Modified Particle Swarm Optimization With Chaotic Attraction Strategy for Modular Design of Hybrid Powertrains

Zhou, Quan; He, Yinglong; Zhao, Dezong; Ji, Li; Li, Yanfei; Williams, Howard; Xu, Hongming

doi:10.1109/tte.2020.3014688

Cited by 26 publications

(7 citation statements)

References 45 publications

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“…Zhou et al developed a modified accelerated particle swarm optimization (MAPSO) algorithm for modular design considering the design variables which are from discontinuously varying domains (e.g. integers) [29]. The results suggested that the proposed MAPSO can achieve global optimal results in a more computationally efficient manner compared to both Pareto methods (NSGA-II) and weighted-sum methods (PSO, APSO).…”

Section: Ai Methods For Powertrain Designmentioning

confidence: 99%

Artificial Intelligence and Its Roles in the R&D of Vehicle Powertrain Products

Zhou

2022

IJAMM

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Review Artificial Intelligence and Its Roles in the R&D of Vehicle Powertrain Products Quan Zhou , * , Ji Li , and Hongming Xu , * Birmingham CASE Automotive Research and Education Centre, University of Birmingham, Birmingham, B15 2TT UK * Correspondence: q.zhou@bham.ac.uk (Q.Z.); h.m.xu@bham.ac.uk (H.X.) Received: 5 October 2022 Accepted: 20 November 2022 Published: 25 December 2022 Abstract: Decarbonization requires global actions, and the transport sector is the main battlefield since it contributes more than 20% of CO 2 emissions. Vehicle electrification is an effective routine to reduce vehicle carbon emissions, but it increases the complexity of the vehicle systems, especially the powertrain systems. The rapid development of artificial intelligence (AI) is promoting the development of new automation technologies that can benefit the automotive industry. This paper reviews the key milestones of AI technology development for vehicle research and development (R&D) and highlights the advantage of AI-based methods in powertrain design and control. An outlook of future research directions will be discussed, and conclusions will be summarized.

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Section: Ai Methods For Powertrain Designmentioning

confidence: 99%

Artificial Intelligence and Its Roles in the R&D of Vehicle Powertrain Products

Zhou

2022

IJAMM

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“…In this research field, heuristic algorithms are usually adopted to handle NP-complete issues. Recently, University of Birmingham proposed the improved evolutionary algorithm with chaos attractive policy for the energy management scheme design for on-road vehicles [3,4] and off-road vehicles [5]. However, these single-objective optical solutions are proven to be hard to build a Pareto frontier with good distribution, particularly for two or more objectives which need to be considered.…”

Section: Introductionmentioning

confidence: 99%

Electrothermally-Aware Multi-objective Modular Design: A Case Study on Series-Parallel Hybrid Propulsion Systems

Liu

Zhu

et al. 2022

2022 IEEE 5th International Electrical and Energy Conference (CIEEC)

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This paper introduces an effective modular design solution for series-parallel hybrid propulsion systems (HPSs) based on a battery electrothermal model and a sub-objective related to temperature to concurrently consider both the battery electrical and thermal behaviors. To ensure that more optimal design options are provided, a Pareto-augmented collaborative optimization (PACO) framework is proposed to integrate three multi-objective evolutionary algorithms (MOEAs), aiming to extend the range of the Pareto frontier. Furthermore, two real driving cycles taken from worldwide harmonized light vehicles are utilized to evaluate the performance of the optimized vehicle systems. The modelling results show that the decomposed MOEA (MOEA/D) within PACO is the main contributor to the performance improvement in the modular design of HPSs, which leads to the reduction of intergenerational distance by over 2.7% and increase of the hypervolume by over 17.6%, in comparison with two state-of-the-art evolutionary algorithms.

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“…Metaheuristic algorithms (e.g. Generic Algorithms [33], [34] and Particle Swarm Optimization (PSO) algorithms [35], [36]) have been proposed to conduct global searches in learning conditions, and are more computationally efficient than exhaustive searching. The PSO algorithm, which mimics the intelligence of birds flocking, is one of the most widely used AI-learning algorithms in automotive engineering.…”

Section: Introductionmentioning

confidence: 99%

Global Optimization of the Hydraulic-Electromagnetic Energy-Harvesting Shock Absorber for Road Vehicles With Human-Knowledge-Integrated Particle Swarm Optimization Scheme

Zhou

Guo

et al. 2021

IEEE/ASME Trans. Mechatron.

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Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.When citing, please reference the published version. Take down policyWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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Modified Particle Swarm Optimization With Chaotic Attraction Strategy for Modular Design of Hybrid Powertrains

Cited by 26 publications

References 45 publications

Artificial Intelligence and Its Roles in the R&D of Vehicle Powertrain Products

Artificial Intelligence and Its Roles in the R&D of Vehicle Powertrain Products

Electrothermally-Aware Multi-objective Modular Design: A Case Study on Series-Parallel Hybrid Propulsion Systems

Global Optimization of the Hydraulic-Electromagnetic Energy-Harvesting Shock Absorber for Road Vehicles With Human-Knowledge-Integrated Particle Swarm Optimization Scheme

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