Open Circuit Voltage Estimation Research Articles

A novel approach of battery pack state of health estimation using artificial intelligence optimization algorithm

An accurate battery pack state of health (SOH) estimation is important to characterize the dynamic responses of battery pack and ensure the battery work with safety and reliability. However, the different performances in battery discharge/charge characteristics and working conditions in battery pack make the battery pack SOH estimation difficult. In this paper, the battery pack SOH is defined as the change of battery pack maximum energy storage. It contains all the cells' information including battery capacity, the relationship between state of charge (SOC) and open circuit voltage (OCV), and battery inconsistency. To predict the battery pack SOH, the method of particle swarm optimization-genetic algorithm is applied in battery pack model parameters identification. Based on the results, a particle filter is employed in battery SOC and OCV estimation to avoid the noise influence occurring in battery terminal voltage measurement and current drift. Moreover, a recursive least square method is used to update cells' capacity. Finally, the proposed method is verified by the profiles of New European Driving Cycle and dynamic test profiles. The experimental results indicate that the proposed method can estimate the battery states with high accuracy for actual operation. In addition, the factors affecting the change of SOH is analyzed.

Estimation of open-circuit voltage of Cu(In,Ga)Se2 solar cells before cell fabrication

Cu(In,Ga)Se2 (CIGS) absorbers with several Ga/III, Ga/(In + Ga), profiles were fabricated by the so-called “multi layer precursor method” under the control of Ga flux rate during film's deposition. Open-circuit voltage (VOC) of CIGS solar cell is principally dependent on average band-gap energy (Eg) in space-change region (SCR) of below 1.3 eV. This average Eg in SCR, principally controlled by average Ga/III ratio in SCR, is estimated by the peak position of (220/204)-oriented CIGS films on soda-lime grass (SLG) substrates investigated by grazing incidence X-ray diffraction (GIXRD) with 0.1° incident angle, whereas the average Eg in SCR is predicted by Raman or photoluminescence (PL) peak positions of CIGS absorbers on both SLG and stainless steel (SUS) substrates. Ultimately, with the average Eg in SCR of below 1.3 eV, the (220/204) peak position (GIXRD) can be well used as a predictor of the VOC on rigid SLG substrates. On the other hand, Raman peak and PL peak positions can be well utilized as indicators of the VOC on not only rigid SLG but also flexible SUS substrates before solar cell fabrication. These are fast and non-destructive methods to evaluate the Ga content, Eg near CIGS surface, and corresponding VOC for high cell performance.

State-of-charge estimation of electric vehicles using Kalman filter for a harsh environment; a heuristic method for current measurement error calibration

In this paper, it has been studied how to estimate state-of-charge (SOC) with accuracy and robustness in a harsh environment. Several methods have been used for that purpose. More specifically, in the process of estimation of open circuit voltage (OCV), least mean square (LMS) is used to calculate parameters of ECM, and Kalman filter (KF) is proposed to estimate SOC using both current integral and SOC obtained from OCV. The actual current pattern is used to verify the method in a harsh environment. This method can obtain the result that errors in SOC estimation, occurred in the whole process, is relatively smaller than other methods. Accurate SOC enables more efficient battery management. As a result, the utilisation of a battery can be increased using the method this paper proposes.

Design, Testing, and Validation of a Simplified Control Scheme for a Novel Plug-In Hybrid Electric Vehicle Battery Cell Equalizer

In order to meet cost targets for hybrid electric (HEV), plug-in hybrid electric (PHEV), and all-electric vehicles (EV), an improvement in the battery life cycle and safety is essential. Recently, lithium batteries, in the form of lithium-ion, lithium-polymer, or lithium iron phosphate have been explored. Despite research initiatives, lithium-based batteries have not yet been able to meet steep energy demands, long lifetime, and low cost of vehicular propulsion applications. One practical approach to improve performance is to use power electronics intensive cell voltage equalizers, in conjunction with on-board energy storage devices. The purpose of this paper is to introduce a simplified control scheme, based on open-circuit voltage estimation, for a novel cell equalizer configuration, with the potential to fulfil expectations of the following: 1) low cost; 2) large currents; and 3) high efficiency. Issues, such as the limitations on maximum and minimum cell voltage, noise, and quantization errors, are explored. Finally, a comprehensive comparison between the theoretical test results and practical equalization test results is presented.

Battery open-circuit voltage estimation by a method of statistical analysis

The basic task of a battery management system (BMS) is the optimal utilization of the stored energy and minimization of degradation effects. It is critical for a BMS that the state-of-charge (SoC) be accurately determined. Open-circuit voltage (OCV) is directly related to the state-of-charge of the battery, accurate estimation of the OCV leads to an accurate estimate of the SoC. In this paper we describe a statistical method to predict the open-circuit voltage on the basis of voltage curves obtained by charging batteries with different currents. We employ a dimension reduction method (Karhunen–Loeve expansion) and linear regression. Results of our modelling approach are independently validated in a specially designed experiment.