Battery Degradation Research Articles

Optimizing Energy Management and Sizing of Photovoltaic Batteries for a Household in Granada, Spain: A Novel Approach Considering Time Resolution

As residential adoption of renewable energy sources increases, optimizing rooftop photovoltaic systems (RTPVs) with Battery Energy Storage Systems (BESSs) is key for enhancing self-sufficiency and reducing dependence on the grid. This study introduces a novel methodology for sizing Home Energy Management Systems (HEMS), with the objective of minimizing the cost of imported energy while accounting for battery degradation. The battery model integrated nonlinear degradation effects and was evaluated in a real case study, considering different temporal data resolutions and various energy management strategies. For BESS capacities ranging from 1 to 5 kWh, the economic analysis demonstrated cost-effectiveness, with a Net Present Value (NPV) ranging from 54.53 € to 181.40 € and discounted payback periods (DPBs) between 6 and 10 years. The proposed HEMS extended battery lifespan by 22.47% and improved profitability by 21.29% compared to the current HEMS when applied to a 10 kWh BESS. Sensitivity analysis indicated that using a 5 min resolution could reduce NPV by up to 184.68% and increase DPB by up to 43.12% compared to a 60 min resolution for batteries between 1 and 5 kWh. This underscores the critical impact of temporal resolution on BESS sizing and highlights the need to balance accuracy with computational efficiency.

Deep reinforcement learning-based energy management system enhancement using digital twin for electric vehicles

Compared to conventional engine-based powertrains, electrified powertrain exhibit increased energy efficiency and reduced emissions, making electrification a key goal for the automotive industry. For a vehicle with a hybrid energy storage system, its performance and lifespan are substantially affected by the energy management system. Reinforcement learning-based methods are gaining popularity in vehicle energy management, but most of the literature in this area focuses on pure simulation, while hardware implementation is still limited. This paper introduces the digital twin methodology to enhance the reinforcement learning-based energy management system for battery and ultracapacitor electric vehicles. The digital twin model can exploit the bilateral interdependency between the virtual model and the actual system, which improves the control performance of the energy management system in real-time control. The physical model is established based on a hardware-in-the-loop simulation platform. In addition, battery degradation is also considered for prolonging the battery lifespan to reduce operating costs. The validation results of the trained reinforcement learning agent illustrate that the digital twin-enhanced Q-learning energy management system improves the energy efficiency by 7.08 % and reduces the battery degradation by 25.28 %.

Lift-Out Specimen Preparation and Multiscale Correlative Investigation of Li-Ion Battery Electrodes Using Focused Ion Beam-Secondary Ion Mass Spectrometry Platforms.

Advanced characterization is paramount to understanding battery cycling and degradation in greater detail. Herein, we present a novel methodology of battery electrode analysis, employing focused ion beam (FIB) secondary-ion mass spectrometry platforms coupled with a specific lift-out specimen preparation, allowing us to optimize analysis and prevent air contamination. Correlative microscopy, combining electron microscopy and chemical imaging of a liquid electrolyte Li-ion battery electrode, is performed over the entire electrode thickness down to subparticle domains. We observed a distinctive remnant lithiation among interparticles of the anode at the discharge state. Furthermore, chemical mapping reveals the nanometric architecture of advanced composite active materials with a lateral resolution of 16 nm and the presence of a solid electrolyte interface on the particle boundaries. We highlight the methodological advantages of studying interfaces and the ability to conduct high-performance chemical and morphological correlative analyses of battery materials and comment on their potential use in other fields.

Revealing the Failure Mechanisms of Lithium Metal Solid‐State Batteries with Solid Inorganic Electrolytes by In situ Electron Microscopy

Lithium metal solid‐state batteries (LMSSBs) are considered to be one of the ultimate choices for future energy storage systems because of their high theoretical energy density and enhanced safety. However, the development of LMSSBs has been seriously hindered by some practical issues, such as Li dendrite penetration in the solid‐state electrolytes (SSEs) and uncontrolled interphase growth at the Li/SSE interface, which can cause severe battery degradation, failure, and even safety hazards. To construct safe high‐performance LMSSBs, it is crucial to gain an in‐depth understanding of the failure mechanisms induced by these challenges, especially through direct visualization of the failure processes. In this review, the recent progress on the mechanistic study of LMSSBs by in situ electron microscopy is summarized. In situ transmission electron microscopy (TEM) and scanning electron microscopy (SEM) offer an opportunity to probe the battery failure mechanism by observing the associated physical and chemical processes at nano/atomic resolution. The failure causes of Li dendrites growth and interphase formation are classified and discussed, followed by the corresponding solutions to address these issues. Additionally, the emerging perspectives on future research directions in this field are also summarized.

A comprehensive framework for estimating the remaining useful life of Li-ion batteries under limited data conditions with no temporal identifier

The escalating applications of Lithium-ion (Li-ion) batteries in renewable energy and electric vehicles underscore the need for enhanced prognostics and health management systems to reduce the risk of sudden failures. Remaining useful life (RUL) determination is one of the most critical tasks in the field of battery prognostics nowadays. Even though statistical and machine learning (ML) methods have proven effective in research setups, many challenges prevent applying these prediction methods to real-life scenarios. These challenges include (1) scarcity of run-to-failure datasets with similar experimental conditions, (2) low data granularity when presented in capacity vs. discharge cycle pairs, and (3) lack of “temporal identifiers” in real-life scenarios. A temporal identifier is any label that provides knowledge about the current degradation state of a working battery. The research question developed for this study was, ‘Can the remaining useful life of a Li-ion battery having limited data without a temporal identifier be predicted?’ The specific aims were to estimate the temporal identifier of limited data and to predict the remaining useful life (RUL). An innovative framework incorporating reliability analysis and deep learning addresses these specific aims. Experimental data is used to test the framework's capabilities, limiting the training dataset to only three batteries and the testing dataset to a small sample (< 10 data points) of another battery. This new approach enabled the RUL prediction to achieve errors as low as ∼5 cycles and root mean square error of 6.24 cycles, outperforming other benchmark studies on Li-ion battery RUL prediction that use more battery degradation data without temporal identifier.

Aging-aware co-optimization of topology, parameter and control for multi-mode input- and output-split hybrid electric powertrains

Existing research has focused on battery intrinsic optimization, Hybrid Energy Storage System (HESS), and powertrain control perspectives to enhance battery life. However, the impact of powertrain physical architecture -control multi-layer co-optimization on battery life has been neglected. Therefore, this study innovatively proposes a multi-mode dedicated hybrid transmission (DHT)—from the perspective of powertrain architecture—to reduce battery life degradation by switching between input split (IS) and output split (OS) modes. In order to fully optimize the battery degradation potential, a topology-parameter-control multi-layer co-optimization (TPCMC) method is proposed in this paper. In the topology layer, graph theory method is used for automatic generation, screening and modeling. In the parameter and control layer, a novel near-optimal energy management strategy, Rapid Dynamic Programming+ (Rapid-DP+), is proposed by introducing Pareto optimization, slope filtering, and Battery Life Aging Optimization (BLAO) methods, which improves the computational efficiency by 3500 times with 9.09–21.1 % battery life optimization. The results show that, relative to the Toyota Prius, the optimized powertrain improves the acceleration by 41.3 %, fuel economy by 6.08 %–8.1 %, and battery life degradation by 9.9 %–22.5 % under different driving cycles. This paper contributes to battery life degradation in the electrified transportation through an advanced TPCMC approach.

Optimal battery electric bus system planning considering heterogeneous vehicles, opportunity charging, and battery degradation

Battery electric buses (BEBs) have been seen as a viable alternative for sustainable mobility. The charger placement and fleet configuration play a crucial role in the operational efficiency of BEBs. In this study, a joint optimization model is developed to strategically plan the charger placement and fleet configuration for a BEB system. Distinct from existing research, we simultaneously incorporate opportunity charging, heterogeneous vehicles, and battery degradation to integratively minimize the costs of charger installation, BEB purchase, energy consumption, and battery degradation. Based on a model reformulation, a heuristic-exact hybrid solution method is customized to solve the model. A real-world case study from Oslo, Norway is provided to demonstrate the optimization model and solution method. The results indicate that the proposed model can reduce the total costs by 21.27% in comparison to the conventional model. Sensitivity analyses are further conducted to provide insightful findings and managerial implications for BEB systems.

State-of-health estimation and classification of series-connected batteries by using deep learning based hybrid decision approach

In rechargeable battery control and operation, one of the primary obstacles is safety concerns where the battery degradation poses a significant factor. Therefore, in recent years, state-of-health assessment of lithium-ion batteries has become a noteworthy issue. On the other hand, it is challenging to ensure robustness and generalization because most state-of-health assessment techniques are implemented for a specific characteristic, operating situation, and battery material system. In most studies, health status of single cell batteries is assessed by using analytical or computer-aided deep learning methods. But, the state-of-health characteristics of series-connected battery systems should be also focused with advances of technology and usage, especially electric vehicles. This study presents a data-driven, deep learning-based hybrid decision approach for predicting the state-of-health of series-connected lithium-ion batteries with different characteristics. The paper consists of generating series-connected battery degradation dataset by using of some mostly used datasets. Also, by employing deep learning-based networks along with hybrid-classification aided by performance metrics, it is shown that estimating and predicting the state-of-health can be achieved not only by using sole deep-learning algorithms but also hybrid-classification techniques. The results demonstrate the high accuracy and simplicity of the proposed novel approach on datasets from Oxford University and Calce battery group. The best estimated mean squared error, root mean square error and mean-absolute percentage error values are not more than 0.0500, 0.2236 and 0.7065, respectively which shows the efficiency not only by accuracy but also error indicators. The results show that the proposed approach can be implemented in offline or online systems with best average accuracy of 98.33 % and classification time of 58 ms per sample.

Effects of Contact Loss at Electrolyte/Negative Electrode Interface on Current Density Distribution in Solid-State Batteries

Cyclic volume changes and non-uniform electrodeposition/stripping, among other cycling-induced chemo-mechanical degradation of lithium metal and lithium-alloy solid state batteries, lead to contact loss between the anode and the solid electrolyte separator. Operando experiments have shown accelerated short-circuiting behavior due to contact loss in “anode-free” solid-state batteries. Simulations have shown the relationship between active area fraction and the ratio of effective conductivities in regular-shape active area configurations. Through modeling experiments using imputed active contact area of lithium-metal negative electrode batteries, we quantify the effects of this contact loss. Specifically, we (1) quantify the interfacial resistance due to this contact loss, (2) show non-uniform local current density distribution such that evaluation of what area fraction has current exceeding critical current densities is possible, and (3) show non-uniform reaction distribution at the positive electrode. This work sheds light on the tradeoffs in the design of solid state batteries within the context of contact loss.

A Comparison of Standard SEI Growth Models in the Context of Battery Formation

Growth of the Solid Electrolyte Interphase (SEI) layer on negative electrode particles during the formation cycle is one of the most complex and least understood steps of lithium-ion battery manufacturing. This initial SEI formation significantly impacts battery performance, lifetime, and degradation. Zero-dimensional models, which reduce the complexity of SEI’s morphology, material, and structure, are commonly used to study long-term SEI growth rates and capacity fade. These models are derived based on limiting mechanisms. We aim to compare the most common SEI growth models, focusing on the first few cycles at low C-rates representing formation protocols. Using consistent parameters across models, we seek to understand if they can capture the dynamics of SEI formation. We conducted qualitative comparisons with experimental measurements of Coulombic efficiency in 2032-type coin cells at low C-rate. Our analysis shows that the models predict SEI growth in the first cycle to be higher than in subsequent cycles. However, the difference between cycles in these models is insufficient to explain the experimental results, which indicate a capacity fade during the first cycle that is two orders of magnitude higher than in later cycles. This suggests new models are needed to accurately describe the physics of the formation cycle.

Accelerated commercial battery electrode-level degradation diagnosis via only 11-point charging segments

Accelerated and accurate degradation diagnosis is imperative for the management and reutilization of commercial lithium-ion batteries in the upcoming TWh era. This work proposes a framework combining both deep learning and physical modeling to extend traditional capacity degradation diagnosis to a rapid and accurate degradation diagnosis at the electrode level using only readily measurable charging current and voltage signals. Deep learning is used to rapidly and robustly predict polarization-free incremental capacity analysis (ICA) curves in minutes, which are traditionally obtained in a dozen hours, and the physical model is to quantitatively reveal the electrode-level degradation modes by decoupling them from the ICA curves. It is demonstrated that 11 points collected at any starting state-of-charge (SOC) in a minimum of 2.5 minutes are sufficient to predict reliable ICA curves with a mean root mean square error (RMSE) of 0.2774 Ah/V. Accordingly, batteries can be accurately elevated based on their degradation at both macro and electrode levels. Through transfer learning, such a method can also be adapted to different battery chemistries, emphasizing the enticing potential for rapid promotion of this work in battery degradation diagnosis.

Diagnosis of lithium-ion batteries degradation with P2D model parameters identification: A case study on low temperature charging

The estimation of the state of health (SoH) of a lithium-ion battery is still a hot topic in the scientific research. This publication deals with the combined use of optimized tests, also involving impedance spectroscopy, and physical models to investigate lithium-ion batteries degradation. As a case study, this method is firstly applied on a low-temperature charging degradation campaign, in order to expectedly generate a lithium plating-dominated ageing state. Degradation tests, performed under previously selected combinations of operating conditions, are performed down to 75 % SoH on commercial samples, determining severe ageing rate up to 1.5 % capacity loss per equivalent full cycle. The proposed interpretation methodology identifies the ageing to be dominated by the loss of lithium inventory, consistently with the expected degradation mechanism. Large electrolyte consumption is also detected, which induces a strongly anisotropic utilization of the electrodes during discharge, as confirmed by pseudo-two-dimensional (P2D) model simulations. This activity contributes to verify the reliability of the methodology, elucidate the effect of lithium plating on the performance and underline the effect of the operating conditions at low temperature, paving the way to the application on real-world conditions.

Optimal scheduling strategies for electrochemical energy storage power stations in the electricity spot market

IntroductionThis paper constructs a revenue model for an independent electrochemical energy storage (EES) power station with the aim of analyzing its full life-cycle economic benefits under the electricity spot market.MethodsThe model integrates the marginal degradation cost (MDC), energy arbitrage, ancillary services, and annual operation and maintenance (O&amp;amp;M) costs to calculate the net profits of the EES power station. Using an iterative optimization approach, we determine the optimal MDC and analyze the economic end of life (EOL) for different types of EES power stations.ResultsBy examining real-world examples from the California energy market, we find that the full life-cycle benefits of an EES power station peak when its MDC is optimal, at $45/MWh-throughput. Under these conditions, the economic and physical EOL of commercial/industrial EES power station is 9 years, while the economic EOL of residential-grade EES power station is 8 years, which is shorter than their physical EOL of 9 years.DiscussionThe study further indicates that the economic life of an EES power station is influenced by multiple factors, and operators need to determine the optimal economic EOL to maximize revenue based on battery degradation characteristics, market conditions and operational strategy.

Progressive degradation behavior and mechanism of lithium-ion batteries subjected to minor deformation damage

Minor deformation damage poses a concealed threat to battery performance and safety. This study delves into the progressive degradation behavior and mechanisms of lithium-ion batteries under minor deformation damage induced by out-of-plane compression. The effects of varying initial states of charge and loading speed on battery degradation are also analyzed. It has been observed that a deformation damage degree as low as 3.1 % can cause a significant transient capacity reduction up to 6.3 %. More transient capacity reduction can occur at higher initial state of charges and loading speed. Additionally, the investigation reveals that the capacity decay rate between the normal cell and cells experiencing deformation damage degree of <4.7 % is almost similar, with a maximum difference of merely 1.887 mAh/cycle and a minimum difference of 0.001 mAh/cycle, both observed over 3000 cycles. Non-destructive and destructive analysis methods are used to investigate degradation behaviors of the cells experiencing minor deformation dagame. It is found that such progressive degradation is primarily driven by the loss of active lithium ions, followed by the loss of cathode and anode active materials. It is anticipated that the findings will offer theoretical underpinnings for advancements in battery damage assessment, early failure prediction, and facilitation of loss adjustment and compensation by insurance agencies.

A dynamic pricing strategy and charging coordination of PEV in a renewable-grid integrated charging station

The increasing penetration of Plug-in Electric Vehicles (PEV) in the transportation system has increased the burden on the power system. This has made peak load demand management a challenging task for the power grid. To address this issue, a novel dynamic demand response pricing strategy in a grid-renewable generation integrated charging station environment is proposed in this paper. Renewable energy sources reduce the cost of generation and grid integration makes the system reliable. The proposed strategy models a Stackelberg game to provide dynamic prices for charging, discharging and grid power supplied for charging stations. Uncertainty and economics of renewable generation are considered for effective analysis and evaluation of the feasibility of the proposed strategy. The study considers the bidirectional flow of power and the battery degradation cost. Charging coordination is performed to optimize the cost of charging and discharging and support the grid in peak load demand management. Random charging behaviour and other parameters of PEVs are simulated using a random distribution function to resemble the real-time environment. A numerical case study validates that the proposed strategy has optimized the cost of charging and discharging and the serving capabilities of the charging station are enhanced with existing infrastructure.

Bi-Layer Model Predictive Control strategy for techno-economic operation of grid-connected microgrids

Effective management of renewable energy sources (RESs) alongside energy storage systems is crucial for maintaining stability and enhancing the economic performance of a grid-connected microgrid (MG). To address shortcomings in existing energy management strategies, which often overlook essential elements such as voltage support, battery degradation management, and uncertainties in load demand and solar generation, this study proposes a Bi-Layer Strategy for optimizing the operation of Battery Energy Storage Systems (BESS) within grid-connected MGs. The proposed approach maximizes economic returns, minimizes battery degradation, and provides reactive power support to maintain voltage stability. The method consists of the Energy Management Layer (EML) and the Control Layer (CL). The EML adopts a Model Predictive Control (MPC) framework to optimize the BESS power output over a 24-h rolling horizon based on forecasted solar generation, load demand, and electricity prices. The CL translates the optimized hourly setpoints from the EML into real-time operational commands, ensuring that the BESS adapts to actual grid conditions and maintains stable operation. Simulations conducted on a test MG, comprising 150 households, 500 kW rooftop solar PV, and a 300 kWh Li-Ion BESS demonstrate the strategy’s effectiveness under both fixed-rate and Time-of-Use (ToU) pricing schemes. The strategy reduces costs, mitigates battery degradation, and enhances voltage stability by optimizing BESS operations. Compared to a similar method in the literature, it reduces battery degradation by 24%, leading to net savings of $10,000 AUD, while the compared strategy resulted in a $5000 AUD loss. Real-time validation using the OPAL-RT platform further confirms the strategy’s effectiveness in optimizing BESS operations under real-world conditions.

Adaptive safe reinforcement learning‐enabled optimization of battery fast‐charging protocols

AbstractOptimizing charging protocols is critical for reducing battery charging time and decelerating battery degradation in applications such as electric vehicles. Recently, reinforcement learning (RL) methods have been adopted for such purposes. However, RL‐based methods may not ensure system (safety) constraints, which can cause irreversible damages to batteries and reduce their lifetime. To this end, this article proposes an adaptive and safe RL framework to optimize fast charging strategies while respecting safety constraints with a high probability. In our method, any unsafe action that the RL agent decides will be projected into a safety region by solving a constrained optimization problem. The safety region is constructed using adaptive Gaussian process (GP) models, consisting of static and dynamic GPs, that learn from online experience to adaptively account for any changes in battery dynamics. Simulation results show that our method can charge the batteries rapidly with constraint satisfaction under varying operating conditions.

Tracing Root Causes of Electric Vehicle Fires

As electric vehicles (EVs) emerge as the backbone of modern transportation, the concurrent uptick in battery fire incidents presents a disconcerting challenge. To tackle this issue effectively, it is imperative to pierce beyond the superficial causes of lithium‐ion battery (LIB) failures—such as equipment malfunctions or physical damage—and to excavate the underlying triggers. This nuanced approach is pivotal to refining EV quality, diminishing fire incidents, and bolstering consumer trust. While issues that are readily apparent to consumers, like spontaneous battery degradation, vehicular collisions, or submersion, may seem like the primary culprits, they merely scratch the surface of a more complex problem. The true mechanisms behind these fires involve internal and external short circuits, yet these explanations fall short in providing actionable guidance for manufacturers or end‐users to enhance LIB safety. The heart of the issue is identifying the root causes that bridge failure triggers and resulting fires. A total of 20 root causes are identified, linking them to real‐world scenarios like overcharging causing internal shorts or wire harness issues leading to external shorts. With this insight, stakeholders can more effectively investigate and understand EV LIB fire incidents, bolstering EV safety and reliability, and promoting consumer confidence.

Quantifying the Aging of Lithium-Ion Pouch Cells Using Pressure Sensors

Understanding the behavior of pressure increases in lithium-ion (Li-ion) cells is essential for prolonging the lifespan of Li-ion battery cells and minimizing the safety risks associated with cell aging. This work investigates the effects of C-rates and temperature on pressure behavior in commercial lithium cobalt oxide (LCO)/graphite pouch cells. The battery is volumetrically constrained, and the mechanical pressure response is measured using a force gauge as the battery is cycled. The effect of the C-rate (1C, 2C, and 3C) and ambient temperature (10 °C, 25 °C, and 40 °C) on the increase in battery pressure is investigated. By analyzing the change in the minimum, maximum, and pressure difference per cycle, we identify and discuss the effects of different factors (i.e., SEI layer damage, electrolyte decomposition, lithium plating) on the pressure behavior. Operating at high C-rates or low temperatures rapidly increases the residual pressure as the battery is cycled. The results suggest that lithium plating is predominantly responsible for battery expansion and pressure increase during the cycle aging of Li-ion cells rather than electrolyte decomposition. Electrochemical impedance spectroscopy (EIS) measurements can support our conclusions. Postmortem analysis of the aged cells was performed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) to confirm the occurrence of lithium plating and film growth on the anodes of the aged cells. This study demonstrates that pressure measurements can provide insights into the aging mechanisms of Li-ion batteries and can be used as a reliable predictor of battery degradation.

Sensor Fusion-Based Pulsed Controller for Low Power Solar-Charged Batteries with Experimental Tests: NiMH Battery as a Case Study

Solar energy is considered the major source of clean and ubiquitous renewable energy available on various scales in electric grids. In addition, solar energy is harnessed in various electronic devices to charge the batteries and power electronic equipment. Due to its ubiquitous nature, the corresponding market for solar-charged small-scale batteries is growing fast. The most important part to make the technology feasible is a portable battery charger and the associated controllers to automate battery charging. The charger should consider the case of charging to be convenient for the user and minimize battery degradation. However, the issue of slow charging and premature battery life loss plagues current industry standards or innovative battery technologies. In this paper, a new pulse charging technique is proposed that obviates battery deterioration and minimizes the overall charging loss. The solar-powered battery charger is prototyped and executed as a practical, versatile, and compact photovoltaic charge controller at cut rates. With the aid of sensor fusion, the charge controller is disconnected and reconnects the battery during battery overcharging and deep discharging conditions using sensors with relays. The laboratory model is tested using a less expensive PV panel, battery, and digital signal processor (DSP) controller. The charging behavior of the solar-powered PWM charge controller is studied compared with that of the constant voltage–constant current (CV–CC) method. The proposed method is pertinent for minimizing energy issues in impoverished places at a reasonable price.