Advanced Adaptive Rule-Based Energy Management for Hybrid Energy Storage Systems (HESSs) to Enhance the Driving Range of Electric Vehicles

Vehicles, Journal Year: 2025, Volume and Issue: 7(1), P. 6 - 6

Published: Jan. 18, 2025

The energy storage system (ESS) plays a crucial role in electric vehicles (EVs), impacting their performance and efficiency. While batteries are the standard choice for storage, they come with drawbacks like low power density limited life cycles, which can hinder pure battery (PBEVs). To address these issues, hybrid (HESS) that combines supercapacitor provides more effective solution. delivers consistent power, while manages peak demands regenerative braking energy. This study proposes new management strategy HESS, an advanced adaptive rule-based algorithm. results of algorithms used to verify proposed control was modeled MATLAB/Simulink evaluated across three driving cycles—UDDS, NYCC, Japan1015—while varying states charge supercapacitors. findings indicate HESS significantly alleviates stress compared system, enhancing both efficiency lifespan. Among tested, algorithm yielded best results, increasing number viable drive cycles.

Language: Английский

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An optimized informer model design for electric vehicle SOC prediction

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(3), P. e0314255 - e0314255

Published: March 11, 2025

SOC prediction is of great value to electric vehicle status assessment. Informer model better than other models in prediction, but there still a gap practical application. Therefore, based on the health assessment algorithm, new optimized proposed predict SOC. Firstly, carried out through historical running data obtain matrix. Then, matrix used improve Encoder and Decoder modules accuracy speed model. Subsequently, utilized optimize logic, reduce influence truncation error, further accuracy. Finally, using before after optimization, performed four different datasets. The results indicate that optimizing En-De module Informer, improved by approximately 15%, with increasing about 100%. Furthermore, logic error enhanced Informer's around 20%.

Language: Английский

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Lithium Battery Enhancement Through Electrical Characterization and Optimization Using Deep Learning

World Electric Vehicle Journal, Journal Year: 2025, Volume and Issue: 16(3), P. 167 - 167

Published: March 13, 2025

Research on lithium-ion batteries has been driven by the growing demand for electric vehicles to mitigate greenhouse gas emissions. Despite advances, still face significant challenges in efficiency, lifetime, safety, and material optimization. In this context, objective of research is develop a predictive model based Deep deep-Learning learning techniques. Based Learning techniques that combine Transformer Physicsphysics-Informed informed approaches optimization design electrochemical parameters improve performance lithium batteries. Also, we present training database consisting three key components: numerical simulation using Doyle–Fuller–Newman (DFN) mathematical model, experimentation with half-cell configured zinc oxide anode, set commercial battery discharge curves electronic monitoring. The results show developed Transformer–Physics physics-Informed can effectively integrate deep deep-learning DNF make predictions behavior estimate battery-charge capacity an average error 2.5% concerning experimental data. addition, it was observed could explore new allow evaluation without requiring invasive analysis their internal structure. This suggests assess optimize various applications, which significantly impact industry its use Electric Vehicles (EVs).

Language: Английский

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Experimental investigation on electric vehicle braking system using fuzzy rule-based control strategy

World Journal of Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 10, 2025

Purpose Brake blending in electric vehicles is highly critical for enhancing the overall driving experience, energy recovery and safety. In this paper, a control strategy based on fuzzy rules proposed brake EVs to integrate regenerative braking with conventional friction braking. Existing approaches, such as machine learning (ML) model predictive (MPC), have several limitations compared rule-based (FRBC). ML-based systems require extensive datasets training are computationally intensive. Their performance heavily depends data quality, making real-time adaptation difficult. Therefore, FRBC proposed. Design/methodology/approach Simulations hardware-in-the-loop experiments validate of system by indicating higher efficiency better lesser wear components. Findings The shows steeper activation curve between 1 3 s reaches 85% within third second. This study, therefore, introduces novel directions flexible adaptive frameworks optimize supports development sustainable efficient vehicle operation. Originality/value work aimed at pushing state-of-the-art EV through rigorous simulations experimental validation, view improving recovery, safety experience.

Language: Английский

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Adaptive neuro fuzzy inference system based optimized energy management strategy for the power integration of battery and supercapacitor in electric vehicle

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 126, P. 117073 - 117073

Published: May 16, 2025

Language: Английский

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A Novel Optimal Control Strategy of Four Drive Motors for an Electric Vehicle

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(7), P. 3505 - 3505

Published: March 23, 2025

Based on the mobility requirements of electric vehicles, four-wheel drive (4WD) can significantly enhance driving capability and increase operational flexibility in handling. If output different motors be effectively controlled, energy losses during distribution process reduced, thereby greatly improving overall efficiency. This study presents a simulation platform for an vehicle with four as power sources. also consists cycle, driver, lithium-ion battery, dynamics, management system models. Two rapid-prototyping controllers integrated required circuit to analog-to-digital signal conversion input are utilized carry out hardware-in-the-loop (HIL) simulation. The called NEDC (New European Driving Cycle), FTP-75 (Federal Test Procedure 75) used evaluating performance characteristics response relationship among subsystems. A control strategy, ECMS (Equivalent Consumption Minimization Strategy), is simulated compared average torque mode. method considers demanded powers motor speeds, various combinations search consumption find minimum value. As result, it identify global optimal solution. Simulation results indicate that, mode rule-based control, pure environment HIL UDDS maximum improvement rates efficiency 45 kW 95 systems 6.1% 6.0%, respectively. In 5.1% 4.8%,

Language: Английский

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The Effect of Energy Management in Heating–Cooling Systems of Electric Vehicles on Charging and Range

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(15), P. 6406 - 6406

Published: July 23, 2024

In this study, an energy management model for electric vehicles including the entire vehicle such as cabin, motors, battery, and heating–cooling system was prepared. The heating cooling processes were run according to internationally recognized driving cycles well at constant speeds investigate them under different ambient conditions. managed in line with cabin temperature target determined by considering comfort consumption of each elements process analyzed. Under operating conditions, variation time, instantaneous power, cumulative calculated. effect on consumption, charging rate, range analyzed interpreted. results showed that consumed more when decreased, charge ratio deformation rate increased about 30% –10 °C. Similarly, increased, reached up 40% 40 When outdoor conditions close thermal 23 °C inside total rates reduced less than 10%.

Language: Английский

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