다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

With the rapidly evolving technology of the smart grid and electric vehicles (EVs), the battery has emerged as the most prominent energy storage device, attracting a significant amount of attention. The very recent discussions about the performance of lithium-ion (Li-ion) batteries in the Boeing 787 have confirmed so far that, while battery technology is growing very quickly, developing cells with higher power and energy densities, it is equally important to improve the performance of the battery management system (BMS) to make the battery a safe, reliable, and cost-efficient solution. The specific characteristics and needs of the smart grid and EVs, such as deep charge/discharge protection and accurate state-of-charge (SOC) and state-of-health (SOH) estimation, intensify the need for a more efficient BMS. The BMS should contain accurate algorithms to measure and estimate the functional status of the battery and, at the same time, be equipped with state-of-the-art mechanisms to protect the battery from hazardous and inefficient operating conditions.

Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles

Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles

The battery management system (BMS) is a critical component of electric and hybrid electric vehicles. The purpose of the BMS is to guarantee safe and reliable battery operation. To maintain the safety and reliability of the battery, state monitoring and evaluation, charge control, and cell balancing are functionalities that have been implemented in BMS. As an electrochemical product, a battery acts differently under different operational and environmental conditions. The uncertainty of a battery’s performance poses a challenge to the implementation of these functions. This paper addresses concerns for current BMSs. State evaluation of a battery, including state of charge, state of health, and state of life, is a critical task for a BMS. Through reviewing the latest methodologies for the state evaluation of batteries, the future challenges for BMSs are presented and possible solutions are proposed as well.

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Battery Management Systems in Electric and Hybrid Vehicles

An accurate State-of-Charge (SoC) estimation plays a significant role in battery systems used in electric vehicles due to the arduous operation environments and the requirement of ensuring safe and reliable operations of batteries. Among the conventional methods to estimate SoC, the Coulomb counting method is widely used, but its accuracy is limited due to the accumulated error. Another commonly used method is model-based online iterative estimation with the Kalman filters, which improves the estimation accuracy in some extent. To improve the performance of Kalman filters in SoC estimation, the adaptive extended Kalman filter (AEKF), which employs the covariance matching approach, is applied in this paper. First, we built an implementation flowchart of the AEKF for a general system. Second, we built an online open-circuit voltage (OCV) estimation approach with the AEKF algorithm so that we can then get the SoC estimate by looking up the OCV-SoC table. Third, we proposed a robust online model-based SoC estimation approach with the AEKF algorithm. Finally, an evaluation on the SoC estimation approaches is performed by the experiment approach from the aspects of SoC estimation accuracy and robustness. The results indicate that the proposed online SoC estimation with the AEKF algorithm performs optimally, and for different error initial values, the maximum SoC estimation error is less than 2% with close-loop state estimation characteristics.

Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach

On the investigation of photovoltaic output power reduction due to dust accumulation and weather conditions

This paper presents a new method for the estimation of State of Charge (SOC) for NiMH batteries. Among the conventional methods to estimate SOC, Coulomb Counting is widely used, but this method is not precise due to error integration. Another method that has been proposed to estimate SOC is by using a measurement of the Open Circuit Voltage (OCV). This method is found to be a precise one for SOC estimation. In NiMH batteries, the hysteresis characteristic of OCV is very strong compared to other type of batteries. Another characteristic of NiMH battery to be considered is that the OCV of a NiMH battery under discharging mode is lower than it is under charging mode. In this paper, the OCV is modeled by a simple method based on a hyperbolic function which well known as Takacs’s model. The OCV model is then used for SOC estimation. Although the model is simple, the error is within 10%.

/pdf/soc-estimation-based-on-ocv-for-nimh-batteries-using-an-2soi2959zt.pdf

SOC Estimation Based on OCV for NiMH Batteries Using an Improved Takacs Model

This paper proposes a SOC (State-of-charge) estimator of batteries using the Extended Kalman Filter (EKF). EKF can work properly only with accurate model. Therefore, this paper includes high accuracy electrical battery model for EKF state. The modeling is focused on high-capacity LiPB batteries. The battery model is extracted from single cell of LiPB 40Ah, 3.7V. The dynamic behavior of single cell battery is modeled using a bulk capacitor, two series RC networks and a series resistance. The voltage in bulk capacitor represents OCV (Open Circuit Voltage) related to SOC value. EKF is employed to estimate the OCV, and then OCV is converted to SOC value. EKF is tested with experiments data obtained by MACCOR battery tester. EKF is improved with single varying parameter of bulk capacitor which follows the SOC value. The test of estimator algorithm is done for full region of SOC. The test results show the error of estimation can reach max 5%SOC at some points.

SOC estimation of LiPB batteries using Extended Kalman Filter based on high accuracy electrical model

This paper propose about using PID control system based on Kp, Ki, and Kd parameter determination with scheduling process from fuzzy logic. Control system is used to arrange speed of three phase induction motor using IFOC method. This method can be minimized the main problem from speed control of induction motor which is a transient condition. The robustness validation from this system use testing process of dynamic speed which is compared with the other control system to know the system performance in transient condition such as (rise time, overshoot, undershoot and settling time). The result shows using the proposed system has better performance responses which is requiring 0.001 seconds time in transient condition up to steady state condition without overshoot and undershoot problem.

/pdf/fuzzy-gain-scheduling-of-pid-fgs-pid-for-speed-control-three-479x68bepi.pdf

Fuzzy Gain Scheduling of PID (FGS-PID) for Speed Control Three Phase Induction Motor Based on Indirect Field Oriented Control (IFOC)

This paper describes about the electrical modeling of NiMH battery to estimate the State-of-Charge (SOC). The relationship between the SOC and the Open Circuit Voltage (OCV) has the hysteresis characteristic and it is modeled based on the Takacs model in this paper. The modeling includes both of major loop and minor loop. The original Takacs model is improved with additional polynomial function to approach the experimental data as similar as possible. To verify the improvement by using the proposed Takacs model, the results of the original Takacs model and the improved one for the major and minor loops are compared. The modeling error in major loop based on the improved Takacs model is less than 4% SOC. In minor loop modeling, the error of modeling is less than 6% SOC, but there is no significant difference with the improved Takacs model.

Novie Ayub Windarko

Papers

On the investigation of photovoltaic output power reduction due to dust accumulation and weather conditions

SOC Estimation Based on OCV for NiMH Batteries Using an Improved Takacs Model

SOC estimation of LiPB batteries using Extended Kalman Filter based on high accuracy electrical model

Fuzzy Gain Scheduling of PID (FGS-PID) for Speed Control Three Phase Induction Motor Based on Indirect Field Oriented Control (IFOC)

Hysteresis modeling for estimation of State-of-Charge in NiMH battery based on improved Takacs model