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Several space agencies have announced plans to return humans to the Moon in the near future. The objectives of these missions include using the Moon as a stepping-stone towards crewed missions to Mars, to test advanced technology, and to further exploration of the Moon for scientific research and in-situ resource utilization. To meet these objectives, it will be necessary to establish and operate a lunar base. As a result, a wide variety of tasks that may pose a number of crew health and safety risks will need to be performed on the surface of the Moon. Therefore, to ensure sustainable human presence on the Moon and beyond, it is essential to anticipate potential risks, assess the impact of each risk, and devise mitigation strategies. To address this, a nine-week intensive investigation was performed by an international, interdisciplinary and intercultural team on how to maximize crew safety on the lunar surface through a symbiotic relationship between astronauts and robots.

The application of high strength steels in tube hydroforming is being considered as one of the most effective ways to achieve the overall weight reduction without compromising the vehicle safety (crashworthiness). In this paper, the tensile behaviour of high strength dual-phase steel DP600 was investigated. The microstructure, mechanical performance and damage evolution was evaluated. A new finite element (FE) model based on crystal plasticity theory was developed to investigate large strain phenomena in multi-phase materials.

This paper benchmarks three different lithium-ion (Li-ion) battery voltage modelling approaches, a physics-based approach using an Extended Single Particle Model (ESPM), an equivalent circuit model, and a recurrent neural network. The ESPM is the selected physics-based approach because it offers similar complexity and computational load to the other two benchmarked models. In the ESPM, the anode and cathode are simplified to single particles, and the partial differential equations are simplified to ordinary differential equations via model order reduction. Hence, the required state variables are reduced, and the simulation speed is improved. The second approach is a third-order equivalent circuit model (ECM), and the third approach uses a model based on a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN)). A Li-ion pouch cell with 47 Ah nominal capacity is used to parameterize all the models.

Accurate battery state of charge (SOC) estimation is essential for safe and reliable performance of electric vehicles (EVs). Lithium-ion batteries, commonly used for EV applications, have strong time-varying and non-linear behaviour, making SOC estimation challenging. In this paper, a processor in the loop (PIL) platform is used to assess the execution time and memory use of different SOC estimation algorithms. Four different SOC estimation algorithms are presented and benchmarked, including an extended Kalman filter (EKF), EKF with recursive least squares filter (EKF-RLS) feedforward neural network (FNN), and a recurrent neural network with long short-term memory (LSTM). The algorithms are deployed to two different NXP S32Kx microprocessors and executed in real-time to assess the algorithms' computational load. The algorithms are benchmarked in terms of accuracy, execution time, flash memory, and random access memory (RAM) use.

Using standard tensile testing methods, the material properties of AKDQ and DP600 steels tubes along the axial direction were determined. A novel in-situ optical strain mapping system ARAMIS® was utilized to evaluate the strain distribution during tensile testing along the axial direction. Microstructural and damage characterization was carried out using microscopy and image analysis techniques to compare the damage evolution and formability of both materials. Failure in both steels was observed to occur via a ductile failure mode. AKDQ was found to be the more formable material as it can achieve higher strains, total elongations and thinning prior to failure than the higher strength DP600.

To achieve an accurate estimate of losses in a battery it is necessary to consider the reversible entropic losses, which may constitute over 20% of the peak total loss. In this work, a procedure for experimentally determining the entropic heating coefficient of a lithium-ion battery cell is developed. The entropic heating coefficient is the rate of change of the cell’s open-circuit voltage (OCV) with respect to temperature; it is a function of state-of-charge (SOC) and temperature and is often expressed in mV/K. The reversible losses inside the cell are a function of the current, the temperature, and the entropic heating coefficient, which itself is dependent on the cell chemistry. The total cell losses are the sum of the reversible and irreversible losses, where the irreversible losses consist of ohmic losses in the electrodes, ion transport losses, and other irreversible chemical reactions.

This paper presents a dynamic model for an interior permanent magnet (IPM) machine with a space-vector-modulation-based voltage source inverter. The dynamic model considers spatial harmonics, cross-coupling and magnetic saturation. In order to include the nonlinear electromagnetic characteristics of the IPM machine, the dynamic model is built based on the current-flux look-up tables obtained from finite element analysis (FEA). The model is co-simulated with the drive system, which considers the effects of the modulation technique and the switching frequency. The dynamic performance of a 60/8 IPM machine is analyzed using the dynamic model at different operating conditions and then validated with the torque waveforms obtained from FEA. The results show that dynamic performance can be analyzed accurately and more quickly using the dynamic model presented in this paper.

Multiphase machines continue to increase in popularity in high power applications due to their proven benefits compared to their three-phase counterparts. However, with the increased phase number and, therefore, the increased number of degrees of freedom, the complexity of both modelling and control strategies significantly increases. This paper proposes a dynamic modelling method for six-phase interior permanent magnet machines using the vector space decomposition transformation, which can be extended to machines with any number of phases. The proposed technique considers the nonlinear characteristics of the machine, such as spatial harmonics, magnetic saturation, and cross-coupling, which are based on flux linkage look-up tables from finite element analysis. The main contribution of this paper is the consideration of the effect of harmonic components and asymmetries within the machine windings on losses.

This paper presents a compact, partially laminated busbar design to connect the DC-link capacitor, high-voltage DC (HVDC) connector, and power module using a single integrated busbar. The proposed busbar design is designed for a high-power and high-voltage Silicon Carbide (SiC) traction inverter. The proposed solution eliminates the need for using separate busbars: one for the connection between the HVDC connector and the DC-link capacitor, and the other one between the connection of the DC-link capacitor and the power module. Incorporating two busbars in a single traction inverter increases the total volume of the inverter and the parasitic components. Thus, the main design goals in this paper are minimizing the parasitic inductances, increasing the power density, and achieving a uniform current distribution across the capacitor cores.

A comparative analysis of overmodulation methods is performed in the generalized form in this paper. The generalized form is based on four segmented formulae, which streamlines the execution of the PWM module. The comparative analysis considers five aspects: actual modulation index, harmonic content, transition to six-step operation, modulation index linearization, and execution complexity. The main contributions of this paper are twofold. Firstly, a thorough assessment of conventional overmodulation strategies for dual three-phase PMSM drives is undertaken. Secondly, a modified Minimum Phase Error (MPE) overmodulation method is proposed to extend the overmodulation to six-step operation. The modified MPE is introduced with advantages of wider modulation index range, low harmonic components in voltages and currents, smooth transition to six-step operation, and simple implementation.

Lithium-ion battery (LIB) terminal voltage models are investigated using two modelling approaches. The first model is a third-order Thevenin equivalent circuit model (ECM), which consists of an open-circuit voltage in series with a nonlinear resistance and three parallel RC pairs. The parameters of the ECM are obtained by fitting the model to hybrid pulse power characterization (HPPC) test data. The parametrization of the ECM is performed through quadratic-based programming. The second is a novel modelling approach based on long short-term memory (LSTM) recurrent neural networks to estimate the battery terminal voltage. The LSTM is trained on multiple vehicle drive cycles at six different temperatures, including −20°C, without the necessity of battery characterization tests. The performance of both models is evaluated with four automotive drive cycles at each temperature. The results show that both models achieve acceptable performance at all temperatures.

Battery state-of-charge (SOC) is critical information for the vehicle energy management system and must be accurately estimated to ensure reliable and affordable electrified vehicles (xEV). However, due to the nonlinear temperature, health, and SOC dependent behaviour of Li-ion batteries, SOC estimation is still a significant automotive engineering challenge. Traditional approaches to this problem, such as electrochemical models, usually require precise parameters and knowledge from the battery composition as well as its physical response. In contrast, neural networks are a data-driven approach that requires minimal knowledge of the battery or its nonlinear behaviour. The objective of this work is to present the design process of an SOC estimator using a deep feedforward neural network (FNN) approach. The method includes a description of data acquisition, data preparation, development of an FNN, FNN tuning, and robust validation of the FNN to sensor noise.

Battery terminal voltage modelling is crucial for various applications, including electric vehicles, renewable energy systems, and portable electronics. Terminal voltage models are used to determine how a battery will respond under load and can be used to calculate run-time, power capability, and heat generation and as a component of state estimation approaches, such as for state of charge. Previous studies have shown better voltage modelling accuracy for long short-term memory (LSTM) recurrent neural networks than other traditional methods (e.g., equivalent circuit and electrochemical models). This study presents two new approaches – sequence training and data shuffling – to improve LSTM battery voltage models further, making them an even better candidate for the high-accuracy modelling of lithium-ion batteries. Because the LSTM memory captures information from past time steps, it must typically be trained using one series of continuous data.