RESEARCH ON ENERGY MANAGEMENT STRATEGY OF FUEL CELL VEHICLE BASED ON DYNAMIC PROGRAMMING

Wang Zhifu; Xu Song; Luo Wei

doi:10.19912/j.0254-0096.tynxb.2022-0852

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (10) : 550-556. DOI: 10.19912/j.0254-0096.tynxb.2022-0852

RESEARCH ON ENERGY MANAGEMENT STRATEGY OF FUEL CELL VEHICLE BASED ON DYNAMIC PROGRAMMING

Wang Zhifu^1,2, Xu Song^1,2, Luo Wei³

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Abstract

For the 100 km hydrogen consumption rate of fuel cell trucks, the energy management strategy of dynamic programming algorithm （DP） is firstly used to derive the energy allocation under different power demands. Then the allocated motor power （P_re） and power cell SOC are used as inputs for fuzzy logic control, and the genetic algorithm （GA） is used to optimize the fuzzy rules and the affiliation function. Finally, the whole vehicle model and control strategy are jointly simulated and validated by Cruise-Matlab/Simulink environment designed under CHTC-LT operating conditions. The simulation results show that the fuel cell hybrid truck fuel economy is improved by 5.2% under the fuzzy logic control strategy compared with the power following strategy, and the control power cell state of charge （SOC） variation is stabilized within a reasonable range.

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fuel cells / dynamic programming / energy management / fuel economy / power control

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Wang Zhifu, Xu Song, Luo Wei. RESEARCH ON ENERGY MANAGEMENT STRATEGY OF FUEL CELL VEHICLE BASED ON DYNAMIC PROGRAMMING[J]. Acta Energiae Solaris Sinica. 2023, 44(10): 550-556 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0852

References

[1] 李宾. 基于出行工况预测的燃料电池汽车能量管理策略研究[D]. 长春: 吉林大学, 2021.
LI B.Research on energy management strategy of fuel cell vehicle based on driving condition prediction[D]. Changchun: Jilin University, 2021.
[2] KWON L, CHO D S, AHN C.Degradation-conscious equivalent consumption minimization strategy for a fuel cell hybrid system[J]. Energies, 2021, 14(13): 3810.
[3] 江冬冬. 基于模型预测控制的混合动力车辆能量管理[D]. 杭州: 浙江大学, 2020.
JIANG D D.Energy management of hybrid electric vehicles based on model predictive control[D]. Hangzhou: Zhejiang University, 2020.
[4] 王志福, 罗崴, 徐崧, 等. 燃料电池汽车能量管理策略综述[J]. 电池, 2022, 52(3): 328-332.
WANG Z F, LUO W, XU S, et al.A review of energy management strategy for fuel cell vehicle[J]. Battery bimonthly, 2022, 52(3): 328-332.
[5] 冯坚, 韩志玉, 高晓杰, 等. 基于动态规划算法和路况的增程式电动车能耗分析[J]. 同济大学学报(自然科学版), 2019, 47(S1): 115-119.
FENG J, HAN Z Y, GAO X J, et al.Energy consumption analysis of a range-extender electric vehicle based on a dynamic programming algorithm and road conditions[J]. Journal of Tongji University(natural science edition), 2019, 47(S1): 115-119.
[6] YIN H, ZHOU W H, LI M, et al.An adaptive fuzzy logic-based energy management strategy on battery/ultracapacitor hybrid electric vehicles[J]. IEEE transactions on transportation electrification, 2016, 2(3): 300-311.
[7] 牛礼民, 张泉泉, 朱奋田, 等. 基于全局优化算法的增程式电动汽车模糊控制策略[J]. 重庆交通大学学报(自然科学版), 2022, 41(2): 137-145.
NIU L M, ZHANG Q Q, ZHU F T, et al.Fuzzy control strategy for extended range electric vehicle based on global optimization algorithm[J]. Journal of Chongqing Jiaotong University(natural science), 2022, 41(2): 137-145.
[8] ANSAREY M, SHARIAT PANAHI M, ZIAATI H, et al.Optimal energy management in a dual-storage fuel cell hybrid vehicle using multi-dimensional dynamic programming[J]. Journal of power sources, 2014, 250: 359-371.
[9] 刘正杰, 朱云芳, 戴朝华, 等. 增程式燃料电池混合动力有轨电车电源系统优化配置[J]. 太阳能学报, 2022, 43(3): 67-73.
LIU Z J, ZHU Y F, DAI C H, et al.Optimization of power system configuration for extended-range fuel cell hybrid tram[J]. Acta energiae solaris sinica, 2022, 43(3): 67-73.
[10] 王永红, 董日康, 武志斐. 基于模糊理论的燃料电池汽车能量管理策略[J]. 科技和产业, 2021, 21(6): 255-258.
WANG Y H, DONG R K, WU Z F.Fuel cell vehicle energy management strategy based on fuzzy theory[J]. Science technology and industry, 2021, 21(6): 255-258.
[11] 何洪文, 石曼, 曹剑飞, 等. 基于动态规划的再生制动能量管理策略[J]. 重庆理工大学学报(自然科学), 2021, 35(2): 74-80.
HE H W, SHI M, CAO J F, et al.Regenerative braking energy management strategy based on dynamic programming[J]. Journal of Chongqing University of Technology(natural science), 2021, 35(2): 74-80.
[12] 李岩, 袁弘宇, 于佳乔, 等. 遗传算法在优化问题中的应用综述[J]. 山东工业技术, 2019(12): 242-243, 180.
LI Y, YUAN H Y, YU J Q, et al.Review on the application of genetic algorithm in optimization problems[J]. Shandong industrial technology, 2019(12): 242-243, 180.