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| 1 | Multi-mode energy management strategy for hydraulic hub-motor auxiliary system based on improved global optimization algorithm显示文摘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%. | ZENG XiaoHua WU ZiQiao WANG Yue SONG DaFeng LI GuangHan | 2020 | Science China(Technological Sciences)2020，63，10： | 1 |
| 2 | A multi-objective power flow optimization control strategy for a power split plug-in hybrid electric vehicle using game theory显示文摘Power flow optimization control,which governs the energy flow among engine,battery,and motor,plays a very important role in plug-in hybrid electric vehicles(PHEVs).Its performance directly affects the fuel economy of PHEVs.For the purpose of improving fuel economy,the electric system including battery and motor will be frequently scheduled,which would affect battery life.Therefore,a multi-objective optimization mechanism taking fuel economy and battery life into account is necessary,which is also a research focus in field of hybrid vehicles.Motivated by this issue,this paper proposes a multi-objective power flow optimization control strategy for a power split PHEV using game theory.Firstly,since the demand power of driver which is necessary for the power flow optimization control,cannot be known in advance,the demand power of driver can be modelled using a Markov chain to obtain predicted demand power.Secondly,based on the predicted demand power,the multi-objective optimization control problem is transformed into a game problem.A novel non-cooperative game model between engine and battery is established,and the benefit function with fuel economy and battery life as the optimization objective is proposed.Thirdly,under the premise of satisfying various constraints,the participants of the above game maximize their own benefit function to obtain the Nash equilibrium,which comprises of optimal power split scheme.Finally,the proposed strategy is verified compared with two baseline strategies,and results show that the proposed strategy can reduce equivalent fuel consumption by about 15%compared with baseline strategy 1,and achieve similar fuel economy while greatly extend battery life simultaneously compared with baseline strategy 2. | WANG WeiDa WANG WeiQi YANG Chao LIU Cheng YANG LiuQuan SUN XiaoXia XIANG ChangLe | 2021 | Science China(Technological Sciences)2021，64，12： | 1 |
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