Intelligent Renewable Energy System: A Focus on Hydrogen Fuel Cells and Battery Storage with AI

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (22 August 2022) | Viewed by 23086

Special Issue Editors

National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, No.5 Zhongguancun South Street, Beijing 100081, China
Interests: electric vehicles; energy management; integrated control; modelling and simulation; battery systems
State Key Lab of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Interests: proton exchange membrane fuel cell; fuel cell vehicles; powertrains; energy management
School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350116, China
Interests: fuel cells; electric vehicles; battery management system; energy storage; energy management system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Renewable energy generation and storage are key to carbon neutrality. Hydrogen fuel cells and battery storage, as representatives of renewable energy carriers, have the advantages of high-energy conversion efficiency, simple structure and well flexibility, and can be widely used as a mobility and stationary power source, such as the electrification of transportation and households in remote areas. At present, the efficient designs and utilizations of the renewable energy system are still an urgent matter in the global new energy research field. Fortunately, inspired by advanced multi-disciplinary integration with artificial intelligence (AI), big data analysis, machine learning, data-driven, automation control and other system design, modeling, control and management methods, new and efficient solutions of renewable energy systems can be expected. The Special Issue of “Intelligent Renewable Energy System: A Focus on Hydrogen Fuel Cells and Battery Storage with AI” aims to cover recent advances and future perspectives related to fuel cells and battery storage with system design, modeling, control strategy, energy management, AI methods, and applications on mobility and stationary scenarios. The Special Issue welcomes outstanding research papers, as well as review articles, devoted to innovative suggestions for AI-based or advanced modeling and control technologies in the field of renewable energy, especially for the hydrogen fuel cell and battery storage.

The main topics of this Special Issue include but are not limited to:

  • Hydrogen fuel cell and electrolyzer design, modeling, and control;
  • Fuel cell vehicle powertrains;
  • Advances in battery storage technologies;
  • Battery management system (BMS);
  • Energy management system (EMS);
  • Electric vehicle control;
  • Fuel cell combined heat and power (CHP);
  • Thermal management of renewable energy systems;
  • Advanced Grid-to-Vehicle and Vehicle-to-Grid (V2G and G2V) systems;
  • Charging/discharging infrastructure;
  • DC/DC converters;
  • Data-driven control of renewable energy system;
  • Development of artificial intelligence in the new energy field;
  • Real-time simulation and analysis tools for hydrogen fuel cells;
  • Battery thermal system optimization;
  • Electrification of remoted areas;
  • Deep learning and machine learning for renewable energy system;
  • Data-driven-based models and physics-based models;
  • Hydrogen storage and renewable hydrogen production.

Prof. Dr. Hongwen He
Prof. Dr. Liangfei Xu
Prof. Dr. Ya-Xiong Wang
Guest Editors

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Published Papers (11 papers)

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Research

21 pages, 29625 KiB  
Article
Contact Fatigue State Identification of Specimen Based on Heterogeneous Data and Evidence Theory
by Xiang Chen, Yu Liu, Yuan Fu, Qiancheng Gu and Yan Yang
Appl. Sci. 2022, 12(17), 8509; https://0-doi-org.brum.beds.ac.uk/10.3390/app12178509 - 25 Aug 2022
Cited by 1 | Viewed by 939
Abstract
In order to accurately realize the contact fatigue state identification of specimen, a new method based on vibration and image heterogeneous data, as well as on D-S evidence theory, is proposed. Firstly, combined with the bearing public data set from CWRU, the vibration [...] Read more.
In order to accurately realize the contact fatigue state identification of specimen, a new method based on vibration and image heterogeneous data, as well as on D-S evidence theory, is proposed. Firstly, combined with the bearing public data set from CWRU, the vibration signal imaging methods such as SDP, GAF and GRI, as well as neural network models such as VGG16, ResNet and S-T, were compared and analyzed. It is determined that the SDP method is used to visualize the vibration signal, and the two state identification evidence bodies based on the vibration information source are obtained through the VGG16 and ResNet models. Secondly, combined with image monitoring signals, the fatigue defect identification method based on automatic weighting threshold and the detection error dynamic compensation method based on fatigue defect edge features are used to quantify the fatigue damage area and obtain the state identification evidence body based on the image information source. On this basis, a state identification network model based on vibration and image spatiotemporal heterogeneous data is constructed, and the D-S evidence theory is used to realize the contact fatigue state identification of the specimen. The results show that fusion of vibration and image data can achieve information complementarity and may identify the contact fatigue state of the specimen more accurately. The accuracy of state identification after fusion is 98.67%, which is at least 3% higher than that of a single information source. This research is of great significance for the accurate acquisition of material contact fatigue properties and has certain reference value for the heterogeneous data fusion from different sources. Full article
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25 pages, 7896 KiB  
Article
Gearbox Fault Diagnosis Based on Multi-Sensor and Multi-Channel Decision-Level Fusion Based on SDP
by Yuan Fu, Xiang Chen, Yu Liu, Chan Son and Yan Yang
Appl. Sci. 2022, 12(15), 7535; https://0-doi-org.brum.beds.ac.uk/10.3390/app12157535 - 27 Jul 2022
Cited by 7 | Viewed by 1351
Abstract
In order to deal with the shortcomings (such as poor robustness) of the traditional single-channel vibration signal in the comprehensive monitoring of the gearbox fault state, a multi-channel decision-level fusion algorithm was proposed based on symmetrized dot pattern (SDP) analysis, with the visual [...] Read more.
In order to deal with the shortcomings (such as poor robustness) of the traditional single-channel vibration signal in the comprehensive monitoring of the gearbox fault state, a multi-channel decision-level fusion algorithm was proposed based on symmetrized dot pattern (SDP) analysis, with the visual geometry group 16 network (VGG16) fault diagnosis model. Firstly, the SDP method was used to convert the vibration signal of a single multi-channel sensor into an imaging arm. Secondly, the obtained image arm was input into the VGG16 convolutional neural network in order to train the fault diagnosis model that can be obtained. Then, the SDP images of the signals that were to be measured from multiple multi-channel sensors were input into the fault diagnosis model, and the diagnosis results of multiple multi-channel sensors could then be obtained. Experimentally, it was demonstrated that the diagnostic results of multi-channel sensors one, two, and three were more accurate than those of single-channel sensors one, two, and three, by 3.01%, 16.7%, and 5.17%, respectively. However, the fault generation was not generated in a single direction, but rather multiple directions. In order to improve the comprehensiveness of the raw vibration data, a fusion method using DS (Dempster–Shafer) evidence theory was proposed in order to fuse multiple multi-channel sensors, in which the accuracy achieved 99.93% when sensor one and sensor two were fused, which was an improvement of 8.88% and 1.02% over single sensors one and two, respectively. When sensor one and sensor three were fused, the accuracy reached 99.31%, which was an improvement of 8.31% and 6.17% over single sensors one and three, respectively. When sensor two and sensor three were fused, the accuracy reached 99.91%, which was an improvement of 1.00% and 6.74% over single sensors two and three, respectively. When three sensors were fused simultaneously, the accuracy reached 99.99%, which was 8.93%, 1.08%, and 6.81% better than single sensors one, two, and three, respectively. Therefore, it can be proved that the number of sensor channels has a great influence on the diagnosis results. Full article
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20 pages, 62210 KiB  
Article
Adaptive Sliding Mode Control Integrating with RBFNN for Proton Exchange Membrane Fuel Cell Power Conditioning
by Xuelian Xiao, Jianguo Lv, Yuhua Chang, Jinzhou Chen and Hongwen He
Appl. Sci. 2022, 12(6), 3132; https://0-doi-org.brum.beds.ac.uk/10.3390/app12063132 - 18 Mar 2022
Cited by 6 | Viewed by 1540
Abstract
Proton exchange membrane fuel cells (PEMFC) are considered a promising solution for renewable energy application. To meet industrial requirements, the power source consisting of PEMFC is required to be power regulator to generate a stable and desired current and/or voltage under various working [...] Read more.
Proton exchange membrane fuel cells (PEMFC) are considered a promising solution for renewable energy application. To meet industrial requirements, the power source consisting of PEMFC is required to be power regulator to generate a stable and desired current and/or voltage under various working conditions. In this article, the adaptive sliding mode control integrating with the radial basis function neural network (RBFNN) approach for DC/DC buck converter-based PEMFC is presented to address perturbations from inner parameters as well as external disturbances in terms of power conditioning. Sliding mode control (SMC) and backstepping schemes are integrated to tackle the nonlinear and coupled outputs resulting in large control errors and slow response caused by PEMFC characteristics. To accurately estimate the parametric uncertainties and disturbance injections, such as buck converter parameter varying and PEMFC operation point changing, the RBFNN adaptive law is developed according to the defined Lyapunov and Gaussian functions overcoming the limitations of non-/linear parameter estimating. Simulations and experiments on the PEMFC power supply prototype governed by the DS1104 board are carried out. The comparative results indicate that the proposed RBFNN estimation associated with the backstepping SMC can reduce up to 7.5% overshoot and smooth PEMFC voltage and inductor current when disturbance changes in a voltage regulation experiment. Thus, the proposed method can regulate the current or voltage of a PEMFC power supply with robustness, adaptivity, and no chattering phenomenon. Full article
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12 pages, 9075 KiB  
Article
An Improved Gated Recurrent Unit Neural Network for State-of-Charge Estimation of Lithium-Ion Battery
by Jianlong Chen, Chenlei Lu, Cong Chen, Hangyu Cheng and Dongji Xuan
Appl. Sci. 2022, 12(5), 2305; https://0-doi-org.brum.beds.ac.uk/10.3390/app12052305 - 22 Feb 2022
Cited by 16 | Viewed by 2091
Abstract
State-of-charge (SOC) estimation of lithium-ion battery is a key parameter of the battery management system (BMS). However, SOC cannot be obtained directly. In order to predict SOC accurately, we proposed a recurrent neural network called gated recurrent unit network that is based on [...] Read more.
State-of-charge (SOC) estimation of lithium-ion battery is a key parameter of the battery management system (BMS). However, SOC cannot be obtained directly. In order to predict SOC accurately, we proposed a recurrent neural network called gated recurrent unit network that is based on genetic algorithm (GA-GRU) in this paper. GA was introduced to optimize the key parameters of the model, which can improve the performance of the proposed network. Furthermore, batteries were tested under four dynamic driving conditions at five temperatures to establish training and testing datasets. Finally, the proposed method was validated on dynamic driving conditions and compared with other deep learning methods. The results show that the proposed method can achieve high accuracy and robustness. Full article
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23 pages, 6345 KiB  
Article
Extreme Learning Machine Using Bat Optimization Algorithm for Estimating State of Health of Lithium-Ion Batteries
by Dongdong Ge, Zhendong Zhang, Xiangdong Kong and Zhiping Wan
Appl. Sci. 2022, 12(3), 1398; https://0-doi-org.brum.beds.ac.uk/10.3390/app12031398 - 28 Jan 2022
Cited by 16 | Viewed by 2146
Abstract
An accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for the safe and reliable operation of electric vehicles. As a single hidden-layer feedforward neural network, extreme learning machine (ELM) has the advantages of a fast learning speed and [...] Read more.
An accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for the safe and reliable operation of electric vehicles. As a single hidden-layer feedforward neural network, extreme learning machine (ELM) has the advantages of a fast learning speed and good generalization performance. The bat algorithm (BA) is a swarm intelligence optimization algorithm based on bat echolocation for foraging. In this study, BA was creatively applied to improve the ELM neural network, forming a BA-ELM model, and it was applied to SOH estimation for the first time. First, through Pearson and Spearman correlation analysis, six variables were determined as the input variables of the model. The actual remaining capacity of the battery was determined as the output variable. Second, BA was used to optimize the connection weights and bias in ELM to construct the BA-ELM model. Third, the battery data set was trained and tested with BA-ELM, ELM, Elman, back propagation (BP), radial basis function (RBF), and general regression neural network (GRNN) models. Five statistical error indicators, and the radar chart, scatter plot, and violin diagram were used to compare the estimation effects. The results show that the evaluation function of BA-ELM can converge quickly and effectively optimize the network model of ELM. The RMSE of the BA-ELM model was 0.5354%, and the MAE was 0.4326%, which is the smallest among the 6 models. The RMSE values of the other model were 2.27%, 3.53%, 3.07%, 3.86%, 3.24%, respectively, indicating the BA-ELM has good potential for future applications. Full article
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13 pages, 3244 KiB  
Article
Performance and Parameter Sensitivity Analysis of the PEMFC Flow Channel with Porous Baffles
by Cong Chen, Dongji Xuan, Mingge Wu, Shengnan Liu and Yunde Shen
Appl. Sci. 2021, 11(24), 11942; https://0-doi-org.brum.beds.ac.uk/10.3390/app112411942 - 15 Dec 2021
Cited by 3 | Viewed by 1318
Abstract
In this paper, a method to improve the performance of PEMFCs using porous material as a flow channel baffle is proposed. The results show that PEMFCs with four porous baffles flow channels have better performance at high current density compared with the traditional [...] Read more.
In this paper, a method to improve the performance of PEMFCs using porous material as a flow channel baffle is proposed. The results show that PEMFCs with four porous baffles flow channels have better performance at high current density compared with the traditional flow channel. The structural parameters of the flow channel explored in this study include porosity, the thickness of the baffle and the number of baffles, and their influence on the performance of PEMFCs. Sensitivity analysis results show that the performance of the PEMFCs with the porous baffle channel is the most sensitive to baffle thickness, and the thickness and baffle could be appropriately adjusted. The number of plates and porosity of the baffle are adjusted to improve the performance of the PEMFCs. Full article
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22 pages, 5834 KiB  
Article
Online SoC Estimation of Lithium-Ion Batteries Using a New Sigma Points Kalman Filter
by Dongdong Ge, Zhendong Zhang, Xiangdong Kong and Zhiping Wan
Appl. Sci. 2021, 11(24), 11797; https://0-doi-org.brum.beds.ac.uk/10.3390/app112411797 - 12 Dec 2021
Cited by 7 | Viewed by 2494
Abstract
The accurate state of charge (SoC) online estimation for lithium-ion batteries is a primary concern for predicting the remaining range in electric vehicles. The Sigma points Kalman Filter is an emerging SoC filtering technology. Firstly, the charge and discharge tests of the battery [...] Read more.
The accurate state of charge (SoC) online estimation for lithium-ion batteries is a primary concern for predicting the remaining range in electric vehicles. The Sigma points Kalman Filter is an emerging SoC filtering technology. Firstly, the charge and discharge tests of the battery were carried out using the interval static method to obtain the accurate calibration of the SoC-OCV (open circuit voltage) relationship curve. Secondly, the recursive least squares method (RLS) was combined with the dynamic stress test (DST) to identify the parameters of the second-order equivalent circuit model (ECM) and establish a non-linear state-space model of the lithium-ion battery. Thirdly, based on proportional correction sampling and symmetric sampling Sigma points, an SoC estimation method combining unscented transformation and Stirling interpolation center difference was designed. Finally, a semi-physical simulation platform was built. The Federal Urban Driving Schedule and US06 Highway Driving Schedule operating conditions were used to verify the effectiveness of the proposed estimation method in the presence of initial SoC errors and compare with the EKF (extended Kalman filter), UKF (unscented Kalman filter) and CDKF (central difference Kalman filter) algorithms. The results showed that the new algorithm could ensure an SoC error within 2% under the two working conditions and quickly converge to the reference value when the initial SoC value was inaccurate, effectively improving the initial error correction ability. Full article
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17 pages, 4150 KiB  
Article
Online Active Set-Based Longitudinal and Lateral Model Predictive Tracking Control of Electric Autonomous Driving
by Wenhui Fan, Hongwen He and Bing Lu
Appl. Sci. 2021, 11(19), 9259; https://0-doi-org.brum.beds.ac.uk/10.3390/app11199259 - 05 Oct 2021
Cited by 1 | Viewed by 1837
Abstract
Autonomous driving is a breakthrough technology in the automobile and transportation fields. The characteristics of planned trajectories and tracking accuracy affect the development of autonomous driving technology. To improve the measurement accuracy of the vehicle state and realise the online application of predictive [...] Read more.
Autonomous driving is a breakthrough technology in the automobile and transportation fields. The characteristics of planned trajectories and tracking accuracy affect the development of autonomous driving technology. To improve the measurement accuracy of the vehicle state and realise the online application of predictive control algorithm, an online active set-based longitudinal and lateral model predictive tracking control method of autonomous driving is proposed for electric vehicles. Integrated with the vehicle inertial measurement unit (IMU) and global positioning system (GPS) information, a vehicle state estimator is designed based on an extended Kalman filter. Based on the 3-degree-of-freedom vehicle dynamics model and the curvilinear road coordinate system, the longitudinal and lateral errors dimensionality reduction is carried out. A fast-rolling optimisation algorithm for longitudinal and lateral tracking control of autonomous vehicles is designed and implemented based on convex optimisation, online active set theory and QP solver. Finally, the performance of the proposed tracking control method is verified in the reconstructed curve road scene based on real GPS data. The hardware-in-the-loop simulation results show that the proposed MPC controller has apparent advantages compared with the PID-based controller. Full article
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17 pages, 6727 KiB  
Article
Fundamental Numerical Analysis of a Porous Micro-Combustor Filled with Alumina Spheres: Pore-Scale vs. Volume-Averaged Models
by Qingqing Li, Jiansheng Wang, Jun Li and Junrui Shi
Appl. Sci. 2021, 11(16), 7496; https://0-doi-org.brum.beds.ac.uk/10.3390/app11167496 - 16 Aug 2021
Cited by 2 | Viewed by 1705
Abstract
Inserting porous media into the micro-scale combustor space could enhance heat recirculation from the flame zone, and could thus extend the flammability limits and improve flame stability. In the context of porous micro-combustors, the pore size is comparable to the combustor characteristic length. [...] Read more.
Inserting porous media into the micro-scale combustor space could enhance heat recirculation from the flame zone, and could thus extend the flammability limits and improve flame stability. In the context of porous micro-combustors, the pore size is comparable to the combustor characteristic length. It is insufficient to treat the porous medium as a continuum with the volume-averaged model (VAM). Therefore, a pore-scale model (PSM) is developed to consider the detailed structure of the porous media to better understand the coupling among the gas mixture, the porous media and the combustor wall. The results are systematically compared to investigate the difference in combustion characteristics and flame stability limits. A quantified study is undertaken to examine heat recirculation, including preheating and heat loss, in the porous micro-combustor using the VAM and PSM, which are beneficial for understanding the modeled differences in temperature distribution. The numerical results indicate that PSM predicts a scattered flame zone in the pore areas and gives a larger flame stability range, a lower flame temperature and peak solid matrix temperature, a higher peak wall temperature and a larger Rp-hl than a VAM counterpart. A parametric study is subsequently carried out to examine the effects of solid matrix thermal conductivity (ks) on the PSM and VAM, and then the results are analyzed briefly. It is found that for the specific configurations of porous micro-combustor considered in the present study, the PSM porous micro-combustor is more suitable for simplifying to a VAM with a larger Φ and a smaller ks, and the methods can be applied to other configurations of porous micro-combustors. Full article
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18 pages, 17267 KiB  
Article
Structure Optimization of Battery Thermal Management Systems Using Sensitivity Analysis and Stud Genetic Algorithms
by Jiahui Chen, Dongji Xuan, Biao Wang and Rui Jiang
Appl. Sci. 2021, 11(16), 7440; https://0-doi-org.brum.beds.ac.uk/10.3390/app11167440 - 13 Aug 2021
Cited by 7 | Viewed by 1950
Abstract
Battery thermal management systems (BTMS) are hugely important in enhancing the lifecycle of batteries and promoting the development of electric vehicles. The cooling effect of BTMS can be improved by optimizing its structural parameters. In this paper, flow resistance and heat dissipation models [...] Read more.
Battery thermal management systems (BTMS) are hugely important in enhancing the lifecycle of batteries and promoting the development of electric vehicles. The cooling effect of BTMS can be improved by optimizing its structural parameters. In this paper, flow resistance and heat dissipation models were used to optimize the structure of BTMS, which were more efficient than the computational fluid dynamics method. Subsequently, five structural parameters that affect the temperature inside the battery pack were analyzed using single-factor sensitivity analysis under different inlet airflow rates, and three structural parameters were selected as the constraints of a stud genetic algorithm. In this stud genetic algorithm, the maximal temperature difference obtained by the heat dissipation model was within 5K as the constraint function, where the objective function minimized the overall area of the battery pack. The BTMS optimized by the stud genetic algorithm was reduced by 16% in the maximal temperature difference and saved 6% of the battery package area compared with the original BTMS. It can be concluded that the stud genetic algorithm combined with the flow resistance network and heat dissipation models can quickly and efficiently optimize the air-cooled BTMS to improve the cooling performance. Full article
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19 pages, 6947 KiB  
Article
Analyzing and Modeling of Water Transport Phenomena in Open-Cathode Polymer Electrolyte Membrane Fuel Cell
by Wei-Wei Yuan, Kai Ou, Seunghun Jung and Young-Bae Kim
Appl. Sci. 2021, 11(13), 5964; https://0-doi-org.brum.beds.ac.uk/10.3390/app11135964 - 26 Jun 2021
Cited by 5 | Viewed by 2582
Abstract
Water management is one issue that must be surpassed to ensure high membrane proton conductivity and adequate reactant transport in the membrane-electrode assembly (MEA) simultaneously. A well-designed water management system is based on a comprehensive understanding of water transport in the inner part [...] Read more.
Water management is one issue that must be surpassed to ensure high membrane proton conductivity and adequate reactant transport in the membrane-electrode assembly (MEA) simultaneously. A well-designed water management system is based on a comprehensive understanding of water transport in the inner part of the polymer electrolyte membrane (PEM) fuel cell. In this work, the water transport phenomena in the MEA PEM fuel cell are analyzed by using a mathematical model. The transport of diluted species interface is used to model the transport of water in the ionomer phase in the catalytic layer and the membrane domains. The molecular flux of water is defined using Nernst–Planck equations, including migration and Fickian diffusion using parameters obtained experimentally for diffusivity and mobility based on water drag for a fully humidified membrane. The proposed model 1D model includes anode gas channel, cathode gas channel, anode gas diffusion layer (GDL), cathode GDL, anode catalyst layer, cathode catalyst layer, and proton exchange membrane. Water activity, ionomer conductivity, and output voltage are predicted by changing the humidity on the anode side of the fuel cell. Full article
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