Co-Optimization of Velocity and Charge-Depletion for Plug-In Hybrid Electric Vehicles: Accounting for Acceleration and Jerk Constraints

2021 ◽  
Author(s):  
Di Chen ◽  
Mike Huang ◽  
Anna G. Stefanopoulou ◽  
Youngki Kim
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Numerical Approach ◽  
Sequential Optimization ◽  
Mixed Integer ◽  
Hybrid Powertrain ◽  
Hybrid Electric ◽  
Computationally Intensive ◽  
Low Dimensional ◽  
And Control

Abstract Recent advances in vehicle connectivity and automation technologies promote advanced control algorithms that co-optimize the longitudinal dynamics and powertrain operation of hybrid electric vehicles. Typically, a sequential optimization with the vehicle dynamics optimized followed by powertrain optimization is adopted to manage a number of complexities such as the inherent mixed-integer nature of the hybrid powertrain, the numerous state and control variables, the differing time scales of vehicle and powertrain subsystems, time-varying state constraints, and large horizon lengths. Instead, we solve the offline optimization problem in a centralize manner assuming exact knowledge of the lead vehicle's position over the entire trip by applying a discrete-time single shooting-based numerical approach, Discrete Mixed-Integer Shooting (DMIS), including a linearly increasing computational complexity to the problem horizon. In particular, the hierarchical problem structure is exploited to decompose the computationally intensive Hamiltonian minimization step into a set of low-dimensional optimizations. DMIS allows us to compute the direct fuel minimization problem including the vehicle and powertrain dynamics in a centralized manner to its full horizon while systematically tuning weighting factors that penalize passenger discomfort. For the first time, this study reveals that practically implemented sequential optimization exhibits similar fuel optimality as co-optimization when a certain level of passenger comfort is required.

Sensitivity and Correlation Analysis and Implications in Scalable Product Family Design for Plug-In Hybrid Electric Vehicles

2015 ◽  
Author(s):  
Zhila Pirmoradi ◽  
G. Gary Wang
Keyword(s):  
Correlation Analysis ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Electric Energy ◽  
Product Family ◽  
Green Energy ◽  
Product Family Design ◽  
Hybrid Electric ◽  
Market Needs ◽  
And Control

Plug-in Hybrid Electric Vehicles (PHEVs) bear great promises for increasing fuel economy and decreasing greenhouse gas emissions by the use of advanced battery technologies and green energy resources. The design of a PHEV highly depends on several factors such as the selected powertrain configuration, control strategy, sizes of drivetrain components, expected range for propulsion purely by electric energy, known as AER, and the assumed driving conditions. Accordingly, design of PHEV powertrains for diverse customer segments requires thorough consideration of the market needs and the specific performance expectations of each segment. From the manufacturing perspective, these parameters provide the opportunity of mass customization because of the high degree of freedom, especially when the component sizes and control parameters are simultaneously assessed. Based on a nonconventional sensitivity and correlation analysis performed on a simulation model for power-split PHEVs in this study, the product family design (PFD) concept and its implications will be investigated, and limitations of PFD for such a complex product along with directions for efficient family design of PHEVs will be discussed.

Study and Investigation of Energy Management Techniques Used in Electric/Hybrid Electric Vehicles

2021 ◽  
Vol 54 (4) ◽  
pp. 599-606
Author(s):  
Punyavathi Ramineni ◽  
Alagappan Pandian
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Internal Combustion Engines ◽  
Hybrid Electric Vehicles ◽  
Vital Role ◽  
Combustion Engines ◽  
Key Factor ◽  
Hybrid Electric ◽  
And Control ◽  
Intelligent Controllers

Many pollution-related issues are raising due to the usage of conventional internal combustion engines (ICEs) vehicles. Electric Vehicles/ Hybrid electric vehicles (EVs/HEVs) are the finest solutions to overcome those problems associated with ICE-based vehicles. The EVs are introduced with a signal energy source (SES), which is not a successful attempt, especially during transient vehicles, driving, etc. Multiple energy sources (MES) EVs are introduced to attain better performance than the SES vehicles, which is obtained by combining two sources like battery/fuel cells, ultracapacitor. In this contest, energy management (EMNG) plays a vital role in sharing the load to the sources as per the EVs requirement. In the case of MES-based EVs, the controller always plays a significant role in the related EMNG system because it is the key factor in improving vehicle efficiency. In this article, a study has mainly been done related to several conventional, intelligent controllers and control algorithms to do the proper EMNG between sources present in the EV.

scholarly journals Multiphase mixed-integer nonlinear optimal control of hybrid electric vehicles

Automatica ◽  
2021 ◽  
Vol 123 ◽  
pp. 109325 ◽  
Author(s):  
Nicolò Robuschi ◽  
Clemens Zeile ◽  
Sebastian Sager ◽  
Francesco Braghin
Keyword(s):  
Optimal Control ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Nonlinear Optimal Control ◽  
Mixed Integer ◽  
Hybrid Electric

Modeling and Control Strategy of Series Hybrid Electric Vehicles

2014 ◽  
Vol 543-547 ◽  
pp. 1246-1249
Author(s):  
Liang Zhang ◽  
Bin Jiao ◽  
Lei Li
Keyword(s):  
Electric Vehicles ◽  
Control Strategy ◽  
Power Distribution ◽  
Hybrid Electric Vehicles ◽  
System Structure ◽  
Modeling And Control ◽  
Powertrain Control ◽  
Hybrid Electric ◽  
And Control ◽  
Made In

The modeling method and control strategy for series hybrid electric vehicles were presented in this paper. Firstly, the system structure and operation principles are discussed systematically; and then a control strategy is proposed based on the modeling of powertrain. Control strategy focus on the multi-modes switch logic and power distribution. In the last part of this paper, the simulation made in MATLAB/Simulink was introduced, which results indicate that the model and control strategy are correct.

scholarly journals Fuel economy improvement and emission reduction of 48 V mild hybrid electric vehicles with P0, P1, and P2 architectures with lithium battery cell experimental data

2021 ◽  
Vol 13 (10) ◽  
pp. 168781402110360
Author(s):  
Yiqun Liu ◽  
Y Gene Liao ◽  
Ming-Chia Lai
Keyword(s):  
Experimental Data ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Emission Reduction ◽  
Hybrid Electric Vehicles ◽  
Simulation Software ◽  
Hybrid Powertrain ◽  
Battery Cell ◽  
Hybrid Electric ◽  
Mild Hybrid

This paper intends to provide design selections of hybrid powertrain architectures in 48 V mild hybrid electric vehicles. Based on the location of the electric machine in the driveline, the hybrid powertrain architectures can be categorized into five groups, P0, P1, P2, P3, and P4. This paper uses simulation software to investigate the fuel economy improvements and emission reduction of 48 V mild hybrid electric vehicles with P0, P1, and P2 architectures. A baseline conventional and a 12 V start/stop vehicle models based on the production vehicle are built for comparison. The 48 V battery pack model is based on experimental data including open-circuit voltage and internal resistance of a 20 Ah lithium polymer battery cell. Four standard driving cycles are used to assess the fuel economy and emissions of the vehicle models. With features of engine idle elimination, electric power assist, and regenerative braking, the 48 V P0 and P1 respectively gains average 13.5% and 15.5% simulated fuel economy compared to baseline vehicle. The 48 V P2 enables feature of electric launch/driving and improves the fuel economy by average 18.5% better than baseline vehicle. The 48 V mild hybrid system seems to be one of the promising techniques to meet future fuel economy standards and emission regulations.

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