The constant-current constant-voltage (CC-CV) charging method is a common approach to charge batteries with a single charger. Recently, with the emergence of high power-density batteries, e.g., supercapacitors, several chargers are typically connected in parallel to provide the required charging power in many applications. However, the classical decentralized control scheme for the multicharger system leads to the current imbalance, which deteriorates the reliability of the charging system. In this article, we extend the CC-CV charging protocol to multicharger systems using a cooperative control method, which can alleviate the current imbalance among chargers effectively. First, the motivation and charging system modeling of this work are provided. Then, a cooperative CC charging protocol and a cooperative CV charging protocol are presented, respectively, and a switching logic is designed to achieve the switching between them based on the supercapacitor voltage. The closed-loop model of the proposed charging system is developed using the block diagram. A laboratory testbed is built to verify the effectiveness of the proposed method. Experiment results show that the proposed method provides a better current balancing and voltage regulation performance when compared with existing decentralized control methods.

Automatic train operation systems of high-speed trains are critical to guarantee operational safety, comfort, and parking accuracy. However, implementing optimal automatic operation control is challenging due to the train's uncertain dynamics and actuator saturation. To address this issue, this paper develops a data-driven Koopman model based predictive control method for automatic train operation systems. The proposed control scheme is designed within a data-driven framework. First, using operational data of trains and the Koopman operator, an explicit linear Koopman model is built to characterize the train dynamics. Then, a model predictive controller is designed based on the Koopman model under comfort and actuator constraints. Furthermore, an online update mechanism for the Koopman model is developed to cope with the changing dynamic characteristics of trains, which reduces the accumulation errors and improves control performance. Stability analysis of the closed-loop control system is provided. Comparative simulation results validate the effectiveness of the proposed control approach.

Supercapacitor energy storage systems (ESS) play a significant role in light rail vehicles (LRV) with no need for overhead lines and the pantograph. As the ESS of LRV demand short charging time and high-power supply, it is crucial to achieve the rapid energy balancing for series-connected supercapacitor modules of the ESS. To address this issue, this paper proposes a distributed control scheme for state-of-energy (SoE) balancing of supercapacitor modules. In order to provide the sufficient balancing current, the push-pull circuit is introduced to transfer the energy among supercapacitor modules in the low working voltage. Then the distributed SoE balancing strategy is proposed by regulating the balancing circuit according to the SoE deviation of neighboring supercapacitor modules. The ESS prototype is built to validate the proposed strategy can reduce the balancing time and improve the energy conversion efficiency.

The switched resistor circuit has been a popular cell balancing scheme in low-power supercapacitor applications. However, the cell balancing performance of the circuit is vulnerable to current variations and resistance deviations with the existing asynchronous cell-balancing (ACB) method. To address this challenge, in this paper, we propose a synchronized cell-balancing (SCB) method based on the consensus theory, with the aim of synchronizing cell voltages during the charging process. First, the cyber-physical modeling of the cell balancing system is presented, where the physical layer models the circuit components and the cyber layer represents the communication topology among cells. Second, a distributed voltage tracker is proposed using a consensus tracking protocol, which benefits from a good scalability. A compensator is designed to stabilize the closed-loop multiple-in-multiple-out (MIMO) system and frequency-domain methods are applied to determine the compensator parameters. A laboratory testbed has been built to verify the effectiveness of the proposed method. Experiment results show that the SCB method has a better tolerance of current variations and resistance deviations than the ACB method.

In this paper, we investigate the consensus problem over communication networks where the communication channels are represented by stable Linear Time Invariant (LTI) systems. We give the necessary and sufficient condition to reach consensus under our protocol and show that the consensus value is not only determined by the agents' initial states and the network topology but also by the channels' dynamics. We further show that if the H

This paper studies the cooperative control problem for a class of multiagent dynamical systems with partially unknown nonlinear system dynamics. In particular, the control objective is to solve the state consensus problem for multiagent systems based on the minimisation of certain cost functions for individual agents. Under the assumption that there exist admissible cooperative controls for such class of multiagent systems, the formulated problem is solved through finding the optimal cooperative control using the approximate dynamic programming and reinforcement learning approach. With the aid of neural network parameterisation and online adaptive learning, our method renders a practically implementable approximately adaptive neural cooperative control for multiagent systems. Specifically, based on the Bellman's principle of optimality, the Hamilton–Jacobi–Bellman (HJB) equation for multiagent systems is first derived. We then propose an approximately adaptive policy iteration algorithm for multiagent cooperative control based on neural network approximation of the value functions. The convergence of the proposed algorithm is rigorously proved using the contraction mapping method. The simulation results are included to validate the effectiveness of the proposed algorithm.