Driving Cycles for Emission Measurements Under European Conditions

This paper presents system analysis, modeling and simulation of an EV with two independent rear wheel drives. The traction control system is designed to guarantee the EV dynamics and stability in case of no differential gears. Using two electrics in-wheel motors give the possibility to have a torque and speed control in each wheel. This control level improves the EV stability and the safety. The proposed traction control system uses the vehicle speed, which is different from wheels speed characterized by slip in the driving mode, an input. In this case, a generalized neural network algorithm is proposed to estimate the vehicle speed. In terms of the analysis and the simulations carried out, the conclusion can be drawn that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed control approach operates satisfactorily.

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“…The test cycle is the urban ECE-15 cycle (Fig. 6) [15]. A driving cycle is a series of data points representing the vehicle speed versus time.…”

Section: Simulation Resultsmentioning

confidence: 99%

Analysis, Modeling and Neural Network Traction Control of an Electric Vehicle without Differential Gears

Haddoun

Benbouzid

Abdessemed

et al. 2007

2007 IEEE International Electric Machines &Amp; Drives Conference

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“…Figure 8 gives the global configuration of the sensorless neuro-fuzzy sliding mode control scheme and also shows how the EV dynamics will be taken into account. The test cycle is the urban ECE-15 cycle (André et al, 1995). A driving cycle is a series of data points representing the vehicle speed versus time.…”

Section: Scenariomentioning

confidence: 99%

Sliding mode control and neuro-fuzzy network observer for induction motor in EVs applications

Ghariani

Hachicha

Ltifi

et al. 2011

IJEHV

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In order to deal with the major problems of electric vehicle (EV) propelled by an induction motor (IM), a neuro-fuzzy sliding mode control was designed. The proposed scheme uses an adaptive observer that is based on a full order model of the IM in indirect vector-controlled drive. Moreover, it is evaluated on an EV global model taking into account the vehicle dynamics, the thermal effect and the parameters variations. The neuro-fuzzy network is used to adaptively adjust the parameters prediction. However, the sliding mode control can offer many good properties such as good performance against unmodelled dynamics, insensitivity to external disturbance rejection and fast dynamic. Simulations results carried out on a test vehicle, show that the proposed controller is superior to PID one at response speed, steady-state tracking error and resisting perturbation whenever driving or braking. This shows that the proposed scheme is good candidate for EV's propulsion.

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“…Os ciclos de condução são construídos reconectando sequências cinemáticas selecionadas aleatoriamente. Este método vem sendo amplamente aplicado na construção dos ciclos de condução europeus (André et al, 1995).…”

Section: Construção De Ciclos Com Classificação Padronizadaunclassified

“…De acordo com André et al (1995) Para Dai et al (2008), apesar de diferenciar as condições cinemáticas da condução, este método não necessariamente diferencia as emissões associadas a essas atividades.…”

Section: Construção De Ciclos Com Classificação Padronizadaunclassified

Construção de ciclos de condução para estimativa de emissões veiculares para ônibus urbanos.

Filho¹,

Fernando²

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AGRADECIMENTOSAos meus pais pelo amor e incentivo contínuo à minha educação.À minha esposa Débora pelo carinho, apoio e compreensão.Aos colegas Athos Bernardes, pelo interesse e colaboração no desenvolvimento das complexas planilhas de aquisição e análise de dados, e Milton Shikishima, pela dedicação e empenho na instrumentação e acompanhamento do veículo de teste utilizado. Sem eles a realização desse trabalho não teria sido possível. ABSTRACTThe main objective of this research is to develop driving cycles for urban buses from experimental data, with the purpose of estimating pollutant emissions. The study presents different methodologies for the construction of driving cycles, with emphasis on a procedure that uses the VSP concept (Vehicle Specific Power), a variable with high correlation to pollutant emissions. Data from speed variation were collected from a bus performing urban endurance tests, driving through many types of urban roads on a predefined route. Segments from the entire route were selected to represent different operating conditions for buses, during peak and off-peak periods, for which several driving cycles were developed. Pollutant emissions were estimated for these cycles. The results show that road characteristics and adverse traffic conditions can impact directly on VSP distribution and therefore lead to higher pollutant emissions.

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Driving Cycles for Emission Measurements Under European Conditions

Cited by 63 publications

References 0 publications

Analysis, Modeling and Neural Network Traction Control of an Electric Vehicle without Differential Gears

Analysis, Modeling and Neural Network Traction Control of an Electric Vehicle without Differential Gears

Sliding mode control and neuro-fuzzy network observer for induction motor in EVs applications

Construção de ciclos de condução para estimativa de emissões veiculares para ônibus urbanos.

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