This page has only limited features, please log in for full access.
Short‐term traffic flow prediction plays a crucial role in research and application of intelligent transportation system. Neural network algorithm can use the big data for training and has more advantages over other prediction models in traffic features extraction. However, it is still a problem to extract the spatiotemporal features of traffic flow in a simple and sufficient way to improve the prediction accuracy. In this paper, a double‐branch deep residual gated convolutional neural network (RGCNN) is proposed to extract features from both time and space based on three‐dimensional traffic data, and scaled exponential linear units is used as an activation function to enhance the convergence effect of network training. In order to increase the ability of the network to fit the traffic data, an analogous self‐attention (ASA) is designed, which retains the advantages of attention while hardly increasing training costs. Simulation experiments are carried out in real traffic data sets, the simulation results of traffic flow prediction tasks in different prediction horizons show that the prediction performance of the proposed prediction model (ASA‐RGCNN) is superior to that of other common prediction models and the proposed model can be applied to the predicting task under different traffic conditions. By visualising ASA weights at different traffic flow levels, the impact of space‐time traffic data on the prediction task can also be found out.
Zhao Zhang; Xiaohong Jiao. A deep network with analogous self‐attention for short‐term traffic flow prediction. IET Intelligent Transport Systems 2021, 15, 902 -915.
AMA StyleZhao Zhang, Xiaohong Jiao. A deep network with analogous self‐attention for short‐term traffic flow prediction. IET Intelligent Transport Systems. 2021; 15 (7):902-915.
Chicago/Turabian StyleZhao Zhang; Xiaohong Jiao. 2021. "A deep network with analogous self‐attention for short‐term traffic flow prediction." IET Intelligent Transport Systems 15, no. 7: 902-915.
A novel control strategy for the adaptive real-time energy management of a commuter pull-in hybrid vehicle is proposed. The proposed strategy can adapt to various driving conditions so that fuel economy can be improved further in practice. Its main feature is that a fuzzy inference system (FIS) for online estimation of the reference SOC and an adaptive update law with traffic recognition are blended into the main frame of an adaptive-equivalent consumption minimization strategy (A-ECMS). The FIS is established through an adaptive neuro-fuzzy inference system (ANFIS) that is offline trained by the traffic information extracted from historical traffic data and the reference state of charge (SOC) optimized by dynamic programming (DP). The adaptive update law with traffic recognition means that the adaptive equivalent factor (A-EF) of the real-time A-ECMS is adjusted online according to the traffic information in the real route besides the SOC of the vehicle battery. This is because the initial A-EF and the proportional–integral coefficients of the A-EF adjuster are mappings of the SOC and the traffic road segment, and the mappings are optimized by particle swarm optimization (PSO) according to the different initial SOC and the real historical driving cycles of each segment. The proposed strategy is carried out on the simulation test platform integrated GT-Suite simulator and MATLAB/Simulink. The simulation results show that the proposed strategy can reach an optimal energy distribution on a near global optimal level (close to the level of dynamic programming (DP) under the deterministic driving condition). Compared with a rule-based (RB) strategy, the traditional ECMS, an A-ECMS with the linear SOC reference, an A-ECMS with the EF optimized by PSO and an A-ECMS with the A-EF adjusted by a fixed PI feedback controller of the SOC, the fuel consumption is reduced by an average of 22.98% 10.26% 6.52% 2.33% and 5.91% respectively.
Ping Li; Xiaohong Jiao; Yang Li. Adaptive real-time energy management control strategy based on fuzzy inference system for plug-in hybrid electric vehicles. Control Engineering Practice 2020, 107, 104703 .
AMA StylePing Li, Xiaohong Jiao, Yang Li. Adaptive real-time energy management control strategy based on fuzzy inference system for plug-in hybrid electric vehicles. Control Engineering Practice. 2020; 107 ():104703.
Chicago/Turabian StylePing Li; Xiaohong Jiao; Yang Li. 2020. "Adaptive real-time energy management control strategy based on fuzzy inference system for plug-in hybrid electric vehicles." Control Engineering Practice 107, no. : 104703.
The concurrent optimization of powertrain component parameters and energy management strategy for a hybrid hydraulic vehicle (HHV) is the key to implementing improved fuel economy while satisfying driving performance criteria. In this article, which considers coupled parameters and conflicting objectives in the optimization, an improved multi-objective particle swarm optimization (IMOPSO) is proposed from the perspective of inertia weight, and global and local optimal information to overcome the problem of multi-objective particle swarm optimization (MOPSO) falling into local optimization prematurely. The IMOPSO is applied to the component parameter optimization to find the Pareto optimal solution set that provides a wide range of options for HHV powertrain design successfully. In order to improve the management control effect of the equivalent consumption minimization strategy (ECMS), the equivalence factors (EFs) are optimized offline by the IMOPSO to obtain the EF map between different torque demands and the state of charge of the accumulator, and further, to establish the online ECMS with the EFs optimized by the IMOPSO (I-ECMS). The simulation results verify the advantage of the IMOPSO-based component parameter optimization and the proposed I-ECMS.
Zhong Wang; Xiaohong Jiao. Optimization of the powertrain and energy management control parameters of a hybrid hydraulic vehicle based on improved multi-objective particle swarm optimization. Engineering Optimization 2020, 1 -20.
AMA StyleZhong Wang, Xiaohong Jiao. Optimization of the powertrain and energy management control parameters of a hybrid hydraulic vehicle based on improved multi-objective particle swarm optimization. Engineering Optimization. 2020; ():1-20.
Chicago/Turabian StyleZhong Wang; Xiaohong Jiao. 2020. "Optimization of the powertrain and energy management control parameters of a hybrid hydraulic vehicle based on improved multi-objective particle swarm optimization." Engineering Optimization , no. : 1-20.
Achieving robust longitudinal speed control for hybrid electric vehicles (HEVs) through precise position tracking of electric throttle control system (ETCS) can improve engine fuel economy and vehicle longitudinal speed performance. Whereas, nonlinearities resulting from friction, gearbox, and return springs of ETCS, uncertain system parameters related to production deviations and device aging, disturbance from the air flow fluctuation on the throttle plate, and unknown road grade and uncertain preceding vehicle acceleration make control design challenging. Aiming at this issue, a speed cascade control scheme considering car-following scenario is investigated for a parallel ETCS controlled HEV in this paper, of which contains a primary speed adaptive controller and a secondary electronic throttle adaptive nonlinear active disturbance rejection controller with the adaptive gains extended state observer. The distinction from the existing relevant literatures is that the inherent characteristics of nonlinearity and uncertainty in the ETCS and longitudinal velocity kinematics, and the car following scenarios are explicitly taken into account in the design of the cascade control for ETCS controlled HEVs. Both simulation and rapid-control-prototype (RCP) experimental results demonstrate the effectiveness and practicality of the proposed scheme and the advantages over other existing research strategies.
Jiaqi Xue; Xiaohong Jiao. Speed cascade adaptive control for hybrid electric vehicle using electronic throttle control during car-following process. ISA Transactions 2020, 110, 328 -343.
AMA StyleJiaqi Xue, Xiaohong Jiao. Speed cascade adaptive control for hybrid electric vehicle using electronic throttle control during car-following process. ISA Transactions. 2020; 110 ():328-343.
Chicago/Turabian StyleJiaqi Xue; Xiaohong Jiao. 2020. "Speed cascade adaptive control for hybrid electric vehicle using electronic throttle control during car-following process." ISA Transactions 110, no. : 328-343.
Zitao Sun; Xiaohong Jiao. Adaptive prescribed performance servo control of an automotive electronicthrottle system with actuator constraint. TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 2020, 28, 956 -968.
AMA StyleZitao Sun, Xiaohong Jiao. Adaptive prescribed performance servo control of an automotive electronicthrottle system with actuator constraint. TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES. 2020; 28 (2):956-968.
Chicago/Turabian StyleZitao Sun; Xiaohong Jiao. 2020. "Adaptive prescribed performance servo control of an automotive electronicthrottle system with actuator constraint." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 28, no. 2: 956-968.
Bashash S, Moura S J, Forman J C, et al. Plug-in hybrid electric vehicle charge pattern optimization for energy cost and battery longevity. J Power Sources, 2011, 196: 541–549 Wang Y, Jiao X, Sun Z, et al. Energy management strategy in consideration of battery health for PHEV via stochastic control and particle swarm optimization algorithm. Energies, 2017, 10: 1894 Hou C, Ouyang M, Xu L, et al. Approximate Pontryagin’s minimum principle applied to the energy management of plug-in hybrid electric vehicles. Appl Energy, 2014, 115: 174–189 Guan X M, Zhang X J, Lv R L, et al. A large-scale multi-objective flights conflict avoidance approach supporting 4D trajectory operation. Sci China Inf Sci, 2017, 60: 112202 Yasui Y. JSAE-SICE benchmark problem 2: fuel consumption optimization of commuter vehicle using hybrid powertrain. In: Proceedings of the 10th World Congress on Intelligent Control and Automation, Beijing, 2012. 606–611 Onori S, Spagnol P, Marano V, et al. A new life estimation method for lithium-ion batteries in plug-in hybrid electric vehicles applications. Int J Power Electron, 2012, 4: 302–319 Sasaki M, Shen T. EV bus system control strategy design with consideration of battery lifetime model. In: Proceedings of the 10th International Power and Energy Conference, Ho Chi Minh City, 2012. 213–217 Download references This work was supported by National Natural Science Foundation of China (Grant No. 61573304) and Natural Science Foundation of Hebei Province (Grant No. F2017203210). Vehicle speed and battery degradation data were provided by Dr. Yuji Yasui and Mr. Masakazu Sasaki. Correspondence to Xiaohong Jiao. Reprints and Permissions Wang, Y., Jiao, X. Multi-objective energy management for PHEV using Pontryagin’s minimum principle and particle swarm optimization online. Sci. China Inf. Sci. 64, 119204 (2021). https://doi.org/10.1007/s11432-018-9595-3 Download citation Received: 06 June 2018 Revised: 04 August 2018 Accepted: 05 September 2018 Published: 11 March 2020 DOI: https://doi.org/10.1007/s11432-018-9595-3
Yuying Wang; Xiaohong Jiao. Multi-objective energy management for PHEV using Pontryagin’s minimum principle and particle swarm optimization online. Science China Information Sciences 2020, 64, 1 -3.
AMA StyleYuying Wang, Xiaohong Jiao. Multi-objective energy management for PHEV using Pontryagin’s minimum principle and particle swarm optimization online. Science China Information Sciences. 2020; 64 (1):1-3.
Chicago/Turabian StyleYuying Wang; Xiaohong Jiao. 2020. "Multi-objective energy management for PHEV using Pontryagin’s minimum principle and particle swarm optimization online." Science China Information Sciences 64, no. 1: 1-3.
Hybrid hydraulic technology has the advantages of high-power density and low price and shows good adaptability in construction machinery. A complex hybrid powertrain architecture requires optimization and management of power demand distribution and an accurate response to desired power distribution of the power source subsystems in order to achieve target performances in terms of fuel consumption, drivability, component lifetime, and exhaust emissions. For hybrid hydraulic vehicles (HHVs) that are used in construction machinery, the challenge is to design an appropriate control scheme to actually achieve fuel economy improvement taking into consideration the relatively low energy density of the hydraulic accumulator and frequent load changes, the randomness of the driving conditions, and the uncertainty of the engine dynamics. To improve fuel economy and adaptability of various driving conditions to online energy management and to enhance the response performance of an engine to a desired torque, a hierarchical model predictive control (MPC) scheme is presented in this paper using the example of a spray-painting construction vehicle. The upper layer is a stochastic MPC (SMPC) based energy management control strategy (EMS) and the lower layer is an MPC-based tracking controller with disturbance estimator of the diesel engine. In the SMPC-EMS of the upper-layer management, a Markov model is built using driving condition data of the actual construction vehicle to predict future torque demands over a finite receding horizon to deal with the randomness of the driving conditions. A multistage stochastic optimization problem is formulated, and a scenario-based enumeration approach is used to solve the stochastic optimization problem for online implementation. In the lower-layer tracking controller, a disturbance estimator is designed to handle the uncertainty of the engine, and the MPC is introduced to ensure the tracking performance of the output torque of the engine for the distributed torque from the upper-layer SMPC-EMS, and therefore really achieve high efficiency of the diesel engine. The proposed strategy is evaluated using both simulation MATLAB/Simulink and the experimental test platform through a comparison with several existing strategies in two real driving conditions. The results demonstrate that the proposed strategy (SMPC+MPC) improves miles per gallon an average by 7.3% and 5.9% as compared with the control strategy (RB+PID) consisting of a rule-based (RB) management strategy and proportional-integral-derivative (PID) controller of the engine in simulation and experiment, respectively.
Zhong Wang; Xiaohong Jiao. Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle. Applied Sciences 2020, 10, 745 .
AMA StyleZhong Wang, Xiaohong Jiao. Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle. Applied Sciences. 2020; 10 (3):745.
Chicago/Turabian StyleZhong Wang; Xiaohong Jiao. 2020. "Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle." Applied Sciences 10, no. 3: 745.
Zesong Pu; Xiaohong Jiao; Chao Yang; Shengnan Fang. An Adaptive Stochastic Model Predictive Control Strategy for Plug-in Hybrid Electric Bus During Vehicle-Following Scenario. IEEE Access 2020, 8, 13887 -13897.
AMA StyleZesong Pu, Xiaohong Jiao, Chao Yang, Shengnan Fang. An Adaptive Stochastic Model Predictive Control Strategy for Plug-in Hybrid Electric Bus During Vehicle-Following Scenario. IEEE Access. 2020; 8 ():13887-13897.
Chicago/Turabian StyleZesong Pu; Xiaohong Jiao; Chao Yang; Shengnan Fang. 2020. "An Adaptive Stochastic Model Predictive Control Strategy for Plug-in Hybrid Electric Bus During Vehicle-Following Scenario." IEEE Access 8, no. : 13887-13897.
A hybrid electric vehicle (HEV) is a product that can greatly alleviate problems related to the energy crisis and environmental pollution. However, replacing such a battery will increase the cost of usage before the end of the life of a HEV. Thus, research on the multi-objective energy management control problem, which aims to not only minimize the gasoline consumption and consumed electricity but also prolong battery life, is necessary and challenging for HEV. This paper presents an adaptive equivalent consumption minimization strategy based on a recurrent neural network (RNN-A-ECMS) to solve the multi-objective optimal control problem for a plug-in HEV (PHEV). The two objectives of energy consumption and battery loss are balanced in the cost function by a weighting factor that changes in real time with the operating mode and current state of the vehicle. The near-global optimality of the energy management control is guaranteed by the equivalent factor (EF) in the designed A-ECMS. As the determined EF is dependent on the optimal co-state of the Pontryagin’s minimum principle (PMP), which results in the online ECMS being regarded as a realization of PMP-based global optimization during the whole driving cycle. The time-varying weight factor and the co-state of the PMP are map tables on the state of charge (SOC) of the battery and power demand, which are established offline by the particle swarm optimization (PSO) algorithm and real historical traffic data. In addition to the mappings of the weight factor and the major component of the EF linked to the optimal co-state of the PMP, the real-time performance of the energy management control is also guaranteed by the tuning component of the EF of A-ECMS resulting from the Proportional plus Integral (PI) control on the deviation between the battery SOC and the optimal trajectory of the SOC obtained by the Recurrent Neural Network (RNN). The RNN is trained offline by the SOC trajectory optimized by dynamic programming (DP) utilizing the historical traffic data. Finally, the effectiveness and the adaptability of the proposed RNN-A-ECMS are demonstrated on the test platform of plug-in hybrid electric vehicles based on GT-SUITE (a professional integrated simulation platform for engine/vehicle systems developed by Gamma Technologies of US company) compared with the existing strategy.
Lu Han; Xiaohong Jiao; Zhao Zhang. Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging. Energies 2020, 13, 202 .
AMA StyleLu Han, Xiaohong Jiao, Zhao Zhang. Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging. Energies. 2020; 13 (1):202.
Chicago/Turabian StyleLu Han; Xiaohong Jiao; Zhao Zhang. 2020. "Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging." Energies 13, no. 1: 202.
The mode transition of single-shaft parallel hybrid electric vehicles (HEVs) between engine and motor has an important impact on power and drivability. Especially, in the process of mode transition from the pure motor-drive operating mode to the only engine-drive operating mode, the motor starting engine and the clutch control problem have an important influence on driving quality, and solutions have a bit of room for improving dynamic performance. In this paper, a novel mode transition control method is proposed to guarantee a fast and smooth mode transition process in this regard. First, an adaptive sliding mode control (A-SMC) strategy is presented to obtain the desired torque trajectory of the clutch transmission. Second, a proportional-integral (PI) observer is designed to estimate the actual transmission torque of the clutch. Meanwhile, a fractional order proportional-integral-differential (FOPID) controller with the optimized control parameters by particle swarm optimization (PSO) is employed to realize the accurate position tracking of the direct current (DC) motor clutch so as to ensure clutch transmission torque tracking. Finally, the effectiveness and adaptability to system parameter perturbation of the proposed control approach are verified by comparison with the traditional control strategy in a MATLAB environment. The simulation results show that the driving quality of the closed-loop system using the proposed control approach is obviously improved due to fast and smooth mode transition process and better adaptability.
Jingang Ding; Xiaohong Jiao. A Novel Control Method of Clutch During Mode Transition of Single-Shaft Parallel Hybrid Electric Vehicles. Electronics 2019, 9, 54 .
AMA StyleJingang Ding, Xiaohong Jiao. A Novel Control Method of Clutch During Mode Transition of Single-Shaft Parallel Hybrid Electric Vehicles. Electronics. 2019; 9 (1):54.
Chicago/Turabian StyleJingang Ding; Xiaohong Jiao. 2019. "A Novel Control Method of Clutch During Mode Transition of Single-Shaft Parallel Hybrid Electric Vehicles." Electronics 9, no. 1: 54.
To improve the real-time capability, adaptivity, and efficiency of the energy management strategy in the actual driving cycle, a real-time energy management strategy is investigated for commute hybrid electric vehicles, which integrates mode switching with variable threshold and adaptive equivalent consumption minimization strategy. The proposed strategy includes offline and online parts. In the offline part based on the historical traffic data on the route of the commute vehicle, particle swarm optimization is applied to optimize all the thresholds of mode switching, equivalence factor of the equivalent consumption minimization strategy, and the engine torque and speed at the engine-alone propelling mode so as to establish their mappings on the battery state of charge and power demand. In the online part, the established mappings are involved in the energy management supervisor to generate timely appropriate mode switching signals, and an adaptive equivalence factor for instantaneous optimization equivalent consumption minimization strategy and the optimal engine torque and speed at engine-alone propelling mode. To fully demonstrate the effectiveness of the proposed strategy, the simulation results and comparison with some other strategies and the benchmark dynamic programming strategy are presented by implementing the strategies on the GT-SUITE test platform. The comparison result indicates that the control effect of the proposed energy management strategy is much nearer to that of the benchmark dynamic programming than those of other strategies (the rule-based control, the conventional equivalent consumption minimization strategy, the adaptive equivalent consumption minimization strategy, the rule-based-equivalent consumption minimization strategy, and the stochastic dynamic programming strategy) with the respective improvement in fuel efficiency by 25.9%, 13.25%, 4.6%, 1.32%, and 1.13%.
Yang Li; Xiaohong Jiao. Energy management strategy for hybrid electric vehicles based on adaptive equivalent consumption minimization strategy and mode switching with variable thresholds. Science Progress 2019, 103, 1 .
AMA StyleYang Li, Xiaohong Jiao. Energy management strategy for hybrid electric vehicles based on adaptive equivalent consumption minimization strategy and mode switching with variable thresholds. Science Progress. 2019; 103 (1):1.
Chicago/Turabian StyleYang Li; Xiaohong Jiao. 2019. "Energy management strategy for hybrid electric vehicles based on adaptive equivalent consumption minimization strategy and mode switching with variable thresholds." Science Progress 103, no. 1: 1.
To further improve fuel consumption performance of hybrid electric vehicles (HEVs) running on commute route in the face of time-varying traffic information, this paper investigates a real-time energy management strategy based on the adaptive equivalent consumption minimization strategy (A-ECMS) framework with traffic information recognition. The proposed management strategy integrates the global near optimization and the real-time performance. The simple traffic recognition is constructed by utilising k-means clustering algorithm to deal with the historical traffic data to form four clusters. The adaptive equivalence factor of the A-ECMS is designed as a three-dimensional mapping on each cluster and the system states by employing stochastic dynamic programming (SDP) policy iteration to solve offline the stochastic optimal control problem formulated by each cluster statistical characteristic. In real-time energy management controller online, the instantaneous power split is performed by the ECMS with a proper equivalent factor, which is obtained from mappings according to the cluster recognised by the current traffic situation and the state-of-charge (SOC). The effectiveness of the designed control strategy is verified by the simulation test conducted on GT-suite HEV simulator over real driving cycles.
Yang Li; Xiaohong Jiao. Real‐time energy management for commute HEVs using modified A‐ECMS with traffic information recognition. IET Intelligent Transport Systems 2019, 13, 729 -737.
AMA StyleYang Li, Xiaohong Jiao. Real‐time energy management for commute HEVs using modified A‐ECMS with traffic information recognition. IET Intelligent Transport Systems. 2019; 13 (4):729-737.
Chicago/Turabian StyleYang Li; Xiaohong Jiao. 2019. "Real‐time energy management for commute HEVs using modified A‐ECMS with traffic information recognition." IET Intelligent Transport Systems 13, no. 4: 729-737.
To improve tracking performance of engine speed in the face of nonlinearity and time-varying uncertainty, this article investigates the double closed-loop cascade active disturbance rejection control strategy for automotive engine control system. In this cascade control arrangement, the outer active disturbance rejection speed controller with the extended state observer for the speed error and its integral, and disturbance from load torque and time-varying uncertainty, drives the set-point of the inner loop to keep the engine speed to its set-point; meanwhile, the inner active disturbance rejection pressure controller with the extended state observer for the pressure error and its integral, and disturbance from the air mass flow rate leaving the intake manifold and the pumping fluctuation of air charge, manages the throttle valve to match the pressure with the set-point requested by the outer active disturbance rejection speed controller. The observer gains and controller gains of active disturbance rejection speed controller and active disturbance rejection pressure controller are determined by the linear matrix inequalities ensuring the stability and disturbance attenuation level of the closed-loop system. The effectiveness is validated by implementing the proposed strategy and a series of related control schemes in the simulator of a real V6 engine.
Meiyu Feng; Xiaohong Jiao; Zhong Wang. Cascade active disturbance rejection control–based double closed-loop speed tracking control for automotive engine. International Journal of Engine Research 2019, 21, 1541 -1554.
AMA StyleMeiyu Feng, Xiaohong Jiao, Zhong Wang. Cascade active disturbance rejection control–based double closed-loop speed tracking control for automotive engine. International Journal of Engine Research. 2019; 21 (8):1541-1554.
Chicago/Turabian StyleMeiyu Feng; Xiaohong Jiao; Zhong Wang. 2019. "Cascade active disturbance rejection control–based double closed-loop speed tracking control for automotive engine." International Journal of Engine Research 21, no. 8: 1541-1554.
Aiming at the frequent start-stop of a straight-manipulator aerial platform vehicle for sandblasting and spray painting hull, a hydraulic energy recovery and reuse unit consisting of accumulator and pump/motor is connected in parallel on the original series hybrid system. The main purpose of the modified hybrid powertrain is to exploit the operational characteristics of the hydraulic pump/motor to recover the kinetic energy during vehicle braking. And while, during the climbing and acceleration process, the stored energy in accumulator is quickly released to meet the intermittent and high power requirements of the system. Firstly, the fuel-electric-hydraulic hybrid power system is presented for an actual aerial platform vehicle. And then a logical control strategy of the energy management is designed according to vehicle driving condition. The simulation comparison result is given between the fuel-electric-hydraulic hybrid system and the original series hybrid system.
Zhong Wang; Xiaohong Jiao; Zesong Pu; Lu Han. Energy Recovery and Reuse Management for Fuel-electric-hydraulic Hybrid Powertrain of a Construction Vehicle. IFAC-PapersOnLine 2018, 51, 390 -393.
AMA StyleZhong Wang, Xiaohong Jiao, Zesong Pu, Lu Han. Energy Recovery and Reuse Management for Fuel-electric-hydraulic Hybrid Powertrain of a Construction Vehicle. IFAC-PapersOnLine. 2018; 51 (31):390-393.
Chicago/Turabian StyleZhong Wang; Xiaohong Jiao; Zesong Pu; Lu Han. 2018. "Energy Recovery and Reuse Management for Fuel-electric-hydraulic Hybrid Powertrain of a Construction Vehicle." IFAC-PapersOnLine 51, no. 31: 390-393.
A kind of double closed-loop servo control strategy is presented in this paper to realize accurate and fast position tracking of throttle valve for the automobile electronic throttle control system (ETCS). The control structure includes an extended state observer (ESO) compensating for the total disturbances resulted from nonlinearities of friction and return springs and uncertainties of some physical parameters, and two PI-type controllers with disturbance compensators of the outer position loop and the inner current loop. The proposed scheme is featured by transforming the control gains determination for the two PID-type controllers into the derivation of state feedback gains. And the transformation is carried on by means of the augmented deviation equations constructed by the ESO-estimated unmeasurable current and angular velocity of throttle. And control parameters of the two feedback loop controllers and ESO can be instructively determined by solving the linear matrix inequalities (LMIs) obtained from the Lyapunov stability analysis of the whole closed-loop system. The feasibility of the proposed strategy is validated in both simulation and hardware-in-the loop (HIL) test platform.
Jiaqi Xue; Xiaohong Jiao; Zitao Sun. ESO-Based Double Closed-loop Servo Control for Automobile Electronic Throttle. IFAC-PapersOnLine 2018, 51, 979 -983.
AMA StyleJiaqi Xue, Xiaohong Jiao, Zitao Sun. ESO-Based Double Closed-loop Servo Control for Automobile Electronic Throttle. IFAC-PapersOnLine. 2018; 51 (31):979-983.
Chicago/Turabian StyleJiaqi Xue; Xiaohong Jiao; Zitao Sun. 2018. "ESO-Based Double Closed-loop Servo Control for Automobile Electronic Throttle." IFAC-PapersOnLine 51, no. 31: 979-983.
Xiaohong Jiao; Guanghui Li; Hui Wang. Adaptive finite time servo control for automotive electronic throttle with experimental analysis. Mechatronics 2018, 53, 192 -201.
AMA StyleXiaohong Jiao, Guanghui Li, Hui Wang. Adaptive finite time servo control for automotive electronic throttle with experimental analysis. Mechatronics. 2018; 53 ():192-201.
Chicago/Turabian StyleXiaohong Jiao; Guanghui Li; Hui Wang. 2018. "Adaptive finite time servo control for automotive electronic throttle with experimental analysis." Mechatronics 53, no. : 192-201.
During sandblasting and spray painting for hull, the tracking performance of the end-effector of manipulator to the outer surface of the hull in uniform velocity is seriously affected by uncertainty of the electrohydraulic actuator and deflection of the manipulator. To effectively improve the tracking performance of the tipposition/velocity, a novel tracking control strategy is proposed, which is based on active disturbance rejection control (ADRC) with extended state observer (ESO) and setting position feedforward control with deflection compensation. First, to reduce tracking error caused by the deflection, the reference positions of two cylinders driving telescopic and luffing motion are calculated with consideration of the deflection influence according to manipulator geometry. And then, ADRC technique is adopted to design the position servo controller of the luffing/telescopic cylinder with the help of the estimate for the uncertainty of the electro-hydraulic system by the ESO. The stability of the whole closed-loop system and the convergence of tracking error are guaranteed theoretically, simultaneously, simulation carried out in Matlab/Simulink environment with physical parameters of a real system demonstrates the effectiveness and superiority of the proposed control strategy compared with existing control schemes. Furthermore, the experimental result is given to show the feasibility and availability of the control method.
Zhong Wang; Xiaohong Jiao; Meiyu Feng. Tip-position/velocity Tracking Control of Manipulator for Hull Derusting and Spray Painting based on Active Disturbance Rejection Control. International Journal of Control, Automation and Systems 2018, 16, 1916 -1926.
AMA StyleZhong Wang, Xiaohong Jiao, Meiyu Feng. Tip-position/velocity Tracking Control of Manipulator for Hull Derusting and Spray Painting based on Active Disturbance Rejection Control. International Journal of Control, Automation and Systems. 2018; 16 (4):1916-1926.
Chicago/Turabian StyleZhong Wang; Xiaohong Jiao; Meiyu Feng. 2018. "Tip-position/velocity Tracking Control of Manipulator for Hull Derusting and Spray Painting based on Active Disturbance Rejection Control." International Journal of Control, Automation and Systems 16, no. 4: 1916-1926.
This paper presents an energy management strategy for plug-in hybrid electric vehicles (PHEVs) that not only tries to minimize the energy consumption, but also considers the battery health. First, a battery model that can be applied to energy management optimization is given. In this model, battery health damage can be estimated in the different states of charge (SOC) and temperature of the battery pack. Then, because of the inevitability that limiting the battery health degradation will increase energy consumption, a Pareto energy management optimization problem is formed. This multi-objective optimal control problem is solved numerically by using stochastic dynamic programming (SDP) and particle swarm optimization (PSO) for satisfying the vehicle power demand and considering the tradeoff between energy consumption and battery health at the same time. The optimization solution is obtained offline by utilizing real historical traffic data and formed as mappings on the system operating states so as to implement online in the actual driving conditions. Finally, the simulation results carried out on the GT-SUITE-based PHEV test platform are illustrated to demonstrate that the proposed multi-objective optimal control strategy would effectively yield benefits.
Yuying Wang; Xiaohong Jiao; Zitao Sun; Ping Li. Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm. Energies 2017, 10, 1894 .
AMA StyleYuying Wang, Xiaohong Jiao, Zitao Sun, Ping Li. Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm. Energies. 2017; 10 (11):1894.
Chicago/Turabian StyleYuying Wang; Xiaohong Jiao; Zitao Sun; Ping Li. 2017. "Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm." Energies 10, no. 11: 1894.
In order to enhance the rapidity and accuracy of throttle opening trajectory tracking for vehicle electronic throttle control (ETC) systems, a finite time servo control strategy is investigated by incorporating the particle swarm optimization (PSO) identification technique with the finite time stability theory. The PSO technique is adopted to identify the uncertain physical parameters of a real vehicle ETC system. In the PSO-based identification algorithm, the integrated square error between the actual and model throttle opening angles is regarded as the fitness function, and the mutation operation is added to prevent the particles from falling into local optimum. The designed servo controller is comprised of a feed-forward controller for trajectory tracking accuracy, a nonlinearity compensator for friction and return spring, and a feedback controller for finite time stability by utilizing additional power integrator and backstepping technique. The effectiveness of the proposed control strategy is verified by both the comparison results with the existing strategy in MATLAB/Simulink environment and the experiment results carried out on the electronic throttle hardware-in-loop test platform in several actual operating cases.
Guanghui Li; Xiaohong Jiao. Synthesis and validation of finite time servo control with PSO identification for automotive electronic throttle. Nonlinear Dynamics 2017, 90, 1165 -1177.
AMA StyleGuanghui Li, Xiaohong Jiao. Synthesis and validation of finite time servo control with PSO identification for automotive electronic throttle. Nonlinear Dynamics. 2017; 90 (2):1165-1177.
Chicago/Turabian StyleGuanghui Li; Xiaohong Jiao. 2017. "Synthesis and validation of finite time servo control with PSO identification for automotive electronic throttle." Nonlinear Dynamics 90, no. 2: 1165-1177.
In order to improve the transient and static performances of an engine speed control system in a wide speed range, this paper presents an adaptive double closed-loop control strategy. The control scheme possesses intake manifold pressure inner closed-loop adaptive proportional-integral control and engine speed outer closed-loop adaptive proportional-integral control for achieving the tracking precision in a wide range of speed, as well as adaptive nonlinearity and feedforward compensators for overcoming parameter uncertainty and nonlinearity. The whole closed-loop system's stability and the speed tracking convergence are ensured theoretically by the Lyapunov stability theory and the LaSalle invariant principle. The effectiveness of the proposed control strategy is validated through the operation results on the simulator of a V6 engine exploited by the Research Committee of the Society of Instrument and Control Engineers of Japan.
Meiyu Feng; Xiaohong Jiao. Double closed-loop control with adaptive strategy for automotive engine speed tracking system. International Journal of Adaptive Control and Signal Processing 2017, 31, 1623 -1635.
AMA StyleMeiyu Feng, Xiaohong Jiao. Double closed-loop control with adaptive strategy for automotive engine speed tracking system. International Journal of Adaptive Control and Signal Processing. 2017; 31 (11):1623-1635.
Chicago/Turabian StyleMeiyu Feng; Xiaohong Jiao. 2017. "Double closed-loop control with adaptive strategy for automotive engine speed tracking system." International Journal of Adaptive Control and Signal Processing 31, no. 11: 1623-1635.