Characterization and identification towards dynamic-based electrical modeling of lithium-ion batteries

Lithium-ion batteries are widely recognized as a crucial enabling technology for the advancement of electric vehicles and energy storage systems in the grid.The design of battery state estimation and control algorithms in battery management systems is usually based on battery models,which interpret crucial battery dynamics through the utilization of mathematical functions.Therefore,the investigation of battery dynamics with the purpose of battery system identification has garnered considerable attention in the realm of battery research.Characterization methods in terms of linear and nonlinear response of lithium-ion batteries have emerged as a prominent area of study in this field.This review has undertaken an analysis and discussion of characterization methods,with a particular focus on the motivation of battery system identification.Specifically,this work encompasses the incorporation of frequency domain nonlinear characterization methods and dynamics-based battery electrical models.The aim of this study is to establish a connection between the characterization and identification of battery systems for researchers and engineers specialized in the field of batteries,with the intention of promoting the advancement of efficient battery technology for real-world applications.

SOC distribution-based modeling for lithium-ion battery for electric vehicles using numerical optimization

In order to simulate electrical characteristics of a lithium-ion battery used in electric vehicles in a good manner,a three-layer battery model is established.The charge of the lithium-ion battery is assumed to distribute among the three layers and their interaction is used to depict hysteresis and relaxation effect observed in the lithium-ion battery.The model parameters are calibrated and optimized through a numerically nonlinear least squares algorithm in Simulink Parameter Estimation Toolbox for an experimental data set sampled in a hybrid pulse test of the battery.Evaluation results showed that the established model is able to provide an acceptable accuracy in estimating the State of Charge of the lithium-ion battery in an open-loop fashion for a sufficiently long time and to describe the battery voltage behavior more accurately than a commonly used battery model.The battery modeling accuracy can thereby satisfy the requirement for practical electric vehicle applications.

Modeling the dynamic behavior of a lithium-ion battery for electric vehicles using numerical optimization

In order to simulate the dynamical behavior of a lithium ion traction battery used in elec tric vehicles, an equivalent circuit based battery model was established. The methodology in the guide document of the ADVISOR software was used to determine the initial parameters of the model as a function of state of charge （ SoC ） over an experimental data set of the battery. A numerically nonlinear least squares algorithm in SIMULINK design optimization toolbox was applied to further op timize the model parameters. Validation results showed that the battery model could well describe the dynamic behavior of the lithinm ion battery in two different battery loading situations.

An analytical model for predicting battery lifetime

We used an analytical high-level battery model to estimate the battery lifetime for a given load.The experimental results show that this model to predict battery lifetime under variable loads is more appropriate than that under constant loads.

Dynamic Cell Modeling for Accurate SOC Estimation in Autonomous Electric Vehicles

This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 Ah, optimized for power-needy applications. The AEV operates in a harsh environment with rate requirements up to ±25C and highly dynamic rate profiles, unlike portable-electronic applications with constant power output and fractional C rates. SOC estimation methods effective in portable electronics may not suffice for the AEV. Accurate SOC estimation necessitates a precise cell model. The proposed SOC estimation method utilizes a detailed Kalman-filtering approach. The cell model must include SOC as a state in the model state vector. Multiple cell models are presented, starting with a simple one employing “Coulomb counting” as the state equation and Shepherd’s rule as the output equation, lacking prediction of cell relaxation dynamics. An improved model incorporates filter states to account for relaxation and other dynamics in closed-circuit cell voltage, yielding better performance. The best overall results are achieved with a method combining nonlinear autoregressive filtering and dynamic radial basis function networks. The paper includes lab test results comparing physical cells with model predictions. The most accurate models obtained have an RMS estimation error lower than the quantization noise floor expected in the battery-management-system design. Importantly, these models enable precise SOC estimation, allowing the vehicle controller to utilize the battery pack’s full operating range without overcharging or undercharging concerns.

Letter to the Editor Re “Fractional Modeling and SOC Estimation of Lithium-ion Battery”

A Recent paper by Ma et al.,claims to estimate the state of charge of Lithium-ion batteries with a fractionalorder impedance model including a Warburg and a constant phase element(CPE)with a maximum error of 0.5%[1].The proposed equivalent circuit model from[1]is reproduced in Fig.1.

Review of lithium-ion battery state of charge estimation

The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries,thereby improving discharge efficiency and extending cycle life.In this study,the key lithium-ion battery SOC estimation technologies are summarized.First,the research status of lithium-ion battery modeling is introduced.Second,the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed.Third,the development status and advantages and disadvantages of SOC estimation methods are summarized.Finally,the current research problems and prospects for development trends are summarized.

Application of Digital Twin in Smart Battery Management Systems

Lithium-ion batteries have always been a focus of research on new energy vehicles,however,their internal reactions are complex,and problems such as battery aging and safety have not been fully understood.In view of the research and preliminary application of the digital twin in complex systems such as aerospace,we will have the opportunity to use the digital twin to solve the bottleneck of current battery research.Firstly,this paper arranges the development history,basic concepts and key technologies of the digital twin,and summarizes current research methods and challenges in battery modeling,state estimation,remaining useful life prediction,battery safety and control.Furthermore,based on digital twin we describe the solutions for battery digital modeling,real-time state estimation,dynamic charging control,dynamic thermal management,and dynamic equalization control in the intelligent battery management system.We also give development opportunities for digital twin in the battery field.Finally we summarize the development trends and challenges of smart battery management.

State of Health Estimation of LiFePO_(4) Batteries for Battery Management Systems

When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside the batteries.To optimize battery usage,a battery management system(BMS)is used to estimate possible aging effects while different load profiles are requested from the grid.This is specifically seen in a case when the vehicle is connected to the net(online through BMS).During this process,the BMS chooses the optimized load profiles based on the least aging effects on the battery pack.The major focus of this paper is to design an algorithm/model for lithium iron phosphate(LiFePO4)batteries.The model of the batteries is based on the accelerated aging test data(data from the beginning of life till the end of life).The objective is to develop an algorithm based on the actual battery trend during the whole life of the battery.By the analysis of the test data,the complete trend of the battery aging and the factors on which the aging is depending on is identified,the aging model can then be recalibrated to avoid any differences in the production process during cell manufacturing.The validation of the model was carried out at the end by utilizing different driving profiles at different C-rates and different ambient temperatures.A Linear and non-linear model-based approach is used based on statistical data.The parameterization was carried out by dividing the data into small chunks and estimating the parameters for the individual chunks.Self-adaptive characteristic map using a lookup table was also used.The nonlinear model was chosen as the best candidate among all other approaches for longer validation of 8-month data with real driving data set.

Battery Charger for Electric Vehicles based on a Wireless Power Transmission

In this paper,the case of a battery charger for electric vehicles based on a wireless power transmission is addressed.The specificity of every stage of the overall system is presented.Based on calculated and measured results,relevant capacitive compensations of the transformer and models are suggested and discussed in order to best match the operating mode and aiming at simplifying as much as possible the control and the electronics of the charger.

Visualizing surface-enriched Li storage with a nanopore-array model battery

The coupling of model batteries and surface-sensitive techniques provides an indispensable platform for interrogating the vital surface/interface processes in battery systems.Here,we report a sandwich-format nanopore-array model battery using an ultrathin graphite electrode and an anodized aluminum oxide(AAO)film.The porous framework of AAO regulates the contact pattern of the electrolyte with the graphite electrode from the inner side,while minimizing contamination on the outer surface.This model battery facilitates repetitive charge-discharge processes,where the graphite electrode is reversibly intercalated and deintercalated,and also allows for the in-situ characterizations of ion intercalation in the graphite electrode.The ion distribution profiles indicate that the intercalating Li ions accumulate in both the inner and outer surface regions of graphite,generating a high capacity of~455 mAh·g^(-1)(theory:372 mAh·g^(-1)).The surface enrichment presented herein provides new insights towards the mechanistic understanding of batteries and the rational design strategies.

Data-Based Interpretable Modeling for Property Forecasting and Sensitivity Analysis of Li-ion Battery Electrode

Lithium-ion batteries have become one of the most promising technologies for speeding up clean automotive applications,where electrode plays a pivotal role in determining battery performance.Due to the strongly-coupled and highly complex processes to produce battery electrode,it is imperative to develop an effective solution that can predict the properties of battery electrode and perform reliable sensitivity analysis on the key features and parameters during the production process.This paper proposes a novel tree boosting model-based framework to analyze and predict how the battery electrode properties vary with respect to parameters during the early production stage.Three data-based interpretable models including AdaBoost,LPBoost,and TotalBoost are presented and compared.Four key parameters including three slurry feature variables and one coating process parameter are analyzed to quantify their effects on both mass loading and porosity of battery electrode.The results demonstrate that the proposed tree model-based framework is capable of providing efficient quantitative analysis on the importance and correlation of the related parameters and producing satisfying early-stage prediction of battery electrode properties.These can benefit a deep understanding of battery electrodes and facilitate to optimizing battery electrode design for automotive applications.

Modeling and SOC estimation of lithium iron phosphate battery considering capacity loss

Modeling and state of charge(SOC)estimation of Lithium cells are crucial techniques of the lithium battery management system.The modeling is extremely complicated as the operating status of lithium battery is affected by temperature,current,cycle number,discharge depth and other factors.This paper studies the modeling of lithium iron phosphate battery based on the Thevenin’s equivalent circuit and a method to identify the open circuit voltage,resistance and capacitance in the model is proposed.To improve the accuracy of the lithium battery model,a capacity estimation algorithm considering the capacity loss during the battery’s life cycle.In addition,this paper solves the SOC estimation issue of the lithium battery caused by the uncertain noise using the extended Kalman filtering(EKF)algorithm.A simulation model of actual lithium batteries is designed in Matlab/Simulink and the simulation results verify the accuracy of the model under different operating modes.

Theoretical model of lithium iron phosphate power battery under high-rate discharging for electromagnetic launch

Due to the large error of the traditional battery theoretical model during large-rate discharge for electromagnetic launch,the Shepherd derivative model considering the factors of the pulse cycle condition,temperature,and life is proposed by the Naval University of Engineering.The discharge rate of traditional lithium-ion batteries does not exceed 10C,while that for electromagnetic launch reaches 60C.The continuous pulse cycle condition of ultra-large discharging rate causes many unique electrochemical reactions inside the cells.The traditional model cannot accurately describe the discharge characteristics of the battery.The accurate battery theoretical model is an important basis for system efficiency calculation,precise discharge control,and remaining capacity prediction.To this purpose,an experimental platform for electromagnetic launch is built,and discharge characteristics of the battery under different rate,temperature,and life decay are measured.Through the experimental test and analysis,the reason that the traditional model cannot accurately characterize the large-rate discharge process is analyzed.And a novel battery theoretical model is designed with the help of genetic algorithm,which is integrated with the electromagnetic launch topology.Numerical simulation is compared with the experimental results,which verifies the modeling accuracy for the large-rate discharge.On this basis,a variety of discharge conditions are applied to test the applicability of the model,resulting in better results.Finally,with the continuous cycle-pulse condition in the electromagnetic launch system,the stability and accuracy of the model are confirmed.

Cost-efficient Thermal Management for a 48V Li-ion Battery in a Mild Hybrid Electric Vehicle

The 48V mild hybrid system is a cost-efficient solution for original equipment manufacturers to meet increasingly stringent fuel consumption requirements.However,hybrid functions such as auto-stop/start and brake regeneration are unavailablewhen a 48V battery is at very low temperature because of its limited charge and discharge capability.Therefore,it is important to develop cost-efficient thermal management to warm-up the battery of a 48V mild hybrid electric vehicle(HEV)to recover hybrid functions quickly in cold climate.Following the model-based“V”process,we first define the requirements and then design different mechanisms to heat a 48V battery.Afterward,we build a 48V battery model in LMS AMESim and conduct co-simulation with simplified battery management system and hybrid control unit algorithms in MATLAB Simulink for analysis.Finally,we carry out a series of vehicle experiments at low temperature and observe the effect of heating to validate the design.Both simulation results and experimental data show that a cold 48V battery placed in a cabin with hot air can be heated effectively in the developed“Enhanced Generator Mode with 48V Battery”mode.The entire design is in a newly developed software that cyclically charges and discharges a 48V battery for quick warm-up in cold temperature without needing any additional hardware such as a heater,making it a cost-efficient solution for HEVs.

Recent Progress and Future Trends on the State of Charge Estimation Methods to Improve Battery-storage Efficiency: A Review

Battery storage systems are subject to frequent charging/discharging cycles,which reduce the operational life of the battery and reduce system reliability in the long run.As such,several Battery Management Systems(BMS)have been developed to maintain system reliability and extend the battery’s operative life.Accurate estimation of the battery’s State of Charge(SOC)is a key challenge in the BMS due to its non-linear characteristics.This paper presents a comprehensive review on the most recent classifications and mathematical models for SOC estimation.Future trends for SOC estimation methods are also presented.

Physical-based training data collection approach for data-driven lithium-ion battery state-of-charge prediction

Data-Driven approaches for State of Charge(SOC)prediction have been developed considerably in recent years.However,determining the appropriate training dataset is still a challenge for model development and validation due to the considerably varieties of lithium-ion batteries in terms of material,types of battery cells,and operation conditions.This work focuses on optimization of the training data set by using simple measurable data sets,which is important for the accuracy of predictions,reduction of training time,and application to online esti-mation.It is found that a randomly generated data set can be effectively used for the training data set,which is not necessarily the same format as conventional predefined battery testing protocols,such as constant current cycling,Highway Fuel Economy Cycle,and Urban Dynamometer Driving Schedule.The randomly generated data can be successfully applied to various dynamic battery operating conditions.For the ML algorithm,XGBoost is used,along with Random Forest,Artificial Neural Network,and a reduced-order physical battery model for comparison.The XGBoost method with the optimal training data set shows excellent performance for SOC prediction with the fastest learning time within 1 s,a short running time of 0.03 s,and accurate results with a 0.358%Mean Absolute Percentage Error,which is outstanding compared to other Data-Driven approaches and the physics-based model.

Integrated energy-oriented cruising control of electric vehicle on highway with varying slopes considering battery aging

Eco-driving strategies for vehicles with conventional powertrains have been studied for years and attempt to reduce fuel consumption by optimizing the driving velocity profile.For electric vehicles(EVs)with regenerative braking,the speed profile with the best energy efficiency should be different from conventional vehicles.This paper proposes an energy-oriented cruising control strategy for EVs with a hierarchical structure to realize eco-cruising on highways with varying slopes.The upper layer plans the energy-optimized vehicle velocity,and the lower layer calculates the torque allocation between the front and rear axles.However,the resulting speed profile with varying velocity may cause a high charge and discharge rate of the battery,resulting in rapid battery fading.To extend the battery life,we make a tradeoff between the energy consumption and wear of the battery by formulating an optimal control problem,where driving comfort and travel time are also considered.An indirect optimal control method is implemented to derive the optimal control rule.As an extension,the control rule for avoiding rear-end collisions is presented and simulated for driving in the real world.

Whole-lifetime Coordinated Service Strategy for Battery Energy Storage System Considering Multi-stage Battery Aging Characteristics

One battery energy storage system(BESS)can be used to provide different services,such as energy arbitrage(EA)and frequency regulation(FR)support,etc.,which have different revenues and lead to different battery degradation profiles.This paper proposes a whole-lifetime coordinated service strategy to maximize the total operation profit of BESS.A multi-stage battery aging model is developed to characterize the battery aging rates during the whole lifetime.Considering the uncertainty of electricity price in EA service and frequency deviation in FR service,the whole problem is formulated as a twostage stochastic programming problem.At the first stage,the optimal service switching scheme between the EA and FR services are formulated to maximize the expected value of the whole-lifetime operation profit.At the second stage,the output power of BESS in EA service is optimized according to the electricity price in the hourly timescale,whereas the output power of BESS in FR service is directly determined according to the frequency deviation in the second timescale.The above optimization problem is then converted as a deterministic mixed-integer nonlinear programming(MINLP)model with bilinear items.Mc Cormick envelopes and a bound tightening algorithm are used to solve it.Numerical simulation is carried out to validate the effectiveness and advantages of the proposed strategy.

A life-prediction method for lithium-ion batteries based on a fusion model and an attention mechanism

The current life-prediction models for lithium-ion batteries have several problems, such as the construction of complex feature structures, a high number of feature dimensions, and inaccurate prediction results. To overcome these problems, this paper proposes a deep-learning model combining an autoencoder network and a long short-term memory network. First, this model applies the characteristics of the autoencoder to reduce the dimensionality of the high-dimensional features extracted from the battery data set and realize the fusion of complex time-domain features, which overcomes the problems of redundant model information and low computational efficiency. This model then uses a long short-term memory network that is sensitive to time-series data to solve the long-path dependence problem in the prediction of battery life. Lastly, the attention mechanism is used to give greater weight to features that have a greater impact on the target value, which enhances the learning effect of the model on the long input sequence. To verify the efficacy of the proposed model, this paper uses NASA's lithium-ion battery cycle life data set.