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Battery management systems (BMSs) are critical to ensure the efficiency and safety of high-power battery energy storage systems (BESSs) in vehicular and stationary applications. Recently, the proliferation of battery big data and cloud computing advancements has led to the development of a new generation of BMSs, named Cloud BMS (CBMS), aiming to improve the performance and safety of BESSs. The CBMS is a cyber-physical system with connectivity between the physical BMS and a cloud-based virtual BMS, which is realized through a communication channel such as Internet of Things. Compared to the traditional BMS, the CBMS offers significantly higher computational resources, leveraging the implementation of advanced digital twin models and best-in-class algorithms in the BMS software, which will provide superior performances. However, as for any other CPS, the CBMS creates vulnerabilities against cyberattacks and if not properly secured, could end up damaging the BESS and/or causing dangerous, expensive, and life-threatening situations. Cybersecurity of the CBMSs has thus become a trending topic and several works have been published in this area in recent years. This paper conducts a scoping review to address different topics related to BMS cybersecurity. The CBMS architecture is presented, and the potential cyberattack surfaces are identified. Different possible attack scenarios, including attack points, attack types, and their impact at the component level (BMS and BESS) and system level (vehicle or grid), are discussed. In addition, the paper provides a review of potential countermeasures to protect the CBMS against cyberattacks. The paper also includes a review of the applicable standards and regulations that relate to this trending topic. Finally, based on the reviewed gaps, potential future research domains on BMS cybersecurity topics are identified and presented at the end of the paper. [ABSTRACT FROM AUTHOR]

The performance of electric vehicle (EV) drivetrains depends on the power capability of individual components, including the battery pack, motor drive, and electric motor. To ensure safety, maximum power must be limited by considering the constraint of the weakest component in the drivetrain. While there exists a large body of work that discusses state-of-power (SoP) estimation for individual components, there is no work that considers all the components' limiting factors at once. Moreover, research on how to use these limits to adjust the performance at the system level has been rare. In this paper, the SoPs of the components are used to estimate the state-of-function (SoF) of the EV drivetrain. The SoF is defined as the maximum charge/discharge power that can be sourced and/or sunk by the drivetrain without violating the safety limits of its components. The component-level SoP estimations are fulfilled using several digital algorithms based on recursive least-squares (RLS) and Kalman filters (KFs), as well as by taking into account specific limiting conditions such as high driving altitude and ambient temperatures. An EV driven by a hybrid energy storage system based on a battery/supercapacitor, and a permanent-magnet synchronous motor is considered the use case. Based on the drivetrain SoF estimation, we propose two de-rating schemes to ensure that the drivetrain safety limits will be respected: adaptive cruise control and adaptive adjustment of pedal sensitivity. The de-rating schemes are introduced to a so-called recommender system that is implemented in MATLAB/STATEFLOW. The recommender system provides advisory feedback to the driver to switch to a different driving mode to ensure safety. The simulation results over a standard drive cycle using MATLAB/SIMULINK and STATEFLOW show the effectiveness of the proposed design at both component and system levels. The paper also proposes an implementation concept for the integration of the proposed recommender system into the advanced driver assistance system (ASAS). [ABSTRACT FROM AUTHOR]

In this article, an efficient method for finite-time secure dynamic state estimation in cyber–physical systems (CPSs) subjected to unknown inputs and cyber-attacks is proposed. The proposed approach is based on a set of local finite-time state estimators operating over the subsets of sensory nodes, which are designed to estimate the states of the CPS subjected to unknown inputs. When cyber-attacks compromise some sensory nodes, the estimation results of the local finite-time estimators which use the measurements of the attacked sensors can be corrupted. An efficient detection algorithm is thus proposed to identify the valid local estimators that are completely devoid of the attacked sensory nodes. The information of the valid local estimators is then used to achieve secure state estimation and localization of the launched cyber-attack. The necessary and sufficient conditions for the feasibility and finite-time convergence of the proposed estimation mechanism are analytically derived and proven. The effectiveness of the proposed method is demonstrated by testing it on a dc electric motor. Likewise, some online software-in-the-loop tests are conducted to demonstrate the real-time feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Modular converters or reconfigurable battery energy storage systems are a promising approach to eliminate the dependence on the weakest element in previously hard-wired battery packs and to combine heterogeneous batteries (so-called mixed-battery systems). But their need for expensive sensors and complex monitoring as well as control subsystems hinders their progress. Estimating the parameters of each module can substantially reduce the number of required sensors and/or communication components. However, the existing estimation methods for cascaded modular circuits neglect important parameters such as the internal resistance of the battery, resulting in large systematic errors and bias. This article proposes an online estimator based on a dual Kalman filter (DKF) that exploits the slow dynamics of the battery compared to the load. The DKF algorithm estimates the open-circuit voltage (OCV) and internal resistance of each module by measuring only the output voltage and current of the system. Compared with the state-of-the-art, the proposed method is simpler and cheaper with only two sensors compared to $ \geq N + 2$ ($N$ is the number of modules). Furthermore, the proposed algorithm achieves a fast convergence through an optimal learning rate. Simulations and experimental results confirm the ability of the proposed approach, achieving <1.5% and <5% estimation error for OCV and the internal resistance, respectively. [ABSTRACT FROM AUTHOR]

This article introduces an efficient modeling approach based on the Wiener structure to reinforce the capacity of classical equivalent circuit models (ECMs) in capturing the nonlinearities of lithium-ion (Li-ion) batteries. The proposed block-oriented modeling architecture is composed of a simple linear ECM followed by a static output nonlinearity block, which helps achieving a superior nonlinear mapping property while maintaining the real-time efficiency. The observability of the established battery model is analytically proven. This article also introduces an efficient parameter estimator based on extended-kernel iterative recursive least squares algorithm for real-time estimation of the parameters of the proposed Wiener model. The proposed approach is applied for state-of-charge (SoC) estimation of 3.4-Ah 3.6-V nickel-manganese-cobalt-based Li-ion cells using the extended Kalman filter (EKF). The results show about 1.5% improvement in SoC estimation accuracy compared with the EKF algorithm based on the second-order ECM. A series of real-time tests are also carried out to demonstrate the computational efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Parasitic capacitance has a considerable impact on conductive electromagnetic interference (EMI), especially on the common mode (CM) noise. In vehicular applications, such as more electric aircraft and electrical vehicles, the value of CM stray capacitance can vary during the drive cycle, due to some change in environmental, and operating conditions, resulting in fluctuations in EMI level. Designing EMI filters according to potentially overestimated CM capacitance results in additional weight, volume, and cost which should be avoided. In this paper, for the first time, the parasitic capacitance relevant to CM noise is estimated in real-time using unscented Kalman filter (UKF). The UKF is employed due to its ability in dealing with nonlinearity and stochastic nature of the EMI as well as the measurement noises. The proposed method is tested on a typical dc–dc buck converter in different operating conditions. The effectiveness of the proposed method is tested using some hardware-in-the-loop experiments using dSPACE1104 development platform. [ABSTRACT FROM AUTHOR]

Dynamic-state estimation plays a critical role in achieving real-time wide-area monitoring of power systems. On the other hand, false data injection (FDI) attacks are substantial threats, which can undesirably ruin the state estimation results. To tackle this problem, an effective secure hybrid dynamic-state estimation approach that involves a dynamic model of the attack vector is proposed in this article. In the proposed method, an initial estimation of the system states is first obtained using a designed unknown input observer (UIO). Subsequently, based on the system, UIO models, and the initial estimations of the states, a dynamic model for the attack vector is extracted. Ultimately, the attack model is augmented with the main system model for coestimation of the attack and the system states using a Kalman filter. The onset of the FDI attack is rapidly detected by the accurate estimation of the attack vector. The effectiveness of the proposed approach is demonstrated under different FDI attack scenarios by a thorough theoretical analysis as well as simulations on IEEE 14-bus and 57-bus test systems. In order to show that the proposed method can keep up with typical scan rates of commercial phasor measurement units, a series of software-in-the-loop experiments are also conducted and the real-time feasibility of the proposed approach is guaranteed. [ABSTRACT FROM AUTHOR]

This study proposes an effective diagnosis method for single and multiple open‐switch faults (OSFs) in three‐phase permanent magnet synchronous motor (PMSM) drives. The proposed method is based on the real‐time estimation of the three‐phase motor currents using three Kalman filters (KFs). Three residual signals are defined as the differences between the three‐phase measured and estimated stator currents. The averaged normalised residual signals are used as diagnostic criteria for the detection of OSFs. The proposed method accurately detects the OSFs, localises the faulty switches and effectively discriminates between the OSFs and current sensor faults. The main superiorities of the proposed KF‐based method are its fast detection time and high robustness to measurement noises/errors and load variations. Moreover, the proposed method is useful for both closed‐loop and open‐loop PMSM drives. The proposed method can be embedded as a subroutine in the drive control unit without any hardware extension. The effectiveness of the proposed method is confirmed through extensive simulations and several hardware‐in‐the‐loop experiments on a testbed with a 1.5 kW PMSM and dSPACE1104 control board. In addition, the real‐time feasibility of the proposed method using low‐cost microprocessor technology is guaranteed by testing it on TMS320F28335 from Texas Instruments©. [ABSTRACT FROM AUTHOR]

Online accurate estimation of supercapacitor state-of-health (SoH) and state-of-energy (SoE) is essential to achieve efficient energy management and real-time condition monitoring in electric vehicle (EV) applications. In this article, for the first time, unscented Kalman filter (UKF) is used for online parameter and state estimation of the supercapacitor. In the proposed method, a nonlinear state-space model of the supercapacitor is developed, which takes the capacitance variation and self-discharge effects into account. The observability of the considered model is analytically confirmed using a graphical approach. The SoH and SoE are then estimated based on the supercapacitor online identified model with the designed UKF. The proposed method provides better estimation accuracy over Kalman filter (KF) and extended KF algorithms since the linearization errors during the filtering process are avoided. The effectiveness of the proposed approach is demonstrated through several experiments on a laboratory testbed. An overall estimation error below 0.5% is achieved with the proposed method. In addition, hardware-in-the-loop experiments are conducted and real-time feasibility of the proposed method is guaranteed. [ABSTRACT FROM AUTHOR]

In order to improve the dependability and security of transformer differential protection, the energisation conditions should be effectively distinguished from internal fault conditions. In this study, a new method with a low computational burden and high robustness against measurement noises is proposed for discriminating between the transformer inrush current and internal faults. In the proposed method, sine waves are fitted to the sample points of the normalised differential currents for different phases using recursive extended least‐squares (RELS) algorithm in an online manner. The extended kernel enables the algorithm to effectively estimate the dynamics of the measurement noises. Three residual signals which are defined as the differences between the normalised differential currents and the fitted sine wave signals are considered as the decision criteria. Based on the selected criteria, the energisation condition is identified in about a half‐cycle of power system frequency. A large number of simulation and experimental test cases are used to demonstrate the effectiveness of the proposed method. The results show that the proposed algorithm achieves an accuracy of ∼98% and has a relatively low operating time, while the computational burden for the embedded implementation is favorably low. [ABSTRACT FROM AUTHOR]

This paper deals with a new application for a special sort of Rogowski coil (RC) sensor in real-time estimation of diode turn-OFF energy loss. The turn-OFF energy loss of power diode is a key metric in designing power electronics circuits. The estimation accuracy of the diode turn-OFF energy loss significantly depends on the accurate extraction of diode reverse-recovery parameters during the turn-OFF process. By far, many methods have been proposed for offline estimation of diode reverse-recovery parameters. However, online estimation of these parameters can also provide worthwhile information for real-time monitoring of power diode and operating power electronics circuit. Specifically, the online estimated energy loss includes information which can be interpreted to obtain general ideas about the diode junction temperature, health status of snubber circuits, and so on. In this paper, a novel approach for online estimation of turn-OFF energy loss of power diodes using an efficient double-layer printed circuit board RC (DLPCBRC) sensor is proposed. The sensor is designed and utilized to acquire the key reverse-recovery parameters of the diode. Based on the extracted reverse-recovery parameters, a sufficiently good estimation of diode turn-OFF energy loss is obtained in real-time. The simulation and experimental results on a typical diode in a boost DC–DC converter are presented to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, an efficient method based on the Kalman filter (KF) theory is proposed for fast and accurate detection and compensation of current transformer (CT) saturation. During the unsaturated regions, the algorithm uses the current sample points together with extended Kalman filter (EKF) for obtaining a model, which suitably represents the current signal behavior. The acquired model is used to reconstruct the original current waveform during the saturated regions. The onset and end points of the CT saturation intervals are detected by defining a criterion based on the estimation error of the EKF algorithm. The proposed method is very profitable as it requires only one sample point to detect the CT saturation and it also copes with the problems of measurement noises and harmonics. These advantages are confirmed by a large number of test cases obtained using PSCAD/EMTDC software as well as by real-time hardware implementation. [ABSTRACT FROM AUTHOR]

Differential protection is the most common type of protection in power transformers. However, inrush current due to transformer energization may appear as fault current to the protective relay, causing a false trip of the transformer. Therefore, discrimination of the transformer inrush current from internal faults is necessary to improve the security of the protection scheme. In this paper, a new method for discrimination of the transformer inrush current from an internal fault current is proposed. First, the nonlinear state-space model of a real single-phase transformer is derived, which incorporates the nonlinear phenomena of hysteresis and magnetic saturation. Based on the derived model, an Extended Kalman Filter (EKF) is used for estimation of the primary winding current. A residual signal is defined as the difference between the measured and estimated currents. When a healthy transformer is energized, the EKF perfectly estimates the primary winding current and, hence, the residual signal is almost zero. However, when the transformer is faulty, the EKF cannot effectively estimate the current due to the existing large model mismatches. Consequently, a large residual signal is created. The superiorities of the proposed algorithm are demonstrated using different experimental scenarios. [ABSTRACT FROM PUBLISHER]

Supercapacitor is being widely used in electric transportation applications, particularly in electric vehicles (EVs). In the EV, the supercapacitor mainly serves as energy storage system for accelerating the vehicle and/or capturing the braking energy due to its prominent features such as high power density and long life cycle. However, during the vehicle braking and/or accelerating, deep and fast supercapacitor charging‐discharging cycles increase the electric and thermal stresses on the supercapacitor bank. Under such circumstances, condition monitoring of the supercapacitor bank is essential in EVs for safe operation of the storage system. In this paper, an effective method for condition monitoring and fault diagnosis of the supercapacitor banks is proposed. First, it is shown that the equivalent series resistance (ESR) and double‐layer capacitance (CDL) can be used as the key signatures for indicating the state‐of‐health. Subsequently, the recursive extended least‐square algorithm (RELS) is used for online estimation of the ESR and capacitance of the supercapacitor bank. Based on a residual signal which is defined as the difference between the voltages of the supercapacitor bank and RELS algorithm, the abnormal conditions of the supercapacitor bank are distinguished. Experimental results are presented to confirm the feasibility and precision of the proposed method. [ABSTRACT FROM AUTHOR]

This study presents a new resistive capacitor switching transient limiter (RCSTL) for mitigating power capacitor switching transients. The RCSTL is composed of three limiting resistors, a three‐phase full bridge diode rectifier, a thyristor and a three‐phase coupling transformer. The limiting resistors are connected in series with the capacitor bank and the thyristor is installed at the DC side of the diode rectifier. During the capacitor bank energising, the thyristor remains off and hence, the secondary side of the coupling transformer acts as open circuit. Consequently, high impedance including the transformer's magnetic reactance in parallel with the limiting resistors is inserted in series with the capacitor bank. Under such circumstance, the limiting resistors restrain the capacitor switching transients. In the steady‐state, the control system triggers the thyristor and the coupling transformer acts as short circuit. As a result, the limiting resistors are bypassed by the coupling transformer and thus, the RCSTL has no significant effect on the circuit. Analytical analysis is performed to describe different operation modes of the proposed RCSTL. A single stage of a three‐phase wye‐connected capacitor bank with 25 kVAR is energised and the performance of the proposed limiter is investigated. Both the simulation and experimental results confirm the effectiveness of the RCSTL. [ABSTRACT FROM AUTHOR]