The Experts below are selected from a list of 168 Experts worldwide ranked by ideXlab platform

Scott J Moura - One of the best experts on this subject based on the ideXlab platform.

  • real time capacity estimation of lithium ion batteries utilizing thermal dynamics
    IEEE Transactions on Control Systems and Technology, 2020
    Co-Authors: Dong Zhang, Satadru Dey, Hector E Perez, Scott J Moura
    Abstract:

    Increasing longevity remains one of the open challenges for Lithium-Ion (Li-Ion) Battery technology. We envision a health-conscious advanced Battery management system, which implements monitoring and control algorithms that increase Battery lifetime while maintaining performance. For such algorithms, real-time Battery capacity estimates are crucial. In this paper, we present an online capacity estimation scheme for Li-Ion batteries. The key novelty lies in: 1) leveraging thermal dynamics to estimate Battery capacity and 2) developing a hierarchical estimation algorithm with provable convergence properties. The algorithm consists of two stages working in cascade. The first stage estimates Battery core temperature and heat generation based on a two-state thermal model, and the second stage receives the core temperature and heat generation estimation to estimate state-of-charge and capacity. Results from numerical simulations and experimental data illustrate the performance of the proposed capacity estimation scheme.

  • state of charge estimation of parallel connected Battery cells via descriptor system theory
    arxiv:eess.SY, 2020
    Co-Authors: Dong Zhang, Luis D Couto, Sebastien Benjamin, Wente Zeng, D F Coutinho, Scott J Moura
    Abstract:

    This manuscript presents an algorithm for individual Lithium-Ion (Li-Ion) Battery cell state of charge (SOC) estimation when multiple cells are connected in parallel, using only terminal voltage and total current measurements. For Battery packs consisting of thousands of cells, it is desirable to estimate individual SOCs by only monitoring the total current in order to reduce sensing cost. Mathematically, series connected cells yield dynamics given by ordinary differential equations under classical full voltage sensing. In contrast, parallel connected cells are evidently more challenging because the dynamics are governed by a nonlinear descriptor system, including differential equations and algebraic equations arising from voltage and current balance across cells. An observer with linear output error injection is formulated, where the individual cell SOCs and local currents are locally observable from the total current and voltage measurements. The asymptotic convergence of differential and algebraic states is established by considering local Lipschitz continuity property of system nonlinearities. Simulation results on LiNiMnCoO$_2$/Graphite (NMC) cells illustrate convergence for SOCs, local currents, and terminal voltage.

Ala A. Hussein - One of the best experts on this subject based on the ideXlab platform.

  • A Novel Neural Network with Gaussian Process Feedback for Modeling the State-of-Charge of Battery Cells
    2020 IEEE Energy Conversion Congress and Exposition (ECCE), 2020
    Co-Authors: Abdallah Chehade, Ala A. Hussein
    Abstract:

    This paper proposes a new method for estimating the state-of-charge (SOC) of Lithium-Ion (Li-Ion) Battery cells. The method is based on a neural network with Gaussian process feedback. The proposed model enhances the estimation accuracy even for aged Battery cells by correlating the SOC trends between consecutive charge-discharge cycles. Derivation of the model followed by experimental evaluation on different Battery cells with different ageing conditions are presented.

  • derivation and comparison of open loop and closed loop neural network Battery state of charge estimators
    Energy Procedia, 2015
    Co-Authors: Ala A. Hussein
    Abstract:

    Abstract This paper presents two artificial neural network (ANN) based algorithms for Battery state-of-charge (SOC) estimation. The SOC is an important quantity that must be estimated in real-time in many applications. ANN is a mathematical model that consists of interconnected artificial neurons inspired by biological neural networks and is used to predict the output of a dynamic system based on some historical data of that system. The first algorithm presented in this paper has an open-loop structure and known as nonlinear input output (NIO) feed-forward algorithm, while the second is closed loop called nonlinear autoregressive with exogenous input (NARX) feed-back algorithm. A pulse-discharge test is performed on a commercial Lithium-Ion (Li-Ion) Battery cell in order to collect data to evaluate those methods. Results are presented and compared.

  • Experimental modeling and analysis of Lithium-Ion Battery temperature dependence
    2015 IEEE Applied Power Electronics Conference and Exposition (APEC), 2015
    Co-Authors: Ala A. Hussein
    Abstract:

    Battery performance is strongly dependent on the ambient temperature. For example, at moderate temperatures, the Battery performance is optimal, whereas at extreme temperatures, the Battery performance is not optimized and sometimes unexpected. In order to predict the Battery behavior, a model that involves the Battery's underlying dynamics is usually used. The majority of dynamic Battery models are derived at only one single temperature (room temperature), which can easily lead to failure in predicting the Battery performance when the temperature varies. Therefore, adding some temperature dependence to those models can make the Battery management system more reliable, safer, and moreover, prolong the Battery lifetime. In this paper, a 3.6V/1100mAh Lithium-Ion (Li-Ion) Battery cell is tested at temperature between -30°C and +50°C and its main parameters are measured. The measured parameters include the discharge capacity, the charge and discharge resistance, and the open-circuit voltage, which comprise the main parameters of equivalent electric-circuit based models. Experimental testing results and observations are presented in this paper.

Dong Zhang - One of the best experts on this subject based on the ideXlab platform.

  • real time capacity estimation of lithium ion batteries utilizing thermal dynamics
    IEEE Transactions on Control Systems and Technology, 2020
    Co-Authors: Dong Zhang, Satadru Dey, Hector E Perez, Scott J Moura
    Abstract:

    Increasing longevity remains one of the open challenges for Lithium-Ion (Li-Ion) Battery technology. We envision a health-conscious advanced Battery management system, which implements monitoring and control algorithms that increase Battery lifetime while maintaining performance. For such algorithms, real-time Battery capacity estimates are crucial. In this paper, we present an online capacity estimation scheme for Li-Ion batteries. The key novelty lies in: 1) leveraging thermal dynamics to estimate Battery capacity and 2) developing a hierarchical estimation algorithm with provable convergence properties. The algorithm consists of two stages working in cascade. The first stage estimates Battery core temperature and heat generation based on a two-state thermal model, and the second stage receives the core temperature and heat generation estimation to estimate state-of-charge and capacity. Results from numerical simulations and experimental data illustrate the performance of the proposed capacity estimation scheme.

  • state of charge estimation of parallel connected Battery cells via descriptor system theory
    arxiv:eess.SY, 2020
    Co-Authors: Dong Zhang, Luis D Couto, Sebastien Benjamin, Wente Zeng, D F Coutinho, Scott J Moura
    Abstract:

    This manuscript presents an algorithm for individual Lithium-Ion (Li-Ion) Battery cell state of charge (SOC) estimation when multiple cells are connected in parallel, using only terminal voltage and total current measurements. For Battery packs consisting of thousands of cells, it is desirable to estimate individual SOCs by only monitoring the total current in order to reduce sensing cost. Mathematically, series connected cells yield dynamics given by ordinary differential equations under classical full voltage sensing. In contrast, parallel connected cells are evidently more challenging because the dynamics are governed by a nonlinear descriptor system, including differential equations and algebraic equations arising from voltage and current balance across cells. An observer with linear output error injection is formulated, where the individual cell SOCs and local currents are locally observable from the total current and voltage measurements. The asymptotic convergence of differential and algebraic states is established by considering local Lipschitz continuity property of system nonlinearities. Simulation results on LiNiMnCoO$_2$/Graphite (NMC) cells illustrate convergence for SOCs, local currents, and terminal voltage.

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

  • design of minimum cost degradation conscious lithium ion Battery energy storage system to achieve renewable power dispatchability
    Applied Energy, 2020
    Co-Authors: Yang Li, Yixin Su, Binyu Xiong, Mahinda Vilathgamuwa, Jinrui Tang, S S Choi, Yu Wang
    Abstract:

    Abstract The application of Lithium-Ion (Li-Ion) Battery energy storage system (BESS) to achieve the dispatchability of a renewable power plant is examined. By taking into consideration the effects of Battery cell degradation evaluated using electrochemical principles, a power flow model (PFM) of the BESS is developed specifically for use in system-level study. The PFM allows the long-term performance and lifetime of the Battery be predicted as when the BESS is undertaking the power dispatch control task. Furthermore, a binary mode BESS control scheme is proposed to prevent the possible over-charge/over-discharge of the BESS due to the uncertain renewable input power. Analysis of the resulting new dispatch control scheme shows that a proposed adaptive BESS state of energy controller can guarantee the stability of the dispatch process. A particle swarm optimization algorithm is developed and is incorporated into a computational procedure for which the optimum Battery capacity and power rating are determined, through minimizing the capital cost of the BESS plus the penalty cost of violating the dispatch power commitment. Results of numerical examples used to illustrate the proposed design approach show that in order to achieve hourly-constant power dispatchability of a 100-MW wind farm, the minimum-cost Li-Ion BESS is rated 31-MW/22.6-MWh.

Jitendra Vagdoda - One of the best experts on this subject based on the ideXlab platform.

  • Cloud-Based Battery Condition Monitoring and Fault Diagnosis Platform for Large-Scale Lithium-Ion Battery Energy Storage Systems
    Energies, 2018
    Co-Authors: Darshan Makwana, Amit Adhikaree, Jitendra Vagdoda
    Abstract:

    Performance of the current Battery management systems is limited by the on-board embedded systems as the number of Battery cells increases in the large-scale Lithium-Ion (Li-Ion) Battery energy storage systems (BESSs). Moreover, an expensive supervisory control and data acquisition system is still required for maintenance of the large-scale BESSs. This paper proposes a new cloud-based Battery condition monitoring and fault diagnosis platform for the large-scale Li-Ion BESSs. The proposed cyber-physical platform incorporates the Internet of Things embedded in the Battery modules and the cloud Battery management platform. Multithreads of a condition monitoring algorithm and an outlier mining-based Battery fault diagnosis algorithm are built in the cloud Battery management platform (CBMP). The proposed cloud-based condition monitoring and fault diagnosis platform is validated by using a cyber-physical testbed and a computational cost analysis for the CBMP. Therefore, the proposed platform will support the on-board health monitoring and provide an intelligent and cost-effective maintenance of the large-scale Li-Ion BESSs.