As awareness of human footprint grows, solutions are investigated to reduce greenhouse gas emissions (GHG). To reduce the carbon intensity of electricity production, a massive deployment of renewable energy sources (RESs) has become a mandatory goal. A significant challenge behind the deployment of RESs is the frequency regulation of such systems due to the high penetration of inverters based on energy storage systems (ESS). To overcome this issue, this manuscript proposes, as the main contribution, to use a virtual inertia emulator-based model predictive control. MPC is an optimization-based control strategy that aims at finding the optimal control actions of a system by predicting its future behaviors. This control solution is compared to a proportional (P) and a proportional integrator (PI) controller. The results of this study show that the MPC controller reduce the minimum ESS capacity by 55% and 2%, and the ESS energy throughput by 86% and 36 % compared to a P and PI controller. Those results suggest that the MPC controller can help to diminish the CAPEX and ESS aging compared to a P or PI controller.

Estimation of a lithium battery electrical impedance can provide relevant information regarding its characteristics. Currently, electrochemical impedance spectroscopy (EIS) constitutes the most recognized and accepted method. Although highly precise and robust, EIS is usually performed during laboratory testing and is not suitable for any on-board application, such as in battery electric vehicles (BEVs) because it is an instrumentally and computationally heavy method. To address this issue and on-line system applications, this manuscript describes, as a main contribution, a passive method for battery impedance estimation in the time domain that involves the voltage and current profile induced by the battery through its ordinary operation without injecting a small excitation signal. This method has been tested on the same battery with different passive voltage and current profile and has been validated by achieving similar results. Compared to the original idea presented in the published conference paper, this manuscript explains, in detail, the previously developed method of transforming the battery impedance from the frequency domain to time domain. Moreover, this impedance measurement is used to estimate more robustly the battery state of charge (SoC) through Kalman filters. In the original published conference paper, only an extended Kalman filter (EKF) was applied. However, in this manuscript, an EKF and an unscented Kalman filter (UKF) are used and their performances are compared.

Developing an efficient online predictive modeling system (PMS) is a major issue in the field of electrified vehicles as it can help reduce fuel consumption, greenhouse gasses (GHG) emission, but also the aging of power-train components, such as the battery. For this manuscript, a model predictive control (MPC) has been considered as PMS. This control design has been defined as an optimization problem that uses the projected system behaviors over a finite prediction horizon to determine the optimal control solution for the current time instant. In this manuscript, the MPC controller intents to diminish simultaneously the battery aging and the equivalent fuel consumption. The main contribution of this manuscript is to evaluate numerically the impacts of the vehicle battery model on the MPC optimal control solution when the plug hybrid electric vehicle (PHEV) is in the battery charge sustaining mode. Results show that the higher fidelity model improves the capability of accurately predicting the battery aging.

Model predictive control strategy (MPC) has been identified as an efficient path for reducing fuel consumption, greenhouse gasses (GHG) emission, or degradation of power-train components for electrified vehicles. MPC is an optimization-based control strategy that aims at finding the optimal control actions of a system by predicting its future behaviors. As the main contribution, this paper provides a Pareto-front analysis of the objective function taking into account the equivalent fuel consumption and the battery aging when the PHEV is in the charge sustaining (CS) mode. The results show that the MPC controller can decrease the battery capacity fade by 45% for only increasing the equivalent vehicle fuel consumption of 0.1% compared to an engine on-off thermostat control strategy.

In Electrified Vehicles, the cost, efficiency, and durability of electrified vehicles are dependent on the energy storage system (ESS) components, configuration and its performance. This paper, pursuing a minimal size tactic, describes a methodology for quantitatively and qualitatively investigating the impacts of a full bandwidth load on the ESS in the HEV. However, the methodology can be extended to other electrified vehicles. The full bandwidth load, up to the operating frequency of the electric motor drive (20 kHz), is empirically measured which includes a frequency range beyond the usually covered frequency range by published standard drive cycles (up to 0.5 Hz). The higher frequency band is shown to be more efficiently covered by a Hybrid Energy Storage System (HESS) which in this paper is defined as combination of a high energy density battery, an Ultra-Capacitor (UC), an electrolytic capacitor, and a film capacitor. In this paper, the harmonic and dc currents and voltages are measured through two precision methods and then the results are used to discuss about overall HEV efficiency and durability. More importantly, the impact of the addition of high-band energy storage devices in reduction of power loss during transient events is disclosed through precision measurement based methodology.