generation applications because of its high modularity and high power quality. However, reliability is one of the most important issues and the system may suffer from great financial loss after fault occurs. In this paper, based on conventional fundamental phase shift compensation and third harmonic injection, a hybrid compensation fault-tolerant method is proposed to improve the post-fault performance in the FGBESS. By adjusting initial phase offset and amplitude of injected component, the optimal third harmonic injection is generated in an asymmetric system under each faulty operation. Meanwhile, the optimal redundancy solution under each fault condition is also elaborated comprehensively with a comparison of the presented three fault-tolerant strategies. This takes full advantage of battery utilization and minimizes the loss of energy capacity. Finally, the effectiveness and feasibility of the proposed methods are verified by results obtained from simulations and a 10 kW experimental platform.

The synthesis of ring-contracted derivatives of erythromycin A via intramolecular transesterification under microwave irradiation of 8,9-anhydroerythromycin A 6,9-hemiketal and its derivatives is described. It was found that microwave irradiation could significantly improve the yields and shorten the reaction times under either solvent-containing (method A) or solvent-free (method B) conditions.

A series of novel 3-alkyl-1,5-diaryl-1H-pyrazoles were synthesized as combretastatin A-4 (CA-4) analogues and evaluated for antiproliferative activity against three human cancer cell lines (SGC-7901, A549 and HT-1080). Most of the target compounds displayed moderate to potent antiproliferative activity, and 7k was found to be the most potent compound. Structure-activity relationships indicated that compounds with a trimethoxyphenyl A-ring at the N-1 position of the pyrazole skeleton were more potent than those with the A-ring at the C-5 position. Tubulin polymerization and immunostaining experiments revealed that 7k potently inhibited tubulin polymerization and disrupted tubulin microtubule dynamics in a manner similar to CA-4. Computational modelling demonstrated that the binding of 7k to the colchicine binding site on microtubules may involve a similar mode as CA-4.

Due to the widespread deployment of distributed energy resources (DERs) and the liberalization of electricity market, traditional distribution networks are undergoing a transition to active distribution systems (ADSs), and the traditional deterministic planning methods have become unsuitable under the high penetration of DERs. Aiming to develop appropriate models and methodologies for the planning of ADSs, the key features of ADS planning problem are analyzed from the different perspectives, such as the allocation of DGs and ESS, coupling of operation and planning, and high-level uncertainties. Based on these analyses, this comprehensive literature review summarizes the latest research and development associated with ADS planning. The planning models and methods proposed in these research works are analyzed and categorized from different perspectives including objectives, decision variables, constraint conditions, and solving algorithms. The key theoretical issues and challenges of ADS planning are extracted and discussed. Meanwhile, emphasis is also given to the suitable suggestions to deal with these abovementioned issues based on the available literature and comparisons between them. Finally, several important research prospects are recommended for further research in ADS planning field, such as planning with multiple micro-grids (MGs), collaborative planning between ADSs and information communication system (ICS), and planning from different perspectives of multi-stakeholders.

Lithium ion batteries are widely used in portable electronics and transportations due to their high energy and high power with low cost. However, they suffer from capacity degradation during long cycling, thus making it urgent to study their decay mechanisms. Commercial 18650-type LiCoO2 + LiNi0.5Mn0.3Co0.2O2/graphite cells are cycled at 1 C rate for 700 cycles, and a continuous post-mortem analysis is performed. Based on these tests, the decay mechanism of the cells is finally proposed. The changes of differential capacity curves of the full cells show that the loss of active materials, loss of lithium ions and cell polarization are the main factors contributing to capacity loss. Non-fully charging of the electrodes is also one of the reasons, but only takes up a minor portion. Impedance results indicate that the charge transfer resistance becomes larger during cycling, especially at low state of charge. Morphology and surface chemistry analysis demonstrates that the electrode particles after cycling exhibit some minor cracks and some additional layers are formed on surfaces of both the cathode and anode electrodes. All of these effects may contribute to the impedance increase, and consequently lead to degradation of the full cells. Thus, a good protection of the surface of the cathode and anode shows great potential to improve the capacity maintenance and prolong the cycle life of the cells.

A novel, fully decentralized strategy to coordinate charge operation of electric vehicles is proposed in this paper. Based on stochastic switching control of on-board chargers, this strategy ensures high-efficiency charging, reduces load variations to the grid during charging periods, achieves charge completion with high probability, and accomplishes approximate “valley-filling”. Further improvements on the core strategy, including individualized power management, adaptive strategies, and battery support systems, are introduced to further reduce power fluctuation variances and to guarantee charge completion. Stochastic analysis is performed to establish the main properties of the strategies and to quantitatively show the performance improvements. Compared with the existing decentralized charging strategies, the strategies proposed in this paper can be implemented without any information exchange between grid operators and electric vehicles (EVs), resulting in a communications cost reduction. Additionally, it is shown that by using stochastic charging rules, a grid-supporting battery system with a very small energy capacity can achieve substantial reduction of EV load fluctuations with high confidence. An extensive set of simulations and case studies with real-world data are used to demonstrate the benefits of the proposed strategies.