A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles

Moura, Scott J.; Fathy, Hosam K.; Callaway, Duncan S.; Stein, Jeffrey L.

doi:10.1109/tcst.2010.2043736

Cited by 500 publications

(195 citation statements)

References 27 publications

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“…The optimal torque control strategy will impose the engine's speed to follow the electric motor's speed fluctuation while at the same time operating at optimal torque for that rotating speed to ensure good efficiency. It has a good transient operation than other control strategies, but the battery charge will deplete faster and shorten the car's autonomy (Opila et al, 2012;Moura et al, 2011).…”

Section: Hev Control Methodsmentioning

confidence: 99%

Fuel consumption evaluation of a hybrid electric car over aggressive cycles for thermal engine optimization

Asus

2018

Int. j. adv. appl. sci.

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This paper investigates fuel consumption of a thermal engine in a hybrid electric vehicle system over aggressive cycles in order to optimize its energy consumption. The model of the system is first developed using data obtained from experiments over two aggressive driving cycles and then used to validate the model and further used as a platform to test other control strategies. The car's actual control strategy operates on high torque region of the engine to sustain battery charge and caused high fuel consumption. Based on previous researches, there are two optimal control strategies that can be implemented for a series hybrid electric vehicle system; the dynamic programming control method and the optimal torque control method. Result analysis shows that the operations of the engine are different between the two control methods; it is concentrated on high speed region for the dynamic programming control method and at the middle of the engine map for the optimal torque control method.

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Section: Hev Control Methodsmentioning

confidence: 99%

Fuel consumption evaluation of a hybrid electric car over aggressive cycles for thermal engine optimization

Asus

2018

Int. j. adv. appl. sci.

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“…The road load is then given by [16]: (15) with is the aerodynamic drag force, is the rol ling resistance force, is the profile force of the roa d, and is the Stokes or viscous friction force. The vehicle velocity is obtained using the classical dynamics relationship with the traction and road load forces, and [17]: (16) with the mass of the vehicle.…”

Section: Modeling and Dynamics Of The Ev Load Torque Emulatormentioning

confidence: 99%

Sensorless Fault-Tolerant Control of an Induction Motor Based Electric Vehicle

Roubache¹,

Chaouch²,

Said³

2016

Journal of Electrical Engineering and Technology

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-This paper describes a sensorless fault-tolerant control (FTC) for a high-performance induction motor drive that propels an electric-vehicle (EV). The effect of the fault on the induction motor (IM) can be modeled by an exogenous signal from a stable autonomous system. An additive term is added to the nominal offset and serves to control the effect of the defect (FTC aspect). The proposed sensorless FTC for IM is realized in presence of both rotor and stator electrical faults. Consequently, the extended kalman filter (EKF) is proposed to estimate the rotor flux and the rotor speed using the measured stator currents and voltages. Therefore, a classical EV traction system is studied using an induction motor drive. Indeed, the induction motor based powertrain is coupled to DC machine-based load torque emulator taking into account the electric vehicle mechanics and aerodynamics. Simulation and experimental results confirm widely the feasibility and the effectiveness of the proposed FTC of IM based SMC in the electric vehicle application.

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“…Since the load descriptions are time invariant, the type should be considered semi-predictive; the models can describe and predict the evolution of a transient episode, but not the occurrence of a transient after stationary operation. Despite the simplicity of the models, from a prediction point of view, these have found use in several applications such as power split control in hybrid electric vehicles [42,59,81], route prediction [38,83] and risk assessment [32,82,95]. Just as in the case of map-or database based predictions, since the whole w(x) map is known beforehand, the optimal control actions as functions of the state can be precalculated.…”

Section: Predictionmentioning

confidence: 99%

Optimal Predictive Control of Wheel Loader Transmissions

Nilsson

2015

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The cover illustrations show wheel loaders at work. The transmissions of present heavy wheel loaders are in general based on torque converters. The characteristics of this component suits these machines, especially in that it enables thrust from zero vehicle speed without risk of stalling the engine, without active control. Unfortunately, the component also causes losses which might become large compared to the transmitted power. One approach for mitigating these losses is to switch to a continuously variable transmission.Changing to such a system greatly increases the possibility, and the need, for actively selecting the engine speed, and here a conict emerges. A low engine speed is desired for high eciency but a high speed is required for high power.Heavy wheel loaders often operate according to a common repeating pattern known as the short loading cycle.This cycle is extremely transient, which makes the choice of engine operating point both important and dicult. At the same time, the repeating pattern in the operation enables a rough prediction of the future operation. One way to use the uncertain prediction is to use optimization techniques for selecting the best control actions. This requires a method for detecting the operational pattern and producing a prediction from this, to formulate a manageable optimization problem, and for solving this, and nally to actually control the machine according to the optimization results.This problem is treated in the four papers that are included in this dissertation.The rst paper describes a method for automatically detecting when the machine is operating according to any of several predened patterns. The detector uses events and automata descriptions of the cycles, which makes the method simple yet powerful. In the evaluations over 90% of the actual cycles are detected and correctly identied. The detector also enables a quick analysis of large datasets. In several of the following papers this is used to condense measured data sequences into statistical cycles for the control optimization.In the second paper dynamic programming and Pontryagin's maximum principle is applied to a simplied system consisting of a diesel engine and a generator. Methods are developed based on the maximum principle analysis, for nding the fuel optimal trajectories at output power steps, and the simplicity of the system enables a deeper analysis of these solutions. The methods are used to examine and visualize the mechanisms behind the solutions at power transients, and the models form the basis for the models in the following papers.The third paper describes two dierent concepts for implementing dynamic programming based optimal control of a hydrostatic transmission. In this system one load component forms a stochastic state constraint, and the concepts present two dierent strategies for handling this constraint. The controller concepts are evaluated through simulations, in terms of implementability, robustness against uncertainties in the prediction and fuel savings. generell kunskap om hur m...

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A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles

Cited by 500 publications

References 27 publications

Fuel consumption evaluation of a hybrid electric car over aggressive cycles for thermal engine optimization

Fuel consumption evaluation of a hybrid electric car over aggressive cycles for thermal engine optimization

Sensorless Fault-Tolerant Control of an Induction Motor Based Electric Vehicle

Optimal Predictive Control of Wheel Loader Transmissions

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