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The penetration of electric vehicles (EVs) in vehicle-to-grid (V2G) interaction can effectively assist the grid in achieving frequency regulation and peak load balancing. However, the customer perceives that participating in V2G services would result in the additional cycling of the battery and the accelerated aging of the EVs' power battery, which has become a major obstacle to the widespread adoption of V2G services. Most existing methods require long-term cycling data and battery parameters to quantify battery aging, which is not suitable for the V2G scenario with large-scale and short-time intervals. Consequently, the real-time and accurate quantification of battery aging for optimization remains a challenge. This study proposes a charging scheduling method for EVs that can accurately and online quantify battery aging. Firstly, V2G scheduling is formulated as an optimization problem by defining an online sliding window to collect real-time vehicle information on the grid, enabling online optimization. Secondly, battery aging is more accurately quantified by proposing a novel amplitude-based rain-flow cycle counting (MRCC) method, which utilizes the charging information of the battery within a shorter time period. Lastly, an intelligent optimization algorithm is employed to optimize the charging and discharging power of EVs, aiming to minimize grid fluctuations and battery aging. The proposed method is validated using a V2G scenario with 50 EVs with randomly generated behaviors, and the results demonstrate that the proposed online scheduling method not only reduces the EFCC of the battery by 8.4%, but also achieves results close to global optimization. [ABSTRACT FROM AUTHOR]

Silicon is a promising anode material for next‐generation lithium‐ion batteries. However, the volume change and the voltage hysteresis during lithiation and delithiation are two substantial drawbacks to their lifetime and performance. The reason for the voltage hysteresis in amorphous silicon nanoparticles covered by a solid‐electrolyte interphase (SEI) is investigated. Concentration gradients inside the nanoscale silicon cannot produce the massive stresses necessary to cause the reported voltage hysteresis. The chemo‐mechanical model shows that plastic deformation of the stiff, inorganic SEI during lithiation and delithiation reproduces the observed silicon open‐circuit voltage hysteresis. Additionally, the viscous behavior of the SEI explains the difference between the voltage hysteresis observed at low currents and after relaxation. It is concluded that the visco‐elastoplastic behavior of the SEI is the origin of the voltage hysteresis in silicon nanoparticle anodes. Thus, consideration of the SEI mechanics is crucial for further improvements. [ABSTRACT FROM AUTHOR]

A huge increase in fast−charging stations will be necessary for the transition to EVs. Nevertheless, charging a battery pack at a higher C−rate impacts its state of health, accelerating its degradation. The present paper proposes a different and innovative approach that considers the daily routine of an EV Li−ion battery based on a standard driving cycle, including charging phases when the depth of discharge is 90%. Through dynamic modeling of the EV battery system, the state of charge evolution is determined for different charging C−rates, considering both real discharging and charging current profiles. Finally, by applying a suitable post−processing procedure, aging test features are defined, each being related to a specific EV battery working mode, including charging at a particular C−rate, considering the global battery operation during its lifespan. It is demonstrated that, according to the implemented procedure, fast−charging cycles at 50 kW reduce battery lifespan by about 17% with respect to charge in a 22 kW three−phase AC column, in parity with the discharge rate. Thus, this work can provide a deep insight into the expected massive penetration of electric vehicles, providing an estimate of battery useful life based on charging conditions. [ABSTRACT FROM AUTHOR]

Battery lifetime is an important parameter in the life cycle assessment (LCA) of a plug-in hybrid-electric vehicle (PHEV). This paper aims to study the impact of various parameters on the battery aging of a PHEV. For this purpose, model-based use cases are generated, the outputs of which are the daily driven distances for a period of one year, recharge scenarios, and battery temperature. A combined aging model (calendar and cycling aging) is used to calculate the capacity lost by the battery at the end of one year of use. The thermal model of the battery is using an electro-thermal coupling equation, for which the ambient temperature is modeled using daily minimum and maximum temperature data varying throughout the year for different cities. Finally, a sensitivity analysis is carried out using the conditioned variance method to identify the most important input parameters which largely affect the output of this study. The results of this study show that battery size, annual mileage, external temperature, and charging behavior are the most important parameters to be considered in the aging study of the battery of a PHEV personal car. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are currently the pioneers of green transition in the transportation sector. The nickel-manganese-cobalt (NMC) technology, in particular, has the largest market share in electric vehicles (EVs), offering high specific energy, optimized power performance, and lifetime. The aging of different lithium-ion battery technologies has been a major research topic in the last decade, either to study the degradation behavior, identify the associated aging mechanisms, or to develop health prediction models. However, the lab-scale standard test protocols are mostly utilized for aging characterization, which was deemed not useful since batteries are supposed to age dynamically in real life, leading to aging heterogeneity. In this research, a commercial NMC variation (4-4-2) was aged with a pragmatic standard-drive profile to study aging behavior. The characterized measurable parameters were statistically investigated before performing an autopsy on the aged battery. Harvested samples of negative and positive electrodes were analyzed with Scanning Electron Microscopy (SEM) and the localized volumetric percentile of active materials was reported. Loss of lithium inventory was found to be the main aging mechanism linked to 20% faded capacity due to heavy electrolyte loss. Sparsely distributed fluorine from the lithium salt was found in both electrodes as a result of electrolyte decomposition. [ABSTRACT FROM AUTHOR]

The increasingly widespread use of electric vehicles requires proper planning of the charging infrastructure. In addition to the correct identification of the optimal positions, this concerns the accurate sizing of the charging station with respect to energy needs and the management of power flows. In particular, if we consider the presence of a renewable energy source and a storage system, we can identify strategies to maximize the use of renewable energy, minimizing the purchase costs from the grid. This study uses real charging data for some public stations, which include "normal" chargers (3 kW and 7 kW) and "quick" ones (43 kW and 55 kW), for the optimal sizing of a photovoltaic system with stationary storage. Battery degradation due to use is included in the evaluation of the overall running costs of the station. In this study, two different cost models for battery degradation and their influence on energy flow management are compared, along with their impact on battery life. [ABSTRACT FROM AUTHOR]

The utilization of the complete Li-ion battery charge curve provides access to a multitude of critical battery states, which are indispensable for evaluating the safety and dependability of battery-powered devices. Nonetheless, the diminishing health of batteries, coupled with the challenges associated with data collection from battery management systems, presents a substantial hurdle in obtaining complete charging curves. In this study, we introduce an innovative neural network architecture, demands only a segment of the charging curve as input in order to prognosticate the complete constant-current charging curve. Further, this model can be enhanced by optional external data, (e.g., ambient temperature, total battery charge), along with composite inputs, (e.g., battery temperature–voltage–capacity sequence), thereby augmenting its operational performance. This method undergoes rigorous validation across three diverse battery datasets encompassing various data inputs, charging profiles, and temperatures. These assessments reveal an exceptional level of accuracy, with an average error rate falling below 9.35 mAh for 1.1 Ah batteries in the absence of external information, and dipping below 7.37 mAh for 1.1 Ah batteries when incorporating external data. The promising outcomes derived from these validations unequivocally affirm the effectiveness of our proposed model in accurately estimating battery charge curve predictions. • Model forecasts full charging curve from a segment efficiently. • Merges external info with curve data, enriching predictions. • Novel structure and mechanisms enhance accuracy and efficiency. [ABSTRACT FROM AUTHOR]

The dilemma between battery operating revenue and aging has not been addressed appropriately by previous studies. This paper presents a co-optimization method of battery full life-cycle scheduling and lifespan for its maximal lifetime value, involving usage limits, nonlinear aging characteristics, and time-value-of-money effects. The resulting dispatch model consists of numerous large-scale non-convex nonlinear programming (NLP) problems with the nonlinearity appearing in the equality constraint, which cannot be solved efficiently through conventional methods. Thus, we design an exact and fast solution approach including the equivalent problem transformation, a modified pegging algorithm, and a novel acceleration algorithm. The proposed methods are illustrated and validated by numerical tests using real historical data from Pecan St. Project. Test results suggest that it is not the case that the longer service life, the more battery lifetime profits can be generated. The proposed scheduling method can improve the lifetime profits than the conventional methods. For a battery with a 15-year design life, the optimal scheduling and lifespan with a 5-day resolution can be obtained within half an hour. [ABSTRACT FROM AUTHOR]

The battery capacity loss and power degradation due to aging process is one of the concerns particularly in vehicle electrification. This paper investigates the effects of charging/discharging current C‐rate, operating ambient temperature, and cycling on the battery cell aging and aging progress. Two groups of aging experiments are conducted on the lithium‐ion polymer battery cells with 20 Ah capacities. The first group of aging tests investigate the effects of charging/discharging C‐rate and operating ambient temperature on the battery cell capacity after 300 cycles. After 300 cycles, the cell with 3 C‐rates charging/discharging cycling currents under 40°C ambient temperature retains 64.53% usable capacity, comparing to 94.61% usable capacity in the cell with 1 C‐rate charging/discharging cycling currents under 20°C. The second group of tests investigate the cell aging progress during 800 aging cycles under normal working conditions (1 C‐rate charging, 2 C‐rate discharging, and 25°C ambient temperature). The results show that the cell's usable capacity is 75.17% after 800 cycles. The internal resistance of the cell could be 0.0235 Ω after 300 cycles of 3 C‐rate charging/discharging cycles under 40°C, or as high as 0.034 Ω after 800 cycles of 1 C‐rate charging/2 C‐rate discharging cycles under 25°C. [ABSTRACT FROM AUTHOR]

In recent years, with the development of battery technology, hybrid electric ship (HES), as a promising solution to reduce the fuel consumption and emissions, has become a research hotspot. However, frequent use of the battery will accelerate the aging of the battery, and the replacement of scrapped battery will increase the cost of the ship. Therefore, it is necessary to consider delaying battery aging into the energy control strategy of HES. The equivalent consumption minimization strategy (ECMS) is a feasible energy control strategy because it can be implemented in real time. However, under the condition of uncertain initial state of charge (SOC) of the battery, ECMS cannot effectively reduce the fuel consumption unless the equivalent factor (EF) is optimized in real time. In this paper, an adaptive equivalent consumption minimization strategy (A‐ECMS) is proposed, which extracts the global optimal EF trajectory from the dynamic programming (DP) solution and uses the back propagation (BP) neural network to adjust the EF in real time. A trade‐off between the fuel consumption and battery aging is made in the cost function by introducing a weight coefficient. Finally, the effectiveness and the adaptability of the proposed strategy are verified in MATLAB. [ABSTRACT FROM AUTHOR]

Braking energy recovery is one of the main technologies affecting the economic performance of an electric vehicle. To improve economy from as much recovered braking energy as possible on the premise of ensuring vehicle security is the goal of regenerative braking control strategy. However, due to the non-linear and multi-objective characteristics of hybrid braking system, finding the optimal regenerative braking control strategy, considering safety, economy, and comfort, remains a challenge. Considering the efficient characteristics of regenerative braking system and battery aging, a swarm intelligence-based predictive regenerative braking control strategy is proposed. Particle swarm optimisation is used as the main part of the strategy, the ant colony algorithm is used to modify the iterative process of particle swarm optimisation to avoid convergence to a locally optimal solution, and model predictive control theory is applied in the control strategy to realise the optimal control. Then, under emergency braking conditions and urban cycling conditions, the stability and economy of proposed strategy are test by the simulation experiments. Finally, to reduce the computational complexity of the control strategy, an equivalent control strategy is proposed based on the nearest point method, and its effectiveness is verified by hardware-in-loop experiment. [ABSTRACT FROM AUTHOR]

Li-ion batteries have become the standard power source for electric vehicles (EVs) as the alternative of choice to reduce CO2 emissions. But before becoming a reliable technology, Li-ion batteries must deal with two significant challenges: undesirable electrochemical reactions caused by excessive charging rates and considerable time for an EV to get charged. It is necessary to employ balanced current profiles that prevent both serious battery degradation effects and the inconvenience to end users. In this work, the authors propose a safe exploration deep reinforcement learning (SDRL) approach in order to determine optimal charging profiles under variable operating conditions. One of the main advantages of RL techniques is that they can learn from interaction with the real or simulated system while incorporating the nonlinearity and uncertainty derived from fluctuating environmental conditions. However, since RL techniques must explore undesirable states before obtaining an optimal policy, no safety guarantees are provided. The proposed approach aims at maintaining zero-constraint violations throughout the learning process through the integration of a safety layer that corrects the action if a constraint is likely to be violated. The proposed method is tested in the equivalent circuit of a Li-ion battery under varying conditions. Results reveal that with the integration of this safety layer, SDRL is able to find safe optimized charging policies while considering a trade-off between the charging speed and the battery lifespan, including a 30% reduction in charging time while still maintaining temperatures within permissible limits and up to 38% of battery life conservation, compared with benchmark methods. Moreover, our approach does not experience episodes demonstrating restriction violations throughout the pre-training, training, and evaluation phases. [ABSTRACT FROM AUTHOR]

[Display omitted] • Modeling EV's powertrain, battery pack TMS, detailed cabin, and HVAC systems. • Assessments of EV performance under annual dynamic environmental conditions. • Using vehicle-based dynamic temperature and power profiles for aging calculation. • Battery aging calculation for 10 years, including 22 working days for each month. • At the end of the 10th year the total capacity loss of the battery is 7.1%. In this article, a comprehensive study of the aging behavior of an electric vehicle battery pack considering the vehicle's operation under real driving conditions, such as ambient temperature, humidity, and solar irradiation conditions, in all different months of the year for a 10-year time horizon with two different working scenarios is investigated for the first time. The vehicle duty cycle represents a personal vehicle traveling once at 7:00 AM from home to work and returning at 5:00 PM from work to home for scenario 1. For the second scenario, 8:30 AM is set for outbound trips, and 6:30 PM is set for return trips via the WLTP driving cycle. The target temperature for the cabin and battery pack are set to 22 °C and 28 °C, respectively. The results indicate the highest energy consumption is for the cold season. The EV's energy consumptions in scenario 2 for outbound and return trips were 20 % less and 0.6 % more than the EV's energy consumptions in scenario 1 for outbound and return trips, respectively. Next, the battery aging was evaluated for ten years, resulting in 7.16 % and 6.99 % total capacity loss for scenario 1 and scenario 2, respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • Mildly oxide graphene (MGO) coatings on aluminum in Li-ion batteries are studied. • Coatings are applied by electrophoretic deposition (EPD) and drop-casting. • EPD coating of MGO reduces corrosion and improves battery capacity. • Drop-cast MGO separates easily from aluminum and degrades battery performance. Preventing current collector corrosion is critical in Li-ion batteries. This study investigates mildly oxidized graphene (MGO) coating on aluminium (Al) foil for corrosion resistance and battery performance for the first time. Electrophoretic deposition (EPD) and drop-casting are used to apply this coating. Polarization tests and SEM analysis are performed on coatings to determine the optimal EPD conditions. Optimal conditions for EPD of MGO on aluminium are 5 V and 40 s. Using LiNi 0.8 Co 0.15 Al 0.05 O 2 (NCA) cathodes, the performance of cells on MGO-coated and uncoated aluminium (Bare-Al) is evaluated. EPD sample shows a capacity of 175 mAh/g at C/2, 35 mAh/g more than the Bare-Al sample despite a 67.3 Ω increase in charge transfer resistance (R ct). Due to inadequate adhesion, the drop-casted sample displays a 136 mAh/g capacity at C/2, poor rate capability, and higher R ct (335.48 Ω). MGO-coated aluminium batteries perform better under the EPD method because of improved corrosion resistance of current collectors, as demonstrated by reduced corrosion currents in the polarization curves and reduced conductivity losses in incremental capacity (ICA) results. [ABSTRACT FROM AUTHOR]

Lithium‐ion technologies have become the most attractive and selected choice for battery electric vehicles. However, the understanding of battery aging is still a complex and nonlinear experience which is critical to the modeling methodologies. In this work, a comprehensive lifetime modeling twin framework following semi‐empirical methodology has been developed to predict the crucial degradation outputs accurately in terms of capacity fade and resistance increase. The constructed model considers all the relevant aging influential factors for commercial nickel manganese cobalt (NMC) Li‐ion cells based on long‐term laboratory‐level investigation and combines both the cycle life and the calendar life aspects. To demonstrate robustness, the model is validated with a real‐life worldwide harmonized light‐duty test cycle (WLTC). The model can precisely predict the capacity fade and the internal resistance growth with a root‐mean‐squared error (RMSE) of 1.31% and 0.56%, respectively. The developed model can be used as an advanced online tool forecasting the lifetime based on dynamic profiles. [ABSTRACT FROM AUTHOR]

During the satellite's operations, simulation tools perform an important role in ensuring the space mission success. In this sense, the models implemented in the context of an operational satellite simulator that enables analysis of health status and maintenance during operations shall reflect the current satellite behavior with high fidelity. Moreover, it is complicated to obtain all analytical models of a satellite's disciplines, considering its aging. This paper proposes an Artificial Neural Network (ANN) to reproduce the battery voltage behavior of a large sun-synchronous remote sensing satellite, the CBERS-4, currently in operation. Using the genetic algorithm to find the best network architecture of ANN, the neural model for this application presented an error of less than 1%, demonstrating its feasibility to obtain a high fidelity model for an operational simulator enabling extend analyses. The paper addresses advanced techniques aligned with the space industry's future, increasing the ability to analyze a large amount of data and improve the space system's operation. [ABSTRACT FROM AUTHOR]

Research shows that datacenter backup batteries have shown great potential in peak power shaving. The use of batteries for peak power shaving can greatly improve the power usage efficiency of datacenter, thereby saving a lot of construction cost of datacenter power infrastructure. However, due to the accelerated battery aging phenomenon, the battery often needs to be replaced several times during the life cycle of a data center. This makes the battery cost an important part of the cost of the data center. Under the more advanced distributed backup system, the battery cost accounts for a greater proportion. Therefore, how to use batteries more economically has become a key issue for cost saving. In this paper, a revenue model for predicting battery availability is proposed, which can evaluate whether the aging battery in the traditional sense still has practical value, and how to balance the performance degradation and backup reliability problem in the aging battery use. This paper also proposes an optimized battery control method, which reduces the cost of data center backup power. [ABSTRACT FROM AUTHOR]

The discharge rate of the energy storage battery in the operation of the microgrid system is random, which makes it difficult to accurately predict its life. Aiming at the problem of battery life prediction in microgrid operation scenarios, a degradation model for life estimation of microgrid energy storage batteries is proposed. In the paper, the effect of discharge rate on energy storage is equivalent to state of charge and depth of discharge of energy storage, and the combined effects of calendar decay, cyclic decay and solid electrolyte interface film formation mechanism are considered to obtain a more realistic reflection of the life degradation characteristics of the microgrid energy storage battery. The cycle test of the battery under different operating conditions and the fitting experiment of the model to the attenuation data verify the prediction accuracy and applicability of the model. [ABSTRACT FROM AUTHOR]

As many safety- or mission-critical electric systems become equipped with batteries, we need to achieve seemingly conflicting goals: G1) timely execution of power-consuming tasks (for safety or mission) while G2) minimizing battery aging (for system sustainability). To this end, this paper proposes a novel scheduling framework for a set of power-consuming real-time tasks, which efficiently utilizes run-time slack (i.e., system idle time identified at run-time) to find the tasks' schedule that achieves both G1 and G2. The proposed framework is not only applicable to any existing prioritization policy (e.g., EDF and FP) but has also been proven to reduce battery aging by up to 32.6% without compromising G1. [ABSTRACT FROM AUTHOR]

• ESS degradation cost based on battery lifetime is considered for power management of nanogrid with PV system. • Multi-objective optimization representing power management determines individual power consumption of schedulable electric appliances and ESS charging/discharging. • P2P power trading is performed between nanogrids based on cooperative game model. • ESS degradation cost is decreased by 32.75% by the proposed power management. This study presents a peer-to-peer (P2P) trading strategy for power management of nanogrids with renewable energy sources (RESs), taking battery lifetime of energy storage system (ESS) into consideration. Nanogrid requires more analytical power management as individual control of each electric appliance should be carried out, considering its rated power. With the increasing penetration of distributed energy resources, P2P trading can be applied to nanogrids equipped with RESs and ESSs. For the power management of a nanogrid, multi-objective optimization is used to minimize grid power consumption, electricity cost, and delay of using electric appliances in appliance scheduling. In this paper, a battery aging model or degradation cost model is considered in the multi-objective optimization to prolong the ESS lifetime. The electricity from RESs and ESSs used for P2P trading is determined to increase the profitability of nanogrids. Depending on the amount of electricity allocated to P2P trading, a nanogrid can be a seller or buyer in P2P trading. P2P trading is carried out based on the cooperative game theory to maximize the welfare of both sellers and buyers. As compared to power management involving P2P trading without considering ESS lifetime, the proposed one in consideration of ESS lifetime decreases the electricity cost of nanogrid by 2.86% on average, and the degradation cost of ESS by 32.75% on average. [ABSTRACT FROM AUTHOR]

Temperature is a major factor that affects battery aging. Therefore, charging stations exposed to the external environment and charge point operator (CPO) operation strategy can significantly affect battery aging. In particular, when providing power services that may require a high C-rate, electric vehicle (EV) battery aging can be aggravated in certain environments. Therefore, battery aging management according to the provided power service needs to be dealt with from the perspective of the CPO, which establishes the operation strategy, in addition to the EV. This study focuses on EV battery aging management considering the charging station operating environment from the point of view of a CPO. Battery aging model considers the mutual influence between charging station's ambient temperature, EV cabin temperature, battery temperature, and battery temperature management system (BTMS). Based on this, the CPO establishes an optimal smart charging strategy considering the BTMS operating conditions and the power consumption of EVs working in an independent environment. Based on this, the technical and economic effects of battery aging management according to participation in power services were analyzed. Case study results show that indoor stations can reduce management costs for battery aging by up to 13.31% compared to that in outdoor stations. • The operation of charge point operators considering EV battery aging was analyzed. • The effect of the charging station operating environment on the battery aging was modeled. • An optimal smart charging strategy considering total management cost is proposed. • Indoor/outdoor charging cases were compared and analyzed. [ABSTRACT FROM AUTHOR]

A battery energy storage system (BESS) is an effective solution to mitigate real-time power imbalance by participating in power system frequency control. However, battery aging resulted from intensive charge–discharge cycles will inevitably lead to lifetime degradation, which eventually incurs high-operating costs. This study proposes a deep reinforcement learning-based data-driven approach for optimal control of BESS for frequency support considering the battery lifetime degradation. A cost model considering battery cycle aging cost, unscheduled interchange price, and generation cost is proposed to estimate the total operational cost of BESS for power system frequency support, and an actor–critic model is designed for optimising the BESS controller performance. The effectiveness of the proposed optimal BESS control method is verified in a three-area power system. [ABSTRACT FROM AUTHOR]

A battery energy storage system (BESS) is an effective solution to mitigate real‐time power imbalance by participating in power system frequency control. However, battery aging resulted from intensive charge–discharge cycles will inevitably lead to lifetime degradation, which eventually incurs high‐operating costs. This study proposes a deep reinforcement learning‐based data‐driven approach for optimal control of BESS for frequency support considering the battery lifetime degradation. A cost model considering battery cycle aging cost, unscheduled interchange price, and generation cost is proposed to estimate the total operational cost of BESS for power system frequency support, and an actor–critic model is designed for optimising the BESS controller performance. The effectiveness of the proposed optimal BESS control method is verified in a three‐area power system. [ABSTRACT FROM AUTHOR]

Previous studies have proposed to use cascaded multilevel topologies in stationary battery energy storage systems (BESSs) or in BESSs of electric vehicles, due to the balancing capability or the high efficiency of these topologies. Because of the elimination of the direct current (dc) bus, the batteries in these topologies are expected to experience large current ripples. However, whether these current ripples can accelerate the aging of the batteries has not yet been dedicatedly investigated, although it is a determining factor for the feasibility of this category of topologies. Therefore, this article first summarizes the existing studies regarding the influence of current ripples on the aging of lithium batteries, which proves the necessity of an experimental investigation. Then, a long-term aging experiment on battery cells is conducted, in order to examine the influence of the current ripples in cascaded multilevel topologies. According to the experimental results and the conclusions in previous studies, the ripples in cascaded multilevel topologies generally have a negligible influence on the aging of batteries, except in certain scenarios. These scenarios can be identified by the three preconditions, first, current ripples must contain micro charge and discharge cycles; second, the microcycles are below 10 Hz; third, low frequency microcycles must contribute a large amount of charge throughput. [ABSTRACT FROM AUTHOR]

The interest in installing photovoltaic (PV)-based microgrids has increased significantly in the last few years due to the urgent need for reducing greenhouse gas emissions and improving the reliability as well as the quality of power supply, particularly in developing countries. The design of a microgrid is challenging since multiple components and complicated operating conditions have to be taken into account. In this article, a comprehensive method for optimal design of a class of residential PV-battery microgrids is proposed to determine the optimal number of lead-acid batteries and PV panels, the optimal battery bank depth of discharge (DOD) value, and the optimal tilt angle of the PV panels. The aim of the optimization is to minimize the levelized cost of energy (LCOE) where the limitation of the annual total loss of the power supply and the operational constraints are considered. In addition, a detailed model for battery lifetime estimation is introduced based on the physico-chemical mechanism of the lead-acid battery. Moreover, the effect of design parameters, e.g., the size and DOD of the battery bank on the battery capacity degradation, the PV-panel number and battery lifetime on LCOE, is investigated. Computation results demonstrate that the proposed method is capable of optimizing the size of the microgrid components and the other design parameters while satisfying all technical and operational constraints. [ABSTRACT FROM AUTHOR]

This article focuses on the design of a half-full bridge (HFB) balancing link that can reduce the cost of redistributive battery balancer in electrified vehicle applications by combining the functionalities of a battery balancer and auxiliary power module (APM). The benefits of redistributive balancing are quantified based on existing datasets of commercial battery cells. The power rating for the electric vehicle's APM and balancing capability are discussed. A HFB module prototype is designed to validate the proposed concept. How to reduce the current ripple on the battery cells is presented in order to minimize the degradation of the cells due to highly dynamic current. Nickel-manganese-cobalt cells are installed on the module to investigate the balancing performance in real life. The efficiency map is also given for the prototype. Compared with existing balancing methods, the improvement under the same driving cycle is shown in the long-term simulation. [ABSTRACT FROM AUTHOR]

Summary: With the increasing popularity of combining residential photovoltaic systems with battery storages, research, industry, and customers look for ways to determine if such an investment is economically profitable. Simulation programs may serve to predict the profitability and lifetime of the system. In this paper, we use techno‐economic analysis with a specific account of battery degradation to determine profitability and lifetime of a residential photovoltaic (PV) battery system under different energy management and tariff regimes. This work presents two case studies: the first being a techno‐economic comparison for a residential PV‐battery system in New South Wales, Australia and Germany, and the second analyzing the profitability and degradation impact of three different operation strategies for a battery storage in Australia. The results reveal that site‐specific conditions (i.e., geographical and energy‐economic constraints) may have a significant impact on the ideal system configuration and ultimately the anticipated battery lifetime. Furthermore, statistical analysis of different storage operation strategies applied to various prosumer load and generation profiles reveals the effects of storage dispatch strategies on battery aging. [ABSTRACT FROM AUTHOR]

The prognosis and health management of lithium-ion batteries are extremely important issues for operating performance as well as the cost of energy storage systems in vehicular applications. This is achieved through the estimation of the state-of-health and the prediction of remaining useful life (RUL). This paper presents a lithium battery prognosis model considering the battery-aging conditions. The proposed model is developed based on the Rao-Blackwellization particle filter, which is able to estimate the posterior values of the aging indicators, i.e., capacity and resistance, and to predict the RUL. The particularity of the proposed model is that it considers the aging conditions of batteries as inputs of the prognosis model. In order to validate the proposed method, experiments have been carried out under different aging conditions for three types of lithium-ion batteries. The proposed model performances have been evaluated. A comparison against the particle filter prognosis model is presented. Results highlight the effectiveness of the proposed technique to predict the RUL for different cases: initial conditions, types of lithium-ion batteries, and aging conditions. The RUL prediction using the proposed prognosis model presents a maximum relative error of 6.64%, which is low compared to 14.3% when a simple particle filter prognosis model is used. [ABSTRACT FROM AUTHOR]

Summary: The rapid increase of renewable energy sources made coordinated control of the distributed and intermittent generation units a more demanded task. Matching demand and supply is particularly challenging in islanded microgrids. In this study, we have demonstrated a mixed‐integer quadratic programming (MIQP) method to achieve efficient use of sources within an islanded microgrid. A unique objective function involving fuel consumption of diesel generator, degradation in a lithium‐ion battery energy storage system, carbon emissions, load shifting, and curtailment of the renewable sources is constructed, and an optimal operating point is pursued using the MIQP approach. A systematic and extensive methodology for building the objective function is given in a sequential and explicit manner with an emphasis on a novel model‐based battery aging formulation. Performance of the designed system and a sensitivity analysis of resulting battery dispatch, diesel generator usage, and storage aging against a range of optimization parameters are presented by considering real‐world specifications of the Semakau Island, an island in the vicinity of Singapore. [ABSTRACT FROM AUTHOR]

Conditions such as the temperature and pressure experienced by lithium-ion battery components are dependent on cell geometry and can vary widely within a large cell. The resulting uneven degradation is challenging to study at the full cell level but can be revealed upon disassembly and post mortem analysis. In this work, we report localized lithium plating in automotive-grade, prismatic lithium-ion cells, also under cycling conditions generally considered to be mild (e.g., 5–65 %SOC, 23 °C, 0.5C cycle rate). Dead lithium content is quantified using 7Li nuclear magnetic resonance spectroscopy in both electrode and separator samples, corresponding to substantial capacity fade (26–46%) of the full cells. Severe lithium plating is typically initiated in regions near the positive tab, in which both the separators and negative electrodes are ultimately deactivated. High pressure arises during cycling, and we propose a deactivation mechanism based on high local stress due to electrode expansion and external constraint. Further, we develop a model to demonstrate that component deactivation can result in lithium plating even under mild cycling conditions. Notably, components harvested from regions with no detected lithium plating maintained adequate electrochemical performance. [Display omitted] • Detection of lithium plating at ambient temperature and low charge rate. • Quantitative mapping of lithium plating using 7Li NMR spectroscopy. • Parallel cycling of harvested material segments to reconstruct full cell current distribution. • Mechanism of pore closure and deactivation of electrode and separator. • Model of lithium plating propagation in deactivated regions. [ABSTRACT FROM AUTHOR]

Abstract An extensive parametric study of an alternative battery model, the fractional differential model (FDM), is presented for the first time over the entire lifetime of 20Ah NMC cells. The evolution of the FDM's model parameters, identified in the time-domain, is compared to electrochemical impedance spectroscopy results to assess their physico-chemical significance. This comprehensive parametric analysis shows that the FDM is able to capture relevant physico-chemical information with regards to the ohmic resistance over the entire SoC range and battery lifetime. Furthermore, the FDM's electrical performance is compared to the conventional first order RC battery model over the entire battery lifetime. Thanks to the FDM's non-integer derivatives for system states, it is able to capture intrinsic fractional derivative properties such as diffusion dynamics, charge transfer and memory hysteresis, which results in an improved simulation accuracy compared to the RC model. The comparative study showed an improved simulation accuracy of the FDM up to 85% in the lowest SoC range and a minimum improvement of 40% over the entire SoC range, considering the entire battery lifetime. Inherently proving that the FDM's ability to capture highly nonlinear battery behavior in the low SoC region persists over battery lifetime. [ABSTRACT FROM AUTHOR]

Abstract When developing an energy management strategy (EMS) including a battery aging model for plug-in hybrid electric vehicles, the trade-off between the energy consumption cost (ECC) and the equivalent battery life loss cost (EBLLC) should be considered to minimize the total cost of both and improve the life cycle value. Unlike EMSs with a lower State of Charge (SOC) boundary value given in advance, this paper proposes a model predictive control of EMS based on an optimal battery depth of discharge (DOD) for a minimum sum of ECC and EBLLC. First, the optimal DOD is identified using Pontryagin's Minimum Principle and shooting method. Then a reference SOC is constructed with the optimal DOD, and a model predictive controller (MPC) in which the conflict between the ECC and EBLC is optimized in a moving horizon is implemented. The proposed EMS is examined by real-world driving cycles under different preview horizons, and the results indicate that MPCs with a battery aging model lower the total cost by 1.65%, 1.29% and 1.38%, respectively, for three preview horizons (5, 10 and 15 s) under a city bus route of about 70 km, compared to those unaware of battery aging. Meanwhile, global optimization algorithms like the dynamic programming and Pontryagin's Minimum Principle, as well as a rule-based method, are compared with the predictive controller, in terms of computational expense and accuracy. Highlights • A model predictive energy management strategy is proposed for PHEVs. • The energy management strategy considers the battery aging. • Optimal battery depth of discharging is identified. • Real-world driving cycles are used to examine the controller. • Typical methods are compared to the proposed method. [ABSTRACT FROM AUTHOR]

Abstract As Li-ion batteries (LIBs) see increasing usage for electric vehicles, the effect of transportation history on battery state-of-health remains unclear. In particular, the influence of dynamic impact on battery behavior is investigated in terms of its effect on battery aging. Standard LiCoO 2 electrodes were dynamically impacted using a nano-impactor setup, and the relationship between impact cycle number (N = 0, 3, 12, and 1000) and electrochemical performance is studied. Li-ion half cells constructed from impacted electrodes display decreasing first cycle Coulombic and energy efficiency with number of impacts, with electrochemical impedance spectroscopy analysis revealing an increase in the charge-transfer resistance. Accelerated capacity fading through long-term cycling is observed for N = 1000 impacts, with significant voltage hysteresis and decay occurring by cycle 50. Additional ex-situ material characterization of cycled electrodes reveals a negative Raman shift by ∼2–3 cm−1 for the A 1g and E g bands for the N = 1000 case as compared to pristine LiCoO 2 , correlating to retention of material stress and electrochemical degradation via capacity fading. These results connect mechanical and electrochemical analysis of LIB electrodes, elucidating the relationship between dynamic impact and cell aging. [ABSTRACT FROM AUTHOR]

Energy harvesting is a key technology for futureenergy neutralubiquitous wireless communication systems. Among the many available sources of energy, RF harvesting is a natural choice for wireless communications since the devices can be efficiently designed for RF harvesting operations. To achieve energy autonomy a rechargeable energy storage is needed. However, the bursty nature of energy arrival associated with wireless power transfer and the energy usage pattern induced by devices’ activity may cause considerable stress to the battery and reduce its life span. In fact, deep charging and discharging cycles degrade the battery state of health, that is, the maximum amount of energy that can be stored. In this paper, a framework for the optimization of wireless nodes’ transmission and charging strategy is presented, where the battery aging rate is made explicit as a performance metric. The proposed framework is based on MDP theory, where the embedded stochastic model captures the energy arrival and data generation processes, and the control variable takes the form of a transmission power selection and energy acceptance/rejection. The tradeoff between the achievable throughput and the aging rate is explored for different parameters setting. [ABSTRACT FROM AUTHOR]

Abstract Aquoeus zinc-ion batteries have become an interesting alternative to the traditional technologies used for energy storage from renewable energy sources. Among them, systems based on Prussian Blue derivates have attracted great attention due to their high discharge potential, good cyclability and rate capability. In this work we have observed that both anion and co-cations are playing a critical role in the cycle stability of copper hexacynoferrate. By using post-mortem X-ray diffraction, it is observed that a partial transformation of copper hexacyanoferrate structure occurs via substitution of the transition metals in the lattice. The phase transformation mitigate the energy losses, and in certain conditions allows retaining up to 82% of the initial energy provided after 500 cycles. Highlights • The intercalation of zinc in CuHCF in presence of sodium salt was studied. • A phase transformation upon cycling was observed. • The new phase formed presents higher intercalation potential than original CuHCF. • The co-intercalation of Na partially mitigates the phase transformation. [ABSTRACT FROM AUTHOR]

The article presents a statistical model of mechanical degradation in the negative electrode of lithium ion batteries. During battery operation, nano-cracks nucleate and grow caused by the impact of diffusion-induced stress during Li-ion intercalation. Particle agglomeration is another mechanical effect that contributes to morphological changes. The presented model employs a population balance formalism to describe the propagation of the particle density distribution function in the electrode. A set of kinetic and transport equations accounts for structure-transforming processes at the level of individual particles. These processes alter the particle density distribution function, and cause changes in battery performance. A parametric study reveals the population of small particles and the width of the initial particle size distribution (PSD) as the main parameters that determine changes in electrochemical performance and capacity fade. The model is applied to experimental data in order to isolate and quantify the impact of various degradation mechanisms. [ABSTRACT FROM AUTHOR]

This paper describes an approach to compute the maximum value of energy storage systems (ESS) in grid applications under uncertain energy prices. The value obtained is based on an optimal operation (consisting of charge/discharge sequences) of the ESS. In other words, it is the maximum value that may be obtained when the ESS charge/discharge sequence is adapted to the expected operational conditions. To obtain that optimal value, this paper describes a dynamic program approach, with the particularity that the switching decisions are optimized considering an uncertain price evolution and a dynamic calculation of the aging cost. A practical implementation of this approach is proposed, in which the problem is conveniently sliced into matrices corresponding to single decisions. It is shown that such an arrangement, combined with shift and re-indexing operators, provides a fast solution to the optimization problem consisting of a huge number of decision evaluations. The algorithm is then applied to a number of European electricity markets, with a particular focus on arbitrage. The particularities of the algorithm solutions are analyzed, and it is shown that not considering the imperfect foresight and the aging impacts leads to considerable errors in valuing an ESS. [ABSTRACT FROM AUTHOR]

Understanding the mechanical aging of lithium ion batteries influencing the binder stability is of particular interest for enhanced battery life. In this study we present an indentation method to investigate the changes in the elasticity of PVdF in NCM electrodes with high reproducibility. To determine changes in elasticity by calculating the indentation work ( η it ), a 50 μm flat punch indenter was used. In addition, a drying procedure for DMC washed samples was evaluated to reduce the effect of the washing procedure on the elasticity due to swelling of the binder. NCM electrodes soaked with electrolyte and electrodes after formation were investigated, showing a significant decrease in elasticity due to the contact with the LiPF 6 containing organic carbonate solvent based electrolyte and due to the electrochemical formation procedure. Further electrochemical aging reduced the elasticity to nearly ≈50% compared to the pristine electrode. Method development and the obtained results are discussed in detail. The developed method provides a low standard deviation and high reproducibility. Hence, it is a valid methodology for the quantification of related aging mechanisms taking place in lithium ion batteries. [ABSTRACT FROM AUTHOR]

• A comprehensive model based on battery aging effect is developed for MHP-BTMS. • A framework combing deep learning and multi-objective genetic algorithm. • BTMS shows poor cooling capacity due to SEI formation inside the aged battery. • Two optimization strategies are proposed for multi-objective optimization. • The trade-off between battery temperature and performance is achieved. Battery thermal management and electrochemical performance are critical for efficient and safe operation of battery pack. In this research, a multi-physics model considering the battery aging effect is developed for micro heat pipe battery thermal management system (MHP-BTMS). A novel multi-variables global optimization framework combining multi-physics modeling, deep learning and multi-objective optimization algorithms is established for optimizing the structural parameters of MHP-BTMS to improve battery thermal management and electrochemical performance simultaneously. It is found that MHP-BTMS fails to control the temperature of aged battery pack due to the higher heat generation caused by solid electrolyte interphase formation. After 1000 cycles, the maximum temperature and maximum temperature difference were increased by 3.32 K, 2.49 K, 2.04 K and 1.78 K, 1.46 K, 1.26 K, respectively. It is also found that the battery electrochemical performance during the cycling is highly related to battery thermal behaviors. MHP-BTMS with 0.004/s inlet velocity achieved the best performance in preventing SEI formation and battery aging effect, which was lower by 7.01 nm (SEI) and 1.65% (aging), 2.31 nm and 0.58% as compared to 0.002 and 0.003 m/s cases. Besides, MHP-BTMS with optimized inlet velocity, MHP arrangement and cold plate can improve cooling performance and electrochemical performance. Multi-variables global optimization can provide the optimal structure parameters of MHP-BTMS under the different combinations of weighted coefficients and optimization strategies to achieve the trade-off between battery thermal issues and electrochemical performance. In addition, it is demonstrated that the weighted coefficients and optimization strategies in this novel framework can be changed according to the actual needs in engineering applications. [ABSTRACT FROM AUTHOR]

[Display omitted] • Fast, cheap method for quantifying plated lithium on commercial LIBs anodes. • Lab-made device with H 2 sensitive sensor used to quantify deposited Li. • Plated Li quantification can be introduced in aging models for LIBs. The deposition of metallic lithium is a degradation mechanism, also known as lithium plating, that might occur at the negative electrode surface of Li-ion battery cell, especially during fast charging, low temperature charging and high state of charge. The loss of cyclable lithium may lead to irreversible capacity fade and might also bring significant safety hazards. In this context, the experimental quantification of plated lithium on the negative electrode surface could be used to accurately validate electrochemical and physical models that might be used to predict plated lithium without cell opening. Also, the determination of plated Li may help in the evaluation of the safety and aging of commercial cells from different manufacturers for a specific application. This work shows a fast and cost-effective method that can be used to quantify plated lithium on commercial Li-ion battery graphite electrodes. [ABSTRACT FROM AUTHOR]

In this article, we investigate the potential for battery life prolongation through optimized charging under consideration of individual mobility requirements. Based on a comprehensive battery aging model we introduce a continuous quadratic programming model to derive battery life optimal charging (OPT). The strategy indicates when and how much to charge to maximize the potential range throughout the battery life. We find that OPT has the potential to more than double the expected battery life compared to simple and often abundant recharging activities as observable today. The degree of battery life prolongation strongly depends on the operating temperature. Since optimal charging would require deterministic knowledge of future trips and corresponding charging levels we investigate a more convenient charging heuristic derived from “As-Late-As-Possible” (ALAP) charging. ALAP charging considers range buffers between 5% and 60% over the range required until the next re-charging opportunity. We analyze the trade-off between (long-term) battery life and (short-term) range flexibility. We find that for decreasing temperatures the trade-off between battery life and flexibility is solved with increasing range buffers. From our results battery degradation aware charging heuristics can be easily derived and applied in real-world settings. [ABSTRACT FROM AUTHOR]

Regenerative braking is a key technology for hybrid electric vehicles (HEVs) to improve fuel economy, and it is a multi-objective control problem, which should ensure vehicle braking safety, recover more energy, and protect components from aging. As is known, battery is the most sensitive component in hybrid powertrain, so a large recover current can cause damage to the battery and reduce its life. However, the damage to is usually ignored in regenerative braking. Therefore, this paper proposed a hierarchical control strategy with battery aging consideration to solve the problem. In the up-level controller, the control targets are to recover more energy and minimize aging of the battery in general braking mode, and ensuring the vehicle braking safety in emergency braking mode at the same time. The low-level controller receives the commands of the up-level controller, and controls the pneumatic braking system and the electric motor (EM). The constraints of maximum EM torque and maximum battery charging power are set to protect the EM and the battery. Simulation tests are designed to indicate the effects of regenerative braking on battery aging and the control effectiveness of the proposed method, and controller-in-the-loop tests are carried out to verify the real-time calculation performance. [ABSTRACT FROM AUTHOR]

Battery energy storage systems have gained increasing interest for serving grid support in various application tasks. In particular, systems based on lithium-ion batteries have evolved rapidly with a wide range of cell technologies and system architectures available on the market. On the application side, different tasks for storage deployment demand distinct properties of the storage system. This review aims to serve as a guideline for best choice of battery technology, system design and operation for lithium-ion based storage systems to match a specific system application. Starting with an overview to lithium-ion battery technologies and their characteristics with respect to performance and aging, the storage system design is analyzed in detail based on an evaluation of real-world projects. Typical storage system applications are grouped and classified with respect to the challenges posed to the battery system. Publicly available modeling tools for technical and economic analysis are presented. A brief analysis of optimization approaches aims to point out challenges and potential solution techniques for system sizing, positioning and dispatch operation. For all areas reviewed herein, expected improvements and possible future developments are highlighted. In order to extract the full potential of stationary battery storage systems and to enable increased profitability of systems, future research should aim to a holistic system level approach combining not only performance tuning on a battery cell level and careful analysis of the application requirements, but also consider a proper selection of storage sub-components as well as an optimized system operation strategy. [ABSTRACT FROM AUTHOR]

This paper focuses on Hybrid Energy Storage Systems (HESS), consisting of a combination of batteries and Electric Double Layer Capacitors (EDLC), for electric urban busses. The aim of the paper is to develop a methodology to determine the hybridization percentage that allows the electric bus to work with the highest efficiency while reducing battery aging, depending on the chosen topology, control strategy, and driving cycle. Three power electronic topologies are qualitatively analyzed based on different criteria, with the topology selected as the favorite being analyzed in detail. The whole system under study is comprised of the following elements: a battery pack (LiFePO4 batteries), an EDLC pack, up to two DC-DC converters (depending on the topology), and an equivalent load, which behaves as an electric bus drive (including motion resistances and inertia). Mathematical models for the battery, EDLCs, DC-DC converter, and the vehicle itself are developed for this analysis. The methodology presented in this work, as the main scientific contribution, considers performance variation (energy efficiency and battery aging) and hybridization percentage (ratio between batteries and EDLCs, defined in terms of mass), using a power load profile based on standard driving cycles. The results state that there is a hybridization percentage that increases energy efficiency and reduces battery aging, maximizing the economic benefits of the vehicle, for every combination of topology, type of storage device, control strategy, and driving cycle. [ABSTRACT FROM AUTHOR]

A new approach using Dirichlet process mixture model (DPMM) and particle filtering (PF) method to predict remaining discharge time (RDT) of ongoing discharge processes of lithium-ion batteries is proposed. Different voltage trajectory patterns are proposed to describe the discharge process at different periods of a battery’s life. Each pattern is represented by the same empirical model based on the physical discharge behavior of lithium-ion batteries, with different parameters to distinguish itself. A DPMM is developed to automatically discover these voltage trajectory patterns from historical monitoring data, without specifying the number of patterns in advance. The trajectory parameters for each pattern can also be learned simultaneously, which are used as the initial parameters for online prognostics. The developed DPMM is able to discover new patterns as more trajectory data become available. During online prognostics, voltage trajectory pattern is constantly identified with new voltage data, then initial parameters for identified pattern and PF-based method are combined to predict the RDT of the ongoing discharge process. A case study demonstrating the proposed approach is presented. It also demonstrates that this approach improves accuracy of RDT prediction compared with benchmark PF-based method. [ABSTRACT FROM AUTHOR]

Due to their decreasing cost, lithium-ion batteries (LiB) are becoming increasingly attractive for grid-scale applications. In this paper, we investigate the use of LiB for providing secondary reserve and show how the achieved cost savings could be increased by using model-based optimization techniques. In particular, we compare a maximum use dispatch strategy with two different cost-minimizing strategies. For the estimation of state-dependent battery usage cost, we combine an existing electro-thermal LiB model of a mature lithium-iron-phosphate battery cell with corresponding semiempirical calendar and cycle aging models. We estimate the benefit of storage operation from the system operator's point of view by gauging the avoided cost of activated reserve. Our evaluation is based on two years worth of data from the German reserve market. The proposed cost minimizing dispatch strategies yield significantly better results than a dispatch strategy that maximizes battery utilization. [ABSTRACT FROM AUTHOR]

Battery energy storage systems (BESS) coupled with rooftop-mounted residential photovoltaic (PV) generation, designated as PV-BESS, draw increasing attention and market penetration as more and more such systems become available. The manifold BESS deployed to date rely on a variety of different battery technologies, show a great variation of battery size, and power electronics dimensioning. However, given today's high investment costs of BESS, a well-matched design and adequate sizing of the storage systems are prerequisites to allow profitability for the end-user. The economic viability of a PV-BESS depends also on the battery operation, storage technology, and aging of the system. In this paper, a general method for comprehensive PV-BESS techno-economic analysis and optimization is presented and applied to the state-of-art PV-BESS to determine its optimal parameters. Using a linear optimization method, a cost-optimal sizing of the battery and power electronics is derived based on solar energy availability and local demand. At the same time, the power flow optimization reveals the best storage operation patterns considering a trade-off between energy purchase, feed-in remuneration, and battery aging. Using up to date technology-specific aging information and the investment cost of battery and inverter systems, three mature battery chemistries are compared; a lead-acid (PbA) system and two lithium-ion systems, one with lithium-iron-phosphate (LFP) and another with lithium-nickel-manganese-cobalt (NMC) cathode. The results show that different storage technology and component sizing provide the best economic performances, depending on the scenario of load demand and PV generation. [ABSTRACT FROM AUTHOR]

• A model coupling electrochemistry, battery aging and heat transfer is developed. • The comparison between different coolants and nanofluids is performed. • BTMS shows poor cooling capacity due to SEI formation inside the aged battery. • Dispersing nanoparticle into BTMSs can improve thermal behaviors of aged battery. • Nanoparticle shapes have a significant effect on BTMS overall performance. In this research, a novel model considering electrochemistry, battery aging and heat transfer is developed for the design and optimization of battery thermal management system (BTMS) to ensure efficient and durable operation of batteries. The multiphysics behaviors in different working cycles of BTMSs are analyzed and compared. It is found that solid electrolyte interphase (SEI) formation inside the aged battery pack leads to the higher heat generation rate, which is the main reason that BTMSs only provide effective cooling performance in the initial working cycles but fail to control the battery temperature after 1000 cycles. Meanwhile, BTMS with water provides the lowest maximum temperature and temperature difference with the lowest pressure loss, which were 5.14 K, 4.33 K, 3.79 K and 3.94 K, 3.51 K, 3.2 K and 2772.7 Pa, 3980.9 Pa, 5271.8 Pa lower than those of BTMS with EO, were 2.17 K, 1.66 K, 1.37 K and 1.79 K, 1.43 K, 1.19 K and 544.4 Pa, 758.1 Pa, 984.3 Pa lower than those of BTMS with EG. In addition, BTMS with water also showed the best performance in controlling SEI formation and capacity fade, leading the highest average potential. Furthermore, dispersing nanoparticles into BTMSs can further enhance the cooling performance with a higher pressure loss, and BTMS with water-based nanofluid achieves the best performance. Besides, the cooling performance of BTMS increases with increasing volume fraction of nanoparticles, although the pressure loss is also higher. Nanoparticle shapes also have a significant effect on battery thermal behaviors and electrochemical performance. With brick-shaped nanoparticles, BTMS well cools the battery pack and reduces the battery capacity fade. For comparison, BTMS with spherical-shaped nanoparticles achieves the lowest pressure loss with providing favorable thermal manage for battery pack. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are playing an increasingly important role in industrial applications such as electrical vehicles and energy storage systems. Their working performance and operation safety are significantly impacted by state of health (SOH), which will decrease after cycles of charging and discharging. This paper has proposed a novel two-stage SOH estimation method that can realize SOH estimation flexibly, rapidly and robustly. In the first stage, eight typical 300-s voltage profiles are used for describing the whole charging process and multiple aging features are extracted. Then, a novel stacking ensemble model with five base models is proposed. In the second stage, a Shapley additive explanation approach is introduced to obtain the contributions of features and understand why a prediction is made, thus reducing the concern of applying black-box model. The performance of the proposed model is verified using two different battery degradation datasets and the results show that the accuracy of the proposed model is better than conventional machine learning models including lightGBM, XGBoost, RF, SVR, and GPR. In addition, with various forms of noise interference, the proposed stacking model is proved to be more robust than conventional machine learning models. • State of health is estimated rapidly and flexibly by short-term voltage profiles. • Multiple aging features based on the short-term voltage profiles are proposed. • An accurate and robust stacking ensemble model is proposed. • SHAP values are deployed to interpret the black-box data-driven model. [ABSTRACT FROM AUTHOR]

This paper reports a two-dimensional modeling to predict the aging effect on the variation of the electrical and thermal behaviors of a lithium-ion battery (LIB) cell under the constant current (CC) charge and discharge cycling over a long time. To account for the aging effects of the LIB cell due to cycling, the key modeling parameters are expressed as a function of cycling number. In order to validate the modeling methodology introduced in this paper, the modeling results towards the changes of the discharge curves and two-dimensional temperature distributions of the aged LIB cell during CC discharge at different C rates are compared with the experimental measurements after every thousand cycles. The electrical and thermal behaviors predicted by the modeling for the aged LIB cell show good agreement with the experimental data. [ABSTRACT FROM AUTHOR]