This paper proposes a comprehensive novel multi-terminal HVDC protection scheme based on artificial neural network (ANN) and high frequency components detected from fault current signals only. The method is shown to accurately detect, classify and locate overhead line faults. Unlike existing travelling wave based methods which must capture the initial wavefront and require high sampling rates, the new approach is more robust since it gives accurate fault detection and fault location over a range of windowed post-fault signals. Furthermore, the proposed method is fault resistance independent meaning even a very high fault impedance has no effect on accurate fault location. A three-terminal VSC-HVDC system is modelled in PSCAD/EMTDC, which is used for obtaining the fault current data for transmission line terminals. The method is verified by studying different cases with a range of fault resistances in various fault locations, and in addition, external faults. The results show that the proposed method gives fast (<5 ms) and reliable (100%) fault detection and classification and accurate location (<1.16%) for DC line faults

The accelerating market penetration of electric vehicles (EVs) raises important questions for both industry and academia: how to deal with potentially millions of retired batteries (RBs) from EVs, and how to extend the potential value of these batteries after they are retired. It is therefore critical to deepen our understanding of the comprehensive performance of RBs in appropriate applications, such as stationary energy storage with less-demanding on power capacity. The following literature review evaluates the opportunity of the emerging RB market in detail. Meanwhile, various specifically technical issues and solutions for battery reuse are compiled, including aging knee, life predicting, and inconsistency controlling. Furthermore, the risks and benefits of battery reuse are highlighted referring to transportation electrification and entire industrial chain. Also, current policy shortcomings and uncertainties are outlined, and policy recommendations are provided for relevant participants. Six typical application scenarios are selected, and high-value business models for battery reuse are explored from different techno-economic aspects. Insights from this review indicate that as the entire recycling chain is completed, battery reuse will be essential to the future energy market and will play an important role in the future development of low-carbon energy.

This article proposes a synergistic approach that traverses the battery optimal size simultaneously against the optimal power management based on deep reinforcement learning (DRL). A fuel cell hybrid electric vehicle (FC-HEV) with the FC/battery hybrid powertrain is used as the study case. The battery plays a key role in current transportation electrification, and the optimal sizing of the battery is critical for both system technical performances and economical revenues, especially in the hybrid design. The optimal battery design should coordinate the static sizing study against the dynamic power distribution for a given system, but few works provided the synergistic consideration of the two parts. In this study, the interaction happens in each sizing point with the optimal power sharing between the battery and the FC, aiming at minimizing the summation of hydrogen consumption, FC degradation, and battery degradation. Under the proposed framework, the power management is developed with deep Q network (DQN) algorithm, considering multiobjectives that minimize hydrogen consumption and suppress system degradation. In the case study, optimal sizing parameters with lowest cost are determined. Leveraged by the optimal size, the hybrid system economy with synergistic approach is improved by 16.0%, compared with the conventional FC configuration.

This paper proposes a multi-dimensional size optimization framework and a hierarchical energy management strategy (HEMS) to optimize the component size and the power of a plug-in hybrid electric vehicle (PHEV) with the hybrid energy storage system (HESS). In order to evaluate the performance of size optimization and power optimization, a PHEV with a battery energy storage system (BESS) is used as a comparison reference, and the dynamic programming (DP) algorithm is set as a benchmark for comparison. The size optimization method explores the optimal configuration of the system, including the maximum power of the system, the maximum power and capacity of the battery, and the maximum power and capacity of the supercapacitor (SC). Compared with the BESS, the size-optimized HESS reduces the capacity of the system by 31.3% and improves the economy by 37.8%. The HEMS can simultaneously optimize vehicle fuel consumption and suppress battery aging. Its upper layer uses the DP algorithm to optimize fuel economy, and the lower layer apply the linear programming (LP) method to improve battery life. Based on the size optimization results and HEMS, compared with the benchmark, the battery aging rate has been reduced by 48.9%, and the vehicle economy has increased by 21.2%.