State of Charge (SOC) and State of Health (SOH) estimation on lithium polymer battery via Kalman filter

Topan, Paris Ali; Ramadan, M. Nisvo; Fathoni, Ghufron; Cahyadi, Adha Imam; Wahyunggoro, Oyas

doi:10.1109/icstc.2016.7877354

Cited by 82 publications

(35 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section reviews some of the key issues that can affect the overall State of Health (SOH) of those batteries, e.g., the ability to deliver power compared to a new pack, and provides a review of several current research areas that help address the issues. Note, SOH is an important indicator of battery functionality that predicts the number of times the battery can be charged and discharged before its life is terminated [92,93].…”

Section: Li-ion Battery Lifespanmentioning

confidence: 99%

Current Li-Ion Battery Technologies in Electric Vehicles and Opportunities for Advancements

et al. 2019

View full text Add to dashboard Cite

Over the past several decades, the number of electric vehicles (EVs) has continued to increase. Projections estimate that worldwide, more than 125 million EVs will be on the road by 2030. At the heart of these advanced vehicles is the lithium-ion (Li-ion) battery which provides the required energy storage. This paper presents and compares key components of Li-ion batteries and describes associated battery management systems, as well as approaches to improve the overall battery efficiency, capacity, and lifespan. Material and thermal characteristics are identified as critical to battery performance. The positive and negative electrode materials, electrolytes and the physical implementation of Li-ion batteries are discussed. In addition, current research on novel high energy density batteries is presented, as well as opportunities to repurpose and recycle the batteries.

show abstract

Section: Li-ion Battery Lifespanmentioning

confidence: 99%

Current Li-Ion Battery Technologies in Electric Vehicles and Opportunities for Advancements

et al. 2019

View full text Add to dashboard Cite

show abstract

“…reduced by the KF algorithm because the recursive equations are repeatedly evaluated during system operation [96]. Recently, the EKF has attracted increasing attention and become one of the most commonly used methods to estimate the battery SoC even when the initial SoC is unknown [97].…”

Section: Adaptive Filter-based Estimation Methodsmentioning

confidence: 99%

“…The uncertainty value of SoC is reduced by the KF algorithm because the recursive equations are repeatedly evaluated during system operation [96].…”

Section: Adaptive Filter-based Estimation Methodsmentioning

confidence: 99%

“…As the KF has the observability of the system as a requirement, a virtual-measurement-based method can be implemented to account for a local loss of observability, like is presented in [91]. This approach uses the iterative process of the KF described in Figure 8 for estimating not only SoC but also other battery parameters, like the KF implemented in [96] estimates both SoC and SoH. It is common to find a combination of CC or OCV methods with the KF method to estimate SoC value, as is presented in [58,101].…”

Section: Real Batterymentioning

confidence: 99%

See 1 more Smart Citation

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

2017

View full text Add to dashboard Cite

Abstract:Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.

show abstract

“…Most manufacturers do not recommend reaching very low levels of SOC (i.e., lower than 10 percent) as this can cause permanent damage to the battery terminals and/or negatively impact the state-of-health of the battery more rapidly. The state-of-health refers to the ability of the battery to store and deliver energy compared with a new battery [13]. In other words, we note the compromise that must be exercised between the battery bank size (and hence cost), and ensuring the state-of-health of the batteries continues to be high for a longer period of time.…”

Section: Introductionmentioning

confidence: 99%

State-of-Charge Effects on Standalone Solar-Storage Systems in Hot Climates: A Case Study in Saudi Arabia

Elshurafa

Aldubyan

2019

Sustainability

View full text Add to dashboard Cite

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.

show abstract

State of Charge (SOC) and State of Health (SOH) estimation on lithium polymer battery via Kalman filter

Cited by 82 publications

References 8 publications

Current Li-Ion Battery Technologies in Electric Vehicles and Opportunities for Advancements

Current Li-Ion Battery Technologies in Electric Vehicles and Opportunities for Advancements

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

State-of-Charge Effects on Standalone Solar-Storage Systems in Hot Climates: A Case Study in Saudi Arabia

Contact Info

Product

Resources

About