This paper proposes a low-complexity State-of-Charge (SoC) estimation strategy for lithium-ion batteries under aging. The approach reformulates a second-order equivalent circuit model into a higher-order linear time-varying structure, enabling the use of a classical Kalman filter with linear output (KFLO). This avoids both the linearization errors of the EKF and the computational overhead of the UKF. A hybrid identification scheme updates internal resistance online via event-driven current variations while recursively correcting cycle efficiency. Experimental validation on progressively aged cells shows that the estimator maintains robust accuracy, achieving RMSE below 2.5% and MAE below 1.3% even for aged batteries, outperforming EKF and UKF benchmarks. Results demonstrate the method’s suitability for resource-constrained BMS applications requiring long-term reliability.

The use of lithium-ion batteries requires careful monitoring, especially regarding the estimation of key internal parameters. Among these, the state of charge (SoC) is particularly important since it cannot be measured directly, requiring the development of reliable estimators. Model-based approaches are widely used for this purpose, equivalent circuit models (ECMs) being especially popular due to their ability to intuitively represent dynamics using electrical analogs. Despite their low complexity, ECMs typically exhibit nonlinear output tions that must be properly handled to ensure accurate estimation. In this context, the present work proposes SoC estimator based on a second-order ECM with a linearized output. The proposed formulation is derived an immersion-based transformation that increases the system order, yielding in a model with a linear and state-affine dynamics containing a current-dependent parameter. The main difference in this approach that the basis transformation exactly converts the originally non-linear system into a linear parameter varying (LPV) model. This allows the use of low-complexity estimators for estimating the SoC, such as the classic Kalman filter in this work. An observability analysis is conducted to guarantee estimation convergence, and a Kalman Filter (KF) is designed for SoC estimation. Results using real data obtained in an electromobility use case demonstrate that the proposed approach achieves a root mean square error (RMSE) below 1.2% under different conditions.

This paper presents an approach for state of charge (SoC) estimation in Li-ion batteries using the Extended Kalman Filter (EKF) within the Single Particle Model (SPM) describing battery electrochemical phenomena. Accurate SoC estimation is crucial for battery management, enhancing safety, lifespan, and reliability. Whereas traditional methods struggle with complex dynamics, this paper investigates the opportunity of combining EKF's estimation performance with SPM accuracy, with model parameters being assumed as properly identified. It is shown that SoC estimation is robust to noise and model simplifications. The EKF was validated in an electromobility use case against a previously proposed two-level SoC observer. MATLAB (R)/Simulink (R) simulations confirm the EKF's robustness and estimation accuracy, while its tuning complexity appears as a slight drawback against the two-level observer, more simply to adjust. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

This paper proposes a predictive approach in voltage-mode control of a DC-DC boost power electronic converter. Typical proportional-integral control with a first-order prefilter has been used in order to compensate for the boost converter dynamics and to ensure steady-state voltage error. A predictor structure derived from classic Smith prediction approach has been employed in order to maintain stable operation in the presence of a right-plane zero in the control channel transfer function. The resulting control algorithm is lightweight, constitutes a cost-effective real-time solution applicable to converters operating at high switching frequency and can be embedded on cost-effective microcontrollers. Numerical simulation results validate the proposed approach and show superior performance with respect to a baseline control system.

This paper investigates the problem of calibration and validation of a battery electrochemical model as a mandatory step towards accurate estimation of battery important variables, like state of charge (SoC) and state of health (SoH). Here, the Single Particle Model (SPM) is considered, which mathematically describes the battery internal governing phenomena by means of parabolic partial differential equations (PDEs), but whose parameters are notoriously difficult to measure or estimate. After suitable approximation of this model through a linear finite-dimensional model, a systematic procedure of SPM calibration is here proposed and validated against real data issued from battery cycling in an electric vehicular application, i.e., under standard driving cycle scenarios. In a novel approach of SoC estimation, the suitably calibrated SPM, together with measures of voltage and current, allow to analytically connect the internal spatially distributed ions' concentrations to the equlibrium concentration, which, at its turn, is an image of battery SoC. Results suggest that SPM can reliably predict the battery internal ions' concentrations and be further used for SoC accurate estimation. Copyright (c) 2024 The Authors.

This paper proposes a two-level hierarchical estimation structure of a lithium-ion battery state of charge (SoC) based on the parabolic-partial-differential-equation Single Particle Model (SPM). Using a suitable linear finite-dimensional approximation of this model, it is proposed that the SoC estimation to be done in two dynamically-separated levels, consistent with the multi-time-scale nature of battery internal phenomena: a fast level implementing a dynamic inversion, in charge of retrieving surface concentrations of lithium based on output voltage measures, and a slow level, in which internal lithium concentrations are estimated by employing a Luenberger observer. After having first suitably calibrated the model, this structure is easy to tune and allows avoiding estimation convergence issues frequently occurring in the case of a global, one-level estimation and which are partly due to a numerically ill conditioned linear approximated model. SoC estimation accuracy is validated by using real data issued from battery cycling in an electric vehicular application, that is, under standard driving cycle scenarios. Estimation errors - computed against Coulomb counting taken as baseline estimation of battery SoC - are smaller than 3.5%, thus suggesting very satisfactory performance.

This paper presents a demonstrator featuring closed-loop coupling of a robust-control-designed Power Management System (PMS) with a simulator – in the form of a mathematical model – of a multiple-storage-based microgrid, playing the role of the plant. The PMS ensures a reliability-aware coordinated control of the different energy storages, of various technologies, supplying a typically irregular load. Some usual applications are in stationary or mobile microgrids, like power supply systems on board of electric vehicles, the latter being the use case exemplified here. Being designed upon the hardware-in-the loop simulation (HILS) principle, the demonstrator allows calibration and real-time validation of the robust PMS and its pertinent closed-loop real-time tests in laboratory conditions. The rapid prototyping system dSPACETM MicroAutoBox II is employed for embedding the microgrid model, whereas the robust PMS is embedded on a TI C2000 microcontroller unit. General design requirements and architecture of the demonstrator are detailed, then its customization to the considered application case is presented. Finally, a set of illustrative real-time tests – obtained by using a standard driving cycle – are presented and discussed.

Hybridization of energy storage units is a topic of interest in the nowadays context of transition towards more sustainable ways of energy production and consumption. Extensive research continues to be devoted to this topic, whereas various technical solutions have already been successfully implemented for a plethora of applications. This paper aims in a first step at overviewing the necessity of storage units in an increasingly renewable-based, decentralized energy production, then at explaining the role of hybridization of different storage technologies for more versatility and flexibility in what is generally known as a smart grid context. Then, the focus is on how to make the different energy storage sources to optimally and robustly cooperate towards a common goal. It is about obtaining a “fusion” of different heterogeneous sources. The answer to this problem obviously requires advanced control approaches being employed. An overview of most effective and widely used control strategies is envisaged – accompanied by some illustrative application results – out of which robust control techniques are given a special attention. This paper ends by attempting a look towards the future, namely by identifying some open questions and research directions worthy to further investigate.

This paper focuses on interaction between two closed-connected high-voltage direct current (HVDC) lines. This interaction is studied by employing high-fidelity nonlinear modelling in MATLAB/Simulink software environment. In order to describe the mechanism behind the HVDCs interaction, both nonlinear time-domain simulations and modal analysis of the coupled HVDC links have been performed. System small-signal stability has been assessed, and the path of interactions has been identified by computing the participations of various states in the oscillatory modes; this sets the preliminaries for a global, grid-oriented HVDCs control design approach. After the detailed analysis of newly identified coupling oscillations, the minimal modelling requirements to put them into evidence have also been studied; this significantly facilitates the analysis in a realistic large-scale grid context.

Prosumers are households that are both producers and consumers of electricity. In this paper, an Energy Management System (EMS) for a DC-microgrid-based prosumer is designed and simulated, whose aim is to coordinate operation of a photovoltaic (PV) system and a battery for supplying a DC load; interaction with the utility grid - modelled as an infinite-power source - is minimized, such that power is either injected or drawn only when strictly necessary. The PV system and the battery are paralleled on a common DC-link by means of a boost converter and a synchronous buck converter, respectively, while an inverter ensures connection to the utility grid. In order to design and simulate the individual components of the microgrid, MATLAB®/Simulink® tools were used. During the study, different operating scenarios were created with respect to the PV power, load and the state of charge (SoC) of the battery. Thus, the following operation modes were identified: following mode, discharge mode, charging mode - referring to the battery operating modes that depend of its SoC. The aim of the EMS is to ensure the requirements of a prosumer application and switch between these different modes. Simulation results have shown that the developed EMS can achieve effective coordination within the microgrid, and all goals are reached.

This paper reports on the operation assessment of an electrical power generation system based upon cross-flow water turbines. The specific power take-off system has been tested in real-world conditions in a variety of scenarios and the experimental results have confirmed previous modeling assessments. Dynamical proprieties have been precisely identified and stable system operation over the entire operating range has been achieved. Steady-state characterization in terms of power coefficient has also been done, allowing the assessment of power generation system global efficiency and enabling the building of more precise models to be used in further simulations and assessments for power grid integration. Maximum power point tracking and other specific operation regimes such as angular position synchronization have also been validated.

This paper focuses on interaction between two closed-connected high-voltage DC (HVDC) lines. This interaction is studied by employing electromagnetic transients (EMT)-based nonlinear modeling in MATLAB (R)/Simulink (R)) software environment. In order to describe the mechanism behind the HVDCs interaction, both nonlinear time-domain simulations and modal analysis of the coupled HVDC links, have been performed. System small-signal stability has been assessed and the path of interactions has been identified by computing the participations of various states in the oscillatory modes.

This paper approaches the problem of output power prediction for an off-shore wind park. To this end, a so called wind deficiency factor for each turbine and for each wind direction sector is identified using past data. This identification is done by using the effective wind speed concept that can establish a link between output power of each wind turbine and meteorological mast measures in terms of wind speed and direction. Based on forecast wind speed and direction, a wind park simulator that uses the previously-identified deficiency factors, computes future output power time evolutions. Numerical simulations show the feasibility of the proposed approach.

This paper proposes a general frequency-separation-based strategy of coordinating power sources within off-grid applications. The application chosen to illustrate this strategy is an electric vehicle equipped with two power sources-a battery and an ultracapacitor (UC)-for which coordination problem can be formulated and solved as a linear quadratic Gaussian (LQG) optimal control problem. The two power sources are controlled to share the stochastically variable load according to their respective frequency range of specialization: low-frequency variations of the required power are supplied by the main source, the battery, whereas high-frequency variations are provided by the UC. The studied system is a bilinear one; it can be modeled as a linear parameter varying system. An LQG-based optimal control structure is designed and coupled with a gain-scheduling structure to cover the entire operating range. In this way, load regulation performance and the variations of battery current are conveniently traded off to preserve battery reliability and lifetime. Real-time experiments on a dedicated test rig-based on employing a real UC-validate the proposed optimal power flow management approach.

In this note, an alternative to classical methods for observer design is discussed, based on a dual control approach: it is indeed highlighted how an observer (at least approximate) can be obtained for a system by designing a control law for an auxiliary copy of this system, so that it tracks the system output. An important ingredient in this approach is the use of high-gain in the control design. The strategy is illustrated by various examples, including the case of unknown input reconstruction.

This paper deals with an adaptive frequency-based power sharing method between batteries and ultracapacitors (UC) as power sources within an electric vehicle. An adaptive frequency splitter is used for routing the low-frequency content of power demand into the battery and its high-frequency content into the UC system, taking profit from the UC as a peak power unit. Autonomy may thus be increased while preserving battery state of health and ensuring that UC voltage variations remain confined within certain desired range. Results obtained by real-time experiments on a dedicated test rig validate the proposed energy management approach and recommend it to be applied as power source coordination method to microgrids in general.

This chapter deals with modeling methodologies used for obtaining simplified – in the sense of reduced order – power electronic converter models, which are able to represent their low-frequency average behavior and are more easily employed in simulation or control law design.

Interest in variable-structure control is justified by the necessity of robustly controlling systems whose structure switches between several configurations. Power electronic converters are such class of systems because they can be described by differential equations with discontinuous right-hand sides (i.e., discontinuous inputs). Moreover, they exhibit nonlinear behavior which can in some applications render unsuitable standard linear control approaches. Good control performance such as large bandwidth is ensured because the switching solution is obtained directly without any other form of supplementary modulation (PWM, sigma-delta modulation).

This paper deals with off-shore wind park model identification for output power prediction purpose. The investigated solution is based upon identifying the so-called wind deficiency factor for each turbine and for each wind direction sector. This is done by employing the effective wind speed concept that can establish a link between output power of a wind turbine and the meteorological mast measures. Numerical simulations show the feasibility of the proposed approach.