The optimal state of charge (SoC) balancing control for series-connected lithium-ion battery cells is presented in this paper. A modified SoC balancing circuit for two adjacent cells, based on the principle of a bidirectional Cuk converter, is proposed. The optimal SoC balancing problem is established to minimize the SoC differences of cells and the energy loss subject to constraints of the normal SoC operating range, the balancing current, and current of cells. This optimization problem is solved using the sequential quadratic programming algorithm to determine the optimal duties of PWM signals applied to the SoC balancing circuits. An algorithm for the selection of the initial points for the optimal problem-solving process is proposed. It is applied in cases where the cost function has no decreasing part. Experimental tests are conducted for seven series-connected Samsung cells. The optimal SoC balancing control and SoC estimation algorithms are coded in MATLAB and embedded in LabVIEW to control the SoC balancing in real time. The test results show that the differences between the SoCs of cells converges to the desired range using the proposed optimal SoC balancing control strategy.

The AC battery utilizing second-life time batteries has gained great interest currently with the advantages of both power solutions and economic benefits. In this system, the power converters play a crucial role in the stable and effective operation of the system. This paper focused on the AC/DC stage with the chosen topology being the interleaved full bridge (IFB) converter due to its flexibility and the ability to increase the power rate of the system. For the sake of high-performance operation, various pulse width modulation (PWM) methods for this converter are analyzed. First, based on the theory of the traditional PWM methods for a full bridge inverter in combination with the interleaved technique, this paper proposed three interleaved PWM methods for the IFB converter. Secondly, the proposed methods are theoretically compared in terms of the output current, common-mode voltage, and power losses. Finally, the evaluation is carried out by both the simulation and the experimental prototype, in which the results are in good agreement with the theoretical analysis.

In this paper, a tracking control approach is developed based on an adaptive reinforcement learning algorithm with a bounded cost function for perturbed nonlinear switched systems, which represent a useful framework for modelling these converters, such as DC–DC converter, multi-level converter, etc. An optimal control method is derived for nominal systems to solve the tracking control problem, which results in solving a Hamilton–Jacobi–Bellman (HJB) equation. It is shown that the optimal controller obtained by solving the HJB equation can stabilize the perturbed nonlinear switched systems. To develop a solution to the translated HJB equation, the proposed neural networks consider the training technique obtaining the minimization of square of Bellman residual error in critic term due to the description of Hamilton function. Theoretical analysis shows that all the closed-loop system signals are uniformly ultimately bounded (UUB) and the proposed controller converges to optimal control law. The simulation results of two situations demonstrate the effectiveness of the proposed controller.