A multi-energy system (MES) is an integrated energy infrastructure that simultaneously models and optimizes the coupling and conversion processes among multiple energy carriers (e.g., electricity, thermal energy, and hydrogen, etc.) across diverse end-use sectors, enabling coordinated operation for improved efficiency, flexibility, and sustainability. Integrating renewable energy sources (RESs) into MES systems drives the adaptation of energy storage systems (ESSs) and the development of strategies to mitigate their intermittency. Indeed, this paper provides a sophisticated model of dispatchable ESSs in integrated energy hubs, demonstrating how storage units optimize the operational cost of combined heat and power (CHP) systems by jointly accounting for renewable generation and energy buffer states. The proposed controller employs relaxed model predictive control (RMPC), reducing computational time by managing complex decision variables more efficiently than standard MPC, making it suitable for residential energy management. Numerical simulations confirm that the control strategy effectively operates the integrated system, meeting constraints, and demands while minimizing device costs and maximizing profits.

The Virtual Synchronous Generator (VSG) represents a promising grid-forming (GFM) control approach that emulates the dynamic characteristics of synchronous generators through the implementation of virtual inertia and damping. This paper investigates the influence of including the inner current control and the current reference angle on the transient stability of VSG. While incorporating current control into the stability analysis improves the accuracy of the results, it also results in a reduced domain of attraction (DoA) for the system. To address these limitations, this paper proposed an advanced control strategy, which incorporates the standard VSG with additional feedback and feedforward functions to enhance VSG transient stability by expanding DoA. Numerical simulations demonstrate that the proposed control surpasses conventional VSG methods in terms of adaptability, stability, and reliability under various disturbances, such as three-phase faults and load increases.

Lithium-ion battery diagnosis is critical for ensuring optimal performance and mitigating safety risks; however, challenges remain due to overlapping electrochemical processes and dynamic operating conditions. This study introduces the dynamic impedance spectra to enable a comprehensive diagnosis of LIBs’ behaviors with active battery charging. For starters, the voltage drifts caused by the state of charge (SOC) shifts and polarization effects are eliminated by applying a moving average filter. The time-resolved impedance spectra and the corresponding equivalent circuit model (ECM) parameters are extracted and continuously updated to decouple and monitor electrochemical processes across timescales. Kramers-Kronig (K-K) validation confirms the dynamic impedance measurement validity, with residuals below 0.5%. The analysis of ECM parameter evolution provides in-situ insights into the electrochemical dynamics and material properties during the battery charging process.

The growth of new energy vehicles boosts lithium battery use, but also raises reuse challenges for retired ones. To address the need for echelon utilization of retired lithium-ion batteries, this paper proposes an intelligent sorting method that integrates time-series and image features, aiming to match retired batteries with suitable application scenarios and extend their service life. First, an experimental platform is established to collect data from retired batteries. A clustering analysis combining voltage curves with an improved K-means algorithm is conducted to enhance the capability of handling nonlinear data. Next, the Gramian Angular Field (GAF) encoding method is employed to convert time-series data into images, which are then efficiently classified using the ConvNeXt deep network, improving the clarity and discriminative power of feature representation. Finally, a decision fusion framework based on grid search and weight optimization is constructed to integrate the results of clustering and image classification, thereby improving the accuracy and robustness of the model. Experimental results demonstrate that the proposed multi-feature fusion approach effectively identifies the condition and sorting of retired batteries, achieving a screening accuracy of 94.41%, thereby providing a reliable solution for their intelligent reuse.

The intermittency of renewable energy sources (RESs) leads to the incorporation of energy storage systems into microgrids (MGs). In this article, a novel strategy based on model predictive control is proposed for the management of a wind–solar MG composed of RESs and a hydrogen energy storage system. The system is involved in the daily and regulation service markets, characterized by different timescales. The long-term operations related to the daily market are managed by a high-layer control, which schedules the hydrogen production and consumption to meet the load demand, maximizes the revenue by participating in the electricity market, and minimizes the operational costs. The short-term operations related to the real-time market are managed by a low-layer control (LLC), which corrects the deviations between the actual and forecasted conditions, by optimizing the power production according to the participation in the market and the short-term dynamics and constraints of the equipment. In addition, the LLC is in charge of smoothing the power provided to the grid. Numerical simulations demonstrate that the strategy effectively operates the MG by satisfying constraints and energy demands while minimizing device costs. Moreover, when compared to other strategies, the controller yields fewer state switches in the hydrogen devices, thus extending their lifespan. The efficacy of the control strategy is further validated through a lab-scale MG setup.

The predictive energy management strategy (PEMS) offers potential advantages in enhancing the driving economy of electrified vehicles using vehicle speed prediction. However, realizing accurate predictions in practical contexts remains a challenge. Departing from conventional PEMS that rely on historical speed or static traffic data, we introduce a real-time traffic-aware PEMS for improved performance. To better understand the interplay between the host vehicle and its surrounding traffic, we use a Transformer network as the predictor that employs the speeds and relative distances of the surrounding six vehicles to forecast future speed sequences for the host vehicle. To augment this data-driven approach, we develop a dual-predictor strategy based on the deep ensemble technique. This strategy measures the Transformer’s output uncertainty to gauge prediction reliability and introduce an automated threshold mechanism. Based on this threshold and real-time uncertainties, the strategy chooses between the Transformer and an exponential predictor to achieve improved prediction outcomes. A reinforcement learning method is integrated as the PEMS optimizer. For validation, we generate training data with traffic information based on the next generation simulation (NGSIM) dataset and create a test scenario in the SUMO simulator. The results confirm that speed predictions based on real-time traffic data surpass traditional PEMS, either directly inputting traffic data or excluding it. The Transformer predictor significantly outperforms the state-of-the-art predictor. Importantly, our dual-predictor design amplifies prediction accuracy by 27.2% against the standard single-network predictor under non-training conditions. Overall, our PEMS enhances driving economy by 11.1% relative to traffic-unaware models and 8.0% over non-Transformer schemes.

Due to the high dependency between reliability of power device and its junction temperature, monitoring their thermal behavior in real time is very essential. In this paper, a lookup table-based electro-thermal real-time simulation model is developed to quickly estimate the junction temperature of the power devices. Output series interleaved boost converter with high voltage gain and low input current ripple, which is dedicated for fuel cell application, is selected as a case study. The thorough design and implementation process are presented and the developed model is simulated with a 200 nanoseconds time step on NI FlexRIO PXIe-7975R real-time platform. Moreover, the effectiveness and accuracy of the FPGA-based electro-thermal model are validated by comparison the results from PLECS software.

The durability and reliability are two main obstacles for the widespread commercialization deployment of Proton Exchange Membrane Fuel Cell (PEMFC). Prognostic and Health Management (PHM) could supervise the State of Health (SoH) and make the right action at the right time to extend the lifetime of PEMFC. In the Remaining Useful Life (RUL) prediction, the Health Indicators (HIs) are able to reflect the degradation state and an efficient Health Indicator (HI) could make sure the prediction accuracy. In general, the voltage and power are the most commonly used HIs because they are easy to measure or calculate. Besides, the current and voltage sensors are convenient to be installed and the voltage and power are always supervised for the control purpose. Nevertheless, these two HIs are more suitable for static operating conditions and assume that their deviation is only influenced by the ageing phenomenon. In the dynamic or time-varying operating conditions, the voltage and power are synthetically influenced by the operating parameters and deterioration factors. So some novel HIs (e.g., virtual stack voltage, average resistance) should be used for the dynamic working conditions. Moreover, two HIs, i.e., polarization resistance and power loss, are proposed in this paper. Finally, the performances of different HIs are evaluated based on the experimental data.

Nowadays distribution power systems are used in different applications such as aircraft, ships, submarines and hybrid electric vehicles. However, the interaction between individually designed power subsystems may cause instability. Moreover, in these applications, the constant power load (CPL) also poses challenges for system dynamic response and stability. Thus, the main objective is to stabilize the cascaded system supplying the CPL. An interconnection and damping assignment (IDA) passivity-based control (PBC) scheme for LC filter cascaded with buck converter supplying CPL is proposed. The plant is described by port-controlled Hamiltonian (PCH) form. Particularly, an adaptive interconnection matrix is developed to achieve internal links in PCH system. A modified IDA-PBC and its proof are presented to perfect the implementation for the CPL application. Simulation results are given to illustrate the effectiveness of the proposed approach.

A novel control scheme for two phases floating interleaved boost DC-DC converter (FIBC) is presented in this paper. The proposed controller is based on second order sliding mode method combined with an extend state observer (ESO), which comprises two loops: the outer loop is a voltage regulation loop whereas the inner loop is an instantaneous current regulation loop. The outer loop is accomplished by SOSM and ESO, which aims to regulate the output voltage of the converter and reject the disturbance of the load variations. The SOSM strategy is also applied to inner loop to make the current track its reference accurately. Through the proposed approach, the system can obtain a stronger robustness, and the chattering problem is also suppressed. Both theoretical analysis and simulation results are demonstrated. Moreover, in order to verify the effectiveness of the proposed control scheme, comparisons with dual loops super-twisting control scheme (ST + ST) and outer active disturbance reject control plus inner super-twisting control scheme (ADRC + ST) in case of parameter uncertainties and loads disturbances have been made in this paper.

This paper aims to the design and control of a de-de floating interleaved boost converter with high voltage gain. Firstly, the process of the components sizing is presented in detail. Secondly, the controller with dual-loop cascade structure, both based on super-twisting sliding mode algorithm, are proposed to deal with the uncertainties of the converter. The control goals of constant converter output voltage, equal current sharing between phases and constant switching frequency (for phase interleaving to reduce the input current ripple) are achieved. The stability of the sliding mode inner loop and the outer loop are both proven using Lyapunov stability theorem. The effectiveness and the robustness of the proposed controller are validated by the simulation and experiment results.

It is well know that the interaction between poorly damped LC input filter with dc-dc converter lead to degradation of dynamic performance and fault scenario of the system. This problem also often occurs in fuel cell systems. Due to the relatively low and unregulated output voltage, the high gain boost converter is need in such application. A floating interleaved boost converter (FIBC) is selected as a good candidate to achieve this desired effect. In order to ensure the system stability, this paper addresses a method which permits to design a fault-tolerant stabilizing system for the proposed converter and the filter. It consists in implementing an active stabilizer for each switch. Afterward, a method to design fault-tolerant stabilizing system is developed. The simulation results are reported to verify the effectiveness of the proposed method.

Due to their properties such as high energy density, lithium-ion battery packs are more and more widespread for many kinds of applications. Although a classical Battery Management System (BMS) can improve the performance and safety of a battery pack, it cannot deal with issues related to aging and cell faults. This paper presents a fault-response approach for battery packs operating at the cell level. It uses a reconfigurable topology combined with agents, that can increase the resilience of a pack in the face of faults. A co-simulation technique based on Matlab/Simulink and the JADE platform is used to test and validate the proposed approach.

One of the main keys for modern high efficiency power converters for traction applications is implementation of soft-switching technology. Moreover, its control technologies seeks to increase the power density with high frequency. However, this kind of architecture, involving high frequency resonance, is too complex for the real-time simulation with a limited bandwidth. This paper proposes a reduced-order model for this soft-switching circuit by time-domain analysis approach which exhibits a valuable characteristic for the real-time simulation. A variable matrix and subsystem methods are used to solve interface voltages/current from the torn system. Successful implementation of the proposed model on a field programmable gate array (FPGA) device is reported and its application for traction power supply system is implemented with a hardware in the loop (HIL) test. Fixed point operators are used to ensure good accuracy and low computational latency. Comparison between the experiment results and traction auxiliary analysis shows its stability.

The electric vehicle technology has been adopting PEM fuel cells for hybrid applications over the past decades. Fuel cell systems are often low voltage systems. The key requirements of a boost converter for such application are need of high-voltage. The second-order low-pass filter is often used to filter the high frequency current ripple. It is known that the interaction between the poorly damped filter with the converter leads to degradation of dynamic performance and instability of the system. This paper addresses a novel wide stability control strategy to solve this problem without increasing the weight and size. In this paper, the mathematical model of the system is built. The dynamic stability of the system is investigated based on root locus and Lyapunov first method. The simulation results are reported to verify the effectiveness of the proposed method.

An optimized state of charge (SOC) estimation method is critical for energy control strategy in hybrid energy system. For an embedded system, the executed algorithm should be less time consuming and also robust on measurement noise from sensors. Moreover, the estimation method should also be insensitive to initial SOC for the purpose of avoiding battery relaxing time in real application. The proposed method in this paper combines adaptive unscented Kalman filter (AUKF) and multivariate adaptive regression splines (MARS) to meet the above demands of embedded hybrid energy system. Samples which consist of battery current, terminal voltage and temperature are used to for MARS model training. The effectiveness and robustness of the proposed method is validated by experimental test. Also, the proposed method is compared with least squares support vector machine (LSSVM) based method in estimated accuracy and time consumption. Experiment results indicate that the proposed method is less time consuming as well as good accuracy is guaranteed.

This paper presents a short review of the aging mechanism modeling of proton exchange membrane (PEM) fuel cell in automotive applications. The fuel cell aging mechanism is particularly important for fuel cell transportation applications, because the degradation rates of a fuel cell could increase very fast when operating at different transient conditions, such as: load cycles, start-stop cycles, fuel starvation, high temperature or low humidification. The objective of this paper is to give a state-of-the-art introduction of models for different PEM fuel cell aging phenomena. In the first part of this paper, a summary and analyses of the different aging phenomena that can be observed in PEM fuel cell are made based on literature review. Secondly, a summary of some mathematical PEMFC aging phenomena models is presented, with a focus on the models that describe the aging phenomena related specifically to automotive applications.

In order to combine energy sources with different dynamic responses, this paper proposes one universal distributed control method to implement power sharing in frequency spectrum. The energy elements with fast dynamic responses such as battery can be used to absorb high frequency power components, while the ones with slow dynamic responses such as Fuel Cells supply smooth energy. The method is based on the droop control technology adding carefully designed forward path filter, and the disturbance response can be adjust according to the requirement. Simulations and experiments are conducted to verify the proposed method to loads stepping.

This paper presents hardware-in-the-loop (HIL) validation of an air supply method for the fuel cell in automotive applications. A 12 kW proton exchange membrane fuel cell (PEMFC) model is developed and implemented in dSPACE. The performances of the fuel cell stack and compressor during a classic European Driving Cycle are tested. The experimental results show that the compressor consumes up to 25 % of the fuel cell generated power in the worst operating point, whereas only 4.2 % on average.

In this paper, a real-time 2D PEMFC multi-physiques model is developed for fuel cell vehicle simulation purpose. For the consideration of real time model performance, an extrapolation method of a 1D channel pressure model into a 2D pressure map is used in the presented model. The developed PEMFC model is validated with experimental test. From the 2D modeling approach, different channel configuration types are also compared and discussed. The presented PEMFC model is developed for real-time fuel cell vehicle simulation and hardware-in-the-loop applications.

The residential buildings take a large part of the electricity consumption. The building microgrid will play an important role in the future, to improve the energy efficiency, reduce environmental impacts, as well as enhance power system stability. This paper proposes a distributed tariff-driven droop gain-scheduling method, to solve the power distribution between different energy resources as well as the grid connected converter, for residential building microgrid operating in grid-connected mode. Tariff-driven functions are used to adjust droop gains in the voltage control loop. In this way, the power distribution ratio can be changed automatically according to the tariff conditions, as time-of-use electricity tariff. And also near-optimization in economic can be implemented, with properly chosen parameters in tariff functions. A residential building DC microgrid including solid oxide fuel cell, photovoltaic panel and grid connected converter is modeled with MATBLB/Simulink and used to verify the proposed method.