Implementation of comfort constraints in dynamic programming for hybrid vehicle energy management

Debert, Maxime; Padovani, Thomas Miro; Colin, Guillaume; Chamaillard, Yann; Guzzella, Lino

doi:10.1504/ijvd.2012.047392

Cited by 17 publications

(14 citation statements)

References 12 publications

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“…SOC, vehicle speed and power demand at the wheel) of HEV/PHEV without transmission as the entry condition of the SDP [16]- [23]. However, it would introduce an additional state variable, current gear, into the optimize process of the SDP if similar method is adopted for PHEB with AMT [5], [12], [14]. The process of optimization is simplified as Fig.…”

Section: Stochastic Dynamic Programmingmentioning

confidence: 99%

“…u are optimal control strategies obtained from the SDP under the economic and dynamic gearshift logic, respectively. In this paper, the control variable is the engine torque as shown in (14). And the corresponding contents in this section have been illustrated in online part of figure 5.…”

Section: Adaptive Factor For Gearshift and Torque Splitmentioning

confidence: 99%

“…The equivalent consumption minimization strategy (ECMS) [9]- [11], the global optimal approach, deterministic dynamic programming (DDP) [12]- [14] have been developed to optimize the power split both for plug-in and conventional HEVs. However, they might not be directly applied in practical for their strict limits on knowing the full driving cycle as a prior [2], [6].…”

Section: Introductionmentioning

confidence: 99%

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Application-Oriented Stochastic Energy Management for Plug-in Hybrid Electric Bus With AMT

Zhang

Yan

Yang

et al. 2016

IEEE Trans. Veh. Technol.

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Taking the complex but regular characteristic of bus routine into account, the stochastic dynamic programming (SDP) might be a more potential method to optimize the energy management for plug-in hybrid electric bus (PHEB). However, the discrete transmission system and the continuous power system make it a complicated multi-dimensional optimal problem especially for PHEB with automated mechanical transmission (AMT), and the optimal decisions, which obtained based on historical data, might not always well satisfy the driver's expectation to vehicle maneuverability under various driving conditions. To solve these problems, an adaptive approach based on the SDP is proposed in this paper. Exhaustively, the SDP is propelled into the input of the transmission to only optimize the torque-split under the special gearshift logic, which reduces the dimensions of optimization and obtains more applicable optimal sequences. Then, an adaptive factor, which trade-off the vehicle fuel economy and drivability in real time by dynamically adjusting the gearshift points and the torque split, is developed for the variation of the complicated bus driving cycles. The simulation results demonstrate that the proposed method could well respond the variations of the driving conditions (e.g. road grade and vehicle load etc.). Furthermore, the performance of the proposed method is detailed discussed by comparing with different control strategies. More significantly, the proposed approach has a great potential to apply in actual.Index Terms-Plug-in hybrid electric bus (PHEB), stochastic dynamic programming (SDP), adaptive factor, optimal energy management.W 0018-9545 (c) be obtained from the IEEE by sending a request to pubs-permissions@ieee.org.2 Furthermore, R. Johri et al. proposed a simultaneous optimization of power split and gear shift logic for HEV using SDP [21]. Great contributes have been made for above researches on optimizing the power management to some extent. However, most of them mainly concentrate on the conventional HEVs or PHEVs without transmission and seldom take the bus routine into account. Considering the versatile characteristic of driving conditions, it is a complicated multi-dimensional optimal problem especially for energy management of PHEB with AMT.[21] proposed a good method to simultaneous optimal the power split and gear shift, however, the optimal results, which are mapped as two independent and constant lookup-tables, might not always well respond the variable driving conditions. Due to the strictly inter-corresponding relationship between power split and gearshift, it would break the optimal decisions and highly deteriorate the vehicle fuel economy if any changes happened on the optimal gearshift to obtain the good maneuverability. Moreover, the simultaneous optimization would highly increase the calculate burden, and the complicated optimized results would jeopardize the real-time operation. Therefore it is necessary to decouple the gearshift logic from the whole optimization.This paper proposes an adaptive...

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Section: Stochastic Dynamic Programmingmentioning

confidence: 99%

Section: Adaptive Factor For Gearshift and Torque Splitmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application-Oriented Stochastic Energy Management for Plug-in Hybrid Electric Bus With AMT

Zhang

Yan

Yang

et al. 2016

IEEE Trans. Veh. Technol.

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“…as in [7]. The proposed methodology is to attribute a higher penalty to transitions most inconvenient for the driver, implying, for example, torque interruption, jerk, engine start, etc.…”

Section: Drivability Constraintmentioning

confidence: 99%

“…Moreover, since the battery state of charge x is directly impacted by the chosen control, it is necessary to respect its dynamics:ẋ = f (x, u) It is possible to add constraints to the energy management strategy, such as pollutant emission [4,5], engine events [6] or gear events [6,7], and this is what we will show in this paper. The first section recalls the classical optimal energy management strategy with the Pontryagin maximum principle.…”

Section: Introductionmentioning

confidence: 99%

Towards a Friendly Energy Management Strategy for Hybrid Electric Vehicles with Respect to Pollution, Battery and Drivability

et al. 2014

Self Cite

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The paper proposes a generic methodology to incorporate constraints (pollutant emission, battery health, drivability) into on-line energy management strategies (EMSs) for hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs). The integration of each constraint into the EMS, made with the Pontryagin maximum principle, shows a tradeoff between the fuel consumption and the constraint introduced. As state dynamics come into play (catalyst temperature, battery cell temperature, etc.), the optimization problem becomes more complex. Simulation results are presented to highlight the contribution of this generic strategy, including constraints compared to the standard approach. These results show that it is possible to find an energy management strategy that takes into account an increasing number of constraints (drivability, pollution, aging, environment, etc.). However, taking these constraints into account increases fuel consumption (the existence of a trade-off curve). This trade-off can be sometimes difficult to find, and the tools developed in this paper should help to find an acceptable solution quickly.

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Energy-saving mechanism of series–parallel hybrid transmissions — A dynamic programming method based on matrix operations

Zhao,

Xu,

et al. 2024

Energy Conversion and Management

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Implementation of comfort constraints in dynamic programming for hybrid vehicle energy management

Cited by 17 publications

References 12 publications

Application-Oriented Stochastic Energy Management for Plug-in Hybrid Electric Bus With AMT

Application-Oriented Stochastic Energy Management for Plug-in Hybrid Electric Bus With AMT

Towards a Friendly Energy Management Strategy for Hybrid Electric Vehicles with Respect to Pollution, Battery and Drivability

Energy-saving mechanism of series–parallel hybrid transmissions — A dynamic programming method based on matrix operations

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