Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

Online parameter identification is essential for the accuracy of the battery equivalent circuit model(ECM).The traditional recursive least squares(RLS)method is easily biased with the noise disturbances from sensors,which degrades the modeling accuracy in practice.Meanwhile,the recursive total least squares(RTLS)method can deal with the noise interferences,but the parameter slowly converges to the reference with initial value uncertainty.To alleviate the above issues,this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM.RLS converges quickly by updating the parameters along the gradient of the cost function.RTLS is applied to attenuate the noise effect once the parameters have converged.Both simulation and experimental results prove that the proposed method has good accuracy,a fast convergence rate,and also robustness against noise corruption.

Calculation model of edge carbon atoms in graphite particles for anode of lithium-ion batteries

Based on the hexagonal crystallite model of graphite,the electrochemical characteristics of carbon atoms on the edge and basal plane were proposed by analyzing graphite crystal structure and bonds of carbon atoms in different sites.A spherical close-packed model for graphite particle was developed.The fractions of surface carbon atoms（SCA） and edge carbon atoms（ECA） were derived in the expression of crystallographic parameters and particle size,and the effects of ECA on the initial irreversible capacity and the mechanisms of action were analyzed and verified.The results show that the atoms on the edge are more active for electrochemical reactions,such as electrolyte decomposition and tendency to form stable bond with other atoms and groups.For the practical graphite particle,corresponding modifying factors were introduced to revise the difference in calculating results.The revised expression is suitable for the calculation of the fractions of SCA and ECA for carbon materials such as graphite,disordered carbon and modified graphite.

基于Gassing模型的锂电池SOC估计与参数辨识

具有非线性、时变性的Gassing锂电池模型具有以下两大特点:模型考虑了析气现象,使其适用范围拓广到SOC>85%的临界情况;模型考虑了温度的动态变化对系统状态变量的影响。针对该模型,基于相关的试验数据,采用双Kalman滤波算法(DKF),同时实现了对模型参数的辨识和对SOC的在线估计。台架试验和实车验证表明,该算法在非临界情况和临界情况下均可以较准确地在线估计SOC(估计误差在4%以内),并且对SOC的初值误差具有较好的鲁棒性。

Battery Modeling: A Versatile Tool to Design Advanced Battery Management Systems

Fundamental physical and (electro) chemical principles of rechargeable battery operation form the basis of the electronic network models developed for Nickel-based aqueous battery systems, including Nickel Metal Hydride (NiMH), and non-aqueous battery systems, such as the well-known Li-ion. Refined equivalent network circuits for both systems represent the main contribution of this paper. These electronic network models describe the behavior of batteries during normal operation and during over (dis) charging in the case of the aqueous battery systems. This makes it possible to visualize the various reaction pathways, including convention and pulse (dis) charge behavior and for example, the self-discharge performance.

Co-estimation of state-of-charge and state-of-temperature for large-format lithium-ion batteries based on a novel electrothermal model

The safe and efficient operation of the electric vehicle significantly depends on the accurate state-of-charge(SOC)and state-of-temperature(SOT)of Lithium-ion(Li-ion)batteries.Given the influence of cross-interference between the two states indicated above,this study establishs a co-estimation framework of battery SOC and SOT.This framwork is based on an innovative electrothermal model and adaptive estimation algorithms.The first-order RC electric model and an innovative thermal model are components of the electrothermal model.Specifically,the thermal model includes two lumped-mass thermal submodels for two tabs and a two-dimensional(2-D)thermal resistance network(TRN)submodel for the main battery body,capable of capturing the detailed thermodynamics of large-format Li-ion batteries.Moreover,the proposed thermal model strikes an acceptable compromise between the estimation fidelity and computational complexity by representing the heat transfer processes by the thermal resistances.Besides,the adaptive estimation algorithms are composed of an adaptive unscented Kalman filter(AUKF)and an adaptive Kalman filter(AKF),which adaptively update the state and noise covariances.Regarding the estimation results,the mean absolute errors(MAEs)of SOC and SOT estimation are controlled within 1%and 0.4°C at two temperatures,indicating that the co-estimation method yields superior prediction performance in a wide temperature range of 5–35°C.

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.

Optimization of Electrolyte Conductivity for Li-ion Batteries Based on Mass Triangle Model

Mass triangle model was applied to lithium ion battery for electrolyte conductivity forecasting. Seven kinds of electrolytes with different proportions of 3 solvents were prepared. The solvent proportions of the seven electrolytes varied so as to make the seven coordinate points distribute in the ternary coordinate system to form a forcasting region by the connection of them. Their conductivities were tested and the conductivity value in the forecasting region was calculated based on the tested value by mass triangle model. Conductivity isolines formed in the region and blank area showing no forecasted value existed simultaneously. Optimized electrolyte with superior conductivity was selected according to conductivity variation trendency combined with the attention paid to the no-value-shown blank area. The conductivity of optimized electrolytefre[ethyl carbonate（EC）]：m[propylene carbonate（PC）]：m[ethylmethyl carbonate（EMC）]=0.19：0.22：0.59} was 0.745 mS/cm at-40℃, increased by a factor of 51.4% compared to 0.492 mS/cm of common electrolyte[m（EC）：m（PC）：m（EMC）=l：l：l]. The accuracy of mass triangle model was demonstrated from the perspective that the maximum value existed in the blank area, Batteries with this optimized electrolyte exhibited a better performance.

基于析气现象的锂电池系统建模

基于析气现象和热力学原理对锂电池系统在临界和非临界情况下的动力学特性进行了较为细致的研究,建立了以荷电状态(SOC,State of Charge)、温度、开路电压和内阻为状态变量的系统动力学模型并应用Matlab/Simulink软件实现了相应的动态仿真。仿真结果表明,该模型不仅能反映锂电池在非临界情况下的动力学特性,而且在一定程度上还能较为准确地描述临界情况的非线性动力学特性。

锂离子电池系统建模仿真

动力电池测试是动力电池研究的基础,而建立和选择合适准确的电池模型对于电池管理系统开发具有重要意义.为了解决电动汽车仿真时电池模型选取的问题,分别系统的建立了Thevenin模型,Gassing模型以及AVL电池模型,其中,Thevenin模型和AVL电池模型实质上分别为一阶等效电路模型和三阶等效电路模型,而Gassing模型是一种半机理半经验模型.三种模型在非极限工况下,都具有较高的精度,Thevenin模型和AVL电池能够更加准确地描述电池极化环节,AVL电池模型和Gassing模型在剧烈变化工况下误差很小,而Gassing模型能够很好地描述电池在极限工况下的动态特性,且可以反映出温度与内阻的动态变化.

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.

PSO-based Optimization for Constant-current Charging Pattern for Li-ion Battery

A particle swarm optimization algorithm to search for an optimal five-stage constant-current charge pattern is proposed.The goal is to maximize the objective function for the proposed charge pattern based on the charging capacity,time,and energy efficiency,which all share the same weight.Firstly,an equivalent circuit model is built and battery parameters are identified.Then the optimal five-stage constant-current charge pattern is searched using a particle swarm optimization algorithm.At last,comparative experiments using the constant current-constant voltage(CC-CV)method are performed.Although the charging SOC of the proposed charging pattern was 2.5%lower than that of the CC-CV strategy,the charging time and charging energy efficiency are improved by 15.6%and 0.47%respectively.In particular,the maximum temperature increase of the battery is approximately 0.8℃lower than that of the CC-CV method,which indicates that the proposed charging pattern is more secure.

Calculation Model of Effective Thermal Conductivity of a Spiral-wound Lithium Ion Battery

This paper proposed an analytical model which can calculate the effective thermal conductivity (ETC) of a spiral-wound Lithium-ion battery (Li-ion battery). It bases on a two-dimensional energy balance with both radial and spiral heat transfer, as well as internal thermal contact resistance (TCR) considered simultaneously and studies the influence of winding layers and winding tension on the ETC. Results show that the analytical data are in good agreement with the numerical results. With the winding layers decreased and the winding tension enhanced, the ETC of Li-ion battery increases gradually. The radial temperature in Li-ion battery is also investigated which demonstrates a relatively higher temperature when considering the internal TCR.