scholarly journals A Particle-Swarm-Optimization-Based Approach for the State-of-Charge Estimation of an Electric Vehicle When Driven Under Real Conditions

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Aramis Perez ◽  
Francisco Jaramillo ◽  
Cesar Baeza ◽  
Martin Valderrama ◽  
Vanessa Quintero ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Battery Pack ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Soc Estimation ◽  
Laboratory Conditions

Recent developments in lithium-ion (Li-ion) storage technology have enabled a revolution in the automotive industry. Fully electric vehicles (EVs) operate under the most diverse combination of driving and environmental conditions affecting the autonomy range. In other words, an equal state-of-charge (SOC) on two same model EV does not mean the same traveling distance since the conditions such as the state-of-health (SOH) of the battery, type of driver and even the type of route will influence the EV performance. Typically, SOC estimation algorithms are proposed and validated under controlled laboratory conditions. However, when real conditions are present, it is necessary to incorporate new tools capable of handling the diverse variability present in all the conditions. For instance, the topography of the route influences the current that the battery pack delivers, and the performance on the same route can be affected by the SOH. One of the main concerns for EV owners is that once a battery pack is installed, it becomes almost impossible to perform laboratory tests under controlled conditions. This paper proposes a novel approach to estimate the SOC by extending an existing SOC model (obtained in laboratory conditions) with the novelty of the assistance of Particle-Swarm-Optimization (PSO) to estimate the model parameters using real EV driving data. The data was obtained by a real-driving experiment, which consists on driving the EV in a complete discharge cycle on a highway. During this experiment, the initial SOC was at 100\%, and the idea was to discharge the battery pack driving through a highway where the driving conditions are almost uniform making it possible to characterize the SOC curve. Then, PSO is used to estimate the model parameters, and afterwards the model is validated in different types of routes. The obtained results show that the proposed approach can estimate the SOC satisfactorily. In this regard, this type of real-driving experiment can be performed by any driver, and by combining the particular results with the proposed approach, the users can personalize the SOC estimation model to their vehicles, and even more, create their own knowledge base of their EV performance through time. Therefore, the real-driving experiment can be replicated when needed to update the model parameters, thus allowing a better understanding of the actual SOH of the battery pack. Furthermore, by combining the obtained model with the elevation profile of a given route, the user can assess where to stop in case that a recharge is necessary.

scholarly journals An All-Region State-of-Charge Estimator Based on Global Particle Swarm Optimization and Improved Extended Kalman Filter for Lithium-Ion Batteries

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 321 ◽  
Author(s):  
Xin Lai ◽  
Wei Yi ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Extended Kalman Filter ◽  
Particle Swarm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Soc Estimation

In this paper, a novel model parameter identification method and a state-of-charge (SOC) estimator for lithium-ion batteries (LIBs) are proposed to improve the global accuracy of SOC estimation in the all SOC range (0–100%). Firstly, a subregion optimization method based on particle swarm optimization is developed to find the optimal model parameters of LIBs in each subregion, and the optimal number of subregions is investigated from the perspective of accuracy and computation time. Then, to solve the problem of a low accuracy of SOC estimation caused by large model error in the low SOC range, an improved extended Kalman filter (IEKF) algorithm with variable noise covariance is proposed. Finally, the effectiveness of the proposed methods are verified by experiments on two kinds of batteries under three working cycles, and case studies show that the proposed IEKF has better accuracy and robustness than the traditional extended Kalman filter (EKF) in the all SOC range.

scholarly journals Real-Time State-of-Charge Estimation via Particle Swarm Optimization on a Lithium-Ion Electrochemical Cell Model

Batteries ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 4 ◽  
Author(s):  
Arun Chandra Shekar ◽  
Sohel Anwar
Keyword(s):  
Particle Swarm Optimization ◽  
Single Cell ◽  
Real Time ◽  
Cell Model ◽  
Particle Swarm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Swarm Optimization ◽  
Soc Estimation ◽  
Electrochemical Model

With the ever-increasing usage of lithium-ion batteries, especially in transportation applications, accurate estimation of battery state of charge (SOC) is of paramount importance. A majority of the current SOC estimation methods rely on data collected and calibrated offline, which could lead to inaccuracies in SOC estimation under different operating conditions or when the battery ages. This paper presents a novel real-time SOC estimation of a lithium-ion battery by applying the particle swarm optimization (PSO) method to a detailed electrochemical model of a single cell. This work also optimizes both the single-cell model and PSO algorithm so that the developed algorithm can run on an embedded hardware with reasonable utilization of central processing unit (CPU) and memory resources while estimating the SOC with reasonable accuracy. A modular single-cell electrochemical model, as well as the proposed constrained PSO-based SOC estimation algorithm, was developed in Simulink©, and its performance was theoretically verified in simulation. Experimental data were collected for healthy and aged Li-ion battery cells in order to validate the proposed algorithm. Both simulation and experimental results demonstrate that the developed algorithm is able to accurately estimate the battery SOC for 1C charge and 1C discharge operations for both healthy and aged cells.

scholarly journals Joint Estimation of SOC of Lithium Battery Based on Dual Kalman Filter

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1412
Author(s):  
Hao Wang ◽  
Yanping Zheng ◽  
Yang Yu
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Filter Method ◽  
Model Parameters ◽  
Soc Estimation ◽  
Online Identification ◽  
Kalman Filter Method

In order to improve the estimation accuracy of the state of charge (SOC) of electric vehicle power batteries, a dual Kalman filter method based on the online identification of model parameters is proposed to estimate the state of charge in lithium-ion batteries. Here, we build the first-order equivalent circuit model of lithium-ion batteries and derive its online identification model based on extended Kalman (EKF). Considering that the noise value in the EKF algorithm is difficult to select through experiments to achieve the best filtering effect, this paper combines an improved particle swarm optimization algorithm (IPSO) with EKF to realize online model parameter identification. At the same time, the EKF filtering method derived from the state space equation is also used in SOC estimation. It constitutes a dual Kalman filter method for online identification of model parameters and SOC estimation. The experimental and simulation results show that the IPSO–EKF algorithm can adaptively adjust the noise value according to the complex operating conditions of electric vehicles. Compared with the EKF algorithm, our algorithm can identify battery model parameters more accurately. The dual Kalman filter method composed of the IPSO–EKF algorithm and EKF applied to SOC estimation achieved a higher accuracy in the final algorithm verification.

scholarly journals A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 987 ◽  
Author(s):  
Qiaohua Fang ◽  
Xuezhe Wei ◽  
Haifeng Dai
Keyword(s):  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Battery Pack ◽  
Prediction Algorithm ◽  
Model Parameters ◽  
Battery Management System ◽  
Discharge Energy ◽  
Difference Model ◽  
Energy Prediction

The remaining discharge energy prediction of the battery pack is an important function of a battery management system. One of the key factors contributing to the inaccuracy of battery pack remaining discharge energy prediction is the inconsistency of the state and model parameters. For a batch of lithium-ion batteries with nickel cobalt aluminum oxide cathode material, after analyzing the characteristics of battery model parameter inconsistency, a “specific and difference” model considering state of charge and R0 inconsistency is established. The dual time-scale dual extended Kalman filter algorithm is proposed to estimate the state of charge and R0 of each cell in the battery pack, and the remaining discharge energy prediction algorithm of the battery pack is established. The effectiveness of the state estimation and remaining discharge energy prediction algorithm is verified. The results show that the state of charge estimation error of each cell is less than 1%, and the remaining discharge energy prediction error of the battery pack is less than 1% over the entire discharge cycle. The main reason which causes the difference between the “specific and difference” and “mean and difference” models is the nonlinearity of the battery’s state of charge - open circuit voltage curve. When the nonlinearity is serious, the “specific and difference” model has higher precision.

scholarly journals Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1054
Author(s):  
Kuo Yang ◽  
Yugui Tang ◽  
Zhen Zhang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Soc Estimation ◽  
Battery Model

With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

scholarly journals Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 122
Author(s):  
Peipei Xu ◽  
Junqiu Li ◽  
Chao Sun ◽  
Guodong Yang ◽  
Fengchun Sun
Keyword(s):  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Maximum Error ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Soc Estimation ◽  
Capacity Degradation ◽  
Battery Capacity

The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%.

scholarly journals Short-term power load forecasting based on support vector machine and particle swarm optimization

2018 ◽  
Vol 13 ◽  
pp. 174830181879706 ◽  
Author(s):  
Song Qiang ◽  
Yang Pu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Influencing Factors ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Forecast Accuracy ◽  
Support Vector ◽  
Model Parameters ◽  
Short Term ◽  
Swarm Optimization

In this work, we summarized the characteristics and influencing factors of load forecasting based on its application status. The common methods of the short-term load forecasting were analyzed to derive their advantages and disadvantages. According to the historical load and meteorological data in a certain region of Taizhou, Zhejiang Province, a least squares support vector machine model was used to discuss the influencing factors of forecasting. The regularity of the load change was concluded to correct the “abnormal data” in the historical load data, thus normalizing the relevant factors in load forecasting. The two parameters are as follows Gauss kernel function and Eigen parameter C in LSSVM had a significant impact on the model, which was still solved by empirical methods. Therefore, the particle swarm optimization was used to optimize the model parameters. Taking the error of test set as the basis of judgment, the optimization of model parameters was achieved to improve forecast accuracy. The practical examples showed that the method in the work had good convergence, forecast accuracy, and training speed.

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