Blending gear shift strategy design and comparison study for a battery electric city bus with AMT

Lin, Cheng; Zhao, Mingjie; Pan, Hong; Yi, Jiang

doi:10.1016/j.energy.2019.07.004

Cited by 49 publications

(25 citation statements)

References 30 publications

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“…It should be indicated that the instantaneous driving balancing torque resist T with respect to the current vehicle velocity and acceleration could be calculated from Eq. (19), and the adjustment torque ΔT can be calculated by the difference between the expected vehicle velocity and the actual vehicle velocity. Δ…”

Section: G Driver Modelmentioning

confidence: 99%

Energy Management of the Power-Split Hybrid Electric City Bus Based on the Stochastic Model Predictive Control

Yang

Huang

et al. 2021

IEEE Access

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Section: G Driver Modelmentioning

confidence: 99%

Energy Management of the Power-Split Hybrid Electric City Bus Based on the Stochastic Model Predictive Control

Yang

Huang

et al. 2021

IEEE Access

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“…The DP is a widely used optimization method for vehicle energy consumption analysis [30][31][32]. In this paper, the DP is adopted to explore the optimal energy economy of each vehicle so that the comparison is more convincing.…”

Section: Energy Management Strategy Design Based On Dynamic Programmingmentioning

confidence: 99%

Comparison on Energy Economy and Vibration Characteristics of Electric and Hydraulic in-Wheel Drive Vehicles

et al. 2021

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This paper compares the energy economy and vertical vibration characteristics of in-wheel drive electric vehicles (IEVs), in-wheel drive electric hydraulic hybrid vehicles (IHVs) and centralized drive electric vehicles (CEVs). The dynamic programming (DP) algorithm is used to explore the optimal energy consumption of each vehicle. The energy economy analysis shows that the IEV consumes more energy than the CEV due to its relatively lower electric motor efficiency, even with fewer driveline components. The IHV consumes much more energy than the IEV and CEV because of the energy loss in the hydraulic driveline. The vertical vibration analysis demonstrates that both IEV and IHV degrade the vehicle driving comfort due to increased unsprung mass. Taking the advantage of high power density of the hydraulic motor, IHV have less unsprung mass when compared with the IEV, which helps to mitigate the vibration problems caused by increased unsprung mass.

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“…A minimum waiting period or shift interval is maintained between every consecutive gearshift to avoid frequent gear shifting as well as to ensure riding comfort. The shift interval must be greater than the maximum gearshift time, because the gearshift time is not necessarily the same for every gear change in a transmission system with more than two gears for both upshift and downshift [36,49]. Based on the study conducted by Zhang et al [46], a minimum shift interval of 4 s is assumed to be sufficient for an acceptable level of riding comfort.…”

Section: Gearshift Controlmentioning

confidence: 99%

“…Although these techniques are applied on throttle demand vs. vehicle speed-based gearshift schedule and cannot be directly linked to the proposed schedule in relation to road grade and vehicle acceleration, these could be considered as a general guideline. In this study, the weighting factor is set manually considering mainly two factors [49], i.e., avoiding frequent gearshift to ensure rider comfort and kick-downshifting during high acceleration demand. Therefore, the weighting factor is not necessarily the same at all vehicle speeds.…”

mentioning

confidence: 99%

Gear Ratio Optimization along with a Novel Gearshift Scheduling Strategy for a Two-Speed Transmission System in Electric Vehicle

2020

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A novel gearshift scheduling strategy has been framed for a two-speed transmission system in electric vehicles that can save energy during hilly driving and frequently changing driving conditions through efficient electric motor operation. Unlike the traditional approach, the proposed gearshift strategy is based on the preferred vehicle speed range, vehicle acceleration, and road grade to ensure desired vehicle performances with minimum energy consumption. Meanwhile, the vehicle speed range is chosen around the electric motor rated speed, and two gearshift schedules in relation to vehicle acceleration and road grade are developed based on the motor torque generating capacity and efficiency. Appropriate gear is selected through a combined assessment of the required vehicle speed, acceleration, and road grade information. A guideline is developed and explained for the primary gearshift schedule. Next, the gear ratios and gearshift schedules are optimized combinedly in a Simulink environment using the gradient descent method and pattern search method on three driving cycles separately. Depending on the driving scenarios, around 4% to 7.5% energy saving has been experienced through optimization, while the gear ratios and gearshift schedule in relation to the road grade are found to be major contributors to the vehicle economic driving compared to that with the gearshift schedule for vehicle acceleration.

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Blending gear shift strategy design and comparison study for a battery electric city bus with AMT

Cited by 49 publications

References 30 publications

Energy Management of the Power-Split Hybrid Electric City Bus Based on the Stochastic Model Predictive Control

Energy Management of the Power-Split Hybrid Electric City Bus Based on the Stochastic Model Predictive Control

Comparison on Energy Economy and Vibration Characteristics of Electric and Hydraulic in-Wheel Drive Vehicles

Gear Ratio Optimization along with a Novel Gearshift Scheduling Strategy for a Two-Speed Transmission System in Electric Vehicle

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