Big-Data Based Online State of Charge Estimation and Energy Consumption Prediction for Electric Vehicles

Zhang, Zhiyun; Huang, Miaohua; Chen, Yupu; Gao, D. M.

doi:10.4271/2016-01-1200

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…In further research articles, proposed algorithms rely on live-information such as traffic or weather, which is normally not available in the vehicle without some sort of connectivity. 21,23,[32][33][34][35] However, system architecture and performance are not investigated.…”

Section: Range Estimation Routing and Charge Planningmentioning

confidence: 99%

“…In the articles, the system architectures are only vaguely described and the feasibility of the proposed concepts regarding system performance is not investigated. In further research articles, proposed algorithms rely on live‐information such as traffic or weather, which is normally not available in the vehicle without some sort of connectivity 21,23,32‐35 . However, system architecture and performance are not investigated.…”

Section: Related Workmentioning

confidence: 99%

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Evaluating system architectures for driving range estimation and charge planning for electric vehicles

Thorgeirsson

Vaillant²,

Scheubner

et al. 2020

Softw Pract Exp

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Due to sparse charging infrastructure and short driving ranges, drivers of battery electric vehicles (BEVs) can experience range anxiety, which is the fear of stranding with an empty battery. To help eliminate range anxiety and make BEVs more attractive for customers, accurate range estimation methods need to be developed. In recent years, many publications have suggested machine learning algorithms as a fitting method to achieve accurate range estimations. However, these algorithms use a large amount of data and have high computational requirements. A traditional placement of the software within a vehicle's electronic control unit could lead to high latencies and thus detrimental to user experience. But since modern vehicles are connected to a backend, where software modules can be implemented, high latencies can be prevented with intelligent distribution of the algorithm parts. On the other hand, communication between vehicle and backend can be slow or expensive. In this article, an intelligent deployment of a range estimation software based on ML is analyzed. We model hardware and software to enable performance evaluation in early stages of the development process. Based on simulations, different system architectures and module placements are then analyzed in terms of latency, network usage, energy usage, and cost. We show that a distributed system with cloud-based module placement reduces the end-to-end latency significantly, when compared with a traditional vehicle-based placement. Furthermore, we show that network usage is significantly reduced. This intelligent system enables the application of complex, but accurate range estimation with low latencies, resulting in an improved user experience, which enhances the practicality and acceptance of BEVs.

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Section: Range Estimation Routing and Charge Planningmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Evaluating system architectures for driving range estimation and charge planning for electric vehicles

Thorgeirsson

Vaillant²,

Scheubner

et al. 2020

Softw Pract Exp

View full text Add to dashboard Cite

show abstract

“…Zhang, et al proposed a big data-based algorithm to build a battery pack dynamic model and a probabilistic model for energy consumption prediction [11].…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation and Prediction Algorithm Suggestion for Determining SOC of Lithium Polymer Battery in a Parallel Hybrid Electric Vehicle

et al. 2018

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The necessity of hybrid vehicles and electric vehicles is widely known for reasons such as fossil fuel depletion, climate change, emission norms mandated by regulations, and so on. Expansion of the hybrid vehicle market is a realistic way to respond to fuel efficiency regulations. Hybrid electric vehicles are continuously challenged to meet cross-attribute performance while minimizing energy usage and component cost in a highly competitive automotive market. Current optimization strategy for a parallel hybrid requires much computational time and relies heavily on the drive cycle to accurately represent driving conditions in the future. With increasing application of the lithium-ion battery technology in the automotive industry, development processes and validation methods for the battery management system (BMS) have attracted attention. The purpose of this study is to propose an algorithm to analyze charging characteristics and improve accuracy for determining state of charge (SOC), the equivalent of a fuel gauge for the battery pack, during the regenerative braking period of a TMED type parallel hybrid electric vehicle.

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Density-based clustering multiple linear regression model of energy consumption for electric vehicles

Chen

Huang

Tao

2022

Sustainable Energy Technologies and Assessments

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Big-Data Based Online State of Charge Estimation and Energy Consumption Prediction for Electric Vehicles

Cited by 3 publications

References 11 publications

Evaluating system architectures for driving range estimation and charge planning for electric vehicles

Evaluating system architectures for driving range estimation and charge planning for electric vehicles

Experimental Evaluation and Prediction Algorithm Suggestion for Determining SOC of Lithium Polymer Battery in a Parallel Hybrid Electric Vehicle

Density-based clustering multiple linear regression model of energy consumption for electric vehicles

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