Battery electricity bus charging schedule considering bus journey’s energy consumption estimation

He, Jia; Yan, Na; Zhang, Jian; Wang, Tao; Chen, Yanyan; Tang, Tie-Qiao

doi:10.1016/j.trd.2022.103587

Cited by 14 publications

(3 citation statements)

References 26 publications

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“…In this problem, bus-to-trip assignments are optimized to achieve the minimal operating cost, with explicit consideration of the BEBs' charging requirements simultaneously. The third type is the integrated electric bus planning, which incorporates BEB scheduling into other high-level planning problems, such as the deployment of charging infrastructure and line planning (An, 2020;He et al, 2023;Liu et al, 2023aLiu et al, , 2023b. This type of planning takes a holistic approach to address electric bus scheduling problems and considers the interdependence of various planning factors.…”

Section: Electric-fleet Bus Scheduling Problemmentioning

confidence: 99%

“…Meanwhile, the stochasticity in travel time is also considered in their model, thereby enhancing the practicality and efficacy of their recommended charging plans. Later, studies in the area are further improved by He et al (2023). They made a significant contribution by creating an estimation method that provides highly accurate energy consumption values, enabling a more realistic optimization of the charging strategy.…”

Section: Solely Optimizing Charging Schedulementioning

confidence: 99%

See 1 more Smart Citation

Vehicle and Charging Scheduling of Electric Bus Fleets: A Comprehensive Review

Zhang,

Han,

Peng

et al. 2023

J. Int. Con. Veh.

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Transit electrification has emerged as an unstoppable force, driven by the considerable environmental benefits it offers. However, the adoption of battery electric buses is still impeded by their limited flexibility, a constraint that necessitates adjustments to current bus scheduling plans. Consequently, this study aspires to offer a thorough review of articles focused on battery electric bus scheduling. Moreover, we provide a comprehensive review of 42 papers on electric bus scheduling and related studies, with a focus on the most recent developments and trends in this research domain. Despite this extensive review, our findings reveal a paucity of research that takes into account the robustness of electric bus scheduling. Furthermore, we highlight the critical areas of considering diverse charging modes in electric bus scheduling and integrated planning of electric buses, which have not been adequately explored but hold the potential to greatly boost the effectiveness of electric bus systems. Through this synthesis, we hope that readers could acquire a thorough comprehension of the studies in this field and be motivated to address the identified research gaps, thus propelling the progress of transit electrification.

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Section: Electric-fleet Bus Scheduling Problemmentioning

confidence: 99%

Section: Solely Optimizing Charging Schedulementioning

confidence: 99%

Vehicle and Charging Scheduling of Electric Bus Fleets: A Comprehensive Review

Zhang,

Han,

Peng

et al. 2023

J. Int. Con. Veh.

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“…The increasing pressure of energy consumption and environmental crisis has resulted in the accelerated development of new energy device technology, exemplified by electric vehicles, which provide a new development solution to reduce energy waste and environmental pollution [1][2][3]. Energy storage PACK is a type of energy storage system used to store energy for electric devices and vehicles.…”

Section: Introductionmentioning

confidence: 99%

SOC estimation and fault identification strategy of energy storage battery PACK: Based on adaptive sliding mode observer

Xueyi,

Tinglong

2024

The Journal of Engineering

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Accurate state of charge (SOC) estimation and fault identification and localization are crucial in the field of battery system management. This article proposes an innovative method based on sliding mode observation theory for SOC estimation and short‐circuit fault location. The core of this new method is the design of an adaptive sliding mode observer, which reduces jitter by introducing adaptive switching gain, establishes an internal loop of gain and error, and improves the performance of SOC estimation. In addition, recursive least squares method was used to identify the key parameters of the model. Secondly, based on obtaining the SOC of each battery cell in series with the energy storage PACK, the specificity of the faulty battery cell in SOC change trend is utilized to identify and locate the short‐circuit fault of the energy storage PACK. The simulation and test results show that the designed adaptive sliding mode observer can significantly improve the estimation accuracy of SOC and has better stability. Compared to the commonly used Kalman estimation and BP neural network estimation methods, the designed method has improved accuracy by 5.53% and 3.42%, respectively. In addition, based on the accurate identification of SOC, the short‐circuit fault diagnosis results of the battery PACK have a high accuracy, confirming the feasibility and effectiveness of the designed strategy that includes SOC estimation and short‐circuit fault identification and positioning, and has broad application prospects.

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