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Register a new userThe promise of energy-efficient battery-powered urban aircraft
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Improvements in rechargeable batteries are enabling several electric urban air mobility (UAM) aircraft designs with up to 300 miles of range with payload equivalents of up to 7 passengers. We find that novel UAM aircraft consume between 130 Wh/passenger-mile up to ~1,200 Wh/passenger-mile depending on the design and utilization, relative to an expected consumption of over 220 Wh/passenger-mi for terrestrial electric vehicles and 1,000 Wh/passenger-mile for combustion engine vehicles. We also find that several UAM aircraft designs are approaching technological viability with current Li-ion batteries, based on the specific power-and-energy while rechargeability and lifetime performance remain uncertain. These aspects highlight the technological readiness of a new segment of transportation.
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We give the definition of s-Vector control by using the complex power vector in the p-q plane to quantify the feasible operational region of battery energy storage system (BESS) and to find the optimal power set-point solution. The s-Vector control features in the avoidance of using costly optimization solvers to solve optimization models online which is also an execution time bottleneck in real-time control. The advantages of s-Vector control are fast and stable computational efficiency. We validate a s-Vector based real-time controller of BESS to provide frequency response and voltage support as ancillary services for the power grid. The objective is to maximize the utility of the BESS asset. We formulate the dynamic capability curve of the DC-AC converter and the security requirements of the battery cells as constrains of the control system. The initial power set-points are obtained based on the traditional droop control approach. Based on s-Vector control, a fast optimization solution algorithm is proposed to find the optimal power set-point and guarantee the security. According to the experimental validation in our 720 kVA / 560 kWh BESS on EPFL campus, we achieve 100 ms of refreshing the real-time control loop. As a benchmark, using optimization solver requires 200 ms to update the real-time control loop.
Repurposing automotive batteries to second-use battery energy storage systems (2-BESS) may have environmental and economic benefits. The challenge with second-use batteries is the uncertainty and diversity of the expected packs in terms of their chemistry, capacity and remaining useful life. This paper introduces a new strategy to optimize 2-BESS performance despite the diversity or heterogeneity of individual batteries while reducing the cost of power conversion. In this paper, the statistical distribution of the power heterogeneity in the supply of batteries is considered when optimizing the choice of power converters and designing the power flow within the battery energy storage system (BESS) to maximize battery utilization. By leveraging a new lite-sparse hierarchical partial power processing (LS-HiPPP) approach, we show a hierarchy in partial power processing (PPP) partitions power converters to a) significantly reduce converter ratings, b) process less power to achieve high system efficiency with lower cost (lower efficiency) converters, and c) take advantage of economies of scale by requiring only a minimal number of sets of identical converters. The results demonstrate that LS-HiPPP architectures offer the best tradeoff between battery utilization and converter cost and had higher system efficiency than conventional partial power processing (C-PPP) in all cases.
The innovative concept of Electric Aircraft is a challenging topic involving different control objectives. For instance, it becomes possible to reduce the size and the weight of the generator by using the battery as an auxiliary generator in some operation phases. However, control strategies with different objectives can be conflicting and they can produce undesirable effects, even instability. For this reason an integrated design approach is needed, where stability can be guaranteed in any configuration. In other words, the design of the supervisory controller must be interlaced with that of low-level controllers. Moreover, uncertainties and noisy signals require robust control techniques and the use of adaptiveness in the control algorithm. In this paper, an aeronautic application aiming at recharging batteries and to use the battery to withstand generator overloads is addressed. Detailed and rigorous stability proofs are given for any control configuration, including the switching phases among different control objectives. Effectiveness of the proposed strategies is shown by using a detailed simulator including switching electronic components.
Recent data-driven approaches have shown great potential in early prediction of battery cycle life by utilizing features from the discharge voltage curve. However, these studies caution that data-driven approaches must be combined with specific design of experiments in order to limit the range of aging conditions, since the expected life of Li-ion batteries is a complex function of various aging factors. In this work, we investigate the performance of the data-driven approach for battery lifetime prognostics with Li-ion batteries cycled under a variety of aging conditions, in order to determine when the data-driven approach can successfully be applied. Results show a correlation between the variance of the discharge capacity difference and the end-of-life for cells aged under a wide range of charge/discharge C-rates and operating temperatures. This holds despite the different conditions being used not only to cycle the batteries but also to obtain the features: the features are calculated directly from cycling data without separate slow characterization cycles at a controlled temperature. However, the correlation weakens considerably when the voltage data window for feature extraction is reduced, or when features from the charge voltage curve instead of discharge are used. As deep constant-current discharges rarely happen in practice, this imposes new challenges for applying this method in a real-world system.
The paper provides a comprehensive battery storage modelling approach, which accounts for operation- and degradation-aware characteristics, i.e., variable efficiency, internal resistance growth, and capacity fade. Based on the available experimental data from the literature, we build mixed-integer linear programming compatible lithium iron phosphate (LiFePO$_4$) battery model that can be used in problems related to various applications, i.e., power system, smart grid, and vehicular applications. Such formulation allows finding the globally optimal solution using off-the-shelf academic and commercial solvers. In the numerical study, the proposed modelling approach has been applied to realistic scenarios of peak-shaving, where the importance of considering the developed models is explicitly demonstrated. For instance, a time-varying operation strategy is required to obtain the optimal utilization of the LiFePO$_4$ battery storage. Particularly, during the battery operational lifetime its optimal average SoC may change by up to $20%$, while the duration of charging process may increase by $75%$. Finally, using the same LiFePO$_4$ benchmark model from the literature, we compare the results of using the proposed approach to the state-of-the-art in the optimal sizing and scheduling problems. The proposed approach led to a $12.1%$ reduction of battery investment and operating costs compared to the state-of-the-art method.
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