Identification Of Battery Model Parameters Research Articles

Cloud-Based Optimization of a Battery Model Parameter Identification Algorithm for Battery State-of-Health Estimation in Electric Vehicles

Connectivity and cloud computing are key elements in the future of electric mobility. They allow manufacturers to provide advanced fleet management and predictive diagnostic services. In particular, cloud computing dramatically enhances data availability and enables the use of more complex and accurate state estimation algorithms for electric vehicle lithium-ion batteries. A tuning procedure for a moving window least squares algorithm to estimate the parameters of a 2-RC equivalent circuit battery model is presented in this paper. The tuning procedure uses real data collected from a test vehicle and uploaded to the Stellantis-CRF cloud. The tuned algorithm was applied to eight months of road tests and showed very small estimation errors. The errors are comparable to other literature data, even when the literature results were obtained in laboratory tests. The estimated model parameters are tracked through time and seem accurate enough to show the first signs of battery aging.

Lithium battery model parameter identification based on the GA-LM algorithm

ABSTRACT The accuracy of lithium battery model parameters is the key to lithium battery state estimation. The offline parameter identification method for lithium batteries requires the nonlinear fitting of the voltage rebound curve of the hybrid pulse discharge experiment. The genetic algorithm has a strong global search ability, but it is easy to fall into local solutions. The Levenberg-Marquardt algorithm has a strong local optimization ability, but the algorithm cannot converge when the prior value is unknown. Given the above problems, this paper proposes a parameter identification method based on the Genetic-Levenberg-Marquardt (GA-LM) algorithm, which takes the sum of the squared model voltage errors as the objective function, and predicts the initial value of the parameter vector through the GA, providing the LM algorithm with prior value. In the case of unknown prior values, the GA-LM algorithm can achieve high-precision nonlinear optimization. Finally, the simulation test under the conditions of constant current discharge and hybrid pulse power discharge. The mean absolute error, mean relative error, and root mean square error of the model voltage in the two working conditions are 7.23 mV, 0.20%, 9.61 mV, and 13.37 mV, 0.37%, 15.44 mV, which shows that the algorithm has high accuracy.

Lithium-ion battery health estimation with real-world data for electric vehicles

Complex environments and variable working conditions lead to irreversible attenuation of battery pack capacity in electric vehicles (EVs). Online capacity estimation is of great significance for battery pack management and maintenance. This work proposes a state-of-health (SOH) attenuation model considering driving mileage and seasonal temperature for battery health estimation. Firstly, a variable forgetting factor recursive least square (VFFRLS) algorithm is proposed for battery model parameter identification. It adaptively adjusts the forgetting factor according to current fluctuations. Then, an extended Kalman-particle filter (EPF) algorithm is proposed for online capacity estimation. In addition, a battery pack SOH attenuation model is constructed considering seasonal temperature and driving mileage. Finally, the performance of the proposed model and algorithm is verified with nine months of actual vehicle data. The experimental results show that the proposed parameter identification and capacity estimation algorithm can accurately estimate the model parameters and capacity. The average capacity of the battery module decreases with the total mileage. The compensation of monthly driving mileage and ambient temperature factors effectively improves the accuracy of SOH model.

On-Line Multi-Time Scale Adaptive Parameter Identification Based on Improved Lithium-Ion Batteries Hysteresis Characteristic-Electrical Equivalent Circuit Modeling

Accurate identification of model parameters is a key aspect of lithium battery state estimation. To accurately identify battery model parameters, this paper establishes Hysteresis Characteristic-Electrical Equivalent Circuit (HC-EEC) modeling by analyzing the influence of the hysteresis effect on the battery State of Charge (SOC). For the high-precision identification of battery model parameters, an Online Multi-Time Scale Adaptive Parameter Identification Strategy (OM-TSAPIS) is proposed in this paper. According to the different dynamic response links in the HC-EEC model, the strategy performs parameter identification through different time scale links and uses the adaptive step size as the starting identification condition for the multi-time scale links, thereby improving the parameter identification accuracy of the HC-EEC model. The absolute average error of OM-TSAPIS was 0.0437 mV and 0.298 mV under the Urban Dynamometer Driving Schedule (UDDS) and Beijing Bus Dynamic Street Test (BBDST) conditions, respectively. Simulation results show that the identification accuracy of the proposed algorithm is high.

Online identification of battery model parameters and joint state of charge and state of health estimation using dual particle filter algorithms

Online identification of battery model parameters and joint state of charge and state of health estimation using dual particle filter algorithms

Online Parameters and State of Charge Co-estimation of Lithium-Ion Battery in Varying Temperature Using Joint Extended Kalman Filter

This paper compares the mode based lithium-ion battery state of charge (SOC) estimations using offline and online parameters under varying temperature. An innovative offline identification method based on genetic algorithm (GA) is used for off-line identification of battery model parameters. The common extended Kalman filter (EKF) and the joint extended Kalman filter (JEKF) are implemented as the algorithms to implement SOC estimation with offline and online parameters. The SOC estimations by JEKF using online parameters and by EKF using offline parameters from mismatched temperature are compared. The results are as follows. When battery temperature is inaccurate, the inaccurate temperature can result in inaccurate offline parameters parameters, which will further increase the SOC estimation errors by EKF using offline parameters. In contrast, SOC estimation accuracy by JEKF are still accurate when no temperature information is provided, because the parameters are online updated by JEKF.

A Novel Adaptive Particle Swarm Optimization Algorithm Based High Precision Parameter Identification and State Estimation of Lithium-Ion Battery

Lithium-ion batteries are widely used in new energy vehicles, energy storage systems, aerospace and other fields because of their high energy density, long cycle life and high-cost performance. Accurate equivalent modeling, adaptive internal state characterization and accurate state of charge estimation are the cornerstones of expanding the application market of lithium-ion batteries. According to the highly nonlinear operating characteristics of lithium-ion batteries, the Thevenin equivalent model is used to characterize the operating characteristics of lithium-ion batteries, particle swarm optimization algorithm is used to process the measured data, and adaptive optimization strategy is added to improve the global search ability of particles, and the parameters of the model are identified innovatively. Combined with extended Kalman algorithm and Sage-Husa filtering algorithm, the state-of-charge estimation model of lithium ion battery is constructed. Aiming at the influence of fixed and inaccurate noise initial value in traditional Kalman filtering algorithm on SOC estimation results, Sage-Husa algorithm is used to adaptively correct system noise. The experimental results under HPPC condition show that the maximum error of the model is less than 1.5%. Simulation results of SOC estimation algorithm under two different operating conditions show that the maximum estimation error of adaptive extended Kalman algorithm is less than 0.05, which realizes high-precision lithium battery model parameter identification and high-precision state-of-charge estimation.

Joint State-of-Charge and State-of-Available-Power Estimation Based on the Online Parameter Identification of Lithium-Ion Battery Model

This article presents a joint state-of-charge (SOC) and state-of-available-power (SOAP) estimation method based on online battery model parameter identification. First, the SOAP of lithium-ion batteries is analyzed thoroughly, and a safe operating area border-based (SOAB-based) SOAP estimation is proposed. Second, based on the adaptive battery-state estimator (ABSE) and improved ABSE, a joint SOC and SOAB-based SOAP estimation method is proposed. The joint estimation results show that the improved ABSE achieves higher accuracy than the ABSE at different battery aging states. The open-loop accuracy evaluation results show that the improved ABSE identifies the battery model parameters more accurately, and the ABSE algorithm error source lies in its identified Rp being much higher than the actual value when the battery is charged/discharged at a high current. The ABSE does not consider the influence of load current on the equivalent circuit model parameters, so it is not suitable for SOAP estimation in theory. The improved ABSE proposed by our team can eliminate this modeling error, identify the battery model parameters, and estimate the SOC and SOAB-based SOAP more accurately. This improved ABSE is an effective algorithm for estimating the battery state when the battery is charged/discharged with a high current.

A Surrogate-Assisted Teaching-Learning-Based Optimization for Parameter Identification of the Battery Model

Lithium-ion batteries are widely used as power sources in industrial applications. Electrochemical models and simulations are crucial to disclose many details that cannot be directly measured through experiments. Parameter identification of an accurate electrochemical model is much more cost-effective than direct and destructive measurement methods. However, the complex structure and strong nonlinearity of electrochemical models will make the parameter identification very difficult. Additionally, time-consuming electrochemical simulations can significantly limit the identification efficiency. This article proposes a surrogate-model-based scheme to achieve high-efficiency parameter identification of an electrochemical battery model. To be specific, the proposed method is implemented by the close integration of an evolutionary algorithm and a surrogate model. A sensitivity-based identification strategy is first designed to alleviate the difficulty of optimization. Then, a surrogate model is developed from historical data to gradually approach the objective function used for parameter evaluations. Finally, an evolutionary algorithm is employed to find promising solutions by minimizing the output of the surrogate model. Simulations and experimental studies demonstrate the effectiveness and high efficiency of the proposed method.

A New Dynamic Parameters V-RC Model of Battery Parameter Identification

Different state of charge (SOC), batteries have different model parameters. This paper proposes a dynamic parameter identification method for battery models under different SOC. Firstly, dynamic parameter V-RC model of battery was designed according to the hysteresis loops characteristics and the polarization characteristics of the battery voltage during the charge-discharge process. Next, the test program was designed which can complete the lithium battery charge and discharge data acquisition, including identification of battery model parameters for AC signal injection. The relationship between OCV and SOC are obtained by using the least squares curve fitting method, which realizes the function fitting of the model’s parameters. Then, battery transient response experiment indicating that have different parameters under different SOC. Finally, test results can show that open-circuit voltage (OCV) error of the new model is lower than constant parameter model OCV error. The error of the fixed parameter model OCV is 2.6%, while the OCV error of the dynamic parameter model is 1.6%. Dynamic parameter VRC model can more accurately reflect the actual state of the battery.

Accurate lithium-ion battery modeling with inverse repeat binary sequence for electric vehicle applications

The attribute of battery current excitation signal significantly influences the battery model parameter identification accuracy. However, currently the studies mainly focus on the selection of battery models and the improvement of algorithm, and overlook the influence of excitation signal. More importantly, the conventional excitation signals, which are unsuited to the processes that subjected to the nonlinear effects, can lead to poor estimation accuracy of model parameters. Therefore, this paper proposes a novel excitation signal design method called inverse repeat binary sequence (IRBS). The theoretical analysis shows that the antisymmetric characteristic of IRBS can overcome the adverse effects of the direct current component and even-order nonlinearities for parameter estimation. Then, the design parameters of the excitation signal are determined for real application by analysing the single-sided amplitude spectrum of the typical battery test loading profiles of electric vehicles, and model parameters are estimated by means of particle swarm optimization algorithm. Finally, the experimental results of different temperatures based on the LiNiMnCoO2 lithium-ion battery validate that IRBS is feasible, and has the higher accuracy than three commonly used excitation signal design methods.

State of Charge Estimation for Lithium Battery Based on Adaptively Weighting Cubature Particle Filter

Accurate estimation of lithium battery state of charge is very important for ensuring the operation of battery management system, realizing the energy management strategy of electric vehicles, reducing mileage anxiety and promoting the sustainable development of electric vehicles. In this paper, several studies are carried out for state of charge estimation of lithium-ion battery: (1) Aiming at the problem of parameter identification of battery model, an optimal identification method of model parameters based on ant lion optimization algorithm is proposed. (2) An adaptive weighting Cubature particle filter (AWCPF) method is proposed for SOC estimation. The proposed AWCPF method is based on particle filter (PF) algorithm, while the Cubature Kalman filter (CKF) algorithm is utilized to generate the proposal distribution for PF algorithm, which can retrain the particles degradation problem in PF algorithm. To solve the problem that the CKF algorithm is sensitive to noise, comparing with fixed sigma point weights of the conventional CKF, the weights of sigma points are adaptively adjusted based on state and measurement residual vectors. Furthermore, the process noise and measurement noise are estimated iterative. In this paper, experimental verification of different initial values of SOC under various working conditions is carried out. The results show that the proposed AWCPF algorithm based SOC estimation method has high estimation accuracy, strong robustness, fast convergence speed, with the maximum SOC estimation error is less than 1%.

Study on the Characteristics of a High Capacity Nickel Manganese Cobalt Oxide (NMC) Lithium-Ion Battery—An Experimental Investigation

The use of high-capacity batteries as the battery pack of electric vehicles is the current development trend. In order to better design battery packages and battery management systems and develop related battery estimation technology, the related characteristics of high capacity battery cells need to be studied in depth. Capacity and pulse tests of batteries at different temperatures are carried out in this paper. Through these experimental data, the electrical characteristics of different parameters of the high capacity battery, such as capacity characteristic data, internal resistance characteristic data, OCV-SOC characteristic relation curve, power data and temperature rise are analyzed. The specific parameters of the battery in the second order equivalent circuit model are obtained by using the off-line parameter identification method. These parameters results can be used as comparison data and reference data. It is beneficial to the on-line parameter identification of battery model and the estimation of battery state, so as to shorten the development time and improve the quality of the development.

An Improved Battery On-line Parameter Identification and State-of-charge Determining Method

Abstract To improve the estimation accuracy of battery's inner state for battery management system, an improved online parameters identification algorithm for equivalent circuit battery model is researched. To reduce the computation cost, the existing methods regarded the open circuit voltage over time as a constant value. However, when the sampling intervals are bigger, the estimation error of the battery state-of-charge calculated by the traditional method can be reach to 10% or more. Compared with the existing battery model parameter identification method, this study proposes a new online estimation method and which can estimate the battery open-circuit voltage in different sampling intervals with high accuracy. The results of the experiment, which uses Federal Urban Driving Schedule test to verify the parameters identification approach, show the proposed approach can accurately identify the model parameters within 1% maximum terminal voltage estimation error, and the state-of-charge error which calculated by the open circuit voltage estimates can be efficiently reduced to an accepted level.

Adaptive model parameter identification for large capacity Li-ion batteries on separated time scales

The accurate identification of battery model parameters is critical to the development of the battery management system (BMS). For large capacity Li-ion batteries, different internal processes happen inside the cell during charging and discharging, which introduce the complex dynamics that occur on different time scales. The multi time-scaled effect of the battery dynamics imposes difficulties on the design of an accurate parameter identification algorithm. As an original contribution, we propose a novel adaptive identification algorithm of the model parameters on separated time scales. The battery dynamics are described with a second-order ECM (equivalent circuit model), where the slow dynamics and fast dynamics are described separately. The parameter identification algorithm is composed of two separated modules, of which one is for the identification of slow dynamics and the other is for the identification of fast dynamics. The two modules are executed on separated time scales. The identification module for slow dynamics is based on extended Kalman filtering (EKF) while the module for fast dynamics is based on recursive least squares (RLS). The coupling of the two modules is through the voltage response of the slow dynamics. To make the algorithm more adaptive, the operation time scale of the slow identification module is not constant, but dependent on current profiles. Validation with experimental results shows that the proposed identification strategy performs better than the traditional RLS based identification methods.

Research on Parameter Identification of Battery Model Based on Adaptive Particle Swarm Optimization Algorithm

Research on Parameter Identification of Battery Model Based on Adaptive Particle Swarm Optimization Algorithm

Two Time-Scaled Battery Model Identification With Application to Battery State Estimation

Electrified propulsion systems have now become an increasingly popular option for automotive companies to meet the more stringent emissions standards. A well-designed battery state estimation (BSE) system, which includes state-of-charge and state-of-health estimation, is one of the most important aspects of a successful electrified propulsion system design. Among different methods, model-based state estimation has proven to be very successful in their accuracy and implementability. A relatively newer approach to model-based BSE is to identify the battery model parameters (typically a low-order control-oriented model) in real time. This allows the battery model parameter to adjust to changing characteristics of the battery, and thus further improving the robustness of the design. However, standard identification algorithms used have very limited capability in performing this identification successfully due to the frequency response characteristics of the battery. In this brief, we describe a two time-scaled battery model parameter identification method, where the slower and faster battery dynamics are identified separately. Compared with standard approach to real-time battery model identification, where no such separation is made, this method can generate a model whose frequency response is much closer to that of the actual battery. Furthermore, this method uses the standard least squares regression method, which can be easily implemented in real time in the form of recursive least squares. Using this identification method, we show how battery SoC can be estimated. Laboratory battery cell data is used to illustrate the difference between this method and the more standard approach. Then, battery pack collected from a test vehicle is used to demonstrate the SoC estimation capability.