A new state‐of‐charge estimation method for electric vehicle lithium‐ion batteries based on multiple input parameter fitting model

The estimation of state‐of‐charge (SOC) is crucial to determine the remaining capacity of the Lithium‐Ion battery, and thus plays an important role in many electric vehicle control and energy storage management problems. The accuracy of the estimated SOC depends mostly on the accuracy of the battery model, which is mainly affected by factors like temperature, State of Health (SOH), and chemical reactions. Also many characteristic parameters of the battery cell, such as the output voltage, the internal resistance and so on, have close relations with SOC. Battery models are often identified by a large amount of experiments under different SOCs and temperatures. To resolve this difficulty and also improve modeling accuracy, a multiple input parameter fitting model of the Lithium‐Ion battery and the factors that would affect the accuracy of the battery model are derived from the Nernst equation in this paper. Statistics theory is applied to obtain a more accurate battery model while using less measurement data. The relevant parameters can be calculated by data fitting through measurement on factors like continuously changing temperatures. From the obtained battery model, Extended Kalman Filter algorithm is applied to estimate the SOC. Finally, simulation and experimental results are given to illustrate the advantage of the proposed SOC estimation method. It is found that the proposed SOC estimation method always satisfies the precision requirement in the relevant Standards under different environmental temperatures. Particularly, the SOC estimation accuracy can be improved by 14% under low temperatures below 0 °C compared with existing methods. Copyright © 2017 John Wiley & Sons, Ltd.     

An alternating current heating method for lithium‐ion batteries from subzero temperatures

An alternating current (AC) heating method for lithium‐ion batteries is proposed in the paper. Effects of current frequency, amplitudes and waveforms on the temperature evolution and battery performance degradation are respectively investigated. First, a thermal model is established to depict the heat generation rate and temperature status, whose parameters are calibrated from the AC impedance measurements under different current amplitudes and considering battery safe operating voltage limits. Further experiments with different current amplitudes, frequencies and waveforms on the 18650 batteries are conducted to validate the effectiveness of the AC heating. The experimental data recorded by appropriate measurement instrument are of great consistence with simulation results from the thermal model. At high frequency, the temperature rises prominently as the current increases, and high frequency serves as a good innovation to reduce the battery degradation. However, efficient temperature rise can be obtained from high impedance at low frequencies. Typically, 600 s is needed to heat up the battery from ?24 °C to 7.79 °C with sinusoidal waveform and approximately from ?24 °C to 25.6 °C with rectangular pulse waveform at 10A and 30 Hz. The model and experiments presented have shown potential value in battery thermal management studies for electric vehicle (EV)/hybrid electric vehicle (HEV) applications at subzero temperatures. Copyright © 2016 John Wiley & Sons, Ltd.     

Cycling degradation testing and analysis of a LiFePO4 battery at actual conditions

This paper presents a degradation testing of a lithium‐ion battery developed using real world drive cycles obtained from an electric vehicle (EV). For this, a data logger was installed in the EV, and real world drive cycle data were collected. The EV battery system consists of 3 lithium‐ion battery packs with a total of 20 battery modules in series. Each module contains 6 series by 49 parallel lithium‐ion cells. The vehicle was driven in the province of Ontario, Canada, and several drive cycles were recorded over a 3‐month period. However, only 4 drive cycles with statistical analysis are reported in this paper. The reported drive cycles consist of different modes: acceleration, constant speed, and deceleration in both highway and city driving at ?6°C, 2°C, 10°C, and 23°C ambient temperatures with all accessories on. Additionally, individual cell characterization was conducted using a C/25 (0.8A) charge‐discharge cycle and hybrid pulse power characterization (HPPC). The Thevenin battery model was constructed in MATLAB along with an empirical degradation model and validated in terms of voltage and SOC for all drive cycles reported. The presented model closely estimated the profiles observed in the experimental data. Data collected from the drive cycles showed that a 4.6% capacity fade occurred over the 3 months of driving. The empirical degradation model was fitted to these data, and an extrapolation estimated that 20% capacity fade would occur after 900 daily drive cycles.     

Passive thermal management of the lithium‐ion battery unit for a solar racing car

In this study, a three‐dimensional numerical model is developed to investigate the thermal and electrical characteristics of 18 650 lithium‐ion battery cells that are used in the solar racing car of Dokuz Eylül University, i.e., SOLARIS. The Newman, Tiedemann, Gu, and Kim (NTGK) battery model of ANSYS Fluent software is implemented to resolve the coupled multiphysics problem. In the analysis, only the discharging period of the battery is considered. Before going through parametric studies under variable weather conditions, time‐wise variations of the cell temperature and the battery voltage are evaluated both experimentally and numerically under two different ambient conditions of 0°C and 25°C. Comparative results revealed that reasonable predictions are achieved with the current battery model, and the difference between the predicted battery surface temperature and experimental data is less than 1°C. Following the model validation, the battery performance is numerically examined by applying the battery model to a real race procedure of SOLARIS. Phase change materials (PCMs) with different amounts and melting temperatures are implemented around the batteries, and transient analyses are conducted under real weather conditions. The current study aims to keep the battery temperature of a solar racing car above a certain limit to prevent the overcooling and maintain higher charging capacity. Implementation of PCM with a melting temperature of 26°C yields 3.15% of capacity increment, and such a performance improvement corresponds to 15.51 Wh of extra energy that can be extracted from an individual battery.     

A validation study of lithium‐ion cell constant c‐rate discharge simulation with Battery Design Studio®

We compare battery performance simulations from a commercial lithium‐ion battery modeling software package against manufacturer performance specifications and laboratory tests to assess model validity. A set of commercially manufactured spiral wound lithium‐ion cells were electrochemically tested and then disassembled and physically characterized. The Battery Design Studio® (BDS) software was then used to create a mathematical model of each battery, and discharge simulations at constant C‐rates ranging from C/5 to 2C were compared against laboratory tests and manufacturer performance specifications. Results indicate that BDS predictions of total energy delivered under our constant C‐rate battery discharge tests are within 6.5% of laboratory measurements for a full discharge and within 2.8% when a 60% state of charge window is considered. Average discrepancy is substantially lower. In all cases, the discrepancy in simulated vs. manufacturer specifications or laboratory results of energy and capacity delivered was comparable to the discrepancy between manufacturer specifications and laboratory results. Results suggest that BDS can provide sufficient accuracy in discharge performance simulations for many applications. Copyright © 2012 John Wiley & Sons, Ltd.     

A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

Lithium‐ion battery state‐of‐health estimation is one of the vital issues for electric vehicle safety. In this work, a joint model‐based and data‐driven estimator is developed to achieve accurate and reliable state‐of‐health estimation. In the estimator, an increase in ohmic resistance extracted from the Thevenin model is defined as the health indicator to quantify the capacity degradation. Then, a linear state‐space representation is constructed based on the data‐driven linear regression. Furthermore, the Kalman filter is introduced to trace capacity degradation based on the novel state space representation. A series of battery aging datasets with different dynamic loading profiles and temperatures are obtained to demonstrate the accuracy and robustness of the proposed method. Results show that the maximum error of the Kalman filter is 2.12% at different temperatures, which proves the effectiveness of the proposed method.     

A novel multiobjective charging optimization method of power lithium‐ion batteries based on charging time and temperature rise

Power lithium‐ion batteries have been widely utilized in energy storage system and electric vehicles, because these batteries are characterized by high energy density and power density, long cycle life, and low self‐discharge rate. However, battery charging always takes a long time, and the high current rate inevitably causes great temperature rises, which is the bottleneck for practical applications. This paper presents a multiobjective charging optimization strategy for power lithium‐ion battery multistage charging. The Pareto front is obtained using multiobjective particle swarm optimization (MOPSO) method, and the optimal solution is selected using technique for order of preference by similarity to ideal solution (TOPSIS) method. This strategy aims to achieve fast charging with a relatively low temperature rise. The MOPSO algorithm searches the potential feasible solutions that satisfy two objectives, and the TOPSIS method determines the optimal solution. The one‐order resistor‐capacitor (RC) equivalent circuit model is utilized to describe the model parameter variation with different current rates and state of charges (SOCs) as well as temperature rises during charging. And battery temperature variations are estimated using thermal model. Then a PSO‐based multiobjective optimization method for power lithium‐ion battery multistage charging is proposed to balance charging speed and temperature rise, and the best charging stage currents are obtained using the TOPSIS method. Finally, the optimal results are experimentally verified with a power lithium‐ion battery, and fast charging is achieved within 1534 s with a 4.1°C temperature rise.     

Thermal analysis of high‐power lithium‐ion battery packs using flow network approach

High‐power applications of lithium‐ion batteries require efficient thermal management systems. In this work, a lumped capacitance heat transfer model is developed in conjunction with a flow network approach to study performance of a commercial‐size lithium‐ion battery pack, under various design and operating conditions of a thermal management system. In order to assess the battery thermal management system, capabilities of air, silicone oil, and water are examined as three potential coolant fluids. Different flow configurations are considered, and temperature dispersions, cell‐averaged voltage distributions, and parasitic losses due to the fan/pump power demand are calculated. It is found that application of a coolant with an appropriate viscosity and heat capacity, such as water, in conjunction with a flow configuration with more than one inlet will result in uniform temperature and voltage distributions in the battery pack while keeping the power requirement at low, acceptable levels. Simulation results are presented and compared with literature for model validation and to show the superior capability of the proposed battery pack design methodology. Copyright © 2014 John Wiley & Sons, Ltd.     

Subspace‐based modeling and parameter identification of lithium‐ion batteries

For reliable and safe operation of lithium‐ion batteries in electric vehicles, the monitoring of state‐of‐charge and state‐of‐health is necessary. However, these internal states cannot be measured directly, which are usually estimated through model‐based techniques. Therefore, an accurate application‐oriented battery model is of significant importance. The purpose of this paper is to present a novel method on battery modeling and parameter identification. In this work, a state‐space model with clear mathematical and electrochemical meanings is proposed on the basis of the electrochemical basics of lithium‐ion batteries. The frequency‐domain characteristics of the lithium‐ion batteries are also investigated. On the basis of the frequency analysis, an identification test profile that can excite the dynamic characteristics of the battery fully and persistently is proposed. A subspace‐based algorithm is then adopted to identify the parameters of the battery model. The performance and robustness of the estimated model are validated through some experiments and simulations. The validation results show that the proposed method can achieve an acceptable accuracy with the maximum error being less than 2%. Copyright © 2013 John Wiley & Sons, Ltd.     

Data‐driven lithium‐ion battery states estimation using neural networks and particle filtering

The state of charge and state of health estimations are two of the most crucial functions of a battery management system, which are the quantified evaluation of driving mileage and remaining useful life of electric vehicles. This paper investigates a novel data‐driven–enabled battery states estimation method by combining recurrent neural network modeling and particle‐filtering–based errors redress. First, a recurrent neural network with long‐short time memory is employed to learn the long‐term nonlinear relation between batteries states and measurable signals of lithium‐ion batteries, such as current, voltage, and temperature. Second, to denoise the estimation errors of the neural network model, particle filtering is employed to smooth the state of charge estimation results. Third, the terminal voltage difference of battery is highly related to the internal resistance of the battery, which is thus taken as a new input to track the internal resistance of the battery. The performance of the proposed method is verified by multiple comparisons with conventional techniques under randomized loading profiles and different temperatures.     

Study on electrochemical and thermal characteristics of lithium‐ion battery using the electrochemical‐thermal coupled model

The higher specific energy leads to more heat generation of a battery, which affects the performance and cycle life of a battery and even results in some security problems. In this paper, the capacity calibration, Hybrid Pulse Power Characteristic (HPPC), constant current (dis)charging, and entropy heat coefficient tests of chosen 11‐Ah lithium‐ion batteries are carried out. The entropy heat coefficient increases firstly and then decreases with the increase of the depth of discharge (DOD) and reaches the maximum value near 50% DOD. An electrochemical‐thermal coupled model of the chosen battery is established and then verified by the tests. The simulation voltage and temperature trends are in agreement with the test results. The maximum voltage and temperature error is within 2.06% and 0.4°C, respectively. Based on the established model, the effects of adjustable parameters on electrochemical characteristic are systematically studied. Results show that the average current density, the thickness of the positive electrode, the initial and maximum lithium concentration of the positive electrode, and the radius of the positive electrode particle have great influence on battery capacity and voltage. In addition, the influence degree of the internal resistance of the solid electrolyte interface (SEI) layer, the thickness of negative electrode, and the initial and maximum lithium concentration of the negative electrode on the capacity and voltage is associated with certain constraints. Meanwhile, the influences of adjustable parameters related to thermal characteristic are also systematically analyzed. Results show that the average current density, the convective heat transfer coefficient, the thickness, and the maximum lithium concentration of the positive electrode have great influence on the temperature rise. Besides, the uniformity of the temperature distribution deteriorates with the increase of the convective heat transfer coefficient.     

Multi‐objective design optimization for mini‐channel cooling battery thermal management system in an electric vehicle

Lithium‐ion battery packs have been generally used as the power source for electric vehicles. Heat generated during discharge and limited space in the battery pack may bring safety issues and negative effect on the battery pack. Battery thermal management system is indispensable since it can effectively moderate the temperature rise by using a simple system, thereby improving the safety of battery packs. However, the comprehensive investigation on the optimal design of battery thermal management system with liquid cooling is still rare. This article develops a comprehensive methodology to design an efficient mini‐channel cooling system, which comprises thermodynamics, fluid dynamics, and structural analysis. The developed methodology mainly contains four steps: the design of the mini‐channel cooling system and computational fluid dynamics analysis, the design of experiments and selection of surrogate models, formulation of optimization model, and multi‐objective optimization for selection of the optimum scheme for mini‐channel cooling battery thermal management system. The findings in the study display that the temperature difference decreases from 8.0878 to 7.6267 K by 5.70%, the standard temperature deviation decreases from 2.1346 to 2.1172 K by 0.82%, and the pressure drop decreases from 302.14 to 167.60 Pa by 44.53%. The developed methodology could be extended for industrial battery pack design process to enhance cooling effect thermal performance and decrease power consumption.     

A hybrid lithium‐ion battery model for system‐level analyses

We describe an advanced lithium‐ion battery model for system‐level analyses such as electric vehicle fleet simulation or distributed energy storage applications. The model combines an empirical multi‐parameter model and an artificial neural network with particular emphasis on thermal effects such as battery internal heating. The model is fast and can accurately describe constant current charging and discharging of a battery cell at a variety of ambient temperatures. Comparison to a commonly used linear kilowatt‐hour counter battery model indicates that a linear model overestimates the usable capacity of a battery at low temperatures. We highlight the importance of including internal heating in a battery model at low temperatures, as more capacity is available when internal heating is taken into account. Copyright © 2016 John Wiley & Sons, Ltd.     

Li‐ion battery dynamics model parameter estimation using datasheets and particle swarm optimization

Dynamics modeling is of fundamental importance to Li‐ion battery design and manufacturing. Accurate dynamics models established can be used to optimize the operation strategy, manage the life cycle, and thus ensure the economic and safe operation of the batteries. The Li‐ion dynamics model examined in this paper has integrated both empirical and electrochemical aspects, and it has been specifically validated for electric vehicle applications using experimental data. Previously, the model parameter estimation was done by manually picking three key points from the discharge curve obtained from the datasheet. The approach is quite subjective and error prone. The resulted model may deviate greatly from the experimental curve. To address this issue, this paper proposes to use particle swarm optimization to more objectively estimate the model parameters. Results from case studies show that the proposed approach provides more accurate estimation of the true parameters, and thus the new approach can more precisely capture the battery dynamics. In addition, this approach is generic because it is independent of specific battery chemistries and applicable to many different types of Li‐ion batteries. Copyright © 2016 John Wiley & Sons, Ltd.     

Multi‐phase method of estimation and adaptation of parameters of electrical battery models

Mathematical modeling of the battery lifetime is an important tool for the design of more efficient batteries, as well as for the optimization of their use. The electrical models class is among the classes of mathematical models used for this purpose, and a fundamental step to their application is the correct estimation of their parameters. This paper performs the mathematical modeling of Lithium‐Ion Polymer batteries lifetime through the electrical model of Tremblay, in which a multi‐phase method of estimation and adaptation of parameters is proposed, divided into three phases: discovery, learning, and inference. The multi‐phase method is based on two Artificial Intelligence techniques: genetic algorithms and artificial neural networks. The proposed method is validated by the simulation and experimental studies. From the results, it is concluded that the application of the multi‐phase method improves the effective accuracy of the Tremblay model, when it comes to adapt its parameters to the battery during runtime. For constant discharge currents, the average error reduction was 79%, when compared to the best set of parameters obtained by GA without the adaptation process. For variable current discharge curves, the method was able to reduce the error more than 35%. This method can be applied to other battery lifetime prediction models.     

A wavelet transform‐adaptive unscented Kalman filter approach for state of charge estimation of LiFePo4 battery

LiFePo₄ battery is widely used in electric vehicles; however, its flatness and hysteresis of the open‐circuit voltage curve pose a big challenge to precise state of charge (SOC) estimation. The issue is discussed and addressed in this paper. First, a cell model with hysteresis is built to describe real‐time dynamic characteristics of the LiFePo₄ battery. Second, the model parameters and SOC are estimated independently to avoid the possibility of cross interference between them. For model identification, an adaptive unscented Kalman filter (AUKF) algorithm is used to identify the cell parameters as they change slowly. While SOC could change rapidly, wavelet transform AUKF algorithm is put forward to estimate SOC. In the novel algorithm, the measurement noise can be estimated and updated online. Finally, the performance of the proposed method is verified under dynamic current condition. The experimental results show that estimated value based on the proposed method is more accurate than unscented Kalman filter‐based method and AUKF‐based algorithm. Meanwhile, the proposed estimator also has the merits of fast convergence and good robustness against the initialization uncertainty.     

Degradation mechanisms in Li‐ion batteries: a state‐of‐the‐art review

One of the most prominent energy storage technologies which are under continuous development, especially for mobile applications, is the Li‐ion batteries due to their superior gravimetric and volumetric energy density. However, limited cycle life of Li‐ion batteries inhibits their extended use in stationary energy storage applications. To enable wider market penetration of Li‐ion batteries, detailed understanding of the degradation mechanisms is required. A typical Li‐ion battery comprised of an active material, binder, separator, current collector, and electrolyte, and the interaction between these components plays a critical role in successful operation of such batteries. Degradation of Li‐ion batteries can have both chemical and mechanical origins and manifests itself by capacity loss, power fading or both. Mechanical degradation mechanisms are associated with the volume changes and stress generated during repetitive intercalation of Li ions into the active material, whereas chemical degradation mechanisms are associated with the parasitic side reactions such as solid electrolyte interphase formation, electrolyte decomposition/reduction and active material dissolution. In this study, the main degradation mechanisms in Li‐ion batteries are reviewed. Copyright © 2017 John Wiley & Sons, Ltd.     

Correlating uncertainties of a lithium‐ion battery – A Monte Carlo simulation

Safety issues raised from a lithium‐ion battery during operation can be attributed to the variation of its temperature, which is, in turn, associated with the uncertainties in the parameters such as material properties and operating conditions. In this study, a Monte Carlo simulation of a mechanistic lithium‐ion battery model is conducted to capture the probabilistic nature of uncertainties in the parameters and their relative importance to the temperature of a lithium‐ion battery cell. Sensitivity analysis is statistically performed, and the varied parameters are ranked according to their contributions to the variation of the battery temperature. Statistical analysis is also conducted on the distribution of the temperature and deviation from its normality has been identified. These analyses can provide valuable information for manufactures in the area of resource partitioning for quality and safety control. Copyright © 2015 John Wiley & Sons, Ltd.     

Thermal analysis of a Li‐ion battery module under realistic EV operating conditions

The thermal behavior of a Li‐ion battery module that belongs to the battery system of an actual electric vehicle prototype was numerically investigated. Realistic driving loads and passive cooling conditions were considered. A combination of a vehicle dynamics model, an equivalent electric circuit battery model, and a 3D finite‐element thermal model was used in the analysis. Temperature and electric potential measurements, performed at the cell and module levels, were first used for model calibration. Electric currents, associated with the ARTEMIS driving cycles, were then calculated and applied in the battery model to predict the heat sources for the thermal model. It was found that the temperature increase corresponding to urban transportation requirements in European countries is tolerable. Nevertheless, road and highway applications would result in a temperature increase that accelerates cell ageing, and an active cooling strategy is required. Copyright © 2012 John Wiley & Sons, Ltd.