Research on the Energy-Saving Strategy of Path Planning for Electric Vehicles Considering Traffic Information

Zhu, Guanghai; Lin, Jian‐Bin; Liu, Qingwu; He, Haibo

doi:10.3390/en12193601

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…In addition to regenerative braking, some energy saving measures have been put forward one after another. Recently, Zhu et al [109] proposed a path planning method for electric vehicles considering traffic information. This approach models real transportation networks and enables energy consumption prediction with the help of deep learning.…”

Section: Prospects For Other Technologies To Improve Energy Saving Ef...mentioning

confidence: 99%

Regenerative Braking of Electric Vehicles Based on Fuzzy Control Strategy

Yin,

Ma,

et al. 2023

Processes

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Regenerative braking technology is a viable solution for mitigating the energy consumption of electric vehicles. Constructing a distribution strategy for regenerative braking force will directly affect the energy saving efficiency of electric vehicles, which is a technical bottleneck of battery-powered electric vehicles. The distribution strategy of the front- and rear-axle braking forces of electric vehicles that possess integrated front-wheel-drive arrangements is established based on the Economic Commission of Europe (ECE) regulations, which enables the clarification of the total braking force of the front axle. The regenerative braking torque model of the motor is adjusted to optimize the ratio of motor braking force to the whole front-axle braking force. The regenerative braking process of electric vehicles is influenced by many factors, such as driving speed and braking intensity, so regenerative braking presents characteristics of nonlinearity, time variability, delay, and incomplete models. By considering the impact of fuzzy controllers having better robustness, adaptability, and fault tolerance, a fuzzy control strategy is employed in this paper to accomplish the regenerative braking force distribution on the front axle. A regenerative braking model is created on the Simulink platform using the braking force distribution indicated above, and experiments are run under six specific operating conditions: New European Driving Cycle (NEDC), World Light-Duty Vehicle Test Cycle (WLTC), Federal Test Procedure 72 (FTP-72), Federal Test Procedure 75 (FTP-75), China Light-Duty Vehicle Test Cycle-Passenger (CLTC-P), and New York City Cycle (NYCC). The findings demonstrate that in six typical cycling road conditions, the energy saving efficiency of electric vehicles has greatly increased, reaching over 15%. The energy saving efficiency during the WLTC driving condition reaches 25%, and it rises to 30% under the FTP-72, FTP-75, and CLTC-P driving conditions. Furthermore, under the NYCC road conditions, the energy saving efficiency exceeded 40%. Therefore, our results verify the effectiveness of the regenerative braking control strategy proposed in this paper.

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Section: Prospects For Other Technologies To Improve Energy Saving Ef...mentioning

confidence: 99%

Regenerative Braking of Electric Vehicles Based on Fuzzy Control Strategy

Yin,

Ma,

et al. 2023

Processes

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“…Energy-efficient path planning aims to optimize resource usage, ensuring vehicles operate within their energy constraints while completing tasks [5]. Reducing energy consumption aligns with sustainability goals reducing carbon emissions and environmental impact [6]. Optimizing energy usage reduces operational costs by minimizing recharging frequency and maintenance expenses [7].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Energy Efficiency through Path Planning for Off-Road Missions of Unmanned Tracked Electric Vehicle

İnal,

Cansever,

Yalçın

et al. 2024

Vehicles

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The primary objective of this research is to address the existing gap about the use of a path-planning algorithm that will reduce energy consumption in off-road applications of tracked electric vehicles. The study focuses on examining various off-road terrains and their impact on energy consumption to validate the effectiveness of the proposed solution. To achieve this, a tracked electric vehicle energy model that incorporates vehicle dynamics is developed and verified using real vehicle driving data logs. This model serves as the foundation for devising a strategy that can effectively enhance the energy efficiency of off-road tracked electric vehicles in real-world scenarios. The analysis involves a thorough examination of different off-road terrains to identify strategies that can adapt to diverse landscapes. The path planning strategy employed in this study is a modified version of the A*, called the Energy-Efficient Path Planning (EEPP) algorithm, specifically tailored for the dynamic energy consumption model of off-road tracked electric vehicles. The energy consumption of the produced paths is then compared using the validated energy consumption model of the tracked electric vehicle. It is important to note that the identification of an energy-efficient path heavily relies on the characteristics of the vehicle and the dynamic energy consumption model that has been developed. Furthermore, the algorithm takes into account real-world and practical considerations associated with off-road applications during its development and evaluation process. The results of the comprehensive analysis comparing the EEPP algorithm with the A* algorithm demonstrate that our proposed approach achieves energy savings of up to 6.93% and extends the vehicle’s operational range by 7.45%.

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“…Reference [19] proposed a multi-path planning model to address urban path planning problems considering TF information. Reference [20] proposed an energy-saving path planning method for EVs that considers traffic information, integrating EVs driving status data with traffic information using sequence-to-sequence techniques.…”

Section: Introductionmentioning

confidence: 99%

Optimal Routing and Charging Strategy of Electric Vehicles Based on Traffic Flow Prediction

Wang

Zhao

et al. 2023

Preprint

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With the increasing number of electric vehicles (EVs) in recent years, road congestion is becoming a common phenomenon, which not only prolongs travel time but also causes anxiety for EV users. Therefore, this paper proposes a time-varying shortest path search method for traffic flow (TF) and establishes an EV route planning model based on this method to plan the optimal path for users. Firstly, a convolutional neural network (CNN) is used to predict TF and a new queuing model is established to calculate the charging queuing time. Then, a path planning model considering mid-way charging is established based on the predicted TF data and charging queuing model. Finally, the performance of the proposed method is tested using road network maps of different scales, and a case study on the optimal path of EV with mid-way charging under the minimum objective function is conducted based on a real traffic network. The results show that the proposed time-varying shortest path search method under the TF network can quickly calculate the optimal path and has great potential for solving practical problems.

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Research on the Energy-Saving Strategy of Path Planning for Electric Vehicles Considering Traffic Information

Cited by 7 publications

References 29 publications

Regenerative Braking of Electric Vehicles Based on Fuzzy Control Strategy

Regenerative Braking of Electric Vehicles Based on Fuzzy Control Strategy

Enhanced Energy Efficiency through Path Planning for Off-Road Missions of Unmanned Tracked Electric Vehicle

Optimal Routing and Charging Strategy of Electric Vehicles Based on Traffic Flow Prediction

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