Energy Management Improvement of Hybrid Electric Vehicles via Combined GPS/Rule-Based Methodology

Ramadan, Haïtham Saad Mohamed; Becherif, Mohamed; Claude, F.

doi:10.1109/tase.2017.2650146

Cited by 64 publications

(26 citation statements)

References 36 publications

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“…To improve prediction accuracy, intelligent transportation systems (ITSs) with some vehicular telemetry technologies including onboard GPSs, geographical information systems (GISs), and advanced traffic flow modeling techniques [49,62] can be used to access information of the traffic or road conditions. Based on the traffic data from telematics, the battery SOC profile can be scheduled systematically by considering the effect of road conditions, battery and real-time implementation ability, which can efficiently improve fuel economy based on the MPC strategy [63].…”

Section: Model Predictive Control (Mpc)mentioning

confidence: 99%

Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

Kong

Liang

et al. 2019

Applied Sciences

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This paper presents a comprehensive review of energy management control strategies utilized in hybrid electric vehicles (HEVs). These can be categorized as rule-based strategies and optimization-based strategies. Rule-based strategies, as the most basic strategy, are widely used due to their simplicity and practical application. The focus of rule-based strategies is to determine and optimize the optimal threshold for mode switching; however, they fall into a local optimal solutions. To have better performance in energy management, optimization-based strategies were developed. The categories of the existing optimization-based strategies are identified from the latest literature, and a brief study of each strategy is discussed, which consists of the main research ideas, the research focus, advantages, disadvantages and improvements to ameliorate optimality and real-time performance. Deterministic dynamic programming strategy is regarded as a benchmark. Based on neural network and the large data processing technology, data-driven strategies are put forward due to their approximate optimality and high computational efficiency. Finally, the comprehensive performance of each control strategy is analyzed with respect to five aspects. This paper not only provides a comprehensive analysis of energy management control strategies for HEVs, but also presents the emphasis in the future.

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Section: Model Predictive Control (Mpc)mentioning

confidence: 99%

Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

Kong

Liang

et al. 2019

Applied Sciences

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“…As such, HEVs have gained the attention of major car manufacturers and are widely used in civil, industrial, and military fields. Over the years, engineers have conducted many studies on the energy management strategies that can be adopted by HEVs [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. In terms of the working principle, such strategies can be roughly divided into two types: rule-based and optimization-based.…”

Section: Introductionmentioning

confidence: 99%

“…In terms of the working principle, such strategies can be roughly divided into two types: rule-based and optimization-based. Ramadan et al [1] established a Petri net strategy based on a global positioning system (GPS) and used battery capacity management to reduce fuel consumption. Li et al [2] proposed a torque-leveling threshold-changing strategy for parallel HEVs, which reduces hydrogen fuel consumption while maintaining the battery's state of charge (SOC) at close to the ideal value.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Model Predictive Control for Heavy-Duty Hybrid Electric Vehicles Using Random Power Prediction Method

Chen¹,

Liao²,

Ma³

2020

IEEE Access

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“…Multi-power source systems are also taken seriously because of their wide efficiency range and multi-mode flexibility [1]. At present, the drive control strategy of a multi-power driven vehicle is mainly designed based on the static characteristics of the transmission system components [2]. In [3], a rule-based control strategy for a dual-motor coupling driven pure electric vehicle is designed to obtain the optimal acceleration performance.…”

Section: Introductionmentioning

confidence: 99%

“…If the static model is used as the basis of the driving strategy, its dynamic and economy performance have a large deviation from the actual running. (2) The actual operation of the motor is affected by the control parameters, and there is always torque fluctuation. However, these characteristics cannot be reflected in the static model.…”

Section: Introductionmentioning

confidence: 99%

Motor Vector Control Based on Speed-Torque-Current Map

Jia

Xiao

et al. 2019

Applied Sciences

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In order to effectively extend the mileage of pure electric vehicles, the influence of the electromechanical energy conversion principle and the dynamic control of a permanent magnet synchronous motor (PMSM) on the performance of pure electric vehicles are studied with a dual-motor drive system as a carrier in this paper. A vector control strategy based on the speed-torque-current map of a motor is proposed, considering the bus voltage fluctuation influenced by battery charge and discharge. By constructing a complete vehicle model including the dynamic control model of the motor, the power distribution control strategy of a dual-motor coupling mode considering the dynamic characteristics of the motor is developed and simulated on the MATLAB platform. The results show that the dynamic model of the motor is closer to the actual running conditions. The proposed control strategy effectively reduces the demand power, decreases the energy consumption of the electric drive system, and extends the mileage of the vehicle.

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Energy Management Improvement of Hybrid Electric Vehicles via Combined GPS/Rule-Based Methodology

Cited by 64 publications

References 36 publications

Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

Nonlinear Model Predictive Control for Heavy-Duty Hybrid Electric Vehicles Using Random Power Prediction Method

Motor Vector Control Based on Speed-Torque-Current Map

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