Temperature and humidity are two important interconnected factors in the performance of PEMFCs (Proton Exchange Membrane Fuel Cells). The fuel and oxidant humidity and stack temperature in a fuel cell were analyzed in this study. There are many factors that affect the temperature and humidity of the stack. We adopt the fuzzy control method of multi-input and multi-output to control the temperature and humidity of the stack. A model including a driver, vehicle, transmission motor, air feeding, electrical network, stack, hydrogen supply and cooling system was established to study the fuel cell performance. A fuzzy controller is proven to be better in improving the output power of fuel cells. The three control objectives are the fan speed control for regulating temperature, the solenoid valve on/off control of the bubble humidifier for humidity variation and the speed of the pump for regulating temperature difference. In addition, the results from the PID controller stack model and the fuzzy controller stack model are compared in this research. The fuel cell bench test has been built to validate the effectiveness of the proposed fuzzy control. The maximum temperature of the stack can be reduced by 5 °C with the fuzzy control in this paper, so the fuel cell output voltage (power) increases by an average of approximately 5.8%.

In remote measurement systems, the lead wire resistance of the resistance sensor will produce a large measurement error. In order to ensure the accuracy of remote measurement, a novel lead-wire-resistance compensation technique is proposed, which is suitable for a two-wire resistance temperature detector. By connecting a zener diode in parallel with the resistance temperature detector (RTD) and an interface circuit specially designed for it, the lead-wire-resistance value can be accurately measured by virtue of the constant voltage characteristic of the zener diode when reverse breakdown occurs, and compensation can thereby be made when calculating the resistance of RTD. Through simulation verification and practical circuit testing, when the sensor resistance is in 848–2120 Ω scope and the lead wire resistance is less than 50 Ω, the proposed technology can ensure the measuring error of the sensor resistance within ±1 Ω and the temperature measurement error within ±0.3 °C for RTDs performing 1000 Ω at 0 °C. Therefore, this method is able to accurately compensate the measurement error caused by the lead wire resistance in two-wire RTDsand is suitable for most applications.

This paper aims to design thermal dummy cells (TDCs) that can be used in the development of lithium-ion battery thermal management systems. Based on physical property and geometry of real 18,650 cylindrical cells, a three-dimensional model of TDCs was designed, and it is used to numerically simulate the thermal performance of TDCs. Simulations show that the TDC can mimic the temperature change on the surface of a real cell both at static and dynamic current load. Experimental results show that the rate of heating resistance of TDC is less than 0.43% for temperatures between 27.5 °C and 90.5 °C. Powered by a two-step voltage source of 12 V, the temperature difference of TDCs is 1 °C and 1.6 °C along the circumference and the axial directions, respectively. Powered by a constant voltage source of 6 V, the temperature rising rates on the surface and in the core are higher than 1.9 °C/min. Afterwards, the proposed TDC was used to simulate a real cell for investigating its thermal performance under the New European Driving Cycle (NEDC), and the same tests were conducted using real cells. The test indicates that the TDC surface temperature matches well with that of the real battery during the NEDC test, while the temperature rise of TDC exceeds that of the real battery during the suburban cycle. This paper demonstrates the feasibility of using TDCs to replace real cells, which can greatly improve safety and efficiency for the development of lithium-ion battery thermal management systems.

The formation mechanism of alternating current (AC) of battery is analyzed theoretically for a typical motor drive circuit model. The effect of direct current link (DC-link) capacitor on battery current is calculated and discussed, and the result showed that the presence of this capacitor will weaken the amplitude of AC current, especially when the heating frequency is close to the resonance point of the resistor and inductor paralleled with capacitor (RL-C) of the DC-link capacitor and motor stator circuit. Classical proportion integration (PI) control theory is introduced into in-vehicle internal heating technology of battery for the first time to propose a voltage-feedback preheating control strategy. The control frequency and the ratio of non-zero vector and dead zone are discussed and determined. The battery current is adjusted indirectly by dynamically changing the duty cycle of zero vector to maintain the minimum voltage of the battery at a constant value and above the lower limit. The comparison experiment results show that the heating rate of the proposed strategy is improved by 45 % compared with the fixed duty cycle strategy in the temperature range from -30 °C to -10 °C.

The implementation of unmanned intelligent construction on the concrete surfaces of an airport effectively improves construction accuracy and reduces personnel investment. On the basis of three known common tracked vehicle dynamics models, reference trajectory planning and trajectory tracking controller algorithms need to be designed. In this paper, based on the driving characteristics of the tracked vehicle and the requirements of the stepping trajectory, a quartic polynomial trajectory planning algorithm was selected with the stability of the curve as a whole and the end point as the optimization goal, combining the tracked vehicle dynamics model, collision constraints, start–stop constraints and other boundary conditions. The objective function of trajectory planning was designed to effectively plan the reference trajectory of the tracked vehicle’s step-by-step travel. In order to realize accurate trajectory tracking control, a nonlinear model predictive controller with transverse-longitudinal integrated control was designed. To improve the real-time performance of the controller, a linear model predictive controller with horizontal and longitudinal decoupling was designed. MATLAB 2023A and CoppeliaSim V4.5.1 were used to co-simulate the two controller models. The experimental results show that the advantages and disadvantages of the tracked vehicle dynamics model and controller design are verified.