A new technique to observe the electromagnetic torque of a permanent magnet synchronous machine (PMSM) based on flux linkage estimation considering unknown magnetic condition is presented in this paper. The magnetic behavior uncertainties including permanent magnet flux, linear self-inductances, linear coupling-effect inductances and magnetic saturations are taken into account as a set of new variables in the model of machine. Regarding the new model, an appropriate observer based on Kalman-like algorithm is proposed to observe those variables. Then, the variables are collected and used to estimate the stator flux linkages and consequently the machine torque. The observability as well as the stability of the observer for the proposed system is theoretically investigated. The simulation and experimental results are represented for different scenarios. They prove the effectiveness of the proposed method.

The parameter extraction of parameters for Li-ion batteries is regarded as a critical topic for assessing the performance of battery energy storage systems (BESSs). The supply–demand algorithm (SDA) is used in this work to identify a storage system’s unknown parameters. The parameter-extracting procedure is represented as a nonlinear optimization task in which the state of charge (SOC) is approximated using nonlinear features related to the battery current and the initial SOC condition. Furthermore, the open-circuit voltage is approximated using the resulting SOC, which is performed in a nonlinear formula, as well. When used in the dynamic nonlinear BESS model, the SDA was used to verify the fitness values and standard deviation error. Furthermore, the results that were acquired using SDA are compared to recently developed approaches, which are the gradient-based, tuna swarm, jellyfish, heap-based, and forensic-based optimizers. Simulated studies were paired with experiments for the 40 Ah Kokam Li-ion battery and the ARTEMIS driving-cycle pattern. The numerical outcomes showed that the proposed SDA is an approach which is excellent at identifying the parameters. Furthermore, when compared to the other current optimization techniques, for both the Kokam Li-ion batteries and the ARTEMIS drive-cycle pattern, the suggested SDA exhibited substantial precision.

Electric Vehicle (EV) bidirectional charger technology is growing in importance. It defines the fact of returning the electricity stored in the batteries of EV to Grid (V2G), to Home (V2H), to Load (V2L), or in one word V2X mode. The EV onboard charger is divided into two parts: AC-DC and DC-DC converters. The isolated bidirectional DC-DC LLC resonant converter is used to improve the charger efficiency within both battery power and voltage ranges. It is controlled by varying the switching frequency based on a small signal modeling approach using the gain transfer function inversion method. The dimensions of the DC-DC LLC converter directly affect the charger cost. Moreover, they cause an important control frequency saturation zone, especially in V2X mode, where the switching frequency is out of the feasibility zone. The new challenge in this paper is to design an optimization strategy to minimize the LLC converter cost and improve the control frequency feasibility zone, for a wide variation of battery voltage and converter power, in the charging (G2V) and discharging (V2X) modes simultaneously. For our best knowledge, this optimization problem, in the case of a bidirectional (G2V and V2X) charger, is not yet considered in the literature. An optimal design that considers the control stability equations in the optimization algorithm is elaborated. The obtained results show a significant converter cost decrease and important expansion of control frequency feasibility zones. A comparative study between initial and optimized values, in G2V and V2X modes, is generated according to the converter efficiency.

Bidirectional DC–DC converters are frequently chosen for applications requiring high power density such as in bidirectional electric vehicle (EV) chargers. Vehicle to Everything (V2X) technology makes the EV battery an electrical energy source. In this article, the use of a DC–DC LLC converter used in a bidirectional EV charger is reviewed. Different modeling approaches of the DC–DC LLC converter, such as small and large signal modeling, are discussed. Common modulation strategies applied to the DC–DC LLC converter in V2X mode, such as Pulse Frequency Modulation (PFM), Pulse Width Modulation (PWM) and Phase-Shift Modulation (PSM), are presented. The new challenge is to present the main characteristics and limitations of each modulation strategy in order to cover the whole operating zone of the EV charger in V2X mode. Furthermore, different control strategies based on a small or large signal model combined with different modulation strategies are highlighted. Linear and nonlinear controllers applied to the DC–DC LLC converter are discussed. Robust controllers are mainly highlighted regarding their advantage in ensuring the control robustness with respect to unexpected disturbances. A comparative study among modulation strategies as well as different control algorithms is conducted in terms of control performance and converter efficiency in V2X mode.

For the self-sensing control of interior permanent magnet synchronous motors (IPMSM), the rotor position and speed estimation based on high-frequency (HF) injection techniques generally employs two parts after the signal injection: signal processing/heterodyning process and tracking. This article proposes significant improvements for these two parts. First, the low-pass filters used in the classical heterodyning process of HF injection techniques are removed, which leads to the proposal of a novel heterodyning process in order to improve the estimation procedure in terms of delays, cost/complexity of implementation, and harmonics. Second, an independent machine parameters estimation based on step-by-step sliding mode observer using only the sign of the rotor position estimation error is developed. Thanks to this observer, the tuning of position, speed, and acceleration observer gains is achieved in a decoupled manner, which ensures states convergence separately in finite time. Third, the machine acceleration is estimated to improve the estimation procedure in transient modes. These contributions enhance significantly the estimation procedure in all operating ranges. A comparative study of the robustness/performance of the proposed strategy and classical tracking algorithms is carried out. Both simulations and experimental tests on an IPMSM rig are conducted in the framework of electric propulsion benchmark used in automotive applications, showing good agreement with the expected results.