Energy Management Improvement Based on Fleet Learning for Hybrid Electric Buses

López, Jon Ander; Herrera, Víctor Isaac; Camblong, Haritza; Milo, Aitor; Gaztañaga, Haizea

doi:10.1109/vppc.2018.8605025

Cited by 8 publications

(11 citation statements)

References 7 publications

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“…where m f • T s is the fuel mass consumption at each time step (T s =1 s), determined by the split factor (U), within the urban route length (N) [10]. Therefore the optimized parameter is the split factor.…”

Section: B Fuzzy Logic Tuningmentioning

confidence: 99%

“…II) is performed. For the real driving operation conditions, driving disruptions have been considered, such as passenger, auxiliary and traffic disruptions [10]. Additionally, in this simulation, continuous operations monitoring is done during 15 days.…”

Section: Stage 2: Urban Bus Operationmentioning

confidence: 99%

“…6 the proposed EMS comparison is shown. The proposed EMS has been first compared against a DP off-line optimization and with the proposed rule-based non-adaptive EMS named LUT (Look up table based EMS) [10]. The operation costs of the proposed approach increase compared to the DP global optimization which has a value of 31.5%.…”

Section: A Proposed Ems Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Li-ion Battery Aging Conscious Intelligent Energy Management Strategy for Hybrid Electric Buses

Ibarra¹,

Lucu²,

Goitia-Zabaleta³

et al. 2019

Perspectivas

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The aim of this paper is to propose a battery aging conscious energy management strategy. The initial design of an energy management strategy is a significant point, in order to fulfill the efficiency goals in a short term. However, with the aging, the initial conditions vary. The new trend of digitalization allows to monitor the operation, having the possibility to improve the performance of the initial proposed strategy in a long term. Therefore, a methodology for updating the energy management strategy along the bus lifetime is proposed, in order to improve the operation costs and extend the battery lifetime. This methodology is based on a dynamic programming optimization, tuning the membership functions in a fuzzy logic control. The simulation results show a reduction of the operation costs up to 47% together with a BT lifetime extension of around 2.94%.

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Section: B Fuzzy Logic Tuningmentioning

confidence: 99%

Section: Stage 2: Urban Bus Operationmentioning

confidence: 99%

See 1 more Smart Citation

Li-ion Battery Aging Conscious Intelligent Energy Management Strategy for Hybrid Electric Buses

Ibarra¹,

Lucu²,

Goitia-Zabaleta³

et al. 2019

Perspectivas

Self Cite

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“…In this regard, a methodology for analyzing, processing, and making decisions based on this processed data was proposed in [10], to improve the techno-economic performance of the whole fleet. Focusing on the decision making element of the methodology as mentioned above, the contribution of this paper lies in an approach for managing a BT life system for an entire fleet.…”

Section: Introductionmentioning

confidence: 99%

“…This technique is focused on extending the fleet's BT life.This The analysis in this paper was based on the fleet described in Table I. The fleet is composed of the following two power-trains, with the respective models presented in detail in [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Bus-to-Route and Route-to-Bus Approaches in Hybrid Electric Buses Fleet for Li-ion Battery Lifetime Extension

López¹,

Perez²,

Milo³

et al. 2019

Perspectivas

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The aim of this paper is to propose a methodology for managing the Li-ion battery lifetime of a whole fleet with the aim to improve the total cost of ownership of hybrid electric buses. This approach has been addressed from two points of view the bus-to-route and route-to-bus approaches. The bus-to-route optimization is focused on the energy management strategy generation of each bus of the fleet. A techno-economic, route energetic evaluation and battery aging analysis of the fleet have been performed. From the outcome of this analysis, the buses have been grouped, according the state of health of each bus. Based on the analysis and classification, the route-to-bus approach is applied. This technique lies on both, a re-evaluation of the energy management system and/or the re-organization of the buses according to the state of health of each bus. Increases of BT lifetimes up to 10.7% are obtained with the proposed approach.

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Energy Management Improvement Based on Fleet Digitalization Data Exploitation for Hybrid Electric Buses

López¹,

Herrera

Camblong

et al. 2019

Computational Intelligence and Optimization Methods for Control Engineering

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Energy Management Improvement Based on Fleet Learning for Hybrid Electric Buses

Cited by 8 publications

References 7 publications

Li-ion Battery Aging Conscious Intelligent Energy Management Strategy for Hybrid Electric Buses

Li-ion Battery Aging Conscious Intelligent Energy Management Strategy for Hybrid Electric Buses

Bus-to-Route and Route-to-Bus Approaches in Hybrid Electric Buses Fleet for Li-ion Battery Lifetime Extension

Energy Management Improvement Based on Fleet Digitalization Data Exploitation for Hybrid Electric Buses

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