Prediction of Demand Power of Electric Drive Armored Vehicle Based on Improved Grey Markov Chain

doi:10.3969/j.issn.1000-1093.2021.10.009

Abstract

Abstract: A combined power prediction method based on improved grey Markov chain is proposed to solve the problem of low accuracy of demand power prediction for electric drive armored vehicles. The improved grey prediction method and Markov chain prediction method are used to predict the main power and residual power in the load demand power. The predicted results of demand and residual powers at each prediction moment are algebraically added to establish an improved grey Markov chain combination prediction method. The simulated results show that，compared with the traditional prediction method，the proposed demand power combination prediction method can better predict the strong randomness of load power and accurately predict the variation trend of demand power，and the prediction accuracy is increased by 16.49%，which can provide effective reference information for the energy management strategy.

Key words: electricdrivearmoredvehicle, loaddemand, greymodel, Markovchain, powerprediction

CLC Number:

TJ810.2

LIU Chunguang， CHEN Luming， ZHANG Yunyin， ZHANG Zheng， XU Haoxuan. Prediction of Demand Power of Electric Drive Armored Vehicle Based on Improved Grey Markov Chain[J]. Acta Armamentarii, 2021, 42(10): 2130-2144.

References

［1］项宇.新型电传动装甲车辆综合电力系统能量管理策略研究[D].北京:装甲兵工程学院，2016.
XIANG Y.Research on energy management strategy of integrated power system for new electric drive armored vehicles[D].Beijing:Academy of Armored Forces Engineering，2016.(in Chinese)
[2] 应天杏.基于功率需求预测的燃料电池汽车能量管理自适应控制策略研究[D].上海:上海交通大学，2018.
YING T X.Research on adaptive control strategy for energy ma-nagement of fuel cell vehicle based on power demand prediction[D].Shanghai:Shanghai Jiao Tong University，2018.(in Chinese)
[3] FRANCO F L，RICCO M，MANDRIOLI R，et al.Electric vehicle aggregate power flow prediction and smart charging system for distributed renewable energy self-consumption optimization[J].Energies，2020，13(19):418002.
[4] 付翔，铁鑫，刘会康.基于需求功率预测的混合动力越野车能量管理[J].江苏大学学报（自然科学版），2021，42(1):67-76.
FU X，TIE X，LIU H K.Energy management of hybrid off road vehicles based on demand power prediction[J].Journal of Jiangsu University (Natural Science Edition)，2021，42(1):67-76. (in Chinese)
[5] 廖自力，项宇，刘春光，等.电传动装甲车辆混合动力系统功率流控制策略[J].兵工学报，2017，38(12):2289-2300.
LIAO Z L，XIANG Y，LIU C G，et al.Power flow control strategy of hybrid power system of electric drive armored vehicle[J].Acta Armamentarii，2017，38(12):2289-2300.(in Chinese)
[6] 张杰，马晓军，刘春光，等.双侧独立电驱动履带车辆反馈线性化解耦与预测行驶控制[J].兵工学报，2021，42(4): 697-705.
ZHANG J，MA X J，LIU C G，et al.Feedback linearization decoupling and predictive driving control for dual independent electric drive tracked vehicle[J].Acta Armamentarii，2021，42(4): 697-705. (in Chinese)
[7] 王大鹏.灰色预测模型及中长期电力负荷预测应用研究[D].武汉:华中科技大学，2013.
WANG D P.Research on grey prediction models and their applications in medium- and long-term power load forecasting[D].Wuhan:Huazhong University of Science and Technology，2013.(in Chinese)
[8] 王子赟，纪志成.基于灰色-辨识模型的风电功率短期预测[J].电力系统保护与控制，2013，41(12):79-85.
WANG Z Y，JI Z C.Grey-identification model based wind power generation short-term prediction[J].Power System Protection and Control 2013，41(12):79-85. (in Chinese)
[9] YIN K D，GENG Y，LI X M.Improved grey prediction model based on exponential grey action quantity[J].Journal of Systems Engineering and Electronics，2018，29(3):560-570.
[10] 曾波，尹小勇，孟伟.实用灰色预测建模方法及其MATLAB程序实现[M].北京:科学出版社，2019.
ZENG B，YIN X Y, MENG W.Practical grey prediction modeling method and its MATLAB program implementation[M]. Beijing: Science Press，2019.(in Chinese)
[11] 邓聚龙.灰色系统理论教程[M].武汉:华中科技大学出版社，1990.
DENG J L.The grey system theory tutorial[M].Wuhan:Huazhong University of Science and Technology Press，1990.(in Chinese)
[12] CHEN L M，LIAO Z L，MA X J.Nonlinear model predictive control for heavy-duty hybrid electric vehicles using random power prediction method[J]. IEEE Access，2020，8: 202819-202835.
[13] 孙超.混合动力汽车预测能量管理研究[D].北京:北京理工大学，2016.
SUN C.Research on predictive energy management of hybrid electric vehicle[D].Beijing: Beijing Institute of Technology，2016. (in Chinese)
[14] 罗云馨.基于浮动车数据的城市道路交通评价分析[D]. 成都:电子科技大学，2019.
LUO Y X.Evaluation and analysis of urban road traffic based on floating car data[D].Chengdu: University of Electronic Science and Technology of China，2019.(in Chinese)
[15] 杨超.轻型汽车实际道路行驶排放特性研究[D].长春:吉林大学，2020.
YANG C.Study on real driving emission characteristics of light vehicle[D].Changchun:Jilin University，2020.(in Chinese)
[16] LI Q，ZHOU Q，HE Y，et al.Driver-identified supervisory control system of hybrid electric vehicles based on spectrum-guided fuzzy feature extraction[J].IEEE Transactions on Fuzzy Systems，2020，28(11):2691-2701.
[17] 季学武，费聪，何祥坤，等.基于LSTM网络的驾驶意图识别及车辆轨迹预测[J].中国公路学报，2019，32(6): 34-42.
JI X W，FEI C，HE X K，et al.Intention recognition and trajectory prediction for vehicles using LSTM network[J].China Journal of Highway and Transport，2019，32(6):34-42.(in Chinese)
[18] NIJHUIS M，GIBESCU M，COBBEN J F G.Bottom-up Markov Chain Monte Carlo approach for scenario based residential load modelling with publicly available data[J].Energy and Buildings，2016，112:121-129.
[19] ZOU Y，KONG Z H，LIU T，et al.A real-time Markov chain driver model for tracked vehicles and its validation: its adaptability via stochastic dynamic programming[J].IEEE Transactions on Vehicular Technology，2017，66(5):3571-3582.
[20] 傅妍芳，王赞，苏一昶，等.基于马尔可夫链的军用Ad hoc网络可用性建模[J].兵工学报，2021，42(1):65-73.
FU Y F，WANG Z，SU Y T，et al. Availability modeling of military Ad Hoc network based on Markov chain[J].Acta Armamentarii，2021，42(1):65-73.(in Chinese)
[21] ZHANG M，SHI S M，LIN N，et al.High-efficiency driving cycle generation using a Markov chain evolution algorithm[J]. IEEE Transactions on Vehicular Technology， 2019，68(2):1288-1301.
[22] LIU T，WANG B，YANG C L.Online Markov chain-based energy management for a hybrid tracked vehicle with speedy Q-learning[J].Energy，2018，160:544-555.
[23] 袁伟，付锐，郭应时，等.基于马尔可夫链的驾驶人视觉转移特征[J].长安大学学报(自然科学版)，2012，32(6): 88-93.
YUAN W，FU R，GUO Y S，et al. Driver's visual transition cha-racteristics based on the Markov chain[J].Journal of Chang'an University (Natural Science Edition)，2012，32(6):88-93. (in Chinese)
[24] 翁志坚，邱晨杰，邱福祥，等.基于马尔可夫优化的灰色GM(1，1) 沉降预测模型及应用[J].科学技术与工程，2020，20(29): 12065-12070.
WENG Z J，QIU C J，QIU F X，et al.An optimized GM(1，1) grey prediction model based on Markov chain and its application[J].Science Technology and Engineering，2020，20(29): 12065-12070. (in Chinese)
[25] 贾俊平，何晓群，金勇进.统计学[M].第7版.北京:中国人民大学出版社，2018.
JIA J P，HE X Q，JIN Y J.Statistics [M].7th ed. Beijing:China Renmin University Press，2018.(in Chinese)

[1]	WANG Zhenfeng， LI Fei， WANG Xinyu， YANG Jiansen， QIN Yechen. State Estimation of Suspension System Based on Interacting Multiple Model Unscented Kalman Filter [J]. Acta Armamentarii, 2021, 42(2): 242-253.
[2]	FU Yanfang， WANG Zan， SU Yichang， DAI Fei， ZHONG Lianjiong， GUO Dengdeng， LU Ying， LIANG Hongtao. Availability Modeling of Military Ad Hoc Network Based on Markov Chain [J]. Acta Armamentarii, 2021, 42(1): 65-73.
[3]	ZHAI Yuyao, SHI Xianjun, L Jiapeng. Research on Evaluation Method for Testability Index and Modeling of Missile System Based on GSPN [J]. Acta Armamentarii, 2019, 40(10): 2070-2079.
[4]	PAN Xian-jun, ZHANG Wei, ZHAO Tian, GUO Xiao-Qiang. Fractional Order Discrete Grey Model and Its Application in Spare Parts Demand Forecasting [J]. Acta Armamentarii, 2017, 38(4): 785-792.
[5]	LIAO Zi-li, XIANG Yu, LIU Chun-guang, LI Jia-qi. Power Flow Control Strategy of Hybrid Power System of Electric Drive Armored Vehicle [J]. Acta Armamentarii, 2017, 38(12): 2289-2300.