Fuel Economy
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Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5713
Author(s):  
Aaron Rabinowitz ◽  
Farhang Motallebi Araghi ◽  
Tushar Gaikwad ◽  
Zachary D. Asher ◽  
Thomas H. Bradley

In this study, a thorough and definitive evaluation of Predictive Optimal Energy Management Strategy (POEMS) applications in connected vehicles using 10 to 20 s predicted velocity is conducted for a Hybrid Electric Vehicle (HEV). The presented methodology includes synchronous datasets gathered in Fort Collins, Colorado using a test vehicle equipped with sensors to measure ego vehicle position and motion and that of surrounding objects as well as receive Infrastructure to Vehicle (I2V) information. These datasets are utilized to compare the effect of different signal categories on prediction fidelity for different prediction horizons within a POEMS framework. Multiple artificial intelligence (AI) and machine learning (ML) algorithms use the collected data to output future vehicle velocity prediction models. The effects of different combinations of signals and different models on prediction fidelity in various prediction windows are explored. All of these combinations are ultimately addressed where the rubber meets the road: fuel economy (FE) enabled from POEMS. FE optimization is performed using Model Predictive Control (MPC) with a Dynamic Programming (DP) optimizer. FE improvements from MPC control at various prediction time horizons are compared to that of full-cycle DP. All FE results are determined using high-fidelity simulations of an Autonomie 2010 Toyota Prius model. The full-cycle DP POEMS provides the theoretical upper limit on fuel economy (FE) improvement achievable with POEMS but is not currently practical for real-world implementation. Perfect prediction MPC (PP-MPC) represents the upper limit of FE improvement practically achievable with POEMS. Real-Prediction MPC (RP-MPC) can provide nearly equivalent FE improvement when used with high-fidelity predictions. Constant-Velocity MPC (CV-MPC) uses a constant speed prediction and serves as a “null” POEMS. Results showed that RP-MPC, enabled by high-fidelity ego future speed prediction, led to significant FE improvement over baseline nearly matching that of PP-MPC.


2021 ◽  
Vol 202 ◽  
pp. 104496
Author(s):  
Christopher R. Knittel ◽  
Shinsuke Tanaka
Keyword(s):  

2021 ◽  
Author(s):  
Boris Zhmud ◽  
Arthur Coen ◽  
Karima Zitouni

Author(s):  
Priyank Kothari

Abstract: Aerodynamic drag is the force that opposes an object’s motion. When a vehicle no matter the size, is designed to allow air to move fluidly over its body, aerodynamic drag will have less of an impact on its performance and fuel economy. Heavy trucks burn a significant amount of fuel as to overcome the air resistance. More than 50% of an 18-wheeler’s fuel is spent reducing aerodynamic drag on the highways. Keywords: Aerodynamics, Heavy vehicles, ANSYS, Aerodynamic Drag, Fuel efficiency.


Author(s):  
Tao Deng ◽  
Ke Zhao ◽  
Haoyuan Yu

In the process of sufficiently considering fuel economy of plug-in hybrid electric vehicle (PHEV), the working time of engine will be reduced accordingly. The increased frequency that the three-way catalytic converter (TWCC) works in abnormal operating temperature will lead to the increasing of emissions. This paper proposes the equivalent consumption minimization strategy (ECMS) to ensure the catalyst temperature of PHEV can work in highly efficient areas, and the influence of catalyst temperature on fuel economy and emissions is considered. The simulation results show that the fixed equivalent factor of ECMS has great limitations for the underutilized battery power and the poor fuel economy. In order to further reduce fuel consumption and keep the emission unchanged, an equivalent factor map based on initial state of charge (SOC) and vehicle mileage is established by the genetic algorithm. Furthermore, an Adaptive changing equivalent factor is achieved by using the following strategy of SOC trajectory. Ultimately, adaptive equivalent consumption minimization strategy (A-ECMS) considering catalyst temperature is proposed. The simulation results show that compared with ordinary ECMS, HC, CO, and NOX are reduced by 14.6%, 20.3%, and 25.8%, respectively, which effectively reduces emissions. But the fuel consumption is increased by only 2.3%. To show that the proposed method can be used in actual driving conditions, it is tested on the World Light Vehicle Test Procedure (WLTC).


Author(s):  
Naser Sina ◽  
Vahid Esfahanian ◽  
Mohammad Reza Hairi Yazdi

Plug-in hybrid electric buses are a viable solution to increase the fuel economy. In this framework, precise estimation of optimal state-of-charge trajectory along the upcoming driving cycle appears to play a pivotal role in the way to approach the globally optimal fuel economy. This paper aims to conduct a parametric study on the key factors affecting the estimation of optimal state-of-charge trajectory, including trip information availability and trip segment distance, and to provide a guideline for the design and implementation of predictive energy management systems. To accomplish this, the dynamic programming algorithm is employed to obtain the solution of optimal control problem for the sampled driving cycles in a particular bus route. A large database comprising of driving features of the cycles and the optimal solution is developed which then is used to construct a neural network based estimator for obtaining the optimal state-of-charge trajectory. The main results show promising performance of the proposed method with about 76% reduction in the root mean square error of the estimated trajectory comparing to the linear state-of-charge trajectory assumption. Moreover, the robustness of the estimator is verified through simulation and it is observed that appropriate choice of trip segment distance is vital to improve the estimation accuracy, especially in case of uncertain prediction of trip information.


Author(s):  
Xiaohu Yang ◽  
Rong Yang ◽  
Shenglan Tan ◽  
Xionghou Yu ◽  
Liang Fang

To improve the fuel economy and reduce the exhaust emissions of a hybrid electric city bus (HECB) with dual planetary gear, a vehicle model is proposed based on the coupling mechanism between engine and battery motor in the gear. Then, two kinds of adaptive equivalent consumption minimization strategy (ECMS) algorithms based on fuzzy proportional-integral (PI) controller: Fuzzy PA-ECMS and Fuzzy MPGA-ECMS (MGPA: multiple population genetic algorithm), are established to improve the control effect of ECMS with equivalent factor (EF) as the core. Firstly, an approximate expression of optimal EF is derived based on the Pontryagin’s minimum principle (PMP). Subsequently, the deviation between the reference state of charge (SOC) and the actual SOC and the corresponding variation rate are calculated, which are combined with the fuzzy logic controller to adjust the EF. Finally, the driving style is introduced to rectify the trajectory of EF. According to different driving conditions (different initial values of SOC), the PI parameters of EF are optimized offline by multiple population genetic algorithm. Meanwhile, the interval of PI parameters is continuously optimized by the fuzzy controller. Then, simulation experiments are conducted to verify the efficacy of the two proposed algorithms. The simulation results show that compared to the conventional bus and rule-based control strategy, the proposed Fuzzy PA-ECMS algorithm can improve the fuel economy of bus by 41.70% and 5.29%, respectively. Further, compared to Fuzzy PA-ECMS, Fuzzy MPGA-ECMS algorithm can improve the fuel economy of bus by 0.3%.


Author(s):  
Qilun Zhu ◽  
Robert Prucka

Abstract This research proposes an Iterative Dynamic Programming (IDP) algorithm that generates an optimal supervisory control policy for Hybrid Electric Vehicles (HEVs) considering transient powertrain dynamics. The proposed algorithm tries to solve the 'curse of dimensionality and the 'curse of modeling' of conventional Dynamic Programming (DP). The proposed IDP algorithm iteratively updates the DP formulation using a machine learning (ML) based powertrain model. The ML model is recursively trained using the outputs from the driving cycle simulation with a high-fidelity model. Once the reduced model converges to the high-fidelity model accuracy, the resulting control policy yields a 9.1% Fuel Economy (FE) improvement compared to the baseline non-predictive rule-based control for the UDDS driving cycle. A conventional DP control strategy based on a quasi-static powertrain model and a perfect preview of future power demand yields 14.2% FE improvement. However, the FE improvement reduces to 5.7% when the policy is validated with the high-fidelity model. It is concluded that capturing the transient powertrain dynamics is critical to generating a realistic fuel economy prediction and relevant powertrain control policy. The proposed IDP strategy employs targeted state-space exploration to leverage the improving state trajectory from previous iterations. Compared to conventional fixed state-space sampling methods, this method improves the accuracy of the DP policy against discretization error. It also significantly reduces the computational load of the relatively high number of states of the transient powertrain model.


Author(s):  
Heet Patel

Abstract: Traditional vehicles are designed to bring out the best performance, good fuel economy, fewer emissions, and good high-speed stability. In this process of designing a vehicle, the underbody geometry of a car plays a vital role and is often neglected because of its complicated design bits. Though the presence of uneven surfaces causes the layers of air to separate resulting in generating turbulence. This report is about designing an active rear diffuser of a car. The rear diffuser is an aerodynamic device that is installed in the end part of the underbody of a car. Diffuser now a day is quite a common aerodynamic device that is used in performance cars. The main moto of attaching a diffuser is to reduce the wake produced behind the car and help the streamlines to converge better. The prime focus of this study is to design an active rear diffuser that will not only help in providing great high-speed stability and aerodynamic efficiency but will also use the aerodynamic forces adversely to help the car stop faster and on its track. This is made possible first by understanding the effects of diffuser angle on the aerodynamic forces acting on the car. Further, to actually transform the computational values into a working model, an electronic circuit is designed which mimics the exact movement of the diffuser according to the speed and other driving conditions. Keywords: Adaptive, diffuser, automobile, aerodynamic, aerodynamic Drag, aerodynamic Lift


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