In electric vehicles and stationary storage applications, the increasing demand for Lithium-ion battery (LIB) systems highlights the need for precise state-of-health (SOH) estimation. Traditional approaches often rely on resource-heavy computations or impractical setups, such as controlled charging or high-frequency sampling, misaligned with the constraints of large-scale battery management systems (BMS) that operate with limited computational power and low-frequency data collected over seconds. This paper proposes a computationally efficient algorithm, using a nonlinear input-output neural network (NIONN) for state-of-charge (SOC) estimation and internal resistance derived from voltage drops during current transients for SOH estimation. Furthermore, temperature compensation through polynomial fitting enhances the accuracy of the estimate. Validated on the NASA battery aging dataset, the proposed approach achieves an average error below 3%. With only 41 arithmetic operations and adaptability to diverse conditions. The proposed method with real-time SOH monitoring for electric vehicles and large-scale energy storage offers a practical, robust solution tailored to BMS limitations.

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.

The paper proposes an Asymmetrical Modular Multilevel Converter (AMMC) suitable for low/medium-voltage dc-ac conversions with very high output quality. The modules' dc-links of the AMMC are charged to a binary exponential sequence to produce a large number of output levels using only a few modules.The concept of using asymmetrical dc-links for high-quality output is not entirely new. However, the practicality of existing approaches is relatively low and challenged by the difficulties in maintaining the required dc-link voltages as well as suppressing their interaction with the output, which often requires multiple isolated dc/dc converters. We solve this problem by aligning the modules in the Marquardt MMC inverter module configuration that offers more control freedom, hence the term AMMC. Furthermore, we introduce a highly effective switched-inductor charge transfer and balancing mode between modules and even across arms. We accordingly modify the underlying conventional chopper modules so that the dc-link voltage control can be completely sensorless. The proposed AMMC is tested in a lab setup with four modules per arm reaching 32 output levels. In contrast to the low benefit of an additional module in MMC due to only linear improvement of the output granularity, each further module halves the finest voltage step. The components to maintain the graded voltage sequence and the underlying inductive charge transfer only a fraction (< 10%) of the load current so that relatively low-power devices can be used.

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.

Modular reconfigurable batteries, also known as smart batteries, are gaining significant traction, mainly due to the large environmental incentives and falling price of electronic components. Although they have many advantages compared to a hard-wired battery pack, complex monitoring circuit and numerous sensor requirement make it harder to compete with conventional systems in a cost-driven application. This paper proposes a novel approach to estimate parameters of each individual battery module without any direct measurement at their terminals. The proposed algorithm uses the output voltage and current of the load combined with the exact knowledge of the modules’ states to estimate the open-circuit voltage, ohmic resistance, and polarization resistance in the electric circuit model for each battery module. The method combined with Kalman filter demonstrates the feasibility of this method through simulations, where the proposed method achieves above 98% and 96% accuracies for estimation of the open-circuit voltage and equivalent resistance of the battery, respectively. Additionally, the method can decouple the two resistances with

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.

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.

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.

The use of PFC rectifiers has assumed an increasingly preponderance, contributing in a decisive way to improve the power quality indices, since they allow to operate with sinusoidal current on the ac-side and with controlled voltage on the dc-side. In this paper is proposed a novel PFC rectifier that allows five-levels of voltage. As noted in the paper, it presents a number of interesting advantages when compared to the conventional five-level PFC rectifier, mainly because it requires less passive and active semiconductors to produce the different voltage levels and requires less hardware resources to implement the gate-driver circuits. The proposed five-level PFC (5L-PFC) rectifier operates in boost mode and has a single dc-link (although with a midpoint to achieve the various levels), being an important feature for applications in smart grids (e.g., smart electrical appliances and electric mobility chargers). The key topics of the 5L-PFC rectifier are addressed and discussed based on the analysis of the principle of operation. As current control strategy it was adopted the model predictive control. Experimental results in steady and transient state were considered for an effective validation of the 5L-PFC rectifier, verifying the operation with: sinusoidal current on the ac-side; multi-level operation with five-levels; controlled dc-link voltage.

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.