Analysis of impact factors for plug-in hybrid electric vehicles energy management

Khayyer, Pardis; Wollaeger, James; Onori, Simona; Marano, Vincenzo; Özgüner, Ü.; Rizzoni, Giorgio

doi:10.1109/itsc.2012.6338895

Cited by 21 publications

(12 citation statements)

References 12 publications

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“…For the EMS, a discrete adaptation scheme based on feedback from SOC is employed ( [21]). As mentioned before, for charge-depleting vehicle operation, as generally seen in PHEVs, the optimal SOC trajectory is approximately a quasi-linear decreasing function of the travelled distance ( [22][23][24]). However, when the final SOC target is set higher with respect to that encountered when looking for the global optimal solution in terms of fuel consumption (i.e., when the DP algorithm used is free to exploit all the energy available in the battery cells), it is seen for some driving cycles that a linear SOC reference defined in the time domain is more representative of the optimal solution.…”

Section: E Equivalence Factor Adaptationmentioning

confidence: 88%

“…where is the current covered distance and b is the proportional gain. In (24), the two previous values of the equivalence factor are used to stabilize the output. This expression corresponds to an AutoRegressive Moving Average (ARMA) filter ( [4]).…”

Section: E Equivalence Factor Adaptationmentioning

confidence: 99%

“…For charge-sustaining operation, the SOC reference is usually regarded as a constant ( [21]). Instead, for charge-depleting operation, like that generally seen in PHEVs, the optimal SOC trajectory has been found to be approximately a quasi-linear decreasing function of the travelled distance, which in literature is referred to as a blended strategy ( [22][23][24]). Finally, in order to properly initialize the equivalence factor, a pre-computed offline map can be used in which the optimal values are stored for several different cycles based on the total travelled distance and the average speed [4].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Equivalent Consumption Minimization Strategy With Rule-Based Gear Selection for the Energy Management of Hybrid Electric Vehicles Equipped With Dual Clutch Transmissions

et al. 2020

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Based on observations of the behaviour of the optimal solution to the problem of energy management for plug-in hybrid electric vehicles, a novel real-time Energy Management Strategy (EMS) is proposed. In particular, dynamic programming results are used to derive a set of rules aiming at reproducing the optimal gearshift schedule in electric mode while the Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) is employed to decide the powertrain operating mode and the current gear when power from the internal combustion engine is needed. In terms of total fuel consumption, simulations show that the proposed approach yields results that are close to the optimal solution and also outperforms those of the A-ECMS, a well-known EMS. One of the main aspects that differentiates the strategy here proposed from previous works is the introduction of a model to use physical considerations to estimate the energy consumption during gearshifts in dual-clutch transmissions. This, together with a series of properly tuned fuel penalties allows the controller to yield results in which there is no gear hunting behaviour.

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Section: E Equivalence Factor Adaptationmentioning

confidence: 88%

Section: E Equivalence Factor Adaptationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Equivalent Consumption Minimization Strategy With Rule-Based Gear Selection for the Energy Management of Hybrid Electric Vehicles Equipped With Dual Clutch Transmissions

et al. 2020

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“…The required torque ( ) and speed ( ) of the wheel can be calculated via the future vehicle velocity V( ) [9,13]. For all of the reachable value of ( ), the admissible set ( ) of can be determined and then the equivalent fuel consumption with respect to different combination of and will be obtained by traversing all of the points in ( ).…”

Section: Constructing and Solving Of The Dynamic Programming Equationmentioning

confidence: 99%

Energy Management Strategy Based on the Driving Cycle Model for Plugin Hybrid Electric Vehicles

Wang

Cui

et al. 2014

Abstract and Applied Analysis

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The energy management strategy (EMS) for a plugin hybrid electric vehicle (PHEV) is proposed based on the driving cycle model and dynamic programming (DP) algorithm. A driving cycle model is constructed by collecting and processing the driving data of a certain school bus. The state of charge (SOC) profile can be obtained by the DP algorithm for the whole driving cycle. In order to optimize the energy management strategy in the hybrid power system, the optimal motor torque control sequence can be calculated using the DP algorithm for the segments between the traffic intersections. Compared with the traditional charge depleting-charge sustaining (CDCS) strategy, the test results on the ADVISOR platform show a significant improvement in fuel consumption using the EMS proposed in this paper.

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“…Furthermore, for the real-time control strategies of PHEVs, a good vehicle performance, in terms of fuel economy, power, and battery life, cannot be ensured, if the driving conditions are not considered. The slope is proven to be an important factor that influences the fuel consumption of the HEV [21,22]. PHEVs usually operate in the hybrid-driving mode while driving uphill, and the engine and motor coordinately work together to ensure that the engine operates in the high-efficiency region.…”

Section: Introductionmentioning

confidence: 99%

Optimal Energy Management Strategy for a Plug-in Hybrid Electric Vehicle Based on Road Grade Information

Liu

et al. 2017

Energies

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Abstract:Energy management strategies (EMSs) are critical for the improvement of fuel economy of plug-in hybrid electric vehicles (PHEVs). However, conventional EMSs hardly consider the influence of uphill terrain on the fuel economy and battery life, leaving vehicles with insufficient battery power for continuous uphill terrains. Hence, in this study, an optimal control strategy for a PHEV based on the road grade information is proposed. The target state of charge (SOC) is estimated based on the road grade information as well as the predicted driving cycle on uphill road obtained from the GPS/GIS system. Furthermore, the trajectory of the SOC is preplanned to ensure sufficient electricity for the uphill terrain in the charge depleting (CD) and charge sustaining (CS) modes. The genetic algorithm is applied to optimize the parameters of the control strategy to maintain the SOC of battery in the CD mode. The pre-charge mode is designed to charge the battery in the CS mode from a reasonable distance before the uphill terrain. Finally, the simulation model of the powertrain system for the PHEV is established using MATLAB/Simulink platform. The results show that the proposed control strategy based on road-grade information helps successfully achieve better fuel economy and longer battery life.

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Analysis of impact factors for plug-in hybrid electric vehicles energy management

Cited by 21 publications

References 12 publications

Adaptive Equivalent Consumption Minimization Strategy With Rule-Based Gear Selection for the Energy Management of Hybrid Electric Vehicles Equipped With Dual Clutch Transmissions

Adaptive Equivalent Consumption Minimization Strategy With Rule-Based Gear Selection for the Energy Management of Hybrid Electric Vehicles Equipped With Dual Clutch Transmissions

Energy Management Strategy Based on the Driving Cycle Model for Plugin Hybrid Electric Vehicles

Optimal Energy Management Strategy for a Plug-in Hybrid Electric Vehicle Based on Road Grade Information

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