A robust approach to state of charge assessment based on moving horizon optimal estimation considering battery system uncertainty and aging condition

2020 ◽  
Vol 270 ◽  
pp. 122508 ◽  
Author(s):  
Hongbin Ren ◽  
Hongwei Zhang ◽  
Zepeng Gao ◽  
Yuzhuang zhao
2019 ◽  
Vol 11 (12) ◽  
pp. 3471 ◽  
Author(s):  
Elshurafa ◽  
Aldubyan

In this paper, we quantify the economic and environmental implications of operating a standalone photovoltaic-battery system (PVB) while varying the battery’s minimum allowable state of charge (MSOC), the load profile, and simultaneously incorporating ambient temperature effects in hot climates. To that end, Saudi Arabia has been chosen for this case study. Over a project lifetime of 25 years, we find that, contrary to the widely accepted norm of 50% being a reasonable MSOC, a lower MSOC can bestow economic benefits. For example, a MSOC of 20% results in a lower number of batteries required throughout the lifetime of the project—while still meeting demand. For a village of 1000 homes, this translates to a saving of $47 million in net present value. Further, incorporating temperature effects results in deducing more realistic costs that are 125% higher than the ideal scenario (i.e., when temperature is not modeled). This difference stems from underestimating the actual number of batteries needed throughout the project lifetime. Compared to a diesel-powered microgrid, and for a village of 1000 homes, a PVB would, on an annual basis, avoid emitting 5000 tons of carbon and avoid burning 2 million liters of diesel.


2014 ◽  
Vol 953-954 ◽  
pp. 790-795
Author(s):  
Yuan Bin Yu ◽  
Zhou Cai ◽  
Kai Peng ◽  
Wen Qiang Lv

Accurate battery state of charge is the prerequisite and precondition for optimal control of hybrid vehicles. This article will be based on the established dynamic model of battery, estimate the battery state of charge in real time. Firstly, analysis the application limitations of Kalman filtering algorithm estimates battery state of charge. Secondly, for some uncertain parameters contained in the model of battery system, paper proposes a parameter line identification extended Kalman filter algorithm to estimate the battery state of charge. Finally, experimental verification algorithm dynamic conditions in the battery state of charge estimation accuracy and effectiveness.


2022 ◽  
Vol 70 (1) ◽  
pp. 67-78
Author(s):  
Daniel Lehmann ◽  
Diego Hidalgo Rodriguez ◽  
Michel Brack

Abstract In the decentralized renewable driven electric energy system, economically viable battery systems become increasingly important for providing grid-related services. End of 2016, STEAG has successfully started the commercial operation of six 15 MW large scale battery systems which have been incorporated in STEAG’s primary control pool. During the commissioning phase, extensive effort has been spent in optimizing the operational efficiency of these systems with promising results. However, the operation experience has shown that there is still significant potential for improving the system behavior as well as reducing the aging of the battery cells. By analyzing historical data of the charging power associated with the state of charge management, opportunities for significantly reducing the operational costs have been identified. By means of more involved model-based control strategies, which adequately consider the specific characteristics of the battery system, and by using mathematical optimization and artificial intelligence, adapting the state of charge management strategy to new applications, these additional cost savings can be obtained. Apart from giving insights into the operational experience with large scale battery systems, the contribution of this paper lies in proposing strategies for reducing the operational costs of the battery system using classical approaches as well as mathematical optimization and neural networks. These approaches will be illustrated by simulation results.


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