Applied Sciences

Editorial

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3 pages, 169 KiB

Open AccessEditorial

Battery Management System for Future Electric Vehicles

by Bedatri Moulik and Dirk Söffker

Appl. Sci. 2020, 10(15), 5095; https://doi.org/10.3390/app10155095 - 24 Jul 2020

Cited by 4 | Viewed by 2437

Abstract

The future of electric vehicles relies nearly entirely on the design, monitoring, and control of the vehicle battery and its associated systems. Along with an initial optimal design of the cell/pack-level structure, the runtime performance of the battery needs to be continuously monitored [...] Read more.

The future of electric vehicles relies nearly entirely on the design, monitoring, and control of the vehicle battery and its associated systems. Along with an initial optimal design of the cell/pack-level structure, the runtime performance of the battery needs to be continuously monitored and optimized for a safe and reliable operation and prolonged life. Improved charging techniques need to be developed to protect and preserve the battery. The scope of this Special Issue is to address all the above issues by promoting innovative design concepts, modeling and state estimation techniques, charging/discharging management, and hybridization with other storage components. Full article

(This article belongs to the Special Issue Battery Management System for Future Electric Vehicles)

Research

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24 pages, 7629 KiB

Open AccessArticle

Empirical Thermal Performance Investigation of a Compact Lithium Ion Battery Module under Forced Convection Cooling

by Akinlabi A. A. Hakeem and Davut Solyali

Appl. Sci. 2020, 10(11), 3732; https://doi.org/10.3390/app10113732 - 28 May 2020

Cited by 14 | Viewed by 3184

Abstract

Lithium ion batteries (LiBs) are considered one of the most suitable power options for electric vehicle (EV) drivetrains, known for having low self-discharging properties which hence provide a long life-cycle operation. To obtain maximum power output from LiBs, it is necessary to critically [...] Read more.

Lithium ion batteries (LiBs) are considered one of the most suitable power options for electric vehicle (EV) drivetrains, known for having low self-discharging properties which hence provide a long life-cycle operation. To obtain maximum power output from LiBs, it is necessary to critically monitor operating conditions which affect their performance and life span. This paper investigates the thermal performance of a battery thermal management system (BTMS) for a battery pack housing 100 NCR18650 lithium ion cells. Maximum cell temperature (Tmax) and maximum temperature difference (ΔTmax) between cells were the performance criteria for the battery pack. The battery pack is investigated for three levels of air flow rate combined with two current rate using a full factorial Design of Experiment (DoE) method. A worst case scenario of cell Tmax averaged at 36.1 °C was recorded during a 0.75 C charge experiment and 37.5 °C during a 0.75 C discharge under a 1.4 m/s flow rate. While a 54.28% reduction in ΔTmax between the cells was achieved by increasing the air flow rate in the 0.75 C charge experiment from 1.4 m/s to 3.4 m/s. Conclusively, increasing BTMS performance with increasing air flow rate was a common trend observed in the experimental data after analyzing various experiment results. Full article

(This article belongs to the Special Issue Battery Management System for Future Electric Vehicles)

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18 pages, 3111 KiB

Open AccessArticle

Torque and Battery Distribution Strategy for Saving Energy of an Electric Vehicle with Three Traction Motors

by Yi-Hsiang Tseng and Yee-Pien Yang

Appl. Sci. 2020, 10(8), 2653; https://doi.org/10.3390/app10082653 - 11 Apr 2020

Cited by 2 | Viewed by 4365

Abstract

A torque and battery distribution (TBD) strategy is proposed for saving energy for an electric vehicle (EV) that is driven by three traction motors. Each traction motor is driven by an independent inverter and a battery pack. When the vehicle is accelerating or [...] Read more.

A torque and battery distribution (TBD) strategy is proposed for saving energy for an electric vehicle (EV) that is driven by three traction motors. Each traction motor is driven by an independent inverter and a battery pack. When the vehicle is accelerating or cruising, its vehicle control unit determines the optimal torque distribution of the three motors by particle swarm optimization (PSO) theory to minimize energy consumption on the basis of their torque–speed–efficiency maps. Simultaneously, the states of charge (SOC) of the three battery packs are controlled in balance for improving the driving range and for avoiding unexpected battery depletion. The proposed TBD strategy can increase 7.7% driving range in the circular New European Driving Cycle (NEDC) of radius 100 m and 28% in the straight-line NEDC. All the battery energy can be effectively distributed and utilized for extending the driving range with an improved energy consumption efficiency. Full article

(This article belongs to the Special Issue Battery Management System for Future Electric Vehicles)

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20 pages, 4276 KiB

Open AccessArticle

Development of a Comprehensive Model for the Coulombic Efficiency and Capacity Fade of LiFePO₄ Batteries under Different Aging Conditions

by Ting-Jung Kuo

Appl. Sci. 2019, 9(21), 4572; https://doi.org/10.3390/app9214572 - 28 Oct 2019

Cited by 7 | Viewed by 2921

Abstract

In this paper, a comprehensive model for LiFePO₄ batteries is proposed to ensure high efficiency and safe operation. The proposed model has a direct correlation between its parameters and the electrochemical principles to estimate the state of charge (SoC) and the remaining [...] Read more.

In this paper, a comprehensive model for LiFePO₄ batteries is proposed to ensure high efficiency and safe operation. The proposed model has a direct correlation between its parameters and the electrochemical principles to estimate the state of charge (SoC) and the remaining capacity of the LiFePO₄ battery. This model was based on a modified Thévenin circuit, Butler–Volmer kinetics, the Arrhenius equation, Peukert’s law, and a back propagation neural network (BPNN), which can be divided into two parts. The first part can be represented by the dual exponential terms, responsive to the Coulomb efficiency; the second part can be described by the BPNN, estimating the remaining capacity. The model successfully estimates the SoC of the batteries that were tested with an error of 1.55%. The results suggest that the model is able to accurately estimate the SoC and the remaining capacity in various environments (discharging C rates and temperatures). Full article

(This article belongs to the Special Issue Battery Management System for Future Electric Vehicles)

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17 pages, 3808 KiB

Open AccessArticle

Small-Signal Modeling and Analysis for a Wirelessly Distributed and Enabled Battery Energy Storage System of Electric Vehicles

by Yuan Cao and Jaber Abu Qahouq

Appl. Sci. 2019, 9(20), 4249; https://doi.org/10.3390/app9204249 - 11 Oct 2019

Cited by 11 | Viewed by 2243

Abstract

This paper presents small-signal modeling, analysis, and control design for wireless distributed and enabled battery energy storage system (WEDES) for electric vehicles (EVs), which can realize the active state-of-charge (SOC) balancing between each WEDES battery module and maintain operation with a regulated bus [...] Read more.

This paper presents small-signal modeling, analysis, and control design for wireless distributed and enabled battery energy storage system (WEDES) for electric vehicles (EVs), which can realize the active state-of-charge (SOC) balancing between each WEDES battery module and maintain operation with a regulated bus voltage. The derived small-signal models of the WEDES system consist of several sub-models, such as the DC-DC boost converter model, wireless power transfer model, and the models of control compensators. The small-signal models are able to provide deep insight analysis of the steady-state and dynamics of the WEDES battery system and provide design guidelines or criteria of the WEDES controller. The derived small-signal models and controller design are evaluated and validated by both MATLAB^®/SIMULINK simulation and hardware experimental prototype. Full article

(This article belongs to the Special Issue Battery Management System for Future Electric Vehicles)

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16 pages, 3994 KiB

Open AccessArticle

SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization

by Xiangwei Guo, Xiaozhuo Xu, Jiahao Geng, Xian Hua, Yan Gao and Zhen Liu

Appl. Sci. 2019, 9(19), 4177; https://doi.org/10.3390/app9194177 - 06 Oct 2019

Cited by 20 | Viewed by 2939

Abstract

State of charge (SOC) estimation is generally acknowledged to be one of the most important functions of the battery management system (BMS) and is thus widely studied in academia and industry. Based on an accurate SOC estimation, the BMS can optimize energy efficiency [...] Read more.

State of charge (SOC) estimation is generally acknowledged to be one of the most important functions of the battery management system (BMS) and is thus widely studied in academia and industry. Based on an accurate SOC estimation, the BMS can optimize energy efficiency and protect the battery from being over-charged or over-discharged. The accurate online estimation of the SOC is studied in this paper. First, it is proved that the second-order resistance capacitance (RC) model is the most suitable equivalent circuit model compared with the Thevenin and multi-order models. The second-order RC equivalent circuit model is established, and the model parameters are identified. Second, the reasonable optimization of model parameters is studied, and a reasonable optimization method is proposed to improve the accuracy of SOC estimation. Finally, the SOC is estimated online based on the adaptive unscented Kalman filter (AUKF) with optimized model parameters, and the results are compared with the results of an estimation based on pre-optimization model parameters. Simulation experiments show that, without affecting the convergence of the initial error of the AUKF, the model after parameter optimization has a higher online SOC estimation accuracy. Full article

(This article belongs to the Special Issue Battery Management System for Future Electric Vehicles)

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20 pages, 4160 KiB

Open AccessFeature PaperArticle

Cooperative Optimization of Electric Vehicles and Renewable Energy Resources in a Regional Multi-Microgrid System

by Jin Chen, Changsong Chen and Shanxu Duan

Appl. Sci. 2019, 9(11), 2267; https://doi.org/10.3390/app9112267 - 31 May 2019

Cited by 17 | Viewed by 2563

Abstract

By integrating renewable energy sources (RESs) with electric vehicles (EVs) in microgrids, we are able to reduce carbon emissions as well as alleviate the dependence on fossil fuels. In order to improve the economy of an integrated system and fully exploit the potentiality [...] Read more.

By integrating renewable energy sources (RESs) with electric vehicles (EVs) in microgrids, we are able to reduce carbon emissions as well as alleviate the dependence on fossil fuels. In order to improve the economy of an integrated system and fully exploit the potentiality of EVs’ mobile energy storage while achieving a reasonable configuration of RESs, a cooperative optimization method is proposed to cooperatively optimize the economic dispatching and capacity allocation of both RESs and EVs in the context of a regional multi-microgrid system. An across-time-and-space energy transmission (ATSET) of the EVs was considered, and the impact of ATSET of EVs on economic dispatching and capacity allocation of multi-microgrid system was analyzed. In order to overcome the difficulty of finding the global optimum of the non-smooth total cost function, an improved particle swarm optimization (IPSO) algorithm was used to solve the cooperative optimization problem. Case studies were performed, and the simulation results show that the proposed cooperative optimization method can significantly decrease the total cost of a multi-microgrid system. Full article

(This article belongs to the Special Issue Battery Management System for Future Electric Vehicles)

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23 pages, 2071 KiB

Open AccessArticle

Adaptive Dual Extended Kalman Filter Based on Variational Bayesian Approximation for Joint Estimation of Lithium-Ion Battery State of Charge and Model Parameters

by Jing Hou, Yan Yang, He He and Tian Gao

Appl. Sci. 2019, 9(9), 1726; https://doi.org/10.3390/app9091726 - 26 Apr 2019

Cited by 30 | Viewed by 3206

Abstract

An accurate state of charge (SOC) estimation is vital for the safe operation and efficient management of lithium-ion batteries. At present, the extended Kalman filter (EKF) can accurately estimate the SOC under the condition of a precise battery model and deterministic noise statistics. [...] Read more.

An accurate state of charge (SOC) estimation is vital for the safe operation and efficient management of lithium-ion batteries. At present, the extended Kalman filter (EKF) can accurately estimate the SOC under the condition of a precise battery model and deterministic noise statistics. However, in practical applications, the battery characteristics change with different operating conditions and the measurement noise statistics may vary with time, resulting in nonoptimal and even unreliable estimation of SOC by EKF. To improve the SOC estimation accuracy under uncertain measurement noise statistics, a variational Bayesian approximation-based adaptive dual extended Kalman filter (VB-ADEKF) is proposed in this paper. The variational Bayesian inference is integrated with the dual EKF (DEKF) to jointly estimate the lithium-ion battery parameters and SOC. Meanwhile, the measurement noise variances are simultaneously estimated in the SOC estimation process to compensate for the model uncertainties, so that the adaptability of the proposed algorithm to dynamic changes in battery characteristics is greatly improved. A constant current discharge test, a pulse current discharge test, and an urban dynamometer driving schedule (UDDS) test are performed to verify the effectiveness and superiority of the proposed algorithm by comparison with the DEKF algorithm. The experimental results show that the proposed VB-ADEKF algorithm outperforms the traditional DEKF algorithm in terms of SOC estimation accuracy, convergence rate, and robustness. Full article

(This article belongs to the Special Issue Battery Management System for Future Electric Vehicles)

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