• Title, Summary, Keyword: Battery State Estimation

Search Result 97, Processing Time 0.051 seconds

Novel State-of-Charge Estimation Technique of the Lead-acid Battery by Using EKF Considering Hysteresis Phenomenon (히스테리시스 현상을 고려한 확장칼만필터를 이용한 새로운 납축전지의 충전상태 추정방법)

  • Duong, Van-Huan;Choi, Woojin
    • Proceedings of the KIPE Conference
    • /
    • /
    • pp.317-318
    • /
    • 2013
  • State-of-Charge (SOC) is one of the most important indicators for the battery management system. Thus its precise estimation is crucial not only for effectively utilizing the energy but also preventing critical situations from happening to the powertrain of the vehicle. However, lead-acid battery is time-variant and highly nonlinear, and the hysteresis phenomenon causes large errors in estimating SOC. This paper proposes a novel SOC estimation technique for the lead-acid battery by using Extended Kalman Filter (EKF) considering hysteresis effect. The validity of the proposed technique is verified through the experiments.

  • PDF

Enhanced Coulomb Counting Method for State-of-Charge Estimation of Lithium-ion Batteries based on Peukert's Law and Coulombic Efficiency

  • Xie, Jiale;Ma, Jiachen;Bai, Kun
    • Journal of Power Electronics
    • /
    • v.18 no.3
    • /
    • pp.910-922
    • /
    • 2018
  • Conventional battery state-of-charge (SoC) estimation methods either involve sophisticated models or consume considerable computational resource. This study constructs an enhanced coulomb counting method (Ah method) for the SoC estimation of lithium-ion batteries (LiBs) by expanding the Peukert equation for the discharging process and incorporating the Coulombic efficiency for the charging process. Both the rate- and temperature-dependence of battery capacity are encompassed. An SoC mapping approach is also devised for initial SoC determination and Ah method correction. The charge counting performance at different sampling frequencies is analyzed experimentally and theoretically. To achieve a favorable compromise between sampling frequency and accumulation accuracy, a frequency-adjustable current sampling solution is developed. Experiments under the augmented urban dynamometer driving schedule cycles at different temperatures are conducted on two LiBs of different chemistries. Results verify the effectiveness and generalization ability of the proposed SoC estimation method.

Estimation of State-of-charge and Sensor Fault Detection of a Lithium-ion Battery in Electric Vehicles (전기자동차용 리튬이온전지를 위한 SOC 추정 및 센서 고장검출)

  • Han, Man-You;Lee, Kee-Sang
    • The Transactions of The Korean Institute of Electrical Engineers
    • /
    • v.63 no.8
    • /
    • pp.1085-1091
    • /
    • 2014
  • A model based SOC estimation scheme using parameter identification is described and applied to a Lithium-ion battery module that can be installed in electric vehicles. Simulation studies are performed to verify the effect of sensor faults on the SOC estimation results for terminal voltage sensor and load current sensor. The sensor faults should be detected and isolated as soon as possible because the SOC estimation error due to any sensor fault seriously affects the overall performance of the BMS. A new fault detection and isolation(FDI) scheme by which the fault of terminal voltage sensor and load current sensor can be detected and isolated is proposed to improve the reliability of the BMS. The proposed FDI scheme utilizes the parameter estimation of an input-output model and two fuzzy predictors for residual generation; one for terminal voltage and the other for load current. Recently developed dual polarization(DP) model is taken to develope and evaluate the performance of the proposed FDI scheme. Simulation results show the practical feasibility of the proposed FDI scheme.

A Study on the Algorithm of Battery SOH Estimation for Battery Management System(BMS) (배터리관리시스템(BMS)을 이용한 배터리 잔존수명(SOH) 추정 알고리즘에 관한 연구)

  • Seo, Cheol-Sik;Moon, Jong-Hyun;Park, Jae-Wook;Kim, Geum-Soo;Kim, Dong-Hee
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
    • /
    • /
    • pp.317-320
    • /
    • 2008
  • This paper presents the battery management system(BMS) for the optimum conditions of the lead-Acid battery in UPS. The proposed system controls the over and under currents of battery for protecting and it was applied algorithm for optimum conditions to estimate the State Of Charge(SOC) and State Of Health(SOH) in charge or discharge mode. It approved the performance and the algorithm for the estimation of SOH, through the experiments which using the charge and discharge tester and the field tests.

  • PDF

SOC Estimation Algorithm for the Lithium-Ion Battery by Using a Linear State Observer (선형 상태 관측기를 이용한 리튬이온 배터리의 SOC 추정 알고리즘)

  • Tran, Ngoc-Tham;Choi, Woojin
    • Proceedings of the KIPE Conference
    • /
    • /
    • pp.60-61
    • /
    • 2014
  • Lithium-Ion batteries have become the best tradeoff between energy, power density and cost of the energy storage system in many portable high electric power applications. In order to manage the battery efficiently State of Charge (SOC) of the battery needs to be estimated accurately. In this paper a model-based approach to estimate the SOC of the Lithium-Ion battery based on the estimation of the battery impedance is proposed. The validity and feasibility of the proposed algorithm is verified by the experimental results.

  • PDF

Adaptive On-line State-of-available-power Prediction of Lithium-ion Batteries

  • Fleischer, Christian;Waag, Wladislaw;Bai, Ziou;Sauer, Dirk Uwe
    • Journal of Power Electronics
    • /
    • v.13 no.4
    • /
    • pp.516-527
    • /
    • 2013
  • This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

Comparative Study of Non-Electrochemical Hysteresis Models for LiFePO4/Graphite Batteries

  • Ma, Jiachen;Xie, Jiale;Bai, Kun
    • Journal of Power Electronics
    • /
    • v.18 no.5
    • /
    • pp.1585-1594
    • /
    • 2018
  • The estimation of $LiFePO_4$/graphite battery states suffers from the prominent hysteresis phenomenon between the respective open-circuit voltage curves towards charging and discharging. A lot of hysteresis models have been documented to investigate the hysteresis mechanism. This paper reviews and deeply interprets four non-electrochemical hysteresis models and some improvements. These models can be conveniently incorporated into commonly used equivalent circuit models to reproduce battery behaviors. Through simulation and experimental comparisons of voltage predictions and state-of-charge estimations, the pros and cons of these models are presented.

A Fuzzy H Filter Design for State of Charge Estimation (잔존충전용량 추정을 위한 퍼지 H 필터 설계)

  • Yoo, Seog-Hwan;Wu, Xuedong
    • Journal of Korean Institute of Intelligent Systems
    • /
    • v.20 no.2
    • /
    • pp.214-219
    • /
    • 2010
  • This paper deals with a nonlinear fuzzy $H_{\infty}$ filter design for SOC(state of charge) estimation in Lithium polymer battery. The dynamic equation of the battery cell is modeled as a T-S fuzzy system and the filter is designed via solutions of linear matrix inequalities. In order to illustrate the performance of the designed filter, a computer simulation is performed using the experimental data with the UDDS(urban dynamometer driving schedule) current profile.

SOC/SOH Estimation Method for AGM Battery by Combining ARX Model for Online Parameters Identification and DEKF Considering Hysteresis and Diffusion Effects (파라미터 식별을 위한 ARX 모델과 히스테리시스와 확산 효과를 고려한 이중 확장 칼만필터의 결합에 의한 AGM 배터리의 SOC/SOH 추정방법)

  • Tran, Ngoc-Tham;Choi, Woojin
    • Proceedings of the KIPE Conference
    • /
    • /
    • pp.401-402
    • /
    • 2014
  • State of Charge (SOC) and State of Health (SOH) are the key issues for the application of Absorbent Glass Mat (AGM) type battery in Idle Start Stop (ISS) system which is popularly integrated in Electric Vehicles (EVs). However, battery parameters strongly depend on SOC, current rate and temperature and significantly change over the battery life cycles. In this research, a novel method for SOC, SOH estimation which combines the Auto Regressive with external input (ARX) method using for online parameters prediction and Dual Extended Kalman Filter (DEKF) algorithm considering hysteresis is proposed. The validity of the proposed algorithm is verified by the simulation and experiments.

  • PDF

SOC Estimation of Flooded Lead Acid Battery Using an Adaptive Unscented Kalman Filter (적응형 Unscented 칼만필터를 이용한 플러디드 납축전지의 SOC 추정)

  • Khan, Abdul Basit;Choi, Woojin
    • Proceedings of the KIPE Conference
    • /
    • /
    • pp.59-60
    • /
    • 2016
  • Flooded lead acid batteries are still very popular in the industry because of their low cost as compared to their counterparts. State of Charge (SOC) estimation is of great importance for a flooded lead acid battery to ensure its safe working and to prevent it from over-charging or over-discharging. Different types of Kalman Filters are widely used for SOC estimation of batteries. The values of process and measurement noise covariance of a filter are usually calculated by trial and error method and taken as constant throughout the estimation process. While in practical cases, these values can vary as well depending upon the dynamics of the system. Therefore an Adaptive Unscented Kalman Filter (AUKF) is introduced in which the values of the process and measurement noise covariance are updated in each iteration based on the residual system error. A comparison of traditional and Adaptive Unscented Kalman Filter is presented in the paper. The results show that SOC estimation error by the proposed method is further reduced by 3 % as compared to traditional Unscented Kalman Filter.

  • PDF