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International Journal of Energy for a Clean Environment
SJR: 0.195 SNIP: 0.659 CiteScore™: 1.2

ISSN Imprimir: 2150-3621
ISSN En Línea: 2150-363X

International Journal of Energy for a Clean Environment

Formerly Known as Clean Air: International Journal on Energy for a Clean Environment

DOI: 10.1615/InterJEnerCleanEnv.2014007911
pages 241-256

MULTIOBJECTIVE FORECAST AND OPTIMIZATION FOR SECONDARY BATTERY PRODUCTION IN CHINA

Weijun Sun
Beijing Key Laboratory of Environmental Science and Engineering, School of Chemical Engineering and Environment, Beijing Institute of Technology, Beijing 100081, China
Yajuan Yu
Beijing Key Laboratory of Environmental Science and Engineering, School of Chemical Engineering and Environment, Beijing Institute of Technology, Beijing 100081, China
Dong Wang
Beijing Key Laboratory of Environmental Science and Engineering, School of Chemical Engineering and Environment, Beijing Institute of Technology, Beijing 100081, China
Xiang Wang
Beijing Key Laboratory of Environmental Science and Engineering, School of Chemical Engineering and Environment, Beijing Institute of Technology, Beijing 100081, China
Yuhan Liang
Beijing Key Laboratory of Environmental Science and Engineering, School of Chemical Engineering and Environment, Beijing Institute of Technology, Beijing 100081, China
Kai Huang
College of Environmental Science and Engineering, Beijing Forestry University, Beijing, 100083, China

SINOPSIS

Currently in China, PbA, NiMH, and Li-ions have occupied the secondary battery market. However, the use of secondary batteries has severe environmental impact to humans, and the negative influences of different types are not the same. Therefore it is significant to build up yield optimization and adjust the industry structure for coordination of environmental and economic concerns. Yield structure is determined by policy, economic effect, environmental impact, and other factors, so this article sets up multiobjective goals and simulates three yield-optimization scenarios. "Business as usual" (BAU) focuses on policy direction, the "low-investment model" (LIM) takes economic effects into consideration, and the "environmental emphasis model" (EEM) focuses on the environment. The application of BAU, LIM, and EEM models, serving as tools to analyze the yield optimization and industry structure of secondary batteries, is helpful for decision makers and experts to put forward prospective policy for a sustainable society. Based on the three models, the results of secondary battery yield optimization are analyzed. Compared with total production, investment, and annual environmental impact from the three models, EEM has the lowest negative environmental impact, while investment and the annual production of the other two are within a reasonable range. That means EEM is an economical and environmentally friendly optimization model, balancing the high electrochemical performance and fast social development, while not sacrificing the environment.


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