Analysis of regenerative braking efficiency &amp;#x2014; A case study of two electric vehicles operating in the Rotterdam area

Sterkenburg, Stefan van; Rietveld, E.; Rieck, Frank; Veenhuizen, Bram; Bosma, H.

doi:10.1109/vppc.2011.6043109

Cited by 30 publications

(16 citation statements)

References 2 publications

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“…Its maximum power is 23 kW. Figure 4 shows the variations of torque and e ciency contours of the MG against its revolution [28]. The selected motor is used as a generator through braking, which charges the battery.…”

Section: Models Of Phev Main Componentsmentioning

confidence: 99%

A modified control strategy for parallel hybrid electric vehicles equipped with continuously variable transmission

2016

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Abstract. This paper aims to establish a control strategy for parallel hybrid electric vehicles equipped with full-toroidal Continuously Variable Transmission (CVT). First, the advantages of CVT are elucidated. Afterwards, a modi ed control strategy on the base of Baseline static Control Strategy (BCS) is proposed. Employing this strategy, in some moments, the engine operates in its fuel-optimal point to decrease the vehicle Fuel Consumption (FC). It is demonstrated that the modi cations in BCS are applicable using a CVT as the power train. In order to investigate the implemented modi cation, an optimization on the proposed control strategy and BCS in SC03 driving cycle is accomplished and then, the optimized control strategies are compared. It will be demonstrated that the proposed method is superior to BCS in terms of FC in SC03 driving cycle. Finally, in order to examine generality of the comparison, the optimized control strategies are compared in other driving cycles. It is revealed that the optimized state of the proposed control strategy is advantageous in these cycles.

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Section: Models Of Phev Main Componentsmentioning

confidence: 99%

A modified control strategy for parallel hybrid electric vehicles equipped with continuously variable transmission

2016

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“…3, in our vehicle model, there are bidirectional energy flows between the vehicle socket and wheels through the AC/DC converter, battery, DC/AC converter, motor/generator and transmission. EV socket-to-wheels efficiency is taken as 69%, based on the average values of EV components found in reviewed literature (Smith 2010a;Campanari et al 2009;Hayes et al 2011;Van Sterkenburg et al 2011;van Vliet et al 2011).…”

Section: Model Assumptionsmentioning

confidence: 99%

“…Energy recovered flows through the wheels to the battery. Values range from 59% to 84% (Van Sterkenburg et al 2011;Smith 2010a;Campanari et al 2009), resulting in a mean value of 69%. Rolling resistance (μ r ) has a direct impact on fuel consumption.…”

Section: Model Assumptionsmentioning

confidence: 99%

Transport energy consumption in mountainous roads. A comparative case study for internal combustion engines and electric vehicles in Andorra

Travesset-Baro

Rosas-Casals

Jover

2015

Transportation Research Part D: Transport and Environment

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This paper analyses transport energy consumption of conventional and electric vehicles in mountainous roads. A standard round trip in Andorra has been modelled in order to characterise vehicle dynamics in hilly regions. Two conventional diesel vehicles and their electric-equivalent models have been simulated and their performances have been compared. Six scenarios have been simulated to study the effects of factors such as orography, traffic congestion and driving style. The European fuel consumption and emissions test and Artemis urban driving cycles, representative of European driving cycles, have also been included in the comparative analysis. The results show that road grade has a major impact on fuel economy, although it affects consumption in different levels depending on the technology analysed. Electric vehicles are less affected by this factor as opposed to conventional vehicles, increasing the potential energy savings in a hypothetical electrification of the car fleet. However, electric vehicle range in mountainous terrains is lower compared to that estimated by manufacturers, a fact that could adversely affect a massive adoption of electric cars in the short term.

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“…According to previous studies, a variety of factors affect energy consumption, including travel-related factors [4,5], environment-related factors [6][7][8][9], vehicle-related factors [10][11][12][13][14], roadway-related factors [15], traffic-related factors [16][17][18], driver-related factors [3,[19][20][21][22], the health and degradation condition of the battery [23][24][25][26], the efficiency of braking energy recovery [27][28][29][30][31], and the charge and discharge character of the battery [32]. Some previous studies collected the information through driving-cycle experiments in the lab [4,24] and Global Positioning System (GPS) observations in the real world [32][33][34], but some results showed the significant difference between experiments and real-world conditions [35,36], leading to a relative low accuracy and poor practicality of models.…”

Section: Introductionmentioning

confidence: 99%

Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations

2017

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Improving the estimation accuracy for the energy consumption of electric vehicles (EVs) would greatly contribute to alleviating the range anxiety of drivers and serve as a critical basis for the planning, operation, and management of charging infrastructures. To address the challenges in energy consumption estimation encountered due to sparse Global Positioning System (GPS) observations, an estimation model is proposed that considers both the kinetic characteristics from sparse GPS observations and the unique attributes of EVs: (1) work opposing the rolling resistance; (2) aerodynamic friction losses; (3) energy consumption/generation depending on the grade of the route; (4) auxiliary load consumption; and (5) additional energy losses arising from the unstable power output of the electric motor. Two quantities, the average energy consumption per kilometer and the energy consumption for an entire trip, were focused on and compared for model fitness, parameter, and effectiveness, and the latter showed a higher fitness. Based on sparse GPS observations of 68 EVs in Aichi Prefecture, Japan, the traditional linear regression approach and a multilevel mixed-effects linear regression approach were used for model calibration. The proposed model showed a high accuracy and demonstrated a great potential for application in using sparse GPS observations to predict the energy consumption of EVs.

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Analysis of regenerative braking efficiency — A case study of two electric vehicles operating in the Rotterdam area

Cited by 30 publications

References 2 publications

A modified control strategy for parallel hybrid electric vehicles equipped with continuously variable transmission

A modified control strategy for parallel hybrid electric vehicles equipped with continuously variable transmission

Transport energy consumption in mountainous roads. A comparative case study for internal combustion engines and electric vehicles in Andorra

Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations

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