ACCOUNTING CALENDAR AND CYCLIC AGEING FACTORS IN DIAGNOSTIC AND PROGNOSTIC MODELS OF SECOND-LIFE EV BATTERIES APPLICATION IN ENERGY STORAGE SYSTEMS
The rapid expansion of the electric vehicle market has significantly increased the demand for lithium-ion batteries, posing challenges for manufacturers and policymakers regarding efficient use and recycling. When these batteries reach the end of their primary lifecycle, their repurposing for second...
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General Energy Institute of the National Academy of Sciences of Ukraine
2024
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System Research in Energy| _version_ | 1871104371137183744 |
|---|---|
| author | Kostenko, Ganna |
| author_facet | Kostenko, Ganna |
| author_institution_txt_mv | [
{
"author": "Ganna Kostenko",
"institution": null
}
] |
| author_sort | Kostenko, Ganna |
| baseUrl_str | https://systemre.org/index.php/journal/oai |
| collection | OJS |
| datestamp_date | 2026-07-18T12:57:48Z |
| description | The rapid expansion of the electric vehicle market has significantly increased the demand for lithium-ion batteries, posing challenges for manufacturers and policymakers regarding efficient use and recycling. When these batteries reach the end of their primary lifecycle, their repurposing for secondary applications such as energy storage becomes critical to addressing environmental and resource management issues. This paper focuses on applying second-life batteries in energy storage systems, emphasizing the importance of accounting for calendar and cyclic aging factors to optimize battery performance and longevity. Calendar aging refers to the degradation that occurs over time due to chemical reactions within the battery, even when it is not in use. This type of aging is influenced by temperature, state of charge (SOC), and storage conditions. Cyclic aging, on the other hand, results from repeated charging and discharging cycles, which cause mechanical and chemical changes within the battery, leading to capacity fade and increased internal resistance. The combined effects of these aging processes necessitate the development of high-precision diagnostic and prognostic models to manage the performance and longevity of second-life batteries effectively. In Ukraine, the adoption of electric vehicles is accelerating, leading to an influx of used electric vehicles. This situation necessitates the prompt development of strategies for repurposing these batteries for energy storage applications. The complexities associated with final recycling processes make secondary use an attractive interim solution. By repurposing used EV batteries, Ukraine can mitigate immediate challenges related to battery waste and resource scarcity while supporting the transition to renewable energy sources. This paper highlights the need for an integral degradation index (DI) that combines calendar and cyclic aging factors with stochastic influences to provide a comprehensive measure of battery health. Such an index is essential for optimizing battery management practices, including the scheduling of charging and discharging cycles, to extend the operational life of secondary batteries. The study also presents practical recommendations for implementing these models in various energy storage scenarios, ranging from residential solar energy systems to industrial grid support and electric vehicle charging stations. By adopting optimized battery management strategies, the potential for extending the lifespan of secondary batteries and reducing operational costs is significant. This approach supports sustainable energy practices and aligns with global efforts to promote renewable energy sources and circular economy principles. |
| doi_str_mv | 10.15407/srenergy2024.03.021 |
| first_indexed | 2026-03-24T02:03:21Z |
| format | Article |
| fulltext |
Системні дослідження в енергетиці. 2024. 3(79) 21
МОДЕЛЮВАННЯ, ОПТИМІЗАЦІЯ
ТА ПРОГНОЗУВАННЯ В ЕНЕРГЕТИЦІ
____________________________________________________________
ISSN 2786-7102 (Online), ISSN 2786-7633 (Print)
https://doi.org/10.15407/srenergy2024.03.021
UDC 621.311:621.33
Ganna Kostenko, https://orcid.org/0000-0002-8839-7633
General Energy Institute of NAS of Ukraine, 172, Antonovycha St., Kyiv, 03150, Ukraine
e-mail: Kostenko_HP@nas.gov.ua
_______________________________________________________________________________________
ACCOUNTING CALENDAR AND CYCLIC AGEING FACTORS IN
DIAGNOSTIC AND PROGNOSTIC MODELS OF SECOND-LIFE
EV BATTERIES APPLICATION IN ENERGY STORAGE SYSTEMS
Abstract. The rapid expansion of the electric vehicle market has significantly increased the demand for
lithium-ion batteries, posing challenges for manufacturers and policymakers regarding efficient use and
recycling. When these batteries reach the end of their primary lifecycle, their repurposing for secondary
applications such as energy storage becomes critical to addressing environmental and resource management
issues. This paper focuses on applying second-life batteries in energy storage systems, emphasizing the
importance of accounting for calendar and cyclic aging factors to optimize battery performance and
longevity. Calendar aging refers to the degradation that occurs over time due to chemical reactions within
the battery, even when it is not in use. This type of aging is influenced by temperature, state of charge (SOC),
and storage conditions. Cyclic aging, on the other hand, results from repeated charging and discharging
cycles, which cause mechanical and chemical changes within the battery, leading to capacity fade and
increased internal resistance. The combined effects of these aging processes necessitate the development of
high-precision diagnostic and prognostic models to manage the performance and longevity of second-life
batteries effectively. In Ukraine, the adoption of electric vehicles is accelerating, leading to an influx of used
electric vehicles. This situation necessitates the prompt development of strategies for repurposing these
batteries for energy storage applications. The complexities associated with final recycling processes make
secondary use an attractive interim solution. By repurposing used EV batteries, Ukraine can mitigate
immediate challenges related to battery waste and resource scarcity while supporting the transition to
renewable energy sources. This paper highlights the need for an integral degradation index (DI) that
combines calendar and cyclic aging factors with stochastic influences to provide a comprehensive measure
of battery health. Such an index is essential for optimizing battery management practices, including the
scheduling of charging and discharging cycles, to extend the operational life of secondary batteries. The
study also presents practical recommendations for implementing these models in various energy storage
scenarios, ranging from residential solar energy systems to industrial grid support and electric vehicle
charging stations. By adopting optimized battery management strategies, the potential for extending the
lifespan of secondary batteries and reducing operational costs is significant. This approach supports
sustainable energy practices and aligns with global efforts to promote renewable energy sources and circular
economy principles.
Keywords: Lithium-Ion Battery, Electric Vehicle, Energy Storage, Battery Degradation, Calendar Ageing,
Cyclic Ageing, Integral Degradation Index, Remaining Useful Life, State of Health.
1. Introduction
The expansion of the electric vehicle market is increasing the demand for lithium-ion batteries, which
poses challenges for manufacturers and policymakers to use and recycle these resources. Once their primary life
cycle is complete, depleted batteries carry environmental and social risks due to toxic emissions. It is predicted
that by 2030, about 75 % of used batteries from electric vehicles can be used in energy storage systems, creating
opportunities for their reuse and contributing to sustainable resource management [1–4]. The importance of using
secondary electric vehicle (EV) batteries lies in the growing need for sustainable energy solutions. As the
adoption of electric vehicles increases globally, a significant number of batteries will reach the end of their
primary automotive life. Repurposing these batteries for energy storage systems (ESS) not only extends their
Системні дослідження в енергетиці. 2024. 3(79) 22
lifecycle but also addresses critical issues such as resource efficiency, waste reduction, and the high costs
associated with manufacturing new batteries. This practice contributes to a circular economy, where resources
are reused and recycled, reducing the environmental impact of battery disposal.
In Ukraine, the development of electric transport is progressing rapidly [5–7], and the market is seeing an
influx of used electric vehicles [8, 9]. Consequently, the issue of secondary use of these batteries will arise sooner
than in many other countries. This urgency is compounded by the complexities associated with the final recycling
of batteries. By repurposing used EV batteries for energy storage, Ukraine can address immediate challenges
related to battery waste and resource scarcity while supporting the transition to renewable energy sources.
Estimated forecast of secondary EV batteries availability in Ukraine is represented in Fig. 1.
To this end, global automakers are implementing initiatives to repurpose used batteries for power systems,
which improves the integration of renewable energy sources and ensures efficient use of energy. In this context,
special attention should be paid to the analysis of the mechanisms of battery degradation, which determine both
calendar and cyclic aging. High temperatures and state of charge are critical factors contributing to the rapid
decline in battery efficiency and capacity, and understanding these processes is key to developing strategies for
their effective repurposing and utilization [10–13].
Figure 1. Estimated forecast of secondary EV batteries availability in Ukraine
Accurate diagnostic and prognostic models for secondary energy storage systems are essential to
maximize the performance and lifespan of repurposed EV batteries [14–16]. These models must account for
factors such as calendar aging and cyclic aging, which significantly impact battery degradation. By considering
these factors, the models can provide a more precise prediction of the battery's remaining useful life (RUL) and
state of health (SOH). Such detailed modeling is vital for various applications, from residential and commercial
energy storage to grid stability and electric vehicle charging infrastructure. Developing and implementing these
advanced models is key to unlocking the full potential of secondary EV batteries, thereby supporting both global
and national energy sustainability goals.
The purpose of this article is to formalize an integral degradation index, which combines the effects of
both calendar and cyclic aging, is crucial for a comprehensive assessment. This index helps in optimizing usage
patterns and maintenance schedules, ensuring that the secondary batteries perform reliably and efficiently over
their extended lifecycle. develop diagnostic and prognostic models for optimizing the use of second-life EV
batteries in energy storage systems. The article focuses on considering factors such as calendar aging, cyclic
aging, and the introduction of an integral degradation index, which allows for more accurate assessments of the
RUL and SOH of lithium-ion batteries.
Системні дослідження в енергетиці. 2024. 3(79) 23
2. Methods and Materials
There is a wealth of research dedicated to various aspects of lithium-ion batteries, both in EVs and
stationary energy storage systems. These studies cover a wide range of topics, including but not limited to, the
following:
Battery Chemistry and Materials: Research in this area focuses on the development and optimization of
electrode materials, electrolytes, and other components to enhance the performance, safety, and longevity of
lithium-ion batteries [17–21]. Innovations in battery chemistry, such as the use of advanced materials like silicon
anodes and solid-state electrolytes, aim to increase energy density and reduce degradation rates.
Battery Management Systems (BMS): Effective battery management systems are crucial for monitoring
and controlling the performance of lithium-ion batteries [18, 22–24]. Studies explore advanced BMS algorithms
that optimize charging and discharging cycles, balance cell performance, and extend battery lifespan. These
systems play a key role in ensuring the safety and reliability of batteries in both EVs and stationary applications
[25, 26].
Aging and Degradation Mechanisms: Understanding the aging and degradation processes in lithium-ion
batteries is critical for predicting their lifespan and performance [10, 27–30]. Research in this area investigates
the effects of cyclic and calendar aging, temperature, state of charge (SOC), depth of discharge (DOD), and other
operational factors on battery health. These studies provide insights into the physical and chemical changes that
occur over time, helping to develop better models for predicting battery life.
Thermal Management: Efficient thermal management is essential for maintaining the performance and
safety of lithium-ion batteries [10, 25, 26]. Studies examine various cooling and heating strategies, thermal
management materials, and system designs to prevent overheating, reduce thermal gradients, and improve
overall battery efficiency. Thermal management is particularly important in high-power applications like EVs
and large-scale energy storage systems.
Safety and Reliability: Ensuring the safety and reliability of lithium-ion batteries is a major focus of
research [13, 14, 27]. This includes studying the causes and prevention of thermal runaway, developing safer
battery designs, and implementing robust safety protocols. Research also explores diagnostic tools and
techniques for early detection of potential failures and anomalies in battery systems.
Recycling and Second-Life Applications: With the growing adoption of lithium-ion batteries, there is
increasing interest in sustainable end-of-life solutions. Studies on battery recycling aim to recover valuable
materials and reduce environmental impact [31, 32]. Additionally, research on second-life applications explores
the repurposing of used EV batteries for stationary energy storage, examining their performance, economic
viability, and environmental benefits [33–38].
Electrochemical Modeling and Simulation: Advanced modeling and simulation techniques are used to
predict the behavior of lithium-ion batteries under various conditions [39–45]. These models help in
understanding complex electrochemical processes, optimizing battery design, and improving performance.
Research includes both empirical and physics-based models, which are essential for developing accurate battery
management systems and diagnostic tools.
Energy Storage System Integration: Integrating lithium-ion batteries into broader energy systems, such as
renewable energy grids and microgrids, is a significant area of research [32–37]. Studies focus on optimizing
the performance of battery storage systems in combination with solar, wind, and other renewable energy sources.
This includes investigating energy management strategies, grid stability, and economic implications of battery
storage integration.
These diverse research efforts collectively contribute to advancing the technology and application of
lithium-ion batteries, ensuring their effective use in both electric vehicles and stationary energy storage systems.
In secondary applications, such as stationary energy storage systems, the ability to accurately assess and predict
battery life allows for efficient management of battery life, ensuring the stability of power supply and optimizing
the efficiency of systems.
Системні дослідження в енергетиці. 2024. 3(79) 24
Understanding the operating modes and stressors that influence the acceleration of degradation (as shown
in Fig. 2) is critical for implementing tools to assess battery health and predict remaining useful life.
Figure 2. Framework for Predicting State of Health and Remaining Useful Life of Repurposed EV Batteries
A widely accepted classification divides the main modes of degradation operating in lithium-ion batteries
into three: loss of lithium supply; loss of active material in the electrodes and an increase in the internal resistance
of the element [10, 14, 17]. It is mostly associated with the consumption of lithium ions due to adverse reactions
such as the formation of a solid electrolyte layer on the surface of the graphite negative electrode, electrolyte
decomposition reactions, or lithium degradation reactions [36, 37]. Such adverse reactions irreversibly consume
lithium ions, making them unavailable for subsequent charge/discharge cycles. The loss of active material in the
electrodes usually arises from a combination of factors. One of them is the structural degradation of electrodes
due to volumetric changes in active materials during cycling. This causes mechanical stress, which leads to
cracks in the particles and a decrease in the density of lithium storage sites. Other factors include the chemical
decomposition and dissolution of transition metals in the electrolyte and the modification of the solid electrolyte
layer [38, 39]. An increase in resistance can be caused by the formation of parasitic phases, such as a layer of
solid electrolyte on the surface of the electrode, as well as a loss of electrical contact within the porous electrode.
Several failure mechanisms have been identified as leading to second-stage degradation, including lithium
plating, electrode saturation, resistance increase, and mechanical deformation. Neural networks have been
employed to forecast this degradation phase, generally assuming that capacity loss determines battery lifespan
and that aging models hold true until the battery's SOH drops to 70 %.
Calendar Aging depends on time and storage conditions (temperature, SOC) [10, 49]. The calendar aging
model can be described by the following equation:
𝑄𝑐𝑎𝑙𝑒𝑛𝑑𝑎𝑟(𝑡) = 𝑘𝑐𝑎𝑙𝑒𝑛𝑑𝑎𝑟 ⋅ 𝑒
𝐸𝑎
𝑅 𝑇 ⋅ 𝑡 ⋅ 𝑓(𝑆𝑂𝐶), (1)
where:
𝑄𝑐𝑎𝑙𝑒𝑛𝑑𝑎𝑟(𝑡) – loss of capacity due to calendar aging; 𝑘𝑐𝑎𝑙𝑒𝑛𝑑𝑎𝑟– reaction rate constant; 𝐸𝑎 – activation
energy; R – universal gas constant; T is the temperature; t is the time; f(SOC) is a function that describes the
dependence on the SOC.
In summary, both temperature and SOC directly impact battery calendar aging. The resulting capacity
fade and resistance increase are non-linear over time, indicating a strong interaction between these factors and
the aging behavior.
Cycling Aging depends on the number of charge/discharge cycles, depth of discharge (DOD), charging
rate (C-rate), and temperature [49]. The cyclic aging model can be described by the following equation:
𝑄𝑐𝑦𝑐𝑙𝑒(𝑡) = 𝑘𝑐𝑦𝑐𝑙𝑒 ⋅ (𝐷𝑂𝐷)𝛼 ⋅ (𝐶𝑟𝑎𝑡𝑒)𝛽 ⋅ 𝑒
𝐸𝑎
𝑅 𝑇 ⋅ 𝑁𝑐𝑦𝑐𝑙𝑒(𝑡), (2)
Системні дослідження в енергетиці. 2024. 3(79) 25
where: 𝑄𝑐𝑦𝑐𝑙𝑒(𝑡) – loss of capacity due to cyclic aging; 𝑘𝑐𝑦𝑐𝑙𝑒 – reaction rate constant for cyclic aging; DOD –
depth of discharge; Crate – charging/discharging speed; α and β – model parameters; 𝑁𝑐𝑦𝑐𝑙𝑒(𝑡) – the number
of charge/discharge cycles.
Additionally, high current peaks during charging or discharging can induce significant energy transfer,
which accelerates aging. These peaks can cause thermal and mechanical stress, leading to further degradation.
The proposed Integral Degradation Index (DI) takes into account not only calendar and cyclic aging, but
also random variables that simulate component in operating conditions, such as temperature, depth of discharge,
and others, and allows to obtain a more accurate estimate of the overall degradation of the battery, taking into
account various operational factors [46]. The inclusion of a stochastic component in the degradation index for
secondary batteries is crucial due to the unique characteristics and usage history of these batteries. Secondary
EV batteries have experienced diverse and often unpredictable usage patterns. This variability makes their
degradation behavior more complex and less predictable than that of new batteries. Unlike new batteries that
typically start their life under controlled and consistent conditions, secondary batteries have been subjected to
varied driving conditions, charging habits, and environmental exposures. These inconsistent operational
environments, including different temperatures, SOC, DOD, and charge/discharge rates, significantly impact the
degradation process. A stochastic component can account for these random and variable factors, providing a
more accurate and individualized assessment of the battery's current state.
Secondary batteries may exhibit unpredictable aging effects due to their complex history of cyclic and
calendar aging. The rate of degradation can vary significantly over time and may not follow a simple linear
pattern. Incorporating a stochastic element allows the degradation index to reflect these random aging effects,
improving the accuracy of RUL and SOH predictions. Moreover, the remaining capacity of secondary batteries
can vary widely due to the heterogeneous nature of their prior usage. Batteries with similar initial capacities may
degrade differently based on their usage history. The stochastic component helps address this variability,
providing a more realistic measure of the remaining capacity and the battery's ability to meet future energy
demands. This enhanced predictive accuracy is critical for optimizing battery management strategies, scheduling
maintenance, and planning for replacements. By reflecting the actual performance more closely, the stochastic
approach ensures that the degradation index is robust and applicable to real-world scenarios, thereby supporting
the efficient and reliable use of secondary batteries in energy storage systems.
Incorporating factors such as inconsistent operational environments, diverse usage histories, and
unpredictable aging effects, our integral degradation index is designed to provide an accurate and individualized
assessment of secondary batteries. This index reflects the random and variable factors impacting battery health,
enabling improved RUL and SOH predictions.
𝐷𝐼(𝑇) = 𝑄𝑡𝑜𝑡𝑎𝑙(𝑡) = 𝑄𝑐𝑎𝑙𝑒𝑛𝑑𝑎𝑟(𝑡) + 𝑄𝑐𝑦𝑐𝑙𝑒(𝑡) ± 𝛥𝑄(𝑡)
= 𝑘𝑐𝑎𝑙𝑒𝑛𝑑𝑎𝑟 ⋅ 𝑒
𝐸𝑎
𝑅 𝑇 ⋅ 𝑡 ⋅ 𝑓(𝑆𝑂𝐶) + 𝑘𝑐𝑦𝑐𝑙𝑒 ⋅ (𝐷𝑂𝐷)𝛼 ⋅ (𝐶𝑟𝑎𝑡𝑒)𝛽 ⋅ 𝑒
𝐸𝑎
𝑅 𝑇 ⋅ 𝑁𝑐𝑦𝑐𝑙𝑒(𝑡) (3)
± √(
𝜕𝑄
𝜕𝑇
∆𝑇)
2
+ (
𝜕𝑄
𝜕𝑆𝑂𝐶
∆𝑆𝑂𝐶)
2
+ (
𝜕𝑄
𝜕𝐷𝑂𝐷
∆𝐷𝑂𝐷)
2
+ (
𝜕𝑄
𝜕𝐶𝑟𝑎𝑡𝑒
∆𝐶𝑟𝑎𝑡𝑒)
2
where: 𝑄𝑡𝑜𝑡𝑎𝑙(𝑡) – total loss of battery capacity, taking into account the factors of calendar and cyclic
aging; 𝛥𝑄(t) – taking into account the uncertainty of the state/behaviour of secondary batteries under operating
conditions; ∆T, ∆𝑆𝑂𝐶, ∆𝐷𝑂𝐷, ∆𝐶𝑟𝑎𝑡𝑒 are random variables, respectively, temperature, state of charge, depth of
discharge, charge/discharge rate with a certain distribution (e.g., normal), derivatives represent
𝜕𝑄
𝜕𝑇
𝜕𝑄
𝜕𝑆𝑂𝐶
,
𝜕𝑄
𝜕𝐷𝑂𝐷
,
𝜕𝑄
𝜕𝐶𝑟𝑎𝑡𝑒
the sensitivity of the battery capacity to changes in each of these parameters.
By integrating a stochastic component, we account for the complexities and variability inherent in the
prior usage of secondary batteries, ensuring a realistic measure of their remaining capacity and performance
capabilities. This approach enhances predictive accuracy, optimizes battery management strategies, and supports
the efficient and reliable application of secondary batteries in energy storage systems.
Системні дослідження в енергетиці. 2024. 3(79) 26
This indicator can be used to develop and adjust optimal battery charging and discharging modes,
minimizing the impact of adverse factors and extending the life cycle of the battery. In empirical battery aging
studies, data is usually collected under constant storage or cycling conditions. However, real-world applications
often involve variable conditions. Historical charging and discharging patterns, known as path dependency,
significantly impact battery performance and characteristics. Considering path dependency makes testing more
complex, requiring dynamic cyclic aging tests with fluctuating SOC, Depth of Discharge (DOD), temperature,
and discharge rates. Previous research indicates that path dependency is particularly relevant for lithium-ion
batteries under high C-rate conditions.
The Remaining Useful Life (RUL) of a battery is a critical metric that indicates how long a battery can
continue to operate effectively before it reaches a specified critical state of health (SOH). Knowing the initial
SOH and the critical SOH value, we can calculate the RUL using the following formula:
𝑅𝑈𝐿 =
𝑆𝑂𝐻0−𝑆𝑂𝐻𝑐𝑟𝑖𝑡𝑖𝑐𝑎𝑙
𝐷𝐼
(4)
where: 𝑆𝑂𝐻0 – the initial state of health of the battery when it starts its secondary use; 𝑆𝑂𝐻𝑐𝑟𝑖𝑡𝑖𝑐𝑎𝑙 – the critical
state of health at which the battery is considered no longer effective for the intended application, 𝐷𝐼 – the
degradation index, which represents the rate of degradation per unit time.
The equation 4 helps to determine the number of years or cycles the battery can still be useful before
reaching the critical SOH.
Capacity Loss is an essential measure that shows the decrease in the battery's capacity over time due to
degradation. It can be calculated using the following formula:
𝐿𝑜𝑠𝑠 = 𝑆𝑂𝐻0 − (𝑆𝑂𝐻0 × (1 − 𝐷𝐼 × 𝑡)) (5)
The equation 5 calculates the difference between the initial capacity and the reduced capacity after a
certain period or number of cycles, indicating the extent of capacity loss due to degradation over time.
Remaining Cycles Estimation refers to the total number of full charge-discharge cycles a battery can
undergo before it reaches the end of its useful life. It can be determined using the degradation index:
𝐶𝑦𝑐𝑙𝑒𝑠 =
𝑆𝑂𝐻0−𝑆𝑂𝐻𝑐𝑟𝑖𝑡𝑖𝑐𝑎𝑙
𝐷𝐼 ×𝐷𝑂𝐷
(6)
In essence, the equation 6 can be understood as the remaining useful life (RUL) of the battery, determined
by the difference between the initial and critical SOH, divided by the product of the degradation index (DI) and
the depth of discharge (DOD). This approach allows for a more precise estimation of battery cycles by factoring
in how deeply the battery is discharged in each cycle and the overall rate of degradation.
These formulas are essential for evaluating the performance and longevity of secondary batteries in
various scenarios. They allow for precise calculations and predictions about the battery's useful life, capacity
loss over time, and the expected number of cycles, facilitating effective planning and management of battery
storage systems.
3. Results and discussions
The scenario approach in this work allows for a detailed study of the various operating conditions of
secondary batteries and the development of optimal strategies for their use. Each scenario is characterized by
unique technical and economic features that affect the battery's operating mode, aging, and residual capacity.
Taking these differences into account enables the creation of more accurate forecasting and management models,
which contribute to maximizing the efficiency of battery use in specific conditions.
In Table 1, the scenarios for the use of secondary electric vehicle (EV) batteries in various energy storage
applications are presented. Each scenario is characterized by specific calendar and cyclic aging factors, typical
battery capacities, and unique charging/discharging features. The scenarios range from residential self-
consumption to commercial and industrial energy storage, island installations, electric vehicle fast charging,
hybrid power systems for power and frequency regulation, and hybrid systems for local communities. This
detailed categorization helps to understand the different operational demands and aging characteristics that
secondary EV batteries must withstand, ensuring their optimal utilization and extended lifecycle in diverse
energy storage applications.
Системні дослідження в енергетиці. 2024. 3(79) 27
Table 1. General Characteristics of Calendar and Cyclic Aging for Different Scenarios
Scenario Calendar
Aging
Cyclic
Aging
Typical
Battery
Capacity
(kWh)
Charging/Discharging Features
Scenario 1 Residential consumers
with rooftop PV
Low High 20-50 High cycle frequency due to daily
charging/discharging cycles
Scenario 2 Commercial storage
systems with distributed
generation
Medium High 50-100 Regular charging/discharging
dependent on generation and
consumption
Scenario 3 Grid storage with solar
generation
Medium High 500 Intensive use during the daytime
solar generation cycle
Scenario 4 Grid storage with wind
generation
Medium Medium 500 Variable cycles depending on
unpredictable wind generation
Scenario 5 Fast charging stations for
electric vehicles
Low Very high 200 Very fast charging/discharging to
provide high power
Scenario 6 Storage for frequency
regulation
High Low 100 Mostly maintaining grid stability
with rare charging/discharging
cycles
Scenario 7 Storage for power
regulation
High Medium 150 More frequent charging/discharging
cycles for power regulation
Scenario 8 Storage for self-
sufficiency of local
communities
Medium High 200-300 Combination of
charging/discharging depending on
consumer needs and generation
In Table 2, the specific parameters for each scenario are outlined, including initial and final state of health,
initial depth of discharge, charge/discharge rate, and average state of charge. These parameters provide a detailed
view of the operational conditions and performance expectations for secondary EV batteries in various energy
storage applications. By understanding these parameters, it becomes possible to tailor the management and
maintenance strategies for each scenario, ensuring the effective and prolonged use of repurposed EV batteries in
different contexts.
Table 2. Scenario-Specific Parameters for Secondary EV Battery Application
Scenario Initial SOH (%) Final SOH (%) Initial DOD
(%)
C-rate Average SOC
(%)
Scenario 1 80 40 85 C/20 50
Scenario 2 80 60 85 C/10 60
Scenario 3 80 60 70 C/5 70
Scenario 4 80 60 70 C/5 70
Scenario 5 80 60 90 1.5C 85
Scenario 6 80 60 50 C/2 40
Scenario 7 80 60 60 C 50
Scenario 8 80 40 80 C/3 60
Each scenario describes a specific application of secondary EV batteries, highlighting the corresponding
strategy, detailed description, and expected impact. By implementing these tailored strategies, operators can
optimize the performance and longevity of repurposed EV batteries, ensuring their efficient utilization in various
energy storage contexts. The table provides a comprehensive overview of the practical measures that can be
taken to address the unique demands and aging characteristics of batteries in different scenarios.
Table 3 compares the regular operating parameters with optimized parameters to demonstrate how
effective management can significantly extend the lifespan of secondary batteries.
Системні дослідження в енергетиці. 2024. 3(79) 28
Table 3. Comparative Analyses of Regular and Enhanced (Optimized) Operating Modes Efficiency
Scenario Regular operating mode Enhanced (optimized) operating mode
DI
RUL
(years)
Capacity
Loss
(%/year)
Cycle Count
(cycles/year)
DI
RUL
(years)
Capacity
Loss
(%/year)
Cycle Count
(cycles/year)
Scenario 1 0.020 5.0 1.60 365 0.010 10.0 0.80 200
Scenario 2
0.018 6.7 1.20 300 0.010
10.0
0.80 150
Scenario 3 0.019 5.3 1.40 250 0.011 9.1 0.90 150
Scenario 4 0.017 7.1 1.00 200 0.010 10.0 0.80 150
Scenario 5 0.021 4.8 1.70 400 0.012 8.3 1.00 200
Scenario 6 0.015 8.0 0.80 100 0.008 12.5 0.60 100
Scenario 7 0.016 7.5 0.90 150 0.009 11.1 0.72 120
Scenario 8 0.018 6.7 1.20 250 0.010 10.0 0.80 150
The Table 4 outlines strategies for extending the remaining useful life and reducing capacity loss of
secondary EV batteries through effective operational management.
Table 4. Strategies for Extending RUL and Reducing Capacity Loss through Operational Management
Scenario Strategy Description Expected Impact
Scenario 1 Time-of-use optimization Align battery
charging/discharging with peak
PV generation and household
demand, limit DOD to 70–80 %
Extends RUL by reducing full
cycles per year and lowers
capacity loss with moderate
DOD levels
Scenario 2 Peak shaving and load
shifting
Charge during off-peak hours,
discharge during peak times,
maintain DOD at 70–80 %,
controlled charging rate (C/10)
Extends RUL by preventing
excessive DOD and high current
rates, reduces capacity loss
Scenario 3 Daytime cycling with SOC
stabilization
Charge during high solar
generation, discharge during
peak usage, maintain SOC range
40–80 %, avoid deep discharges
Enhances RUL by preventing
deep discharges, reduces
capacity loss by maintaining
stable SOC
Scenario 4 Variable load management
with controlled cycling
Predict wind patterns, manage
charging/discharging, limit
DOD to 60–70 %, avoid high-
rate discharges
Prolongs RUL by adapting to
variable wind patterns, reduces
capacity loss through moderate
DOD and rate limits
Scenario 5 High-power buffering and
managed charging
Use battery as buffer for peak
loads, manage charging to avoid
overloading, reduce DOD to
80 %, use active cooling
Extends RUL by reducing
thermal and cyclic stress, lowers
capacity loss with controlled
high-power management
Scenario 6 Minimal cycling with
responsive power
management
Keep battery at high SOC (50–
70 %), minimize full
charge/discharge cycles,
implement low-rate discharges
Increases RUL by minimizing
full cycles, reduces capacity loss
through strategic SOC
management
Scenario 7 Frequent but controlled
cycling with SOC balancing
Regular but controlled power
regulation, maintain SOC range
40–80 %, limit charge/discharge
rates (C/2 or C)
Prolongs RUL by preventing
deep discharges, reduces
capacity loss through balanced
SOC and controlled cycling
Scenario 8 Community-based energy
management with hybrid
generation support
Integrate battery storage with
solar and wind sources, maintain
SOC range 40–80%, control
DOD
Enhances battery performance,
extends RUL, reduces capacity
loss by maintaining stable SOC
and controlled DOD
These strategies focus on optimizing the operational parameters of secondary batteries, such as SOC range,
DOD, and charge/discharge rates, tailored to the specific requirements of each scenario. By implementing these
measures, the RUL can be extended, and capacity loss minimized, ensuring more efficient and sustainable battery
use.
Системні дослідження в енергетиці. 2024. 3(79) 29
The table presents various strategies for optimizing battery operation modes across eight energy storage
scenarios. Implementing these strategies can significantly extend battery life (RUL) and reduce capacity loss by
controlling DOD, stabilizing SOC, and managing thermal conditions. Some strategies are suitable for multiple
scenarios. For example, the peak shaving and load shifting strategy with controlled charging (Scenario 2) is
similar to the strategy for power regulation storage systems (Scenario 7), where regular but controlled cycling
with SOC balancing is utilized. Individual strategies are required for scenarios with specific operating conditions.
For instance, the high-power buffering and managed charging strategy (Scenario 5) is specifically designed for
fast charging stations for electric vehicles, where batteries are subjected to high thermal and cyclic stresses.
Additionally, scenarios for energy independence of local communities (Scenario 8) require integration with
hybrid generation sources and a corresponding management strategy to maintain stable SOC and controlled
DOD.
Fig. 3 illustrates the state of health degradation of batteries over time across eight different scenarios. Each
scenario represents a unique application of battery storage. The SOH is plotted against time, showing two
degradation strategies: a regular operating mode and an enhanced operating mode.
Figure 3. Second-Life Battery Health Degradation Over Time for Different Usage Scenarios with Regular and
Enhanced Strategies
In the regular operating mode, batteries are managed with standard charging and discharging practices. In
the enhanced operating mode, advanced strategies are employed to minimize degradation, such as limiting DOD,
maintaining optimal SOC ranges, and controlling charge/discharge rates. Each graph compares the SOH trends
for the regular and enhanced strategies over a specified period, highlighting the impact of these strategies on
extending battery life.
The graphs demonstrate a clear distinction between the regular and enhanced operating modes across all
scenarios. In Scenario 1 (residential consumers with rooftop PV), the regular mode shows a steep decline in
SOH, reaching the critical threshold much sooner than the enhanced mode. This pattern is consistent across all
scenarios, indicating the effectiveness of enhanced strategies in slowing down the degradation process.
For instance, in Scenario 5 (fast charging stations for electric vehicles), the regular mode results in a rapid
decrease in SOH due to high stress from frequent fast charging cycles. However, the enhanced mode significantly
extends the battery's useful life by mitigating these stresses. Similarly, in Scenarios 6 and 7, which involve
frequency and power regulation, the enhanced strategies maintain higher SOH levels over time compared to the
regular modes.
Overall, the enhanced strategies consistently improve battery longevity, making them essential for
applications requiring prolonged battery use. The comparative analysis underscores the importance of adopting
optimized battery management practices to enhance the performance and lifespan of energy storage systems.
Optimizing operational parameters such as DOD, SOC, C-rate, and maintaining a controlled temperature
environment can significantly extend the lifespan of secondary batteries from the regular 5–6 years to 10–15
Системні дослідження в енергетиці. 2024. 3(79) 30
years. This highlights the importance of effective battery management strategies to enhance both performance
and longevity.
Diagnostic models are essential for real-time monitoring and evaluation of the state of health (SOH) and
performance of secondary batteries. Applying optimized parameters is particularly important for secondary
batteries, which have already experienced significant use and therefore require precise management to maximize
their remaining lifespan and utility. Prognostic models predict the future state of secondary batteries, including
their RUL and capacity loss, based on historical and real-time data. The use of optimized parameters improves
the reliability of these predictions and is particularly important for secondary batteries to ensure their continued
viability.
4. Future Researches in Optimization of Operating Modes
Numerous studies in Ukraine have looked into various aspects of electric transportation, power generation,
energy usage, and energy storage systems [47–52]. These studies also consider factors like system resilience,
operational modes, and the roles of consumers and regulators. But there still is a significant gap in the exploration
of electric vehicle batteries and their potential for secondary applications as well as modes of their use.
Implementing the optimized parameters for secondary batteries (such as controlled DOD, SOC, C-rate, and
temperature) enables significant improvements in the management of these batteries. This approach allows for
the optimization of operating modes, ensuring that secondary batteries are used in the most efficient and effective
manner possible. Here’s how this optimization is achieved:
Improved Performance and Longevity: By maintaining DOD between 20–60 %, SOC within 40–80 %,
and C-rate in the range of C/10 to C/5, the stress on the battery is minimized. This results in a reduction of
degradation rates and prolongs the battery's useful life. Proper temperature control (15–25 °C) further ensures
that the batteries operate within an optimal thermal range, reducing thermal stress and chemical degradation.
Enhanced Reliability: Real-time monitoring and adjustment of the battery’s operating conditions ensure
that any deviations from the optimal parameters are promptly addressed. This enhances the reliability of the
battery system, preventing unexpected failures and maintaining consistent performance.
Cost Efficiency: Optimizing the operating modes reduces the frequency of battery replacements and
maintenance interventions. This leads to significant cost savings over the battery’s lifecycle. By extending the
lifespan of secondary batteries from 5–8 years to potentially 10–15 years, the overall cost efficiency of the energy
storage system is greatly improved.
Environmental Benefits: Extending the lifespan of secondary batteries reduces the need for new battery
production, thereby decreasing the environmental impact associated with battery manufacturing and disposal.
This contributes to more sustainable energy practices and aligns with global efforts to reduce carbon footprints.
Adaptability to Various Applications: Enhanced operating modes allow secondary batteries to be
effectively utilized in a range of applications, from residential solar energy storage to industrial grid support and
electric vehicle charging stations. This flexibility ensures that secondary batteries can meet the specific demands
of different energy storage scenarios.
By continuously monitoring the performance and degradation patterns of the batteries, operators can make
informed decisions about when to replace or repurpose the batteries, thereby maximizing their lifespan and
efficiency. By adopting these practices, energy storage systems can achieve greater reliability, reduce operational
costs, and contribute to a more sustainable energy infrastructure.
5. Conclusions
This study underscores the critical importance of diagnostic and prognostic models in managing the
operational regimes of secondary EV batteries used in energy storage systems. By incorporating factors such as
calendar and cyclic aging, these models provide a comprehensive framework for predicting the remaining useful
life and state of health of secondary batteries, which have already undergone significant usage.
The introduction of the integral degradation index (DI) is a key element in these models. By considering
multiple factors like Depth of Discharge, State of Charge, C-rate, and temperature, the DI offers a holistic
Системні дослідження в енергетиці. 2024. 3(79) 31
measure of battery degradation. This index helps in maintaining optimized parameters, which is crucial for
secondary batteries to ensure precise management and to maximize their remaining lifespan and utility. The
integral degradation index allows us to combine all aspects of battery aging, such as calendar and cyclic aging,
into one generalized parameter. This simplifies the analysis of the battery's health and allows you to adequately
assess its remaining lifespan. As a result, users and energy storage operators can get a holistic picture of battery
health without having to analyze many individual parameters. Taking into account stochastic factors in
degradation models will allow you to more accurately simulate the real operating conditions of batteries and
assess the impact of random changes on their condition.
Through real-time monitoring of DOD, SOC, C-rate, and temperature, diagnostic models ensure precise
insights into battery conditions, enabling timely interventions and optimal maintenance strategies. This is
particularly crucial for secondary batteries, which are more susceptible to accelerated degradation if not managed
properly. Integrating temperature sensors and employing machine learning algorithms for anomaly detection
further enhances the accuracy and reliability of these models. Prognostic models, on the other hand, offer
predictive analytics for RUL and capacity loss, utilizing historical and real-time data to forecast future battery
performance. By applying the integral degradation index (DI) and optimized parameters, these models can
simulate various usage scenarios, providing valuable insights for optimizing battery usage across different
applications. This approach not only extends the lifespan of secondary batteries but also reduces operational
costs and enhances overall system efficiency.
The strategies outlined for different scenarios demonstrate how tailored operational management can
significantly extend RUL and reduce capacity loss of secondary EV batteries. From residential self-consumption
with rooftop PV systems to industrial storage with solar and wind generation, each strategy emphasizes the
importance of maintaining optimal operational parameters to maximize battery utility and longevity.
In conclusion, the integration of advanced diagnostic and prognostic models, along with the use of the
integral degradation index (DI), is essential for the effective utilization of secondary EV batteries in energy
storage systems. These models ensure that secondary batteries operate under the most favorable conditions,
enhancing their performance and extending their useful life. As the demand for sustainable energy solutions
grows, the optimization of secondary battery usage through these models will play a pivotal role in achieving
energy resilience and sustainability.
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Системні дослідження в енергетиці. 2024. 3(79) 34
Theoretical and Applied Problems (ITTAP 2023). CEUR Workshop Proceedings, 3628, 43–54. URL: https://ceur-
ws.org/Vol-3628/paper4.pdf; Available at SSRN: URL: https://ssrn.com/abstract=4709501 (Last accessed:
18.05.2024).
УРАХУВАННЯ ФАКТОРІВ КАЛЕНДАРНОГО ТА ЦИКЛІЧНОГО
СТАРІННЯ В ДІАГНОСТИЧНИХ І ПРОГНОСТИЧНИХ
МОДЕЛЯХ ЗАСТОСУВАННЯ ВТОРИННИХ БАТАРЕЙ
ЕЛЕКТРОМОБІЛІВ В СИСТЕМАХ ЗБЕРІГАННЯ ЕНЕРГІЇ
Ганна Костенко, https://orcid.org/0000-0002-8839-7633
Інститут загальної енергетики НАН України, вул. Антоновича, 172, м. Київ, 03150, Україна
e-mail: Kostenko_HP@nas.gov.ua
Анотація. Швидке розширення ринку електромобілів значно збільшило попит на літій-іонні
акумулятори, що створює виклики для виробників та політиків щодо ефективного використання та
утилізації. Коли ці акумулятори досягають кінця свого первинного використання, їх повторне
застосування для зберігання енергії стає критично важливим для вирішення багатьох екологічних та
ресурсних проблем. Ця стаття зосереджена на вторинному застосуванні акумуляторів
електромобілів у системах зберігання енергії з урахуванням факторів календарного та циклічного
старіння для оптимізації продуктивності та довговічності акумуляторів. Календарне старіння
стосується деградації, що відбувається з часом через хімічні реакції в акумуляторі, навіть коли він
не використовується. На цей тип старіння впливають: температура, стан заряду та умови
зберігання. Циклічне старіння, з іншого боку, є результатом повторних циклів заряджання та
розряджання, що спричиняє механічні та хімічні зміни в акумуляторі, призводячи до зменшення
ємності та збільшення внутрішнього опору. Комбінований вплив цих процесів старіння вимагає
розробки високоточних діагностичних та прогнозних моделей для ефективного управління
продуктивністю та довговічністю вторинних акумуляторів. Розвиток ринку вживаних
електромобілів в Україні викликає необхідність швидкого розроблення стратегій для повторного
використання цих акумуляторів у системах зберігання енергії. Складнощі, пов’язані з остаточними
процесами утилізації, роблять повторне використання привабливим проміжним рішенням. Повторне
використання акумуляторів електромобілів дозволяє Україні пом’якшити наявні проблеми, пов’язані
з токсичними відходами при остаточній переробці, водночас підтримуючи перехід до відновлюваних
джерел енергії. У даній статті підкреслюється необхідність створення інтегрального індексу
деградації DI, що поєднує фактори календарного та циклічного старіння зі стохастичними впливами
для надання комплексної оцінки стану акумулятора. Такий показник є важливим для оптимізації
практик управління акумуляторами, включаючи планування циклів заряджання та розряджання для
продовження експлуатаційного терміну вторинних акумуляторів. Дослідження містить практичні
рекомендації щодо впровадження цих моделей у різних сценаріях зберігання енергії, від побутових
сонячних енергосистем до промислової підтримки мережі та станцій заряджання електромобілів.
Прийняття оптимізованих стратегій управління акумуляторами має значний потенціал для
продовження терміну служби вторинних акумуляторів та зниження операційних витрат. Такий
підхід підтримує сталу енергетику і відповідає глобальним зусиллям щодо сприяння відновлюваним
джерелам енергії та принципам циркулярної економіки.
Ключові слова: літій-іонні батареї, електромобіль, зберігання енергії, деградація батареї, календарне
старіння, циклічне старіння, інтегральний індекс деградації, залишковий термін корисного
використання, стан здоров’я батареї.
Надійшла до редколегії: 18.06.2024
https://ssrn.com/abstract=4709501
|
| id | systemreorg-article-856 |
| institution | System Research in Energy |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2026-07-19T01:23:02Z |
| publishDate | 2024 |
| publisher | General Energy Institute of the National Academy of Sciences of Ukraine |
| record_format | ojs |
| resource_txt_mv | systemreorg/a5/5e93854047b314e06419dd6cba72e7a5.pdf |
| spelling | systemreorg-article-8562026-07-18T12:57:48Z ACCOUNTING CALENDAR AND CYCLIC AGEING FACTORS IN DIAGNOSTIC AND PROGNOSTIC MODELS OF SECOND-LIFE EV BATTERIES APPLICATION IN ENERGY STORAGE SYSTEMS Урахування факторів календарного та циклічного старіння в діагностичних і прогностичних моделях застосування вторинних батарей електромобілів в системах зберігання енергії Kostenko, Ganna Lithium-Ion Battery, Electric Vehicle, Energy Storage, Battery Degradation, Calendar Ageing, Cyclic Ageing, Integral Degradation Index, Remaining Useful Life, State of Health. літій-іонні батареї, електромобіль, зберігання енергії, деградація батареї, календарне старіння, циклічне старіння, інтегральний індекс деградації, залишковий термін корисного використання, стан здоров’я батареї. The rapid expansion of the electric vehicle market has significantly increased the demand for lithium-ion batteries, posing challenges for manufacturers and policymakers regarding efficient use and recycling. When these batteries reach the end of their primary lifecycle, their repurposing for secondary applications such as energy storage becomes critical to addressing environmental and resource management issues. This paper focuses on applying second-life batteries in energy storage systems, emphasizing the importance of accounting for calendar and cyclic aging factors to optimize battery performance and longevity. Calendar aging refers to the degradation that occurs over time due to chemical reactions within the battery, even when it is not in use. This type of aging is influenced by temperature, state of charge (SOC), and storage conditions. Cyclic aging, on the other hand, results from repeated charging and discharging cycles, which cause mechanical and chemical changes within the battery, leading to capacity fade and increased internal resistance. The combined effects of these aging processes necessitate the development of high-precision diagnostic and prognostic models to manage the performance and longevity of second-life batteries effectively. In Ukraine, the adoption of electric vehicles is accelerating, leading to an influx of used electric vehicles. This situation necessitates the prompt development of strategies for repurposing these batteries for energy storage applications. The complexities associated with final recycling processes make secondary use an attractive interim solution. By repurposing used EV batteries, Ukraine can mitigate immediate challenges related to battery waste and resource scarcity while supporting the transition to renewable energy sources. This paper highlights the need for an integral degradation index (DI) that combines calendar and cyclic aging factors with stochastic influences to provide a comprehensive measure of battery health. Such an index is essential for optimizing battery management practices, including the scheduling of charging and discharging cycles, to extend the operational life of secondary batteries. The study also presents practical recommendations for implementing these models in various energy storage scenarios, ranging from residential solar energy systems to industrial grid support and electric vehicle charging stations. By adopting optimized battery management strategies, the potential for extending the lifespan of secondary batteries and reducing operational costs is significant. This approach supports sustainable energy practices and aligns with global efforts to promote renewable energy sources and circular economy principles. Швидке розширення ринку електромобілів значно збільшило попит на літій-іонні акумулятори, що створює виклики для виробників та політиків щодо ефективного використання та утилізації. Коли ці акумулятори досягають кінця свого первинного використання, їх повторне застосування для зберігання енергії стає критично важливим для вирішення багатьох екологічних та ресурсних проблем. Ця стаття зосереджена на вторинному застосуванні акумуляторів електромобілів у системах зберігання енергії з урахуванням факторів календарного та циклічного старіння для оптимізації продуктивності та довговічності акумуляторів. Календарне старіння стосується деградації, що відбувається з часом через хімічні реакції в акумуляторі, навіть коли він не використовується. На цей тип старіння впливають: температура, стан заряду та умови зберігання. Циклічне старіння, з іншого боку, є результатом повторних циклів заряджання та розряджання, що спричиняє механічні та хімічні зміни в акумуляторі, призводячи до зменшення ємності та збільшення внутрішнього опору. Комбінований вплив цих процесів старіння вимагає розробки високоточних діагностичних та прогнозних моделей для ефективного управління продуктивністю та довговічністю вторинних акумуляторів. Розвиток ринку вживаних електромобілів в Україні викликає необхідність швидкого розроблення стратегій для повторного використання цих акумуляторів у системах зберігання енергії. Складнощі, пов’язані з остаточними процесами утилізації, роблять повторне використання привабливим проміжним рішенням. Повторне використання акумуляторів електромобілів дозволяє Україні пом’якшити наявні проблеми, пов’язані з токсичними відходами при остаточній переробці, водночас підтримуючи перехід до відновлюваних джерел енергії. У даній статті підкреслюється необхідність створення інтегрального індексу деградації DI, що поєднує фактори календарного та циклічного старіння зі стохастичними впливами для надання комплексної оцінки стану акумулятора. Такий показник є важливим для оптимізації практик управління акумуляторами, включаючи планування циклів заряджання та розряджання для продовження експлуатаційного терміну вторинних акумуляторів. Дослідження містить практичні рекомендації щодо впровадження цих моделей у різних сценаріях зберігання енергії, від побутових сонячних енергосистем до промислової підтримки мережі та станцій заряджання електромобілів. Прийняття оптимізованих стратегій управління акумуляторами має значний потенціал для продовження терміну служби вторинних акумуляторів та зниження операційних витрат. Такий підхід підтримує сталу енергетику і відповідає глобальним зусиллям щодо сприяння відновлюваним джерелам енергії та принципам циркулярної економіки. General Energy Institute of the National Academy of Sciences of Ukraine 2024-07-01 Article Article application/pdf https://systemre.org/index.php/journal/article/view/856 10.15407/srenergy2024.03.021 System Research in Energy; No. 3 (79) (2024): System Research in Energy; 21-34 Системні дослідження в енергетиці; № 3 (79) (2024): Системні дослідження в енергетиці; 21-34 2786-7102 2786-7633 en https://systemre.org/index.php/journal/article/view/856/766 Copyright (c) 2024 Ganna Kostenko https://creativecommons.org/publicdomain/zero/1.0 |
| spellingShingle | Lithium-Ion Battery Electric Vehicle Energy Storage Battery Degradation Calendar Ageing Cyclic Ageing Integral Degradation Index Remaining Useful Life State of Health. Kostenko, Ganna ACCOUNTING CALENDAR AND CYCLIC AGEING FACTORS IN DIAGNOSTIC AND PROGNOSTIC MODELS OF SECOND-LIFE EV BATTERIES APPLICATION IN ENERGY STORAGE SYSTEMS |
| title | ACCOUNTING CALENDAR AND CYCLIC AGEING FACTORS IN DIAGNOSTIC AND PROGNOSTIC MODELS OF SECOND-LIFE EV BATTERIES APPLICATION IN ENERGY STORAGE SYSTEMS |
| title_alt | Урахування факторів календарного та циклічного старіння в діагностичних і прогностичних моделях застосування вторинних батарей електромобілів в системах зберігання енергії |
| title_full | ACCOUNTING CALENDAR AND CYCLIC AGEING FACTORS IN DIAGNOSTIC AND PROGNOSTIC MODELS OF SECOND-LIFE EV BATTERIES APPLICATION IN ENERGY STORAGE SYSTEMS |
| title_fullStr | ACCOUNTING CALENDAR AND CYCLIC AGEING FACTORS IN DIAGNOSTIC AND PROGNOSTIC MODELS OF SECOND-LIFE EV BATTERIES APPLICATION IN ENERGY STORAGE SYSTEMS |
| title_full_unstemmed | ACCOUNTING CALENDAR AND CYCLIC AGEING FACTORS IN DIAGNOSTIC AND PROGNOSTIC MODELS OF SECOND-LIFE EV BATTERIES APPLICATION IN ENERGY STORAGE SYSTEMS |
| title_short | ACCOUNTING CALENDAR AND CYCLIC AGEING FACTORS IN DIAGNOSTIC AND PROGNOSTIC MODELS OF SECOND-LIFE EV BATTERIES APPLICATION IN ENERGY STORAGE SYSTEMS |
| title_sort | accounting calendar and cyclic ageing factors in diagnostic and prognostic models of second-life ev batteries application in energy storage systems |
| topic | Lithium-Ion Battery Electric Vehicle Energy Storage Battery Degradation Calendar Ageing Cyclic Ageing Integral Degradation Index Remaining Useful Life State of Health. |
| topic_facet | Lithium-Ion Battery Electric Vehicle Energy Storage Battery Degradation Calendar Ageing Cyclic Ageing Integral Degradation Index Remaining Useful Life State of Health. літій-іонні батареї електромобіль зберігання енергії деградація батареї календарне старіння циклічне старіння інтегральний індекс деградації залишковий термін корисного використання стан здоров’я батареї. |
| url | https://systemre.org/index.php/journal/article/view/856 |
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