Chromium oxide with high capacity, high voltage, and low cost has drawn much attention as a cathode material for lithium batteries. This study prepares high pure-phase Cr₈O₂₁ through one-step pyrolysis under oxygen atmosphere with CrO₃ as precursor. The chemical composition, morphology, and corresponding electrochemical performance of the samples prepared at different pyrolysis conditions are analyzed through X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, and electrochemical techniques. The results show that the pyrolysis temperature greatly affects the electrochemical performance of chromium oxides. The insufficient decomposition of CrO₃ at lower temperatures results in electrode damage, and the impurity phase of Cr₂O₅ formed during pyrolysis under higher temperatures significantly reduces the specific capacity. Cr₈O₂₁ prepared at 270 ℃ for 24 h exhibits an outstanding performance with a high specific capacity of 357 mAh/g at first discharge under 0.1 C and an average voltage reaching 3 V. The capacity retention exceeds 80% after a 20-cylce test. In addition, high specific capacities of 200 and 363 mAh/g are achieved at -45 ℃ and 65 ℃, respectively, indicating the excellent cycling stability and electroactivity of Cr₈O₂₁.

As typical conversion reaction-type anode materials for sodium-ion batteries (SIBs), iron-base sulfides possess high theoretical capacity, nontoxicity, and nature abundance, making them one of the potential anode materials that can be used in SIBs. However, iron-base sulfides with poor electron/ionic conductivity present a sluggish sodium storage kinetics, which restricts their practical applications. This study utilized iron disulfide (FeS₂) as an example and applies the ion doping strategy to modify the crystal structure and investigate the sample's sodium storage performance. Co²⁺-doped FeS₂ is specifically prepared through a feasible and controllable hydrothermal method. The characterization results reveal that the Co²⁺ amount in the precursor solution plays a key role in regulating the sample's microstructure. Moreover, an enlarged interplanar spacing of (200) is observed in FeS₂. The electrochemical test reveals that the doped materials obtain enhanced rate capabilities and cyclic stability. The optimized sample has revisable specific capacities of 264.3, 224.9, and 193.4 mAh/g at current densities of 1, 2, and 4 A/g, respectively. A 229.8 mAh/g reversible discharge capacity is maintained at 1 A/g current density after 400 cycles, corresponding to 74.6% capacity retention. The kinetics analysis illustrates that the doped FeS₂ presents an improved Na⁺ diffusion coefficient and a capacitive dominated sodium storage mechanism. This study provides theoretical reference for the fabrication of high-performance anode materials for SIBs.

The unstable interface of polyethylene oxide (PEO)-based electrolytes at high voltage seriously hinders their practical application in high-energy density solid-state lithium batteries.This study presents a new approach of constructing high-voltage solid-state lithium batteries based on the PEO/gelatin composite electrolyte. The results indicate that the oxidative decomposition of the hydroxyl groups at the end of the PEO-based electrolyte chain mainly causes the voltage noise behavior of the Li | PEO | NCM811 at voltages greater than 4.0 V. To address this issue, the gelatin carboxyl group is utilized to form hydrogen bonds with the hydroxyl end groups of the PEO chain, slowing down the PEO hydroxyl group oxidation under a high voltage. The hydroxyl terminal oxidation side reaction of the PEO/gelatin composite electrolyte is suppressed at 4.0—4.3 V after gelatin addition. The results show that adding macromolecular gelatin reduces the PEO crystallinity and forms a fast Li ion conductor pathway that improves the ion conductivity of the composite electrolyte, which consequently improves the high-voltage stability of the matched high-voltage cathode, NCM811. This work presents a simple approach of constructing PEO-based polymer electrolytes for high-voltage solid-state lithium batteries.

The proliferation of portable electronic devices and electric vehicles has led to the pressing need for lithium-ion batteries (LIBs) with enhanced energy density and safety. The utilization of wide-electrochemical window electrolytes with non-combustible properties is crucial in achieving the desired battery characteristics. However, conventional carbonate electrolytes suffer from a narrow electrochemical window and easy combustion, consequently limiting the development of high-energy density and -safety batteries. Incorporating fluorinated compounds into electrolytes can improve the film formation, oxidation stability, and combustibility of electrolytes and effectively enhance the overall battery performance. This review paper provides an overview of the recent advancements in the utilization of new fluorinated compounds as lithium salts and solvents in LIB electrolytes. First, the properties of three new fluorinated lithium salts are presented in terms of thermal and electrochemical stabilities, film formation, and passivation ability for aluminum collectors. Next, the physicochemical properties of carbonate, carboxylic acid esters, ethers, aromatic hydrocarbons, and other solvents before and after fluoridation are compared along with the improvements in the ionic conductivity, interfacial formation ability, oxidation resistance, wide temperature performance, and flammability of electrolytes. Focusing on the application of some new fluorocarbonates, fluorinated ethers, and fluorinated aromatic hydrocarbon solvents to LIB electrolytes, we then finally summarize the scientific challenges and the limitations associated with the development and application of fluorine-containing compounds, providing an outlook on their future prospects in LIB electrolyte systems.

Achieving effective recovery and reuse of valuable metal components in the cathode materials of used ternary lithium-ion batteries can promote the stable development of electrochemical energy storage and new energy vehicle business, and realise energy recycling and reuse. At present, the recycling of cathode materials for ternary lithium-ion batteries faces the key problems of lack of mature recycling process and imperfect reuse system. This paper reviews the research progress of resourceful recycling and reuse of cathode materials of used ternary lithium-ion batteries by exploring the recent related literature, and discusses the necessity of recycling of cathode materials of used ternary lithium-ion batteries from the perspectives of resources and environment. For the pretreatment methods of retired lithium-ion batteries, the discharge, disassembly and separation processes are highlighted; for the obtained used ternary cathode materials, the working principles, research status and advantages and disadvantages of the recovery processes such as pyrometallurgical smelting and wet leaching of valuable metals are focused on; for the regeneration strategies of ternary cathode materials, the effective methods of direct regeneration of cathode materials based on leach solution are highlighted, and the possible problems and challenges facing the recycling processes of used For the regeneration strategy of ternary cathode materials, it focuses on the effective method of direct regeneration of cathode materials based on leaching solution, and looks forward to the possible problems and challenges in the future recycling process of waste used ternary lithium-ion battery. The comprehensive analyses show that suitable pretreatment, recovery and regeneration strategies provide important reference value for the green, efficient and low-cost reuse of valuable metals in used ternary lithium-ion batteries.

To analyze the compatibility of low-temperature molten salt and the Q345R storage tank material for heat storage, this study uses the constant-temperature static corrosion method to investigate the corrosion properties of low-alloy steel Q345R in the low-melting point mixed molten salt at 450 ℃ The corrosion mass loss of the sample is measured, and the corrosion rate is calculated with the mixed molten salt. The corrosion kinetics curve of the Q345R material in molten salt is obtained from the 1000 h corrosion test. The corrosion behavior characterization of the Q345R samples in molten salt is measured through scanning electron microscopy, X-ray diffraction, and energy-dispersive spectroscopy. The results show that the corrosion weight loss of the Q345R material in mixed molten salt effectively follows the parabolic corrosion law with the corrosion time increase. Its annual corrosion rate is lower than that in solar salt under the same working temperature conditions. The structure and the composition of the corrosion oxidation products affect the corrosion properties of the Q345R material. The presence of the Cr and Ni elements in the Q345R sample leads to the production of the dense FeCr₂O₄ and MnCr₂O₄ corrosion products on the sample surface, which improves the corrosion resistance of molten salt. Meanwhile, the presence of Ca²⁺ in molten salt results in the CaFeO₄ corrosion product, which has an inhibitory effect on the corrosion. That is, the corrosiveness of low-temperature mixed molten salt to the Q345R material is lower. This work provides a basis for selecting storage tank materials for the practical application of low-temperature mixed molten salt in different fields.

Forming a local turbulence by adding fins in the liquid cooling channel is mainly performed to enhance the heat dissipation performance of a liquid cooling plate; however, this method leads to an increased pressure drop. To address this issue, a new cold plate structure with a baffle, a diversion hole in the channel, and a finned channel wall is designed herein. With regard to reducing the pressure drop and the average temperature, the influence of the number of diversion holes and fins is analyzed and discussed using a single factor method. The results show that the liquid cooling plate exhibits the best comprehensive heat dissipation performance when the numbers of the diversion holes and fins are 4 and 11, respectively. Using the multi-objective optimization method, the distances between different flow holes (X_{\mathrm{3}}) and between the flow holes and the starting point of the baffle (i.e., X_{\mathrm{1}},X_{\mathrm{2}},X_{\mathrm{4}}) are optimized to further optimize the heat dissipation performance of the liquid cooling plate. The results illustrate a further improved comprehensive performance of the model after the multi-objective optimization. The effects of the tilt angle and the opening width of the fin on the average temperature and pressure drop are discussed through orthogonal experiments. The optimization results show the opening width between the fins to have the greatest influence. The average temperature is reduced to 0.869 ℃, denoting a 2.4% reduction. The pressure drop is 18.257 Pa, yielding a 71.6% decrease. The changes in the Nusselt number, pressure drop, and comprehensive evaluation index of different cold plate structures at the Reynolds number changes ranging from 100 to 400 are discussed. This study promotes the application of battery thermal management heat dissipation by providing experimental basis for the research and development of the liquid cooling plate heat dissipation.

Herein, the control model of an energy storage power plant participating in the primary frequency regulation of a power system is analyzed to address the frequency fluctuation problem of a new energy-rich power system and the inconsistent lithium battery state inside the energy storage power plant. The virtual sag and inertia control modes of the electrochemical energy storage for different frequency variation characteristics are investigated. An adaptive regulation method of the energy storage control mode is then proposed based on frequency characteristics and fuzzy control. In relation to this, the energy storage units are grouped by SOH size to suppress the differentiation of the battery health state (i.e., state of health (SOH)) of each energy storage unit. Next, a method of allocating the power output of the energy storage participation in the primary frequency regulation based on the state of charge and the SOH of each battery pack is suggested. Consequently, an integrated control strategy for the electrochemical energy storage participation in the primary frequency regulation considering frequency characteristics and the state of the energy storage cells is presented. The system model is built under the step load and continuous load perturbation conditions. The effectiveness of the integrated control strategy of the electrochemical energy storage in the primary frequency regulation considering the frequency characteristics and the energy storage battery state is verified through a simulation. In terms of battery group state, the proposed strategy can reduce the action depth of energy storage units with a poor SOH, improve the SOH consistency of each energy storage unit battery, and increase the overall service life of an energy storage power plant battery.

Motor-generators (MGs) for converting electric energy into kinetic energy are the key components of flywheel energy storage systems (FESSs). However, the compact diameters, high-power design features of MGs, and vacuum operating settings of FESSs cause the MG rotor's temperature to increase, leading typical cooling water jackets to fail in meeting the heat dissipation needs of high-power density MG rotors. This study expands upon the causes of and harm generated by the heat production of FESS MG rotors and analyzes the calculation methods for the rotor eddy current losses and MG temperature fields. Moreover, this work also presents research progress on the passive and active cooling of MG rotors. Note that passive cooling includes heat radiation and conduction, while active cooling include shollow shaft fluid and heat pipe cooling. The applicability of the methods provided in the FESS is evaluated. The heat buildup can be preventedup to a point. The temperature gradients inside MGs can also be lowered by improving the heat conduction of the insulation materials inside the stators and rotor sand enhancing thermal radiation. Heat pipes have a simple installation, high integration, and excellent heat transfer performance. Unfortunately, the heat transmission effects cannot be proven while the shafts spin. The hollow shaft fluid cooling technology has avery mature, straight forward design and construction and a good heat transfer effect; hence, it can be used as the first choice for the rotor cooling of MGs with high-power density flywheels. Finally, a fresh hollow shaft flow cooling system is put forth to solve the heat dissipation issue in MW FESS MG rotor cooling.

The proposal of the dual-carbon goal brought new opportunities to the development of China's lithium-ion energy storage industry. Correspondingly, the thermal runaway problem of energy storage cabinets became increasingly prominent. Due to its three major attributes of accessibility, effectiveness, and economy, firewater is considered as the most common solution to lithium-ion battery fires. However, the safety of its usage is questioned due to the issue of electrolytic hydrogen production in response to high-voltage energy storage cabinet fires. On the basis of extensive research, this study adopts theoretical analysis and quantitative calculation methods to establish models for the electrolytic hydrogen production rate and the energy storage cabinet concentration under different boundary conditions to explore the safety of using different water qualities to dispose of the thermal runaway schemes of the energy storage cabinets. The results indicate that the electrolytic hydrogen production rate of the energy storage cabinets is influenced by various factors. As the key factors affecting the hydrogen production rate, the electrochemical parameters only slightly affect the hydrogen production rate, while the water conductivity, electrical cabinet insulation protection, and electrolysis temperature significantly affect it. In this work, two mainstream prefabricated energy storage containers in the market are taken as examples. Under extreme conditions (i.e., 90 ℃ water temperature), the energy storage cabinets were completely submerged; the insulation protection of the cabinets and boxes was damaged; and internal ventilation failed. According to the results, when tap water is used for fire extinguishment, the hydrogen concentration remains far below the lower explosion limit for 3 h, indicating the feasibility of fire termination. Seawater used for the same purpose lasts for 1 h, with the hydrogen concentration being less than the lower explosion limit. Fire extinguishment using seawater is feasible when using ventilation measures. Lastly, when industrial-quality alkaline water (30% KOH solution) is used, the generated hydrogen concentration is greater than the lower explosion limit, posing a significant safety risk.

This study aims to obtain the battery model parameters in real time and effectively improve the battery state estimation accuracy. The commonly used system identification and intelligent optimization algorithms have poor real-time performances and low identification accuracies. To address the issue on the equivalent circuit model identification and improve the identification accuracy of the equivalent circuit model parameters, this study establishes a difference equation that identifies the parameters of the second-order resistance-capacitance equivalent circuit model and the Partnership for a New Generation of Vehicles model through a direct discretization method. A multi-innovation auxiliary model extended recursive least squares algorithm with a forgetting factor (FMIAELS) is proposed based on the identification theory of the multi-information algorithm. The FMIAELS algorithm realizes a real-time and accurate identification of the equivalent circuit model parameters by using only the current and the terminal voltage of a battery. The experimental verification results demonstrate that the FMIAELS algorithm accurately identifies the battery model parameters under different temperatures, working conditions, and states of health. The error is about 1/3 that of the common system identification and intelligent optimization algorithms. Moreover, the FMIAELS algorithm accurately identifies the open-circuit voltage (OCV). Under various working conditions, its OCV identification accuracy is significantly better than that of the common system identification and intelligent optimization algorithms, yielding only a 0.22% average error.

Considering the three factors of charging time, charging efficiency, and battery state of health, this study proposes an adaptive multistage constant current charging strategy (i.e., differential voltage platform (DVP)-based multistage strategy) based on the DVP to cope with the multifactor imbalance caused by the complex electrochemical characteristics of lithium-ion batteries during charging. First, an electrical-thermal-aging coupling model is established to simulate the change in the battery parameter characteristics during charging. Next, the charging voltage is processed differentially to realize dynamic optimization and adaptive grading in the charging process. Accordingly, the DVP is used as the constant current switching condition, whereas the improved grey wolf optimizer algorithm is employed to optimize the charging current at each stage. Based on the optimization results, the Pareto optimal frontier analysis is used to compare the effects of different weight value combinations on charging optimization. Finally, the lithium-ion battery charging simulation system is built on the MATLAB/Simulink platform, and the traditional constant current-constant voltage and voltage-based multistage constant current strategies based on the cut-off voltage are compared and tested. The simulation results show that the proposed charging control strategy effectively reduces the battery capacity attenuation caused by charging and shortens the battery charging time.

The accurate prediction of the remaining useful life (RUL) of lithium-ion batteries plays a crucial role in improving the battery life and reducing the probability of accidents. This study combines the advantages of a stacked denoising autoencoder (SDAE) and a transformer to propose a lithium-ion battery RUL prediction network that combines the SDAE, transformer, and efficient channel attention (ECA). Considering the noise pollution brought about by the capacity regeneration phenomenon and the dataset acquisition error during battery usage, the SDAE is used to reconstruct and denoise the input data and extract the features. The sequence information of the reconstructed data is then captured through the transformer network. Finally, the cross-channel integration and interaction of the captured information are performed in combination with the ECA network to realize the RUL prediction of the lithium-ion batteries. This study uses the battery capacity dataset provided by the Center for Advanced Life Cycle Engineering at the University of Maryland. The experimental results show that the proposed algorithm has low error and high accuracy. Compared with that for the suboptimal bi-directional long short-term memory (Bi-LSTM) algorithm, the average RE, mean absolute error (MAE), and root-mean-squared error (RMSE) for the proposed algorithm are relatively reduced by 62.67%, 40.68%, and 34.33%, respectively. Using the B0007 battery capacity dataset provided by the National Aeronautics and Space Administration for generalization verification, the experimental results of the RE, MAE, and RMSE were found to be 1.98%, 3.12%, and 4.16%, respectively. With that being said, the prediction accuracy of the proposed algorithm is higher than that of existing algorithms, such as recurrent neural networks, LSTM, gated recurrent units, and Bi-LSTM. Thus, the generalization of the model is proven.

The remaining useful life (RUL) of lithium-ion batteries is crucial in managing and using energy storage devices. To improve the prediction accuracy, this study proposes a RUL prediction method based on improved deep echo state network (DESN) and neighborhood component analysis (NCA), in which the DESN is optimized by a hybrid differential evolution (DE)-sparrow search algorithm (SSA). First, various health indicators (HIs) are selected to describe the battery aging mechanism by analyzing the capacity decay characteristics of lithium-ion batteries. The NCA is used to reduce the HI dimensionality. Four high-correlation health factors are then obtained as the model input. Next, the DE algorithm (DE) and the SSA are combined to construct the DESSA algorithm, which is used to optimize the DESN network parameters. As a result, the DESSA-DESN prediction model is established. Finally, the validity and the generalization performance of the proposed model are verified using datasets from the National Aeronautics and Space Administration and the Center for Advanced Life Cycle Engineering. The results show that, compared with existing methods (e.g., SSA-DESN and ground penetrating radar), the proposed method more accurately tracks the degradation state of lithium-ion batteries with smaller prediction errors. The root-mean-squared error of the prediction results remains within 1.5%, while the mean absolute error remains within 1%.

The current data-driven available capacity estimation algorithms for lithium-ion batteries encounter various challenges, including an inaccurate aging feature extraction, a low tracking accuracy of the available capacity decline trend, and a long time required for model parameter optimization. This study addresses these issues by proposing an optimized support vector machine regression algorithm that accurately estimates the available capacity of lithium-ion batteries. First, through an analysis of the aging data of a lithium-ion battery, the peak value of the battery capacity increment curve and the corresponding voltage of this peak value are extracted as the characteristic factors for the battery's aging state. The rationality of these characteristic factors is then analyzed using the Pearson correlation coefficient. The sparrow optimization algorithm is used to optimize the kernel function parameters of the support vector machine (SVM) regression algorithm, and the available battery capacity is accurately estimated based on the optimized SVM regression model. Finally, the advanced nature of the sparrow optimization algorithm in parameter optimization is verifiedby comparing different kernel parameter optimization algorithms. The model accuracy is verified by comparing it with the traditional SVM, Gaussian process regression, long short-term memory network, and other algorithms for estimating the available capacity. In conclusion, the proposed optimized support vector regression model effectively tracks the decline trajectory of lithium-ion batteries and accurately estimatestheir available capacity. It obtains better estimation results on different batteries, with the maximum estimation error of available capacity being less than 2%.

Lithium-ion batteries (LIBs) are the main power source of many devices; hence, the accurate prediction of their usable capacity under different operating conditions is crucial. In response to the limitations of the current Peukert equation, which can only be applied to predict the available capacity of LIBs under constant temperature and current discharge conditions, this study proposes an optimization model for predicting the available capacity of LIBs under different operating conditions. The accurate prediction of the available capacity of LIBs under variable temperature and rate conditions is realized by improving the Peukert equation and providing a reasonable coefficient generation method. The discharge performance of LIBs at different temperatures and discharge rates is tested through experiments. The curve between the battery capacity retention rate and the average battery temperature is then fitted. The Arrhenius equation is used for the analysis, and the equation parameters are determined using the least squares method. The calculations conducted under various discharge conditions based on the predicted optimization model verify that the proposed equivalent capacity method accurately predicts the battery's actual discharge capacity. Finally, the predictive optimization model and experiments are used to confirm the effect of temperature on the battery capacity. The impact of the battery capacity on the discharge rate is found to be relatively small when the ambient temperature is above 25 ℃. The ambient temperature below 25 ℃ significantly affects the battery capacity, showing a decreasing to increasing trend with the discharge rate increase. The results indicate that the average temperature significantly affects the battery capacity. Moreover, the high temperature has a smaller effect than the low temperature. Therefore, a temperature compensation coefficient k must be introduced to consider the effect of the average temperature on the battery capacity.

The timely detection and accurate identification of various fault types in battery packs are critical to the safe operation of electric vehicles and battery energy storage systems. Early fault diagnosis is the key to preventing the thermal runaway of lithium-ion batteries in a battery management system. However, existing fault diagnosis methods cannot identify early faults through weak voltage fluctuations. This study proposes an early multiple-fault diagnosis method based on the charging voltage to detect fault batteries with slight voltage changes. First, the battery data are preprocessed, and the features are extracted. The current, state of charge, temperature, and total voltage are selected as the input variables. Next, a voltage prediction model based on the extreme gradient boost (XGBoost) algorithm is established. Compared with the long short-term memory, support vector machine, and random forest methods, the XGBoost-based voltage prediction method effectively reduces the calculation time while improving the prediction accuracy. The voltage residuals obtained through measurement and prediction are used as the fault features, and the mean normalization (MN) method is employed to amplify them. The early faults of the battery packs are classified according to the MN value characteristics of different faults. Finally, density-based spatial clustering of applications with noise (DBSCAN) is used to automatically classify and locate the multiple faults of the battery packs. In conclusion, the clustering method based on DBSCAN identifies multiple faults within 250 s and realizes the accurate classification and location of potential battery unit faults.

This study uses a dual-polarization (DP) model to improve the identification accuracy of the battery model parameters and the model adaptability. According to the different time-varying characteristics of the model parameters, the identification process of the ohmic resistance is first separated to reduce the number of parameters that must be identified by the recursive least square (RLS). The mutual influence of the parameters is then reduced to improve the RLS identification accuracy and decrease the amount of computation. Considering the adaptability of the online and offline identifications of the model parameters to different working conditions, a full working condition-adaptive output equivalent circuit model (ECM) is proposed herein to further improve the model accuracy. Based on the model accuracy and the running speed, a model evaluation method is finally established to verify the superiority of the adaptive output ECM. The simulation experiments show that the RLS-based full working condition-adaptive output ECM has a higher accuracy than the R-DP online model with a known ohmic resistance, DP online model, and DP offline model and achieves a better accuracy and speed balance.

The accurate prediction and assessment of the state of health (SOH) of lithium-ion batteries are extremely important for the safe and stable operation of the battery equipment. Quickly and accurately predicting the SOH can enhance the safety of battery devices and reduce the failure risk. This study proposes an algorithm for estimating the health status of lithium-ion batteries based on the transformer network structure to address the challenge of accurately predicting their SOH. This algorithm utilizes the battery capacity as the SOH indicator, incorporating the average Euclidean distance (AED) and complementary ensemble empirical mode decomposition (CEEMD) methods. First, the AED is used to assess the similarity between the initial cycle capacities of the batteries in the battery database and the battery to be predicted. The batteries in the battery database with similar capacity degradation trends are selected as the training set for improving the model's training speed. The CEEMD method is then employed to decompose the battery capacity curve into the capacity regeneration and degradation trend parts. The degradation models for the lithium-ion batteries are separately established using the transformer network for each component. As a result, the predictions for the SOH of lithium-ion batteries are obtained. This study validates the accuracy of the proposed battery prediction algorithm using two lithium-ion battery datasets from Stanford University and the University of Maryland. These datasets comprise batteries tested under different charge-discharge strategies and testing environments. The root mean square error of the proposed model can be controlled within 4%, demonstrating its high accuracy level. The superiority of the proposed estimation method is validated by comparing it with the commonly used lithium-ion battery health estimation algorithms based on the long short-term memory, recurrent neural network, and gated recurrent unit.

As a distributed energy storage and demand response resource, battery swapping stations can provide greater flexibility for dispatching an integrated energy system. Carbon trading effectively improves the energy utilization efficiency and reduces environmental pollution. This study proposes a low-carbon economic dispatch model for an integrated energy system considering the battery swapping stations. First, the topology of the integrated energy system considering the battery swapping stations is established. Next, a reward and punishment-type stepped carbon trading mechanism is introduced, and a low-carbon economic dispatch model with the lowest system operating and carbon trading costs as the optimization objectives is established. The original model is transformed into a mixed-integer linear programming problem through piecewise linear interpolation. The model is then formulated using Yalmip and solved by employing the Cplex solver. The comparative analysis results of the different operating modes of the battery swapping station reveal that the station with ordered charging and discharging has flexible scheduling advantages in the integrated energy system. This station charges batteries during off-peak hours and flexibly releases energy during peak hours, thereby effectively reducing the load peak and narrowing the peak-valley difference. The reward and punishment-type stepped carbon trading mechanism also provides economic incentives for the integrated energy system. Profits can be obtained by selling surplus carbon quotas, which helps reduce the carbon trading costs. An analysis of the carbon trading price parameters shows that the carbon trading price increase will encourage the integrated energy system to increase the proportion of low-carbon energy, reduce carbon emissions, and maintain the system's economic and environmental sustainability.

To investigate the secondary frequency modulation scenario of the power grid, this study proposes the integrated control strategy of the battery energy storage with an extended service life and performs the economic evaluation of a multi-type energy storage to improve the frequency modulation economy. First, based on the area control error, a battery energy-conventional unit in the grid's secondary frequency modulation model is built to play the fast response characteristic of the energy storage frequency. Next, the battery energy storage system is divided into two parts with different charge and discharge characteristics for integration. The independent tracking frequency modulation power strategy is then proposed to reduce the influence of the frequent charge and discharge on the battery life during the frequency modulation process and extend the service life. Finally, a multi-type battery life curve is fitted. A battery energy storage life evaluation model and a life cycle cost model based on the rainflow-counting method are constructed to compare and analyze the economies of the multi-type battery energy storage under integrated control. The numerical examples based on the actual load disturbance show that the proposed integrated control strategy effectively extends the service life of the battery energy storage in the frequency modulation scenario. The service life is extended several times with lithium and lead-acid batteries. In conclusion, the life cycle cost is significantly reduced.