期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 16, 期 5, 页码 3407-3416出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2019.2957297
关键词
Batteries; Energy management; Optimization; Degradation; Informatics; Estimation; Battery degradation; cost-effective; energy management; optimal sizing; ultracapacitor (UC)
类别
资金
- National Natural Science Foundation of China [51607113]
- Shanghai Sailing Program [16YF1407600, TII-19-2150]
In battery/ultracapacitor (UC) hybrid energy storage systems (HESS), sizing and energy management strategies are crucial, which determine the system cost and performance. However, research on these two problems in a coupled manner for plug-in electric vehicles is still immature. This article aims at resolving this issue in the perspective of minimizing the average operating cost. Both manufacturing cost and system end-of-life timing are incorporated. A quantitative battery degradation model is employed to evaluate the battery dynamic capacity loss and cycle life. Dynamic programming algorithm is then deployed to achieve optimal power distribution between battery and UC. Furthermore, the power management and HESS optimal sizing strategies are unified into a single cost-minimization problem. Combining those efforts, the optimal size of the HESS with minimized average operating cost is solved by simulated annealing method. Optimization results illustrate that a minimum cost of 15.52 USD is achieved with 72 UC cells and 7100 battery cells. A large set of simulation data has proved the optimality of the optimization results. Compared with the battery-only solution, the proposed solution demonstrates 11.9% cost reduction and 21.7% battery cycle life extension under the Urban Dynamometer Driving Schedule. Moreover, the temperature rise of the battery is reduced by 31.1%. Finally, based on the optimal results, the energy management strategy is extended to fit real-time applications by utilizing Markov chain and stochastic dynamic programming.
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