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.

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.

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.

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.

A Comprehensive Review on Charging Topologies and Power Electronic Converter Solutions for Electric Vehicles

Electric vehicles(EVs)are becoming more popular worldwide due to environmental concerns,fuel security,and price volatility.The performance of EVs relies on the energy stored in their batteries,which can be charged using either AC(slow)or DC(fast)chargers.Additionally,EVs can also be used as mobile power storage devices using vehicle-to-grid(V2G)technology.Power electronic converters(PECs)have a constructive role in EV applications,both in charging EVs and in V2G.Hence,this paper comprehensively investigates the state of the art of EV charging topologies and PEC solutions for EV applications.It examines PECs from the point of view of their classifications,configurations,control approaches,and future research prospects and their impacts on power quality.These can be classified into various topologies:DC-DC converters,AC-DC converters,DC-AC converters,and AC-AC converters.To address the limitations of traditional DC-DC converters such as switching losses,size,and high-electromagnetic interference(EMI),resonant converters and multiport converters are being used in high-voltage EV applications.Additionally,power-train converters have been modified for high-efficiency and reliability in EV applications.This paper offers an overview of charging topologies,PECs,challenges with solutions,and future trends in the field of the EV charging station applications.

State of charge estimation for electric vehicles using random forest

This paper introduces an innovative approach to addressing a critical challenge in the electric vehicle(EV)industry—the accurate estimation of the state of charge(SOC)of EV batteries under real-world operating conditions.The electric mobility landscape is rapidly evolving,demanding more precise SOC estimation methods to improve range prediction accuracy and battery management.This study applies a Random Forest(RF)machine learning algorithm to improve SOC estimation.Traditionally,SOC estimation has posed a formidable challenge,particularly in capturing the complex dependencies between various parameters and SOC values during dynamic driving conditions.Previous methods,including the Extreme Learning Machine(ELM),have exhibited limitations in providing the accuracy and robustness required for practical EV applications.In contrast,this research introduces the RF model,for SOC estimation approach that excels in real-world scenarios.By leveraging decision trees and ensemble learning,the RF model forms resilient relationships between input parameters,such as voltage,current,ambient temperature,and battery temperatures,and SOC values.This unique approach empowers the model to deliver precise and consistent SOC estimates across diverse driving conditions.Comprehensive comparative analyses showcase the superiority of the RF over ELM.The RF model not only outperforms in accuracy but also demonstrates exceptional robustness and reliability,addressing the pressing needs of the EV industry.The results of this study not only underscore the potential of RF in advancing electric mobility but also suggest a promising integration of the SOC estimation approach into the battery management system of BMW i3.This integration holds the key to more efficient and dependable electric vehicle operations,marking a significant milestone in the ongoing evolution of EV technology.Importantly,the RF model demonstrates a lower Root Mean Squared Error(RMSE)of 5.902,8%compared to 6.312,7%for ELM,and a lower Mean Absolute Error(MAE)of 4.432,1%versus 5.111,2%for ELM across rigorous k-fold cross-validation testing,reaffirming its superiority in quantitative SOC estimation.

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.

ONE-CELL-STATE METHOD FOR DETERMINATION OF ELECTRONIC STRUCTURE OF INTERMETALLIC COMPOUNDS

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Dynamic Cell Modeling for Accurate SOC Estimation in Autonomous Electric Vehicles

This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 Ah, optimized for power-needy applications. The AEV operates in a harsh environment with rate requirements up to ±25C and highly dynamic rate profiles, unlike portable-electronic applications with constant power output and fractional C rates. SOC estimation methods effective in portable electronics may not suffice for the AEV. Accurate SOC estimation necessitates a precise cell model. The proposed SOC estimation method utilizes a detailed Kalman-filtering approach. The cell model must include SOC as a state in the model state vector. Multiple cell models are presented, starting with a simple one employing “Coulomb counting” as the state equation and Shepherd’s rule as the output equation, lacking prediction of cell relaxation dynamics. An improved model incorporates filter states to account for relaxation and other dynamics in closed-circuit cell voltage, yielding better performance. The best overall results are achieved with a method combining nonlinear autoregressive filtering and dynamic radial basis function networks. The paper includes lab test results comparing physical cells with model predictions. The most accurate models obtained have an RMS estimation error lower than the quantization noise floor expected in the battery-management-system design. Importantly, these models enable precise SOC estimation, allowing the vehicle controller to utilize the battery pack’s full operating range without overcharging or undercharging concerns.

Dynamic NMR and Twisted Intramolecular Charge Transfer Excited States

In this paper the results of dynamic NMR studies on ethylmethylamino-tertiary-butyl-phenylborane (EMABPB) with or without light are reported. The NMR data were recorded on a Bruker 400 MHz NMR equipped with our custom-made optical probe and with our custom-made 450 watts (W) monochromatic light sources. The molecular photochemistry including twisted intramolecular charge-transfer-excited-state (TICT) of the EMABPB in several solvents has been investigated. These results indicate that the aminoborane demonstrates multiple configurations in CD3Cl and CD2Cl2 resulting in the shifts of the signals of the alkyl groups on the nitrogen and boron. This indicates that there are some time-dependent changes at constant temperature over the irradiation interval. At ﹣60°C and the presence of light (λ = 265 nm), we observed a large change in the populations of the two sites, and this by itself indicates a modification in the rotation around the boron nitrogen bond in the excited state. By considering the existence of the TICT state, many important energy technologies may be developed with higher efficiency by controlling the back-electron transfer processes.

Charge-state populations for the neon-XFEL system

The interaction between neon and x-ray free-electron lasers with different laser parameters is systematically studied by solving a set of coupled rate equations. As an example, the evolution of 1s^12 s^22 p^6 configuration is given under different incident photon numbers, pulse widths, and photon energies. We have also determined all of the charge-state populations as a function of three laser pulse parameters by averaging over time. The result shows that the variations of these charge-state populations demonstrate a pattern when the pulse width is shorter than 10 fs: some of the charge-states decrease rapidly,while the others rise but remain relatively constant for pulse width larger than 10 fs. The variation of the average charge with three parameters has also obtained. The average charge decreases for a pulse width shorter than 10 fs but remains basically unchanged for a pulse width longer than 10 fs.

Multiply Charged Anions,Maximum Charge Acceptance,and Higher Electron Affinities of Molecules,Superatoms,and Clusters

The addition of electrons to form gas-phase multiply charged anions(MCAs)normally requires sophisticated experiments or calculations.In this work,the factors stabilizing the MCAs,the maximum electron uptake of gas-phase molecules,X,and the electronic stability of MCAs X^(Q-),are discussed.The drawbacks encountered when applying computational and/or conceptual density functional theory(DFT)to MCAs are highlighted.We develop and test a different model based on the valence-state concept.As in DFT,the electronic energy,E(N,v_(ex)),is a continuous function of the average electron number,N,and the external potential,v_(ex),of the nuclei.The valence-state-parabola is a second-order polynomial that allows extending E(N,v_(ex))to dianions and higher MCAs.The model expresses the maximum electron acceptance,Q_(max),and the higher electron affinities,A_Q,as simple functions of the firstelectron affinity,A_1,and the ionization energy,I,of the"ancestor"system.Thus,the maximum electron acceptance is Q_(max,calc)=1+12A_1/7(I-A_1).The ground-state parabola model of the conceptual DFT yields approximately half of this value,and it is termed Q_(max,GS)=?+A_1/(I-A_1).A large variety of molecules are evaluated including fullerenes,metal clusters,super-pnictogens,super-halogens(OF_3),super-alkali species(OLi_3),and neutral or charged transition-metal complexes,AB_(m )L_n^(0/+/-).The calculated second electron affinity A_(2,calc)=A_1-(7/12)(I-A_1)is linearly correlated to the literature references A_(2,lit) with a correlation coefficient R=0.998.A_2 or A_3 values are predicted for further 24 species.The appearance sizes,n_(ap)^(3-),of triply charged anionic clusters and fullerenes are calculated in agreement with the literature.

Online SOC estimation based on modified covariance extended Kalman filter for lithium batteries of electric vehicles

To offset the defect of the traditional state of charge(SOC)estimation algorithm of lithium battery for electric vehicle and considering the complex working conditions of lithium batteries,an online SOC estimation algorithm is proposed by combining the online parameter identification method and the modified covariance extended Kalman filter(MVEKF)algorithm.Based on the parameters identified on line with the multiple forgetting factors recursive least squares methods,the newly-established algorithm recalculates the covariance in the iterative process with the modified estimation and updates the process gain which is used for the next state estimation to decrease errors of the filter.Experiments including constant pulse discharging and the dynamic stress test(DST)demonstrate that compared with the EKF algorithm,the MVEKF algorithm produces fewer estimation errors and can reduce the errors to 5%at most under the complex charging and discharging conditions of batteries.In the charging process under the DST condition,the EKF produces a larger deviation and lacks stability,while the MVEKF algorithm can estimate SOC stably and has a strong robustness.Therefore,the established MVEKF algorithm is suitable for complex and changeable working conditions of batteries for electric vehicles.

Battery Management System Used in Electric Vehicles

A battery management system(BMS) which contains state of charge(SOC) determination,battery charging control and its optimization,and cell balancing is proposed.Thevenin's model having a RC network characterizing the battery polarization is used for SOC determination.The polarization voltage of the battery model is identified using the nonlinear least square technique.The paper presents a new SOC definition method considering the SOC limit of each cell for battery pack in series.The relationship between the battery current and polarization voltage is analyzed.And then the battery charging approach that the charging current can be adaptively adjusted by the polarization voltage control system is investigated based on fuzzy logic control theory.Cell balancing control strategy is also proposed based on the principle of the maximum use for the capacity and energy of the battery pack.

Numerical analysis of temperature rise within 70MPa composite hydrogen vehicle cylinder during fast refueling

The numerical simulation model for predicting fast filling process of 70 MPa type Ⅲ(with metal liner) hydrogen vehicle cylinder was presented,which has considered turbulence,real gas effect and solid heat transfer issues.Through the numerical analysis method,the temperature distributions of the gas within the solid walls were revealed; adiabatic filling was studied to evaluate the heat dissipation during the filling; the influences of various filling conditions on temperature rise were analyzed in detail.Finally,cold filling was proposed to evaluate the effect on temperature rise and SoC(state of charge) within the cylinder.The hydrogen pre-cooling was proved to be an effective solution to reduce maximum temperature and acquire higher SoC during the filling process.

Theoretical Study on the Structures and Electronic Spectra of the Derivatives of C_(60)-P-2,4,6-Triphenyl Borazinc

The structures and electronic spectra of the derivatives of C60-P-2,4,6-triphenyl borazinc have been studied by using AM 1 method. The calculated results indicate that this kind of compounds has a lower energy difference between HOMO and LUMO. It is found that the electron cloud on unoccupied frontier orbital mainly comes from the contribution of C60, while that on occupied frontier orbital mainly concentrates on the side chain. A long-lived charge-separated state may occur in the objective compounds.

Electronic Structure of Sodium Thiogermanate

Ab initio calculations of the band structure, total and partial densities of states and the spatial distribution of the electron charge density of crystalline Na2GeS3 are performed in the framework of density functional theory in the local density approximation for an exchange-correlation potential. According to the calculation results, sodium thiogermanate is a direct-gap crystal with the top of the valence band and the bottom of the conduction band at the point of the Brillouin zone. The calculated band gap is Eg= 2.51 eV. The nature of the components of the electronic states in different subbands of the valence band is determined. The calculated total density of states in the valence band of the crystal is compared with the known experimental X-ray photoelectron spectrum of Na2GeS3 glass. Based on the maps of the electron density distribution, the nature of the chemical bonds and high mobility of Na+ ions in Na2GeS3 crystal is analyzed.

Power Optimization Strategy Considering Electric Vehicle in Home Energy Management System

With the development of smart grid, residents have the opportunity to schedule their household appliances （HA） for the purpose of reducing electricity expenses and alleviating the pressure of the smart grid. In this paper, we introduce the structure of home energy management system （EMS） and then propose a power optimization strategy based on household load model and electric vehicle （EV） model for home power usage. In this strategy, the electric vehicles are charged when the price is low, and otherwise, are discharged. By adopting this combined system model under the time-of-use electricity price （TOUP）, the proposed scheduling strategy would effectively minimize the electricity cost and reduce the pressure of the smart grid at the same time. Finally, simulation experiments are carried out to show the feasibility of the proposed strategy. The results show that crossover genetic particle swarm optimization algorithm has better convergence properties than traditional particle swarm algorithm and better adaptability than genetic algorithm.