Analysis of Fuel Economy and Pollutant Emissions of Autonomous Vehicle Platoon Based on PLEXE

2018 ◽  
Author(s):  
Fuchun Liu ◽  
Yun He ◽  
Junhong Zhao
Keyword(s):  
Fuel Economy ◽  
Autonomous Vehicle ◽  
Pollutant Emissions ◽  
Vehicle Platoon

Analysis of Fuel Economy of Autonomous Vehicle Platoon

ICTE 2013 ◽  
2013 ◽  
Author(s):  
Jin Zhao ◽  
Rongchen Zhao ◽  
Guangwei Wang ◽  
Xiangnan Zhang
Keyword(s):  
Fuel Economy ◽  
Autonomous Vehicle ◽  
Vehicle Platoon

Robust Approximate Constraint-Following Control for Autonomous Vehicle Platoon Systems

2017 ◽  
Vol 20 (4) ◽  
pp. 1611-1623 ◽  
Author(s):  
Xiaomin Zhao ◽  
Y. H. Chen ◽  
Han Zhao
Keyword(s):  
Autonomous Vehicle ◽  
Vehicle Platoon

scholarly journals Virtual Calibration Method for Diesel Engine by Software in The Loop Techniques

2019 ◽  
Vol 16 (3) ◽  
pp. 6940-6957
Author(s):  
M. C. Cameretti ◽  
E. Landolfi ◽  
T. Tesone ◽  
A. Caraceni
Keyword(s):  
Diesel Engine ◽  
Fuel Economy ◽  
Engine Performance ◽  
Control Unit ◽  
Calibration Method ◽  
Pollutant Emissions ◽  
Engine Control ◽  
Start Of Injection ◽  
Definition Of ◽  
Valid Solution

The calibration of the engine control unit is increased for the development of the whole automotive system. The aim is to calibrate the electronic engine control to match the decreasing emission requirements and increasing fuel economy demands. The reduction of the number of tests on vehicles represents one of the most important requirements for increasing efficiency of the engine calibration process. However, the definition of the design of experiment is not straightforward because the data is not known beforehand, so it is difficult to process and analyse this data to achieve a globally valid model. To reduce time effort and costs the virtual calibration can be a valid solution. This procedure is called software in the loop (SIL) calibration able to develop a process to systematically identify the optimal balance of engine performance, emissions and fuel economy. In this work, a virtual calibration methodology is presented by using a two-stage model to get minimum exhaust emissions of a diesel engine. The data used are from a GT-Power model of a 3L supercharged diesel engine. The model is able to calculate the engine emissions for different engine parameters (such as the start of injection, EGR fraction and rail pressure) and from optimisation process, new injection start maps that reduce pollutant emissions are created.

Fuel economy and NOx emission potential investigation and trade-off of a hybrid electric vehicle based on dynamic programming

2008 ◽  
Vol 222 (10) ◽  
pp. 1851-1864 ◽  
Author(s):  
G-Q Ao ◽  
J-X Qiang ◽  
H Zhong ◽  
X-J Mao ◽  
L Yang ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Cost Function ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Pollutant Emissions ◽  
Power Requirement ◽  
Trade Off ◽  
Fuel Economy Improvement ◽  
Hybrid Electric ◽  
Potential Improvement

Hybrid electric vehicles (HEVs) combined with more than one power source offer additional flexibility to improve the fuel economy and to reduce pollutant emissions. The dynamic-programming-based supervisory controller (DPSC) presented here investigates the fuel economy improvement and emissions reduction potential and demonstrates the trade-off between fuel economy and the emission of nitrogen oxides (NO x) for a state-of-charge-sustaining parallel HEV. A weighted cost function consisting of fuel economy and emissions is proposed in this paper. Any possible engine-motor power pairs meeting with the power requirement is considered to minimize the weighted cost function over the given driving cycles through this dynamic program algorithm. The fuel-economy-only case, the NO x-only case, and the fuel-NO x case have been achieved by adjusting specific weighting factors, which demonstrates the flexibility and advantages of the DPSC. Compared with operating the engine in the NO x-only case, there is 17.4 per cent potential improvement in the fuel-economy-only case. The fuel-NO x case yields a 15.2 per cent reduction in NO x emission only at the cost of 5.5 per cent increase in fuel consumption compared with the fuel-economy-only case.

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.

A Learning Algorithm for Optimal Internal Combustion Engine Calibration in Real Time

2007 ◽  
Author(s):  
Andreas A. Malikopoulos ◽  
Panos Y. Papalambros ◽  
Dennis N. Assanis
Keyword(s):  
Internal Combustion Engine ◽  
Real Time ◽  
Fuel Economy ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Pollutant Emissions ◽  
Engine Operation ◽  
Engine Calibration

Advanced internal combustion engine technologies have increased the number of accessible variables of an engine and our ability to control them. The optimal values of these variables are designated during engine calibration by means of a static correlation between the controllable variables and the corresponding steady-state engine operating points. While the engine is running, these correlations are being interpolated to provide values of the controllable variables for each operating point. These values are controlled by the electronic control unit to achieve desirable engine performance, for example in fuel economy, pollutant emissions, and engine acceleration. The state-of-the-art engine calibration cannot guarantee continuously optimal engine operation for the entire operating domain, especially in transient cases encountered in driving styles of different drivers. This paper presents the theoretical basis and algorithmic implementation for allowing the engine to learn the optimal set values of accessible variables in real time while running a vehicle. Through this new approach, the engine progressively perceives the driver’s driving style and eventually learns to operate in a manner that optimizes specified performance indices. The effectiveness of the approach is demonstrated through simulation of a spark ignition engine, which learns to optimize fuel economy with respect to spark ignition timing, while it is running a vehicle.

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