Driving cycle and road grade on-board predictions for the optimal energy management in EV-PHEVs

Valera, Juan José; Heriz, Borja; Lux, G.; Caus, J.; Bader, Benjamin

doi:10.1109/evs.2013.6914763

Cited by 28 publications

(13 citation statements)

References 12 publications

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“…Although the prediction of the driving cycle, v profile, with the help of traffic, GPS information has gained attention in recent years [35], at present its implementation has not spread yet. Nevertheless, as soon as a v prediction for the next kilometers is available, the presented model in this paper can be easily extended, to include a mechanical submodel of the traction behavior to calculate the future M and n profiles.…”

Section: Model Predictive Controlmentioning

confidence: 99%

Nonlinear Model Predictive Control for Thermal Management in Plug-in Hybrid Electric Vehicles

Lopez-Sanz

Ocampo‐Martínez

Alvarez-Florez³

et al. 2016

IEEE Trans. Veh. Technol.

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Abstract-A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input-Multiple output systems.This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive Copyright (c) 2015 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubspermissions@ieee.orgThe authors wish to acknowledge financial support from the Generalitat de Catalunya (GRC MCIA, Grant n SGR 2014-101).J. Lopez-Sanz and G. sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi-domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2• C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle.Index Terms-nonlinear model predictive control (NMPC), thermal management, plug-in hybrid electric vehicles (PHEV), Li-ion battery cooling.

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Section: Model Predictive Controlmentioning

confidence: 99%

Nonlinear Model Predictive Control for Thermal Management in Plug-in Hybrid Electric Vehicles

Lopez-Sanz

Ocampo‐Martínez

Alvarez-Florez³

et al. 2016

IEEE Trans. Veh. Technol.

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“…Xiang et al [22] and Sun et al [23] developed a radial basis function NNs which can predict the short‐term vehicle velocity using vehicle's historical velocity as reference. In [24], a location‐based velocity prediction method was developed utilising an artificial NN (ANN) and vehicle navigation system. The accuracy of predicted velocity indicates that the approach of ANN combined with navigation system makes it possible to apply to the velocity prediction.…”

Section: Introductionmentioning

confidence: 99%

AER adaptive control strategy via energy prediction for PHEV

Lin

Zhou

2019

IET Intelligent Transport Systems

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“…In recent studies, the future driving cycle was predicted using the Markov chain model [17] and the neural network mode [18] using historical traffic data. In addition, the future driving cycle can be predicted using the traffic information obtained from intelligent transportation systems [19,20]. However, an accurate prediction of the driving cycle usually requires considerable traffic data and involves complex calculations.…”

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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Driving cycle and road grade on-board predictions for the optimal energy management in EV-PHEVs

Cited by 28 publications

References 12 publications

Nonlinear Model Predictive Control for Thermal Management in Plug-in Hybrid Electric Vehicles

Nonlinear Model Predictive Control for Thermal Management in Plug-in Hybrid Electric Vehicles

AER adaptive control strategy via energy prediction for PHEV

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

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