Research on Remaining Driving Range Estimation of Electric Vehicle Based on Dynamic Working Condition

Du, Chang Qing; Du, Gang; Tan, Ke Cheng; Liu, Yong Shan

doi:10.4028/www.scientific.net/amr.945-949.509

Cited by 3 publications

(3 citation statements)

References 8 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Innovative approaches to battery health evaluation and RDR estimation span from physics-based simulations to datacentered methods and artificial intelligence applications [9]. Techniques like Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) networks have manifested impressive accuracy in RDR prediction.…”

Section: Estimation Of Remaining Driving Range (Rdr)mentioning

confidence: 99%

A comprehensive study on battery health assessment and remaining driving range estimation for electric vehicles

Xu,

Jia

2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

View full text Add to dashboard Cite

Electric vehicles (EVs) have emerged as a promising solution to curb greenhouse gas emissions and reduce dependency on fossil fuels. The successful integration of EVs into the mainstream transportation system relies heavily on efficient power battery utilization, making battery health assessment and remaining mileage estimation vital. This paper presents a comprehensive study on the development of intelligent energy management systems for EVs, with a primary focus on predicting battery health status and accurately estimating the remaining mileage (RDR). The study leverages cutting-edge big data analytics and machine learning techniques to enhance battery performance analysis, ensuring optimal energy utilization in electric vehicles.

show abstract

Section: Estimation Of Remaining Driving Range (Rdr)mentioning

confidence: 99%

A comprehensive study on battery health assessment and remaining driving range estimation for electric vehicles

Xu,

Jia

2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

View full text Add to dashboard Cite

show abstract

“…The energy consumption during driving of new energy mining trucks mainly comes from driving resistance. Driving resistance mainly consists of rolling resistance, windward resistance, slope resistance and acceleration resistance (Du Changqing and Shi Yanlong, 2016), for the driving energy consumption when the truck goes uphill, it can be expressed by equation ( 1):…”

Section: Energy Consumption Analysis Of New Energy Mining Trucksmentioning

confidence: 99%

Scheduling Optimization of Electric Trucks for Mines Based on Energy Recovery and Hybrid Genetic Algorithm

Yan,

Yu,

吴

2024

Preprint

View full text Add to dashboard Cite

In the context of increasingly stringent fuel consumption regulations, new energy trucks have become one of the equipment for mining transportation. However, the batteries of new energy trucks consume a significant amount of energy, necessitating rational planning and scheduling to reduce energy consumption. This paper explores a mine transportation scheduling solution based on a hybrid genetic algorithm, with special consideration given to the energy recovery characteristics of electric trucks. Through an analysis of battery energy consumption and recovery during truck operation, and a comprehensive consideration of factors such as transportation time between various mine nodes, road slope and length, as well as node mining and queuing time, the constraints of transportation route selection and vehicle scheduling were examined. The study establishes a multi-objective optimization model that seeks a reasonable structure while taking into account maximizing transportation volume and minimizing costs.The Dijkstra algorithm, combined with the breadth-first search algorithm, is employed to calculate suitable paths. Subsequently, the non-dominated sorting genetic algorithm is utilized for vehicle task allocation through a unique genetic encoding method. Mathematical methods, including entropy weighting, are incorporated to address the multi-objective weighting problem of automatic truck scheduling. To enhance the realism of the solution, the model also accounts for time-varying changes in road congestion and road bifurcation points traversed by truck routes. Finally, simulation experiments validate that the established energy optimization model has a positive impact on mine operations by increasing mining transportation volume, reducing costs, and minimizing wasted time. Additionally, the proposed method exhibits robustness and practicality, demonstrating certain fault adjustment capabilities.

show abstract

“…They used a contact-type, high-power charge-while-driving system, and the installation cost was estimated to be about 1/20 of the cost of a non-contact system. Du established an EV simulation model based on GT-Drive and MATLAB to estimate the remaining driving range under dynamic working conditions [11]. This model used the modified parameters to reduce the initial estimate error.…”

Section: Introductionmentioning

confidence: 99%

Energy Consumption Prediction of Electric Construction Machinery Based on Condition Identification

Lin

Gao

2022

IEEE Access

View full text Add to dashboard Cite

Energy saving and emission reduction have become the consensus of global development. Electric construction machinery has drawn more and more attentions due to its zero emission and high efficiency. However, because of the installed capacity of the battery, the complex working conditions and the time-varying load of construction machinery, the working time of electric construction machinery is hard to estimate. It is important to accurately predict the remaining working time of the whole machine to ensure that the driver can reasonably arrange the operation time. In this paper, the electric loader is studied. To improve the estimation accuracy of the working time of electric loader, the typical working conditions are analyzed. The data of V-type working mode cycles of the actual experimental prototype are analyzed, which provides the basis for the segmentation of working conditions and the extraction of characteristic parameters. The fuzzy C-means clustering algorithm is used, an estimation method of operation energy consumption based on working condition identification is proposed. The results show that the energy consumption estimation method based on the motor average torque proposed in this paper has better estimation accuracy than the traditional estimation method based on the latest unit time energy consumption, which provides an idea for estimating the working time of electric loader and has certain practical significance.

show abstract

Research on Remaining Driving Range Estimation of Electric Vehicle Based on Dynamic Working Condition

Cited by 3 publications

References 8 publications

A comprehensive study on battery health assessment and remaining driving range estimation for electric vehicles

A comprehensive study on battery health assessment and remaining driving range estimation for electric vehicles

Scheduling Optimization of Electric Trucks for Mines Based on Energy Recovery and Hybrid Genetic Algorithm

Energy Consumption Prediction of Electric Construction Machinery Based on Condition Identification

Contact Info

Product

Resources

About