A novel order-reduced thermal-coupling electrochemical model for lithium-ion batteries

Although the single-particle model enhanced with electrolyte dynamics(SPMe)is simplified from the pseudo-twodimensional(P2D)electrochemical model for lithium-ion batteries,it is difficult to solve the partial differential equations of solid–liquid phases in real-time applications.Moreover,working temperatures have a heavy impact on the battery behavior.Hence,a thermal-coupling SPMe is constructed.Herein,a lumped thermal model is established to estimate battery temperatures.The order of the SPMe model is reduced by using both transfer functions and truncation techniques and merged with Arrhenius equations for thermal effects.The polarization voltage drop is then modified through the use of test data because its original model is unreliable theoretically.Finally,the coupling-model parameters are extracted using genetic algorithms.Experimental results demonstrate that the proposed model produces average errors of about 42 mV under 15 constant current conditions and 15 mV under nine dynamic conditions,respectively.This new electrochemicalthermal coupling model is reliable and expected to be used for onboard applications.

Physics-based battery SOC estimation methods:Recent advances and future perspectives

The reliable prediction of state of charge(SOC)is one of the vital functions of advanced battery management system(BMS),which has great significance towards safe operation of electric vehicles.By far,the empirical model-based and data-driven-based SOC estimation methods of lithium-ion batteries have been comprehensively discussed and reviewed in various literatures.However,few reviews involving SOC estimation focused on electrochemical mechanism,which gives physical explanations to SOC and becomes most attractive candidate for advanced BMS.For this reason,this paper comprehensively surveys on physics-based SOC algorithms applied in advanced BMS.First,the research progresses of physical SOC estimation methods for lithium-ion batteries are thoroughly discussed and corresponding evaluation criteria are carefully elaborated.Second,future perspectives of the current researches on physics-based battery SOC estimation are presented.The insights stated in this paper are expected to catalyze the development and application of the physics-based advanced BMS algorithms.

Modeling the rate of corrosion of carbon steel using activated diethanolamine solutions for CO2 absorption

A mechanistic model is developed to investigate the influence of an activator on the corrosion rate of carbon steel in the absorption processes of carbon dioxide(CO2).Piperazine(PZ)is used as the activator in diethanolamine(DEA)aqueous solutions.The developed model for corrosion takes into consideration the effect of fluid flow,transfer of charge and diffusion of oxidizing agents and operating parameters like temperature,activator concentration,CO2 loading and pH.The study consists of two major models:Vapor–liquid Equilibrium(VLE)model and electrochemical corrosion model.The electrolyte-NRTL equilibrium model was used for determination of concentration of chemical species in the bulk solution.The results of speciation were subsequently used for producing polarization curves and predicting the rate of corrosion occurring at the surface of metal.An increase in concentration of activator,increases the rate of corrosion of carbon steel in mixtures of activated DEA.

Physics-informed neural network approach for heat generation rate estimation of lithium-ion battery under various driving conditions

Accurate insight into the heat generation rate(HGR) of lithium-ion batteries(LIBs) is one of key issues for battery management systems to formulate thermal safety warning strategies in advance.For this reason,this paper proposes a novel physics-informed neural network(PINN) approach for HGR estimation of LIBs under various driving conditions.Specifically,a single particle model with thermodynamics(SPMT) is first constructed for extracting the critical physical knowledge related with battery HGR.Subsequently,the surface concentrations of positive and negative electrodes in battery SPMT model are integrated into the bidirectional long short-term memory(BiLSTM) networks as physical information.And combined with other feature variables,a novel PINN approach to achieve HGR estimation of LIBs with higher accuracy is constituted.Additionally,some critical hyperparameters of BiLSTM used in PINN approach are determined through Bayesian optimization algorithm(BOA) and the results of BOA-based BiLSTM are compared with other traditional BiLSTM/LSTM networks.Eventually,combined with the HGR data generated from the validated virtual battery,it is proved that the proposed approach can well predict the battery HGR under the dynamic stress test(DST) and worldwide light vehicles test procedure(WLTP),the mean absolute error under DST is 0.542 kW/m^(3),and the root mean square error under WLTP is1.428 kW/m^(3)at 25℃.Lastly,the investigation results of this paper also show a new perspective in the application of the PINN approach in battery HGR estimation.

KINETICS OF DESULPHURIZATION IN PERMANENT CONTACT OF SLAG AND STEEL

The curve of % S vs reaction time is described quantitatively based on the electrochemical model directly deduced from the principle of electrochemistry as well as the given method for calculating the initial oxygen potential along slag/steel interface.The calculated data is in good agreement with the experimental results. During desulphurization,both L_s~* and L_s,the sulphur partition along the interface and that in the bulk,go up in association with the descending of oxygen potential along interface. The plot of L_s~* against reaction time follows a parabolic curve,while that of L_s is of hyperbola type.Correspondingly,on curves of driving force parameter vs reaction tone,either calculated or experimental,a hump occurs.

Thermal Behavior Analysis of Stacked-type Supercapacitors with Different Cell Structures

As a new energy storage element,supercapacitors have characteristics such as high power density,fast charge and discharge rates,green environmental protection,and long cycle life.Temperature is an important parameter of supercapacitors which significantly influences the stability of the supercapacitors.In this study,the finite element method is used to realize a coupling between a one-dimensional electrochemical model and a three-dimensional thermal model.Then,based on this model,the concept of limited cycle numbers is defined,and different unit quantities,unit size,and the effect of temperature under different temperature environments such as low temperature,room temperature,and high temperature on stacked-type supercapacitors is studied.Finally,stacked-type supercapacitors are compared with rolled-type supercapacitors considering the same cell size,density,and volume approximations.The simulation results show that the higher the number of packaging units,the lower is the limit cycle number.This phenomenon is more pronounced under high current than under low current conditions.Increasing the package size of the porous electrode or separator decreases the limiting cycles.Under the same unit volume scenario,improving the separator size proportion can accurately control the temperature rise at small current values.Under the same material,volume,and density approximations,the temperature rises slowly for stacked-type supercapacitors as compared to rolled-type supercapacitors.This phenomenon is more pronounced with an increase in current.