Design method of energy management strategy for range-extended electric buses based on convex optimization

Gao, Mingming; Du, Jiuyu

doi:10.1109/ifost.2016.7884249

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Moreover, the power loss on the fuel cell is formulated as in Equation 9, 27 P FC,loss t ð Þ = P FC t ð ÞÁ 1 − β:…”

Section: Power Splitting In Hessmentioning

confidence: 99%

“…Moreover, the power loss on the fuel cell is formulated as in Equation ), 27

P_{italicFC, italicloss} (t) = P_{FC} (t) \cdot (1 - β . \frac{P_{FC} (t)}{P_{FC, nom}} + ξ),

where β and ξ are trend‐line coefficients. According to study given in Ref 22 .…”

Section: The Optimization Problemmentioning

confidence: 99%

“…Optimization‐based strategies, as dynamic programming, can lead to a global optimum solution, however it is usually difficult to be implemented as real time control due to high computation times 25 . Convex optimization used in studies 24,26,27 seems to have an increased practical significance, due to its high computational efficiency and global optimization capability. Nevertheless, not all the systems are suitable for linearization and convex optimization is not applicable to non‐linear systems.…”

Section: Introductionmentioning

confidence: 99%

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Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle

2020

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Due to increased environmental pollution and global warming concerns, the use of energy storage units that can be supported by renewable energy resources in transportation becomes more of an issue and plays a vital role in terms of clean energy solutions. However, utilization of multiple energy storage units together in an electric vehicle makes the powertrain system more complex and difficult to control. For this reason, the present study proposes an advanced energy management strategy (EMS) for range extended battery electric vehicles (BEVs) with complex powertrain structure. Hybrid energy storage system (HESS) consists of battery, ultra-capacitor (UC), fuel cell (FC) and the vehicle is propelled with two complementary propulsion machines. To increase powertrain efficiency, traction power is simultaneously shared at different rates by propulsion machines. Propulsion powers are shared by HESS units according to following objectives: extending battery lifetime, utilizing UC and FC effectively. Primarily, to optimize the power split in HESS, a convex optimization problem is formulated to meet given objectives that results 5 years prolonged battery lifetime. However, convex optimization of complex systems can be arduous due to the excessive number of parameters that has to be taken into consideration and not all systems are suitable for linearization. Therefore, a neural network (NN)-based machine learning (ML) algorithm is proposed to solve multi-objective energy management problem. Proposed NN model is trained with convex optimization outputs and according to the simulation results the trained NN model solves the optimization problem within 92.5% of the convex optimization one.

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“…Moreover, the power loss on the fuel cell is formulated as in Equation 9, 27 P FC,loss t ð Þ = P FC t ð ÞÁ 1 − β:…”

Section: Power Splitting In Hessmentioning

confidence: 99%

“…Moreover, the power loss on the fuel cell is formulated as in Equation ), 27

P_{italicFC, italicloss} (t) = P_{FC} (t) \cdot (1 - β . \frac{P_{FC} (t)}{P_{FC, nom}} + ξ),

where β and ξ are trend‐line coefficients. According to study given in Ref 22 .…”

Section: The Optimization Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle

2020

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Integrated Utility-Transit Model for a Comprehensive Transition Plan for Battery-Electric Bus Fleets

Hanna

Shaaban

Salama

2022

2022 IEEE 2nd International Conference on Sustainable Energy and Future Electric Transportation (SeFeT)

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Comprehensive Transition Plan for Battery-Electric Bus Fleets Considering Utility Constraints

Hanna,

Shaaban,

Salama

2023

IEEE Trans. on Ind. Applicat.

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Design method of energy management strategy for range-extended electric buses based on convex optimization

Cited by 4 publications

References 11 publications

Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle

Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle

Integrated Utility-Transit Model for a Comprehensive Transition Plan for Battery-Electric Bus Fleets

Comprehensive Transition Plan for Battery-Electric Bus Fleets Considering Utility Constraints

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