Industrial Grade Adaptive Control Scheme for a Micro-Grid Integrated Dual Active Bridge Driven Battery Storage System

Abstract

In this paper, an industrial grade adaptive control scheme is proposed for a micro-grid integrated dual active bridge driven battery management system (DIBMS). A benchmark industrial grade adaptive control scheme depends on two factors namely robustness and computational resource utilization when such controllers are implemented over processors. The mathematical model of DIBMS system is nonlinear, thus for desired response, non-linear controllers based on sliding mode variable structure control theory suits it well for the state regulation problem of DIBMS, however such controllers utilize high computational resources when practically implemented over processors. Keeping in view the above performance indices, this paper proposes an industrial grade computationally efficient and finite time adaptive robust convergent control for DIBMS system. A proportional integral (PI) scheme is used as central control unit and Hebbian algorithm with double integration of the state error is introduced for online tuning the gains of central control unit. The robustness and computational resource efficiency of the proposed control paradigm is validated using a laboratory scale test bench through TI Launchpad (TMS320F28379D). The superiority of the proposed AI based PI control paradigm is compared with classical PI, integer order sliding mode control (SMC), and fractional order SMC (FOSMC) in terms of computational resource utilization and robustness under all test conditions.

Adaptive Nonlinear Control of Unified Model of Fuel Cell, Battery, Ultracapacitor and Induction Motor Based Hybrid Electric Vehicles

“…To further ensure the robustness and superiority of ATSMC, the experimental analysis has been considered by using HIL setup, which is basically the combination of both software in loop (SIL) and processor in loop (PIL). It is usually used for pre-evaluation before the final deployment of practical applications [41]- [44]. Figure 14 shows the HIL setup and test bench which includes both the controller hardware and the software setups.…”

Energy-Based Combined Nonlinear Observer and Voltage Controller for a PMSG Using Fuzzy Supervisor High Order Sliding Mode in a Marine Current Power System

“…In this part, numerical simulations are presented to show the feasibility of the proposed strategy. Moreover, the experimental validation of the proposed control schemes is provided using processor in the loop experiment [28][29][30]. The simulated conversion system is based on 1.5 MW rated power.…”

“…Thus, the control algorithm is tested in real time. More details about processor in the loop experimentation are reported in [28][29][30]. More importantly, the controller validated using PIL testing is equally efficient when it is tested on an actual hardware plant [28].…”

“…More details about processor in the loop experimentation are reported in [28][29][30]. More importantly, the controller validated using PIL testing is equally efficient when it is tested on an actual hardware plant [28]. Thus, inspired from the above work, the proposed control schemes are tested using a processor in the loop (PIL) experiment and the block diagram of the setup is shown in Figures 22 and 23.…”

A Modular Active Balancing Circuit for Redox Flow Battery Applied in Energy Storage System

“…Therefore, in the actual industrial design, the parameter of MOSFETs and diodes can be calculated based on the abovementioned maximum voltage and current stress to ensure safety. The above three controllers can use conventional proportional integral (PI) control method or other nonlinear control algorithms, such as sliding mode control (SMC) [31], [32], adaptive control [33], fuzzy control [34], model predictive control [35], and so on. Finally, these voltage references are modulated by the triangular carriers in the phase-shift pulse width modulation (PS-PWM) block, obtaining the gating signals to drive the MOSFETs.…”