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For techniques used to estimate battery state of charge (SOC) based on equivalent electric circuit models (ECMs), the battery equivalent model parameters are affected by factors such as SOC, temperature, battery aging, leading to SOC estimation error. Therefore, it is necessary to accurately identify these parameters. Updating battery model parameters constantly also known as online parameter identification can effectively solve this issue. In this paper, we propose a novel strategy based on the sunflower optimization algorithm (SFO) to identify battery model parameters and predict the output voltage in real-time. The identification accuracy has been confirmed using empirical data obtained from CALCE battery group (the center for advanced life cycle engineering) performed on the Samsung (INR 18650 20R) battery cell under one electric vehicle (EV) cycle protocol named dynamic stress test. Comparative analysis of SFO and AFRRLS (adaptive forgetting factor of recursive least squares) is carried out to prove the efficiency of the proposed algorithm. Results show that the calibrated model using SFO has superiority compared with AFFRLS algorithm to simulate the dynamic voltage behavior of a lithium-ion battery in EV application.

In this paper, a battery model suitable for electric vehicle application is analyzed. Open circuit voltage is described by an adaptation of Nernst equation. Thevenin circuit is used to depict the instantaneous and transient regime. Hysteresis effect is outlined by a zero-state correction term. We propose a new algorithm AFFRLS (adaptive forgetting factor recursive least squares) to extract the parameter of the battery model, then to predict the output voltage, and compare it to the original FFRLS (forgetting factor recursive least squares). To evaluate these algorithms, we used experimental data conducted by CALCE Battery Research Group on the Samsung INR 18650-20R battery cell. We fed the data to the algorithms and compared the estimated output voltage for two dynamic tests on MATLAB. Results show that AFFRLS has low distribution in high error range up to 4% less than FFRLS, this means that AFFRLS has a better parameter identification than FFRLS.

One of the problems with the doubly-fed induction generator (DFIG) is its high vulnerability to network perturbations, notably voltage dips, because of its stator windings being coupled directly to the network. As the DFIG’s stator and rotor are electromagnetically mated, the stator current peak occurs during a voltage dip causing an inrush current to the critical converter back-to-back and an overload of the DC-link capacitor. For this purpose, a series of researchers have achieved a linear and non-linear controller with a crowbar-based protection scheme. With this type of protection, the Rotor Side Converter (RSC) is disconnected momentarily, and consequently, its control of both the active and reactive output power of the stator is totally lost, leading to incorrect power quality at the point of common coupling (PCC). In this document, a robust nonlinear controller by Advanced Backstepping with Integral Action Control (ABIAC) is initially employed to monitor the rotor and the network side converters under normal network operations. In the presence of a network fault, an improved protection scheme (IPS) is tacked on to the robust nonlinear control to help enforce the behavior of the DFIG system to be able to overcome the fault. The IPS, which is formed by a crowbar and an RL series circuit, is typically located in the space between the rotor coils and the RSC converter. Compared to a standard crowbar, the developed scheme is successful to limit the rotor transient current and DC-link voltage, also an RSC disengagement to rotor windings can be prevented during the fault. Furthermore, the controllers of both the RSC and the Network Side Converter (NSC) are modified to boost the supply voltage at the PCC. A comparative study is also performed between the IPS without and with modification of the reactive power control loops. The simulation results mean that with the modified controllers during the fault, the amount of reactive power sustainment with ABIAC at the PCC is optimized to 17.5 MVAr instead of 15 MVAr with proportional-integral control (PIC). Therefore, the voltage at the PCC is fort increased in order to comply with the voltage requirements of the farm and absolutely to maintain the connection to the network in case of voltage dip.

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