A Data-driven Online Identification and Control Optimization Approach applied to a Hybrid Electric Powertrain System

2012 ◽  
Vol 45 (2) ◽  
pp. 153-158 ◽  
Author(s):  
Matthias Marx ◽  
Xi Shen ◽  
Dirk Soffker
Keyword(s):  
Data Driven ◽  
Optimization Approach ◽  
Online Identification ◽  
Control Optimization ◽  
Powertrain System ◽  
Hybrid Electric ◽  
Hybrid Electric Powertrain ◽  
And Control

scholarly journals Fundamentals of Force-Controlled Friction Riveting: Part II—Joint Global Mechanical Performance and Energy Efficiency

Materials ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 2489 ◽  
Author(s):  
Gonçalo Pina Cipriano ◽  
Lucian Blaga ◽  
Jorge dos Santos ◽  
Pedro Vilaça ◽  
Sergio Amancio-Filho
Keyword(s):  
Energy Efficiency ◽  
Mechanical Response ◽  
Mechanical Performance ◽  
Tensile Force ◽  
Optimization Approach ◽  
Additional Energy ◽  
Efficient Manner ◽  
Control Optimization ◽  
Friction Parameters ◽  
And Control

The present work investigates the correlation between energy efficiency and global mechanical performance of hybrid aluminum alloy AA2024 (polyetherimide joints), produced by force-controlled friction riveting. The combinations of parameters followed a central composite design of experiments. Joint formation was correlated with mechanical performance via a volumetric ratio (0.28–0.66 a.u.), with a proposed improvement yielding higher accuracy. Global mechanical performance and ultimate tensile force varied considerably across the range of parameters (1096–9668 N). An energy efficiency threshold was established at 90 J, until which, energy input displayed good linear correlations with volumetric ratio and mechanical performance (R-sq of 0.87 and 0.86, respectively). Additional energy did not significantly contribute toward increasing mechanical performance. Friction parameters (i.e., force and time) displayed the most significant contributions to mechanical performance (32.0% and 21.4%, respectively), given their effects on heat development. For the investigated ranges, forging parameters did not have a significant contribution. A correlation between friction parameters was established to maximize mechanical response while minimizing energy usage. The knowledge from Parts I and II of this investigation allows the production of friction riveted connections in an energy efficient manner and control optimization approach, introduced for the first time in friction riveting.

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.

Modeling, control, and performance of a novel architecture of hybrid electric powertrain system

2016 ◽  
Vol 178 ◽  
pp. 454-467 ◽  
Author(s):  
Chenyu Yi ◽  
Bogdan I. Epureanu ◽  
Sung-Kwon Hong ◽  
Tony Ge ◽  
Xiao Guang Yang
Keyword(s):  
Powertrain System ◽  
Hybrid Electric ◽  
And Performance ◽  
Hybrid Electric Powertrain

Integrated System Design and Control Optimization of Hybrid Electric Propulsion System Using a Bi-Level, Nested Approach

2019 ◽  
Author(s):  
Li Chen ◽  
Huachao Dong ◽  
Zuomin Dong
Keyword(s):  
Electric Propulsion ◽  
Fuel Efficiency ◽  
Ghg Emissions ◽  
Propulsion System ◽  
Hybrid Powertrain ◽  
Electric Propulsion System ◽  
Powertrain System ◽  
Marine Propulsion ◽  
Hybrid Electric ◽  
Hybrid Electric Powertrain

Abstract Hybrid electric powertrain systems present as effective alternatives to traditional vehicle and marine propulsion means with improved fuel efficiency, as well as reduced greenhouse gas (GHG) emissions and air pollutants. In this study, a new integrated, model-based design and optimization method for hybrid electric propulsion system of a marine vessel (harbor tugboat) has been introduced. The sizes of key hybrid powertrain components, especially the Li-ion battery energy storage system (ESS), which can greatly affect the ship’s life-cycle cost (LCC), have been optimized using the fuel efficiency, emission and lifecycle cost model of the hybrid powertrain system. Moreover, the control strategies for the hybrid system, which is essential for achieving the minimum fuel consumption and extending battery life, are optimized. For a given powertrain architecture, the optimal design of a hybrid marine propulsion system involves two critical aspects: the optimal sizing of key powertrain components, and the optimal power control and energy management. In this work, a bi-level, nested optimization framework was proposed to address these two intricate problems jointly. The upper level optimization aims at component size optimization, while the lower level optimization carries out optimal operation control through dynamic programming (DP) to achieve the globally minimum fuel consumption and battery degradation for a given vessel load profile. The optimized Latin hypercube sampling (OLHS), Kriging and the widely used Expected Improvement (EI) online sampling criterion are used to carry out “small data” driven global optimization to solve this nested optimization problem. The obtained results showed significant reduction of the vessel LCC with the optimized hybrid electric powertrain system design and controls. Reduced engine size and operation time, as well as improved operation efficiency of the hybrid system also greatly decreased the GHG emissions compared to traditional mechanical propulsion.

Optimal Hybridization of Battery, Engine and Motor for PHEV20

2011 ◽  
Author(s):  
Shashi K. Shahi ◽  
G. Gary Wang ◽  
Liqiang An ◽  
Eric Bibeau
Keyword(s):  
Hybrid Electric Vehicle ◽  
System Analysis ◽  
Operating Cost ◽  
Computational Time ◽  
Design Parameters ◽  
Optimization Approach ◽  
Component Sizing ◽  
Powertrain System ◽  
Hybrid Electric ◽  
Storage Batteries

A plug-in hybrid electric vehicle (PHEV) relies on relatively larger storage batteries than conventional hybrid electric vehicles. The characteristics of PHEV batteries, as well as hybridization of the PHEV battery with the engine and electric motor, play an important role in the design and potential adoption of PHEVs. To exhaustively evaluate all the possible combinations of available types of batteries, motors and engines, the total computational time is prohibitive. This work proposed an integrated optimal design strategy to address this problem. The recently developed Pareto set pursuing (PSP) multi-objective optimization approach is employed to perform optimal hybridization. Each PHEV with chosen battery, motor and engine is designed for optimal component sizing using the Powertrain System Analysis Toolkit (PSAT) software. The methodology is demonstrated with the Toyota Prius PHEV20: PHEV version sized for 20 miles (32.1 km) of all electric range (AER). Fuel economy, operating cost, and green house gases emissions are simultaneously optimized from 4,480 possible combinations of design parameters: 20 batteries, 14 motors, and 16 engines. The hybridization optimization is performed on two different drive cycles—Urban dynamometer driving schedule (UDDS) and Winnipeg weekday duty cycle (WWDC). It was found that battery, motor, and engine work collectively to define an optimal hybridization scheme and the optimal hybridization scheme varies with each driving cycle. The proposed method and software platform could be applied to optimize other powertrain designs.

scholarly journals Advanced rule-based energy management for better fuel economy of hybrid electric tracked vehicle

2021 ◽  
Vol 49 (3) ◽  
pp. 711-718
Author(s):  
Stefan Milićević ◽  
Ivan Blagojević ◽  
Slavko Muždeka
Keyword(s):  
Fuel Economy ◽  
Control Strategy ◽  
Power Distribution ◽  
Tracked Vehicle ◽  
Control Optimization ◽  
Reference Vehicle ◽  
Battery State Of Charge ◽  
Technological Developments ◽  
Hybrid Electric ◽  
And Control

All recent technological developments in the field of power distribution in hybrid electric tracked vehicles are often hard to apply and carry high computational burden which makes them impractical for real-time applications. In this paper, a novel control strategy is proposed for parallel hybrid electric tracked vehicle based on robust and easy to implement thermostat strategy with added merits of power follower control strategy (PFCS). The goal of the control strategy is enhanced fuel economy. Serbian infantry fighting vehicle BVP M80-A is chosen as the reference vehicle. For the purpose of validation, a backward-looking, high fidelity model is created in Simulink environment. Investigation of the results indicates that the proposed control strategy offers 12.8% better fuel economy while effectively maintaining battery state of charge (SOC). Even better results (23.2%) were achieved applying the proposed strategy to a model with an additional generator. It is concluded that further improvements can be made with combined sizing and control optimization.

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