The Experts below are selected from a list of 648 Experts worldwide ranked by ideXlab platform

Rui Xiong - One of the best experts on this subject based on the ideXlab platform.

  • A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries
    Energy, 2018
    Co-Authors: Min Ye, Rui Xiong, Hui Guo, Quanqing Yu
    Abstract:

    Abstract Obtaining an estimation of the parameters and state of charge (SoC) of a lithium-ion battery is crucial for an electric vehicle. The parameters of a battery Model are usually different throughout the battery lifetime. To obtain an accurate SoC and parameters and reduce the computational cost, a double-scale dual adaptive particle filter for online parameters and SoC estimation of lithium-ion batteries is proposed. First, the lithium-ion battery is Modeled using the Thevenin Model. Second, a double-scale dual particle filter is proposed and applied to the battery parameter and SoC estimation. To improve the accuracy and convergence ability to the initial environmental offset, a double-scale dual adaptive particle filter is proposed. Finally, the effectiveness and applicability of the two algorithms are verified by Lithium Nickel Manganese Cobalt Oxide (NMC) batteries of different ages.

  • An Online Model-based Battery Parameter and State Estimation Method Using Multi-scale Dual Adaptive Particle Filters
    Energy Procedia, 2017
    Co-Authors: Min Ye, Rui Xiong, Hao Mu
    Abstract:

    Abstract Accurate estimations of battery parameter and state are very important for battery management in electric vehicles. To improve estimation accuracy and robustness of battery parameter and state, and to reduce computational cost, an online Model-based estimation approach is proposed, Firstly, the lithium-ion battery is Modeled using the Thevenin Model, Then, A multi-scale dual particle filters has been proposed and applied to the battery parameter and state estimation. Finally, to elevate the accuracy and the ability of convergence to initial states’ offset, a multi-scale dual adaptive particle filter was proposed and applied to the battery parameter and state estimation. Experimental results on various degradation states of lithium-ion battery cells further verified the feasibility of the proposed approach.

  • Evaluation of the Model-based state-of-charge estimation methods for lithium-ion batteries
    2016 IEEE Transportation Electrification Conference and Expo (ITEC), 2016
    Co-Authors: Yongzhi Zhang, Rui Xiong, Hongwen He
    Abstract:

    To achieve accurate battery SoC, the Gaussian is applied to construct battery Model. It is able to simulate the time-variable, nonlinear characteristics of battery. To adaptively adjust the Gaussian battery Model parameter set and order, a novel online four-step Model parameter identification and order selection method is proposed. To further evaluate the Gaussian battery Model estimation accuracy, another two kinds of representative battery Models including the combined Model and Thevenin Model are built as comparisons. Results based on three kinds of Kalman filters show that the maximum SoC estimation error of each case is within 2% and the Gaussian Model has the best accuracy for voltage prediction as well as SoC estimation.

  • a novel multi Model probability battery state of charge estimation approach for electric vehicles using h infinity algorithm
    Applied Energy, 2016
    Co-Authors: Hao Mu, Rui Xiong, Weixiang Shen
    Abstract:

    Due to the strong nonlinearity and complex time-variant property of batteries, the existing state of charge (SOC) estimation approaches based on a single equivalent circuit Model (ECM) cannot provide the accurate SOC for the entire discharging period. This paper aims to present a novel SOC estimation approach based on a multiple ECMs fusion method for improving the practical application performance. In the proposed approach, three battery ECMs, namely the Thevenin Model, the double polarization Model and the 3rd order RC Model, are selected to describe the dynamic voltage of lithium-ion batteries and the genetic algorithm is then used to determine the Model parameters. The linear matrix inequality-based H-infinity technique is employed to estimate the SOC from the three Models and the Bayes theorem-based probability method is employed to determine the optimal weights for synthesizing the SOCs estimated from the three Models. Two types of lithium-ion batteries are used to verify the feasibility and robustness of the proposed approach. The results indicate that the proposed approach can improve the accuracy and reliability of the SOC estimation against uncertain battery materials and inaccurate initial states.

  • Research on an Online Identification Algorithm for a Thevenin Battery Model by an Experimental Approach
    International Journal of Green Energy, 2014
    Co-Authors: Rui Xiong, Kai Zhao
    Abstract:

    To improve the estimation accuracy of battery’s inner state for battery management system, an online parameters identification algorithm for Thevenin battery Model is researched. The Thevenin Model and parameters identification algorithm based on recursive least square adaptive filter algorithm was built with the Simulink/xPC Target. The results of hardware-in-loop experiment, which uses Federal Urban Driving Schedule test to verify the parameters identification approach, show the proposed approach can accurately identify the Model parameters within 1% maximum terminal voltage estimation error, and the State of Charge error which calculated by the open circuit voltage estimates can be efficiently reduced to 4%.

Shen Yan-xi - One of the best experts on this subject based on the ideXlab platform.

  • State of charge estimation of lithium-ion battery based on unscented Kalman filter
    Chinese Journal of Power Sources, 2014
    Co-Authors: Shen Yan-xi
    Abstract:

    The capacity of lithium-ion battery and internal parameters obviously vary with temperature, so state of charge of cell exact estimation at various temperatures is the key technology of the battery management system in the electric vehicle. Based on the Thevenin Model, using unscented kalman filter(UKF), the state of charge(SOC)estimation of Li-ion battery at various temperatures and discharge currents was estimated. Experimental study showes that UKF algorithm was adapted to the SOC estimation of Li-ion battery at various discharge currents. With the temperature decreasing, though the UKF convergence rate of estimation of Li-ion battery SOC becomes slow,there is strong correct function to initial error, and steady state accuracy is high. Therefore, UKF algorithm is suitable for the estimation of Li-ion battery SOC at various temperatures and discharge currents.

Mingwang Wang - One of the best experts on this subject based on the ideXlab platform.

  • Online Parameter Identification and Joint Estimation of the State of Charge and the State of Health of Lithium-Ion Batteries Considering the Degree of Polarization
    Energies, 2019
    Co-Authors: Bizhong Xia, Wei Wang, Yongzhi Lai, Mingwang Wang, Guanghao Chen, Jie Zhou, Yadi Yang, Rui Huang, Huawen Wang
    Abstract:

    The state of charge (SOC) and the state of health (SOH) are the two most important indexes of batteries. However, they are not measurable with transducers and must be estimated with mathematical algorithms. A precise Model and accurate available battery capacity are crucial to the estimation results. An improved speed adaptive velocity particle swarm optimization algorithm (SAVPSO) based on the Thevenin Model is used for online parameter identification, which is used with an unscented Kalman filter (UKF) to estimate the SOC. In order to achieve the cyclic update of the SOH, the concept of degree of polarization (DOP) is proposed. The cyclic update of available capacity is thus obtainable to conversely promote the estimation accuracy of the SOC. The estimation experiments in the whole aging process of batteries show that the proposed method can enhance the SOC estimation accuracy in the full battery life cycle with the cyclic update of the SOH, even in cases of operating aged batteries and under complex operating conditions.

  • A Novel Method for Lithium-Ion Battery Online Parameter Identification Based on Variable Forgetting Factor Recursive Least Squares
    Energies, 2018
    Co-Authors: Zizhou Lao, Bizhong Xia, Wei Wang, Wei Sun, Yongzhi Lai, Mingwang Wang
    Abstract:

    For Model-based state of charge (SOC) estimation methods, the battery Model parameters change with temperature, SOC, and so forth, causing the estimation error to increase. Constantly updating Model parameters during battery operation, also known as online parameter identification, can effectively solve this problem. In this paper, a lithium-ion battery is Modeled using the Thevenin Model. A variable forgetting factor (VFF) strategy is introduced to improve forgetting factor recursive least squares (FFRLS) to variable forgetting factor recursive least squares (VFF-RLS). A novel method based on VFF-RLS for the online identification of the Thevenin Model is proposed. Experiments verified that VFF-RLS gives more stable online parameter identification results than FFRLS. Combined with an unscented Kalman filter (UKF) algorithm, a joint algorithm named VFF-RLS-UKF is proposed for SOC estimation. In a variable-temperature environment, a battery SOC estimation experiment was performed using the joint algorithm. The average error of the SOC estimation was as low as 0.595% in some experiments. Experiments showed that VFF-RLS can effectively track the changes in Model parameters. The joint algorithm improved the SOC estimation accuracy compared to the method with the fixed forgetting factor.

  • Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter
    MDPI AG, 2017
    Co-Authors: Bizhong Xia, Zizhou Lao, Wei Wang, Wei Sun, Yongzhi Lai, Guanghao Chen, Ruifeng Zhang, Yong Tian, Zhen Sun, Mingwang Wang
    Abstract:

    State of charge (SOC) estimation is the core of any battery management system. Most closed-loop SOC estimation algorithms are based on the equivalent circuit Model with fixed parameters. However, the parameters of the equivalent circuit Model will change as temperature or SOC changes, resulting in reduced SOC estimation accuracy. In this paper, two SOC estimation algorithms with online parameter identification are proposed to solve this problem based on forgetting factor recursive least squares (FFRLS) and nonlinear Kalman filter. The parameters of a Thevenin Model are constantly updated by FFRLS. The nonlinear Kalman filter is used to perform the recursive operation to estimate SOC. Experiments in variable temperature environments verify the effectiveness of the proposed algorithms. A combination of four driving cycles is loaded on lithium-ion batteries to test the adaptability of the approaches to different working conditions. Under certain conditions, the average error of the SOC estimation dropped from 5.6% to 1.1% after adding the online parameters identification, showing that the estimation accuracy of proposed algorithms is greatly improved. Besides, simulated measurement noise is added to the test data to prove the robustness of the algorithms

Ali Sheikholeslami - One of the best experts on this subject based on the ideXlab platform.

  • A current independent method based on synchronized voltage measurement for fault location on transmission lines
    Simulation Modelling Practice and Theory, 2009
    Co-Authors: Khalil Gorgani Firouzjah, Ali Sheikholeslami
    Abstract:

    Abstract This paper presents a new method based on synchronized voltage measurement technique in order to identify the fault locations in two and three-terminal transmission lines. Due to common problems of current transformers in distance protection of power system and as result increasing cost and reduction of protection accuracy, proposed method is independent of current measurement and based on transmission line terminals voltages measurement. Pre-fault and post-fault voltages at both ends of the line are measured synchronously and used to calculate the fault location. Proposed method calculates the fault location using Thevenin Model of faulted system and transforms the whole parameters to symmetric components. Using proposed technique, fault location can be calculated with a lower than 0.6% error without using current transformers. EMTP/ATP simulation results and mathematical analysis show that proposed fault location technique is independent of fault type, fault resistance, fault inception angle and loading angle of the transmission line.

Hua Gui-sha - One of the best experts on this subject based on the ideXlab platform.

  • A Strategy of Dynamic Estimating State of Charge Based on Li-Ion Battery of Electric Vehicles
    Electrical Measurement & Instrumentation, 2014
    Co-Authors: Hua Gui-sha
    Abstract:

    A state space Model of a Li cell is proposed based on Thevenin Model. The battery parameters always change in the actual operation, so the recursive least square method is chosen to identify the parameters on-line so as to make real-time correction and enhance the adaptability of the system. To overcome the weaknesses of EKF, a new estimation method is put forward based on UKF(Unscented Kalman Filtering) to estimate SOC of Li-ion battery. Experiments are made to compare the performance with the new filter to that with EKF. The result demonstrates that UKF achieve higher filtering accuracy under the same conditions.

  • A strategy research of SOC estimation of lithium battery for electric vehicles
    Chinese Journal of Power Sources, 2013
    Co-Authors: Hua Gui-sha
    Abstract:

    A state space Model of lithium battery based on Thevenin Model was proposed. Due to the change of the battery parameters in the actual operation, the recursive least square method was chosen to identify the parameters on-line. The real-time correction and higher adaptability of the system were obtained. Due to the battery Model nonlinear, extended Kalman filter was applied in estimating state of charge, and a dynamic gain which increased at the beginning of mutation and decreased rapidly after mutation was set up. The experiments and simulations indicate that the extended Kalman filter has an excellent precision, performs well when initial error disturbance happens, and converges to the true value of SOC quickly.