Evaluation of fuel economy for a parallel hybrid electric vehicle

2002 ◽  
Vol 16 (10) ◽  
pp. 1287-1295 ◽  
Author(s):  
Dookhwan Choi ◽  
Hyunsoo Kim
Keyword(s):  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Hybrid Electric

Matching and Optimization for Powertrain System of Parallel Hybrid Electric Vehicle

2013 ◽  
Vol 341-342 ◽  
pp. 423-431
Author(s):  
Jian Ping Gao ◽  
Yue Hui Wei ◽  
Zhen Nan Liu ◽  
Hong Bing Qiao
Keyword(s):  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Dynamic Performance ◽  
Hybrid Powertrain ◽  
Parallel Hybrid Electric Vehicle ◽  
Powertrain System ◽  
Parallel Hybrid ◽  
Hybrid Electric ◽  
Avl Cruise

The parameters matching of the hybrid powertrain system of the hybrid electric vehicle has a directly impact on the performance of the vehicle dynamic and the fuel economy. The preliminary match of the powertrain system base on analysis of Driving Cycle is done, then the software of AVL-Cruise and Matlab are integrated with Isight to optimize parameters of match, by using the Multi-Island GA and NLPQL to establish the combinatorial optimization algorithm. The results show that the fuel economy have been improved by 10.92% without sacrificing the dynamic performance and under the premise of ensuring the limits of the state of charge of battery.

Speed and Throttle Position Forecasting on a Parallel Hybrid Electric Vehicle

2003 ◽  
Author(s):  
Christian M. Muehlfeld ◽  
Sudhakar M. Pandit
Keyword(s):  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Control Algorithm ◽  
Feedforward Control ◽  
Vehicle Simulation ◽  
The Road ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Hybrid Electric

Included in this paper is the forecasting of the speed and throttle position on a thru-the-road parallel hybrid electric vehicle (HEV). This thru-the-road parallel hybrid design is implemented in a 2002 model year Ford Explorer XLT, which is also the Michigan Tech Future Truck. Data Dependent Systems (DDS) forecasting is used in a feedforward control algorithm to improve the fuel economy and to improve the drivability. It provides a one step ahead forecast, thereby allowing the control algorithm to always be a step ahead, utilizing the engine and electric motor in their most efficient ranges. This control algorithm is simulated in PSAT, a hybrid vehicle simulation package, which can estimate the fuel economy and certain performance characteristics of the vehicle. In this paper a fuel economy savings of 2.2% is shown through simulation. Charge sustainability was achieved along with drivability being improved as indicated by the reduction in number of deviations from the speed profile in the driving cycle.

MPC-Based Energy Management of a Parallel Hybrid Electric Vehicle Using Terrain Information

2015 ◽  
Author(s):  
Mohamed Wahba ◽  
Sean Brennan
Keyword(s):  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Simulation Software ◽  
Electrical Machine ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Power Split ◽  
Hybrid Electric

A parallel hybrid electric vehicle (HEV) combines the power produced by electric machines and a combustion engine to enable improved fuel economy. Optimization of the power-split algorithm managing both torque sources can be readily achieved offline, but online implementation results often show great deviation from expected fuel economy due to traffic, hills, and similar effects that are not easily modeled. Of these external influences, the road grade for a travel route is potentially known a priori given a set destination choice from the driver. To examine whether grade information can improve the performance of a hybrid powertrain controller, we first formulate the vehicle model as a low-order dynamic model, recognizing that the primary dynamics of the energy system are slow. A model predictive control (MPC) strategy utilizing the terrain data is then developed to obtain a time-varying power split between the combustion engine and the electrical machine. Simulation results of the HEV model over multiple standard drive cycles, with different terrain profiles and different cost functions, are presented. Testing of the MPC performance compared to Argonne National Lab’s powertrain simulation software Autonomie shows that the MPC strategy utilizing terrain data gives an improvement of up to 2.2% in fuel economy with respect to the same controller without terrain information, on the same route.

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