Automotive Science and Engineering
Volume:13 Issue: 1, Winter 2023

In recent years, the automotive industry has experienced a dramatic mutation in the develop ment of electric vehicles. One of the most important aspects of this type of vehicle is its thermal management. Among the various parts of an electric vehicle that are subjected to thermal management, the battery is of particular importance. Battery cell temperatures may exceed the allowable range due to continuous and high-pressure operation and various weather conditions, and this, in addition to performance, severely affects battery life. Therefore, the appropriate cooling system is essential. In this research, the most common methods of battery cooling are investigated. First, three-dimensional thermal analysis on the battery is performed using the computational fluid dynamics method in transient and steady-state phases.  Then, the effect of changing the cooling flow rate on the maximum temperature of the battery cell as well as the temperature difference of the cells in the battery pack is investigated. The effect of changing inlet coolant temperature change on battery cell temperature distribution is also investigated. The results show that by increasing the flow rate from 0.5 to 1.2 liter per minute, the maximum temperature in the battery pack and the temperature difference between the cells decrease to 44.4 and 2.51 ° C, respectively. Also, by changing the temperature of the inlet coolant from 15 to 30 ° C, the maximum temperature in the battery pack increases up to 42.2 ° C and the temperature difference is negligible.

One of the most important components of fuel cell power systems is the power conditioning subsystem. DC/DC converters play the leading role in the power conditioning subsystem and fuel cell hybridization with other electric power sources and storage. DC/DC converters control the load voltage and, in some cases, the fuel cell current, while current-controlled DC/DC converters control the loading level. Some advantages of designing converters in a multi-layer topology include reduced input current ripple and increased power density. Lower current-rating semiconductor devices can be used due to the current division among the layers and lower values of inductors and capacitors can be used due the lower input current and output voltage ripples, respectively. Furthermore, failure of one layer does not result in a complete system outage; the other layers can deliver a fraction of the nominal power. A fuel cell power system based on a 16 kW proton exchange membrane fuel cell stack and a multi-layer DC/DC boost converter is designed and implemented in this paper. The power system is intended for marine air-independent propulsion systems. The power system is modeled and analyzed using the MATLAB/Simulink software environment. The power system is implemented to verify the analysis and simulation results.

As electric vehicles become more popular, we need to keep improving the lithium-ion batteries that power them. Electrochemical impedance spectroscopy (EIS) is used based on a discrete random binary sequence (DRBS) to reduce excitation time in the low-frequency region and excite the input of the battery. In this paper, voltage and current signals are processed with wavelet transform for impedance evaluation. In using wavelet transform, choosing the most optimal mother wavelet is crucial for impedance evaluation since different mother wavelets can produce different results. We aim to compare three types of continuous Morse mother wavelet, continuous Morlet, and continuous lognormal wavelet, which are among the most important mother wavelets, to determine the best method for impedance evaluation. We used the dynamic time-warping algorithm to quantify the difference between the initial values obtained from standard laboratory equipment and the impedance evaluation through three different continuous wavelets. Our proposed method (lognormal wavelet) has the lowest difference (3.4086) from the initial values compared to the Morlet (3.5504), and Morse (3.5457) methods. As a result, our simulation shows that the lognormal wavelet transform is the best method for impedance evaluation compared to Morlet and Morse wavelets.

In this study, feedback neural networks namely Elman and Jordan are used for prediction of exhaust valve temperature for air cooled engines. Input-output data are extracted from an experimental setup including the valve mechanism of an air cooled engine. Inverse heat transfer problem applying the Adjoint problem is used to address the thermal flux through exhaust valve and seat. Elman and Jordan neural networks are used to predict the transient valve temperature using the experimental data. The results show that Elman and Jordan neural networks predicts well the transient exhaust valve temperature. However, Jordan neural network with training algorithm of Gradient Descent with Adaptive Learning Rate performs better with RMSE error of 16.3 for prediction of exhaust valve temperature.

The primary purpose of each autonomous exit parking system is to facilitate the process of exiting the vehicle, emphasizing the comfort and safety of driving in the absence of almost any human effort. In this paper, the problem of exit parking for autonomous vehicles is addressed. A nonlinear kinematic model is presented based on the geometric relationship of the vehicle velocities, and a linear time-varying discrete-time model of the vehicle is obtained for utilizing the optimal control strategy. The proposed path planning algorithm is based on the minimization of a geometric cost function. This algorithm works for ample space exit parking in Single-Maneuver and tight spaces in Multi-Maneuver exit parking. Finally, an optimal discrete-time linear quadratic control approach is hired to minimize a quadratic cost function. To evaluate the performance of the proposed algorithm, the control system is simulated by MATLAB/Simulink software. The results show that the optimal control strategy is well able to design and follow the desired path in each of the exit parking maneuvers.

In this paper, the consensus of second-order nonlinear self-driving vehicle convoys (SDVCs) is studied. We assume that each self-driving vehicle (SDV) communicates only with one front and one rear SDVs. Each SDV’s nonlinear dynamics consisting of the rolling resistance and the air drag force is a function of SDV’s speed and is investigated in SDVC’s modeling and consensus design. Since the speed is bounded, all vehicles’ nonlinearities are also bounded. Due to engine saturation of each SDV, the control input is limited. We involve this limitation by introducing the arctan(.) function to control protocol. The inter-SDV’s distances are assumed to be constant during motion. The distance tracking error associated with each SDV is defined as distance between it and the leading SDV. The error dynamics of the proposed SDVC is derived after applying the consensus law to each SDV. To prove the internal stability, the Lyapunov theorem is employed. We will prove that under this consensus algorithm, the SDVC will be internal stable. To validate the effectiveness of this method, a SDVC comprising a leading and 6 following SDVs will be studied. It will be verified that under the proposed consensus law, all the SDVs reach a unique consensus.