scholarly journals Analysis of the Current Electric Battery Models for Electric Vehicle Simulation

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2750 ◽  
Author(s):  
Gaizka Saldaña ◽  
José Ignacio San Martín ◽  
Inmaculada Zamora ◽  
Francisco Javier Asensio ◽  
Oier Oñederra
Keyword(s):  
Electric Vehicles ◽  
Lithium Ion ◽  
Electrical Equivalent Circuit ◽  
Vehicle Simulation ◽  
Soc Estimation ◽  
Estimation Capacity ◽  
Runtime Models ◽  
Reduce Emissions ◽  
Real Time Simulations ◽  
Impedance Models

Electric vehicles (EVs) are a promising technology to reduce emissions, but its development enormously depends on the technology used in batteries. Nowadays, batteries based on lithium-ion (Li-Ion) seems to be the most suitable for traction, especially nickel-manganese-cobalt (NMC) and nickel-cobalt-aluminum (NCA). An appropriate model of these batteries is fundamental for the simulation of several processes inside an EV, such as the state of charge (SoC) estimation, capacity and power fade analysis, lifetime calculus, or for developing control and optimization strategies. There are different models in the current literature, among which the electric equivalent circuits stand out, being the most appropriate model when performing real-time simulations. However, impedance models for battery diagnosis are considered very attractive. In this context, this paper compares and contrasts the different electrical equivalent circuit models, impedance models, and runtime models for battery-based EV applications, addressing their characteristics, advantages, disadvantages, and usual applications in the field of electromobility. In this sense, this paper serves as a reference for the scientific community focused on the development of control and optimization strategies in the field of electric vehicles, since it facilitates the choice of the model that best suits the needs required.

scholarly journals Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3284
Author(s):  
Ingvild B. Espedal ◽  
Asanthi Jinasena ◽  
Odne S. Burheim ◽  
Jacob J. Lamb
Keyword(s):  
Electric Vehicles ◽  
Lithium Ion ◽  
Research Area ◽  
State Of Charge ◽  
Stable Operation ◽  
Battery Management ◽  
Soc Estimation ◽  
Current Trends ◽  
Battery Management Systems ◽  
Active Research

Energy storage systems (ESSs) are critically important for the future of electric vehicles. Despite this, the safety and management of ESSs require improvement. Battery management systems (BMSs) are vital components in ESS systems for Lithium-ion batteries (LIBs). One parameter that is included in the BMS is the state-of-charge (SoC) of the battery. SoC has become an active research area in recent years for battery electric vehicle (BEV) LIBs, yet there are some challenges: the LIB configuration is nonlinear, making it hard to model correctly; it is difficult to assess internal environments of a LIB (and this can be different in laboratory conditions compared to real-world conditions); and these discrepancies can lead to raising the instability of the LIB. Therefore, further advancement is required in order to have higher accuracy in SoC estimation in BEV LIBs. SoC estimation is a key BMS feature, and precise modeling and state estimation will improve stable operation. This review discusses current methods use in BEV LIB SoC modelling and estimation. The review culminates in a brief discussion of challenges in BEV LIB SoC prediction analysis.

scholarly journals Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles

Energies ◽  
2014 ◽  
Vol 7 (12) ◽  
pp. 8446-8464 ◽  
Author(s):  
Yong Tian ◽  
Bizhong Xia ◽  
Mingwang Wang ◽  
Wei Sun ◽  
Zhihui Xu
Keyword(s):  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Adaptive Algorithms ◽  
Lithium Ion ◽  
Comparison Study ◽  
Soc Estimation ◽  
Model Based

scholarly journals State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms

2021 ◽  
Vol 12 (1) ◽  
pp. 38
Author(s):  
Venkatesan Chandran ◽  
Chandrashekhar K. Patil ◽  
Alagar Karthick ◽  
Dharmaraj Ganeshaperumal ◽  
Robbi Rahim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Learning Algorithms ◽  
Gaussian Process Regression ◽  
Lithium Ion ◽  
Machine Learning Algorithms ◽  
State Of Charge ◽  
Support Vector ◽  
Soc Estimation

The durability and reliability of battery management systems in electric vehicles to forecast the state of charge (SoC) is a tedious task. As the process of battery degradation is usually non-linear, it is extremely cumbersome work to predict SoC estimation with substantially less degradation. This paper presents the SoC estimation of lithium-ion battery systems using six machine learning algorithms for electric vehicles application. The employed algorithms are artificial neural network (ANN), support vector machine (SVM), linear regression (LR), Gaussian process regression (GPR), ensemble bagging (EBa), and ensemble boosting (EBo). Error analysis of the model is carried out to optimize the battery’s performance parameter. Finally, all six algorithms are compared using performance indices. ANN and GPR are found to be the best methods based on MSE and RMSE of (0.0004, 0.00170) and (0.023, 0.04118), respectively.

scholarly journals State of Charge Estimation Techniques for Lithium-ION Batteries Used in Electric Vehicle Applications

2021 ◽  
Vol 9 (8) ◽  
pp. 2750-2760
Author(s):  
Nikhil P
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
High Energy ◽  
State Of Charge ◽  
High Energy Density ◽  
Estimation Methods ◽  
Estimation Techniques ◽  
Soc Estimation ◽  
Battery Packs

Abstract: Lithium-ion battery packs constitute an important part of Electric vehicles. The usage of Lithium-ion based chemistries as the source of energy has various advantages like high efficiency, high energy density, high specific energy, longevity among others. However, the management of lithium-ion battery packs require a Battery Management System (BMS). The BMS deals with functions like safety, prevention of abusive usage of battery pack, overcharging & over-discharging protection, cell balancing and others. One of the prominent features of the BMS is the estimation of State of charge (SOC). SOC is like a fuel gauge in automobile, it indicates how much more the battery can be used before charging it again. SOC is also required for other functions of BMS like State of Health (SOH) tracking, Range calculation, power & energy availability calculations. However, there is no means of measuring it directly (at least not on-board a vehicle) or estimating it easily. Various techniques should be used to estimate SOC indirectly. This paper starts from classical techniques that have existed since long time and reviews some of the modern & developing methods for SOC estimation. It contains a brief review about most of these SOC estimation methods, thus highlighting the methodology, advantages & disadvantages of each of these techniques. A brief review of other developing SOC estimation techniques is also provided. Keywords: State of Charge, SOC, Lithium-ion battery packs, Electric vehicles, Kalman Filter.

Selection of Power Train Components and Simulation of Dynamic Performance of Electric Vehicles

2010 ◽  
Vol 29-32 ◽  
pp. 2138-2143
Author(s):  
Cheng Jiao Tu ◽  
Xue Zhe Wei ◽  
Hai Feng Dai
Keyword(s):  
Electric Vehicles ◽  
Dynamic Performance ◽  
Lithium Ion ◽  
Simulation Models ◽  
Power Train ◽  
Vehicle Simulation ◽  
Ac Induction Motor ◽  
Train System ◽  
Motor Model ◽  
Selection Of

Electric vehicles have the ability to drastically reduce petroleum use. As the battery used in EVs appears to be the main technical barriers both from a performance and cost perspective, the main efforts have been focused on how to select appropriate power train system for the better dynamic performance. This paper introduces some methods of parameters design of EV’s main power train components, such as the lithium-ion battery and AC induction motor. Then we build a battery model, a motor model and vehicle simulation models using ADVISOR software. Meanwhile, this paper proposes simulation results of the entire vehicle’s dynamic performance, which shows that the power train components’ parameters designed here basically meet the vehicle’s requirements.

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