Evaluation of fluctuating voltage topology with fuel cells and supercapacitors for automotive applications

Xun, Qian; Liu, Yujing

doi:10.1002/er.4622

Cited by 17 publications

(13 citation statements)

References 26 publications

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“…Driving cycle specifications are summarized in Table 2. 19,49,50 Here, UDDS, IST, NYCC, LA92 driving cycles were utilized during training and WLTC driving cycle was used for test procedure.…”

Section: Training and Testing Datasetsmentioning

confidence: 99%

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Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle

2020

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Due to increased environmental pollution and global warming concerns, the use of energy storage units that can be supported by renewable energy resources in transportation becomes more of an issue and plays a vital role in terms of clean energy solutions. However, utilization of multiple energy storage units together in an electric vehicle makes the powertrain system more complex and difficult to control. For this reason, the present study proposes an advanced energy management strategy (EMS) for range extended battery electric vehicles (BEVs) with complex powertrain structure. Hybrid energy storage system (HESS) consists of battery, ultra-capacitor (UC), fuel cell (FC) and the vehicle is propelled with two complementary propulsion machines. To increase powertrain efficiency, traction power is simultaneously shared at different rates by propulsion machines. Propulsion powers are shared by HESS units according to following objectives: extending battery lifetime, utilizing UC and FC effectively. Primarily, to optimize the power split in HESS, a convex optimization problem is formulated to meet given objectives that results 5 years prolonged battery lifetime. However, convex optimization of complex systems can be arduous due to the excessive number of parameters that has to be taken into consideration and not all systems are suitable for linearization. Therefore, a neural network (NN)-based machine learning (ML) algorithm is proposed to solve multi-objective energy management problem. Proposed NN model is trained with convex optimization outputs and according to the simulation results the trained NN model solves the optimization problem within 92.5% of the convex optimization one.

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Section: Training and Testing Datasetsmentioning

confidence: 99%

“…WLTC (The Worldwide harmonized Light vehicles Test Cycles) has both urban and highway driving characteristic. Driving cycle specifications are summarized in Table 2 19,49,50 …”

Section: Neural Networkmentioning

confidence: 99%

Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle

2020

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“…Type Range SOC UC (%) Level limitation [10,90] P support (kW) Level limitation Equation (9) Abbreviation: MPC, model predictive control.…”

Section: Constraints Parametermentioning

confidence: 99%

“…In this work, a hybrid energy storage system (HESS) comprising two different storage technologies is used. One of the ESSs considered is the ultracapacitor (UC), which typically shows high charge and discharge speed, high efficiency and power density, very high cycle‐life and low maintenance . The second storage technology is a hydrogen system consisting of an electrolyzer (EZ) to produce hydrogen from electricity, a tank to store the hydrogen produced, and a fuel cell (FC) to generate electricity from hydrogen.…”

Section: Introductionmentioning

confidence: 99%

Predictive energy management for a wind turbine with hybrid energy storage system

et al. 2019

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Summary Hybrid energy storage systems (HESSs) help mitigating the fluctuations and variable availability of certain renewable sources, such as wind power, as they can provide support in different time scales. Therefore, regulating their state‐of‐charge (SOC) becomes crucial to ensure that the hybrid system complies with generation commitments agreed in time‐ahead markets despite subsequent unexpected wind speed variations. So far, research has been mainly targeted at avoiding extreme SOC situations in the storage devices, whereas the regulation of this parameter to specific values has often been disregarded. A novel approach is proposed in this work, where model predictive control (MPC) is used to regulate the SOC of a HESS under variable wind and grid demand scenarios. The MPC‐based supervisory controller developed for the hybrid system has been implemented and simulated under different situations. This controller monitors the future variation of the SOC with the aim of having the HESS available to develop its assigned functions successfully. The results show that a proper regulation of the SOC in the HESS increases the capacity to manage the active power supplied to the grid by the hybrid system based on wind power, as well as the level of compliance with generation commitments established time ahead.

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“…With the global concern about environmental issues, it has become the consensus of the world to develop renewable energy resources, such as wind [1], hydro [2], fuel cell [3], photovoltaic (PV) [4], [5]. The International Energy Agency (IEA) estimates that the proportion of new energy resources will reach 60% in 2040, and among them, PV and wind energy will account more than 50%.…”

Section: Introductionmentioning

confidence: 99%

LSTM-Attention-Embedding Model-Based Day-Ahead Prediction of Photovoltaic Power Output Using Bayesian Optimization

2019

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Photovoltaic (PV) output is susceptible to meteorological factors, resulting in intermittency and randomness of power generation. Accurate prediction of PV power output can not only reduce the impact of PV power generation on the grid but also provide a reference for grid dispatching. Therefore, this paper proposes an LSTM-attention-embedding model based on Bayesian optimization to predict the day-ahead PV power output. The statistical features at multiple time scales, combined features, time features and wind speed categorical features are explored for PV related meteorological factors. A deep learning model is constructed based on an LSTM block and an embedding block with the connection of a merge layer. The LSTM block is used to memorize and attend the historical information, and the embedding block is used to encode the categorical features. Then, an output block is used to output the prediction results, and a residual connection is also included in the model to mitigate the gradient transfer. Bayesian optimization is used to select the optimal combined features. The effectiveness of the proposed model is verified on two actual PV power plants in one area of China. The comparative experimental results show that the performance of the proposed model has been significantly improved compared to LSTM neural networks, BPNN, SVR model and persistence model. INDEX TERMS LSTM-attention-embedding model, features extraction, deep learning, Bayesian optimization, residual connection.

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Evaluation of fluctuating voltage topology with fuel cells and supercapacitors for automotive applications

Cited by 17 publications

References 26 publications

Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle

Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle

Predictive energy management for a wind turbine with hybrid energy storage system

LSTM-Attention-Embedding Model-Based Day-Ahead Prediction of Photovoltaic Power Output Using Bayesian Optimization

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