| 1. |
- Deng, Pengyi, et al.
(författare)
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Optimal online energy management strategy of a fuel cell hybrid bus via reinforcement learning
- 2024
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Ingår i: Energy Conversion and Management. - 0196-8904. ; 300
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Tidskriftsartikel (refereegranskat)abstract
- An energy management strategy (EMS) based on reinforcement learning is proposed in this study to enhance the fuel economy and durability of a fuel cell hybrid bus (FCHB). Firstly, a comprehensive powertrain system model for the FCHB is established, mainly including the FCHB's power balance, fuel cell system (FCS) efficiency, and aging models. Secondly, the state–action space, state transition probability matrix (TPM), and multi-objective reward function of Q-learning algorithm are designed to improve the fuel economy and the durability of power sources. The FCHB's demand power and battery state of charge (SOC) serve as the state variables and the FCS output power is used as the action variable. Using the demonstration FCHB data, a state TPM is created to represent the overall operation. Finally, an EMS employing Q-learning is formulated to optimize the fuel economy of FCHB, maintain battery SOC, suppress FCS power fluctuations, and enhance FCS lifetime. The proposed EMS is tested and verified through hardware-in-the-loop (HIL) tests. The simulation results demonstrate the effectiveness of the proposed strategy. Compared to a rule-based EMS, the Q-learning-based EMS can improve the energy economy by 7.8%. Furthermore, it is only a 3.7% difference to the best energy economy under dynamic optimization, while effectively reducing the decline and enhancing the durability of the FCS.
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| 2. |
- Wu, Xiaohua, et al.
(författare)
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Online Adaptive Model Identification and State of Charge Estimation for Vehicle-Level Battery Packs
- 2024
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Ingår i: IEEE Transactions on Transportation Electrification. - 2332-7782. ; 10:1, s. 596-607
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Tidskriftsartikel (refereegranskat)abstract
- Accurate state of charge (SOC) estimation of traction batteries plays a crucial role in energy and safety management for electric vehicles. Existing studies focus primarily on cell battery SOC estimation. However, numerical instability and divergence problems might occur for a large-size lithium-ion battery pack consisting of many cells. This paper proposes a high-performance online model identification and SOC estimation method based on an adaptive square root unscented Kalman filter (ASRUKF) and an improved forgetting factor recursive least squares (IFFRLS) for vehicle-level traction battery packs. The model parameters are identified online through the IFFRLS, where the conventional method might encounter numerical stability problems. By updating the square root of the covariance matrix, the divergence problem in the traditional unscented Kalman filter is solved in the ASRUKF algorithm, where the positive semi-definiteness of the covariance matrix is guaranteed. Combined with the adaptive noise covariance matched filtering algorithm and real-time compensation of system error, the proposed method solves the problem of ever-degrading estimation accuracy in the presence of time-varying noise with unknown statistical characteristics. Using a 66.2-kWh vehicle battery pack, we experimentally verified that the proposed algorithm could achieve high estimation accuracy with guaranteed numerical stability. The maximum error of SOC estimation can be bounded by 1%, and the root-mean-square error is as low as 0.47% under real-world vehicle operating conditions.
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| 3. |
- Yang, Fei, et al.
(författare)
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Efficient and Spectrally Stable Blue Perovskite Light-Emitting Diodes Based on Potassium Passivated Nanocrystals
- 2020
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Ingår i: Advanced Functional Materials. - : WILEY-V C H VERLAG GMBH. - 1616-301X .- 1616-3028. ; 30:10
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Tidskriftsartikel (refereegranskat)abstract
- Metal halide perovskites have aroused tremendous interest in the past several years for their promising applications in display and lighting. However, the development of blue perovskite light-emitting diodes (PeLEDs) still lags far behind that of their green and red cousins due to the difficulty in obtaining high-quality blue perovskite emissive layers. In this study, a simple approach is conceived to improve the emission and electrical properties of blue perovskites. By introducing an alkali metal ion to occupy some sites of peripheral suspended organic ligands, the nonradiative recombination is suppressed, and, consequently, blue CsPb(Br/Cl)(3) nanocrystals with a high photoluminescence quantum efficiency of 38.4% are obtained. The introduced K+ acts as a new type of metal ligand, which not only suppresses nonradiative pathways but also improves the charge carrier transport of the perovskite nanocrystals. With further engineering of the device structure to balance the charge injection rate, a spectrally stable and efficient blue PeLED with a maximum external quantum efficiency of 1.96% at the emission peak of 477 nm is fabricated.
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| 4. |
- Zhang, Jibin, et al.
(författare)
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Highly Luminescent and Stable CsPbI3 Perovskite Nanocrystals with Sodium Dodecyl Sulfate Ligand Passivation for Red-Light-Emitting Diodes
- 2021
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Ingår i: The Journal of Physical Chemistry Letters. - : AMER CHEMICAL SOC. - 1948-7185. ; 12:9, s. 2437-2443
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Tidskriftsartikel (refereegranskat)abstract
- CsPbI3 perovskite nanocrystals (NCs) have recently emerged as promising materials for optoelectronic devices because of their superior properties. However, the poor stability of the CsPbI3 NCs induced by easy ligand desorption represents a key issue limiting their practical applications. Herein, we report stable and highly luminescent black-phase CsPbI3 NCs passivated by novel ligands of sodium dodecyl sulfate (SDS). Theoretical calculation results reveal a stronger adsorption energy of SDS molecules at the CsPbI3 surface than that of commonly used oleic acid. As a result, the defect formation caused by the ligand loss during the purification process is greatly suppressed. The optimized SDS- CsPbI3 NCs exhibit significantly reduced surface defects, much enhanced stability, and superior photoluminescence efficiency. The red perovskite light-emitting diodes based on the SDS-CsPbI3 NCs demonstrate an external quantum efficiency of 8.4%, which shows a 4-fold improvement compared to the devices based on the oleic acid-modified CsPbI3 NCs.
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