Lithium-ion (Li-ion) batteries have attracted significant attention in terms of technical features, but to use them within their specific operating region and prevent undue degradation, it is crucial to constantly monitor their state of charge (SOC). However, most available techniques are either too complex, too computationally demanding, or require a large measurement window. This paper proposes a general framework for the data-driven SOC estimation and demonstrates its effectiveness via a very simple Neural Network (NN) classifier. Accordingly, a nonlinear input–output neural network (NIONN) is developed based on the proposed network and then further optimized. Additionally, we present an input engineering process using available data to provide a good accuracy while considering the practical aspects such as complexity as well as the update speed for a low-cost online application. The numerical results from the measurements validate the performance of the proposed general framework. Per extensive optimizations, it is proven that a shallow network structure with 13 neurons in the first hidden layer and 12 neurons in the second layer can achieve <98.3% accuracy and a mean squared error of <10 −5 which is on par with more complex networks with higher neurons and/or hidden layers. Additionally, the optimum observation window was determined to be 5 seconds.

Wireless power transfer (WPT) system is a promising candidate for charging many electrical devices and daily home appliances. Most widely used WPT techniques are inductive coupling based on magnetic fields, and magnetic resonant inductive coupling, which include a resonant circuit in transmitter and receiver coils. In this paper, numerical and analytical analysis of short-range inducting coupling rectangular coils WPT is presented. A numerical methodology based on a three-dimensional discontinuous Galerkin time-domain (DGTD) finite element method is developed to estimate the electromagnetic propagation around WPT rectangular coils. The proposed approach leads to simulations that are fast, robust and accurate, this being the result of a local discretization strategy of the full-wave time-domain Maxwell’s equations on unstructured tetrahedral meshes that is easy to parallelize. The stability of the overall numerical scheme is obtained using an up-wind numerical flux, an implicit second-order accurate time integration scheme, and Whitney basis functions. The numerical methodology is verified based on problems with analytical methods.

Modular multilevel converters (MMCs) are a well-known solution in high-voltage applications. Recently, new topologies such as MMCs with series/parallel (MMSPC) connectivity are attracting large amount focus, due to their improved efficiency as well as self-balancing capability. Although MMSPCs can operate without direct measurement of modules’ voltages in battery-integrated modules, monitoring of the voltage and resistance of each module can still provide useful information to assess the state of each battery. However, including a voltage and current sensor in each module is not a cost-effective approach. This paper proposes a method to estimate the voltage and resistance of each battery-module using only the voltage and current measurement at load terminal. The proposed technique can reduce the number of sensors from 2N + 2 to only 2. Additionally, due to the self-balancing capability, the estimation technique does not require fast convergence and can operate in the background during the idling intervals of the processor. MATLAB simulations confirm the applicability of the proposed approach. The result show a 99 % and also 96 % accuracy for balanced and imbalanced systems.

Modular multilevel converters (MMC) show great potential in high-voltage as well as emerging applications such as grid storage and electro mobility. The main advantages over the two−level converters include flexibility, scalability, and higher degrees of freedom. MMCs with series/parallel connectivity (MMSPC) are a more recent developed technology which can provide stable and efficient sensorless operation. However, although the additional parallel connection between the modules provides many advantages, it can change the equivalent resistance of the system and hence complicate the power loss analysis. This paper presents a simplified conduction loss calculation method based on equivalent resistance analysis for MMSPC. The presented method has the advantage of simplicity with relative accuracy that can speed up the process of loss analysis. Simulation results show that the accuracy of the presented method is above 95% percent for modulation indices above 0.5.

Reducing the computational burden and improving the accuracy are in the two opposite sides of every electrical equivalent circuit model (EECM) for batteries. In this paper, a novel identification method is developed to estimate EECM parameters for any Li-ion battery with the aim of reducing computational burden and improving the accuracy of estimation under high C-rates of charge/discharge cycles for electric vehicle (EV) applications. The proposed parameter identification method is implemented on the second-order EECM. A step by step execution of the new identification method is presented which is based on circuit analysis and Particle Swarm Optimization (PSO). A comparison is carried out between obtained and the Pseudo-Two-Dimension (P2D) electrochemical model results. Moreover, experiments are carried out on two Li-ion battery cells, NCR18650 Panasonic and Mp176065 to verify the accuracy of the proposed method. The included results demonstrate the performance of the proposed method regarding improving accuracy and applicability as well as reducing required memory.

The advantages of modular multilevel converters (MMC) over conventional two-level converters have lead to new emerging topologies and applications. The MMC with series/parallel connectivity (MMSPC) is one of the more recent topologies that can provide stable and efficient sensorless operation by introducing an additional parallel connection state between modules. However, while the parallel functionality has great potential in simplifying control and monitoring, it complicates the analysis of the system. The equivalent resistance of the MMC can be helpful in analyzing the conduction losses and also in designing appropriate heat management systems. Estimating the equivalent resistance of an MMC structure with half-bridge submodules is a straightforward procedure. However, performing a similar analysis for MMSPCs is more difficult, mainly because of the parallel state. In this paper, a simplified semi-analytical equivalent-resistance analysis method for MMSPC with full-bridge submodules (SM) is presented that can increase the speed of analyzing the system. Furthermore, the derived equations are compared to MMC with half-bridge SMs to provide better insight into the effect of varying parameters of the system.

Inductive Power Transfer (IPT) in EV chargers enables range extension opportunity by rapid in-station or dynamic charging of batteries. Enhancement of transferring power and distance ranges and improvement of mutual coupling between coils have always been in the two opposite sides of every wireless power transfer system. A trade-off between transfer efficiency, distance, and power is essential for electrical vehicle applications. Despite previous researches on electromagnetic modeling of IPT systems, it still suffers from noticeable lack of models which shows mutual inductance between the primary and secondary coils and losses in the coils. This paper presents analytical methods to study mutual coupling between coils and considers effects of misalignment on them. A detailed analytical investigation of a pitch rotation misalignment about the y-axis and a lateral translation misalignments in the y-axis direction is presented for a commonly used topology, circular coils, among existing ones applicable to IPT EV chargers. The analytical methodologies have been also evaluated by means of numerical method.

In this paper, a new hybrid charging algorithm suitable for Li-ion battery is proposed with the aim of reducing refilling time and improving battery life cycle. The hybrid algorithm combines constant current constant voltage (CCCV) and pulsed charge (PC) techniques to obtain the suitable way for fast charging and ensure a long lifetime. A step by step execution of the new algorithm is presented. Moreover, the algorithm is implemented using bidirectional DC to DC power converter with Simulink. The charger is able to detect initial battery state of charge (SoC) and take the suitable way to charge it. The included results show the obtained performance of the proposed technique in terms of battery refilling time reduction to return to top of-charge and improving its cycle life.

A three-phase Transformerless hybrid power filter (THSeAF) based on a seven-level Packed U-Cell converter (PUC) to correct major power quality issues related to the more electric aircrafts (MEA) is proposed in this research work. This power quality compensator presents an affordable solution to overcome current related issues as well as ensuring a viable supply to the onboard loads. The PUC converter combines advantages of the flying capacitor and the cascaded H-bridge topologies. This solution is promoting Active power filtering application for higher voltage level with smaller passive component ratings. Based on the Synchronous Reference Frame (SRF) along with the use of the p-q theory, the controller extracts the voltage and current distortions and voltage unbalances. Then, a proportional plus resonant (P+R) controller is producing the gate signals for all three-phase IGBTs. To rectify harmonic currents produced by typical distorted loads, the duty-cycle waveform should produce the according voltage waveform. Beside the proposed technic to improve power system performances on the MEA, this article proposes a universal solution to enhance the power quality problems found in a micro-system and/or distribution level system. Simulation results illustrate the performances of the proposed configuration.

Going forward to reach infrastructure grid by incorporating cluster of renewable and distributed energy sources, storage systems, different kinds of loads, in a huge dimension of information, communication and control technologies, has been established concept of “Smart Grid”. By jointly considering local distributed generation, i.e. two small wind turbines, PP load, i.e. fast charger of electrical vehicles (EVs) on a grid, and a set of linear and non-linear loads, in this paper, power quality features in a smart grid are analyzed comprehensively. The grid has been modeled in MATALB/Simulink. To validate, results are compared with EMTP results.