With the increasing capacity of large-scale electric vehicles, it's necessary to stabilize the fluctuation of charging voltage in order to achieve improvement of lithium-ion battery lifecycle, and the hybrid energy storage system (HESS) including superconducting magnetic energy storage (SMES) and lithium-ion battery is introduced, which is significant to reduce the high frequency discharge cycles and to manage the state of charge (SOC) for batteries with an optimized integration scheme based on power grading approach. With the unscented Kalman filter (UKF) method for battery SOC estimation and the extended Kalman filter (EKF) method for battery internal resistance estimation, the approach for estimating the lithium-ion battery state through adaptive unscented Kalman filter (AUKF) is derived. The AUKF approach takes the form of iterative loop to estimate system parameters and state, so the system strategy has well adaptive characteristics. Experimental results illustrate that the SOC estimation based on the proposed optimized approach for lithium-ion battery in HESS can achieve the well robustness and fast convergence.

The key to control the range extender generation system is to improve the efficiency and reduce the emissions of the electric vehicle (EV). In this paper, based on the purpose of efficiency optimization, both engine and generator are matched to get a public high efficiency region, and a partial power following control strategy was presented. The engine speed is constant in the defined power range, so the output power regulation of the range extender is only realized by the adjustment of the torque of the generator. Engine speed and generator torque were decoupled. An improved proportional resonant (PR) controller is adopted to achieve fast output power regulation. In order to ensure the response characteristics of the control system and to improve the robustness, the impacts on system’s characteristics and stability caused by PR controller and parameters in the inner-current loop were analyzed via frequency response characteristics. A pre-Tustin with deviation compensation is proposed for PR controller’s discretization. A stable and robust power following control method is obtained for the range extender control system. Finally, simulation and experiment of the proposed control strategy illustrated its feasibility and correctness.

The application of Internet of Things technologies in greenhouses is a crucial method to improve agricultural production efficiency and quality. A greenhouse environment monitoring system is designed based on NB-IoT (Narrowband Internet of Things). The system can detect the air temperature, relative humidity, illuminance, and carbon dioxide concentration in greenhouses in real-time and transmit the data to the OneNET cloud platform in a wireless way. Growers can browse the web page through mobile terminals, such as mobile phone, to observe the greenhouse environment information. The experimental results show that the system can detect the greenhouse environment information accurately and run stably and reliably. The automation level of greenhouse environmental monitoring is improved significantly, which lays a good foundation for the automatic control of greenhouse environment.

The accurate estimation of the state of charge (SOC) for lithium-ion batteries’ performance prediction and durability evaluation is of paramount importance, which is significant to ensure reliability and stability for electric vehicles. The SOC estimation approaches based on big data collection and offline adjustment could result in imprecision for SOC estimation under various driving conditions at different temperatures. In the traditional GM(1,1), the initialization condition and the identifying parameter could not be changed as soon as they are confirmed. Aiming at the requirements of battery SOC estimation with non-linear characteristics of a dynamic battery system, the paper presents a method of battery state estimation based on Metabolic Even GM(1,1) to expand battery state data and introduce temperature factors in the estimation process to make SOC estimation more accurate. The latest information data used in the optimized rolling model is introduced through the data cycle updating. The experimental results show that the optimized MEGM(1,1) effectively considers the influence of initial data, and has higher accuracy than the traditional GM(1,1) model in the application of data expansion. Furthermore, it could effectively solve the problem of incomplete battery information and battery capacity fluctuation, and the dynamic performance is satisfactory to meet the requirements of fast convergence. The SOC estimation based on the presented strategy for power batteries at different temperatures could reach the goal of the overall error within 1% under CLTC conditions with well robustness and accuracy.