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- Zhang, Yinping, et al.
(författare)
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Ten cities cross-sectional questionnaire survey of children asthma and other allergies in China
- 2013
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Ingår i: Chinese Science Bulletin. - : Springer Science and Business Media LLC. - 1001-6538 .- 1861-9541. ; 58:34, s. 4182-4189
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Tidskriftsartikel (refereegranskat)abstract
- Asthma, rhinitis and eczema (allergic or non-allergic) have increased throughout the world during the last decades, especially among children. Changes in the indoor environment are suspected to be important causes. China has experienced a dramatic change in indoor environmental exposures during the past two decades. However, such changes and their associations with children's asthma and other health aspects have not been thoroughly studied. China, Children, Homes, Health (CCHH), Phase I, was a cross-sectional questionnaire survey of 48219 children 1-8 years old in 10 Chinese cities during 2010-2012. The questionnaire includes the International Study of Asthma and Allergies in Childhood (ISAAC) core health questions and additional questions regarding housing, life habits and outdoor environment. In health analyses, children aged 3-6 years old were included. The prevalences of doctor diagnosed asthma varied from 1.7% to 9.8% (mean 6.8%), a large increase from 0.91% in 1999 and 1.50% in 2000. The prevalence of wheeze, rhinitis and atopic eczema (last 12 months) varied from 13.9% to 23.7%, 24.0% to 50.8% and 4.8% to 15.8%, respectively. Taiyuan had the lowest prevalences of all illnesses and Shanghai the highest, except for wheezewhere the highest value was for Urumqi. We found (1) no obvious association between disease prevalences and ambient PM10 concentrations and (2) higher prevalences of disease in humid climates with hot summers and cold winters, but with no centrally heated buildings. Associations between the diseases and economic status as indexed by Gross Domestic Product (GDP) requires further study.
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| 2. |
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| 3. |
- Xun, Qian, et al.
(författare)
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A Learning Model Predictive Controller for Energy Management in Fuel Cell Hybrid Electric Vehicles
- 2024
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Ingår i: <em>IEEE Transportation Electrification Conference and Expo, ITEC 2024</em>. - : Institute of Electrical and Electronics Engineers Inc..
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Konferensbidrag (refereegranskat)abstract
- In fuel cell hybrid electric vehicles (FCHEVs), the fuel cell (FC) system serves as the primary power source and a secondary energy storage device such as a battery acts as the energy buffer. However, challenges such as high hydrogen prices and short FC lifespans underscore the need for advanced energy management strategies. This paper introduces a learning model predictive controller (LMPC) to optimize the power allocation between the FC and the battery used in FCHEVs to minimize the hydrogen consumption, enhance the FC lifespan, and maintain a reasonable battery state of charge (SoC). The proposed LMPC is reference-free and leverages the benefits of model predictive control (MPC) and iterative learning control (ILC). The LMPC design outlines the iterative construction of convex terminal sets and terminal costs based on past states and input trajectories. The effectiveness of the proposed LMPC is validated through hardware-in-the-loop (HiL) experiments.
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| 4. |
- Xun, Qian, et al.
(författare)
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A Model Predictive Controller with Adaptive Tuning Weights for Energy Management in Fuel Cell Hybrid Electric Vehicles
- 2024
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Ingår i: <em>2024 IEEE Transportation Electrification Conference and Expo, ITEC 2024</em>. - : Institute of Electrical and Electronics Engineers Inc..
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Konferensbidrag (refereegranskat)abstract
- Fuel cell hybrid electric vehicles (FCHEVs) are recognized as a promising solution for vehicle electrification. However, the adoption of FCHEVs is relatively slow due to various factors such as the high cost of hydrogen and the limited lifespan of fuel cells. Therefore, effective energy management strategies are of great interest. Model predictive control (MPC) is widely employed to deal with energy management in FCHEVs. However, conventional MPC often relies on subjective selection of control weights in the objective function and the performance may be compromised. This paper proposes an optimal weight adaptation method within the MPC framework to enhance its effectiveness. The weights in the objective function are dynamically adjusted online using a moving horizon. Optimization techniques are then applied to fine tune these weights. The effectiveness of the proposed MPC controller with adaptive tuning weights is validated under the UDDS drive cycle.
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