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Electric vertical takeoff and landing (eVTOL) aircraft are emerging as a modern transportation solution aimed at reducing urban traffic congestion and improving the carbon footprint. The power architecture in eVTOLs is defined by the dc bus formed by the battery packs and the power converter used to drive eVTOL motors. A high dc bus voltage is preferred for the power architecture since it can reduce the weight of power cables for a given power rating. However, the impact of high dc bus voltage on the efficiency of the drivetrain power converter must be considered, since reduced efficiency leads to poor battery pack utilization. In this paper, a systematic optimization study is performed considering SiC-based inverter for the drivetrain power converter. Optimal value of dc bus voltage is determined considering the flight profile of eVTOLs. A power converter topology is proposed that can provide optimal performance and enhance the lifetime of the batteries along with providing better monitoring, diagnostics and protection. The optimization strategy is validated experimentally, demonstrating the proposed power architecture's ability to maximize efficiency while enhancing the safety of the battery energy storage system in eVTOLs.

A key function of battery management systems (BMS) in e-mobility applications is estimating the battery state of health (SoH) with high accuracy. This is typically achieved in commercial BMS using model-based methods. There has been considerable research in developing data-driven methods for improving the accuracy of SoH estimation. The data-driven methods are diverse and use different machine-learning (ML) or artificial intelligence (AI) based techniques. Complex AI/ML techniques are difficult to implement in low-cost microcontrollers used in BMS due to the extensive use of non-linear functions and large matrix operations. This paper proposes a computationally efficient and data-lightweight SoH estimation technique. Online impedance at four discrete frequencies is evaluated to derive the features of a linear regression problem. The proposed solution avoids complex mathematical operations and it is well-suited for online implementation in a commercial BMS. The accuracy of this method is validated on two experimental datasets and is shown to have a mean absolute error (MAE) of less than 2% across diverse training and testing data.

Modeling of Li-ion cells is used in battery management systems (BMS) to determine key states such as state-of-charge (SoC), state-of-health (SoH), etc. Accurate models are also useful in developing a cell-level digital-twin that can be used for protection and diagnostics in the BMS. In this paper, a low-complexity model development is proposed based on the equivalent circuit model (ECM) of the Li-ion cells. The proposed approach uses online impedance measurement at discrete frequencies to derive the ECM that matches closely with the results from the electro-impedance spectroscopy (EIS). The proposed method is suitable to be implemented in a microcontroller with low-computational power, typically used in BMS. Practical design guidelines are proposed to ensure fast and accurate model development. Using the proposed method to enhance the functions of a typical automotive BMS is described. Experimental validation is performed using large prismatic cells and small-capacity cylindrical cells. Root-mean-square error (RMSE) of less than 3\% is observed for a wide variation of operating conditions.

Machine learning (ML) provides powerful tools for predictive modeling. ML's popularity stems from the promise of sample-level prediction with applications across a variety of fields from physics and marketing to healthcare. However, if not properly implemented and evaluated, ML pipelines may contain leakage typically resulting in overoptimistic performance estimates and failure to generalize to new data. This can have severe negative financial and societal implications. Our aim is to expand understanding associated with causes leading to leakage when designing, implementing, and evaluating ML pipelines. Illustrated by concrete examples, we provide a comprehensive overview and discussion of various types of leakage that may arise in ML pipelines., Comment: second draft

Grid-interactive power converters are normally synchronized to the grid using phase-locked loops (PLLs). The performance of the PLLs is affected by the non-ideal conditions in the sensed grid voltage such as harmonics, frequency deviations and dc offsets in single-phase systems. In this paper, a single-phase PLL is presented to mitigate the effects of these non-idealities. This PLL is based on the popular second order generalized integrator (SOGI) structure. The SOGI structure is modified to eliminate of the effects of input dc offsets. The resulting SOGI structure has a high-pass filtering property. Hence, this PLL is termed as high-pass generalized integrator based PLL (HGI-PLL). It has fixed parameters which reduces the implementation complexity and aids in the implementation in low-end digital controllers. The HGI-PLL is shown to have least resource utilization among the SOGI based PLLs with dc cancelling capability. Systematic design methods are evolved leading to the design that limits the unit vector THD to within 1% for given non-ideal input conditions in terms of frequency deviation and harmonic distortion. The proposed designs achieve the fastest transient response. The performance of this PLL has been verified experimentally. The results are found to agree with the theoretical prediction., Comment: 22 pages, 13 figures and 2 tables