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The state-of-charge (SOC) estimation approaches based on the pack model can hardly provide precise estimation due to cell difference, while the approaches based on each cell cost high computation resource, which are not suitable for real-time application. The estimation error can be further enlarged without considering balancing current. The SOC estimation approach of the battery pack considering balancing current is proposed, which dynamically searches for the cell with maximum or minimum voltage, and it only needs to calculate the selected cell in every estimation cycle. Compared to the approaches based on the pack model or each single cell, this approach can achieve precise pack SOC and cost less calculation time and resource. It has been verified with ten series-connected 200 Ah Li(NiCoMn)O2 batteries. The SOC estimation error is limited to 0.3% during the charging process, and a reduction of 2.5% is achieved compared to an error of 2.8% based on the pack model. A reduction of 1% is achieved compared to an error of 1.5% based on the pack model during the discharging process. Compared to an error of 1.7% without considering balancing current, a reduction of 1.4% is achieved during the charging process. [ABSTRACT FROM PUBLISHER]

This paper presents a method for an online real-time electrochemical impedance spectroscopy (EIS) measurement of batteries using closed-loop control of power converter. Unlike the previously proposed method which allows the measurement of the ac impedance for a single frequency, the presented method in this paper allows for obtaining the EIS for a spectrum of frequencies by using the information included in a single perturbation cycle, or a few cycles of perturbation to obtain a more accurate EIS with a very wide frequency range. This will result in faster EIS measurement for a spectrum of frequencies and under the same battery operating conditions. The presented method utilizes closed-loop control operation for the EIS measurement functionality, which allows for better control of the output voltage and for upgrading the concept to be able to achieve no added perturbation ripple at the output of the system. The presented online real-time EIS measurement method utilizes a power converter with closed-loop control in order to create an output voltage step-function perturbation at a given frequency to generate battery voltage and current responses. By applying Fourier analysis to these responses, an EIS can be obtained for a range of frequencies equal or higher than the perturbation frequency of the step function. In addition, this paper presents a method to eliminate the added perturbation ripple when two or more power converters are used. The theoretical basis and experimental prototype results are provided to illustrate and validate the presented method. [ABSTRACT FROM PUBLISHER]

Lithium-ion (Li-ion) batteries are nowadays the best tradeoff between performance, cost, and lifetime to store electric power and energy effectively. The reliable determination of the batteries’ instantaneous power capability and energy content is mandatory for many mobile high-energy and power-consuming applications. Herein, the battery impedance and state of charge (\SOC) are the relevant values. In energy storage systems, these values are to be considered for each battery cell individually, due to arising limitations caused by cell-specific variations. Simple algorithms to determine the battery system’s impedance and \SOC are presented including parameter and state-estimation techniques. Furthermore, methods are derived making the impedance and \SOC determination possible for a large number of particular cells in a battery system. The applicability of the demonstrated algorithms for battery control unit implementation is proved incorporating data achieved from load scenarios applied to a cell stack Li-ion battery module. Cell impedance and \SOC variations could be detected precisely. Moreover, a combination of impedance and \SOC spread was identified during typical battery operation. [ABSTRACT FROM PUBLISHER]