Analysis of the Performance of Different Machine Learning Techniques for the Definition of Rule-based Control Strategies in a Parallel HEV

Venditti, Mattia

doi:10.1016/j.egypro.2016.11.087

Cited by 20 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yanqing et al [207], developed an instance-based machine learning algorithm to learn the rolling driving condition that can be predicted by the k-Nearest Neighbor (k-NN) algorithm. Venditti [208], addressed the performance of cluster optimization & rule extraction (CORE) and cluster extraction & rule optimization (CERO) and compare with DP, and provide the almost same result with the small discrepancy about 1.84% and 4.85 %. Langari et al [209] implemented an intelligent energy management agent (IEMA) whose role is to assess the driving environment, by which learning vector quantization (LVQ) network can efficiently determine the driving condition within a limited time period of driving data.…”

Section: Machine Learning Based Emssmentioning

confidence: 93%

Hybrid Sources Powered Electric Vehicle Configuration and Integrated Optimal Power Management Strategy

Gautam¹,

Tariq

Pandey³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Internal Combustion Engine based transportation rapidly increases the impact on the environment. The burning of fossil fuels in the industries and transportation sector breadth of global warming. However, Hybrid Electric Vehicle is not a permanent solution for emission-less road vehicles. Therefore, electric vehicles are the most feasible technologies to attain the goals of energy savings and zero-emission road vehicles. This paper sequentially surveys the key point of vehicles like; Powertrain strategy, configuration, fuel economy, reduced emission, and control strategy expounds in terms of its basic principles, pros, and cons. These key points make the energy management strategy more conclusive for the more efficient vehicle. In addition, this paper review systematically qualitative and quantitative algorithm in all type of EMS used in HEV and compare them with existing approaches in terms of pros & cons through a comprehensive analysis. Furthermore, addressed the potential research gap and provide the directives for further development in power train and ems in every respect.

show abstract

Section: Machine Learning Based Emssmentioning

confidence: 93%

Hybrid Sources Powered Electric Vehicle Configuration and Integrated Optimal Power Management Strategy

Gautam¹,

Tariq

Pandey³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Moreover, data-driven control has been applied for HEV equipped with diesel engines recently. For instance, Venditti 134 studied rule-based control combined with several machine learning methods in parallel HEV. In the article, a homemade clustering algorithm and GA were employed to cluster the dataset and extract the rules, and the comparison was shown.…”

Section: Applications Of Data-driven Approaches In Diesel Enginesmentioning

confidence: 99%

“…Silitonga et al 102 Kowalski et al, 108 Xi et al, 109 Yin et al 110 Wong et al 131 PCA / Antory 89 Arsie et al, 90 Benz et al 91 Subrahmanya et al, 113 Diez-Olivan et al, 114 Flett and Bone 115 Formentin et al, 132 Milanese et al, 133 Venditti 134 ANN was also used for controller design, for example, Sui and Hall 144 developed a double-ANN-based controller in which NN was employed to estimate Jacobian information for the lower-level controller and BPNN was adopted to establish the dosing controller. The designed controller had achieved notable improvement compared with the PID controller.…”

Section: Elmmentioning

confidence: 99%

Data-driven control of automotive diesel engines and after-treatment systems: State of the art and future challenges

Jiang

Yan

Zhang

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

Model-based approaches have played a significant role in automotive electronics for diesel engines and aftertreatment systems, while they also have some drawbacks that cannot be neglected, such as difficult model construction and time-consuming model calibration. To this end, data-driven techniques have been put forward and become a topical issue in diesel engines and aftertreatment systems. This article provides an overview of the recent progress of the last decade in data-driven techniques and relevant applications for diesel engines and aftertreatment systems. Moreover, brief future trends, challenges, and opportunities of data-driven strategies and potential applications in this field are further discussed.

show abstract

“…Briefly, the set of parameters to be considered for pattern recognition determines the effort of offline optimization (number of patterns) and the accuracy of online recognition [90]. Machine learning-based techniques are the state-of-the-art tools that enable better online recognition when a large number of recognized parameters are considered [91].…”

Section: Insight Into Optimal Real-time Methodsmentioning

confidence: 99%

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

2018

View full text Add to dashboard Cite

Abstract:In light of increasing alerts about limited energy sources and environment degradation, it has become essential to search for alternatives to thermal engine-based vehicles which are a major source of air pollution and fossil fuel depletion. Hybrid electric vehicles (HEVs), encompassing multiple energy sources, are a short-term solution that meets the performance requirements and contributes to fuel saving and emission reduction aims. Power management methods such as regulating efficient energy flow to the vehicle propulsion, are core technologies of HEVs. Intelligent power management methods, capable of acquiring optimal power handling, accommodating system inaccuracies, and suiting real-time applications can significantly improve the powertrain efficiency at different operating conditions. Rule-based methods are simply structured and easily implementable in real-time; however, a limited optimality in power handling decisions can be achieved. Optimization-based methods are more capable of achieving this optimality at the price of augmented computational load. In the last few years, these optimization-based methods have been under development to suit real-time application using more predictive, recognitive, and artificial intelligence tools. This paper presents a review-based discussion about these new trends in real-time optimal power management methods. More focus is given to the adaptation tools used to boost methods optimality in real-time. The contribution of this work can be identified in two points: First, to provide researchers and scholars with an overview of different power management methods. Second, to point out the state-of-the-art trends in real-time optimal methods and to highlight promising approaches for future development.

show abstract

Analysis of the Performance of Different Machine Learning Techniques for the Definition of Rule-based Control Strategies in a Parallel HEV

Cited by 20 publications

References 12 publications

Hybrid Sources Powered Electric Vehicle Configuration and Integrated Optimal Power Management Strategy

Hybrid Sources Powered Electric Vehicle Configuration and Integrated Optimal Power Management Strategy

Data-driven control of automotive diesel engines and after-treatment systems: State of the art and future challenges

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

Contact Info

Product

Resources

About