Control and Design Optimization of a Novel Hybrid Electric Powertrain System

2017 ◽  
Author(s):  
Chenyu Yi ◽  
Bogdan Epureanu
Keyword(s):  
Optimal Control ◽  
Design Optimization ◽  
Fuel Economy ◽  
Optimization Problems ◽  
Computational Cost ◽  
Electric Machines ◽  
Hybrid Electric ◽  
Hybrid Electric Powertrain ◽  
And Control ◽  
Electric Powertrains

Control and design optimization of hybrid electric powertrains is necessary to maximize the benefits of novel architectures. Previous studies have proposed multiple optimal and near-optimal control methods, approaches for design optimization, and ways to solve coupled design and control optimization problems for hybrid electric powertrains. This study presents control and design optimization of a novel hybrid electric powertrain architecture to evaluate its performance and potential using physics-based models for the electric machines, the battery and a near-optimal control, namely the equivalent consumption minimization strategy. Design optimization in this paper refers to optimizing the sizes of the powertrain components, i.e. electric machines, battery and final drive. The control and design optimization problem is formulated using nested approach with sequential quadratic programming as design optimization method. Metamodeling is applied to abstract the near-optimal powertrain control model to reduce the computational cost. Fuel economy, sizes of components, and consistency of city and highway fuel economy are reported to evaluate the performance of the powertrain designs. The results suggest an optimal powertrain design and control that grants good performance. The optimal design is shown to be robust and non-sensitive to slight component size changes when evaluated for the near-optimal control.

Analysis of the influence of power coupling type and transmission type of the powertrain on the performance of single-motor hybrid electric vehicles

2021 ◽  
pp. 095440702110339
Author(s):  
Hang Peng ◽  
Datong Qin ◽  
Jianjun Hu ◽  
Zhipeng Chen
Keyword(s):  
Electric Vehicles ◽  
Fuel Economy ◽  
Hybrid Electric Vehicles ◽  
Power Coupling ◽  
Parallel Hybrid ◽  
Hybrid Electric ◽  
Hybrid Electric Powertrain ◽  
Powertrain Configuration ◽  
And Control ◽  
Coupling Type

Existing research on parallel hybrid electric vehicles (HEV) mainly focuses on optimizing the component sizes and control strategies of the single-motor parallel hybrid electric powertrain (SMPHP), and less analyzes the influence of powertrain configuration on the performance of the vehicle. Therefore, the influence of the power coupling type and transmission type of the powertrain configuration on the fuel economy and drivability performance of parallel HEVs is studied in this paper. Considering three types of powertrain topologies (P2 torque-coupled, P2 dual-mode coupled and P3 torque-coupled) and two types of automatic transmissions (DCT and CVT), six typical types of SMPHP configurations to be discussed are determined. To obtain their optimal fuel economy and drivability performance, a multi-objective optimization and analysis method based on dynamic programming and multi-objective particle swarm optimization algorithm is proposed to optimize the component sizes and control variables of powertrain configurations. Finally, the optimal performance and component size optimization results of six typical SMPHP configurations are analyzed and compared, and the influence of powertrain configuration on the performance and components sizing of the SMPHP is obtained, which contributes to the configuration design of the parallel hybrid electric powertrain.

Explicit optimal control policy and its practical application for hybrid electric powertrains

2010 ◽  
Vol 18 (12) ◽  
pp. 1429-1439 ◽  
Author(s):  
Daniel Ambühl ◽  
Olle Sundström ◽  
Antonio Sciarretta ◽  
Lino Guzzella
Keyword(s):  
Optimal Control ◽  
Control Policy ◽  
Practical Application ◽  
Hybrid Electric ◽  
Electric Powertrains

Evolutionary Algorithm Embedded With Bump-Hunting for Constrained Design Optimization

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Kamrul Hasan Rahi ◽  
Hemant Kumar Singh ◽  
Tapabrata Ray
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Computational Cost ◽  
Search Space ◽  
Bump Hunting ◽  
Simulation Based ◽  
Engineering Problems ◽  
Turbine Design

Abstract Real-world design optimization problems commonly entail constraints that must be satisfied for the design to be viable. Mathematically, the constraints divide the search space into feasible (where all constraints are satisfied) and infeasible (where at least one constraint is violated) regions. The presence of multiple constraints, constricted and/or disconnected feasible regions, non-linearity and multi-modality of the underlying functions could significantly slow down the convergence of evolutionary algorithms (EA). Since each design evaluation incurs some time/computational cost, it is of significant interest to improve the rate of convergence to obtain competitive solutions with relatively fewer design evaluations. In this study, we propose to accomplish this using two mechanisms: (a) more intensified search by identifying promising regions through “bump-hunting,” and (b) use of infeasibility-driven ranking to exploit the fact that optimal solutions are likely to be located on constraint boundaries. Numerical experiments are conducted on a range of mathematical benchmarks and empirically formulated engineering problems, as well as a simulation-based wind turbine design optimization problem. The proposed approach shows up to 53.48% improvement in median objective values and up to 69.23% reduction in cost of identifying a feasible solution compared with a baseline EA.

scholarly journals Shape Design Optimization of a Robot Arm Using a Surrogate-Based Evolutionary Approach

2020 ◽  
Vol 10 (7) ◽  
pp. 2223 ◽  
Author(s):  
J. C. Hsiao ◽  
Kumar Shivam ◽  
C. L. Chou ◽  
T. Y. Kam
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Shape Design ◽  
Optimization Approach ◽  
Robot Arm ◽  
Shape Design Optimization ◽  
Robot Arms ◽  
Conflicting Objectives ◽  
Computer Aided

In the design optimization of robot arms, the use of simulation technologies for modeling and optimizing the objective functions is still challenging. The difficulty is not only associated with the large computational cost of high-fidelity structural simulations but also linked to the reasonable compromise between the multiple conflicting objectives of robot arms. In this paper we propose a surrogate-based evolutionary optimization (SBEO) method via a global optimization approach, which incorporates the response surface method (RSM) and multi-objective evolutionary algorithm by decomposition (the differential evolution (DE ) variant) (MOEA/D-DE) to tackle the shape design optimization problem of robot arms for achieving high speed performance. The computer-aided engineering (CAE) tools such as CAE solvers, computer-aided design (CAD) Inventor, and finite element method (FEM) ANSYS are first used to produce the design and assess the performance of the robot arm. The surrogate model constructed on the basis of Box–Behnken design is then used in the MOEA/D-DE, which includes the process of selection, recombination, and mutation, to optimize the robot arm. The performance of the optimized robot arm is compared with the baseline one to validate the correctness and effectiveness of the proposed method. The results obtained for the adopted example show that the proposed method can not only significantly improve the robot arm performance and save computational cost but may also be deployed to solve other complex design optimization problems.

scholarly journals A tool for generating optimal control laws for hybrid electric powertrains

2010 ◽  
Vol 43 (7) ◽  
pp. 81-86
Author(s):  
Nikolce Murgovski ◽  
Jonas Sjöberg ◽  
Jonas Fredriksson
Keyword(s):  
Optimal Control ◽  
Control Laws ◽  
Hybrid Electric ◽  
Electric Powertrains

scholarly journals Model predictive control with random batch methods for a guiding problem

2021 ◽  
pp. 1-24
Author(s):  
Dongnam Ko ◽  
Enrique Zuazua
Keyword(s):  
Optimal Control ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Computational Cost ◽  
Original System ◽  
Time Interval ◽  
Short Time Interval ◽  
Feedback Control Strategy ◽  
Short Time ◽  
And Control

We model, simulate and control the guiding problem for a herd of evaders under the action of repulsive drivers. The problem is formulated in an optimal control framework, where the drivers (controls) aim to guide the evaders (states) to a desired region of the Euclidean space. The numerical simulation of such models quickly becomes unfeasible for a large number of interacting agents, as the number of interactions grows [Formula: see text] for [Formula: see text] agents. For reducing the computational cost to [Formula: see text], we use the Random Batch Method (RBM), which provides a computationally feasible approximation of the dynamics. First, the considered time interval is divided into a number of subintervals. In each subinterval, the RBM randomly divides the set of particles into small subsets (batches), considering only the interactions inside each batch. Due to the averaging effect, the RBM approximation converges to the exact dynamics in the [Formula: see text]-expectation norm as the length of subintervals goes to zero. For this approximated dynamics, the corresponding optimal control can be computed efficiently using a classical gradient descent. The resulting control is not optimal for the original system, but for a reduced RBM model. We therefore adopt a Model Predictive Control (MPC) strategy to handle the error in the dynamics. This leads to a semi-feedback control strategy, where the control is applied only for a short time interval to the original system, and then compute the optimal control for the next time interval with the state of the (controlled) original dynamics. Through numerical experiments we show that the combination of RBM and MPC leads to a significant reduction of the computational cost, preserving the capacity of controlling the overall dynamics.

Power Flow Control in Hybrid Electric Vehicles

2011 ◽  
Author(s):  
Guillermo Becerra ◽  
Jose´ Luis Mendoza-Soto ◽  
Luis Alvarez-Icaza
Keyword(s):  
Flow Control ◽  
Fuel Consumption ◽  
Electric Vehicles ◽  
Power Flow ◽  
Hybrid Electric Vehicles ◽  
Optimization Problems ◽  
Combustion Engine ◽  
Computational Cost ◽  
Power Flow Control ◽  
Hybrid Electric

In this paper a new strategy for controlling the power flow in hybrid electric vehicles is described. The strategy focuses in the planetary gear system where kinematic and dynamic constraints must be satisfied. The aim is to satisfy driver demands and to reduce fuel consumption. The resultant power flow control is continuous and uses the internal combustion engine with the maximum possible efficiency. The strategy is not optimal, although it is inspired by the solution to most optimization problems. The main advantages are that the computational cost is low, when compared to optimization based approaches, and that it is easy to tune. The strategy is tested with simulations using a mathematical model of a power train of a hybrid diesel-electric bus subjected to the power demands of representative urban area driving cycles. Simulation results indicate that the strategy achieves small speed tracking errors and attains good fuel consumption reduction levels.

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