Multi-mode energy management strategy for hydraulic hub-motor auxiliary system based on improved global optimization algorithm
Source abstract
Abstract
Heavy commercial vehicles equipped with a hydraulic hub-motor auxiliary system (HHMAS) often operate under complex road conditions. Selecting appropriate operating mode and realizing reasonable energy management to match unpredictable road conditions are the keys to the driving performance and fuel economy of HHMAS. Therefore, a multi-mode energy management strategy (MM-EMS) based on improved global optimization algorithm is proposed in this study for HHMAS. First, an improved dynamic programming (DP) algorithm for HHMAS is developed. This improved DP algorithm considers the effect of SOC and vehicle speed, thereby preventing the calculation results from falling into local optimization. This algorithm also reduces the dimension of the control variable data grid, and the calculation time is reduced by 35% without affecting the accuracy. Second, a MM-EMS with hierarchical control is proposed. This strategy extracts the optimal control rules from the results of the improved DP algorithm. Then it divides the system’s operating region into two types, namely, single-mode working region and mixedmode working region. In the single-mode working region, mode switching is realized through fixed thresholds. In the mixedmode working region, a linear quadratic regulator (LQR) is adopted to determine a target mode and realize SOC tracking control. Finally, the designed MM-EMS is verified separately in offline simulation and hardware-in-the-loop (HIL) under actual vehicle test cycles. Simulation results show that the results between HIL and offline simulation are largely coincidence. Besides, in comparison with the engine optimal control strategy, the designed MM-EMS can achieve an approximate optimal control, with oil savings of 3.96%.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Electric and Hybrid Vehicle Technologies · Hydraulic and Pneumatic Systems · Fuel Cells and Related Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (32)
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1. Bravo R R S, De Negri V J, Oliveira A A M. Design and analysis of a parallel hydraulic—Pneumatic regenerative braking system for heavy-duty hybrid vehicles. Appl Energy, 2018, 225: 60–7710.1016/j.apenergy.2018.04.102 ↗
2. Amjad S, Neelakrishnan S, Rudramoorthy R. Review of design considerations and technological challenges for successful development and deployment of plug-in hybrid electric vehicles. Renew Sust Energy Rev, 2010, 14: 1104–111010.1016/j.rser.2009.11.001 ↗
3. Zhang H T, Li X G, Liu X Z, et al. Enhancing fuel cell durability for fuel cell plug-in hybrid electric vehicles through strategic power management. Appl Energy, 2019, 241: 483–49010.1016/j.apenergy.2019.02.040 ↗
4. Zeng X H, Li G H, Yin G D, et al. Model predictive control-based dynamic coordinate strategy for hydraulic hub-motor auxiliary system of a heavy commercial vehicle. Mech Syst Signal Process, 2018, 101: 97–12010.1016/j.ymssp.2017.08.029 ↗
5. Filipi Z, Kim Y J. Hydraulic hybrid propulsion for heavy vehicles: Combining the simulation and engine-in-the-loop techniques to maximize the fuel economy and emission benefits. Oil Gas Sci Tech, 2010, 65: 155–17810.2516/ogst/2009024 ↗
6. Midgley W, Cathcart H, Cebon D. Modelling of hydraulic regenerative braking systems for heavy vehicles. P I Mech Eng D-J Automob Eng, 2013, 227: 1072–108410.1177/0954407012469168 ↗
7. Baseley S, Ehret C, Greif E, et al. Hydraulic hybrid systems for commercial vehicles. In: SAE 2007 Commercial Vehicle Engineering Congress & Exhibition. 2007-01-4150
8. Cunningham S. Hydrostatic drive. US Patent, No. 6675575. 2014
9. EauClaire M. Joining forces. OEM Off-Highway, 2008, 23: 40–47
10. Ragavan S V, Kumar J M, Ponnambalam S G. Design of a mecha-tronic drive train with regenerative braking. Appl Mech Mater, 2011, 110–116: 5111–5117
11. Peng J K, He H W, Xiong R. Rule based energy management strategy for a series-parallel plug-in hybrid electric bus optimized by dynamic programming. Appl Energy, 2017, 185: 1633–164310.1016/j.apenergy.2015.12.031 ↗
12. Shi D H, Wang S H, Pisu P, et al. Modeling and optimal energy management of a power split hybrid electric vehicle. Sci China Tech Sci, 2017, 60: 713–72510.1007/s11431-016-0452-8 ↗
13. Zhang Y H, Jiao X H, Li L, et al. A hybrid dynamic programming-rule based algorithm for real-time energy optimization of plug-in hybrid electric bus. Sci China Tech Sci, 2014, 57: 2542–255010.1007/s11431-014-5690-2 ↗
14. Qiu L H, Qian L J, Zomorodi H, et al. Design and optimization of equivalent consumption minimization strategy for 4WD hybrid electric vehicles incorporating vehicle connectivity. Sci China Tech Sci, 2018, 61: 147–15710.1007/s11431-016-9141-x ↗
15. Johri R, Filipi Z. Low-cost pathway to ultra efficient city car: Series hydraulic hybrid system with optimized supervisory control. SAE Int J Engines, 2010, 2: 505–52010.4271/2009-24-0065 ↗
16. Xiang C L, Ding F, Wang W D, et al. MPC-based energy management with adaptive Markov-chain prediction for a dual-mode hybrid electric vehicle. Sci China Tech Sci, 2017, 60: 737–74810.1007/s11431-016-0640-2 ↗
17. Vu T V, Chen C K. A control oriented model and application for control system design of a series hydraulic hybrid vehicle. In: 2014 2nd International Conference on Systems and Informatics (ICSAI). IEEE, 2014. 51–5610.1109/ICSAI.2014.7009258 ↗
18. Qi Y L, Wang W D, Xiang C L, et al. Economic MPC-based transient control for a dual-mode power-split HEV. Sci China Tech Sci, 2017, 60: 1917–193410.1007/s11431-017-9128-4 ↗
19. Bertsekas D P. Dynamic Programming and Optimal Control. Belmont, MA: Athena Scientific, 2005
20. Paganelli G, Ercole G, Brahma A. General supervisory control policy for the energy optimization of charge-sustaining hybrid electric vehicles. JSAE Rev, 2001, 22: 511–51810.1016/S0389-4304(01)00138-2 ↗
21. Serrao L, Onori S, Rizzoni G. ECMS as a realization of Pontryagin’s minimum principle for HEV control. In: 2009 American Control Conference. St. Louis, 2009. 3964–396910.1109/ACC.2009.5160628 ↗
22. Hippalgaonkar R, Ivantysynova M. Optimal power management of hydraulic hybrid mobile machines—Part I: Theoretical studies, modeling and simulation. J Dyn Syst Measur Control, 2016, 138: 05100210.1115/1.4032742 ↗
23. Hippalgaonkar R, Ivantysynova M. Optimal power management of hydraulic hybrid mobile machines—Part II: Machine implementation and measurements. J Dyn Syst Measur Control, 2016, 138: 05100310.1115/1.4032743 ↗
24. Deppen T O, Alleyne A G, Stelson K A, et al. Predictive energy management for parallel hydraulic hybrid passenger vehicle. In: Sci China Tech Sci ASME 2010 Dynamic Systems and Control Conference. Cambridge, 2010. 185–192
25. Yan F J, Wang J M, Huang K S. Hybrid electric vehicle model predictive control torque-split strategy incorporating engine transient characteristics. IEEE Trans Veh Technol, 2012, 61: 2458–246710.1109/TVT.2012.2197767 ↗
26. Borhan H A, Vahidi A, Phillips A M, et al. Predictive energy management of a power-split hybrid electric vehicle. In: 2009 American Control Conference. St. Louis, USA, 2009. 3970–397610.1109/ACC.2009.5160451 ↗
27. Song K, Li F Q, Hu X, et al. Multi-mode energy management strategy for fuel cell electric vehicles based on driving pattern identification using learning vector quantization neural network algorithm. J Power Sources, 2018, 389: 230–23910.1016/j.jpowsour.2018.04.024 ↗
28. Bellman R. Dynamic Programming. North Chelmsford: Courier Corporation, 2013
29. Zhou H C, Xu Z P, Liu L, et al. A rule-based energy management strategy based on dynamic programming for hydraulic hybrid vehicles. Math Problems Eng, 2018, 2018: 949202610.1155/2018/9492026 ↗
30. Zhang X, Göhlich D. Improvement in the vehicle stability of dis-tributed-drive electric vehicles based on integrated model-matching control. P I Mech Eng D-J Automob Eng, 2017, 232: 341–35610.1177/0954407017701284 ↗
31. Lenzo B, Bucchi F, Sorniotti A, et al. On the handling performance of a vehicle with different front-to-rear wheel torque distributions. Veh Syst Dyn, 2019, 57: 1685–170410.1080/00423114.2018.1546013 ↗
32. Zou Y, Guo N, Zhang X. An integrated control strategy of path following and lateral motion stabilization for autonomous distributed drive electric vehicles. P I Mech Eng D-J Automob Eng, 2019, 2019: 095440701988416