Search

Incorporating renewables in the power grid presents challenges for stability, reliability, and operational efficiency. Integrating energy storage systems (ESSs) offers a solution by managing unpredictable loads, enhancing reliability, and serving the grid. Hybrid storage solutions have gained attention for specific applications, suggesting higher performance in some respects. This article compares the performance of hybrid energy storage systems (HESSs) to a single battery, evaluating their energy supply cost and environmental impact through optimization problems. The optimization model is based on a MILP incorporating the energy and degradation terms. It generates an optimized dispatch, minimizing cost or environmental impact of supplying energy to a generic load. Seven technologies are assessed, with an example applied to an industrial site combining a vanadium redox flow battery (VRFB) and lithium battery considering the demand of a local load (building). The results indicate that efficiency and degradation curves have the highest impact in the final costs and environmental functions on the various storage technologies assessed. For the simulations of the example case, a single system only outperforms the hybrid system in cases where lithium efficiency is higher than approximately 87% and vanadium is lower approximately 82%.

Bidirectional charging allows energy from the electric vehicles (EV) to be fed back into the grid, offering the possibility of price-optimized charging. However, such strategies cause higher charging cycles, which affect the cyclic aging of the battery and reduce its service life, resulting in additional costs for the user. Various approaches are used to account for battery degradation in optimizations models of bidirectional charging use-cases. In this paper, a systematic literature review is carried out to identify existing battery degradation models and to determine the most suitable one. In the models under review, degradation is integrated into the optimization's objective function. The review shows that there are mainly two strategies suitable for vehicle-to-grid (V2G) optimization problems: A weighted Ah-throughput model (wAh-model) with a constant degradation cost factor and a performance based model (pb-model) linking the degradation to measurable parameters such as capacity loss. Both models were implemented and analyzed. The results show that the wAh-model is the better optimization option, as in the pb-model the current state of health of the battery has an excessively large impact on the calculated degradation cost. It leads to excess costs due to a higher aging rate at the beginning of life which proves to be not ideal in the optimization. The sensitivity analysis reveals that altering the initial State of Health (SoH) from 95 % in the base scenario to 100 % leads to an increase in average degradation costs by factor 9.71 in the pb-model. From the evaluated base scenario the average degradation costs for the pb-model are 0.45 cent/kWh and for the wAh-model 0.23 cent/kWh. [ABSTRACT FROM AUTHOR]

Due to the complexity of the aging process, maintaining the state of health (SOH) of lithium-ion batteries is a significant challenge that must be overcome. This study presents a new SOH estimation approach based on hybrid Grey Wolf Optimization (GWO) with Bayesian Regularized Neural Networks (BRNN). The approach utilizes health features (HFs) extracted from the battery charging-discharging process. Selected external voltage and current characteristics from the charging-discharging process serve as HFs to explain the aging mechanism of the batteries. The Pearson correlation coefficient, the Kendall rank correlation coefficient, and the Spearman rank correlation coefficient are then employed to select HFs that have a high degree of association with battery capacity. In this paper, GWO is introduced as a method for optimizing and selecting appropriate hyper-p parameters for BRNN. GWO-BRNN updates the population through mutation, crossover, and screening operations to obtain the globally optimal solution and improve the ability to conduct global searches. The validity of the proposed technique was assessed by examining the NASA battery dataset. Based on the simulation results, the presented approach demonstrates a higher level of accuracy. The proposed GWO-BRNN-based SOH estimation achieves estimate assessment indicators of less than 1%, significantly lower than the estimated results obtained by existing approaches. The proposed framework helps develop electric vehicle battery prognostics and health management for the widespread use of eco-friendly and reliable electric transportation. [ABSTRACT FROM AUTHOR]

The use of electrochemical cells is becoming more widespread, especially in the energy industry and battery energy storage systems (BESSs). As we continue to deploy BESSs, it becomes increasingly important for us to understand how these systems age and accurately predict their performance over time. This knowledge is essential for ensuring that the systems operate optimally and can be properly maintained. Since the structure of a BESS is different from a single electrochemical cell, the existing models at the cell level cannot predict and estimate the life of the BESS with suitable accuracy. Furthermore, the test protocols available at the cell level mostly cannot be executed at the BESS level for many reasons. Therefore, in this paper, a review of test protocols for building aging models for BESSs has been performed. After reviewing the protocols for a single electrochemical cell and addressing the differences between BESSs and cells, a review of the works performed on a larger scale has been carried out, and the possible ways for testing the BESS for aging models were investigated. [ABSTRACT FROM AUTHOR]

Abstract The penetration of electrical vehicles (EVs) is exponentially rising to decarbonize the transport sector resulting in the research problem regarding the future of their retired batteries. Landfill disposal poses an environmental hazard, therefore, recycling or reusing them as second-life batteries (SLBs) are the inevitable options. Reusing the EV batteries with significant remaining useful life in stationary storage applications maximizes the economic benefits while extending the useful lifetime before recycling. Following a critical review of the research in SLBs, the key areas were identified as accurate State of Health (SOH) estimation, optimization of health indicators, battery life cycle assessment including repurposing, End-Of-Life (EOL) extension techniques and significance of first-life degradation data on ageing in second-life applications. The inconsistencies found in the reviewed literature showed that the absence of degradation data from first as well as second life, has a serious impact on accurate remaining useful life (RUL) prediction and SOH estimation. This review, for the first time, critically surveyed the recent studies in the field of identification, selection and control of application-based health indicators in relation to the accurate SOH estimation, offering future research directions in this emerging research area. In addition to the technical challenges, this paper also analyzed the economic perspective of SLBs, highlighting the impact of accuracy in second-life SOH estimation and RUL extension on their projected revenue in stationary storage applications. Lack of standard business model based on future market trends of energy and battery pricing and governing policies for SLBs are identified as urgent research gaps.

With the surge of electric vehicles, fast charging has become one of the major challenges for the development of Li-ion and Li metal batteries. The degradation of battery electrodes at fast charging has been identified as among the gating factors. While there have been extensive studies on anode and cathode degradation modes, not sufficient efforts have been made to dive deep into the kinetics of battery charging and its influence on electrode degradation, especially during fast charging. This review presents a comprehensive yet concentrated perspective into such issues. By tracing back to the kinetic origins of battery charging, it is revealed that the intrinsic properties of electrode active materials and the microstructures of electrode are of great importance in determining electrode kinetics. Most of the electrode degradation modes are closely related to the high overpotentials and the spatial inhomogeneity in Li concentration and pertinent characteristics, which are results of the sluggish electrode kinetics during fast charging. Approaches to mitigate electrode degradation are summarized from the aspect of improving electrode kinetics and circumventing detrimental side reactions.

Energy management in the residential sector contributes to energy system dispatching and security with the optimal use of renewable energy systems (RES) and energy storage systems (ESSs) and by utilizing the main grid based on its state. This work focuses on optimal energy flow, ESS parameters, and energy consumption scheduling based on demand response (DR) programs. The primary goals of the work consist of minimizing electricity costs while simultaneously extending the lifetime of ESSs in conjunction with extracting maximum benefits throughout their operational lifespan and reducing CO2 emissions. Effective ESS and photovoltaic (PV) energy usage prices are modeled and an efficient energy flow management algorithm is presented, which considers the life cycle of the ESSs including batteries, electrical vehicles (EVs) and the efficient use of the PV system while reducing the cost of energy consumption. In addition, an optimization technique is employed to obtain the optimal ESS parameters including the size and depth of discharge (DOD), considering the installation cost, levelized cost of storage (LCOS), winter and summer conditions, energy consumption profile, and energy prices. Finally, an optimization technique is applied to obtain the optimal energy consumption scheduling. The proposed system provides all of the possibilities of exchanging energy between EV, battery, PV system, grid, and home. The optimization problem is solved using the particle swarm optimization algorithm (PSO) in MATLAB with an interval time of one minute. The results show the effectiveness of the proposed system, presenting an actual cost reduction of 28.9% and 17.7% in summer and winter, respectively, compared to a base scenario. Similarly, the energy losses were reduced by 26.7% in winter and 22.3% in summer, and the EV battery lifetime was extended from 9.2 to 19.1 years in the winter scenario and from 10.4 to 17.7 years in the summer scenario. The integrated system provided a financial contribution during the operational lifetime of EUR 11,600 and 7900 in winter and summer scenarios, respectively. The CO2 was reduced by 59.7% and 46.2% in summer and winter scenarios, respectively.

As the Electric Vehicle market grows, understanding the implications of battery degradation on the driving experience is key to fostering trust among users and improving End of Life estimations. This study analyses various road types, charging behaviours and Electric Vehicle models to evaluate the impact of degradation on the performance. Key indicators related to the speed, acceleration, driving times and regenerative capabilities are obtained for different degradation levels to quantify the performance decay. Results show that the impact is highly dependent on the road type and nominal battery capacity. Vehicles with long and medium ranges show a robust performance for common driving conditions. Short-range vehicles perform adequately in urban and rural road conditions, but on highways, speed and acceleration reductions of up to 6.7 km/h and 3.96 (km/h)/s have been observed. The results of this study suggest that degradation should not be a concern for standard driving conditions and mid- and long-range vehicles currently dominate the market. In addition, the results are used to define a functional End of Life criterion based on performance loss, beyond the oversimplified 70–80% State-of-Health threshold, which does not consider individual requirements.

With the proliferation of electric vehicles (EVs) and the consequential increase in EV battery circulation, the need for accurate assessments of battery health and remaining useful life (RUL) is paramount, driven by environmentally friendly and sustainable goals. This study addresses this pressing concern by employing data-driven methods, specifically harnessing deep learning techniques to enhance RUL estimation for lithium-ion batteries (LIB). Leveraging the Toyota Research Institute Dataset, consisting of 124 lithium-ion batteries cycled to failure and encompassing key metrics such as capacity, temperature, resistance, and discharge time, our analysis substantially improves RUL prediction accuracy. Notably, the convolutional long short-term memory deep neural network (CLDNN) model and the transformer LSTM (temporal transformer) model have emerged as standout remaining useful life (RUL) predictors. The CLDNN model, in particular, achieved a remarkable mean absolute error (MAE) of 84.012 and a mean absolute percentage error (MAPE) of 25.676. Similarly, the temporal transformer model exhibited a notable performance, with an MAE of 85.134 and a MAPE of 28.7932. These impressive results were achieved by applying Bayesian hyperparameter optimization, further enhancing the accuracy of predictive methods. These models were bench-marked against existing approaches, demonstrating superior results with an improvement in MAPE ranging from 4.01% to 7.12%.

Electric vehicles (EVs) are crucial in mitigating global emissions by replacing internal combustion engines. The capacity of EV batteries, coupled with their charging infrastructure, offers the added advantage of supplying flexible demand capacity and providing demand response benefits to the power grid, which is essential as overall demand increases. EVs ready for vehicle-to-everything (V2X) applications and chargers that support them enhance this flexibility by allowing for varied storage applications. However, to fully harness these benefits, it is vital to consider EV drivers’ charging habits and optimize the charging and discharging controls to minimize battery life impact. This study examines various V2X applications in North America and their effects on battery longevity, considering EV charging patterns. Additionally, it investigates advanced aging-aware optimization algorithms for managing bidirectional charging.

The variability of solar radiation presents significant challenges for the integration of solar photovoltaic (PV) energy into the electrical system. Incorporating battery storage technologies ensures energy reliability and promotes sustainable growth. In this work, an energy analysis is carried out to determine the installation size and the operating setpoint with optimal constant monthly power through an iterative calculation process, considering various operating setpoints and system parameters. A degradation model is integrated according to the curves offered by battery manufacturers and the charge–discharge cycles are calculated using the rainflow method to guarantee a reliable analysis of the plant. Through massive data analysis in a long-term simulation, indicators are generated that allow for establishing a relationship between the energy unavailability of the system and the BESS dimensions.

An energy storage system works in sync with a photovoltaic system to effectively alleviate the intermittency in the photovoltaic output. Owing to its high power density and long life, supercapacitors make the battery–supercapacitor hybrid energy storage system (HESS) a good solution. This study considers the particularity of annual illumination due to climate conditions in Harbin, China. A global optimal PV-HESS sizing method is proposed by constructing a PV-HESS operating cost model and taking the annual system operating cost as the objective function. To consider the effect of battery life degradation due to different charge and discharge rates and charge and discharge times, a semi-empirical model based on the Arrhenius model was used to quantify the battery life degradation. Based on the effects of different seasons and different photovoltaic panel sizes, batteries, and supercapacitors on the optimization results, four scenarios are proposed. The feasibility of the system configuration corresponding to the four scenarios is discussed, and an optimal sizing configuration of the system is obtained. The simulation results show that the proposed method can effectively balance the degradation of the ESS due to irregular charging and discharging and determine the minimum operating cost and a reasonable sizing configuration of the system. [ABSTRACT FROM AUTHOR]

Operating Lithium-ion batteries requires a deep understanding of their performance. Laboratory experiments are conducted mostly under controlled ambient conditions and repetitive loads, whereas real-world operations feature a large spectrum of operating conditions. This leads to a large gap, which in-operation battery field data aims to close. Literature states the necessity for large datasets for field data studies; however, these are not available today. In this article, an overview of the possibilities of sourcing battery field data from Android devices is presented. Today, there are two billion Android devices in active use, and most of them are equipped with a lithium-ion battery and are also connected to the internet. An Android device study featuring multiple devices is conducted, evaluating signal quality and differences. Exemplary data analysis with a focus on state estimation algorithms is then tested on the sourced data. [ABSTRACT FROM AUTHOR]

In this paper, a small-scale PV/Wind/Diesel Hybrid Microgrid System (HMS) for the city of Yanbu, Saudi Arabia is optimally designed, considering the uncertainties of renewable energy resources and battery degradation. The optimization problem is formulated as a multi-objective one with two objective functions: the Loss of Power Supply Probability (LPSP) and the Cost of Electricity (COE). An Improved Decomposition Multi-Objective Evolutionary Algorithm (IMOEAD) is proposed and applied to solve this problem. In this approach, different decomposition schemes are combined effectively to achieve better results than the classical MOEA/D approach. Twelve case studies are investigated based on different scenarios and different numbers of houses (5 and 10 houses). Each time, the suggested approach produced a set of solutions that formed a Pareto front (PF). Considering a variety of parameters, the optimal compromise option can be selected by the designer from the PF. [ABSTRACT FROM AUTHOR]

This paper proposes a bi-level optimization scheduling strategy for integrated photovoltaic (PV) and energy storage systems (ESS) to meet electric vehicle (EV) charging demands while reducing charging costs. First, a battery degradation cost model is developed in order to convert the long-term costs into short-term costs for real-time operation. The upper layer of ESS and power grid operation strategies are obtained by minimizing costs associated with battery degradation and distribution grid costs. The lower layer considers the PV uncertainty and the error caused by the upper layer operation strategy, and obtains the lower layer operation strategy by adding a penalty function to minimize fluctuations in power. Second, the author proposes a global optimization algorithm that combines Particle Swarm Optimization (PSO) and Sequential Quadratic Programming (SQP) in order to solve the above-mentioned models, effectively combining the global search feature of PSO with the local search capability of SQP. Finally, the bi-level optimization scheduling strategy is obtained by solving the model through the algorithm. Simulation results verify the practicality of the scheduling strategy and the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries play a vital role in many systems and applications, making them the most commonly used battery energy storage systems. Optimizing their usage requires accurate state-of-health (SoH) estimation, which provides insight into the performance level of the battery and improves the precision of other diagnostic measures, such as state of charge. In this paper, the classical machine learning (ML) strategies of multiple linear and polynomial regression, support vector regression (SVR), and random forest are compared for the task of battery SoH estimation. These ML strategies were selected because they represent a good compromise between light computational effort, applicability, and accuracy of results. The best results were produced using SVR, followed closely by multiple linear regression. This paper also discusses the feature selection process based on the partial charging time between different voltage intervals and shows the linear dependence of these features with capacity reduction. The feature selection, parameter tuning, and performance evaluation of all models were completed using a dataset from the Prognostics Center of Excellence at NASA, considering three batteries in the dataset. [ABSTRACT FROM AUTHOR]

With the surge of electric vehicles, fast charging has become one of the major challenges for the development of Li-ion and Li metal batteries. The degradation of battery electrodes at fast charging has been identified as among the gating factors. While there have been extensive studies on anode and cathode degradation modes, not sufficient efforts have been made to dive deep into the kinetics of battery charging and its influence on electrode degradation, especially during fast charging. This review presents a comprehensive yet concentrated perspective into such issues. By tracing back to the kinetic origins of battery charging, it is revealed that the intrinsic properties of electrode active materials and the microstructures of electrode are of great importance in determining electrode kinetics. Most of the electrode degradation modes are closely related to the high overpotentials and the spatial inhomogeneity in Li concentration and pertinent characteristics, which are results of the sluggish electrode kinetics during fast charging. Approaches to mitigate electrode degradation are summarized from the aspect of improving electrode kinetics and circumventing detrimental side reactions. [ABSTRACT FROM AUTHOR]

The increasing popularity of electric vehicles is driven by their compatibility with sustainable energy goals. However, the decline in the performance of energy storage systems, such as batteries, due to their degradation puts electric vehicles and hybrid electric vehicles at a disadvantage compared to traditional internal combustion engine vehicles. This paper presents a prognostic-based control framework for hybrid electric vehicles to reduce the cost of operating hybrid electric vehicles by considering the degradation of energy storage systems. The strategy utilizes a degradation forecasting model of electrical components to predict their degradation pattern and uses the prediction to control hybrid electric vehicles via their energy management systems to reduce the degradation of components. A real-time controller hardware-in-the-loop is set up to run the proposed strategy. An hybrid electric vehicle model is developed on Typhoon (i.e., a real-time simulator), which is connected to two layers, energy management and degradation forecasting layer, deployed in Raspberry Pis, respectively. All these components are communicated through CAN communication, where the actual operating condition of the vehicle is sent from Typhoon to each Raspberry Pis to implement the proposed control strategy. With this approach, the cost of operating hybrid electric vehicles can be reduced, making them more competitive than their combustion engine counterparts shown in both numerical simulations and the CHIL experiment.

The lithium-ion batteries used in electric vehicles have a shorter lifespan than other vehicle components, and the degradation mechanism inside these batteries reduces their life even more. Battery degradation is considered a significant issue in battery research and can increase the vehicle’s reliability and economic concerns. This study highlights the degradation mechanisms in lithium-ion batteries. The aging mechanism inside a battery cannot be eliminated but can be minimized depending on the vehicle’s operating conditions. Different operating conditions affect the aging mechanism differently. Knowing the factors and how they impact battery capacity is crucial for minimizing degradation. This paper explains the detailed degradation mechanism inside the battery first. Then, the major factors responsible for the degradation and their effects on the battery during the operation of electric vehicles are discussed. Also, the different techniques used to model the degradation of a battery and predict its remaining life are explained in-depth, along with the techniques to abate the aging process. Finally, this study focuses on the research gaps, difficulties in predicting the lifetime, and reducing the degradation mechanism of a battery used in electric vehicles.

This paper proposes a stochastic programming (SP) method for coordinated operation of distributed energy resources (DERs) in the unbalanced active distribution network (ADN) with diverse correlated uncertainties. First, the three-phase branch flow is modeled to characterize the unbalanced nature of the ADN, schedule DER for three phases, and derive a realistic DER allocation. Then, both active and reactive power resources are co-optimized for voltage regulation and power loss reduction. Second, the battery degradation is considered to model the aging cost for each charging or discharging event, leading to a more realistic cost estimation. Further, copula-based uncertainty modeling is applied to capture the correlations between renewable generation and power loads, and the two-stage SP method is then used to get final solutions. Finally, numerical case studies are conducted on an IEEE 34- bus three-phase ADN, verifying that the proposed method can effectively reduce the system cost and co-optimize the active and reactive power.

As one of the most significant part of carbon neutralisation, the rapid growth of electric vehicle (EV) market in past few years has greatly expedited the transport electrification, which, however, has brought in new challenges to power system including isolated distribution network for commercial and industrial set up. Stochastic and complex EV behaviours would violate network permissible operation region and increase costs for system operators. To address these problems, a chance-constrained smart EV charging strategy in a DC microgrid (DCMG) supporting large office complex is proposed to minimize system cost from distribution network and fleet battery degradation cost from EVs providing ancillary service to the DCMG. When dealing with uncertainties from EVs, a Markov Chain Monte Carlo (MCMC) model is built to couple different parameters in load profiles and characterize the time series of likelihood of charging and discharging. A state-of-charge (SOC) space random walk method is then proposed to solve the resultant massive recursive probabilistic charging requirements. Based on that, a three-stage optimization framework is established to illustrate the work flow in system level. Numerical results verifying the effectiveness of the proposed method are also presented.

To achieve the ambitious goal of net-zero by 2050, the power sector in many countries has been increasingly using renewable energy sources (RES). The power system balance will be significantly challenged due to the intermittency of RES. Lithium-ion battery energy storage systems (BESS) can effectively address the challenge by providing flexibility to fill the mismatch between the intermittent supply and the varying demand, generating revenues for BESS to payback the initial investment. However, every grid participation also results in an inevitable lifetime loss. From the asset owner’s perspective, therefore, it is important to balance the short-term revenue from the fluctuating electrical market with the long-term battery life loss. To achieve this in this study, first, a novel online-applied battery life model is established, which is computationally efficient to comprehensively consider the battery’s historical aging, state of charge (SOC), charge–discharge rate (Crate). Then, an online-applied sliding-window dynamic programming (SWDP) strategy is proposed to optimize short-term grid service revenues and long-term battery life losses. The SWDP is also compared with an offline-applied dynamic programming (DP) strategy. The simulation results show that the cost due to battery life decay is significant, which indicates the importance of tracking the battery life loss in the battery asset management. Additionally, the prediction accuracy of electricity price significantly influences the performance of SWDP strategy. The BESS’s total profit can reduce by half in the considered case study if the electricity prediction error is 12.5%.

This paper proposes a hierarchical energy control strategy for a hybrid dual fuel cell/battery tramway, combining online and offline optimization methods while considering the battery's dynamic behavior. In the upper layer, an online band-pass filter-based extremum-seeking control (BFESC) is employed to estimate the reference power between the dual fuel cell system and battery. In addition, the battery's dynamic behavior is considered a penalty function of the BFESC to maintain its parameters within the desired boundaries. In the middle layer, the power requirement for each fuel cell system is calculated by using an offline method called the map search method. Finally, the fuel cell and battery provide the required power to the DC bus through DC/DC converters, which are controlled by PID controllers in the lower layer. To verify the effectiveness of the proposed control strategy, a simulation model is built in Matlab/Simulink. The results demonstrate that the dual fuel cell/battery system under the control of the proposed energy management strategy (EMS) can operate efficiently while improving the battery's durability. The efficiency of the fuel cell system when using the proposed EMS was lower than 4% compared with the non-constraint EMS. However, the capacity loss of the battery could improve up to 25.9% in high-current and high-SOC cases. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS) is critical for improving the economy of hybrid powertrains and the durability of energy sources. In this paper, a novel EMS based on a twin delayed deep deterministic policy gradient algorithm (TD3) is proposed for a fuel cell hybrid electric bus (FCHEB) to optimize the driving cost of the vehicle. First, a TD3-based energy management strategy is established to embed the limits of battery aging and fuel cell power variation into the strategic framework to fully exploit the economic potential of FCHEB. Second, the TD3-based EMS is compared and analyzed with the deep deterministic policy gradient algorithm (DDPG)-based EMS using real-world collected driving conditions as training data. The results show that the TD3-based EMS has 54.69% higher training efficiency, 36.82% higher learning ability, and 2.45% lower overall vehicle operating cost compared to the DDPG-based EMS, validating the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

We present a novel approach for a battery management system in which adaptive thermal control is employed to balance the capacities of individual groups of cells within a lithium-ion battery pack. Maintaining capacity balance within the battery pack in this manner can significantly extend its cycle life. We explore the physical implementation of this concept and demonstrate that it is a viable way to extend the life of battery packs. The experimental setup consists of three pairs of cells connected electrically in series and supplied with coolant flow from a chiller. All cells are initially in capacity balance and are cooled uniformly for the first 50 fast charge/discharge cycles. Subsequently, cooling is halted to specific cell pairs to deliberately unbalance their capacities. Finally, cooling is selectively restored to correct the capacity imbalance between the cell groups by the end of 100 charge/discharge cycles. These results suggest that adaptive thermal control can be used effectively to maintain capacity balance within the battery pack. [ABSTRACT FROM AUTHOR]

The penetration of electrical vehicles (EVs) is exponentially rising to decarbonize the transport sector resulting in the research problem regarding the future of their retired batteries. Landfill disposal poses an environmental hazard, therefore, recycling or reusing them as second-life batteries (SLBs) are the inevitable options. Reusing the EV batteries with significant remaining useful life in stationary storage applications maximizes the economic benefits while extending the useful lifetime before recycling. Following a critical review of the research in SLBs, the key areas were identified as accurate State of Health (SOH) estimation, optimization of health indicators, battery life cycle assessment including repurposing, End-Of-Life (EOL) extension techniques and significance of first-life degradation data on ageing in second-life applications. The inconsistencies found in the reviewed literature showed that the absence of degradation data from first as well as second life, has a serious impact on accurate remaining useful life (RUL) prediction and SOH estimation. This review, for the first time, critically surveyed the recent studies in the field of identification, selection and control of application-based health indicators in relation to the accurate SOH estimation, offering future research directions in this emerging research area. In addition to the technical challenges, this paper also analyzed the economic perspective of SLBs, highlighting the impact of accuracy in second-life SOH estimation and RUL extension on their projected revenue in stationary storage applications. Lack of standard business model based on future market trends of energy and battery pricing and governing policies for SLBs are identified as urgent research gaps. [ABSTRACT FROM AUTHOR]

This study discusses the use of a retired battery from an electric vehicle for stationary energy storage electric vehicle charging in a residential household. This research provides a novel in-depth examination of the processes that may be necessary to investigate the life loss of a battery, whether new or used. The main contribution is to promote the feasibility of the application from both a technical and economic point of view. The semi-empirical models are then utilized to analyze the life fading that is used in economic studies. In terms of lower initial investment costs for the battery and solar photovoltaics, the numerical calculation demonstrates that the used second-life battery with a DOD of 85% has more advantages over a new battery in the same condition. Additionally, compared to a new battery, a second-life battery gradually loses life and benefits from recycling after a projected 10-year lifespan. These results support the feasibility of the project. A discussion of project hurdles is included in which the hybrid converter modification may be achieved. Policymakers are encouraged to keep this valuable scheme in mind for the sake of margin profit and environmental preservation. [ABSTRACT FROM AUTHOR]

Energy performance contracts are a very promising area for attracting private investment in the renewable energy sector. The concept of energy performance contracting is a well-established mechanism aimed at increasing the energy efficiency of a facility and reducing annual maintenance costs. This paper presents a hierarchical model of a decentralized energy system with renewable energy sources and a battery energy storage system under an energy service agreement. This model reflects the interaction between the client and the performance company. The model includes the main parameters characterizing the energy service contract, such as net present value, contract duration and levelized cost of energy. As an example, a real decentralized power system is considered, which currently only uses diesel generation. In the case of building a photovoltaic system, the optimal equipment composition consists of a 100 kW solar station and storage batteries with a capacity of 240 kW·h. The optimal contract term is 5 years, and diesel fuel savings are 69%. [ABSTRACT FROM AUTHOR]

Electronic applications using lithium-ion batteries are increasingly operated under adverse conditions such as high operating currents and elevated environmental temperatures. Prolonged operation in these adverse conditions induces thermal stresses which can initiate thermal runaway (battery fires). Understanding thermal and performance envelopes for cells is crucial for battery systems' safe operation. To explore the performance and thermal envelope for safe operation, there is a need for computational tools to predict a cell's thermal and electrical behavior in any given environment. This paper introduces a model which predicts temperature and voltage profiles of a cell based on operating conditions (current, environmental temperature, etc.). The technique is developed by (1) establishing a 2-resistor-capacitor (2-RC) equivalent circuit model (ECM), (2) parameterizing the ECM in terms of circuit, degradation, and Arrhenius parameters, (3) calibrating parameters through high-pulse-power-characterization (HPPC) and time-constraint insulated-high-pulse-power (TC-IHPP) tests, (4) validating the ECM through discharging tests, and (5) exploring simulated scenarios and examining the ECM's predictions. Validation results show that with calibrated parameters, the ECM predicts a cell's electrical and temperature behavior reasonably accurately in a thermal environment and thermal runaway under extreme operating conditions. A degradation model predicts that a cell undergoing thermal runaway loses full capacity before failure initiation. Results show that irreversible and reactive heat are driving factors for a cell's heating during cycling at low and high temperatures, respectively. Thus, the ECM was simplified by removing additional parameters, which minimized the computational and experimental work required to set up the model. The simplified ECM (SECM) is suitable for scenarios where only average temperature is desired and can predict average temperature and voltage profiles as the original ECM. Finally, a theoretical model is provided to organize effects of electrical activity, thermal runaway kinetics, and environmental temperature on the likelihood for a cell to fail in thermal runaway. [ABSTRACT FROM AUTHOR]

This paper deals with the optimal control of grid‐connected Battery Energy Storage Systems (BESSs) operating for energy arbitrage. An important issue is that BESSs degrade over time, according to their use, and thus they are usable only for a limited number of cycles. Therefore, the time horizon of the optimization depends on the actual operation of the BESS. We focus on Li‐ion batteries and use an empirical model to describe battery degradation. The BESS model includes an equivalent circuit for the battery and a simplified model for the power converter. In order to model the energy price variations, we use a linear stochastic model that includes the effect of the time‐of‐the‐day. The problem of maximizing the revenues obtained over the BESS lifetime is formulated as a stochastic optimal control problem with a long, operation‐dependent time horizon. First, we divide this problem into a finite set of subproblems, such that for each one of them, the State of Health (SoH) of the battery is approximately constant. Next, we reformulate approximately every subproblem into the minimization of the ratio of two long‐time average‐cost criteria and use a value‐iteration‐type algorithm to derive the optimal policy. Finally, we present some numerical results and investigate the effects of the energy loss parameters, degradation parameters, and price dynamics on the optimal policy. [ABSTRACT FROM AUTHOR]

Battery aging is a complex phenomenon, and precise state of health (SoH) monitoring is essential for effective battery management. This paper presents a data-driven method for SoH estimation based on support vector regression (SVR), utilizing features built from both full and partial discharge capacity curves, as well as battery temperature data. It provides an in-depth discussion of the novel features constructed from different voltage intervals. Moreover, three combinations of features were analyzed, demonstrating how their efficacy changes across different voltage ranges. Successful results were obtained using the full discharge capacity curves, built from the full interval of 2 to 3.4 V and achieving a mean R2 value of 0.962 for the test set, thus showcasing the adequacy of the selected SVR strategy. Finally, the features constructed from the full voltage range were compared with ones built from 10 small voltage ranges. Similar success was observed, evidenced by a mean R2 value ranging between 0.939 and 0.973 across different voltage ranges. This indicates the practical applicability of the developed models in real-world scenarios. The tuning and evaluation of the proposed models were carried out using a substantial dataset created by Toyota, consisting of 124 lithium iron phosphate batteries.

This paper introduces a linear battery aging and degradation model to a multi-energy microgrid sizing model using mixed integer linear programming. The battery aging model and its integration into a larger microgrid sizing formulation are described. A case study is provided to explore the impact of considering battery aging on key results: optimal photovoltaic and storage capacities, optimal distributed energy resources operations strategies, and annual cost and generation metrics. The case study results suggest that considering battery degradation in optimal microgrid sizing problems significantly impacts the perceived value of storage. Depending on capacity loss and lifetime targets, considering battery degradation is shown to decrease optimal storage capacities between 6 and 92% versus scenarios that do not consider battery health. When imposing constant distributed energy resource capacities, inclusion of degradation can decrease optimal annual battery cycling by as much as a factor five and reduce total annual electricity cost savings from otherwise identical photovoltaic and storage systems by 5–12%. These results emphasize that as batteries grow in maturity and ubiquity for distributed energy applications, considering battery health and capacity loss is an essential component of any analytical tool or model to guide system planning and decision-making.

This paper introduces a linear battery aging and degradation model to a multi-energy microgrid sizing model using mixed integer linear programming. The battery aging model and its integration into a larger microgrid sizing formulation are described. A case study is provided to explore the impact of considering battery aging on key results: optimal photovoltaic and storage capacities, optimal distributed energy resources operations strategies, and annual cost and generation metrics. The case study results suggest that considering battery degradation in optimal microgrid sizing problems significantly impacts the perceived value of storage. Depending on capacity loss and lifetime targets, considering battery degradation is shown to decrease optimal storage capacities between 6 and 92% versus scenarios that do not consider battery health. When imposing constant distributed energy resource capacities, inclusion of degradation can decrease optimal annual battery cycling by as much as a factor five and reduce total annual electricity cost savings from otherwise identical photovoltaic and storage systems by 5–12%. These results emphasize that as batteries grow in maturity and ubiquity for distributed energy applications, considering battery health and capacity loss is an essential component of any analytical tool or model to guide system planning and decision-making.

ABSTRACT: Owing to the difficulty of studying materials in real-life battery context, research on metal anodes, suffers from a methodological gap between materials- and device-orientated studies. This gap can be bridged by quantitatively linking the electrical response of the device to the evolution of the material inside the cell. The capability of establishing this link, on the one hand, allows to frame the correct space- and time-scales that are relevant to device research and, on the other hand, helps pinpoint the global observables that can be associated with molecular-level information and imaging. This study contributes to the construction of a conceptual platform, that will enable to rationalize the electrical response of the device on the basis of materials-relevant quantities. To this aim: (i) we have developed a PDE-based mathematical model for the response of a single symmetric cell with metal electrodes; (ii) we have validated it with high-quality data from Zn/Zn symmetric coin-cell cycling in weakly acidic alkaline aqueous electrolyte, containing quaternary ammonium additives, and (iii) we have carried out a parameter-classification task for the experimental data, that notably extended the physico-chemical insight into the mechanism of action of anode-stabilizing additives.

Many studies have evaluated CO2 emission from batteries. However, the impact of Li-ion battery (LiB) degradation on the CO2 emissions from the material through operation phases has not been sufficiently examined. This study aims to clarify the dominant CO2 emission phase and the impact of the degradation of general industrial LiBs from repetitive cycle applications. We developed a model common to general LiB composition and calculated CO2 emissions by the LCA method using the IDEA database. Our model simplifies the degradation process, including capacity decrease and internal resistance increase. We used it in a sensitivity analysis of the carbon intensity of electricity charged to a LiB. The loss mechanism was determined by experimental data for an electric bus with an industrial LiB. The results illustrate that the carbon intensity of electricity affects CO2 emissions dominance, the operation phase for mix (71.3%), and the material phase for renewables (70.9%), and that battery degradation over six years increases the total amount of CO2 emissions by 11.8% for mix and 3.9% for renewables equivalent. Although there are limitations regarding the assumed conditions, the present results will contribute to building a method for monitoring emissions and to standardizing degradation calculations. [ABSTRACT FROM AUTHOR]

Methods for optimization of islanded microgrid systems are usually based on hourly models where each subcomponent is described by a simple algebraic model. There are many studies on this topic, which are usually based on the minimization of total lifetime cost by determining the number of required batteries, wind turbines, PV panels, the positioning of PV panels, etc. In this paper, we further improve the modeling of the microgrid system optimization process by developing a simplified algebraic model that uses one parameter to simulate accelerated battery degradation with respect to depth of discharge. The model consists of simply linearly increasing the degradation of the battery when the state of charge (SOC) becomes lower than a fixed value, and the only model constant is the factor of degradation f. The objective of the paper is to examine the effect of the degree of degradation on the obtained optimal microgrid system parameters. The analysis was performed for several different systems, and the results show that optimal parameters of the system and the overall system cost strongly depend on battery degradation characteristics. The overall system cost can be reduced by 1–6% for lower battery degradation rates and up to 20% for high degradation factor cases. Increasing the degradation factor also has an influence on the ratio of wind turbines to PV panels, and the optimal size of the battery system. [ABSTRACT FROM AUTHOR]

It is well known that dynamic thermal line rating has the potential to use power transmission infrastructure more effectively by allowing higher currents when lines are cooler; however, it is not commonly implemented. Some of the barriers to implementation can be mitigated using modern battery energy storage systems. This paper proposes a combination of dynamic thermal line rating and battery use through the application of deep reinforcement learning. In particular, several algorithms based on deep deterministic policy gradient and soft actor critic are examined, in both single- and multi-agent settings. The selected algorithms are used to control battery energy storage systems in a 6-bus test grid. The effects of load and transmissible power forecasting on the convergence of those algorithms are also examined. The soft actor critic algorithm performs best, followed by deep deterministic policy gradient, and their multi-agent versions in the same order. One-step forecasting of the load and ampacity does not provide any significant benefit for predicting battery action. [ABSTRACT FROM AUTHOR]

In recent years, the goal of lowering emissions to minimize the harmful impacts of climate change has emerged as a consensus objective among members of the international community through the increase in renewable energy sources (RES), as a step toward net-zero emissions. The drawbacks of these energy sources are unpredictability and dependence on nature, leading to unstable load power supply risk. One way to overcome instability in the power supply is by using a battery energy storage system (BESS). Therefore, this study provides a detailed and critical review of sizing and siting optimization of BESS, their application challenges, and a new perspective on the consequence of degradation from the ambient temperature. It also reviews advanced battery optimization planning that considers battery degradation, technologies, degradation, objective function, and design constraints. Furthermore, it examines the challenges encountered in developing the BESS optimization model and evaluates the scope of the proposed future direction to improve the optimized BESS, especially its battery. [ABSTRACT FROM AUTHOR]

This paper addresses the integration of electric vehicle (EV) fleets into industrial smart grids to increase operational flexibility. It focuses on an extended multi-objective optimization problem that minimizes two primary objectives: (i) the electricity expenditure of a company using its employees' EV batteries as temporary distributed energy storage, and (ii) the costs associated with the degradation of EV batteries, given the additional usage from the company's perspective. In this paper, the utilization of an EV fleet is simulated at the individual car level over a one-year period. These optimization problems were balanced by using real-time electricity prices and the effective demand response (DR) of the company's electrical load. The company utilized the EVs as battery storage to offset fluctuating electricity prices, while compensating EV owners with free electricity for the costs incurred through degradation of their batteries. The extent to which the company could compensate EV owners while maintaining the viability of vehicle-to-grid (V2G) services in a non-residential scenario was explored. The results established an equilibrium point at which the financial benefits for the company resulting from V2G services was maximized against the negative financial impact of increased battery degradation for EV owners. The results showed that there is a potential mutual benefit between the company and EV owners, even if the company provided EV owners with free charging (based on a percentage of their battery capacity) for each day of their attendance. This mutually beneficial zone ranged from 3%–10% of the battery capacity for AC charging and 6%–17% for DC charging. Optimal Pareto values indicated an economic trade-off that benefited both stakeholders, with DC charging proving significantly more profitable for the company than AC charging (between 257.5% to 38.1% depending on the amount of free charging provided). The findings emphasize the need for an equitable pricing mechanism considering the different characteristics of EVs based on the operational and financial benefits for both parties to create a balanced pricing framework for V2G. • In-depth study on optimization of vehicle-to-grid and electric vehicle battery degradation using MILP. • Mutual benefit for the company and EV owners in an industrial V2G environment. • DC charging was 257.45% to 38.1% more profitable for the company than AC charging. • Comparing the profitability of different EVs in price-based demand response. • Necessity of a fair pricing mechanism for EV owners participating in V2G. [ABSTRACT FROM AUTHOR]

The over-installation of renewable energy sources (RES) can enhance the profits of RES producers by increasing the total exporting energy; however, RES power curtailments must be applied under over-production of RES plants to ensure their contracted maximum export capacity limits. Battery energy storage systems (BESSs) can be used to reduce the RES curtailments and therefore enhance the profits of producers. This work develops a bidding strategy as a scenario-based stochastic optimization scheme to maximize the expected profits of producers in electricity markets considering battery degradation and maximum export capacity constraints. Using the proposed scheme, a financial analysis is performed to assess the profitability for over-installing hybrid RES plants by calculating the net present value and internal rate of return of the project under different BESS costs. Simulation results, using real data from a wind and photovoltaic plant, demonstrate the effectiveness of the proposed stochastic scheme in enhancing the profit of producers compared to the corresponding deterministic scheme. The performed financial analysis, using the net present value metric, indicates that a maximum over-installation of 150 %– 170 % and 160 %– 180 % for the wind and photovoltaic plant, respectively, is profitable, while the usage of a BESS is profitable when its cost is under 200,000 €/MWh. • A bidding strategy for over-install RES-BESS plants (e.g. , wind-BESS and PV-BESS) is proposed. • A scenario-based stochastic optimization scheme is developed to maximize the expected profits of producers in electricity markets. • The proposed scheme considers battery degradation and maximum export capacity constraints. • A financial analysis is performed to assess the profitability for over-installing hybrid RES plants. [ABSTRACT FROM AUTHOR]

This study analyzes the growth of the solid electrolyte interphase (SEI) layer and lithium plating under various charging conditions. By examining the thermal–electrochemical effect on degradation, the proper charge conditions for alleviating degradation can be understood. First, we analyzed the degradation mechanism during the charging process. After 100 cycles, the SEI layer increased by 30%, resulting in an increase in ohmic loss. The growth of lithium plating increased to approximately 5.2 × 10−2 nm, causing concentration loss. Then, we analyzed the effect of charge rate (C-rate) on degradation. When the C-rate increased from 1C to 1.5C, the charge capacity decreased by 80% at the 100th cycle because a large number of lithium ions reacted at the high C-rate, resulting in a thick SEI layer and lithium plating. The effect of temperature was analyzed as well. When the temperature was 5 °C, the charge capacity decreased by 75% owing to low diffusivity in the electrolyte. Finally, thermal-electrochemical effect on degradation was analyzed. By lowering the C-rate and increasing temperature, 0.3 V of the overpotential can be lowered. In order to mitigate the degradation of the battery, it is recommended to lower the C-rate and higher the temperature as charging proceed. • Thermal-electrochemical effect on Battery degradation mechanism is analyzed. • Growth of SEI affects ohmic loss and growth of lithium plating affects concentration loss. • Generation of SEI and lithium plating actively occurs at high C-rate and low temperature. • By controlling C-rate and temperature, degradation can be alleviated during cycling. [ABSTRACT FROM AUTHOR]

Fast charging of electric vehicles remains a compromise between charging time and degradation penalty. Conventional battery management systems use experience-based charging protocols that are expected to meet vehicle lifetime goals. Novel electrochemical model-based battery fast charging uses a model to observe internal battery states. This enables control of charging rates based on states such as the lithium-plating potential but relies on an accurate model as well as accurate model parameters. However, the impact of battery degradation on the model's accuracy and therefore the fitness of the estimated optimal charging procedure is often not considered. In this work, we therefore investigate electrochemical model-based aging-adaptive fast charging of automotive lithium-ion cells. First, an electrochemical model is identified at the beginning of life for 6 automotive prototype cells and the electrochemically constrained fast-charge is designed. The model parameters are then periodically re-evaluated during a cycling study and the charging procedure is updated to account for cell degradation. The proposed method is compared with two reference protocols to investigate both the effectiveness of selected electrochemical constraints as well as the benefit of aging-adaptive usage. Finally, post-mortem characterization is presented to highlight the benefit of aging-adaptive battery utilization. [Display omitted] • Design of model-based fast-charging protocol to limit lithium plating and temperature rise. • Tracking electrochemical model parameters using reference performance test. • Adaptation of charging protocol to account for cell aging. • Cycling study and post-mortem characterization to validate proposed strategy. [ABSTRACT FROM AUTHOR]

Anisotropic volume change of layered oxide cathode active material such as Ni-rich nickel–manganese–cobalt-oxide (NMC) during cycling significantly contributes to mechanical degradation, emerging as a primary factor leading to instability issues in these high-capacity cathode active materials for lithium-ion batteries. Despite their commendable energy density, these challenges hinder the broader application of NMC, underscoring the need for rational analysis and innovative solutions to address the associated mechanical instability. In this contribution, we utilize a chemo-mechanically coupled cohesive fracture model, complemented by its 3D finite element implementation, to simulate and analyze the performance and cracking behavior of polycrystalline high-Ni nickel–manganese–cobalt cathode active materials. Thereby the two-way coupling between mechanics and diffusion is regarded both in the bulk and at grain boundary. In particular, novel algorithms are introduced to model the electrolyte penetration along the inter-granular cracks, capturing its intricate impact on particle performance. Utilizing simulations on synthetic and reconstructed microstructure samples derived from experimental images, extensive parameter studies were conducted. These investigations unveiled the intricate influence of various microstructure aspects, encompassing grain morphology, grain/particle sizes, and texture. The findings indicate that secondary active material particles exhibiting smaller grains and radially elongated primary particles, coupled with radially oriented high-diffusivity planes, demonstrate enhanced resistance to inter-granular mechanical damage. • An anisotropic chemo-mechanically coupled model with fracture is developed in 3D. • A novel algorithm is introduced to model electrolyte penetration into the cracks. • Microstructure impact on cracking of polycrystalline cathode material is studied. • Grain size and grain morphology have a significant impact on damage mitigation. [ABSTRACT FROM AUTHOR]

• A novel dual-layer correction method is proposed to enhance resilience against uncertainties in integrated energy systems. • AMPC-based energy management framework is integrated with the proposed method by the constraint tightening algorithm. • A multi-objective optimization model is developed including demand response, energy reliability, and battery degradation. • An in-depth quantitative analysis is carried out to assess the annual system behavior under various methodologies. Integrated energy systems (IESs) are increasingly pivotal in the global shift towards sustainable energy frameworks. Within IESs, the energy management system (EMS) plays a critical role, tasked with optimizing energy allocation to achieve objectives like grid stability, energy reliability, and cost-efficiency. A significant challenge for EMS is the inherent unpredictability of renewable energy. Given this, a model predictive control (MPC)-based real-time energy management framework is proposed, which aims to mitigate the impacts of radiation forecast uncertainties in solar-powered IES. A dual-layer correction mechanism is proposed to quantify forecast uncertainty, resulting in uncertain intervals inferred from the hidden Markov model (HMM). Then, the uncertain intervals are adeptly managed within the MPC decision-making framework through the constraint-tightening method. A case study on solar-powered electricity, heating, and hydrogen IES demonstrates the validity of the proposed framework. Five different sets of energy management strategies, such as deterministic analysis, distributionally robust optimization, and the proposed HMM-MPC are subjected to long-term simulations. Comparative analysis reveals that the proposed method enhances various aspects of the performance metrics, including demand response, energy reliability, and system self-sufficiency. [ABSTRACT FROM AUTHOR]

Many thermal management systems are being developed and researched to regulate the battery temperature of an electric vehicle. However, the performance of these systems in controlling the cells' temperatures has been examined at the module or cell size in the literature, leaving out their impact on the environment when they are practically used for battery thermal management in an EV. This study examines the carbon emission of an electric vehicle in Tehran using air-, water-, and phase change material (PCM)-based battery thermal management systems (BTMSs) considering real drive conditions. For every month of a year, battery power variations and temperature changes are considered in the simulated cases with resolution of second, and the battery aging is computed over ten years. The results demonstrate that the simulated vehicle with the PCM-based system consumes 14.68 % and 22.71 % less energy than the air- and water-based systems, respectively. The battery capacity loss for PCM-, air-, and water-based systems at the end of the 10th year are 6.95 %, 7.17 %, and 7.26 %, respectively. Furthermore, it should be noted that the simulated vehicle's carbon emissions with the water-based, air-based, and PCM-based BTMSs are 12.242 kg, 11.241 kg, and 10.042 kg per 100 km, respectively. [Display omitted] • Carbon emission of air-, water-, and PCM-based BTMSs integrated in vehicle. • Modeling real drive conditions including ambient temperature & solar irradiance. • Calculating ageing using temperature and power profiles dependent on the vehicle. • Vehicle with PCM-based BTMS used 15 % & 23 % less energy than air- and water-based. • Carbon emission of EVs with PCM-, air-, & water-based is 10, 11, 12 kg per 100 km. [ABSTRACT FROM AUTHOR]

The safety concerns associated with lithium-ion batteries (LIBs) have led to the development of a novel framework combining advanced machine learning (ML) techniques with multiphysics modeling. Herein, we report an ML framework aiming to predict the occurrence of thermal runaway (TR) in the LIB module by employing a multiphysics model that incorporates thermal, electrochemical, and degradation sub-models. The focus of this research lies in understanding the degradation phenomenon associated with the breakdown of the solid electrolyte interface (SEI) on the negative electrode, which can trigger TR. The developed multiphysics model enables the investigation of electrochemical and degradation processes within batteries under various conditions, including constant charge/discharge and driving cycles. To capture the spatio-temporal temperature change, a graph neural network (GNN) for spatial change is coupled with a Long Short-Term Memory (LSTM) network for temporal evolution to form an integrated framework. The results demonstrate the high accuracy of the ML model in predicting battery temperatures in a module based on spatial and temporal temperature data obtained from temperature sensors attached to the batteries, hence, offering a means to detect TR before it occurs by identifying potential thermal hotspots. • A multiphysics model of thermal runaway in a battery module is developed. • The decomposition of solid electrolyte interface is considered. • Machine learning was implemented to predict temperature during battery operation. • A modeling method was implemented to generate realistic data on EV driving behavior. [ABSTRACT FROM AUTHOR]