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Tian Y.,Tsinghua University | Xia B.,Tsinghua University | Wang M.,Sunwoda Electronic Co. | Sun W.,Sunwoda Electronic Co. | Xu Z.,Sunwoda Electronic Co.
Energies | Year: 2014

State of charge (SOC) estimation is essential to battery management systems in electric vehicles (EVs) to ensure the safe operations of batteries and providing drivers with the remaining range of the EVs. A number of estimation algorithms have been developed to get an accurate SOC value because the SOC cannot be directly measured with sensors and is closely related to various factors, such as ambient temperature, current rate and battery aging. In this paper, two model-based adaptive algorithms, including the adaptive unscented Kalman filter (AUKF) and adaptive slide mode observer (ASMO) are applied and compared in terms of convergence behavior, tracking accuracy, computational cost and estimation robustness against parameter uncertainties of the battery model in SOC estimation. Two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the two algorithms. Comparison results show that the AUKF has merits in convergence ability and tracking accuracy with an accurate battery model, while the ASMO has lower computational cost and better estimation robustness against parameter uncertainties of the battery model. © 2014 by the authors.


Tian Y.,Tsinghua University | Xia B.,Tsinghua University | Xu Z.,Sunwoda Electronic Co. | Sun W.,Sunwoda Electronic Co.
Journal of Power Electronics | Year: 2014

The power-voltage (P-V) characteristic of a photovoltaic (PV) array is nonlinear and time varying with the change in atmospheric conditions. As a result, the maximum power point tracking (MPPT) technique must be applied in PV systems to maximize the generated energy. The incremental conductance (INC) algorithm, one of the MPPT strategies, is widely used for its high tracking accuracy, good adaptability to rapidly changing atmospheric conditions, and easy implementation. This paper presents a modified asymmetrical variable step size INC MPPT method that is based on the asymmetrical feature of the P-V curve. Compared with conventional fixed or variable step size method, the proposed method can effectively improve tracking accuracy and speed. The theoretical foundation and design principle of the proposed approach are validated by the simulation and experimental results.


Xia B.,Tsinghua University | Chen C.,Tsinghua University | Tian Y.,Tsinghua University | Wang M.,Sunwoda Electronic Co. | And 2 more authors.
Energy | Year: 2015

The SOC (state of charge) is the most important index of the battery management systems. However, it cannot be measured directly with sensors and must be estimated with mathematical techniques. An accurate battery model is crucial to exactly estimate the SOC. In order to improve the model accuracy, this paper presents an improved parameter identification method. Firstly, the concept of polarization depth is proposed based on the analysis of polarization characteristics of the lithium-ion batteries. Then, the nonlinear least square technique is applied to determine the model parameters according to data collected from pulsed discharge experiments. The results show that the proposed method can reduce the model error as compared with the conventional approach. Furthermore, a nonlinear observer presented in the previous work is utilized to verify the validity of the proposed parameter identification method in SOC estimation. Finally, experiments with different levels of discharge current are carried out to investigate the influence of polarization depth on SOC estimation. Experimental results show that the proposed method can improve the SOC estimation accuracy as compared with the conventional approach, especially under the conditions of large discharge current. © 2015 Elsevier Ltd.


Xia B.,Tsinghua University | Chen C.,Tsinghua University | Tian Y.,Tsinghua University | Sun W.,Sunwoda Electronic Co. | And 2 more authors.
Journal of Power Sources | Year: 2014

The state of charge (SOC) is important for the safety and reliability of battery operation since it indicates the remaining capacity of a battery. However, as the internal state of each cell cannot be directly measured, the value of the SOC has to be estimated. In this paper, a novel method for SOC estimation in electric vehicles (EVs) using a nonlinear observer (NLO) is presented. One advantage of this method is that it does not need complicated matrix operations, so the computation cost can be reduced. As a key step in design of the nonlinear observer, the state-space equations based on the equivalent circuit model are derived. The Lyapunov stability theory is employed to prove the convergence of the nonlinear observer. Four experiments are carried out to evaluate the performance of the presented method. The results show that the SOC estimation error converges to 3% within 130 s while the initial SOC error reaches 20%, and does not exceed 4.5% while the measurement suffers both 2.5% voltage noise and 5% current noise. Besides, the presented method has advantages over the extended Kalman filter (EKF) and sliding mode observer (SMO) algorithms in terms of computation cost, estimation accuracy and convergence rate. © 2014 Elsevier B.V. All rights reserved.


Tian Y.,Tsinghua University | Xia B.,Tsinghua University | Sun W.,Sunwoda Electronic Co. | Xu Z.,Sunwoda Electronic Co. | Zheng W.,Sunwoda Electronic Co.
Journal of Power Sources | Year: 2014

Accurate estimation for the state of charge (SOC) is one of the most important aspects of a battery management system (BMS) in electric vehicles (EVs) as it provides drivers with the EVs' remaining range. However, it is difficult to get an accurate SOC, because its value cannot be directly measured and is affected by various factors, such as the operating temperature, current rate and cycle number. In this paper, a modified equivalent circuit model is presented to include the impact of different current rates and SOCs on the battery internal resistance, and the impact of different temperatures and current rates on the battery capacity. Besides, a linear-averaging method is presented to calculate the internal resistance and practical capacity correction factors according to data collected from the experimental bench and saved as look-up tables. The unscented Kalman filter (UKF) algorithm is then introduced to estimate the SOC according to the presented model. Experiments based on actual urban driving cycles are carried out to evaluate the performance of the presented method by comparing with two existed methods. Experimental results show that the proposed method can reduce the computation cost and improve the SOC estimation accuracy simultaneously. © 2014 Elsevier B.V. All rights reserved.


Tian Y.,Tsinghua University | Chen C.,Tsinghua University | Xia B.,Tsinghua University | Sun W.,Sunwoda Electronic Co. | And 2 more authors.
Energies | Year: 2014

The state of charge (SOC) is important for the safety and reliability of battery operation since it indicates the remaining capacity of a battery. However, it is difficult to get an accurate value of SOC, because the SOC cannot be directly measured by a sensor. In this paper, an adaptive gain nonlinear observer (AGNO) for SOC estimation of lithium-ion batteries (LIBs) in electric vehicles (EVs) is proposed. The second-order resistor-capacitor (2RC) equivalent circuit model is used to simulate the dynamic behaviors of a LIB, based on which the state equations are derived to design the AGNO for SOC estimation. The model parameters are identified using the exponential-function fitting method. The sixth-order polynomial function is used to describe the highly nonlinear relationship between the open circuit voltage (OCV) and the SOC. The convergence of the proposed AGNO is proved using the Lyapunov stability theory. Two typical driving cycles, including the New European Driving Cycle (NEDC) and Federal Urban Driving Schedule (FUDS) are adopted to evaluate the performance of the AGNO by comparing with the unscented Kalman filter (UKF) algorithm. The experimental results show that the AGNO has better performance than the UKF algorithm in terms of reducing the computation cost, improving the estimation accuracy and enhancing the convergence ability. © 2014 by the authors.


Zhang H.,South China University of Technology | Hu R.,South China University of Technology | Liu H.,South China University of Technology | Sun W.,South China University of Technology | And 5 more authors.
Journal of Materials Chemistry A | Year: 2016

In order to further enhance the reversible capacity and cyclability for lithium storage of Sn-based alloy anode materials, a spherical-shaped Sn-Fe3O4@C ternary-phase composite consisting of nanosized tin (Sn), magnetite (Fe3O4), and graphite (C) was prepared via a two-step process using high-efficiency discharge plasma-assisted milling (P-milling). Ultrafine Sn nanoparticles were embedded and tightly contacted with nanosized Fe3O4, with graphite nanosheets coating the outside to form a multiscale spherical structure. The Sn-Fe3O4@C nanocomposite anodes demonstrate a stable and high capacity of 793 mA h g-1 after 240 cycles between 0.01 and 3.0 V vs. Li/Li+ at 200 mA g-1. Furthermore, a reversible capacity of ∼750 mA h g-1 was obtained after 500 cycles, even when the current density increased to 2000 mA g-1. The high capacity, good cycle performance, and superior high-rate capability characteristics were attributed to the unique nanostructure of the Sn-Fe3O4@C composites. The good dispersion of co-existing Sn and Fe3O4 nanoballs in a spherical carbon matrix resulted in an electrode with high structural stability and fast kinetics for Li ion and electron transfer, which contributed to high reversibility of alloying reactions in Sn and conversion reactions of Fe3O4. Furthermore, the spherical shape of the materials and simple preparation as compared to those of commercial anodes make the Sn-Fe3O4@C composites good candidates for practical applications. © The Royal Society of Chemistry 2016.


Trademark
Sunwoda Electronic Co. | Date: 2012-05-14

Computers; laptop computers; computer peripheral devices; computer peripheral devices; couplers (data processing equipment); notebook computers; sonar navigation, detection system; intercommunication apparatus; navigational instruments; galvanic cells.


Trademark
Sunwoda Electronic Co. | Date: 2011-12-27

Computers; computer peripheral devices; computer software (recorded); electronic pocket translators; time clocks (time recording devices); check on work attendance tool (term linguistically incorrect in the opinion of the International Bureau - Rule 13(2)(b) of the Common Regulations); automatic geter (term linguistically incorrect in the opinion of the International Bureau - Rule 13(2)(b) of the Common Regulations); electronic notice boards; mechanical signs; antennas; telephone apparatus; video telephones; navigation apparatus for vehicles (on-board computers); network communication equipment; vehicle radios; acidimeters for batteries; material for electricity mains (wires, cables); copper wire, insulated; integrated circuits; chips (integrated circuits); electromagnetic coils; capacitors; variometers; couplings, electric; control panels (electricity); low voltage power supply; stable electrical source (term linguistically incorrect in the opinion of the International Bureau - Rule 13(2)(b) of the Common Regulations); turnstiles, automatic; electrolysers; welding electrodes; theft prevention installations, electric; batteries, electric, for vehicles; accumulator jars; battery boxes; plates for batteries; batteries for lighting; anode batteries; high tension batteries chargers for electric batteries; galvanic cells; galvanic batteries; grids for batteries; solar batteries; batteries for pocket lamps; batteries, electric; accumulators, electric; electrified fences.


Trademark
Sunwoda Electronic Co. and Xwoda Electronic Con. Ltd | Date: 2011-04-19

Computers; laptop computers; computer peripheral devices; data processing equipment namely, couplers; notebook computers; sonar; intercommunication apparatus, namely, apparatus for transmission of communication; electric navigational instruments; video telephones; navigation apparatus for vehicles in the nature of on-board computers; electric satellite navigational apparatus; walkie-talkies; tape recorders; radios; audio and video receivers; vehicle radios; apparatus for games adapted for use with an external display screen or monitor; amusement apparatus adapted for use with an external display screen or monitor; headphones; accumulators, electric, for vehicles; batteries, electric, for vehicles; battery jars, namely, battery packs; battery boxes; batteries for lighting; high tension batteries; battery chargers; galvanic cells; galvanic batteries; batteries for pocket lamps; batteries, electric; solar batteries; acidimeters for batteries.

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