Adapted optimal energy management strategy for drivability

Vidal-Naquet, Fabien; Zito, Gianluca

doi:10.1109/vppc.2012.6422678

Cited by 15 publications

(11 citation statements)

References 5 publications

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“…Otherwise, for the optimization, some artificial shift costs can be assigned in order reduce the number of gearshifts. These artificial shift costs are then considered in the cost function of the optimization without including them in the calculation of the fuel consumption [10,26,40]. For the method presented in this paper, the model for the gearshift costs is not restricted to the form presented in Equation (5).…”

Section: Gearbox Modelmentioning

confidence: 99%

“…As reported for instance in [35,36], applying optimization-based methods for the energy management of parallel hybrid vehicles without penalizing engine start or gearshift events can lead to unacceptably frequent engine starts and gearshifts, eventually leading to an increased fuel consumption, excessive wear of components and/or an uncomfortable driving experience. Therefore, in the DP, PMP and SDP approaches, researchers [26,[37][38][39][40][41] have introduced penalties or costs for the changes in the engine or the gear state such that unacceptably frequent starts and shifts can be prevented. To do so in convex optimization-based approaches, heuristics based on signal filtering have been presented so far [29].…”

Section: Introductionmentioning

confidence: 99%

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Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

et al. 2014

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This paper presents a novel method to solve the energy management problem for hybrid electric vehicles (HEVs) with engine start and gearshift costs. The method is based on a combination of deterministic dynamic programming (DP) and convex optimization. As demonstrated in a case study, the method yields globally optimal results while returning the solution in much less time than the conventional DP method. In addition, the proposed method handles state constraints, which allows for the application to scenarios where the battery state of charge (SOC) reaches its boundaries.

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Section: Gearbox Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

et al. 2014

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“…On the other hand, torque interruption cannot be avoided during gearshifts, and the permanently increased primary shaft inertia is an issue : it implies a specific electric motor speed control during gearshifts to avoid damaging the gearbox. For a extended description of the gearshift control algorithms, please refer to Zito [2012].…”

Section: Notes On the Pre-transmission Architecturementioning

confidence: 99%

Stochastic Dynamic Programming based Energy Management of HEV's: an Experimental Validation

Leroy

Vidal-Naquet

Tona

2014

IFAC Proceedings Volumes

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“…In real-time application of the optimization method, if the fuel economy is the only criterion, results will lead to poor drivability [20,21]. Previous research on the multiobjective optimization of fuel economy and drivability mainly focused on the frequent engine on and off events, gear shifting, and improving the engine torque reserve [22][23][24]. Since the speed ratio changes continuously in a continuously variable transmission (CVT), driving problems caused by an automatic transmission can be avoided to a large extent.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Control Strategy for CVT-Based Hybrid Electric Vehicles Considering Drivability Constraints

Zhou

Xiong

et al. 2019

Applied Sciences

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The energy management strategy has a great influence on the fuel economy of hybrid electric vehicles, and the equivalent consumption minimization strategy (ECMS) has proved to be a useful tool for the real-time optimal control of Hybrid Electric Vehicles (HEVs). However, the adaptation of the equivalent factor poses a major challenge in order to obtain optimal fuel consumption as well as robustness to varying driving cycles. In this paper, an adaptive-ECMS based on driving pattern recognition (DPR) is established for hybrid electric vehicles with continuously variable transmission. The learning vector quantization (LVQ) neural network model was adopted for the on-line DPR algorithm. The influence of the battery state of charge (SOC) on the optimal equivalent factor was studied under different driving patterns. On this basis, a method of adaptation of the equivalent factor was proposed by considering the type of driving pattern and the battery SOC. Besides that, in order to enhance drivability, penalty terms were introduced to constrain frequent engine on/off events and large variations of the continuously variable transmission (CVT) speed ratio. Simulation results showed that the proposed method efficiently improved the equivalent fuel consumption with charge-sustaining operations and also took into account driving comfort.

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Adapted optimal energy management strategy for drivability

Cited by 15 publications

References 5 publications

Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

Stochastic Dynamic Programming based Energy Management of HEV's: an Experimental Validation

Real-Time Control Strategy for CVT-Based Hybrid Electric Vehicles Considering Drivability Constraints

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