EnBW Energie Baden-Württemberg AG, or simply EnBW, is a publicly-traded energy company headquartered in Karlsruhe, Germany. As its name indicates, EnBW is based in the German state of Baden-Württemberg.
This study introduces a comprehensive and generic aging model that disentangles and quantifies the isolated effects of individual stress factors on lithium-ion battery degradation. Unlike traditional empirical or semi-empirical models, which often combine stress factors into aggregate predictions, the proposed framework leverages time series analysis to resolve the distinct contributions of temperature, state of charge (SOC), depth of discharge (DOD), and current rate (C-rate). This allows not only the capture of overall degradation trends but also the interpretable parameterization of stress-dependent contributions across the operating ranges of NMC cells. The model achieves high predictive accuracy, with a mean absolute error (MAE) of 1.10% for training and 0.61% for validation against data from realistic load profiles. Key findings reveal that the SOC plays a crucial role in battery aging, particularly at elevated levels, with diminishing negative effects observed beyond 85% SOC. In addition, DOD emerged as the most significant factor, with higher DOD levels correlating with accelerated aging, likely due to mechanical stresses impacting electrode integrity. The analysis showed that while SOC consistently contributes to aging, the C-rate exhibited minimal influence within the tested parameters. This study emphasizes the importance of understanding these aging factors for developing effective battery management strategies. Despite the model’s predictive performance, several limitations were identified, including challenges in representing extreme conditions and accurately modeling non-linear capacity fade. Understanding these limitations is essential for enhancing the model’s applicability and reliability in real-world scenarios. Overall, this approach advances beyond cumulative modeling frameworks by offering interpretable and transferable model parameters, directly linking operating conditions to aging factors and corresponding aging parameters. These insights not only enhance predictive capability but also support the design of optimized battery management strategies aimed at mitigating dominant aging conditions.
Abstract Vertical wind lidar measurements in complex terrain are prone to systematic errors because the underlying assumption of horizontally homogeneous flow is violated. This study employs a two-dimensional source-vortex panel method to approximate arbitrary terrain contours derived from high-resolution digital terrain models, thereby reconstructing the potential-flow velocity field and the associated flow tilt angle relevant for lidar error assessment. The approach is limited by its two-dimensional and inviscid assumptions and by the quality of the underlying DTM data. The implementation is first validated against the pressure distribution of a NACA 0012 airfoil, then applied to two complex sites using DTM data from two different data providers; terrain-section sizes larger than ≈ 500 m are shown to yield converged results. Comparison with 3D ultrasonic anemometer data shows that the panel method reliably reproduces the qualitative pattern of flow deflection, with a mean deviation of < 1° and an RMSE of 2.07° provided that accurate terrain information is available. Consequently, the method offers a computationally inexpensive tool for estimating terrain-related lidar errors when evaluating wind energy resources.
Automated bidirectional charging of electric vehicle fleets offers massive energy system benefits that have barely been leveraged so far. Inductive charging, due to its contactless power transfer, represents a particularly robust solution for automation and avoids the mechanical wear and robotic-handling requirements inherent to conductive systems with high plug-in cycles. This article investigates scalable topologies for inductive charging infrastructure with per-point power levels up to 22 kW. For this purpose, an N-box design architecture is introduced to systematically map power electronics sub-components into functional boxes between the grid interface and charging pads. The proposed framework thus enables a comparative cost assessment of supply schemes, i.e., DC link, 50 Hz AC, and 85 kHz high-frequency power distribution. The results show that using an optimized N-box architecture with grouping of four to seven charging points reduces the total CAPEX costs of an infrastructure from two to 50 parking lots by up to 32% on average compared to scaling state-of-the-art designs from private inductive home charging. Architectural grouping is therefore identified to be more important than the choice of power electronic topology. Further conclusions are listed at the end of the article.
Long-distance hydrogen supply chains using chemical carriers are increasingly seen as a key route connecting renewable-resource-rich regions with industrial hydrogen demand centers. Among these carriers, ammonia offers high volumetric hydrogen density, established synthesis and shipping infrastructure, and the ability to be reconverted to high-purity hydrogen at destination. However, the economic viability of ammonia-based hydrogen trade depends strongly on how variable renewable energy generation interacts with capital-intensive conversion, storage, transport, and reconversion assets over time, an aspect often oversimplified in current assessments.This study develops a techno-economic framework for a green ammonia-to-hydrogen supply chain connecting renewable electricity generation in Chile with hydrogen delivery in Germany. Hourly renewable generation profiles are used to determine electrolyzer utilization, hydrogen production, and upstream ammonia production costs, which are subsequently linked to ammonia storage, maritime transport, and destination-side cracking modules to estimate delivered hydrogen costs in Germany.Three renewable supply configurations are first evaluated using PEM electrolysis: solar-only, wind-only, and hybrid wind–solar. After identifying the most favorable renewable configuration, an additional alkaline-electrolysis case is assessed to quantify the impact of electrolyzer technology selection on system economics. Renewable capacities are selected using deterministic PV–wind capacity sweeps combined with full-year operational simulations, which naturally determine electrolyzer utilization, curtailment, and storage requirements. The results demonstrate that electrolyzer utilization is the primary driver of hydrogen and ammonia production costs, with hybrid wind–solar systems achieving significantly higher utilization than single-resource configurations.For the optimized hybrid setup with alkaline electrolysis, the levelized cost of hydrogen is €2.1 per kg H2, and the levelized cost of ammonia is €0.42 per kg NH3 at the production site. Including maritime shipping and ammonia cracking increases the delivered hydrogen cost to about €5.7 per kg H2. Overall, the results highlight the importance of time-resolved integrated modeling for evaluating the economics of international hydrogen trade.
