scholarly journals Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles

2021 ◽  
Vol 13 (7) ◽  
pp. 168781402110277
Author(s):  
Yankai Hou ◽  
Zhaosheng Zhang ◽  
Peng Liu ◽  
Chunbao Song ◽  
Zhenpo Wang
Keyword(s):  
Electric Vehicles ◽  
Real World ◽  
Regression Models ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Data Driven ◽  
Accurate Estimation ◽  
Support Vector ◽  
Battery Degradation ◽  
Operational Data

Accurate estimation of the degree of battery aging is essential to ensure safe operation of electric vehicles. In this paper, using real-world vehicles and their operational data, a battery aging estimation method is proposed based on a dual-polarization equivalent circuit (DPEC) model and multiple data-driven models. The DPEC model and the forgetting factor recursive least-squares method are used to determine the battery system’s ohmic internal resistance, with outliers being filtered using boxplots. Furthermore, eight common data-driven models are used to describe the relationship between battery degradation and the factors influencing this degradation, and these models are analyzed and compared in terms of both estimation accuracy and computational requirements. The results show that the gradient descent tree regression, XGBoost regression, and light GBM regression models are more accurate than the other methods, with root mean square errors of less than 6.9 mΩ. The AdaBoost and random forest regression models are regarded as alternative groups because of their relative instability. The linear regression, support vector machine regression, and k-nearest neighbor regression models are not recommended because of poor accuracy or excessively high computational requirements. This work can serve as a reference for subsequent battery degradation studies based on real-time operational data.

scholarly journals Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4772 ◽  
Author(s):  
Kaizhi Liang ◽  
Zhaosheng Zhang ◽  
Peng Liu ◽  
Zhenpo Wang ◽  
Shangfeng Jiang
Keyword(s):  
Electric Vehicles ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Data Driven ◽  
Gradient Boosting ◽  
Estimation Model ◽  
Ohmic Resistance ◽  
Extreme Gradient Boosting ◽  
Battery Packs ◽  
Mean Square Errors

Accurate state-of-health (SOH) estimation for battery packs in electric vehicles (EVs) plays a pivotal role in preventing battery fault occurrence and extending their service life. In this paper, a novel internal ohmic resistance estimation method is proposed by combining electric circuit models and data-driven algorithms. Firstly, an improved recursive least squares (RLS) is used to estimate the internal ohmic resistance. Then, an automatic outlier identification method is presented to filter out the abnormal ohmic resistance estimated under different temperatures. Finally, the ohmic resistance estimation model is established based on the Extreme Gradient Boosting (XGBoost) regression algorithm and inputs of temperature and driving distance. The proposed model is examined based on test datasets. The root mean square errors (RMSEs) are less than 4 mΩ while the mean absolute percentage errors (MAPEs) are less than 6%. The results show that the proposed method is feasible and accurate, and can be implemented in real-world EVs.

scholarly journals An Adaptive Data-Driven Approach to Solve Real-World Vehicle Routing Problems in Logistics

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Emir Žunić ◽  
Dženana Đonko ◽  
Emir Buza
Keyword(s):  
Vehicle Routing ◽  
Real World ◽  
Prediction Method ◽  
Transportation Systems ◽  
Data Driven ◽  
Support Vector ◽  
Control Parameters ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Benchmark Instances

Transportation occupies one-third of the amount in the logistics costs, and accordingly transportation systems largely influence the performance of the logistics system. This work presents an adaptive data-driven innovative modular approach for solving the real-world vehicle routing problems (VRPs) in the field of logistics. The work consists of two basic units: (i) an innovative multistep algorithm for successful and entirely feasible solving of the VRPs in logistics and (ii) an adaptive approach for adjusting and setting up parameters and constants of the proposed algorithm. The proposed algorithm combines several data transformation approaches, heuristics, and Tabu search. Moreover, as the performance of the algorithm depends on the set of control parameters and constants, a predictive model that adaptively adjusts these parameters and constants according to historical data is proposed. A comparison of the acquired results has been made using the decision support system with predictive models: generalized linear models (GLMs) and support vector machine (SVM). The algorithm, along with the control parameters, which uses the prediction method, was acquired and was incorporated into a web-based enterprise system, which is in use in several big distribution companies in Bosnia and Herzegovina. The results of the proposed algorithm were compared with a set of benchmark instances and validated over real benchmark instances as well. The successful feasibility of the given routes, in a real environment, is also presented.

scholarly journals A data-driven estimation method for potential passenger demand of last trains in metro based on external traffic data

2019 ◽  
Vol 11 (12) ◽  
pp. 168781401989835
Author(s):  
Wei Li ◽  
Qin Luo
Keyword(s):  
Smart Card ◽  
Real World ◽  
Estimation Method ◽  
Data Driven ◽  
Traffic Data ◽  
Passenger Flow ◽  
Passenger Demand ◽  
Total Potential ◽  
Input Condition ◽  
Smart Card Data

The last train problem for metro is especially important because the last trains are the last chances for many passengers to travel by metro; otherwise, they have to choose other traffic modes like taxis or buses. Among the problems, the passenger demand is a vital input condition for the optimization of last train transfers. This study proposes a data-driven estimation method for the potential passenger demand of last trains. Through the geographic information, external traffic data including taxi and bus are first analyzed separately to match the origin–destination passenger flow during the last train period. A solving solution for taxi and bus is then developed to estimate the potential passenger flow for all the transfer directions of the target stations. Combining the estimated potential passenger flow and the actual passenger flow obtained by metro smart card data, the total potential passenger demand of last trains is obtained. The effectiveness of the proposed method is evaluated using a real-world metro network. This research can provide important guidance and act as a technical reference for the metro operations on when to optimize the last train transfers.

scholarly journals The Data-Driven Multi-Step Approach for Dynamic Estimation of Buildings’ Interior Temperature

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6654
Author(s):  
Stefano Villa ◽  
Claudio Sassanelli
Keyword(s):  
Machine Learning ◽  
Regression Model ◽  
Support Vector Regression ◽  
Regression Models ◽  
Absolute Error ◽  
Data Driven ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Prediction Horizon ◽  
Dynamic Estimation

Buildings are among the main protagonists of the world’s growing energy consumption, employing up to 45%. Wide efforts have been directed to improve energy saving and reduce environmental impacts to attempt to address the objectives fixed by policymakers in the past years. Meanwhile, new approaches using Machine Learning regression models surged in the modeling and simulation research context. This research develops and proposes an innovative data-driven black box predictive model for estimating in a dynamic way the interior temperature of a building. Therefore, the rationale behind the approach has been chosen based on two steps. First, an investigation of the extant literature on the methods to be considered for tests has been conducted, shrinking the field of investigation to non-recursive multi-step approaches. Second, the results obtained on a pilot case using various Machine Learning regression models in the multi-step approach have been assessed, leading to the choice of the Support Vector Regression model. The prediction mean absolute error on the pilot case is 0.1 ± 0.2 °C when the offset from the prediction instant is 15 min and grows slowly for further future instants, up to 0.3 ± 0.8 °C for a prediction horizon of 8 h. In the end, the advantages and limitations of the new data-driven multi-step approach based on the Support Vector Regression model are provided. Relying only on data related to external weather, interior temperature and calendar, the proposed approach is promising to be applicable to any type of building without needing as input specific geometrical/physical characteristics.

scholarly journals Application of Data-Driven Discovery Machine Learning Algorithms in Predicting Geothermal Reservoir Temperature from Geophysical Resistivity Method

2021 ◽  
Vol 9 (4) ◽  
pp. 1-12
Author(s):  
Solomon Namaswa ◽  
John Githiri ◽  
Nicholas Mariita ◽  
Maurice K’Orowe ◽  
Nicholas Njiru
Keyword(s):  
Machine Learning ◽  
Geophysical Methods ◽  
Estimation Method ◽  
Geothermal Field ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Support Vector ◽  
Well Testing ◽  
Subsurface Temperature ◽  
Reservoir Temperature

Geophysical methods including seismology, resistivity, gravity, magnetic and electromagnetic have been put in use for geothermal resource mapping at the Great Olkaria Geothermal field for decades. Reservoir temperature distribution and the electrical conductivity of rocks mainly depend on the same parameters such permeability, porosity, fluid salinity and temperature. This research focused on the integration of Olkaria Domes geothermal well testing temperature and geophysical Electromagnetic resistivity data with the aim of establishing an alternative estimation method for temperature of the reservoir through machine learning Analytics. To achieve this, Data-Driven Discovery Predictive Model Algorithm was built using Python programming language on Anaconda framework. The open-source web based application Jupyter Notebook for coding and visualization was used. Decision Tree Regression, Adaptive Booster Regression, Support Vector Regression and Random Forest Regression were used. The model performance was evaluated using R-Score and Mean Absolute Error metrics. Based on these performance score, the best performing model was suggested to predict subsurface temperature from resistivity. Training the model using the DTR algorithm approach provides superior outputs with R2 of 0.81 and lowest MAE of 29.8. The DTR algorithm could be implemented in determination of subsurface Temperature from resistivity in high temperature hydrothermal fields.

scholarly journals SCADA Data-Based Support Vector Machine Wind Turbine Power Curve Uncertainty Estimation and Its Comparative Studies

2020 ◽  
Vol 10 (23) ◽  
pp. 8685
Author(s):  
Ravi Pandit ◽  
Athanasios Kolios
Keyword(s):  
Support Vector Machine ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Offshore Wind ◽  
Data Driven ◽  
Accurate Estimation ◽  
Support Vector ◽  
Offshore Wind Turbine ◽  
Power Curve ◽  
Power Curves

Power curves, supplied by turbine manufacturers, are extensively used in condition monitoring, energy estimation, and improving operational efficiency. However, there is substantial uncertainty linked to power curve measurements as they usually take place only at hub height. Data-driven model accuracy is significantly affected by uncertainty. Therefore, an accurate estimation of uncertainty gives the confidence to wind farm operators for improving performance/condition monitoring and energy forecasting activities that are based on data-driven methods. The support vector machine (SVM) is a data-driven, machine learning approach, widely used in solving problems related to classification and regression. The uncertainty associated with models is quantified using confidence intervals (CIs), which are themselves estimated. This study proposes two approaches, namely, pointwise CIs and simultaneous CIs, to measure the uncertainty associated with an SVM-based power curve model. A radial basis function is taken as the kernel function to improve the accuracy of the SVM models. The proposed techniques are then verified by extensive 10 min average supervisory control and data acquisition (SCADA) data, obtained from pitch-controlled wind turbines. The results suggest that both proposed techniques are effective in measuring SVM power curve uncertainty, out of which, pointwise CIs are found to be the most accurate because they produce relatively smaller CIs. Thus, pointwise CIs have better ability to reject faulty data if fault detection algorithms were constructed based on SVM power curve and pointwise CIs. The full paper will explain the merits and demerits of the proposed research in detail and lay out a foundation regarding how this can be used for offshore wind turbine conditions and/or performance monitoring activities.

scholarly journals Research on Co-Estimation Algorithm of SOC and SOH for Lithium-Ion Batteries in Electric Vehicles

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 181
Author(s):  
Chang-Qing Du ◽  
Jian-Bo Shao ◽  
Dong-Mei Wu ◽  
Zhong Ren ◽  
Zhong-Yi Wu ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Stress Test ◽  
Equivalent Circuit Model ◽  
Estimation Algorithm ◽  
Maximum Error ◽  
Recursive Least Squares ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Ambient Temperatures ◽  
Soc Estimation

The accurate estimation of the state of charge (SOC) and state of health (SOH) is of great significance to energy management and safety in electric vehicles. To achieve a good trade-off between real-time capability and estimation accuracy, a collaborative estimation algorithm for SOC and SOH is presented based on the Thevenin equivalent circuit model, which combines the recursive least squares method with a forgetting factor and the extended Kalman filter. First, the parameter identification accuracy is studied under a dynamic stress test (DST) and the federal urban driving schedule (FUDS) test at different ambient temperatures (0 °C, 25 °C, and 45 °C). Secondly, the FUDS test is used to verify the SOC estimation accuracy. Thirdly, two batteries with different aging degrees are used to validate the proposed SOH estimation algorithm. Subsequently, the accuracy of the SOC estimation algorithm is studied, considering the influence of updating the SOH. The proposed SOC estimation algorithm can achieve good performance at different ambient temperatures (0 °C, 25 °C, and 45 °C), with a maximum error of less than 2.3%. The maximum error for the SOH is less than 4.3% for two aged batteries at 25 °C, and it can be reduced to 1.4% after optimization. Furthermore, calibrating the capacity as the SOH changes can effectively improve the SOC estimation accuracy over the whole battery life.

Sign in / Sign up

Export Citation Format

Share Document