scholarly journals Application of the Fuel-Optimal Energy Management in Design Study of a Parallel Hybrid Electric Vehicle

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Afshin Pedram Pourhashemi ◽  
S. M. Mehdi Ansarey Movahed ◽  
Masoud Shariat Panahi
Keyword(s):  
Optimal Design ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Performance Metrics ◽  
Design Parameters ◽  
Programming Approach ◽  
Energy Management Strategy ◽  
Dynamic Programming Approach ◽  
Dynamic Measures

In spite of occasional criticism they have attracted, hybrid vehicles (HVs) have been warmly welcomed by industry and academia alike. The key advantages of an HV, including fuel economy and environment friendliness, however, depend greatly on its energy management strategy and the way its design parameters are “tuned.” The optimal design and sizing of the HV remain a challenge for the engineering community, due to the variety of criteria and especially dynamic measures related to nature of its working conditions. This paper proposes an optimal design scheme that begins with presenting an energy management strategy based on minimum fuel consumption in finite driving cycle horizon. The strategy utilizes a dynamic programming approach and is consistent with charge sustenance. The sensitivity of the vehicle’s performance metrics to multiple design parameters is then studied using a design of experiments (DOE) methodology. The proposed scheme provides the designer with a reliable tool for investigating various design scenarios and achieving the optimal one.

scholarly journals Multi-objective Parameters Optimization for HEV Based on improved Particle Swarm Algorithm

2019 ◽  
Vol 118 ◽  
pp. 02005
Author(s):  
Ying Ai ◽  
Yuanjie Gao ◽  
dongsheng Liu
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Controller ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Energy Management Strategy ◽  
Multi Objective

Hybrid electric vehicle fuel consumption and emissions are closely related to its energy management strategy. A fuzzy controller of energy management using vehicle torque request and battery state of charge (SOC) as inputs, engine torque as output is designed in this paper foe parallel hybrid electric vehicle. And a multi-objective mathematical function which purpose on maximize fuel economy and minimize emissions is also established, in order to improve the adaptive ability and the control precision of basic fuzzy controller, this paper proposed an improved particle swarm algorithm that based on dynamic learning factor and adaptive inertia weight to optimize the control parameters. Simulation results based on ADVISOR software platform show that the optimized energy management strategy has a better distribution of engine and motor torque, which helps to improved the vehicle’s fuel economy and exhaust emission performance.

scholarly journals Adaptive Rule-Based Energy Management Strategy for a Parallel HEV

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4472 ◽  
Author(s):  
Rishikesh Mahesh Bagwe ◽  
Andy Byerly ◽  
Euzeli Cipriano dos Santos ◽  
Ben-Miled
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Duty Cycle ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Detailed Knowledge ◽  
Energy Management Strategy ◽  
Rule Based ◽  
Engine Efficiency ◽  
Efficiency Map

This paper proposes an Adaptive Rule-Based Energy Management Strategy (ARBS EMS) for a parallel hybrid electric vehicle (HEV). The aim of the strategy is to facilitate the aftermarket hybridization of medium- and heavy-duty vehicles. ARBS can be deployed online to optimize fuel consumption without any detailed knowledge of the engine efficiency map of the vehicle or the entire duty cycle. The proposed strategy improves upon the established Preliminary Rule-Based Strategy (PRBS), which has been adopted in commercial vehicles, by dynamically adjusting the regions of operations of the engine and the motor. It prevents the engine from operating in highly inefficient regions while reducing the total equivalent fuel consumption of the vehicle. Using an HEV model developed in Simulink®, both the proposed ARBS and the established PRBS strategies are compared over an extended duty cycle consisting of both urban and highway segments. The results show that ARBS can achieve high MPGe with different thresholds for the boundary between the motor region and the engine region. In contrast, PRBS can achieve high MPGe only if this boundary is carefully established from the engine efficiency map. This difference between the two strategies makes the ARBS particularly suitable for aftermarket hybridization where full knowledge of the engine efficiency map may not be available.

scholarly journals Research on energy management strategy considering battery life for plug-in hybrid electric vehicle

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879776 ◽  
Author(s):  
Jianjun Hu ◽  
Zhihua Hu ◽  
Xiyuan Niu ◽  
Qin Bai
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Battery Life ◽  
Energy Management Strategy ◽  
Driving Cycles ◽  
Comprehensive Test ◽  
Hybrid Electric

To improve the fuel efficiency and battery life-span of plug-in hybrid electric vehicle, the energy management strategy considering battery life decay is proposed. This strategy is optimized by genetic algorithm, aiming to reduce the fuel consumption and battery life decay of plug-in hybrid electric vehicle. Besides, to acquire better drive-cycle adaptability, driving patterns are recognized with probabilistic neural network. The standard driving cycles are divided into urban congestion cycle, highway cycle, and urban suburban cycle; the optimized energy management strategies in three representative driving cycles are established; meanwhile, a comprehensive test driving cycle is constructed to verify the proposed strategies. The results show that adopting the optimized control strategies, fuel consumption, and battery’s life decay drop by 1.9% and 3.2%, respectively. While using the drive-cycle recognition, the features of different driving cycles can be identified, and based on it, the vehicle can choose appropriate control strategy in different driving conditions. In the comprehensive test driving cycle, after recognizing driving cycles, fuel consumption and battery’s life decay drop by 8.6% and 0.3%, respectively.

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