State estimation based on Kalman Filter (KF) has been widely considered for state-of-charge (SoC) prediction of lithium-ion (Li-ion) batteries. However, the KF is a model-based approach, and its performance declines when faced with modeling inaccuracies resulting from the nonlinear and time-varying nature of Li-ion batteries. To tackle the modeling uncertainties, a novel hybrid variant of KF is deployed, in which priori estimations are attained similar to the KF algorithm while the posterior estimations are obtained using a recurrent neural network (RNN) integrated into the KF architecture. The proposed method yields enhanced robustness in maintaining the accuracy of SoC estimations in the presence of model mismatches, e.g. due to the aging of batteries. In the proposed method, named KalmanNet, the RNN learns from battery data to predict the optimum Kalman gains for the minimization of SoC estimation error. The proposed method is trained and tested using actual battery data of a high-capacity pouch Li-ion cell replicating real-life driving cycles of electric vehicles. The significance of the results is twofold: 1- When exposed to a model mismatch of ±5%, the SoC estimation accuracy is improved by about 0.5% compared to EKF. 2- The size of required training data is reduced by 20% compared to similar machine learning algorithms.

In this paper, an effective method based on adaptive extended Kalman filter (AEKF) is proposed for detection of random FDIs against battery state-of-charge algorithms on cloud battery management platforms. First, the battery model is established and used with the AEKF to predict the battery response. Second, a residual signal (RS) is defined as the difference between the AEKF-based estimated battery voltage and the received voltage measurement. The FDIs are then detected based on a hybrid detection criterion mixing the Chi-squared test and Euclidean detector. The proposed mixed strategy improves the detection accuracy in terms of false negatives and false positives caused by noises and changes in battery operation. Regarding the latter point, the AEKF is equipped with a dedicated recursive least squares filter to accommodate real-time model changes. The proposed algorithm is developed and verified based on actual battery data related to high-capacity lithium-ion cells. The method is exposed to different case studies considering normal and attack conditions and a remarkable detection accuracy of about 98% is attained with no false positive in the presence of current and voltage noises up to ±10 mA and ± 3 mV.

In this paper, an analysis and performance review of a unique hybrid high-power lithium-iron phosphate cell (HP-LFP) with a high cycle life and fast charge/discharge rate is presented. The new hybrid cell has been developed under the framework of the EU-funded project Hybrid Energy Storage Station (HEROES). The proposed HP-LFP cell simultaneously achieves an optimized energy and power throughput making it useful for applications where both power and energy are demanded such as different types of electric vehicles (EVs), microgrids, and charge stations. The cathode of the hybrid HP-LFP cell is similar to the LFP batteries while the supercapacitor-like anode is based on hard carbon. Thus, the HP-LFP cell has some similarities to the recent lithium-ion capacitors (LiCs) but it behaves differently than the existing LFP batteries and supercapacitors. To distinguish the differences, HP-LFP cell samples are tested, characterized, and analyzed to review their performance compared to the existing technologies. Different standard tests including the open-circuit test, hybrid pulse power characteristics test, and capacity test are fulfilled. The test data is used for model parameterization and parameter sensitivity analysis with respect to the cell's state of charge, temperature, and charge rate. The cell performance has been validated through mission profile testing in an EV fast charge station. Based on the analysis, the paper provides a comparison between different energy storage cells.

This paper presents a design of a bidirectional DC- DC power electronic converter system enabling cyclic operation for a Reversible Solid Oxide Electrolysis Cell (RSO EC) stack for steam electrolysis. The cyclic operation of the RSOEC stack is investigated, and two different equivalent circuit models are presented for the mathematical representation of the stack's electrical dynamics: The well-established steady-state Resistive model and a novel Voigt model. From these, two combined mathematical models of the bidirectional Buck-Boost converter supplying the RSOEC stack are derived using the small-signal averaging technique. For tracking the cyclic output current reference to an RSOEC stack, two Proportional Integral Derivative with derivative Filter (PIDF) controllers are designed using the two combined mathematical models derived. Finally, the performances of the two PIDF controllers for the bidirectional DC-DC converter systems are compared and validated through simulations. The simulation results confirm the bidirectional Buck-Boost converter's ability to deliver cyclic bidirectional output current and demonstrate that the control tuned based on the Voigt mathematical representation of the RSOEC stack yields superior closed-loop performance in accordance with the control design requirements.

In this paper, an efficient control strategy for three-phase four-switch inverter-fed Brushless DC Motor (BLDCM) drives with trapezoidal back Electromotive Force (EMF) is proposed. In the proposed approach, the outer control loop for adjusting the motor speed is designed using Model Predictive Control (MPC) while the inner control loop based on a hysteresis controller regulates the BLDC phase currents. To effectively adjust the current of the uncontrolled phase in the four-switch inverter, efficient switching strategies for motor and generator modes are suggested. The proposed control scheme achieves favorably low torque ripples and improves the speed transient response in terms of tracking error and speed overshoot/undershoot. Therefore, it can be an ideal candidate for low-cost low-power BLDCM applications. Also, the MPC-based speed control loop is tuned by solving a suitable cost function in an offline manner to minimize the real-time computational effort. Using the foregoing technique, it is shown that the implementation of the proposed MPC-based controller becomes as simple as the PI controller while the MPC-based controller achieves superior control performance. The proposed BLDCM drive scheme is experimentally verified in a Hardware-in-the-Loop (HiL) test setup with a 1200W BLDCM and dSPACE1104 development board. The experimental results demonstrate the benefits of the proposed drive system.

The state of charge (SOC) is an essential indicator needed for a proper and safe operation of a battery. A large majority of methods used to estimate SOC rely on the open circuit voltage (OCV) curve. The OCV-SOC relationship is characteristic of the battery chemistry but it may also be affected by the temperature, aging or even the measurement procedure. In this work, the effect of these factors on the OCV curve is analyzed and their influence on the SOC estimation is studied. For this purpose an extended Kalman filter (EKF) has been used to compare the SOC estimation performance under different conditions. Also, the results obtained by two different methods have been compared, at two different aging states and various temperatures in the range 5-40°C.