State-of-charge (SOC) determination is an increasingly important issue in battery technology. In addition to the immediate display of the remaining battery capacity to the user, precise knowledge of SOC exerts additional control over the charging/discharging process, which can be employed to increase battery life. This reduces the risk of overvoltage and gassing, which degrade the chemical composition of the electrolyte and plates. The proposed model in this paper determines the SOC by incorporating the changes occurring due to terminal voltage, current load, and internal resistance, which mitigate the disadvantages of using impedance only. Electromotive force (EMF) voltage is predicted while the battery is under load conditions; from the estimated EMF voltage, the SOC is then determined. The method divides the battery voltage curve into two regions: 1) the linear region for full to partial SOC and 2) the hyperbolic region from partial to low SOC. Algorithms are developed to correspond to the different characteristic changes occurring within each region. In the hyperbolic region, the rate of change in impedance and terminal voltage is greater than that in the linear region. The magnitude of current discharge causes varying rates of change to the terminal voltage and impedance. Experimental tests and results are presented to validate the new models.

State-of-Charge Determination From EMF Voltage Estimation: Using Impedance, Terminal Voltage, and Current for Lead-Acid and Lithium-Ion Batteries

Control oriented 1D electrochemical model of lithium ion battery

High-power lithium ion batteries are often rated with multiple current and voltage limits depending on the duration of the pulse event. These variable control limits, however, are difficult to realize in practice. In this paper, a linear Kalman filter based on a reduced order electrochemical model is designed to estimate internal battery potentials, concentration gradients, and state-of-charge (SOC) from external current and voltage measurements. A reference current governor predicts the operating margin with respect to electrode side reactions and surface depletion/saturation conditions responsible for damage and sudden loss of power. The estimates are compared with results from an experimentally validated, 1-D, nonlinear finite volume model of a 6 Ah hybrid electric vehicle battery. The linear filter provides, to within ~ 2%, performance in the 30%-70% SOC range except in the case of severe current pulses that draw electrode surface concentrations to near saturation and depletion, although the estimates recover as concentration gradients relax. With 4 to 7 states, the filter has low-order comparable to empirical equivalent circuit models commonly employed and described in the literature. Accurate estimation of the battery's internal electrochemical state enables an expanded range of pulse operation.

Model-Based Electrochemical Estimation and Constraint Management for Pulse Operation of Lithium Ion Batteries

DC microgrids are becoming popular in low-voltage distribution systems due to the better compatibility with photovoltaic panels, electric vehicles, and DC loads. This paper presents a practical dc microgrid developed in the Water and Energy Research Laboratory (WERL) in the Nanyang University of Technology, Singapore. The coordination control among multiple dc sources and energy storages is implemented using a novel hierarchical control technique. The bus voltage essentially acts as an indicator of supply-demand balance. A wireless control is implemented for the reliable operation of the grid. A reasonable compromise between the maximum power harvest and effective battery management is further enhanced using the coordination control based on a central energy management system. The feasibility and effectiveness of the proposed control strategies have been tested by a DC microgrid in WERL.

Implementation of Hierarchical Control in DC Microgrids

Online Estimation of the State of Charge of a Lithium Ion Cell

Charge–discharge behaviour of VRLA batteries: model calibration and application for state estimation and failure detection

Evaluation of VRLA battery under overcharging: model for battery testing

Impedance of valve-regulated lead-acid batteries is analysed in this paper, using a model-based method Both double-layer impedance and electrochemical reaction impedance are evaluated, using current, voltage and temperature data measured in experiments It is shown that the electrochemical impedance is a sensitive indicator that can be utilised to detect small inequalities between batteries while the double-layer impedance is less sensitive

Battery impedance and its relationship to battery characteristics

A model-based method is proposed for the impedance-spectra analysis of valve-regulated lead-acid batteries. The electrochemical and double-layer processes are analyzed in a wide range of frequencies based on current, voltage, and temperature measurements. Both slow and fast dynamics of the positive and negative electrodes are evaluated. The method is shown to be accurate, enabling the detection of differences in capacity or other parameters even in slightly differing batteries. An exact relation between battery impedance and the electrochemical cell model is established giving useful information on the contribution of different elements and processes on the impedance. The proposed method is computationally effective due to the special analytical formulation developed for impedance analysis. In addition to the basic processes, the method also allows analysis of the fast-rate discharge and overcharge processes.

A Method for Battery Impedance Analysis

A method and apparatus are disclosed for simulating the operation of a rechargeable battery. There is obtained (503, 553) a value for at least one parameter that describes an internal state of the battery. Said obtained value is used for calculating (504, 505, 554, 555) a prediction value for a characteristic of the battery that is observable outside the battery. These steps are repeated a multitude of times in order to simulate the operation of the battery over a certain period of time. A difference is detected (507, 556) between a calculated prediction value and a known value of a corresponding characteristic in a corresponding situation. The obtained value of said at least one parameter is corrected (507, 558) before a further prediction value calculating step by an amount that is proportional to said detected difference.

Ander Tenno

Papers

Charge–discharge behaviour of VRLA batteries: model calibration and application for state estimation and failure detection

Evaluation of VRLA battery under overcharging: model for battery testing

Battery impedance and its relationship to battery characteristics

A Method for Battery Impedance Analysis

Method and apparatus for soft-sensor characterization of batteries