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• The multi-objective optimization is done for both excess and deficit power mode with novel objectives. So, both the battery and SC would participate in grid excess and deficit power modes. • Due to the microgrid application, the bus voltage is regulated at the desired value by proper control of the SC current. • The adaptive Kalman filter is used to estimate the grid required current by use of DC-bus voltage deviation. Thus, the proposed control strategy does not need the data of load, generation units, and other storage units. • Determining the control signal of the switches using the average model of the converter instead of the conventional PI. This paper proposes a novel optimization-based power management strategy (PMS) for a battery/supercapacitor hybrid energy storage system (HESS) with a semi-active structure in a DC microgrid application. As the DC bus voltage regulation is the main purpose of the HESS, the supercapacitor control due to direct connection to the DC bus becomes more important. While, in the semi-active structure, there is no control over the supercapacitor. The proposed PMS solved this challenge by considering the supercapacitor current as a control target in determining the reference current of the battery. Extending the battery life span by drawing smooth current from the battery and responding the supercapacitor to load current changes, and charging the battery with a constant current as a new objective function, are the other optimization targets. This multi-objective problem is solved by the Karush–Kuhn–Tucker (KKT) condition which makes the proposed PMS suitable for real-time applications. In addition, in contrast to previous related studies, (i) the load current (grid excess/deficiency power) as one of the important inputs of PMS is estimated by the adaptive Kalman filter (AKF), and (ii) the converter control signal is determined by system average model instead of conventional PI controller. Finally, using simulation, the performance of the proposed PMS has been verified by comparing the results with fuzzy logic, droop control, and adaptive filter-based methods. As well, the presented control strategy has been implemented on an experimental setup, and its performance is investigated. [ABSTRACT FROM AUTHOR]

The growing share of renewable energy technology is continuously transforming the energy sector. Using storage capacities becomes crucial to sustainably guarantee stable energy supply. In particular battery storage systems (BSS) are expected to successfully compete with alternative options for energy storage in the near future. Major issues with state-of-the-art BSS include comparatively high costs, limited efficiency as well as reliability, and a low fault tolerance. A leap in power electronics technology might meet these challenges, as the aforementioned characteristics are predominantly determined by existing power converter and BSS architectures. This paper presents the so-called AC Battery – a novel modular multilevel parallel converter based split battery system for AC applications, enabling dynamic switching of battery cells in parallel and in series. Each individual battery cell may be interconnected to its neighbors according to operational needs, e.g. to provide optimum source resistance, lowest state-of-charge (SOC) cycling, and balanced aging, rendering separate battery management systems (BMS) unnecessary. Applying the proposed technology in grid scale BSS may fundamentally change existing system architectures, as it merges battery storage system and power converter. This forms the basis for a highly integrated power electronics unit that includes bidirectional grid connection capability, energy storage, and BMS. [ABSTRACT FROM AUTHOR]

This paper proposes a novel capacitor condition monitoring for dc-link of power converters employed in distributed energy resources systems. It follows the active methodology, in which an intermittent noncharacteristic signal is injected into the dc-link to estimate the equivalent series resistance (ESR) and capacitance (C) of dc-link capacitors. A real-time stationary discrete wavelet packet transform (RT-SDWPT) performs the capacitor parameter estimations. In this scheme, an interharmonic is used as a noncharacteristic signal to prevent the influence of the system's inherent harmonic components, mitigating estimation errors. Moreover, the interharmonic frequency is chosen in the region influenced by the ESR and C parameters to ensure capacitor degradation condition. Besides, to mitigate quality issues, the interharmonic's amplitude, duration, and the time interval between two signal injections follow the recommendations defined by standards. Furthermore, a parameter deviation detection scheme is employed to adaptively adjust the time interval between two estimations, improving failure detection. Experimental results obtained from a microgrid laboratory setup demonstrate the effectiveness and feasibility of the proposed method. • A wavelet-based approach for monitoring the capacitors degradation is presented. • The capacitors degradation is evaluated through the ESR and C deviation. • An interharmonic signal injection provides the ESR and C estimations. • An adaptive scheme determines the time interval between two estimations. • Experimental results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Being one of the important parameters describing the state of power battery, state of charge (SOC) is essential for the electric vehicle battery management system (BMS). SOC estimation method, which combines the constructed controlled auto-regressive and moving average (CARMA) model with the feedforward-feedback compensation method used for revising SOC by the deviation of terminal voltage, is presented in this paper. Fully taken into account the measurement errors of voltage and current, the CARMA model is employed to estimate the battery open-circuit voltage (OCV). With the good consistency of the OCV-SOC curve under the process of battery charge and discharge cycles within a certain temperature range, OCV is adopted to estimate SOC. BP neural network rather than the high order polynomial approximation is used to capture the strong nonlinear relationship between OCV and SOC with the high precision. It is a big challenge for OCV-based SOC estimation that the flat area of OCV-SOC curve for lithium-ion power battery enlarges the measurement errors of OCV. By analyzing the flat characteristic of ΔSOC-OCV curve, the feedforward-feedback compensation for SOC is used for improving the accuracy of OCV-based SOC estimation. Experiment results confirm the effectiveness of the proposed approach that has evidently advantages over other estimation methods. [ABSTRACT FROM AUTHOR]

• A semi-analytical approach is developed to obtain the BESS power reserve for FFR. • The local minimum power reserve is derived to avoid UFLS under a large contingency. • The proposed method bridges the gap of the trial-and-error approaches. • The effectiveness of the proposed method is validated with an actual network model. • The methodology will advance grid services estimation and battery management. The large-scale renewable energy integration faces a challenge of frequency stability due to low inertia in weak power grids. One solution to mitigate this issue is via the fast frequency response service provided by battery storage systems so that the overall grid response during a large power outage can be improved. The battery power reserve is crucial to ensure the frequency ancillary services provision. However, grid operators currently investigate the contingency reserve of batteries through inefficient processes, such as extensive simulations or trial-and-error methods. Therefore, this paper proposes a semi-analytical methodology to quantitively determine the minimum required battery power reserve for preventing under-frequency load shedding when a severe generation trip occurs. This minimum reserve is derived from the time-variant kinetic energy of a given dispatch case. Case study results of the Mexican network demonstrate that the calculated battery power reserve can provide a cost-effective fast frequency response for a real islanded network. The research outcomes offer valuable insights for battery management and grid ancillary services requirement estimation in the transition to power networks dominated by converter-interfaced renewable energy resources. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries have been widely used as the energy storage systems in personal portable electronics (e.g. cell phones, laptop computers), telecommunication systems, electric vehicles and in various aerospace applications. To prevent the sudden loss of power of battery-powered systems, there are various approaches to estimate and manage the battery's state of charge (SOC). In this paper, an artificial neural network-based battery model is developed to estimate the SOC, based on the measured current and voltage. An unscented Kalman filter is used to reduce the errors in the neural network-based SOC estimation. The method is validated using LiFePO4 battery data collected from the Federal Driving Schedule and dynamical stress testing. [ABSTRACT FROM AUTHOR]

• This paper presents an effective hybrid supercapacitor-battery energy storage (SC-BES) system. • It uses a fuzzy type distributed control system (DCS) to optimally regulate the system transient. • The fuzzy type DCS gains are optimally adjusted by an heuristic algorithm to reduce the design effort. • This mechanism combines the features of fuzzy theory and distributed control, which has a flexible structure. • The high performance of the suggested methodology is represented on a typical wind-diesel test system. This paper presents an effective hybrid supercapacitor-battery energy storage system (SC-BESS) for the active power management in a wind-diesel system using a fuzzy type distributed control system (DCS) to optimally regulate the system transient. It addresses a new online intelligent approach by using a combination of the fuzzy logic and DCS based on the particle swarm optimization techniques for optimal tuning and reduce the design effort of the control system. This mechanism combines the features of online fuzzy theory and distributed control system (DOFCS), which has a flexible structure. The proposed energy management algorithm for the hybrid SC-BESS is well able to repel the peak-impact of the battery storage system during the wind speed and load changes. The high performance of the suggested methodology is represented on a typical wind-diesel test system. [ABSTRACT FROM AUTHOR]

• An adaptive droop control method considering battery power characteristics is proposed. • Virtual battery droop algorithm is combined with the battery online estimation. • Suitable power distribution for batteries is realized in a decentralized way. • SOC balancing among energy storage systems can be achieved. • The proposed control is applied on the microgrid model with DAB converters. With the progress of renewable energy technologies, distributed energy system (DES) has become attractive due to its flexibility and interaction with power systems. Battery energy storage system (BESS) is an indispensable part of DESs, the control strategies of which have a great influence on system performance. In this paper, we present a novel adaptive droop control (ADC) for energy storage batteries. The state and model parameters of energy storage batteries are estimated simultaneously online by dual extended Kalman filter (DEKF) algorithm. Then the droop controller is designed based on the battery parameters by the "virtual battery" algorithm, benefit from which power can be distributed among battery packs adaptively for their power characteristics, meanwhile SOC balancing can be achieved automatically. The proposed control strategy is validated in MATLAB/Simulink environment with a direct-current (DC) microgrid model where battery packs are connected to the DC bus through dual active bridge (DAB) converters. Compared with conventional droop controls, the proposed ADC method considers the battery characteristics and battery aging, and it is more suitable for DES with multiple sets of batteries. [ABSTRACT FROM AUTHOR]

• A Gaussian process regression model with deep learning technology is proposed. • The deep recurrent kernel captures ordering matters and recurrent structures of data. • CP and MWP are applied to evaluate the performance of the probability result. • The GRU-GPR achieved accurate and robust estimation result for two datasets. Accurate and robust state of charge estimation of lithium-ion battery is a challenging task in battery management system. In this paper, a novel data-driven SOC estimation approach for Lithium-ion (Li-ion) batteries is proposed based on the Gaussian process regression framework. Kernel function selection and hyperparameters optimization are critical for Gaussian process regression due to the reason that kernel function could capture rich structure of data. By integrating the structural properties of deep learning with the flexibility of kernel methods, a new deep learning technology called deep recurrent kernel that fully encapsulates GRU structure is introduced to capture ordering matters and recurrent structures in sequential data. The proposed method could not only learn the mapping relationship from one sequence of measured quantities such as voltage, current, temperature to SOC, but also quantify estimation uncertainty which is essential for making informed decisions for battery management system. The performance of proposed methods is evaluated by two experimental datasets, one under a series of electric vehicle drive cycles and another under high rate pulse discharge test. We demonstrate the proposed method achieves satisfactory performance, as well as performs strong robustness against unknown initial SOC and outliers occurred in voltage, current and temperature. [ABSTRACT FROM AUTHOR]

• A novel communication-less control method for DC microgrids is proposed. • The proposed method restores the DC bus voltage with local parameters. • Along with the DC bus voltage restoration, proper load/power sharing and batteries SOC balancing are achieved. • The proposed method ensures system stability. • The proposed method is completely suitable for geographically dispersed DC microgrids. DC bus voltage restoration, proportional load/power-sharing, and batteries' SOC balancing are some of the most critical challenges of DC microgrids. The utilization of communication links and the central controller eliminate these problems. Nevertheless, microgrids controlled in this way, have low modularity, flexibility, reliability, and high costs and complexity. This paper proposes a novel communication-less control method, which solves the mentioned concerns and problems. The energy storage units are controlled by local parameters in order to guarantee the system stability. In this method, a unit (sender unit) estimates the DC bus voltage deviation and injects an AC signal to the DC bus. The droop curve of the units is shifted based on the AC signal and their SOC. The shifted values are specified such that the higher SOC units inject (absorb) more (less) power, and the DC bus voltage is restored. The stability analysis and step response of the system show that the proposed method benefits from an acceptable stability and satisfactory speed. Simulation results confirm that the proposed method can regulate the DC bus voltage and reduce the units' SOC imparity without any communication links and central controller. [ABSTRACT FROM AUTHOR]

• Battery pack equalization strategy based on UCCVC hypothesis is proposed. • The convergence of equalization is obtained in different inconsistent conditions. • The equalization strategy is simulated in fresh and aged scenarios. • The equalization strategy is embedded in a real BMS for practical application analysis. Lithium-ion battery pack capacity directly determines the driving range and dynamic ability of electric vehicles (EVs). However, inconsistency issues occur and decrease the pack capacity due to internal and external reasons. In this paper, an equalization strategy is proposed to solve the inconsistency issues. The difference of inconsistency for lithium-ion battery pack equalization is determined based on the uniform charging cell voltage curves hypothesis. Stability of the sampling voltage interval and convergence of equalization are analyzed experimentally. Finally, the results of simulation and experiment both show that the equalization strategy not only maximizes pack capacity, but also adapts to different consistency scenarios. Pack capacity and consistency in the fresh or aged state are significantly improved after battery equalization. In the real battery module experiment, the maximum absolute errors of open circuit voltage (OCV) and state of charge (SOC) are 21.9 mV and 1.86%, and the capacity is improved by 13.03%. Importantly, the equalization strategy has high precision and competitive simplicity with low computation, making it suitable for on-line equalization in EVs. [ABSTRACT FROM AUTHOR]