Thermal issue is one of the major concerns for safe, reliable, and efficient operation of the vanadium redox flow battery (VRB) energy storage systems. During the design of the operational strategy for a grid-connected VRB system, a suitable mathematical model is needed to predict the dynamic behaviors under various operating conditions. However, conventional VRB models usually neglect the impact of temperature variations on system performance. This work develops an enhanced VRB model with the consideration of the coupling effects between the electrochemical and the thermal behaviors. The proposed model consists of two equivalent circuits. First, the electrochemical behaviors of the VRB are modeled by a second-order RC network taking account of the effects of concentration variation of the vanadium ions and the electrochemical activation. Second, a third-order Cauer network is used to model the heat transfer process in the VRB system, and the dynamic thermal behaviors of stacks, pipes and heat exchangers are characterized. Well-designed experiments and particle swarm optimization algorithm are use to identify the parametric values of the developed model. The proposed modeling method was validated experimentally using a 5kW/3kWh VRB platform, and the results show that the model is capable of accurately predicting the VRB performance under variable temperature conditions. The developed coupled electro-thermal model is then used for simulating and analyzing the performance of a VRB system operated in conjunction with a wind power plant under real-world conditions.

Aiming at the path planning problem of Automated Guided Vehicle (AGV) in intelligent storage, an improved Dijkstra algorithm that combines eight-angle search method and Dijkstra algorithm for path optimization is proposed. The grid method is used to model the storage environment, and the improved Dijkstra algorithm is used to optimize the route of the AGV. The simulation test of the AGV path planning process with Matlab shows that the AGV can effectively avoid obstacles by using the traditional Dijkstra algorithm and the improved Dijkstra algorithm, and then search for a collision-free optimized path from the start point to the end point; and the traditional Dijkstra algorithm In comparison, the path length planned by the improved Dijkstra algorithm is shorter and the turning angle is less, indicating that the improved algorithm is correct, feasible and effective, and has a strong global search ability.

The air feeding system is one of the most important systems in the proton exchange membrane fuel cell (PEMFC) stack, which has a great impact on the stack performance. The main control objective is to design an optimal controller for the air feeding system to regulate oxygen excess at the required level to prevent oxygen starvation and obtain the maximum net power output from the PEMFC stack at different disturbance conditions. This paper proposes a fractional order fuzzy PID controller as an efficient controller for the PEMFC air feed system. The proposed controller was then employed to achieve maximum power point tracking for the PEMFC stack. The proposed controller was optimized using the neural network algorithm (NNA), which is a new metaheuristic optimization algorithm inspired by the structure and operations of the artificial neural networks (ANNs). This paper is the first application of the fractional order fuzzy PID controller to the PEMFC air feed system. The NNA algorithm was also applied for the first time for the optimization of the controllers tested in this paper. Simulation results showed the effectiveness of the proposed controller by improving the transient response providing a better set point tracking and disturbance rejection with better time domain performance indices. Sensitivity analyses were carried-out to test the robustness of the proposed controller under different uncertainty conditions. Simulation results showed that the proposed controller had good robustness against parameter uncertainty in the system.

As we acknowledge that vehicle-to-grid (V2G) network holds obvious predominance in economic benefits, it attracts wide attentions of power system and electric vehicle (EV) users. However, lots of security issues hinder its further development. For example, many existing V2G schemes use the bilinear pairing to implement authentication protocol, resulting in high computational costs. In addition, the system master key in the existing V2G scheme is generated independently by a trusted third party, so the system master key is vulnerable to leakage if the third party is compromised by the adversary. In this article, we propose a novel privacy-preserving authentication scheme for V2G networks. Specifically, this scheme employs the nonsupersingular elliptic curve to construct a lightweight authentication protocol for EV and smart grid. Meanwhile, system master key in the proposed scheme is generated by the key agreement protocol, i.e., a protocol that is constructed using a secure two-party computation between the third party authority and the dispatching center. Security analysis shows that the proposed scheme is provably secure under the random oracle model. Compared with related schemes, the proposed scheme can not only improve the authentication efficiency of V2G networks, but also prevent internal attacker. Experimental results indicate that our scheme is of practical significance for V2G networks.