Relevant bibliographies by topics / Algorithm-driven consumption

Contents

  1. Journal articles
  2. Books
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Algorithm-driven consumption'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Algorithm-driven consumption.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Algorithm-driven consumption"

1

Yuan, Shijiao. "Analysis of Consumer Behavior Data Based on Deep Neural Network Model." Journal of Function Spaces 2022 (September 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/4938278.

Full text
Abstract:
This paper divides the research modes of consumer purchase behavior characteristics into three categories: experience-driven mode, theory-driven mode, and data-driven mode. An analysis algorithm based on customer consumption behavior is proposed, and the idea of combining customer consumption behavior factors such as satisfaction and loyalty is proposed. Through comparison, it is pointed out that the data-driven model is most suitable for analyzing the characteristics of online consumers’ purchasing behavior. Using the decision support of knowledge base, different service schemes for customers
APA, Harvard, Vancouver, ISO, and other styles
2

Zhao, Xinxin, Ming Zhang, and Guangyu Xue. "Data-Driven Algorithm Based on Energy Consumption Estimation for Electric Bus." World Electric Vehicle Journal 14, no. 12 (2023): 329. http://dx.doi.org/10.3390/wevj14120329.

Full text
Abstract:
The accurate estimation of battery state of charge (SOC) for modern electric vehicles is crucial for the range and performance of electric vehicles. This paper focuses on the historical driving data of electric buses and focuses on the extraction of driving condition feature parameters and data preprocessing. By selecting relevant parameters, a set of characteristic parameters for specific driving conditions is established, a process of constructing a battery SOC prediction model based on a Long short-term memory (LSTM) network is proposed, and different hyperparameters of the model are identi
APA, Harvard, Vancouver, ISO, and other styles
3

Yang, Ran, Zhaonan Li, Junhao Qian, and Zhihua Li. "Task-Driven Virtual Machine Optimization Placement Model and Algorithm." Future Internet 17, no. 2 (2025): 73. https://doi.org/10.3390/fi17020073.

Full text
Abstract:
In cloud data centers, determining how to balance the interests of the user and the cloud service provider is a challenging issue. In this study, a task-loading-oriented virtual machine (VM) optimization placement model and algorithm is proposed integrating consideration of both VM placement and the user’s computing requirements. First, the VM placement is modeled as a multi-objective optimization problem to minimize the makespan of the loading tasks, user rental costs, and energy consumption of cloud data centers; then, an improved chaos-elite NSGA-III (CE-NSGAIII) algorithm is presented by c
APA, Harvard, Vancouver, ISO, and other styles
4

Chentouf, Mohamed, and Zine El Abidine Alaoui Ismaili. "A Novel Net Weighting Algorithm for Power and Timing-Driven Placement." VLSI Design 2018 (October 18, 2018): 1–9. http://dx.doi.org/10.1155/2018/3905967.

Full text
Abstract:
Nowadays, many new low power ASICs applications have emerged. This new market trend made the designer’s task of meeting the timing and routability requirements within the power budget more challenging. One of the major sources of power consumption in modern integrated circuits (ICs) is the Interconnect. In this paper, we present a novel Power and Timing-Driven global Placement (PTDP) algorithm. Its principle is to wrap a commercial timing-driven placer with a nets weighting mechanism to calculate the nets weights based on their timing and power consumption. The new calculated weight is used to
APA, Harvard, Vancouver, ISO, and other styles
5

Noguera-Vivo, Jose Manuel, and María del Mar Grandío-Pérez. "Enhancing Algorithmic Literacy: Experimental Study on Communication Students’ Awareness of Algorithm-Driven News." Anàlisi 71 (January 29, 2025): 37–53. https://doi.org/10.5565/rev/analisi.3718.

Full text
Abstract:
This article addresses the need for algorithmic literacy in the field of journalism and media education. Amid the escalating complexity of disinformation in the media landscape, the aim is to enhance users’ awareness and understanding of algorithm-driven content. Through focused research on communication students, the study investigates attitudes, beliefs and knowledge relating to the influence of algorithmic systems on news consumption. Existing scholarship is surveyed to establish the evolving nature of algorithmic literacy, ranging from optimizing search engines to countering misconceptions
APA, Harvard, Vancouver, ISO, and other styles
6

Sambu, Patach Arrojula. "Backend-Driven Efficient Upload Algorithm: Balancing Data Freshness and Device Energy Consumption." European Journal of Advances in Engineering and Technology 9, no. 2 (2022): 56–62. https://doi.org/10.5281/zenodo.13919560.

Full text
Abstract:
In mobile applications, maintaining a balance between data freshness and device energy consumption is a critical yet challenging task. Frequent uploads of event data ensure real-time insights but can significantly drain battery life and consume network data, especially over cellular connections. Conversely, infrequent uploads conserve energy but lead to delayed data availability, impairing the ability of app owners to make timely and informed decisions. This paper proposes a backend-driven efficient upload algorithm that dynamically balances these conflicting requirements. The algorithm levera
APA, Harvard, Vancouver, ISO, and other styles
7

Zhang, Peng, Xin Ma, and Kun She. "A Novel Power-Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China." Complexity 2019 (November 6, 2019): 1–22. http://dx.doi.org/10.1155/2019/1510257.

Full text
Abstract:
Along with the improvement of Chinese people’s living standard, the proportion of residential energy consumption in total energy consumption is rapidly increasing in China year by year. Accurately forecasting the residential energy consumption is conducive to making energy programming and supply plan for the administrative departments or energy companies. By improving the grey action quantity of traditional grey model with an exponential time term, a novel power-driven grey model is proposed to forecast energy consumption as reference data for decision makers. The nonlinear parameter of power-
APA, Harvard, Vancouver, ISO, and other styles
8

Hussain, Asad, Jacopo Cimaglia, Sabrina Romano, Francesco Mancini, and Valerio Re. "Data Driven Disaggregation Method for Electricity Based Energy Consumption for Smart Homes." Journal of Physics: Conference Series 2385, no. 1 (2022): 012006. http://dx.doi.org/10.1088/1742-6596/2385/1/012006.

Full text
Abstract:
Abstract Sustainable energy systems must be capable of ensuring sustainable development by providing affordable and reliable energy to consumers. Hence, knowledge and understanding of energy consumption in the residential sector are indispensable for energy preservation and energy efficiency which can only be possible with the help of consumer participation. New energy efficiency methods are developed due to the global adoption of smart meters that monitor and communicate residential energy consumption. Moreover, energy monitoring of each appliance is not feasible, as it is a costly solution.
APA, Harvard, Vancouver, ISO, and other styles
9

Kassem, Sameh A., Abdulla H. A. EBRAHIM, Abdulla M. Khasan, and Alla G. Logacheva. "FORECASTING ELECTRIC CONSUMPTION OF THE ENTERPRISE USING ARTIFICIAL NEURAL NETWORKS." Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy 7, no. 1 (2021): 177–93. http://dx.doi.org/10.21684/2411-7978-2021-7-1-177-193.

Full text
Abstract:
Energy consumption has increased dramatically over the past century due to many factors, including both technological, social and economic factors. Therefore, predicting energy consumption is of great importance for many parameters, including planning, management, optimization and conservation. Data-driven models for predicting energy consumption have grown significantly over the past several decades due to their improved performance, reliability, and ease of deployment. Artificial neural networks are among the most popular data-driven approaches among the many different types of models today.
APA, Harvard, Vancouver, ISO, and other styles
10

Huang, Kang, Jianjun Wu, Xin Yang, Ziyou Gao, Feng Liu, and Yuting Zhu. "Discrete Train Speed Profile Optimization for Urban Rail Transit: A Data-Driven Model and Integrated Algorithms Based on Machine Learning." Journal of Advanced Transportation 2019 (May 2, 2019): 1–17. http://dx.doi.org/10.1155/2019/7258986.

Full text
Abstract:
Energy-efficient train speed profile optimization problem in urban rail transit systems has attracted much attention in recent years because of the requirement of reducing operation cost and protecting the environment. Traditional methods on this problem mainly focused on formulating kinematical equations to derive the speed profile and calculate the energy consumption, which caused the possible errors due to some assumptions used in the empirical equations. To fill this gap, according to the actual speed and energy data collected from the real-world urban rail system, this paper proposes a da
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Algorithm-driven consumption"

1

Damodaran, A. Managing Arts in Times of Pandemics and Beyond. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192856449.001.0001.

Full text
Abstract:
The work seeks to provide a management perspective about museums, theatres, and related organizations (like auction houses) from the angles of philosophy, policy, organizational design, economics, and technology. The work seeks to examine the unprecedented crisis engendered by the COVID-19 pandemic on arts organizations across the world and the management strategies adopted to handle the pandemic. The work delves into the immense significance of digital technologies such as streaming technologies, algorithm-driven sales, and information storing digital ledgers like blockchains in guiding the f
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Algorithm-driven consumption"

1

Shi, Mingchunjian, Liming Kong, Yongzhong Chen, and Xiang Li. "Environmental Data-Driven Optimization of Building Skin Design by Coupling Genetic Algorithm and Neural Network Algorithm -Taking Shaanxi Xi’an Garment Office Building as Example." In Computational Design and Robotic Fabrication. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3433-0_21.

Full text
Abstract:
Abstract It is complex to design facade skin for different building, in view of the high operational energy consumption that accompanies buildings with excessively large window-to-wall ratios. For public buildings with, the energy load cost relatively large. There are a characteristic of facade which have high wall window rate to consume a lot of energy or increase Insulation cost due to the influence of interfaces. For the treatment of shading in summer, excessive overhang of the eaves often increases the load and structural cost of the roof or increases the external sunshade structure to inc
APA, Harvard, Vancouver, ISO, and other styles
2

Yu, Jiesheng, Yongming Zhang, Zhe Yan, and Ziqi Li. "A Novel AI-Driven Multi-objective Optimization Approach for Energy System Design in Industrial Zone." In Computational Design and Robotic Fabrication. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3433-0_17.

Full text
Abstract:
Abstract In the context of global energy conservation and emission reduction, it is imperative to improve energy efficiency and promote sustainable development. The industry sector has consistently been a pivotal industry in implementing low-carbon practices, particularly in large industrial zones characterized by high energy consumption and carbon emissions. A focal point of research in such contexts revolves around the configuration of energy systems in industrial zone. This paper proposes a novel AI-driven multi-objective optimization approach for energy system design in industrial zone. Ec
APA, Harvard, Vancouver, ISO, and other styles
3

Jin, Yuan, Da Yan, Xingxing Zhang, et al. "District Household Electricity Consumption Pattern Analysis Based on Auto-Encoder Algorithm." In Data-driven Analytics for Sustainable Buildings and Cities. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2778-1_18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Li, Jingyi, and Hong Chen. "Optimization and Prediction of Design Variables Driven by Building Energy Performance—A Case Study of Office Building in Wuhan." In Proceedings of the 2020 DigitalFUTURES. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4400-6_22.

Full text
Abstract:
AbstractThis research focuses on the energy performance of office building in Wuhan. The research explored and predicted the optimal solution of design variables by Multi-Island Genetic Algorithm (MIGA) and RBF Artificial neural networks (RBF-ANNs). Research analyzed the cluster centers of design variable by K-means cluster method. In the study, the RBF-ANNs model was established by 1,000 simulation cases. The RMSE (root mean square error) of the RBF-ANNs model in different energy aspects does not exceed 15%. Comparing to the reference case (the largest energy consumption case in the optimizat
APA, Harvard, Vancouver, ISO, and other styles
5

Eniola, Victor, Kafayat Adeyemi, Mohammed Adamu, et al. "Daily Streamflow Forecasting Using an Enhanced LSTM Neural Network Model." In Modelling the Energy Transition. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-69031-0_8.

Full text
Abstract:
Abstract Oil and gas consumption for power generation has caused irreversible damage to humanity. To address the attendant effects of fossil fuel utilization, renewable energy is a good alternative. International organizations give support to countries in their transition to a green energy future. This implies that the use of renewable energy is widely supported. It is therefore recommended to utilize renewable energy as it is environmentally friendly. One such type of renewables is water energy. Water cycle has streamflow $$\left( {f_{s} } \right)$$ f s as its central component. Having reliab
APA, Harvard, Vancouver, ISO, and other styles
6

Zhu, Xinyu, Tingting Wang, Hongliang Xiao, Jiangfeng Fu, and Xiaobo Zhang. "Optimization of Adaptive Cycle Engine Performance Based on Particle Swarm Optimization." In Advances in Transdisciplinary Engineering. IOS Press, 2025. https://doi.org/10.3233/atde250331.

Full text
Abstract:
According to the aero-engine Performance Seeking Control (PSC) of Adaptive Cycle Engine (ACE) in multiple operating modes, an engine performance optimization control strategy based on Particle Swarm Optimization (PSO) is proposed. PSO algorithm has a simple structure and requires fewer parameter settings, and is suitable for ACE with many control variables. This method was applied to the ACE model with two optimization modes: maximum thrust model and minimum specific fuel consumption model. The simulation results showed 11.7% increase in maximum thrust mode and 0.13% reduction in minimum fuel
APA, Harvard, Vancouver, ISO, and other styles
7

Jiang, Tongqiang, Qi Yang, Tianqi Liu, Wei Dong, Yongchun Jiao, and Qingchuan Zhang. "Risk Classification Study of Carbofuran in Vegetables Based on K-Means++ Algorithm." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde221100.

Full text
Abstract:
This study used dietary exposure assessment method and K-means++ clustering algorithm to construct a carbofuran risk grading model to assess the risk of carbofuran in six vegetable categories in 20 provinces in China using carbofuran sampling and testing data and consumption data in 2020. The clustering algorithm classified the risk level of carbofuran in vegetables into 3 levels: low, medium, and high. The number of low risk combinations accounted for 92.5%, and the high-risk combinations were bulb vegetables in Hebei and leafy vegetables in Shaanxi. This study uses objective data to build a
APA, Harvard, Vancouver, ISO, and other styles
8

Goel, Pawan Kumar. "AI for Energy Efficiency and Conservation." In Practice, Progress, and Proficiency in Sustainability. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-6567-0.ch003.

Full text
Abstract:
The growing energy demand and the need for sustainable resource utilization have led to extensive research into artificial intelligence (AI) applications for energy efficiency and conservation. AI-driven solutions offer novel approaches to tackle complex energy management challenges, enabling precise prediction, optimization, and control of energy consumption patterns. Key areas of focus include smart grid management, building automation, industrial process optimization, and transportation efficiency enhancement. Challenges such as scalability, data integration, and algorithm robustness drive
APA, Harvard, Vancouver, ISO, and other styles
9

Jalasri, M., Soumyashree M. Panchal, Karpagam Mahalingam, R. Venkatasubramanian, R. Hemalatha, and Sampath Boopathi. "AI-Powered Smart Energy Management for Optimizing Energy Efficiency in High-Performance Computing Systems." In Advances in Computational Intelligence and Robotics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-5533-6.ch012.

Full text
Abstract:
The chapter discusses the need for efficient energy consumption in high-performance computing systems and proposes the integration of artificial intelligence and machine learning techniques to optimize energy efficiency. It explores AI-driven techniques like reinforcement learning, neural networks, and predictive analytics for energy-aware scheduling, workload allocation, and adaptive power management. The chapter discusses the effectiveness of AI-driven energy optimization strategies in real-world HPC infrastructures, highlighting potential energy savings while maintaining computational perfo
APA, Harvard, Vancouver, ISO, and other styles
10

Muddamalla, Naresh, T. V. V. Satyanarayana, and K. N. V. Suresh Varma. "WDWWO Combined NN and Its Application to Handover in Heterogeneous Networks." In Bio-Inspired Intelligence for Smart Decision-Making. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-5276-2.ch008.

Full text
Abstract:
In heterogeneous networks, vertical handover (VH) has a significant impact on networking performance including delay, throughput, and call block probability. VH management involves technique complexity, network modelling challenges, and inaccurate handover despite several prior efforts. This chapter discusses hybrid methodology that aims to provide accurate VH maintaining less complexity. Deep residual neural (DRN) and wind-driven water wave optimisation (WDWWO) are combined to perform VH. To address this situation, the use of DRN is combined with WDWWO for weight optimisation, resulting in op
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Algorithm-driven consumption"

1

Bhanuprakash, C., and Manish T. P. "Data-Driven Approaches to Analyze Energy Consumption Using Linear Regression Algorithm." In 2025 3rd International Conference on Smart Systems for applications in Electrical Sciences (ICSSES). IEEE, 2025. https://doi.org/10.1109/icsses64899.2025.11009779.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Barochia, Dev, Hasan Nikkhah, and Burcu Beykal. "Design and Optimization of a Multipurpose Zero Liquid Discharge Desalination Plant." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.142929.

Full text
Abstract:
We study the design and optimization of a multicomponent seawater desalination process with zero liquid discharge (ZLD). The designed process is highly integrated with multiple sub processing units that include humidification-dehumidification, Lithium Bromide absorption chiller, multi-effect evaporators, mechanical vapor compression, and crystallization. Aspen Plus software with E-NRTL and SOLIDS thermodynamic packages are used for modeling and simulation of desalination and crystallization units, respectively. In addition to this, we use data-driven optimization to find the best operating con
APA, Harvard, Vancouver, ISO, and other styles
3

Cao, Yang, Rui Zhong, Jun Yu, and Masaharu Munetomo. "Optimization of Electricity Consumption Forecasting Models via Hyper-Heuristic Algorithm." In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, 2024. http://dx.doi.org/10.1109/docs63458.2024.10704338.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Mahmood, Farhat, Sarah Namany, and Rajesh Govindan. "Data-Driven Reinforcement Learning for Greenhouse Temperature Control." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.189272.

Full text
Abstract:
Efficient temperature control in greenhouses is essential for optimal plant growth, especially in arid regions where the harsh environment poses significant challenges to maintaining a stable microclimate. Maintaining the optimum temperature range directly influences healthy plant development and overall agricultural productivity, impacting crop yields and financial outcomes. However, the greenhouse in the present case study fails to maintain the optimum temperature as it operates based on predefined settings, limiting its ability to adapt to dynamic climate conditions. To maintain an ideal te
APA, Harvard, Vancouver, ISO, and other styles
5

Shi, Mingrui, Xu Yang, Xudong Liu, Jiarui Cui, Jian Huang, and Chaonan Tong. "Energy Consumption Optimization for Public Buildings by Using Data-driven Heuristic Dynamic Programming Algorithm." In 2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2019. http://dx.doi.org/10.1109/ddcls.2019.8909064.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Lu, Xin, Guoqing Han, Peng Dong, Luting Wang, Zhuangzhuang Zhang, and Xingyuan Liang. "Energy Consumption Prediction and Optimization of Electrical Submersible Pump Well System Based on DA-RNN Algorithm." In SPE Symposium and Exhibition - Production Enhancement and Cost Optimisation. SPE, 2024. http://dx.doi.org/10.2118/220625-ms.

Full text
Abstract:
Abstract Electrical submersible pump(ESP) well system is widely used in the oil industry due to its advantages of high displacement and lift capability. However, it is associated with significant energy consumption. In order to conserve electrical energy and enhance the efficiency of petroleum companies, a deep learning-based energy consumption calculation method is proposed and utilized to optimize the most energy-efficient operating regime. The energy consumption of the ESP well system is precisely determined through the application of the Pearson correlation coefficient analysis method, whi
APA, Harvard, Vancouver, ISO, and other styles
7

Miranda, Rachel, Rafaela Pedrassani, Marcus Nogueira, Wallthynay Arruda, Leticia Rodrigues, and Rafael Paes. "Predictive Analytics Model for Natural Gas Transportation Consumption." In 2024 15th International Pipeline Conference. American Society of Mechanical Engineers, 2024. https://doi.org/10.1115/ipc2024-133467.

Full text
Abstract:
Abstract In the current natural gas transportation, operational scheduling heavily relies on daily volume nominations submitted by shippers. However, inaccuracies in these nominations pose significant challenges, leading to imprecise planning and complicating transportation operations. With the advancement of the Fourth Industrial Revolution, driven by automation and artificial intelligence (AI), revolutions are expected across various industries, promising significant improvements in efficiency, safety, and quality. This study focuses on predicting daily consumption in a natural gas distribut
APA, Harvard, Vancouver, ISO, and other styles
8

Rahimi, Mohammad A., Rasoul Salehi, and Aria Alasty. "Designing Gear-Shift Pattern for an Electric Vehicle to Optimize Energy Consumption." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-40457.

Full text
Abstract:
In this paper optimization of energy consumption in an electric vehicle is presented. The main idea of this optimization is based on selecting the best gear level in driving the vehicle. Two algorithms for optimization are introduced which are based on fuzzy rules and fuzzy controllers. In first algorithm, fuzzy controller simulates energy consumption in different gear levels, and chooses the optimum gear level. While in second method, fuzzy controller detects the optimum gear level by measuring the vehicle’s average speed and acceleration. To investigate the performance of these controllers,
APA, Harvard, Vancouver, ISO, and other styles
9

Chen, Yuxuan, Wei Yan, Hua Zhang, Ying Liu, Zhigang Jiang, and Xumei Zhang. "A Data-Driven Design Approach for Carbon Emission Prediction of Machining." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-90465.

Full text
Abstract:
Abstract The issue of carbon emission reduction for manufacturing industry attracts increasing attention. As a major contributor in the manufacturing industry, machining has generated large amounts of carbon emissions through the resource consumption, energy consumption, and waste disposal. The carbon emission prediction of machining is a priori technology for its reduction, and has been established as one of the most crucial research targets. The purpose of this study is to design a carbon emission prediction model of machining through a data-driven approach. First of all, the multiple source
APA, Harvard, Vancouver, ISO, and other styles
10

Wang, Qian, Jatin Goyal, Beshah Ayalew, and Amandeep Singh. "Control Allocation for Multi-Axle Hub Motor Driven Land Vehicles With Active Steering." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59509.

Full text
Abstract:
Multi-axle land vehicles with independent drive actuation on multiple axles offer improved stability and traction on various road surfaces. This is possible by exploiting the redundancy of the drive system to generate additional yaw moment and to maximize the utilization of individual tire-road contacts without significant extra power consumption by the drive motors. This paper aims at improving the efficiency of torque allocation with the addition of active steering while enhancing the dynamic performance of the independent hub motor driven multi-axle vehicles. The control algorithm outlined
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Algorithm-driven consumption' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography