Optimal Selection of Equivalence Factors for ECMS in Mild Hybrid Electric Vehicles

2021 ◽  
Author(s):  
Shailesh Hegde ◽  
Angelo Bonfitto ◽  
Hadi Rahmeh ◽  
Nicola Amati ◽  
Andrea Tonoli
Keyword(s):  
Electric Vehicles ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Shooting Method ◽  
Hybrid Electric Vehicles ◽  
Optimal Solution ◽  
Pollutant Emissions ◽  
Hybrid Electric ◽  
Mild Hybrid ◽  
Selection Of

Abstract The increasing stringent emissions regulation over the years have shifted the focus of automotive industry towards more efficient fuel economy solutions. One such solution is Hybrid electric architecture, which is able to improve the fuel economy and consequently cutting down emissions. A well known control strategy to solve optimization problem for energy management of Hybrid electric vehicles is ECMS (Equivalent Consumption Minimization Strategy). Finding the best control parameters (equivalence factors) of this strategy may become quite involved. This paper proposes a method for the selection of the optimal equivalence factors, for charging and discharging, by applying genetic algorithm in the case of a P0 mild hybrid electric vehicle. This method is a systematic and deterministic way to guarantee an optimal solution with respect to the trial and error method. The proposed ECMS is compared to a technique available in literature, known as the shooting method, which relies only on one equivalence factor for discharging. It is demonstrated that the performance in terms of pollutant emissions are comparable. However, ECMS with GA always guarantees an optimal solution even in the case of heavy accessory load, when shooting method is not valid anymore, as it does not guarantee a charge sustaining condition.

scholarly journals Model Predictive Control for Connected Hybrid Electric Vehicles

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Kaijiang Yu ◽  
Xiaozhuo Xu ◽  
Qing Liang ◽  
Zhiguo Hu ◽  
Junqi Yang ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Control Method ◽  
Fuel Efficiency ◽  
Computation Method ◽  
Hybrid Electric

This paper presents a new model predictive control system for connected hybrid electric vehicles to improve fuel economy. The new features of this study are as follows. First, the battery charge and discharge profile and the driving velocity profile are simultaneously optimized. One is energy management for HEV forPbatt; the other is for the energy consumption minimizing problem of acc control of two vehicles. Second, a system for connected hybrid electric vehicles has been developed considering varying drag coefficients and the road gradients. Third, the fuel model of a typical hybrid electric vehicle is developed using the maps of the engine efficiency characteristics. Fourth, simulations and analysis (under different parameters, i.e., road conditions, vehicle state of charge, etc.) are conducted to verify the effectiveness of the method to achieve higher fuel efficiency. The model predictive control problem is solved using numerical computation method: continuation and generalized minimum residual method. Computer simulation results reveal improvements in fuel economy using the proposed control method.

scholarly journals Comparisons of Real-World Vehicle Energy Efficiency with Dynamometer-Based Ratings and Simulation Models

2021 ◽  
Vol 12 (4) ◽  
pp. 161
Author(s):  
Karim Hamza ◽  
Kang-Ching Chu ◽  
Matthew Favetti ◽  
Peter Keene Benoliel ◽  
Vaishnavi Karanam ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Real World ◽  
Hybrid Electric Vehicle ◽  
Energy Intensity ◽  
Hybrid Electric Vehicles ◽  
Combustion Engine ◽  
Simulation Models ◽  
Hybrid Electric

Software tools for fuel economy simulations play an important role during design stages of advanced powertrains. However, calibration of vehicle models versus real-world driving data faces challenges owing to inherent variations in vehicle energy efficiency across different driving conditions and different vehicle owners. This work utilizes datasets of vehicles equipped with OBD/GPS loggers to validate and calibrate FASTSim (software originally developed by NREL) vehicle models. The results show that window-sticker ratings (derived from dynamometer tests) can be reasonably accurate when averaged across many trips by different vehicle owners, but successfully calibrated FASTSim models can have better fidelity. The results in this paper are shown for nine vehicle models, including the following: three battery-electric vehicles (BEVs), four plug-in hybrid electric vehicles (PHEVs), one hybrid electric vehicle (HEV), and one conventional internal combustion engine (CICE) vehicle. The calibrated vehicle models are able to successfully predict the average trip energy intensity within ±3% for an aggregate of trips across multiple vehicle owners, as opposed to within ±10% via window-sticker ratings or baseline FASTSim.

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.

Modeling and Simulation for Super-Mild Hybrid Electric Vehicles

2013 ◽  
Vol 333-335 ◽  
pp. 2072-2075
Author(s):  
Jian Fei Shi ◽  
Bo Jun Zhang ◽  
Yu Wang
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Model Development ◽  
Vehicle Control ◽  
Transmission System ◽  
System Model ◽  
Simulation Results ◽  
Hybrid Electric ◽  
Mild Hybrid

Analysis the super-mild hybrid electric vehicle and its transmission system, the transmission system model of low-gear is established through bond graph. Establish vehicle control simulation model, development of low-gear control strategy to simulation. The simulation results show that the fuel economy and emission performance are improved.

scholarly journals Drive Cycle Performance of Hybrid-Electric Vehicles

2020 ◽  
Vol 1 (2) ◽  
pp. 1-6
Author(s):  
Godwin Kafui Ayetor ◽  
George Bright Gyamfi ◽  
Ebenezer Tetteh Larnor
Keyword(s):  
Electric Vehicles ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Cycle Performance ◽  
System Efficiency ◽  
Fuel Savings ◽  
Hybrid Electric ◽  
All Speeds ◽  
Electric Powertrains

This paper focuses on the effects of HEV (Hybrid-Electric Vehicles) Powertrains on fuel economy and overall system efficiency. Three different hybrid-electric powertrains: Series; Parallel and Combined have been simulated on ADVISOR by the use of MATLAB platform. Three drive cycles, Urban Dynamometer Driving Schedule (UDDS), New European Driving Cycle (NEDC) and Highway Fuel Economy Transport Cycle (HWFET), were used to determine best Fuel Economy, Overall System Efficiency and Energy usage for each Powertrain.While Parallel Powertrain showed best fuel economy and system efficiency at lower speeds (20 mph) during frequent start-stops, Combined Hybrid showed much more significant fuel savings at constant speeds above 48 mph. In situations where both battery and engine power were required simultaneously, Combined Hybrid showed much higher system efficiency giving credence to its PowerSplit device. In conclusion, the selection of the preferred Powertrain for Hybrid Electric application depends strictly on the application required. The results clearly show that advantages of both Series and Parallel powertrains have not been effectively harnessed in the Combined Powertrain as expected. This highlights the need for a Powertrain which effectively saves fuel at all speeds irrespective of number of idle times or stops. Keywords: Hybrid electric vehicle; zero emissions; combined hybrid; series hybrid; parallel hybrid; electric vehicles; fuel cells

scholarly journals Effective optimal control strategy for hybrid electric vehicle with continuously variable transmission

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401882481 ◽  
Author(s):  
Hangyang Li ◽  
Xiaolan Hu ◽  
Bing Fu ◽  
Jiande Wang ◽  
Feitie Zhang ◽  
...  
Keyword(s):  
Cost Function ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Continuously Variable Transmission ◽  
Optimal Control Strategy ◽  
Variable Transmission ◽  
Hybrid Electric

Hybrid electric vehicles equipped with continuously variable transmission show dramatic improvements in fuel economy and driving performance because they can continuously adjust the operating points of the power source. This article proposes an optimal control strategy for continuously variable transmission–based hybrid electric vehicles with a pre-transmission parallel configuration. To explore the fuel-saving potential of the given configuration, a ‘control-oriented’ quasi-static vehicle model is built, and dynamic programming is adopted to determine the optimal torque split factor and continuously variable transmission speed ratio. However, a single-criterion cost function will lead to undesirable drivability problems. To tackle this problem, the main factors affecting the driving performance of a continuously variable transmission–based hybrid electric vehicle are studied. On that basis, a multicriterion cost function is proposed by introducing drivability constraints. By varying the weighting factors, the trade-off between fuel economy and drivability can be evaluated under a predetermined driving cycle. To validate the effectiveness of the proposed method, simulation experiments are performed under four different driving cycles, and the results indicate that the proposed method greatly enhanced the drivability without significantly increasing fuel consumption. Compared to a single-criterion cost function, the use of multiple criteria is more representative of real-world driving behaviour and thus provides better reference solutions to evaluate suboptimal online controllers.

A unified quantitative analysis of fuel economy for hybrid electric vehicles based on energy flow

2021 ◽  
Vol 292 ◽  
pp. 126040
Author(s):  
Xiaohua Zeng ◽  
Qifeng Qian ◽  
Hongxu Chen ◽  
Dafeng Song ◽  
Guanghan Li
Keyword(s):  
Quantitative Analysis ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Energy Flow ◽  
Hybrid Electric Vehicles ◽  
Hybrid Electric

Multirate Integration in Hybrid Electric Vehicle Virtual Proving Grounds

1998 ◽  
Author(s):  
Dario Solis ◽  
Chris Schwarz
Keyword(s):  
Real Time ◽  
Electric Vehicle ◽  
Time Scales ◽  
Electric Vehicles ◽  
Time Scale ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Differential Algebraic Equations ◽  
Vehicle Systems ◽  
Hybrid Electric

Abstract In recent years technology development for the design of electric and hybrid-electric vehicle systems has reached a peak, due to ever increasing restrictions on fuel economy and reduced vehicle emissions. An international race among car manufacturers to bring production hybrid-electric vehicles to market has generated a great deal of interest in the scientific community. The design of these systems requires development of new simulation and optimization tools. In this paper, a description of a real-time numerical environment for Virtual Proving Grounds studies for hybrid-electric vehicles is presented. Within this environment, vehicle models are developed using a recursive multibody dynamics formulation that results in a set of Differential-Algebraic Equations (DAE), and vehicle subsystem models are created using Ordinary Differential Equations (ODE). Based on engineering knowledge of vehicle systems, two time scales are identified. The first time scale, referred to as slow time scale, contains generalized coordinates describing the mechanical vehicle system that includs the chassis, steering rack, and suspension assemblies. The second time scale, referred to as fast time scale, contains the hybrid-electric powertrain components and vehicle tires. Multirate techniques to integrate the combined set of DAE and ODE in two time scales are used to obtain computational gains that will allow solution of the system’s governing equations for state derivatives, and efficient numerical integration in real time.

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