Design of Minimum Fuel Consumption Energy Management Strategy for Hybrid Marine Vessels with Multiple Diesel Engine Generators and Energy Storage

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.

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Energy management strategy for super capacitor energy storage system based on phase shifted full bridge converter

2018 IEEE International Power Electronics and Application Conference and Exposition (PEAC) ◽

10.1109/peac.2018.8590445 ◽

2018 ◽

Cited By ~ 1

Author(s):

Lujun Wang ◽

Jiong Guo ◽

Feng Ji

Keyword(s):

Energy Storage ◽

Energy Management ◽

Management Strategy ◽

Storage System ◽

Energy Storage System ◽

Energy Management Strategy ◽

Super Capacitor

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

Advances in Mechanical Engineering ◽

10.1177/1687814018797766 ◽

2018 ◽

Vol 10 (9) ◽

pp. 168781401879776 ◽

Cited By ~ 2

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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