Hierarchical Energy Optimization Strategy and Its Integrated Reliable Battery Fault Management for Hybrid Hydraulic-Electric Vehicle

Kamal, Elkhatib; Adouane, Lounis

doi:10.1109/tvt.2018.2805353

Cited by 38 publications

(22 citation statements)

References 46 publications

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“…As HEVs and PHEVs have two coordinate power sources, the torque distribution between the two sources can be optimized for better fuel economy. Kamal and Adouaneet (2018) have developed a suboptimum control strategy based on fuzzy logic and neural networks (NNs) to minimize energy consumption for a hybrid hydraulic-electric vehicle. Discrete state-space dynamic programming (DP) and Pontryagin’s maximum principle (PMP) have been combined for HEV online optimum control (Uebel et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Online optimum velocity calculation under V2X for smart new energy vehicles

Huang

Lian

2021

Transactions of the Institute of Measurement and Control

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In this paper, a vector data net solver is proposed, which can reduce bivariate discrete-time dynamic programming (DP) computation time by 98.0% without losing accuracy. Therefore, for the first time, bivariate discrete-time DP can operate under model predictive control rolling optimization to calculate future optimum vehicle velocity in real time considering future road altitude and instant traffic information. Simulation results indicate that with the solution presented in this paper, front vehicles and the proper windows to pass through front intersections can be constantly considered. Meanwhile, the calculated optimum vehicle velocity almost remains the same as the global optimum solutions. Simulation results are validated by real-car tests, and the test new energy vehicle (NEV) electricity consumption is reduced by up to 48.6%. A comparison experiment is performed between the solution presented in this paper and commonly used adaptive dynamic programming (ADP), and the results indicate that the former has better performance and stability. This paper describes a novel solution for online optimum velocity calculation under vehicle to everything (V2X) environment and can be used by all smart NEVs with autonomous driving or active cruise control functions for lower electricity consumption and better riding comfort.

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Section: Introductionmentioning

confidence: 99%

Online optimum velocity calculation under V2X for smart new energy vehicles

Huang

Lian

2021

Transactions of the Institute of Measurement and Control

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“…In the work of Kheirandish et al, a dynamic fuzzy cognitive network is proposed to describe the behavior of a fuel cell electric bicycle system [27]. Moreover, some other type of fuzzy-logic-based control system are employed for HEV energy management such as neuro-fuzzy [28], geneticfuzzy [29] and Takagi-Sugeno fuzzy [30] control systems. However, such fuzzy-logic-based supervisory control systems are established based on human cognition and their performances are largely limited by empirical knowledge.…”

Section: Introductionmentioning

confidence: 99%

Driver-Identified Supervisory Control System of Hybrid Electric Vehicles Based on Spectrum-Guided Fuzzy Feature Extraction

Zhou

et al. 2020

IEEE Trans. Fuzzy Syst.

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“…In practice, the EMS for HHVs is usually designed as a rule-based (RB) control strategy because it is simple and has real-time implementable structure [6][7][8][9][10]. However, since its control performance depends heavily on rule-switching thresholds defined by the engineering experience, it actually cannot always provide good results during various driving conditions.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle

Zhong

Jiao

2020

Applied Sciences

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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.

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Hierarchical Energy Optimization Strategy and Its Integrated Reliable Battery Fault Management for Hybrid Hydraulic-Electric Vehicle

Cited by 38 publications

References 46 publications

Online optimum velocity calculation under V2X for smart new energy vehicles

Online optimum velocity calculation under V2X for smart new energy vehicles

Driver-Identified Supervisory Control System of Hybrid Electric Vehicles Based on Spectrum-Guided Fuzzy Feature Extraction

Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle

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