Relationship between the Local Vibration Mode Characteristics and the Charge State of Carbon Acceptor in GaAs

The local vibration mode(LVM)of carbon acceptor in GaAs is studied by measuring directly the change in LVM absorption with a NIC-170 SX FT-IR spectrometer.The change in the charge state of carbon acceptor and the temperature dependence of the LVM absorption were investigated also.The contents of the impurities other than carbon were estimated by secondary ion mass spectrometry.It is observed that the frequency,the spectral form and the integrated absorption of the LVM are not affected by the change in charge state of car- bon acceptor.

Charge state effect on the K-shell ionization of iron by xenon ions near the Bohr velocity

Fe K-shell ionization cross sections induced by 2.4-6.0 MeV Xe^20＋ are measured and compared with different binary- encounter-approximation （BEA） models. The results indicate that the BEA model corrected both by the Coulomb repulsion and by the effective nuclear charge （Zeff） agrees well with the experimental data. Comparison of Fe K-shell X-ray emission induced by 5 MeV xenon ions with different initial charge states （20＋, 22＋, 26＋, 30＋） verifies the applicability of the effective nuclear charge （Zeff） correction for the BEA model. It is found that Zeff correction is reasonable to describe direct ionization induced by xenon ions with no initial M-shell vacancies. However, when the M shell is opened, the Zeff corrected BEA model is unable to explain the inner-shell ionization, and the electron transfer by molecular-orbital promotion should be considered.

Simulation studies of tungsten impurity behaviors during neon impurity seeding with tungsten bundled charge state model using SOLPS-ITER on EAST

An investigation into tungsten(W)impurity behaviors with the update of the EAST lower W divertor for H-mode has been carried out using SOLPS-ITER.This work aims to study the effect of external neon(Ne)impurity seeding on W impurity sputtering with the bundled charge state model.As the Ne seeding rate increases,plasma parameters,W concentration(C_(W)),and eroded W flux(Γ_(W)^(Ero))at both targets are compared and analyzed between the highly resolved bundled model‘jett’and the full W charge state model.The results indicate that‘jett’can produce divertor behaviors essentially in agreement with the full W charge state model.The bundled scheme with high resolution in low W charge states(<W^(20+))has no obvious effect on the Ne impurity distribution and thus little effect on W sputtering by Ne.Meanwhile,parametric scans of radial particle and thermal transport diffusivities(D_(⊥)andχ_(e,i))in the SOL are simulated using the‘jett’bundled model.The results indicate that the transport diffusivity variations have significant influences on the divertor parameters,especially for W impurity sputtering.

Effect of vacancy charge state on positron annihilation in silicon

The charge-state-dependent lattice relaxation of mono-vacancy in silicon is studied using the first-principles pseu- dopotential plane-wave method. We observe that the structural relaxation for the first-neighbor atoms of the mono-vacancy is strongly dependent on its charge state. The difference in total electron density between with and without charge states in mono-vacancy and its relevant change due to the localized positron are also examined by means of first-principles simu- lation, demonstrating the strong interplay between positron and electron. Our calculations reveal that the positron lifetime decreases with absolute charge value increasing.

Thermal safety boundary of lithium-ion battery at different state of charge

Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charges(SOCs)has significant implications for reinforcing the thermal safety design of the lithium-ion battery module.This study first investigates the thermal safety boundary(TSB)correspondence at the cells and modules level under the guidance of a newly proposed concept,safe electric quantity boundary(SEQB).A reasonable thermal runaway propagation(TRP)judgment indicator,peak heat transfer power(PHTP),is proposed to predict whether TRP occurs.Moreover,a validated 3D model is used to quantitatively clarify the TSB at different SOCs from the perspective of PHTP,TR trigger temperature,SOC,and the full cycle life.Besides,three different TRP transfer modes are discovered.The interconversion relationship of three different TRP modes is investigated from the perspective of PHTP.This paper explores the TSB of LIBs under different SOCs at both cell and module levels for the first time,which has great significance in guiding the thermal safety design of battery systems.

Charge state modulation on boron site by carbon and nitrogen localized bonding microenvironment for two-electron electrocatalytic H_(2)O_(2)production

Design of electrochemical active boron(B)site at solid materials to understand the relationships between the localized structure,charge state at the B site and electrocatalytic activity plays a crucial role in boosting the green electrochemical synthesis of hydrogen peroxide(H_(2)O_(2))via two-electron oxygen reduction(2eORR)pathway.Herein,we demonstrate a carbon(C)and nitrogen(N)localized bonding microenvironment to modulate the charge state of B site at the boron-carbon nitride solid(BCNs)to realize the efficient selective electrocatalytic H_(2)O_(2)production.The localized chemical structure of N-B-N,N-B-C and C-B-C bonds at B site can be regulated through solid-state reaction between boron nitride(BN)and porous carbon(C)at variable temperatures.The optimized BCN-1100 achieves an outstanding H_(2)O_(2)selectivity of 89%and electron transfer number of 2.2(at 0.55 V vs.RHE),with the production of 10.55mmol/L during 2.5 h and the catalytic stability duration for 15000 cycles.Further first-principles calculations identified the dependency of localized bonding microenvironment on the OOH~*adsorption energies and relevant charge states at the boron site.The localized structure of B site with BNC_(2)-Gr configuration is predicted to be the highest 2eORR activity.

Deep learning-based battery state of charge estimation:Enhancing estimation performance with unlabelled training samples

The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their corresponding SOCs.However,the collection of labelled samples is costly and time-consuming.In contrast,the unlabelled training samples,which consist of the current and voltage data with unknown SOCs,are easy to obtain.In view of this,this paper proposes an improved DNN for SOC estimation by effectively using both a pool of unlabelled samples and a limited number of labelled samples.Besides the traditional supervised network,the proposed method uses an input reconstruction network to reformulate the time dependency features of the voltage and current.In this way,the developed network can extract useful information from the unlabelled samples.The proposed method is validated under different drive cycles and temperature conditions.The results reveal that the SOC estimation accuracy of the DNN trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples.In addition,when the dataset with reduced number of labelled samples to some extent is used to test the developed network,it is found that the proposed method performs well and is robust in producing the model outputs with the required accuracy when the unlabelled samples are involved in the model training.Furthermore,the proposed method is evaluated with different recurrent neural networks(RNNs)applied to the input reconstruction module.The results indicate that the proposed method is feasible for various RNN algorithms,and it could be flexibly applied to other conditions as required.

Neural Network-Based State of Charge Estimation Method for Lithium-ion Batteries Based on Temperature

Lithium-ion batteries are commonly used in electric vehicles,mobile phones,and laptops.These batteries demonstrate several advantages,such as environmental friendliness,high energy density,and long life.However,battery overcharging and overdischarging may occur if the batteries are not monitored continuously.Overcharging causesfire and explosion casualties,and overdischar-ging causes a reduction in the battery capacity and life.In addition,the internal resistance of such batteries varies depending on their external temperature,elec-trolyte,cathode material,and other factors;the capacity of the batteries decreases with temperature.In this study,we develop a method for estimating the state of charge(SOC)using a neural network model that is best suited to the external tem-perature of such batteries based on their characteristics.During our simulation,we acquired data at temperatures of 25°C,30°C,35°C,and 40°C.Based on the tem-perature parameters,the voltage,current,and time parameters were obtained,and six cycles of the parameters based on the temperature were used for the experi-ment.Experimental data to verify the proposed method were obtained through a discharge experiment conducted using a vehicle driving simulator.The experi-mental data were provided as inputs to three types of neural network models:mul-tilayer neural network(MNN),long short-term memory(LSTM),and gated recurrent unit(GRU).The neural network models were trained and optimized for the specific temperatures measured during the experiment,and the SOC was estimated by selecting the most suitable model for each temperature.The experimental results revealed that the mean absolute errors of the MNN,LSTM,and GRU using the proposed method were 2.17%,2.19%,and 2.15%,respec-tively,which are better than those of the conventional method(4.47%,4.60%,and 4.40%).Finally,SOC estimation based on GRU using the proposed method was found to be 2.15%,which was the most accurate.

Subdiffraction optical manipulation of the charge state of nitrogen vacancy center in diamond

As a potential candidate for quantum computation and metrology,the nitrogen vacancy(NV)center in diamond presents both challenges and opportunities resulting from charge-state conversion.By utilizing different lasers for the photon-induced charge-state conversion,we achieved subdiffraction charge-state manipulation.The charge-state depletion(CSD)microscopy resolution was improved to 4.1 nm by optimizing the laser pulse sequences.Subsequently,the electron spin-state dynamics of adjacent NV centers were selectively detected via the CSD.The experimental results demonstrated that the CSD can improve the spatial resolution of the measurement of NV centers for nanoscale sensing and quantum information.

ESTIMATION METHOD ON THE BATTERY STATE OF CHARGE FOR HYBRID ELECTRIC VEHICLE

A combined algorithm for battery state of charge （SOC） estimation is proposed to solve the critical issue of hybrid electric vehicle （HEV）. To obtain a more accurate SOC, both coulomb-accumulation and battery resistance-capacitor （RC） model are weighted combined to compensate the deficiencies of individual methods. In order to solve the key issue of coulomb-accumulation, the battery thermal model is used. Based on the principle of energy conservation, the heat generated from battery charge and discharge process is converted into the equivalent electricity to calculate charge and discharge efficiency under variable current. The extended Kalman filter （EKF） as a closed loop algorithm is applied to estimate the parameters of resistance-capacitor model. The input variables do not increase much computing difficulty. The proposed combined algorithm is implemented by adjusting the weighting factor of coulomb- accumulation and resistance-capacitor model. In the end, four different methods including Ah-efficiency, Ah-Equip, RC-SOC and Combined-SOC are compared in federal testing procedure （FTP） drive cycle. The experiment results show that the proposed method has good robustness and high accuracy which is suitable for HEV application.

Study about thermal runaway behavior of high specific energy density Li-ion batteries in a low state of charge

Lithium-ion batteries are widely used in electric vehicles and electronics, and their thermal safety receives widespread attention from consumers. In our study, thermal runaway testing was conducted on the thermal stability of commercial lithium-ion batteries, and the internal structure of the battery was analyzed with an in-depth focus on the key factors of the thermal runaway. Through the study of the structure and thermal stability of the cathode, anode, and separator, the results showed that the phase transition reaction of the separator was the key factor affecting the thermal runaway of the battery for the condition of a low state of charge.

A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries

In the field of energy storage,it is very important to predict the state of charge and the state of health of lithium-ion batteries.In this paper,we review the current widely used equivalent circuit and electrochemical models for battery state predictions.The review demonstrates that machine learning and deep learning approaches can be used to construct fast and accurate data-driven models for the prediction of battery performance.The details,advantages,and limitations of these approaches are presented,compared,and summarized.Finally,future key challenges and opportunities are discussed.

Review of lithium-ion battery state of charge estimation

The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries,thereby improving discharge efficiency and extending cycle life.In this study,the key lithium-ion battery SOC estimation technologies are summarized.First,the research status of lithium-ion battery modeling is introduced.Second,the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed.Third,the development status and advantages and disadvantages of SOC estimation methods are summarized.Finally,the current research problems and prospects for development trends are summarized.

State of charge estimation of Li-ion batteries in an electric vehicle based on a radial-basis-function neural network

The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.

Co-Estimation of State of Charge and Capacity for Lithium-Ion Batteries with Multi-Stage Model Fusion Method

Lithium-ion batteries(LIBs)have emerged as the preferred energy storage systems for various types of electric transports,including electric vehicles,electric boats,electric trains,and electric airplanes.The energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge(SOC)and capacity in real-time.This study proposes a multistage model fusion algorithm to co-estimate SOC and capacity.Firstly,based on the assumption of a normal distribution,the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable parameters.Secondly,a differential error gain with forward-looking ability is introduced into a proportional–integral observer(PIO)to accelerate convergence speed.Thirdly,a fusion algorithm is developed by combining a multistage model and proportional–integral–differential observer(PIDO)to co-estimate SOC and capacity under a complex application environment.Fourthly,the convergence and anti-noise performance of the fusion algorithm are discussed.Finally,the hardware-in-the-loop platform is set up to verify the performance of the fusion algorithm.The validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2%and 3.3%,respectively.

A Comparative Study of Fractional Order Models on State of Charge Estimation for Lithium Ion Batteries

State of charge(SOC)estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles.Battery fractional order models(FOMs)which come from frequency-domain modelling have provided a distinct insight into SOC estimation.In this article,we compare five state-of-the-art FOMs in terms of SOC estimation.To this end,firstly,characterisation tests on lithium ion batteries are conducted,and the experimental results are used to identify FOM parameters.Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy.The model R(RQ)W shows superior identification accuracy than the other four FOMs.Secondly,the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles,memory lengths,ambient temperatures,cells and voltage/current drifts.The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs.Although more complex models can have better robustness against temperature variation,R(RQ),the simplest FOM,can overall provide satisfactory accuracy.Validation results on different cells demonstrate the generalisation ability of FOMs,and R(RQ)outperforms other models.Moreover,R(RQ)shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.

A new state of charge determination method for battery management system

State of Charge (SOC) determination is an increasingly important issue in battery technology. In addition to the immediate display of the remaining battery capacity to the user, precise knowledge of SOC exerts additional control over the charging/discharging process which in turn reduces the risk of over-voltage and gassing, which degrade the chemical composition of the electrolyte and plates. This paper describes a new approach to SOC determination for the lead-acid battery management system by combining Ah-balance with an EMF estimation algorithm, which predicts the battery’s EMF value while it is under load. The EMF estimation algorithm is based on an equivalent-circuit representation of the battery, with the parameters determined from a pulse test performed on the battery and a curve-fitting algorithm by means of least-square regression. The whole battery cycle is classified into seven states where the SOC is estimated with the Ah-balance method and the proposed EMF based algorithm. Laboratory tests and results are described in detail in the paper.

Fuzzy Model for Estimation of the State-of-Charge of Lithium-Ion Batteries for Electric Vehicles

A fuzzy model was established to estimate the state of charge（SOC） of a lithium-ion battery for electric vehicles.The robust Gustafson-Kessel（GK） clustering algorithm based on clustering validity indices was applied to identify the structure and antecedent parameters of the model.The least squares algorithm was utilized to determine the consequent parameters.Validation results show that this model can provide accurate SOC estimation for the lithium-ion battery and satisfy the requirement for practical electric vehicle applications.

Estimation Method of State-of-Charge For Lithium-ion Battery Used in Hybrid Electric Vehicles Based on Variable Structure Extended Kalman Filter

Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter（VSEKF）-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model（GLM）,which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics（HPPC） test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour（Ah） integration method and the extended Kalman filter（EKF） method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.

State of charge and health estimation of batteries for electric vehicles applications:key issues and challenges

Using electric vehicles(EVs)for transportation is considered as a necessary component for managing sustainable development and environmental issues.The present concerns regarding the environment,such as rapid fossil fuel depletion,increases in air pollution,accelerating energy demands,global warming,and climate change,have paved the way for the electrification of the transport sector.EVs can address all of the aforementioned issues.Portable power supplies have become the lifeline of the EV world,especially lithium-ion(Li-ion)batteries.Li-ion batteries have attracted considerable attention in the EV industry,owing to their high energy density,power density,lifespan,nominal voltage,and cost.One major issue with such batteries concerns providing a quick and accurate estimation of a battery’s state and health;therefore,accurate determinations of the battery’S performance and health,as well as an accurate prediction of its life,are necessary to ensure reliability and efficiency.This study conducts a review of the technological briefs of EVs and their types,as well as the corresponding battery characteristics.Various aspects of recent research and developments in Li-ion battery prognostics and health monitoring are summarized,along with the techniques,algorithms,and models used for current/voltage estimations,state-of-charge(SoC)estimations,capacity estimations,and remaining-useful-life predictions.