In this paper, controlling of a DC-DC bidirectional converter is investigated using model predictive control (MPC) technique. The converter is supposed to connect a battery bank to the DC bus. The main purpose of MPC method is to maintain the DC bus voltage in a predefined rated range, which means injecting active power to the DC bus to increase bus voltage and discharging the battery, and on the contrary absorbing active power from the DC bus to decrease bus voltage and charging the battery. The control strategy also disconnects the battery bank while DC bus voltage is in the predefined rated range. Simulations have been done in PSCAD/MATLAB software interfacing and results show the feasibility of the proposed controlling strategy to conduct active power on both directions.

The automotive energy storage market is currently dominated by the existing Li-ion technologies that are likely to continue in the future. Thus, the on-road electric (and hybrid) vehicles running on the Li-ion battery systems require critical diagnosis considering crucial battery aging. This work aims to provide a guideline for pack-level lifetime model development that could facilitate battery maintenance, ensuring a safe and reliable operational lifespan. The first of the twofold approach is a cell-level empirical lifetime model that is developed from a lab-level aging dataset of commercial LTO cells. The model is validated with an exhaustive sub-urban realistic driving cycle yielding a root-mean-square error of 0.45. The model is then extended to a 144S1P modular architecture for pack-level simulation. The second step provides the pack electro-thermal simulation results that are upscaled from a cell-level and validated 1D electrical model coupled with a 3D thermal model. The combined simulation framework is online applicable and considers the relevant aspects into account in predicting the battery system’s lifetime that results in over 350,000 km of suburban driving. This robust tool is a collaborative research outcome from two Horizon2020 EU projects—GHOST and Vision xEV, showcasing outstanding cell-level battery modeling accuracies.

This article presents a flexible controller algorithm for bidirectional modular cascaded H-bridge (CHB)-based battery storage systems. The primary objectives are to maintain high-quality power on the ac side while balancing dc-side battery voltages under charging or discharging operations. The proposed algorithm is based on a finite-set model-predictive current control (FSMPC) technique, which performs in-phase and phase voltage equalization in two complementary balancing terms by taking advantage of intrinsic voltage vector redundancy characteristic of three-phase CHB converters. Additionally, the proposed modifications to the FSMPC provide lower demand on computational resources of the controller through offline reordering of vectors and adjacent vector introduction, which also brings the least differential-mode $dv/dt$ as an added value. Effects of battery voltage variation in charge/discharge cycles and voltage drop caused by nonideal semiconductors are considered to ensure the most accurate performance during steady-state, transient, and common-mode voltage reduction operation. The proposed approach is an efficient easy-to-implement multiparameter controller, which handles all the tasks needless to any additional control loops. Simulations and experimental prototyping results validate the feasibility of the battery voltage balancer terms to be utilized in battery storage applications with no downside impact on key power exchange functions.

In this paper, model predictive control is applied to control a three-phase inverter. Model predictive control (MPC) is known for its simplicity and ease of use. The discussed inverter is generating pure sinusoidal voltages for sensitive applications such as uninterruptable power supply systems with low voltage total harmonic distortion (THD) demand. This method uses the model of the system to predict the behavior of the voltage on the next sampling interval for each possible switching state. A cost function is also used to decide which switching state to be applied based on minimizing the voltage deviations from the reference value and switching losses. Simulations have been done using PSCAD/MATLAB interfacing and results show the feasibility of MPC to control inverters feeding linear, nonlinear or unbalanced loads.