Challenges in Noise Refinement of a Pure Electric Passenger Vehicle

Padavala, Prasad; Inavolu, Nagasuresh; Thaveedu, Judson Rajkumar; Medisetti, Jaganmohan Rao

doi:10.4271/10-05-01-0004

Cited by 7 publications

(1 citation statement)

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“…In many countries, electric vehicles (EVs) have emerged as a vital strategic industry to promote energy conservation and emission reduction [1][2][3]. To enable the widespread adoption of electric vehicles and alleviate range anxiety (i.e., concerns about insufficient energy storage for a trip), the provision of accessible charging infrastructure is crucial [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention Mechanism

2023

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With the aim of promoting energy conservation and emission reduction, electric vehicles (EVs) have gained significant attention as a strategic industry in many countries. However, the insufficiency of accessible charging infrastructure remains a challenge, hindering the widespread adoption of EVs. To address this issue, we propose a novel approach to optimize the placement of charging stations within a road network, known as the charging station location problem (CSLP). Our method considers multiple factors, including fairness in charging station distribution, benefits associated with their placement, and drivers’ discomfort. Fairness is quantified by the balance in charging station coverage across the network, while driver comfort is measured by the total time spent during the charging process. Then, the CSLP is formulated as a reinforcement learning problem, and we introduce a novel model called PPO-Attention. This model incorporates an attention layer into the Proximal Policy Optimization (PPO) algorithm, enhancing the algorithm’s capacity to identify and understand the intricate interdependencies between different nodes in the network. We have conducted extensive tests on urban road networks in Europe, North America, and Asia. The results demonstrate the superior performance of our approach compared to existing baseline algorithms. On average, our method achieves a profit increase of 258.04% and reduces waiting time by 73.40%, travel time by 18.46%, and charging time by 40.10%.

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Section: Introductionmentioning

confidence: 99%

Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention Mechanism

2023

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Research on noise source separation and sound quality prediction for electric powertrain

Liu

Zhang²,

Huang³

et al. 2022

Applied Acoustics

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Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization

et al. 2022

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Lithium-ion batteries are the current most promising device for electric vehicle applications. They have been widely used because of their advantageous features, such as high energy density, many cycles, and low self-discharge. One of the critical factors for the correct operation of an electric vehicle is the estimation of the battery charge state. In this sense, this work presents a comparison of the state of charge estimation (SoC), tested in four different conduction profiles in different temperatures, which was performed using the Multiple Linear Regression without (MLR) and with spline interpolation (SPL-MLR) and the Generalized Linear Model (GLM). The models were calibrated by three different bio-inspired optimization techniques: Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The computational results showed that the MLR-PSO is the most suitable for SoC prediction, overcoming all other models and important proposals from the literature.

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Challenges in Noise Refinement of a Pure Electric Passenger Vehicle

Cited by 7 publications

References 0 publications

Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention Mechanism

Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention Mechanism

Research on noise source separation and sound quality prediction for electric powertrain

Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization

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