Battery Aging Research Articles

Optimising Grid-Connected PV-Battery Systems for Energy Arbitrage and Frequency Containment Reserve

This study introduces a novel method for optimising the size and control strategy of grid-connected, utility-scale photovoltaic (PV) systems with battery storage aimed at energy arbitrage and frequency containment reserve (FCR) services. By applying genetic algorithms (GA), the optimal configurations of PV generators, inverters/chargers, and batteries were determined, focusing on maximising the net present value (NPV). Both DC- and AC-coupled systems were explored. The performance of each configuration was simulated over a 25-year lifespan, considering varying pricing, solar resources, battery ageing, and PV degradation. Constraints included investment costs, capacity factors, and land use. A case study conducted in Wiesenthal, Germany, was followed by sensitivity analyses, revealing that a 75% reduction in battery costs is needed to make AC-coupled PV-plus-battery systems as profitable as PV-only systems. Further analysis shows that changes in electricity and FCR pricing as well as limits on FCR charging can significantly impact NPV. The study confirms that integrating arbitrage and FCR services can optimize system profitability.

Neural network controller for hybrid energy management system applied to electric vehicles

Hybrid energy storage systems based on batteries and supercapacitors can mitigate the aging of electric vehicle batteries aging by avoiding high currents and rapid discharge cycles. This system requires energy management systems that efficiently split the power during real driving cycles. This paper outlines a design methodology for creating a Multilayer Perceptron neural controller that governs the power distribution between the storage system components. In parallel, a Proportional-Integral-Derivative controller was implemented to ensure voltage regulation in the primary DC bus, ensuring stable operation of the entire system and providing flexibility to the neural design. The controller was tuned using particle swarm optimization, hippopotamus optimization, and differential evolution algorithms, designed to minimize the battery root-mean-square current in a comprehensive vehicle simulation. The training was structured into layers to approach the physical problem and controller optimization facets. The controller’s primary goal is to minimize the battery strain, mitigating stress events and prolonging its lifespan. The energy management neural controller was designed using a simple drive cycle and later validated with a realistic cycle. The findings demonstrate the feasibility of achieving a significant reduction in current, both in peak value and on average, especially when compared to basic energy management strategies. The process uncovered a strategy that prioritizes the use of the supercapacitor in power balance, with this effect being more pronounced during critical load events. The main bus voltage remained stable, with no deviations exceeding 5%, owing to the voltage regulator’s stability. Additionally, the particle swarm optimization and the hippopotamus optimization techniques exhibited notable performance compared to the differential evolution algorithm for this problem. Subsequently, the design was evaluated in a more complex cycle, revealing a slight decrease in average battery current performance. However, it still maintained a significant reduction in peak battery current compared to traditional controllers. Nevertheless, this methodology demonstrated power management optimization efficacy and can be extended to more complex scenarios.

An up-to-date perspective of levelized cost of hydrogen for PV-based grid-connected power-to-hydrogen plants across all Italy

Green hydrogen holds potential for decarbonizing the energy sector, but high production costs are a major barrier. This study provides a comprehensive techno-economic-financial-environmental analysis of PV-based grid-connected hydrogen production plants, targeting hard-to-abate industries having constant hydrogen demand across all Italy. Using real hourly data, the Multi Energy System Simulator (MESS), an in-house developed rule-based tool, was employed and integrated with Genetic Algorithm for optimal plant sizing. The aim is to minimize the Levelized Cost of Hydrogen (LCOH) while complying with regulatory frameworks for green hydrogen incentives access. Key findings show that hydrogen storage is more advantageous than battery storage for supply-side flexibility, and the optimal PV-to-electrolyzer size ratio ranges from 1.8 in Southern Italy to 2.1 in Northern Italy, with hydrogen tank designed for daily storage. Considering photovoltaic, electrolyzer and battery aging models grid dependence increases by 60 % when comparing the first and worst year of operation and leads to a 7 % increase in LCOH. Transitioning from the strictest (hourly) to the least stringent (annual) temporal correlation increases certified green hydrogen by 22 %, while LCOH decreases by only 3 %, suggesting that the environmental benefits of stringent temporal requirements outweigh their moderate economic drawbacks. These findings underscore the need for additional national-level incentives to allow the deployment of this technology and achieving cost parity with grey hydrogen.

Comparing experimental designs for parameterizing semi-empirical and deep learning-based lithium-ion battery aging models

Design of Experiment (DOE) methods can be applied to optimize test plans of cycle life aging studies with the aim to efficiently parameterize lithium-ion battery aging models. Since different DOEs exist and their effect on the prediction performance of battery aging models has not yet been investigated, we conducted a cycle life aging study with six commonly used DOEs (One-factor-at-a-time, Taguchi, Box–Behnken, Central Composite, Full Factorial, and D-optimal) and compare their influence on the prediction performance of a semi-empirical and a deep learning-based battery aging model. The results show that the semi-empirical model benefits the most from statistically optimized test plans. Compared to randomly selecting test plans, applying DOE methods helps to consistently achieve one of the lowest possible prediction errors for a given number of test points. Furthermore, it is shown that a D-optimal test plan and the test plans obtained from response surface methods (Box–Behnken and Central Composite) require only half as many test points as a Full Factorial test design, but still result in semi-empirical models with a high prediction accuracy that is similar to the Full Factorial test design. In contrast, deep learning-based battery aging models benefit significantly less from statistically optimized test plans. The highest prediction accuracy is achieved by the Full Factorial test plan and all other DOEs result in higher prediction errors and are even outperformed by several randomly defined test plans. Instead of using static designs, deep-learning-based models profit from a dynamic test optimization, which reduces the number of tested batteries during cycle life testing based on their information gain. We demonstrate that with our proposed dynamic test reduction algorithm, which analyzes the information gain based on aging features extracted after 100 EFC of cycling, up to 50% of all tested batteries of a Full Factorial test plan can be excluded from the cycle life study without deteriorating the prediction accuracy of the resulting deep learning-based battery aging model.

Exploration of Imbalanced Regression in state-of-health estimation of Lithium-ion batteries

The state of health (SOH) estimation for lithium-ion batteries based on deep learning (DL) has made great progress. However, due to different electrochemical compositions of lithium-ion batteries, different ways of conducting experiments and other factors, the degradation process of some batteries shows longer early degradation time and shorter later degradation time, resulting in a long-tailed distribution of degradation data. This leads to the problem of data imbalance in SOH estimation tasks, which affects the accuracy of SOH estimation. This article explores the long-tailed distribution phenomenon in the field of batteries and the corresponding imbalanced regression problem it brings to the estimation of battery SOH. In addition, a method for improving model performance is proposed. Specifically, we use a quadratic interpolation and standardization method to analyze the battery data to ensure the consistency of data features. By discretized analysis of continuous problems, the label distribution smoothing (LDS) method is applied to deep neural networks to analyze and solve this imbalanced regression problem. By convolution processing with the kernel function and label distribution, the weights corresponding to different labels are calculated, which improves the estimation accuracy. We conducted battery aging experiments and verified that the degradation data follows a long-tailed distribution. The effectiveness of the final method was validated on our experimental data and a publicly available dataset.

Thermal runaway behaviour of a cylindrical lithium-ion battery during charge and discharge processes: A comprehensive numerical study

Lithium-ion batteries (LIBs) may experience thermal runaway (TR) accidents during charge and discharge processes. To ensure the safe operation of batteries, it is very important to analyse the TR characteristics during charge and discharge processes. In this work, we have developed a coupled electrochemical thermal (ECT) – TR (ECT-TR) model for a commercial 18,650 type 2.6 Ah NCM523/graphite battery. This model allows for the examination of TR features under different ambient temperatures, effective heat transfer coefficients (h), and C-rates during both charge and discharge processes. We first analysed the temporal evolution of the battery's surface center temperature and the heat generated by chemical reactions under a spectrum of environmental variables. Subsequently, we introduce a battery risk map that delineates distinct zones, including the safe zone, battery chemical reaction zone, battery failure zone, and TR zone, across varying environmental conditions. The results suggest that the battery's TR risk during the charge process exceeds that during the discharge process, with both exhibiting reduced risk compared to that during the cycle process. However, it is noteworthy that the risk of accelerated battery aging due to chemical reactions is more pronounced in the discharge process compared to the charge process.

Modeling and Analysis of Current Loading Effects on Electric Vehicle’s Lithium-Ion Batteries: A MATLAB-Based Model Approach

Beyond portable mobile devices, lithium-ion batteries play a crucial role in electric vehicle operations and stationary grid power generation. However, the aging of lithium-ion batteries, often accelerated by extreme temperatures and load current influences, requires thorough examination and solution. The high load current, cycling, temperature differential, and operational conditions are factors contributing to the reduction in capacity and shortened lifespan of lithium-ion batteries. In this study, a lithium-ion (LiNixMnyCozO2) battery was modeled by using the MATLAB/Simulink model technique. In order to investigate the effect of resistance build-up in the batteries, the capacity of the batteries (old and new batteries) was analyzed over different usage periods: 360 cycles, 1000 cycles, and 2000 cycles. A cooling system was introduced to explicitly carry out an inductive analysis of the effect of temperature on the performances of the batteries. The effect of load current on the capacity of the battery was examined between 30 A and 100 A. The results showed that the available capacity of a battery is proportional to its usage rate. Generally, when the load current on the batteries (old and new batteries) was 30 A, the battery was ideally in good health even after 1000 cycles for a 2 h discharge time. In addition, the old battery, however, showed a capacity decrease to about 74.15% and 74.94% for scenarios 1 and 2 after 1000 cycles for a 2 h discharge time when the batteries were subjected to a 100 A discharge current. Amongst other factors, scenarios 1 and 2 can be differentiated by whether the battery pack discharges uniformly or non-uniformly, whether the individual cells operate under the same or different discharge cycles, and whether the batteries are with cooling or without a cooling system. The voltage and temperature differences between the old and new batteries, after 2000 cycles for the 100 A load current, are 4.0 V and 5.3 °C (scenario 2), respectively. Moreover, after 360 cycles at a 100 A discharge current, the temperature difference between the old and new batteries was 4.5 °C in scenario 1 and 2.3 °C in scenario 2. Based on the results obtained in this study, useful equations for proper calibration, voltage, and cooling switching time characteristics were proposed. Additionally, the study results indicated that at higher load currents, battery degradation became less affected by temperature differentials. The results of this study will aid in the adequate load optimization and thermal management of lithium-ion batteries for electric vehicle applications.

Aging trajectory prediction of lithium-ion batteries based on mechanical-electrical features via nonlinear autoregressive and regression neural networks

The prediction of the lifespan of lithium-ion batteries (LIBs) is crucial for gaining early insights into battery degradation, enabling the optimization of battery usage and management strategies. This work assesses the expansion and aging characteristics of LIBs under various operating conditions and develops a combined machine-learning method to predict the aging trajectory of LIBs based on mechanical and electrical features. To avoid errors caused by secondary data processing, five mechanical-electrical features are directly extracted from the stress and electrochemical characteristic curves to reflect the battery aging state. A nonlinear autoregressive (NAR) neural network is adopted to predict the change trend of aging features (AFs) using historical data. Subsequently, the nonlinear regression (NR) neural network is employed for aging trajectory prediction using the aging feature dataset, which consists of both historical and predicted data. The experimental results demonstrate that the proposed method can ensure that the mean absolute error (MAE) in aging trajectory prediction is within 0.25 %. The method comprehensively analyzes and utilizes the mechanical-electrical properties of LIBs to enhance the predictive accuracy, expanding the scope of the predictive model in practical applications.

Rapid estimation of lithium-ion battery capacity and resistances from short duration current pulses

Rapid onboard diagnosis of battery state of health enables the use of real-time control strategies that can improve product safety and maximize battery lifetime. However, onboard prediction of battery state-of-health is challenging due to the limitations imposed on diagnostic tests so as not to negatively affect the user experience and impede normal operation. To this end, we demonstrate a lightweight machine learning model capable of predicting a lithium-ion battery’s discharge capacity and internal resistance at various states of charge using only the raw voltage-capacity time-series data recorded during short-duration (100 s) current pulses. Tested on two battery aging datasets, one publicly available and the other newly collected for this work, we find that the best models can accurately predict cell discharge capacity with an average mean-absolute-percent-error of 1.66%. Additionally, we quantize and embed the machine learning model onto a microcontroller and show comparable accuracy to the computer-based model, further demonstrating the practicality of on-board rapid capacity and resistance estimation.

The heating effect analysis of electromagnetic induction heating system based on the multi objective optimization strategy

Lithium-ion battery heating in cold weather is necessary to ensure its low-temperature performance and lifetime, so the multi objective optimization heating strategy based on the non-dominated sorting genetic algorithm II is introduced to improve the heating effect of electromagnetic induction heating system, in which the generated Pareto frontier as the research sample is input in K-medoid algorithm in order to reduce the feasible range of solution sets and then get the optimal induction coil parameters combination, and the temperature rise and terminal voltage output performance of battery are characterized by the electrochemical-thermal coupling model. Firstly, under the optimal parameters combination (N = 180, f = 9 kHZ, IA = 6 A), the battery can be heated from 243.15 K to 293.15 K in 1430 s with the temperature difference of 2.74 K, and the temperature field is mainly generated by the heat conduction from active material to mandrel during the heating process due to skin effect and material property. Secondly, the internal resistance is decreased by more than 3 times after the heating, giving rise to the substantial improvement of energy output ability. Finally, neither noticeable discharging capacity retention loss nor apparent voltage output difference is founded after the 200 repeated heating cycles, as compared with the normal charging-discharging cycle at 293.15 K, so the heating strategy has almost no effect on the battery aging and lifetime deterioration. Therefore, the proposed heating strategy can realize a suitable tradeoff among temperature-rising effect, lifespan and low-temperature operating performance, which has substantial potential to be a feasible method for improving the applicability of electric vehicles in a cold weather.

Fusion State-of-Health Estimation of Lithium-Ion Batteries Based on Improved XGBoost Algorithm and Adaptive Kalman Filter

Abstract Accurately predicting the state-of-health of lithium-ion batteries (LIBs) is of paramount significance for safety and stability of battery systems. This paper introduces a fusion model, which integrates the characteristic of data-driven model and equivalent circuit model to enhance precision. The first step is to preprocess data, including extracting health features, correlation screening, and compressing data. Subsequently, the hyperparameters of XGBoost algorithm are optimized using a weighted artificial bee colony algorithm, resulting in an improved XGBoost (IXGB) data-driven model. Finally, the observed values from the data-driven model and the prior values based on the equivalent circuit model are combined through adaptive Kalman filter (AKF), developing an improved XGBoost and adaptive Kalman filter (IXGB-AKF) fusion model, which makes the most of historical experience and the current state of LIBs. Validation is conducted using publicly available NASA Li-ion Battery Aging Datasets, with different datasets under various operating conditions, including different battery cells, different discharge depths and rates of LIBs. The resulting root mean square error values of the former three operating conditions are 1.834%, 2.570%, and 3.456%, respectively. The results indicate that the IXGB-AKF fusion model exhibits good accuracy and robustness under different operating conditions.

Coupled Electro-Thermal-Aging Battery Pack Modeling—Part 1: Cell Level

This paper presents a modeling approach to capture the coupled effects of electrical–thermal aging in Li-ion batteries at the cell level. The proposed semi-empirical method allows for a relatively high accuracy and low computational cost compared to expensive computer simulations. This is something current models often lack but is essential for system level simulations, relevant for electric vehicle manufacturers. The aging analysis includes both cycling and calendar effects across the lifetime of the cell and reversible and irreversible heat in a lumped-mass model to capture the temperature evolution of the cell in operation. The Thévenin equivalent circuit model with capacitance used to simulate the electrical behavior of the cell was experimentally validated, showing a high correlation with the proposed model during the charging and discharging phases. A maximum error of 3% on the voltage reading was identified during discharge with the complete model. This model was also designed to be used as a stepping stone for a comprehensive model at the module and vehicle levels that can later be used by designers.

Robust estimation of lithium-ion battery state of health based on electro-thermal features and machine learning

Accurately estimating the State of Health (SOH) of lithium-ion (Li-ion) batteries is crucial for preventing overcharging and over-discharging, thereby extending battery lifespan. This paper presents a novel method for SOH estimation in lithium-ion batteries by leveraging electro-thermal features, a backpropagation neural network (BPNN), and a particle swarm optimization (PSO) algorithm. First, three health-related features—constant current charging time (CCCT), relative constant current charging time (RCCCT), and maximum temperature during discharge (MTT)—are extracted as indicators of SOH. The correlation between these features and the SOH is validated using Grey Relational Analysis (GRA). Next, a BP neural network is utilized to model the nonlinear relationship between the extracted features and SOH. The PSO algorithm is then employed to optimize the parameters of the BP neural network, enhancing the accuracy of the SOH estimation. The proposed method, which combines electro-thermal features with the optimized BP neural network, is validated through three independent lithium-ion battery aging experiments. Experimental results demonstrate that the proposed approach achieves high estimation accuracy and exhibits strong generalization performance for SOH prediction.

A method for estimating lithium-ion battery state of health based on physics-informed machine learning

Data-driven approaches are widely applied in estimating the State of Health (SOH) of Lithium-Ion Batteries (LIB). However, these methods often suffer from a lack of interpretability. To address this issue, this article proposes a method called Battery Physics-Informed Neural Network (BPINN) to enhance the interpretability of the Feedforward Neural Network (FNN) in SOH prediction. This article is based on the concept of Physics-Informed Neural Networks (PINN). Features are initially extracted from Incremental Capacity (IC) curves to characterize the battery aging process. Notably, IC curve peaks (P-IC) reflect electrochemical reactions during charge and discharge cycles. The degradation of these peaks is directly related to the loss of active materials, causing SOH reduction. This article transforms the monotonic relationship between P-IC and SOH into a physical constraint, which is embedded into model training. Furthermore, during prediction, a secondary “training” based on physical constraints is applied to the FNN prediction results, enhancing the model's interpretability and accuracy. The proposed method is validated using publicly available battery datasets from NASA and the University of Oxford. Results show BPINN effectively improves SOH prediction accuracy, reducing the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to below 0.4 %.

Optimized multi-stage constant current fast charging protocol suppressing lithium plating for lithium-ion batteries using reduced order electrochemical-thermal-life model

Multi-stage Constant Current (MCC) is a state-of-the-art fast-charging protocol considering battery aging. It divides the charging process into multiple stages, each with a different current amplitude based on specific transition criteria, significantly influencing battery performance, such as charging time, degradation rate, and thermal effects. A key challenge in designing MCC protocols is addressing the lithium plating (LiP), which can accelerate degradation and pose a severe risk of thermal runaway. Since the LiP onset conditions vary between fresh and aged cells, this paper proposes an optimized MCC (O-MCC) charging protocol suppressing LiP based on the battery's state of health. To accurately simulate LiP conditions, different platforms of reduced-order electrochemical-thermal-life models are designed, compared, and optimized for speed and accuracy using a genetic algorithm, resulting in a 36.4 % reduction in computational time while maintaining the accuracy of the Pseudo Two-Dimensional model. The Nonlinear Model Predictive Control algorithm is then used to optimize the MCC protocol, minimizing charging time while preventing LiP throughout life. Experimental results show that O-MCC reduces charging time by 11.7 % and capacity loss by 59.4 %, enhancing battery safety. Additionally, O-MCCs with varying constraints are developed to meet specific demands and simulated at the battery pack level.

An Enhanced State-Space Modeling for Detecting Abnormal Aging in VRLA Batteries

The knowledge of battery aging is an indicator that allows controlling the performance of large battery banks. State of Health (SOH) is typically the metric used, encompassing all possible mechanisms in a percentage indicator, with the Coulomb Counting as the most common method. Hence, an in-depth study of aging based on known models provides proper information for correctly managing batteries. This article proposes an aging-sensitive 3-RC-array-equivalent electrical circuit model to characterize the behavior of batteries throughout their useful life, identifying parametric changes as complementary information to the state of health. This model was validated based on experimental tests with 2 V and 6 Ah VRLA batteries aged according to the manufacturer’s recommended use. The results reveal a proportionality through capacity degradation. Then, a control group of batteries was subjected to overcharge and over-discharge conditions. The information given by Coulomb Counting SOH and the proposed method were evaluated. The proposed method provides additional information to the SOH, enhancing the distinguishing capability between typical aging performance and misused aging performance, resulting in a useful tool capable of identifying the aging associated with parametric changes in a time-invariant system where aging is treated as an imminent multiplicative fault.

Methodology for comparative assessment of battery technologies: Experimental design, modeling, performance indicators and validation with four technologies

An increasing number of applications with diverse requirements incorporate various battery technologies. Selecting the most suitable battery technology becomes a tedious task as several aspects need to be taken into account. Two of the key aspects are the battery characteristics under temperature variations and their degradation. While numerous contributions using tailored assessment methods to evaluate both aspects for a particular application exist in the literature, a general methodology for analysis is necessary to enable a quantitative comparison between different technologies. We propose in this paper a novel methodology, based on performance indicators, to quantify the potential and limitations of a battery technology for diverse applications sharing a similar operational profile. A quantification of phenomena such as the influence of high and low temperatures on the battery, or the effect of cycling and state of charge on battery aging is obtained. In pursuit of these indicators, an experimental procedure and the fitting of aging model parameters that allow their calculation are proposed. As an additional outcome of this work, a general aging model that allows comprehensive analysis of aging behavior is developed and the trade-off between experimental time and accuracy is analyzed to find an optimal experimental time between 2 and 4 months, depending on the studied battery technology. Finally, the proposed methodology is applied to four battery technologies in order to show its potential in a real case-study.

Machine Learning for Battery Quality Classification and Lifetime Prediction Using Formation Data

Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety benefits. Here, we propose a data-driven approach with machine learning to classify the battery quality and predict the battery lifetime before usage only using formation data. We extract three classes of features from the raw formation data, considering the statistical aspects, differential analysis, and electrochemical characteristics. The correlation between over 100 extracted features and the battery lifetime is analysed based on the ageing mechanisms. Machine learning models are developed to classify battery quality and predict battery lifetime by features with a high correlation with battery ageing. The validation results show that the quality classification model achieved accuracies of 89.74% and 89.47% for the batteries aged at 25°C and 45°C, respectively. Moreover, the lifetime prediction model is able to predict the battery end-of-life with mean percentage errors of 6.50% and 5.45% for the batteries aged at 25°C and 45°C, respectively. This work highlights the potential of battery formation data from production lines in quality classification and lifetime prediction.

An Adaptive Combined Method for Lithium‐Ion Battery State of Charge Estimation Using Long Short‐Term Memory Network and Unscented Kalman Filter Considering Battery Aging

AbstractThe accurate estimation of battery state of charge (SOC) enables the reliable and safe operation of lithium‐ion batteries. Data‐driven SOC estimation is considered an emerging and effective solution. However, existing data‐driven SOC estimation methods typically involve direct estimation and lack effective feedback correction. Moreover, battery degradation poses additional challenges to accurate SOC estimation. Therefore, this study proposes an adaptive combined method for battery SOC estimation based on a long short‐term memory (LSTM) network and unscented Kalman filter (UKF) algorithm considering battery aging status. First, an LSTM model is constructed to characterize the battery's dynamic performance instead of traditional battery models. Then, the UKF algorithm is employed to perform SOC estimation through the feedback of terminal voltage prediction. To enhance estimation accuracy under different aging statuses, a proportional‐integral‐derivative controller is employed to correct the capacity fading during the SOC estimation process. Validation results indicate that the terminal voltage prediction model demonstrates exceptional robustness against interference from current and voltage noise. Compared to the traditional estimation method combining the deep learning model and Kalman filter algorithm, the proposed method demonstrates superior estimation accuracy under various complex operating conditions. Furthermore, the proposed method outperforms the traditional method in estimation performance during battery aging.

An Arrhenius-Based Simulation Tool for Predicting Aging of Lithium Manganese Dioxide Primary Batteries in Implantable Medical Devices

This article presents a novel aging-coupled predictive thermo-electrical dynamic modeling tool tailored for primary lithium manganese dioxide (Li-MnO2) batteries in active implantable medical devices (AIMDs). The aging mechanisms of rechargeable lithium batteries are well documented using computationally intensive physics-based models, unsuitable for real-time onboard monitoring in AIMDs due to their high demands. There is a critical need for efficient, less demanding modeling tools for accurate battery health monitoring and end-of-life prediction as well as battery safety assessment in these devices. The presented model in this article simulates the battery terminal voltage, remaining capacity, temperature, and aging during active discharge, making it suitable for real-time health monitoring and end-of-life prediction. We incorporate a first-order dynamic for internal resistance growth, influenced by time, temperature, discharge depth, and load current. By adopting Arrhenius-type kinetics and polynomial relationships, this model effectively simulates the combined impact of these variables on battery aging under diverse operational conditions. The simulation handles both the continuous micro-ampere-level demands necessary for device housekeeping and periodic high-rate pulses needed for therapeutic functions, at a constant ambient temperature of 37 °C, mimicking human body conditions. Our findings reveal a gradual, nonlinear increase in internal resistance as the battery ages, rising by an order of magnitude over a period of 5 years. Sensitivity analysis shows that as the battery ages and load current increases, the terminal voltage becomes increasingly sensitive to internal resistance. Specifically, at defibrillation events, the ∂V∂R trajectory dramatically increases from 10−12 to 10−8, indicating a fourth-order-of-magnitude enhancement in sensitivity. A model verification against experimental data shows an R2 value of 0.9506, indicating a high level of accuracy in predicting the Li-MnO2 cell terminal voltage. This modeling tool offers a comprehensive framework for effectively monitoring and optimizing battery life in AIMDs, therefore enhancing patient safety.