State-of-charge estimation challenges in second-life battery systems
SEP 3, 20259 MIN READ
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Second-Life Battery SOC Estimation Background and Objectives
The evolution of battery technology has witnessed significant advancements over the past decades, with lithium-ion batteries becoming the dominant energy storage solution across various applications, particularly in electric vehicles (EVs). As the first-life cycle of these batteries in EVs typically ends when capacity degrades to 70-80% of original capacity, a substantial opportunity emerges for repurposing these batteries in second-life applications such as stationary energy storage systems, grid support, and renewable energy integration.
State-of-charge (SOC) estimation, a critical parameter indicating the remaining capacity of a battery, presents unique challenges in second-life battery systems that differ substantially from new battery applications. Historically, SOC estimation techniques have evolved from simple voltage measurement methods to sophisticated algorithms incorporating multiple parameters, but these were primarily designed for homogeneous, new battery systems with predictable characteristics.
The technical trajectory of SOC estimation has progressed from coulomb counting and open-circuit voltage methods to more advanced approaches including Kalman filtering, machine learning, and electrochemical impedance spectroscopy. However, these methods face significant limitations when applied to second-life batteries due to their heterogeneous aging patterns, varied usage histories, and inconsistent degradation mechanisms.
The primary objective of addressing SOC estimation challenges in second-life battery systems is to develop robust, adaptive, and accurate methodologies that can accommodate the unique characteristics of repurposed batteries. This includes accounting for capacity fade, impedance increase, and chemical degradation that occurred during first-life usage, which significantly impacts the battery's electrochemical behavior and consequently the accuracy of traditional SOC estimation techniques.
Furthermore, the technical goals extend to creating standardized testing protocols and classification systems for second-life batteries that can facilitate more precise SOC estimation. This involves developing algorithms that can self-calibrate based on the specific degradation profile of each battery module or cell, potentially incorporating historical usage data from the battery's first life when available.
The advancement of SOC estimation techniques for second-life batteries also aims to enhance the economic viability of battery repurposing by extending useful life, improving operational efficiency, and increasing reliability. Accurate SOC estimation directly impacts the performance, safety, and longevity of second-life battery systems, making it a cornerstone technology for sustainable energy storage solutions and circular economy initiatives in the battery sector.
State-of-charge (SOC) estimation, a critical parameter indicating the remaining capacity of a battery, presents unique challenges in second-life battery systems that differ substantially from new battery applications. Historically, SOC estimation techniques have evolved from simple voltage measurement methods to sophisticated algorithms incorporating multiple parameters, but these were primarily designed for homogeneous, new battery systems with predictable characteristics.
The technical trajectory of SOC estimation has progressed from coulomb counting and open-circuit voltage methods to more advanced approaches including Kalman filtering, machine learning, and electrochemical impedance spectroscopy. However, these methods face significant limitations when applied to second-life batteries due to their heterogeneous aging patterns, varied usage histories, and inconsistent degradation mechanisms.
The primary objective of addressing SOC estimation challenges in second-life battery systems is to develop robust, adaptive, and accurate methodologies that can accommodate the unique characteristics of repurposed batteries. This includes accounting for capacity fade, impedance increase, and chemical degradation that occurred during first-life usage, which significantly impacts the battery's electrochemical behavior and consequently the accuracy of traditional SOC estimation techniques.
Furthermore, the technical goals extend to creating standardized testing protocols and classification systems for second-life batteries that can facilitate more precise SOC estimation. This involves developing algorithms that can self-calibrate based on the specific degradation profile of each battery module or cell, potentially incorporating historical usage data from the battery's first life when available.
The advancement of SOC estimation techniques for second-life batteries also aims to enhance the economic viability of battery repurposing by extending useful life, improving operational efficiency, and increasing reliability. Accurate SOC estimation directly impacts the performance, safety, and longevity of second-life battery systems, making it a cornerstone technology for sustainable energy storage solutions and circular economy initiatives in the battery sector.
Market Analysis for Second-Life Battery Applications
The second-life battery market has experienced significant growth in recent years, driven by the increasing number of retired electric vehicle (EV) batteries. By 2030, the global second-life battery market is projected to reach $4.2 billion, with a compound annual growth rate of 23.1% from 2023. This growth is primarily fueled by the expanding EV market, with forecasts suggesting that by 2025, approximately 1.2 million EV batteries will reach their end-of-automotive-life annually.
Energy storage systems represent the largest application segment for second-life batteries, accounting for approximately 43% of the market share. These systems are increasingly deployed in residential, commercial, and utility-scale applications to support renewable energy integration and grid stabilization. The residential energy storage segment is growing particularly fast at 29% annually, as homeowners seek cost-effective solutions for storing solar energy.
Industrial applications constitute the second-largest market segment at 28%, where second-life batteries are utilized in material handling equipment, backup power systems, and manufacturing processes. The telecommunications sector has also emerged as a significant consumer, using these batteries for cell tower backup power in regions with unreliable grid infrastructure.
Geographically, Europe leads the second-life battery market with 38% share, followed by Asia-Pacific at 32% and North America at 24%. Europe's dominance stems from stringent regulations promoting circular economy principles and advanced battery recycling infrastructure. China and South Korea are rapidly expanding their market presence through government-backed initiatives supporting battery reuse.
The market faces several challenges, including the lack of standardized testing protocols for state-of-charge estimation, which creates uncertainty regarding battery performance and reliability. This technical barrier has limited market penetration in critical applications where precise energy management is essential. Additionally, the price gap between new and second-life batteries has narrowed as new battery costs continue to decline, potentially affecting the economic viability of repurposed batteries.
Consumer concerns regarding the safety and longevity of second-life batteries remain significant market barriers. Studies indicate that 67% of potential commercial users cite reliability concerns as their primary hesitation in adopting these solutions. This underscores the importance of developing more accurate state-of-charge estimation methods to build market confidence.
Despite these challenges, the market outlook remains positive, with policy support increasing globally. The European Battery Directive and similar regulations in Asia are creating favorable conditions for market expansion by mandating extended producer responsibility and establishing clear pathways for battery reuse.
Energy storage systems represent the largest application segment for second-life batteries, accounting for approximately 43% of the market share. These systems are increasingly deployed in residential, commercial, and utility-scale applications to support renewable energy integration and grid stabilization. The residential energy storage segment is growing particularly fast at 29% annually, as homeowners seek cost-effective solutions for storing solar energy.
Industrial applications constitute the second-largest market segment at 28%, where second-life batteries are utilized in material handling equipment, backup power systems, and manufacturing processes. The telecommunications sector has also emerged as a significant consumer, using these batteries for cell tower backup power in regions with unreliable grid infrastructure.
Geographically, Europe leads the second-life battery market with 38% share, followed by Asia-Pacific at 32% and North America at 24%. Europe's dominance stems from stringent regulations promoting circular economy principles and advanced battery recycling infrastructure. China and South Korea are rapidly expanding their market presence through government-backed initiatives supporting battery reuse.
The market faces several challenges, including the lack of standardized testing protocols for state-of-charge estimation, which creates uncertainty regarding battery performance and reliability. This technical barrier has limited market penetration in critical applications where precise energy management is essential. Additionally, the price gap between new and second-life batteries has narrowed as new battery costs continue to decline, potentially affecting the economic viability of repurposed batteries.
Consumer concerns regarding the safety and longevity of second-life batteries remain significant market barriers. Studies indicate that 67% of potential commercial users cite reliability concerns as their primary hesitation in adopting these solutions. This underscores the importance of developing more accurate state-of-charge estimation methods to build market confidence.
Despite these challenges, the market outlook remains positive, with policy support increasing globally. The European Battery Directive and similar regulations in Asia are creating favorable conditions for market expansion by mandating extended producer responsibility and establishing clear pathways for battery reuse.
Technical Challenges in Second-Life Battery SOC Estimation
Second-life battery systems face significant technical challenges in state-of-charge (SOC) estimation due to their inherent heterogeneity and degraded performance characteristics. Unlike new batteries with predictable behavior patterns, second-life batteries have undergone varying degrees of degradation during their first-life applications, resulting in altered electrochemical properties that complicate accurate SOC estimation.
The primary challenge lies in the lack of consistent historical usage data. Second-life batteries typically come from diverse sources with limited or no access to their previous operational data, making it difficult to establish reliable baseline parameters for SOC algorithms. This data discontinuity creates significant obstacles for conventional estimation methods that rely on historical performance patterns.
Battery aging effects present another major hurdle. The capacity fade and internal resistance increase experienced during first-life usage vary significantly between individual cells, even those from the same original batch. This non-uniform degradation leads to substantial parameter variations within repurposed battery packs, rendering traditional SOC estimation models less effective as they typically assume relatively homogeneous cell characteristics.
The dynamic operating conditions of second-life applications further complicate SOC estimation. These batteries often face more variable load profiles and environmental conditions than in their original applications, requiring more robust and adaptive estimation algorithms. The conventional models calibrated for specific first-life applications may fail to capture these new operational dynamics accurately.
Temperature sensitivity represents another critical challenge. Aged batteries typically exhibit increased sensitivity to temperature variations, with more pronounced effects on their electrochemical behavior. This heightened sensitivity necessitates more sophisticated thermal modeling within SOC estimation algorithms to account for temperature-dependent performance fluctuations.
Self-discharge rates also become more unpredictable in second-life batteries. The degradation of internal components can lead to accelerated and irregular self-discharge behaviors that conventional SOC models struggle to incorporate accurately, resulting in estimation drift over time, particularly during storage periods.
The economic constraints of second-life applications add another layer of complexity. While sophisticated battery management systems with advanced sensors might improve SOC estimation accuracy, they often prove cost-prohibitive for second-life applications where lower overall system costs are essential for economic viability. This creates a technical challenge of developing sufficiently accurate SOC estimation methods that can operate with limited sensing capabilities.
The primary challenge lies in the lack of consistent historical usage data. Second-life batteries typically come from diverse sources with limited or no access to their previous operational data, making it difficult to establish reliable baseline parameters for SOC algorithms. This data discontinuity creates significant obstacles for conventional estimation methods that rely on historical performance patterns.
Battery aging effects present another major hurdle. The capacity fade and internal resistance increase experienced during first-life usage vary significantly between individual cells, even those from the same original batch. This non-uniform degradation leads to substantial parameter variations within repurposed battery packs, rendering traditional SOC estimation models less effective as they typically assume relatively homogeneous cell characteristics.
The dynamic operating conditions of second-life applications further complicate SOC estimation. These batteries often face more variable load profiles and environmental conditions than in their original applications, requiring more robust and adaptive estimation algorithms. The conventional models calibrated for specific first-life applications may fail to capture these new operational dynamics accurately.
Temperature sensitivity represents another critical challenge. Aged batteries typically exhibit increased sensitivity to temperature variations, with more pronounced effects on their electrochemical behavior. This heightened sensitivity necessitates more sophisticated thermal modeling within SOC estimation algorithms to account for temperature-dependent performance fluctuations.
Self-discharge rates also become more unpredictable in second-life batteries. The degradation of internal components can lead to accelerated and irregular self-discharge behaviors that conventional SOC models struggle to incorporate accurately, resulting in estimation drift over time, particularly during storage periods.
The economic constraints of second-life applications add another layer of complexity. While sophisticated battery management systems with advanced sensors might improve SOC estimation accuracy, they often prove cost-prohibitive for second-life applications where lower overall system costs are essential for economic viability. This creates a technical challenge of developing sufficiently accurate SOC estimation methods that can operate with limited sensing capabilities.
Current SOC Estimation Methods for Aged Batteries
01 Machine learning approaches for SOC estimation
Machine learning algorithms can be employed to accurately estimate the state-of-charge in second-life battery systems. These approaches use historical battery data to train models that can predict SOC under various operating conditions. Neural networks, support vector machines, and other AI techniques can adapt to the degraded and variable characteristics of repurposed batteries, providing more reliable estimations than traditional methods for batteries with unknown or altered characteristics.- Machine learning approaches for SOC estimation: Machine learning algorithms are increasingly used for accurate state-of-charge estimation in second-life battery systems. These approaches can handle the non-linear characteristics and degradation patterns of aged batteries by learning from historical data. Neural networks, support vector machines, and other AI techniques can adapt to the unique characteristics of repurposed batteries, providing more reliable SOC estimates than traditional methods, especially when dealing with batteries that have heterogeneous aging histories.
- Adaptive filtering techniques for SOC estimation: Adaptive filtering methods such as Kalman filters, extended Kalman filters, and particle filters are employed to estimate the state-of-charge in second-life battery systems. These techniques recursively process measurement data to provide real-time SOC estimates while accounting for system uncertainties and measurement noise. The adaptive nature of these filters makes them particularly suitable for second-life batteries where parameters change over time due to aging and previous usage patterns.
- Battery health-aware SOC estimation methods: These methods integrate battery health metrics into the SOC estimation process for second-life batteries. By considering capacity fade, internal resistance increase, and other degradation indicators, these approaches adjust SOC algorithms to account for the actual condition of repurposed batteries. This health-aware estimation is crucial for second-life applications where batteries have undergone significant aging in their first life and exhibit varied degradation patterns.
- Multi-parameter fusion for improved SOC accuracy: This approach combines multiple battery parameters and measurements to enhance SOC estimation accuracy in second-life systems. By fusing data from voltage, current, temperature, impedance, and other measurable parameters, these methods create more robust SOC estimates. The multi-parameter fusion is particularly valuable for second-life batteries where traditional single-parameter methods may fail due to the batteries' degraded and non-uniform characteristics.
- SOC estimation with minimal computational resources: These methods focus on providing accurate SOC estimation for second-life battery systems while requiring minimal computational resources. Using simplified models, lookup tables, or efficient algorithms, these approaches enable SOC estimation in applications with limited processing power or energy constraints. This is particularly important for distributed second-life battery applications where each battery module may need independent SOC estimation capabilities without extensive computing hardware.
02 Adaptive filtering techniques for SOC estimation
Adaptive filtering methods such as Kalman filters, extended Kalman filters, and particle filters can be used to estimate the state-of-charge in second-life battery systems. These techniques continuously update the battery model parameters based on real-time measurements, accounting for the aging and degradation characteristics specific to second-life batteries. This approach helps improve estimation accuracy by dynamically adjusting to changing battery conditions and compensating for model uncertainties.Expand Specific Solutions03 Electrochemical impedance spectroscopy for SOC determination
Electrochemical impedance spectroscopy (EIS) can be utilized to determine the state-of-charge in second-life battery systems. This non-invasive technique measures the impedance of batteries at different frequencies, providing insights into the electrochemical processes occurring within the cells. By analyzing the impedance spectra, it's possible to correlate specific patterns with different SOC levels, even in aged batteries with altered characteristics, making it particularly valuable for second-life applications.Expand Specific Solutions04 Combined estimation methods using multiple parameters
Hybrid approaches that combine multiple estimation methods and parameters can enhance SOC estimation accuracy in second-life battery systems. These methods integrate voltage, current, temperature measurements, and impedance data with advanced algorithms to provide more robust estimations. By fusing different information sources and considering the unique degradation patterns of repurposed batteries, these combined approaches can overcome the limitations of single-parameter methods and adapt to the varied history and condition of second-life batteries.Expand Specific Solutions05 Battery health-aware SOC estimation systems
State-of-charge estimation systems that incorporate battery health assessment can provide more accurate results for second-life batteries. These systems simultaneously evaluate the state-of-health (SOH) and use this information to adjust SOC calculations, accounting for capacity fade and resistance increase in aged cells. By considering the degradation history and current health status of repurposed batteries, these approaches can deliver more reliable SOC estimates throughout the second-life application, enabling better energy management and extended useful life.Expand Specific Solutions
Key Industry Players in Second-Life Battery Market
The second-life battery market is currently in its early growth phase, characterized by increasing adoption but still evolving technical standards. Market size is projected to expand significantly as electric vehicle penetration increases, creating a substantial pool of retired batteries. Technical maturity remains moderate, with state-of-charge estimation presenting a critical challenge. Leading automotive manufacturers like Toyota, Nissan, Honda, and BYD are investing heavily in this area, while battery specialists including Samsung SDI, LG Energy Solution, and CATL are developing advanced battery management systems. Technology companies such as Hitachi, Bosch, and Panasonic are contributing expertise in electronics and energy management. The competitive landscape features collaboration between traditional automotive players and technology specialists to overcome the complex technical barriers in accurately determining remaining capacity in repurposed batteries.
Robert Bosch GmbH
Technical Solution: Bosch has developed a comprehensive Battery Intelligence System (BIS) specifically addressing SOC estimation challenges in second-life applications. Their approach combines traditional battery modeling with advanced machine learning techniques to create adaptive algorithms that can accurately estimate SOC despite the inconsistent characteristics of aged batteries. The system employs a multi-model estimation framework where several parallel algorithms (including equivalent circuit models, electrochemical models, and data-driven models) work simultaneously, with a fusion algorithm selecting the most reliable estimate based on operating conditions and battery history[1]. Bosch's technology incorporates a detailed battery characterization process that identifies specific aging signatures, allowing the system to adjust estimation parameters according to identified degradation mechanisms. Their BMS features a "digital twin" concept where a virtual model of each second-life battery pack is continuously updated based on operational data, enabling more accurate predictions of future performance and SOC estimation corrections[2]. The company has implemented a distributed sensor network within their battery management architecture that provides high-resolution monitoring of cell-level variations, which become more pronounced in second-life applications. Bosch has demonstrated this technology in industrial energy storage systems utilizing repurposed EV batteries, achieving SOC estimation accuracy within 5% even for batteries with significant capacity degradation (>25%)[3]. Their system also incorporates predictive maintenance capabilities that can identify when SOC estimation accuracy is likely to deteriorate based on emerging battery issues.
Strengths: Bosch's multi-model approach provides robust estimation across diverse operating conditions and degradation states. Their digital twin concept enables continuous refinement of battery models based on actual performance data. Weaknesses: The system requires significant computational resources to run multiple parallel models simultaneously. The initial characterization process is complex and time-consuming, potentially limiting scalability for mass deployment of second-life batteries.
Samsung SDI Co., Ltd.
Technical Solution: Samsung SDI has developed an advanced Battery Management System (BMS) specifically designed for second-life applications that addresses state-of-charge (SOC) estimation challenges. Their approach combines multiple estimation methods including coulomb counting, open circuit voltage (OCV) measurement, and machine learning algorithms to compensate for the degraded and inconsistent characteristics of used batteries. The system employs a dual-layer estimation framework where initial SOC is determined using OCV-based methods, while dynamic operation relies on an adaptive Extended Kalman Filter (EKF) that continuously updates battery parameters based on real-time performance data[1]. Samsung's technology incorporates cell-level monitoring with over 100 measurement points per battery pack to account for cell-to-cell variations that become more pronounced in aged batteries. Their BMS also features a self-learning capability that builds historical performance profiles for each second-life battery system, improving estimation accuracy over time as it gathers more operational data[2]. The company has implemented this technology in energy storage systems utilizing repurposed EV batteries, demonstrating SOC estimation accuracy improvements of up to 15% compared to conventional methods when dealing with heterogeneous battery conditions.
Strengths: Samsung's multi-method approach provides redundancy and higher accuracy across varied degradation states. Their self-learning algorithms allow for continuous improvement in estimation accuracy as the system gathers more operational data. Weaknesses: The system requires significant computational resources and extensive initial characterization of each battery pack, potentially increasing implementation costs for smaller-scale second-life applications.
Critical Patents and Research in Second-Life Battery Management
Secondary battery status estimating device
PatentWO2010026930A9
Innovation
- A secondary battery state estimating device that includes a detection unit for measuring battery voltage, current, and temperature, a battery state estimation unit for calculating charging rates and open-circuit voltage, and a parameter estimation unit for correcting open-circuit voltage characteristics by minimizing estimation errors based on changes in battery parameters, ensuring accurate estimation of battery state and capacity over time.
State-of-charge estimating device of secondary battery
PatentActiveUS7352156B2
Innovation
- A state-of-charge estimating device that integrates current detection and terminal voltage detection, using a combination of adaptive digital filtering and current integration methods to estimate open-circuit voltage and SOC, with a second state-of-charge estimating part selecting the appropriate estimation method based on current stability to ensure precise SOC estimation.
Safety and Reliability Considerations for Repurposed Batteries
The repurposing of batteries for second-life applications introduces significant safety and reliability challenges that must be addressed to ensure widespread adoption. Unlike new batteries with known histories, second-life batteries have undergone varying degrees of degradation during their first-life applications, creating inherent uncertainties in their performance characteristics and safety profiles.
A primary concern is the increased risk of thermal runaway in aged cells. Degradation mechanisms such as lithium plating, SEI layer growth, and mechanical stress can compromise the internal structure of battery cells, potentially leading to internal short circuits. These risks are exacerbated by the fact that second-life batteries often comprise cells with heterogeneous aging patterns and degradation levels, even within the same battery pack.
Electrical safety presents another critical challenge. The voltage imbalances between cells in repurposed battery systems can lead to overcharging or deep discharging of individual cells, further accelerating degradation and increasing safety risks. This necessitates sophisticated battery management systems (BMS) specifically designed to handle the unique characteristics of second-life batteries, including enhanced cell balancing capabilities and more conservative operational limits.
Mechanical integrity issues also emerge as batteries age. Physical degradation such as casing deformation, terminal corrosion, or seal deterioration can compromise the structural integrity of battery systems. These conditions may not be immediately apparent during visual inspections but can lead to catastrophic failures under operational stresses.
The reliability of state-of-charge (SOC) estimation becomes particularly problematic in second-life applications. Conventional SOC algorithms typically rely on predictable relationships between voltage, current, and state of charge that become increasingly unreliable as batteries age. This uncertainty can lead to unexpected system shutdowns, reduced usable capacity, or operation outside safe parameters.
Standardized testing protocols for second-life batteries remain underdeveloped, creating challenges in certifying their safety and reliability. Unlike new batteries that undergo rigorous quality control during manufacturing, second-life batteries require specialized testing regimes to account for their unique degradation profiles and operational histories.
To address these challenges, advanced diagnostic techniques are being developed, including impedance spectroscopy, differential voltage analysis, and machine learning approaches that can better characterize the health and safety profile of aged cells. Additionally, adaptive safety systems that continuously monitor and adjust operational parameters based on real-time battery behavior show promise for enhancing the safety margins of second-life battery systems.
A primary concern is the increased risk of thermal runaway in aged cells. Degradation mechanisms such as lithium plating, SEI layer growth, and mechanical stress can compromise the internal structure of battery cells, potentially leading to internal short circuits. These risks are exacerbated by the fact that second-life batteries often comprise cells with heterogeneous aging patterns and degradation levels, even within the same battery pack.
Electrical safety presents another critical challenge. The voltage imbalances between cells in repurposed battery systems can lead to overcharging or deep discharging of individual cells, further accelerating degradation and increasing safety risks. This necessitates sophisticated battery management systems (BMS) specifically designed to handle the unique characteristics of second-life batteries, including enhanced cell balancing capabilities and more conservative operational limits.
Mechanical integrity issues also emerge as batteries age. Physical degradation such as casing deformation, terminal corrosion, or seal deterioration can compromise the structural integrity of battery systems. These conditions may not be immediately apparent during visual inspections but can lead to catastrophic failures under operational stresses.
The reliability of state-of-charge (SOC) estimation becomes particularly problematic in second-life applications. Conventional SOC algorithms typically rely on predictable relationships between voltage, current, and state of charge that become increasingly unreliable as batteries age. This uncertainty can lead to unexpected system shutdowns, reduced usable capacity, or operation outside safe parameters.
Standardized testing protocols for second-life batteries remain underdeveloped, creating challenges in certifying their safety and reliability. Unlike new batteries that undergo rigorous quality control during manufacturing, second-life batteries require specialized testing regimes to account for their unique degradation profiles and operational histories.
To address these challenges, advanced diagnostic techniques are being developed, including impedance spectroscopy, differential voltage analysis, and machine learning approaches that can better characterize the health and safety profile of aged cells. Additionally, adaptive safety systems that continuously monitor and adjust operational parameters based on real-time battery behavior show promise for enhancing the safety margins of second-life battery systems.
Standardization Efforts for Second-Life Battery Systems
The standardization landscape for second-life battery systems is currently fragmented, with various organizations working independently to establish frameworks for battery repurposing. Key international bodies including ISO, IEC, and IEEE have initiated working groups specifically addressing second-life battery applications, focusing on testing protocols, safety requirements, and performance metrics for state-of-charge estimation.
The UL 1974 standard represents one of the first comprehensive frameworks specifically developed for repurposed batteries, establishing guidelines for sorting and grading used batteries based on their remaining capacity and performance characteristics. This standard addresses critical aspects of state-of-charge estimation by defining minimum performance requirements and testing methodologies to ensure accurate assessment of battery health.
In Europe, the European Committee for Electrotechnical Standardization (CENELEC) has developed EN 50604, which includes provisions for second-life applications of lithium batteries. This standard emphasizes the importance of reliable state-of-charge estimation methods as batteries transition from automotive to stationary applications, acknowledging the different operational profiles and requirements in these contexts.
The Global Battery Alliance (GBA) has established the Battery Passport initiative, which aims to create a digital representation of batteries throughout their lifecycle. This effort includes standardized methods for tracking battery health and state-of-charge across multiple use phases, potentially resolving one of the key challenges in second-life applications: the lack of consistent historical performance data.
China's standardization efforts through GB/T standards have also begun addressing second-life batteries, with particular focus on grid integration and energy storage applications. These standards emphasize the need for reliable state-of-charge estimation to ensure grid stability when integrating repurposed batteries into energy storage systems.
Despite these advancements, significant gaps remain in standardization efforts. Current standards often fail to adequately address the heterogeneity of aged battery cells and the resulting challenges in state-of-charge estimation. The variability in degradation patterns across different battery chemistries, manufacturers, and usage histories creates substantial challenges for developing universally applicable standards.
Industry consortia like the Consortium for Battery Innovation (CBI) and the Rechargeable Battery Association (PRBA) are working to bridge these gaps by developing best practices and technical guidelines that specifically address the unique challenges of state-of-charge estimation in second-life applications, including methods for handling increased cell-to-cell variations and reduced accuracy of conventional estimation algorithms.
The UL 1974 standard represents one of the first comprehensive frameworks specifically developed for repurposed batteries, establishing guidelines for sorting and grading used batteries based on their remaining capacity and performance characteristics. This standard addresses critical aspects of state-of-charge estimation by defining minimum performance requirements and testing methodologies to ensure accurate assessment of battery health.
In Europe, the European Committee for Electrotechnical Standardization (CENELEC) has developed EN 50604, which includes provisions for second-life applications of lithium batteries. This standard emphasizes the importance of reliable state-of-charge estimation methods as batteries transition from automotive to stationary applications, acknowledging the different operational profiles and requirements in these contexts.
The Global Battery Alliance (GBA) has established the Battery Passport initiative, which aims to create a digital representation of batteries throughout their lifecycle. This effort includes standardized methods for tracking battery health and state-of-charge across multiple use phases, potentially resolving one of the key challenges in second-life applications: the lack of consistent historical performance data.
China's standardization efforts through GB/T standards have also begun addressing second-life batteries, with particular focus on grid integration and energy storage applications. These standards emphasize the need for reliable state-of-charge estimation to ensure grid stability when integrating repurposed batteries into energy storage systems.
Despite these advancements, significant gaps remain in standardization efforts. Current standards often fail to adequately address the heterogeneity of aged battery cells and the resulting challenges in state-of-charge estimation. The variability in degradation patterns across different battery chemistries, manufacturers, and usage histories creates substantial challenges for developing universally applicable standards.
Industry consortia like the Consortium for Battery Innovation (CBI) and the Rechargeable Battery Association (PRBA) are working to bridge these gaps by developing best practices and technical guidelines that specifically address the unique challenges of state-of-charge estimation in second-life applications, including methods for handling increased cell-to-cell variations and reduced accuracy of conventional estimation algorithms.
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