Design and Experimental Evaluations on Energy Efficient Control Allocation Methods for Overactuated Electric Vehicles: Longitudinal Motion Case

Chen, Yan; Wang, Junmin

doi:10.1109/tmech.2013.2249591

Cited by 121 publications

(66 citation statements)

References 34 publications

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“…The second part is the energy loss of the drive system at the current torque distribution point, which consists of copper loss, iron loss, inverter loss, friction loss, stray loss and transmission loss: In this paper the expression Equation (15) is used to perform the energy-saving control in the penalty function and it is different from that used in [11][12][13][14] shown as below:…”

Section: The Multi-objective Optimization Algorithmmentioning

confidence: 99%

“…Simulation results verified that the proposed strategy could improve the energy efficiency of the drive system, but there was no description of the influence of control strategy on vehicle maneuverability, and no slip ratio constraint was considered which may cause excessive spin of the driving wheels. In [12,13], the authors developed a wheel torque control strategy based on an optimization algorithm, and adaptive energy efficient control allocation (A-EECA) was adopted to reduce the calculation cost. The authors pointed out that the wheel torque distribution was not necessarily optimized at each time step and A-EECA can distribute wheel torque trending in the optimal direction at each step.…”

Section: Introductionmentioning

confidence: 99%

“…The authors pointed out that the wheel torque distribution was not necessarily optimized at each time step and A-EECA can distribute wheel torque trending in the optimal direction at each step. As the optimization at each step was started from the last torque state, the action of torque distribution cannot follow virtual control changes in a timely way, as shown in Figure 6 of [12] and Figure 6, Figure 7 of [13]. Moreover, the influence of the controller on vehicle maneuverability was not shown clearly in the papers above.…”

Section: Introductionmentioning

confidence: 99%

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Wheel Torque Distribution of Four-Wheel-Drive Electric Vehicles Based on Multi-Objective Optimization

Lin

2015

Energies

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Abstract:The wheel driving torque on four-wheel-drive electric vehicles (4WDEVs) can be modulated precisely and continuously, therefore maneuverability and energy-saving control can be carried out at the same time. In this paper, a wheel torque distribution strategy is developed based on multi-objective optimization to improve vehicle maneuverability and reduce energy consumption. In the high-layer of the presented method, sliding mode control is used to calculate the desired yaw moment due to the model inaccuracy and parameter error. In the low-layer, mathematical programming with the penalty function consisting of the yaw moment control offset, the drive system energy loss and the slip ratio constraint is used for wheel torque control allocation. The programming is solved with the combination of off-line and on-line optimization to reduce the calculation cost, and the optimization results are sent to motor controllers as torque commands. Co-simulation based on MATLAB ® and Carsim ® proves that the developed strategy can both improve the vehicle maneuverability and reduce energy consumption.

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Section: The Multi-objective Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wheel Torque Distribution of Four-Wheel-Drive Electric Vehicles Based on Multi-Objective Optimization

Lin

2015

Energies

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“…In [1], [2], [3] over-actuated vehicles are approached with a hierarchical control structure, combining a high-level dynamic Sliding Mode Control with a low-level Energy Efficient Control Allocation (EECA) scheme which explicitly considers torquedependent efficiency functions. In [4] these techniques are tested with the implementation of a longitudinal speed tracking controller in an electric ground vehicle, comparing adaptive, KKT-based and rule-based EECAs. Another one-dimensional motion case is considered in [5], where constrained optimal control problems are first formulated to maximize the cruising range of the ground vehicle modeled in [6] and minimize its traveling time, and are then approximated and reformulated in a nonlinear parametric optimization form, which is simpler to solve.…”

Section: Introductionmentioning

confidence: 99%

“…colin.jones@epfl.ch 3 Institute for Systems and Robotics (ISR), Instituto Superior Tecnico (IST), Lisbon, Portugal. 4 Faculty of Engineering, University of Porto (FEUP), Porto, Portugal.…”

Section: Introductionmentioning

confidence: 99%

An energy efficient trajectory tracking controller for car-like vehicles using Model Predictive Control

Salazar¹,

Alessandretti²,

Aguiar³

et al. 2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-A Model Predictive Control (MPC) strategy for energy efficient motion control of car-like vehicles is presented. First, a nonlinear control law for trajectory tracking is derived and used to design a trajectory tracking MPC controller with convergence guarantees to a desired position trajectory. Then, assuming electric propulsion, a performance index reflecting the energy consumption of the vehicle is derived and combined with the stabilizing stage cost of the MPC controller. The resulting strategy drives the vehicle through energy efficient trajectories around the desired one. The distance between the closed-loop trajectories and the desired one provided by the user is guaranteed to be ultimately bounded. Numerical results show the effectiveness of the proposed control strategy for the case of a car driven through flat land or mountainous territory.

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Optimal Distribution Control Of Non‐Linear Tire Force Of Electric Vehicles With In‐Wheel Motors

Li¹,

Du²,

Li³

2015

Asian Journal of Control

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An over‐actuated control system has the advantage of being able to use redundant actuators to reconfigure the control system and it can realize fault tolerant control. In order to achieve improved vehicle stability and handling performance for electric vehicles with in‐wheel steering and driving motors, the control of the vehicle body slip angle and yaw rate is actually an over‐actuated control problem. To obtain the optimal solution for this control problem, this study proposes a two‐level tire force distribution control method, where the upper level controller calculates the desired lateral and longitudinal forces generated by friction on the tire of each wheel according to the driver's steering and driving inputs. The lower level controller maps the desired tire forces into the input of each steering actuator and driving actuator. Unlike the linear mapping method applied in most of the current research, this study develops a proportional‐integral (PI) controller for each actuator so that the non‐linear tire characteristics can be counteracted. In addition, since the PI controllers for eight actuators (four steering actuators and four driving actuators) have a total of 16 control gains to be determined, a genetic algorithm is applied to accurately determine these control gains. The simulation results are presented to validate the control performance of the proposed tire force allocation method.

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Design and Experimental Evaluations on Energy Efficient Control Allocation Methods for Overactuated Electric Vehicles: Longitudinal Motion Case

Cited by 121 publications

References 34 publications

Wheel Torque Distribution of Four-Wheel-Drive Electric Vehicles Based on Multi-Objective Optimization

Wheel Torque Distribution of Four-Wheel-Drive Electric Vehicles Based on Multi-Objective Optimization

An energy efficient trajectory tracking controller for car-like vehicles using Model Predictive Control

Optimal Distribution Control Of Non‐Linear Tire Force Of Electric Vehicles With In‐Wheel Motors

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