Trip-oriented stochastic optimal energy management strategy for plug-in hybrid electric bus

Du, Yongchang; Zhao, Yue; Wang, Qinpu; Zhang, Yuanbo; Huaicheng, Xia

doi:10.1016/j.energy.2016.09.056

Cited by 65 publications

(27 citation statements)

References 32 publications

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“…22, the probability matrix of demand torque transfer corresponding to each vehicle velocity value is calculated by combining the knowledge of statistical analysis through Eq. (24). Figures 5-8 show the transition probability matrix of demand torque with asynchronous length under different vehicle velocities.…”

Section: Variable ( ) ω Kmentioning

confidence: 99%

“…In the energy management method based on the SDP method, the existing standard driving conditions or vehicle driving history data are utilized as the sample of the random model and the statistical model of the driving cycle is establishes accordingly. Then, the DP method was used to solve the energy management problem represented by the statistical model [13]- [14], [23]- [24]. As a special case of SDP, the energy management method was proposed based on the shortest path SDP (SP-SDP).…”

Section: Introductionmentioning

confidence: 99%

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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: Variable ( ) ω Kmentioning

confidence: 99%

Section: Introductionmentioning

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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“…TPM is the major characteristic element of overall markov decision problem (MDP) and hence governs the overall execution process of MDP. If enough historical data of the driving scenario available, a static TPM can be constructed and can be used for predicting N-step values of future driving scenarios in real-time applications [92], [93]. However, enough historical data is often not available and an updating TPM can be articulated in such a case [94].…”

Section: ) Markov Decision Problem-basedmentioning

confidence: 99%

Intelligent Energy Management Systems for Electrified Vehicles: Current Status, Challenges, and Emerging Trends

Ostadian

Ramoul

Biswas

et al. 2020

IEEE Open J. Veh. Technol.

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By increasing the electrified vehicles trend, it is crucial to have a survey of what is done before and what is the open challenge for improving this industry. Powertrain electrification has heightened the need for an energy management strategy, which has been a continuing concern in the development of electrified vehicles. An energy management control unit manages power flow between different energy sources in an electrified powertrain that directly affects vehicle performance. There are inadequate numbers of studies on the most recent energy management strategies, which are compatible with real-world scenarios. Intelligent energy management strategies requirements are discussed in detail, and it is categorized to principle-based, data-driven, and composite-based methods. Besides, enabling technologies for implementing an energy management system with a comparison of different controller chips are described to give readers an experimental view. Future trends and existing challenges are presented, which generate fresh insight into energy management strategies. Index Terms-data-driven methods, electric vehicles, intelligent energy management strategy, reinforcement learning, powertrain architecture.

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“…Electric buses are powered by an electric motor, asynchronous motor (ASM) type, which operates in electric-motor mode, consuming battery power, or in generator mode, recovering the energy when descending slopes or in particular braking situations. The operating characteristic of the electric propulsion system is described by the motor torque diagram vs. speed, for all the possible traction regimes ( Figure 5) [38,46,47]. Electric buses are powered by an electric motor, asynchronous motor (ASM) type, which operates in electric-motor mode, consuming battery power, or in generator mode, recovering the energy when descending slopes or in particular braking situations.…”

Section: Electric Bus Model Datamentioning

confidence: 99%

Analysis of the Electric Bus Autonomy Depending on the Atmospheric Conditions

2019

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The public-transport sector represents, on a global level, a major ecological and economic concern. Improving air quality and reducing greenhouse gas (GHG) production in the urban environment can be achieved by using electric buses instead of those operating with internal combustion engines (ICE). In this paper, the energy consumption for a fleet of electric buses Solaris Urbino 12e type is analyzed, based on the experimental data taken from a number of 22 buses, which operate on a number of eight urban lines, on a route of approximately 100 km from the city of Cluj-Napoca, Romania; consumption was monitored for 12 consecutive months (July 2018–June 2019). The energy efficiency of the model for the studied electric buses depends largely on the management of the energy stored on the electric bus battery system, in relation to the characteristics of the route traveled, respectively to the atmospheric conditions during the monitored period. Based on the collected experimental data and on the technical characteristics of the electric buses, the influence of the atmospheric conditions on their energy balance was highlighted, considering the interdependence relations between the considered atmospheric conditions.

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Trip-oriented stochastic optimal energy management strategy for plug-in hybrid electric bus

Cited by 65 publications

References 32 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

Intelligent Energy Management Systems for Electrified Vehicles: Current Status, Challenges, and Emerging Trends

Analysis of the Electric Bus Autonomy Depending on the Atmospheric Conditions

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