Hybrid Diesel-Electric Heavy Duty Bus Emissions: Benefits Of Regeneration And Need For State Of Charge Correction

Clark, Nigel N.; Xie, Wenwei; Gautam, Mridul; Lyons, Donald W.; Norton, Paul; Balon, Thomas

doi:10.4271/2000-01-2955

Cited by 17 publications

(6 citation statements)

References 7 publications

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“…Fuel consumption and air pollutant emissions from five diesel hybrid electric buses and diesel buses were measured on the chassis dynamometer by WVU (Clark et al, 2000). The results showed about 13% fuel economy gain and significant air pollutant emission reductions for MY 1998 hybrid buses on the CBD cycle, compared to their diesel counterparts.…”

Section: Hybrid Electric Vehiclesmentioning

confidence: 99%

The GREET Model Expansion for Well-to-Wheels Analysis of Heavy-Duty Vehicles

Cai

Burnham

Wang

et al. 2015

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Section: Hybrid Electric Vehiclesmentioning

confidence: 99%

The GREET Model Expansion for Well-to-Wheels Analysis of Heavy-Duty Vehicles

Cai

Burnham

Wang

et al. 2015

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“…For fuel economy comparison, SOC correction is very necessary. Therefore the SOC correction method in the SAE standards [33] is applied to compensate for the SOC difference. Compared with the rule-based control strategy, the fuel consumptions of energy management strategies based on multiple driving cycle optimization and driving pattern recognition are improved by 4.36% and 11.68%, respectively.…”

Section: Simulationmentioning

confidence: 99%

A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition

et al. 2017

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Abstract:The driving pattern has an important influence on the parameter optimization of the energy management strategy (EMS) for hybrid electric vehicles (HEVs). A new algorithm using simulated annealing particle swarm optimization (SA-PSO) is proposed for parameter optimization of both the power system and control strategy of HEVs based on multiple driving cycles in order to realize the minimum fuel consumption without impairing the dynamic performance. Furthermore, taking the unknown of the actual driving cycle into consideration, an optimization method of the dynamic EMS based on driving pattern recognition is proposed in this paper. The simulation verifications for the optimized EMS based on multiple driving cycles and driving pattern recognition are carried out using Matlab/Simulink platform. The results show that compared with the original EMS, the former strategy reduces the fuel consumption by 4.36% and the latter one reduces the fuel consumption by 11.68%. A road test on the prototype vehicle is conducted and the effectiveness of the proposed EMS is validated by the test data.

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“…In addition, discrepancies between predicted and measured NO X may be attributed in part to off-cycle emissions. The effect of off-cycle emissions on comparison of predicted and actual NO X emissions were described previously in detail by Conley et al 6 The effects of control strategy variation have also been evaluated and discussed in detail by Clark et al, 30,31 Azu, 32 and Messer et al 33 From Figures 11 and 13, the model suggests that the bus engine had a steady idle during vehicle decelerations and low axle power demand, whereas actual fueling appeared to be reduced during decelerations and was evidently more load following than what the model anticipated at light loads. Table 6 shows the summary of the integrated predicted and actual emissions results on the three cycles tested by the vehicle.…”

Section: Modeling Of a Hybrid Electric Vehiclementioning

confidence: 99%

“…The energy storage system state of charge difference must be considered for accurate emissions predictions, 3 as well as the hybrid vehicle energy management strategy. 4 Both factors can affect the distance-specific emissions significantly. The onboard energy storage system in a hybrid electric vehicle destroys the relationship between instantaneous vehicle power demand and instantaneous emissions, because the engine is decoupled from the drive wheels.…”

Section: Introductionmentioning

confidence: 99%

Further Validation of Artificial Neural Network-Based Emissions Simulation Models for Conventional and Hybrid Electric Vehicles

Tóth-Nagy

Conley

Jarrett

et al. 2006

Journal of the Air & Waste Management Association

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With the advent of hybrid electric vehicles, computerbased vehicle simulation becomes more useful to the engineer and designer trying to optimize the complex combination of control strategy, power plant, drive train, vehicle, and driving conditions. With the desire to incorporate emissions as a design criterion, researchers at West Virginia University have developed artificial neural network (ANN) models for predicting emissions from heavyduty vehicles. The ANN models were trained on engine and exhaust emissions data collected from transient dynamometer tests of heavy-duty diesel engines then used to predict emissions based on engine speed and torque data from simulated operation of a tractor truck and hybrid electric bus. Simulated vehicle operation was performed with the ADVISOR software package. Predicted emissions (carbon dioxide [CO 2 ] and oxides of nitrogen [NO x ]) were then compared with actual emissions data collected from chassis dynamometer tests of similar vehicles. This paper expands on previous research to include different driving cycles for the hybrid electric bus and varying weights of the conventional truck. Results showed that different hybrid control strategies had a significant effect on engine behavior (and, thus, emissions) and may affect emissions during different driving cycles. The ANN models underpredicted emissions of CO 2 and NO x in the case of a class-8 truck but were more accurate as the truck weight increased.

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Hybrid Diesel-Electric Heavy Duty Bus Emissions: Benefits Of Regeneration And Need For State Of Charge Correction

Cited by 17 publications

References 7 publications

The GREET Model Expansion for Well-to-Wheels Analysis of Heavy-Duty Vehicles

The GREET Model Expansion for Well-to-Wheels Analysis of Heavy-Duty Vehicles

A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition

Further Validation of Artificial Neural Network-Based Emissions Simulation Models for Conventional and Hybrid Electric Vehicles

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