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While large scale deployment of plug-in electric vehicles (PEVs) offers promising advantages, such as environmental benefits, energy security, and economic stability, it also poses certain cybersecurity related challenges. Unlike power grid security, PEV cybersecurity is significantly underexplored. However, cyberattacks on PEVs may lead to disastrous situations such as out-of-service EVs via denial-of-charging (DoC) or battery pack damage via overcharging. In this article, we attempt to address this issue by exploring control-oriented approaches for PEV cybersecurity. Specifically, we focus on designing algorithms for detecting cyberattacks that can potentially affect PEV battery packs during charging. We discuss two algorithms: first, static detector that utilizes only measured variables, and, second, dynamic detector that utilizes the knowledge of system dynamics along with the measurements. Furthermore, we propose a filter-based design approach for the dynamic detector that considers a multiobjective criterion including stability, robustness, and attack sensitivity. We perform theoretical analysis and simulation studies to evaluate the effectiveness of the algorithms under DoC and overcharging attacks that indicate the superiority of dynamic detector in terms of attack detectability. [ABSTRACT FROM AUTHOR]

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]