Hybrid Pulse Power Characterization Research Articles

Physics-Informed Design of Hybrid Pulse Power Characterization Tests for Rechargeable Batteries

Industry-standard diagnostic methods for rechargeable batteries, such as hybrid pulse power characterization (HPPC) tests for hybrid electric vehicles, provide some indications of state of health (SoH), but lack a physical basis to guide protocol design and identify degradation mechanisms. We develop a physics-based theoretical framework for HPPC tests, which are able to accurately determine specific mechanisms for battery degradation in porous electrode simulations. We show that voltage pulses are generally preferable to current pulses, since voltage-resolved linearization more rapidly quantifies degradation without sacrificing accuracy or allowing significant state changes during the measurement. In addition, asymmetric amounts of information gain between charge /discharge pulses are found from differences in electrode kinetic scales. We demonstrate our approach of physics-informed HPPC on simulated Li-ion batteries with nickel-rich cathodes and graphite anodes. Multivariable optimization by physics-informed HPPC rapidly determines kinetic parameters that correlate with degradation phenomena at the anode, such as solid-electrolyte interphase (SEI) growth and lithium plating, as well as at the cathode, such as oxidation-induced cation disorder. If validated experimentally, standardized voltage protocols for HPPC tests could play a pivotal role in expediting battery SoH assessment and accelerating materials design by providing new electrochemical features for interpretable machine learning of battery degradation.

State-of-charge estimation for lithium-ion batteries based on modified unscented Kalman filter using improved parameter identification

The full and single hybrid pulse power characterization (HPPC) experiments are conducted on nickel cobalt manganese (NCM) and lithium iron phosphate (LFP) batteries to obtain the accurate relationship between state of charge (SOC) and open circuit voltage (OCV). The pseudo random number generated by the RAND function is used as the initial value of the parameter identification equation for the second-order equivalent circuit model (ECM), and the parameter identification of lithium-ion batteries is achieved through least squares fitting. The unscented Kalman filter (UKF) simulation model is modified by the first order low pass filtering (FOLPF) algorithm to improve the accuracy of SOC estimation of batteries. The results show that the goodness of fit between the real and estimated HPPC pulse voltage values at different SOC values is above 0.99 for both NCM and LFP batteries. Based on the parameter identification results, the maximum error of the HPPC voltage estimated by the second-order ECM is within 0.045 V under both full pulse and single pulse testing conditions while the maximum error of SOC estimation is within 0.025. The UKF model modified by the FOLPF algorithm provides a reference for the accurate SOC estimation of batteries.

Development of dual polarization battery model with high accuracy for a lithium-ion battery cell under dynamic driving cycle conditions

Lithium-ion batteries are a key technology for electric vehicles. They are suitable for use in electric vehicles as they provide long range and long life. However, Lithium-ion batteries need to be controlled by a Battery Management System (BMS) to operate safely and efficiently. The BMS continuously controls parameters, such as current, voltage, temperature, state of charge (SoC), and state of health (SoH), and protects the battery against overcharging and discharging, imbalances between cells, and thermal runaways. The battery models and several prediction algorithms that the BMS uses to carry out these checks are essential to the system's performance. This research assesses the Dual Polarization (DP) model's ability to mimic actual battery performance in different dynamic driving conditions. In the study, a battery model for a Lithium–Nickel–Manganese–Cobalt-Oxide (Li-NMC) cell with a nominal capacity of 2 Ah is developed. A DP model was used in the study. Modeling and parameter estimation were performed in MATLAB Simulink/Simscape. Firstly, the model parameters are estimated depending on the SoC using the current and voltage data obtained from the Hybrid Pulse Power Characterization (HPPC) test. A further validation study of the model for low dynamic and high dynamic driving cycles is then presented. Dynamic Stress Test (DST), the US06 Supplemental Federal Test Procedure (SFTP) and Worldwide harmonized Light vehicles Test Procedure (WLTP) cycles were used for model validation. As a result of the study, the model's Root Mean Square (RMS) error values were obtained as 0.0053 V for DST, 0.0059 V for US06, and 0.008 V for WLTP. The obtained model is particularly successful for simulating a battery under dynamic current conditions and for use in control and prediction algorithms.

Comparative analysis of equivalent circuit battery models for electric vehicle battery management systems

Lithium-ion batteries need to be controlled by a Battery Management System (BMS) to operate safely and efficiently. BMS controls parameters, such as current, voltage, temperature, state of charge (SoC),state of health (SoH), state of power (SoP) and etc. The battery models and several prediction algorithms that the BMS uses to carry out these checks are essential to the system's performance. Therefore, the battery model is crucial to the BMS. This model is used to optimize the performance, capacity, lifetime and safety of the battery. Using the accurate battery model for BMS and electric vehicles can improve energy efficiency, extend battery life and reduce safety risks. Therefore, it is important that the model can accurately reflect the battery behavior under different load conditions. In this study, the performance of Rint, Partnership for a New Generation of Vehicles (PNGV), Thevenin, and Dual Polarization (DP) battery models, which are widely known in the literature, to simulate static and dynamic voltage behavior is compared. A 18650 NMC battery was used for this purpose, and Hybrid Pulse Power Characterization (HPPC), Dynamic Stress Test (DST), Worldwide Harmonised Light Vehicle Test Procedure (WLTP), and Constant Current (CC) discharge tests were performed. The performance of the models for the four tests is compared. The maximum error values for WLTP are 2.98 % in Rint, 1.32 % in PNGV, 2.80 % in Thevenin, and 1.09 % in DP. Comparing the performances of models for all tests, it is found that the DP model is the most accurate model under both constant and dynamic current conditions.

An improved parameter identification and radial basis correction-differential support vector machine strategies for state-of-charge estimation of urban-transportation-electric-vehicle lithium-ion batteries

The State estimation and determination of time-varying model parameters are crucial for ensuring the safe management of lithium-ion batteries. This paper designs a limited memory recursive least square algorithm to improve the accuracy of online parameter identification. An adaptive radial basis correction-differential support vector machine model is constructed to correct the state of charge value by considering the dynamic characteristic parameters. It greatly reduces estimation error and noise, while monitoring the critical conditions for safe and reliable online battery operation. The estimation effects of the proposed model are verified under hybrid pulse power characterization and dynamic stress test working conditions. The maximum error values obtained are 0.037 % and 0.336 %, respectively, thus achieving high-precision estimation. The proposed method is adaptive to real-time battery management applications, laying a foundation for robust state estimation of lithium-ion batteries used in urban transportation electric vehicles.

Modeling, Model Calibration, and Characterization of Graphite Anodes from Coal Derived Carbon for Battery Applications

Most current production electric vehicles (EVs) contain cells with a battery pack or module in order to maintain electrical conductivity, prevent fatigue due to vibrations, and provide efficient cooling. Various module hardware has to be designed in order to facilitate the efficient rejection of heat and contain volume change due to operation and battery cell aging. As we see a shift towards EVs both globally and domestically, the time from concept development to vehicle production needs to rapidly decrease. The use of model-based engineering is critical to driving rapid design and virtual prototyping of electric vehicles to ensure that battery module components meet EV requirements.Currently, empirical data is used to characterize and parameterize battery models that are used to drive design decisions at the battery cell and battery module level. Typical vehicle level use profiles such as the Hybrid Pulse Power Characterization (HPPC) profile, constant rate discharge, constant rate charging, and US06 drive cycles are traditionally used to parameterize battery models. In this work, we discuss select cell level testing profiles, evaluate the electrochemical transport and reaction parameters that are estimated using a porous electrode model, and provide insight and recommendations based on data from coal derived graphite for battery applications.References Garrick, T. R., Gao, J., Yang, X., & Koch, B. J. (2021). “Modeling Electrochemical Transport within a Three-Electrode System.” Journal of The Electrochemical Society, 168(1), 010530. Lopata, J. S., Garrick, T. R., Wang, F., Zhang, H., Zeng, Y., & Shimpalee, S. (2023). “Dynamic Multi-Dimensional Numerical Transport Study of Lithium-Ion Battery Active Material Microstructures for Automotive Applications.” Journal of The Electrochemical Society, 170(2), 020530.

Lithium-Ion Cells in Automotive Applications: Tesla 4680 Cylindrical Cell Teardown and Characterization

Battery research depends upon up-to-date information on the cell characteristics found in current electric vehicles, which is exacerbated by the deployment of novel formats and architectures. This necessitates open access to cell characterization data. Therefore, this study examines the architecture and performance of first-generation Tesla 4680 cells in detail, both by electrical characterization and thermal investigations at cell-level and by disassembling one cell down to the material level including a three-electrode analysis. The cell teardown reveals the complex cell architecture with electrode disks of hexagonal symmetry as well as an electrode winding consisting of a double-sided and homogeneously coated cathode and anode, two separators and no mandrel. A solvent-free anode fabrication and coating process can be derived. Energy-dispersive X-ray spectroscopy as well as differential voltage, incremental capacity and three-electrode analysis confirm a NMC811 cathode and a pure graphite anode without silicon. On cell-level, energy densities of 622.4 Wh/L and 232.5 Wh/kg were determined while characteristic state-of-charge dependencies regarding resistance and impedance behavior are revealed using hybrid pulse power characterization and electrochemical impedance spectroscopy. A comparatively high surface temperature of ∼70 °C is observed when charging at 2C without active cooling. All measurement data of this characterization study are provided as open source.

State-of-Charge Estimation of Nickel–Cadmium Batteries Based on Dynamic Modeling of Electrical Characteristics and Adaptive Untrace Kalman Filtering

With the increasing demand for intelligence and automation, and the continuous strengthening of safety and efficiency requirements, the disadvantages of traditional “blind use” of nickel–cadmium batteries have become increasingly prominent, and the lack of state-of-charge (SOC) estimation needs to be changed urgently. For this purpose, a dynamic model of nickel–cadmium battery is established, and an SOC estimation method of nickel–cadmium battery based on adaptive untraced Kalman filter is proposed. Firstly, the experimental platform was built, and the open-circuit voltage and polarization characteristics of nickel–cadmium batteries were analyzed. On this basis, an equivalent circuit model is constructed to reflect the characteristics of nickel–cadmium batteries, and the model parameters were identified by the hybrid pulse power characteristic test; Then, based on the dynamic model, the SOC of the nickel–cadmium battery was estimated by combining with the Sage–Husa adaptive untrace Kalman filtering algorithm. Finally, the SOC estimation effect was verified under two operating conditions: Hybrid pulse power characteristic (HPPC) and constant cyclic charging and discharging power. The experimental results show that the proposed estimation method is insensitive to the initial value of SOC, and can still converge to the real value even if there is 30% error in the initial value. The mean absolute error and root mean square deviation of the final SOC estimation results are both less than 1%. The dynamic model and the proposed SOC estimation method provide valuable reference for the operation control, maintenance, and replacement of nickel–cadmium batteries in the use process.

A novel genetic marginalized particle filter method for state of charge and state of energy estimation adaptive to multi-temperature conditions of lithium-ion batteries

Power lithium-ion batteries are widely used in various fields, the battery management system (BMS) is the main object of battery energy management and safety monitoring, so the accurate collaboration of state of charge (SoC) and state of energy (SoE) is estimated to be essential for the BMS system. In this work, a novel genetic marginal particle filter (GMPF) algorithm to estimate SoC and SoE accurately. The forgetting factor recursive least square (FFRLS) algorithm is used to identify the second-order Thevenin equivalent model parameter, and the genetic algorithm is used to improve the re-sampling process of the traditional particle filtering (PF) algorithm, according to the Rao-Blackwell theory in statistical science, the marginalization of part of the linear state variables during the calculation of particle filtering, the distribution of the post-test is similar to a single Gaussian distribution. The GMPF algorithm is verified under the conditions of the hybrid pulse power characteristic (HPPC) and the Beijing bus dynamic stress test (BBDST) with 15 °C, 25 °C, and 35 °C respectively, and experimental results show that the improved GMPF algorithm can effectively realize the collaborative estimation of the SoC and SoE of power lithium-ion batteries. The mean absolute error of SoC and SoE estimation is always less than 1.56 %, the root-mean-square error is always less than 1.58 %. And the GMPF algorithm is suitable for temperature environments of 15 °C to 35 °C.

A lithium-ion battery equivalent circuit model based on a hybrid parametrization approach

This paper proposes an improved lithium-ion battery equivalent circuit model that can simulate the current-voltage characteristics of the battery under various operating conditions. Specifically, a hybrid parametrization approach is adopted by combining the electrochemical impedance spectroscopy and the hybrid pulse power characterization techniques for the identification and quantification of all loss contributions in a lithium-ion battery cell, for a wide operating frequency range. Moreover, the proposed equivalent circuit model considers the variation of the state of charge, the temperature and the charging-discharging current, and since it can be easily realized by utilizing the existing hardware for the cell equalization, it can be a useful tool for a wide range of real-time applications. The above are realized based on analytical equations of the battery performance with respect to the aforementioned operating parameters' variations. The effectiveness and feasibility of the proposed model is experimentally verified on a commercial, high energy capacity cylindrical lithium-ion battery cell of 3.25 Ah by comparing the model voltage performance with the real voltage of the cell during dynamic operating conditions and with variable surface temperature values.

Improved noise bias compensation-equivalent circuit modeling strategy for battery state of charge estimation adaptive to strong electromagnetic interference

Strong electromagnetic interference, which has a significant impact on the performance and safety of the lithium-ion battery, usually affects the accurate state of charge (SOC). Different optimization strategies are used to estimate the model parameters and the SOC due to the unknown nonlinear characteristics caused by noise. However, the impact of sensor and model errors is treated separately. To express the sensor and model uncertainties, a noise bias compensation-equivalent circuit model (NBC-ECM) is proposed, in which sensor noise and model error voltages are employed in the model structure and the SOC estimation process of the lithium-ion battery. For parameter identification, a singular value decomposition-bias compensation recursive least squares (SVD-BCRLS) algorithm is proposed to identify the characteristic micro-parameters of the battery. Then, a moving window adaptive extended Kalman filtering (MWAEKF) algorithm based on window functions is proposed for accurate SOC estimation of lithium-ion batteries. The stability of the model parameters and the reliability of the proposed algorithm in estimating the SOC are evaluated using different noise factors: current and voltage sensor noises of 10 and 50 mA. Using the proposed SVD-BCRLS-MWAEKF algorithm, the maximum SOC error is 1.3 %, the root mean square error (RMSE) is 0.3972 %, and the mean absolute error (MAE) is 0.2316 % using the noise of 0.05 V/A under the hybrid power pulse characterization (HPPC) operating condition. With the same noise value under the Beijing bus dynamic stress test (BBDST) operating condition, the proposed algorithm SOC has a maximum SOC error of 1.57 %, an RMSE of 0.5638 %, and an MAE of 0.4475 %. Under noise interference conditions, estimation is more accurate compared to static conditions, proving that the proposed algorithm can overcome the uncertainties encountered by lithium-ion batteries for real-time BMS applications.

High‐precision state of charge estimation of lithium‐ion batteries based on improved particle swarm optimization‐backpropagation neural network‐dual extended Kalman filtering

SummaryHigh‐precision state of charge (SOC) estimation is essential for battery management systems (BMSs). In this paper, a new SOC estimation method is proposed. The dual Kalman filter algorithm and backpropagation neural network (particle swarm optimization ‐ backpropagation neural network ‐ double extended Kalman filter [PSO‐BPNN‐DEKF]) are combined to estimate and correct the SOC of lithium‐ion batteries, in which the initial weight and threshold of the BPNN are optimized by particle swarm optimization algorithm. Based on the second‐order RC equivalent circuit model, parameter identification is carried out using the adaptive forgetting factor least squares (AFFRLS) method. Online parameter updates and SOC estimation are realized by DEKF algorithm. Then, the trained PSO‐BPNN is used to predict the SOC estimation error in real time, and the SOC estimation value is corrected by adding prediction errors. The SOC estimates before and after correction under Beijing Dynamic Stress Test (BBDST), dynamic stress test (DST), and hybrid pulse power characterization (HPPC) were compared. Under BBDST, DST, and HPPC tests, the maximum errors of the corrected SOC estimates are 0.0107, 0.0090, and 0.0147, respectively. The root mean square error (RMSE) of the corrected SOC estimates decreased by 94.02%, 83.18%, and 88.03%, respectively, compared with the extended Kalman filtering (EKF). The mean absolute error (MAE) of the corrected SOC estimates remained around 0.1% for all the BBDST dynamic operating conditions at different temperatures. The experimental results demonstrate the accuracy, effectiveness, and temperature adaptability of the proposed algorithm for SOC estimation under complex conditions of lithium‐ion batteries.

Identification of parameters for equivalent circuit model of Li-ion battery cell with population based optimization algorithms

As EVs are sold to worldwide area with different latitudes, it also implies that lithium-ion batteries need to be safely operated in different climatic environments. To ensure the robustness and safeness of EVs, the characteristics of lithium-ion batteries must be taken into considerations. To comprehensively grasp the key information of battery cell in an efficient way, building a battery model is one of the most effective ways. Based on the model, one can get rid of time-consuming experiments. Moreover, the model enables to simulate and observe battery behaviors under extreme test condition safely. In this paper, an equivalent electric circuit (EEC) of a Li-ion battery cell is investigated. Based on 2nd order RC circuit, an Extended Hybrid Pulse Power Characterization (EHPPC) is designed and carried out to observe the dynamic response of battery cell in time domain. Once substantial measurement results are obtained, the parameterization turns out to be an important issue. This paper investigates the most appropriate metaheuristic based method to rapidly and systematically identify the EEC parameters. Totally four different methods, namely Particle Swarm Optimization, Grey Wolf Optimizer, Harmony Search, and Golden Eagle Optimization, are realized and equally compared by 100 implementations. Eventually, a mission profile of driving pattern, Dynamic Stress Testing (DST), is used to validate the proposed EEC model. The 2RC EEC model is able to simulate battery voltage response with root-mean-squared error as low as 0.0045.

Modeling, Model Calibration, and Characterization of Graphite Anodes from Coal Derived Carbon

Most current production electric vehicles (EVs) contain cells with a battery pack or module in order to maintain electrical conductivity, prevent fatigue due to vibrations, and provide efficient cooling. Various module hardware has to be designed in order to facilitate the efficient rejection of heat and contain volume change due to operation and battery cell aging. As we see a shift towards EVs both globally and domestically, the time from concept development to vehicle production needs to rapidly increase. The use of model-based engineering is critical to driving rapid design and virtual prototyping of electric vehicles to ensure that battery module components meet EV requirements.Currently, empirical data is used to characterize and parameterize battery models that are used to drive design decisions at the battery cell and battery module level. Typical vehicle level use profiles such as the Hybrid Pulse Power Characterization (HPPC) profile, constant rate discharging, constant rate charging, and US06 drive cycles are traditionally used to parameterize battery models. In this work, we discuss select cell level testing profiles, evaluate the electrochemical transport and reaction parameters that are estimated using a porous electrode model, and provide insight and recommendations based on data from coal derived graphite for battery applications.

HPPC Test Methodology Using LFP Battery Cell Identification Tests as an Example

The aim of this research was to create an accurate simulation model of a lithium-ion battery cell, which will be used in the design process of the traction battery of a fully electric load-hull-dump vehicle. Discharge characteristics tests were used to estimate the actual cell capacity, and hybrid pulse power characterization (HPPC) tests were used to identify the Thevenin equivalent circuit parameters. A detailed description is provided of the methods used to develop the HPPC test results. Particular emphasis was placed on the applied filtration and optimization techniques as well as the assessment of the quality and the applicability of the acquired measurement data. As a result, a simulation model of the battery cell was created. The article gives the full set of parameter values needed to build a fully functional simulation model. Finally, a charge-depleting cycle test was performed to verify the created simulation model.

Using Reference Electrodes to Validate Parameterization of Individual Electrode Soh in the Single Particle Model with Electrolyte

Most model parametrization techniques for reduced order physics-based Lithium-ion battery models like the single particle model rely on minimizing the error in the measured and predicted cell terminal voltage. The battery terminal voltage is the difference between the voltages of the positive and negative electrode solid phase potentials. If the individual contribution of each electrode overpotential to the terminal voltage, is not measured, there could be multiple combinations of parameters that provide similar RMSE for voltage prediction, yielding non-unique solutions and local minima. Misattributing the parameters for kinetics and diffusion in each electrode could result in increased plating or over-conservative designs at higher operating currents.Adding a reference electrode to the battery allows us to measure the individual electrode potentials experimentally. Using this extra measurement, we can parametrize the kinetic and diffusion reactions in positive and negative electrodes with better confidence [1], and validate the electrode-specific state of health parameterization, specifically the increased resistances due to SEI growth and shift in the stoichiometric window as the cell ages [2,3]. In our experiments, we used a pouch cell manufactured in the U of M Battery Lab with Targray NMC 622 Single Crystal cathode powder, Superior SLC 1520-T anode powder, and a custom fabricated Lithium Iron Phosphate (LFP) reference electrode deposited on a porous electrode layer. Further details of the reference electrode are given in [4]. The model was parametrized using discharge data at different C-rates and a modified Hybrid Pulse Power Characterization test. Finally, the RMSE for both the modeled negative and positive electrode potentials are calculated for the validation data set which uses the US06 Drive cycle.

Determination of half-cell open-circuit potential curve of silicon-graphite in a physics-based model for lithium-ion batteries

Lithium-ion batteries with silicon/graphite anodes have the potential to deliver high theoretical capacity. However, these electrodes exhibit significant hysteresis, which presents challenges in accurately estimating the open-circuit potentials (OCP) of the electrodes within a physics-based model. This paper proposes a method to establish the relationship between the electrode OCP and stoichiometry. Galvanostatic intermittent titration technique (GITT) tests are performed on half-cells to measure the charge and discharge OCP. To account for hysteresis, a hysteresis factor is defined to balance the lithiation and de-lithiation OCP. The estimated open-circuit voltage (OCV) of the full-cell is obtained by subtracting the anode OCP from the cathode OCP. The OCP and hysteresis factor are then optimized by minimizing the error between the measured OCV and the estimated OCV. Two different OCV test methods, namely the incremental method and C/30 galvanostatic method, are compared. The OCV estimation for fresh cells shows good agreement with experimental values, with root-mean-square errors (RMSEs) below 6.682 mV. To evaluate the effectiveness of the obtained OCPs in the full-cell model, the optimized OCPs are incorporated into the physics-based model. Under the Hybrid Pulse Power Characterization (HPPC) test, the electrochemical model utilizing the optimized OCP with the incremental OCV and C/30 galvanostatic OCV exhibits RMSEs of 10.587 mV and 11.016 mV, respectively, in predicting the cell voltage. Finally, the OCP identification method is assessed with cells at different aging states. The OCV predictions for degraded cells maintain RMSEs below 9.074 mV, thus validating the effectiveness of the developed OCP estimation method.

State of charge estimation for the vanadium redox flow battery based on the Sage–Husa adaptive extended Kalman filter

SummaryFocusing on improving the accuracy of vanadium redox flow battery (VRFB) state of charge (SOC) estimation, this paper will combine the extended Kalman filter (EKF) estimator with the Sage–Husa adaptive method, referred to as the SAEKF estimator. Firstly, a second‐order Equivalent Circuit Model (ECM) is established to provide the correct parameterization and degrees of freedom. Next, under a hybrid pulse power characterization (HPPC) text, the parameter estimator in MATLAB/Simulink is utilized to identify the ECM parameters by using nonlinear least squares method with Trust‐Region‐Reflective (TRR) iterative algorithm. Then, during the HPPC test, the estimators implemented on the ARM‐Cortex STM32F103 estimate the VRFB SOC under both the 0°C and 5°C conditions. Finally, the mean absolute error (MAE) of the IEKF estimator are 4.66% at the 0°C and 0.895% at the 5°C. In the meantime, the MAE of the SAEKF estimator are 4.15% at the 0°C and 0.703% at the 5°C. The evaluation factors of the 0°C and 5°C experiments in the SAEKF estimator are smaller than those of the IEKF and EKF estimators, indicating that the SAEKF estimator outperforms the IEKF estimator in VRFB SOC estimation.

Comparison and Analysis of SOC Estimation Based on First-Order and Second-Order Thevenin Battery Models Based on EKF

The accurate modeling and estimation of state-of-charge (SOC) of lithium-ion batteries are of great significance for their utilization efficiency, service life extension and battery management system (BMS) design. This paper aims to provide understanding and guidance for understanding and guidance on different battery models and their parameter selection by comparing and analyzing the performance differences of Extended Kalman Filter (EKF) algorithm based on the first-order Thevenin battery model and the second-order Thevenin battery model. The Battery module in Matlab/Simulink is used to simulate the HPPC (HybridPulse Power Characterization) test of real batteries, and the model is parameterically identified. By establishing Thevenin battery models of different orders and applying the EKF algorithm for SOC estimation, the differences in accuracy, robustness and computational complexity of the two models are compared and analyzed, and experimental verification is carried out under different working conditions.

Adaptive state of charge estimation for lithium‐ion batteries using feedback‐based extended Kalman filter

AbstractThe battery management system (BMS) is a crucial component of electric vehicles (EVs) owing to its sustainable operation. To ensure optimal performance of the BMS, state of charge (SOC) of the equipped battery is required to be effectively and accurately estimated. In this paper, the authors consider high‐order equivalent circuit model (ECM) to capture the dynamic characteristics of lithium‐ion batteries, which are connected in series with internal resistance by 2‐RC networks. The parameters of the RC network are determined by mathematically solving the working conditions of the two states. Moreover, the parameters of the battery can be derived by hybrid pulse power characterization (HPPC) tests. Then, based on the open‐circuit voltage, the proposed feedback‐based extended Kalman filtering (FEKF) algorithm is established. The parameters from the simulation have shown that the highest error is 0.0306 V, the optimal knowledge of which can improve the SOC estimation approach remarkably and can provide a reference value. Afterwards, the non‐linear predicting and corrective techniques are applied to the experiment in the extended calculation process. The original error is reduced by the FEKF algorithm, where the maximum and average errors are 0.0298 and 0.0240 V, respectively. Consequently, the established high‐order ECM utilizing the FEKF algorithm may provide SOC estimation with an error of 1.5% or less, resulting in superb performance from the lithium‐ion battery pack.