Energy management strategy for battery/supercapacitor hybrid electric city bus based on driving pattern recognition

The rapid development of cloud techniques like Vehicle-to-Cloud (V2C) makes it possible to gather more information and develop computationally efficient energy management systems (EMS) for electric vehicles. This paper proposes a novel EMS with low computational cost targeting hybrid battery/ultraca...

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Bibliographic Details
Published in:Energy (Oxford) Vol. 243; p. 122752
Main Authors: Shi, Junzhe, Xu, Bin, Shen, Yimin, Wu, Jingbo
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 15.03.2022
Elsevier BV
Subjects:
Algorithms
batteries
Buses
Buses (vehicles)
Cloud computing
Cluster analysis
Clustering
Computational efficiency
Computer applications
Computing costs
Degradation
Driving conditions
Driving pattern recognition
Dynamic programming
Electric vehicles
electrochemical capacitors
energy
Energy consumption
Energy management
Energy management systems
Hybrid energy storage
Optimal control
Pattern recognition
Power consumption
Unsupervised learning
Vector quantization
Vehicle-to-cloud connectivity
Vehicle-to-cloud connectivity
Hybrid energy storage
Dynamic programming
Driving pattern recognition
Unsupervised learning
ISSN:0360-5442, 1873-6785
Online Access:Get full text
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Summary:The rapid development of cloud techniques like Vehicle-to-Cloud (V2C) makes it possible to gather more information and develop computationally efficient energy management systems (EMS) for electric vehicles. This paper proposes a novel EMS with low computational cost targeting hybrid battery/ultracapacitor electric buses to reduce energy consumption and battery life degradation. In the offline training process, by applying the K-means clustering method with 10 selected features, 16 typical driving conditions are classified. For each driving condition, dynamic programming is employed offline to generate global optimal results, which are then used in control rule extraction for online operation. During the online operation, the proposed EMS executes the designed driving pattern recognition algorithm with V2C assistance to select optimal control rules. The simulation results indicate that the proposed EMS effectively decreases the battery degradation and energy consumption cost by 13.89%, compared with the conventional rule-based strategy. In addition, it is shown that V2C assistance leads to a 6.81% lower cost. Besides, the robustness of the proposed EMS is validated by testing the EMS with highly randomized input with uncertainties up to 15% and long duration of V2C data packet loss up to 10 s. •16 power profile segments are selected from 7208 different power profile segments using the K-means clustering method.•16 optimal control rules based on selected power profiles are extracted from offline DP results.•A practical cloud-based EMS of hybrid-electric city buses considering driving pattern recognition is proposed.•The proposed EMS is evaluated based on electric consumption cost, battery degradation cost, and computational cost.•The proposed EMS provides 13.89% fewer cost than the traditional rule-based method.
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2021.122752