Energy management for battery electric vehicle with automated mechanical transmission

Li, Teng; Zou, Yuan; Liu, Dexing

doi:10.1504/ijvd.2016.073701

Cited by 22 publications

(6 citation statements)

References 29 publications

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“…This reduces the production costs of electric vehicles and facilitates maintenance [18]. In order to improve the efficiency and performance of electric drives, researchers have made improvements to the drive structure of single-motor drives, such as utilizing automated manual transmission (AMT) [19,20], two-speed automatic transmission (AT) [21,22], continuously variable transmission (CVT), and dual-clutch transmission (DCT) [23][24][25]. However, the use of a single motor does not allow the vehicle to operate in the economic speed zone of the motor for long periods of time, which is detrimental to increasing the range of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Efficiency Comparison of Electric Wheel Loader Powertrains with Dual Motor Input in Distributed Driving Modes

Fei,

Han,

Wong

et al. 2023

WEVJ

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The presented research on electric wheel loaders lacks a detailed analysis of drive energy-saving during the shovel preparation phase, which is characterized by a high probability of loader tire skidding. To address this issue, this study examines the energy consumption efficiency of a two-motor distributed drive wheel loader under three drive modes including front motor drive, rear motor drive, and dual-motor drive, taking into account the change in the drive force demand caused by the bucket landing. This study finds that the motor energy conversion efficiency is the greatest in single-motor drive mode when the bucket does not generate positive pressure with the ground. In dual-motor drive mode, the total torque overcome is greater, but the motor energy conversion efficiency is the greatest when the bucket generates the greatest positive pressure with the ground. This study suggests that in future designs of electric loaders, two motors can be used to distribute the drive, but the front and rear motors should be designed to participate in the drive with a certain torque distribution ratio at different speeds and resistance to avoid the phenomenon of the bucket pressing the ground too much.

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Section: Introductionmentioning

confidence: 99%

Efficiency Comparison of Electric Wheel Loader Powertrains with Dual Motor Input in Distributed Driving Modes

Fei,

Han,

Wong

et al. 2023

WEVJ

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show abstract

“…The gear ratios or shift schedules can be constantly optimized by simultaneously considering the vehicle's dynamic and economic performance based on the non-dominated sorting genetic algorithm II (NSGA-II) or multi-objective particle swarm optimization algorithm (MOPSO) [25,26]. As a well-known global optimization algorithm, the dynamic program (DP) has been widely accepted as the most popular way to extract an optimal shift schedule based on a prior known driving cycle [7,11,27]. Moreover, the gearshift process can be converted to a mixed integer nonlinear optimization problem, then solved by the branch and bound method to achieve a preferable performance [28].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Shift Schedule Design Method for Multi-Gear AMT Electric Vehicles Based on Dynamic Programming and Fuzzy Logical Control

Liu,

Du,

Cheng

et al. 2023

Machines

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This paper proposes an adaptive shift schedule design framework based on dynamic programming (DP) algorithm and fuzzy logical control to promote the shift schedule’s adaptability whilst improving the comprehensive performance of the multi-gear automated manual transmission (AMT) electric vehicles in real-time application. First, the DP algorithm is employed to extract an offline optimal gear-shift schedule based on a set of driving conditions, including 11 groups of typical driving cycles. Second, a fuzzy logical controller is formulated considering the variation in the vehicle load and acceleration, where a velocity increment is exported online to adjust the gear-shift velocity of the predesigned DP-based schedule to develop a Fuzzy-DP shift schedule. In addition, multi-objective particle swarm optimization (MOPSO) is utilized to construct a comprehensive shift schedule by simultaneously considering the dynamic and economic performance of the vehicle. Then, the dynamic and economic shift schedules are deployed as the benchmark to examine the performance of the proposed shift schedule. Finally, the effectiveness of the Fuzzy-DP shift schedule is evaluated by comparison with others under various combined driving cycles (including vehicle load and velocity). The comparisons demonstrate the remarkable promotion in the adaptability of the Fuzzy-DP shift schedule in terms of acceleration time, energy-saving potential, and shift frequency. The most significant improvements in the dynamic, economic, and shift frequency can reach 8.86%, 10.12%, and 8.56%, respectively, in contrast to the MOPSO-based shift schedule.

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“…The convex optimization was also proposed to simultaneously optimize the powertrain component size, state-of-health (SOH), and energy management strategy [26]. For a pure electric bus equipped with an automatic mechanical transmission (AMT), a gear-shifting strategy considering battery aging effect was constructed [27]. Also, for a hybrid energy storage system comprising batteries and ultra-capacitors, the battery durability was integrated into the development of its power distribution law [28].…”

Section: Introductionmentioning

confidence: 99%

Model predictive energy management for plug-in hybrid electric vehicles considering optimal battery depth of discharge

Xie

et al. 2019

Energy

157

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Energy management for battery electric vehicle with automated mechanical transmission

Cited by 22 publications

References 29 publications

Efficiency Comparison of Electric Wheel Loader Powertrains with Dual Motor Input in Distributed Driving Modes

Efficiency Comparison of Electric Wheel Loader Powertrains with Dual Motor Input in Distributed Driving Modes

An Adaptive Shift Schedule Design Method for Multi-Gear AMT Electric Vehicles Based on Dynamic Programming and Fuzzy Logical Control

Model predictive energy management for plug-in hybrid electric vehicles considering optimal battery depth of discharge

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