• Energy Research

Accurate prediction of lithium-ion batteries’ (LIBs) state-of-health (SOH) is crucial for the safety and maintenance of LIB-powered systems. This study addresses the variability in degradation trajectories by applying gated recurrent unit (GRU) networks alongside principal component analysis (PCA), Granger causality, and K-means clustering to analyze the relationships between operating conditions—such as temperature and load profiles—and battery performance degradation. This paper uses a publicly accessible dataset derived by aging three prismatic LIB cells under a realistic forklift operation profile. First, we identify the features that are relevant to driving variance, then we employ the winning algorithm of K-means clustering for the classification of operational states. Granger causality later investigates the inter-group relationships. Our GRU-BPP model achieves an RMSE value of 0.167 and an MAE of 0.129 for the reference performance testing (RPT) dataset and an RMSE of 0.032 with an MAE of 0.025 for the aging dataset, thus outperformed benchmark methods such as GRU, LME, and XGBoost. These results further enhance the predictiveness and robustness of this approach and yield a holistic solution to the conventional challenges in battery management and their remaining useful life (RUL) predictions.

This article deals with the major challenge of electric vehicle charging infrastructure in urban areas—installing as many fast charging stations as necessary and using them as efficiently as possible, while considering grid level power limitations. A smart fast charging station with four vehicle access points and an intelligent load management algorithm based on the combined charging system interface is presented. The shortcomings of present implementations of the combined charging system communication protocol are identified and discussed. Practical experiments and simulations of different charging scenarios validate the concept and show that the concept can increase the utilization time and the supplied energy by a factor of 2.4 compared to typical charging station installations.

Abstract This comprehensive overview of the impacting factors on lithium-ion-battery’s (LIB) overall efficiency presents the most relevant influencing factors on a battery’s performance. Dissected into their respective short-term and long-term influences, the working principles behind the efficiency influencing factors are presented. With a strong focus on battery characterisation, charge-profiles and battery management systems (BMSs), the authors present results of their own practical research with a detailed literary analysis, allowing a broad coverage of the complex topic. Finally, the authors present a principle model that indicates the interactions between the different involved components of the battery.

Family farming is in many parts of the world still a significant factor in food production. However, farms in underdeveloped regions often lack the appropriate means to carry out agricultural activities in accordance with the state of the art, i.e. with the help of motorized machinery. On the other hand, especially in tropical regions, they generally own a reliable renewable energy source, principally in the form of solar radiation. In an effort to help to improve the returns of farming in an environmentally acceptable way, the project's objective is to investigate the feasibility of a small size electric farming tractor, which shall be able to use locally generated renewable energy. The crucial technical challenge is (as with other electric vehicles) to store sufficient electrical energy on the tractor for an extension of its operating time. On the other hand, economic competitiveness with comparable combustion engine tractors is a prerequisite for the project's success. Consequently, the project investigates an electric tractor system using alternative technical solutions in order to extend the operating time to a maximum and to compare the tractor's tractive power and its hourly operating cost to a combustion engine tractor of a similar power rating. To this end, an electric prototype tractor in conjunction with an appropriate electric generation, storage and transmission system are considered within this work.

A variety of measures are currently being taken on both the national and international levels in order to mitigate the negative effects of climate change. The promotion of electric mobility is one such measure for the transport sector. As a key component in a more environmentally-friendly, resource-saving, and efficient transport sector, electric mobility promises to create better sustainability. Several challenges still need to be met to exploit its full potential. This requires adapting the car technology, the value chain of vehicles, loads on the electricity network, the power generation for the drive, traffic, and charging infrastructure. The challenges to this endeavor are not only technical in nature, but they also include social acceptance, concerns, and economic, as well as ecological, aspects. This paper seeks to discuss and elucidate these problems, giving special focus to the issues of driving range, phenomenon of range anxiety, charging time, and complexity of the charging infrastructure in Germany. Finally, the development of social acceptance in Germany from 2011 to 2020 is investigated.

Electric mobility is considered a solution to reduce carbon emissions. We expanded a lifecycle assessment with data on technical limitations and driving habits (based on real-world data) in order to identify the environmentally optimal drivetrain for each individual driving behavior with current and projected technologies, focusing on CO2 emissions. By combining all data, an environmentally optimal European drivetrain mix is calculated, which is dominated by fuel-cell electric vehicles (50% in 2020, 47% in 2030), followed by plug-in hybrid-electric vehicles (37%, 40%), battery-electric vehicles (BEV) (5%, 12%), and Diesel vehicles (2%, 1%). Driving behavior defines the most environmental drivetrain and the coexistence of different drivetrains is currently still necessary. Such information is crucial to identify limitations and unmet technological needs for full electrification. If range is not considered a limitation, the environmentally optimal drivetrain mix is dominated by BEVs (71%, 75%), followed by fuel cell electric vehicles (25%, 19%) and plug-in electric vehicles (4%, 6%). This confirms the potential environmental benefits of BEVs for current and future transportation. Developments in battery energy density, charging, and sustainable production, as well as a change in driving behavior, will be crucial to make BEVs the environmentally optimal drivetrain choice.