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Battery Management in Electric Vehicles: Current Status and Future Trends

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A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Modelling, Simulation, Management and Application".

Deadline for manuscript submissions: closed (25 September 2023) | Viewed by 39837

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Dr. Prodip K. Das

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School of Engineering, University of Edinburgh, Edinburgh EH9 3FB, UK
Interests: hydrogen energy systems; fuel cells; Li-ion batteries; electric vehicles; battery fire and safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Li-ion batteries (LiBs) are an essential component of zero-carbon energy transition around the world and for reaching the COP26’s goal of securing global net-zero by the mid-century. However, their rapid growth is accompanied by significant drawbacks. It is expected that their continual demand for use in electric vehicles (EVs) will lead to global environmental and supply chain concerns, as the critical materials used in LiBs (e.g., cobalt, lithium, nickel, graphite, manganese) are finite and mined in only a few regions around the world. This means we will eventually have to deal significant battery waste. However, with appropriate and improved battery management in EVs, we can enhance the performance of these batteries, prolong their life in EVs, enable their use in secondary applications, and promote the recycling and re-use of EV batteries to mitigate global environmental and supply chain concerns. This Special Issue of Batteries aims to explore recent advances and future trends in battery management in EVs that will enable reaching global net-zero by the mid-century.

Potential topics include but are not limited to:

Innovative battery management system (BMS);
Artificial intelligence in battery management;
Enhanced algorithms for battery control and monitoring of the state of charge (SOC), state of health (SOH), state of power (SOP), etc;
Battery diagnostic and prognostic functions;
Thermal management for batteries;
Novel sensing methods to enhance battery safety and BMS’s operation;
Battery aging in EVs and its impact on the secondary applications;
Non-destructive testing and diagnostics for thermal runaway and battery management;
New materials, advanced manufacturing methods, and novel cell and pack design for promoting the recycling and re-use of batteries;
Digital Twins of batteries for performance optimization;
Multi-objective optimization strategies for batteries.

Dr. Prodip K. Das
Guest Editor

Manuscript Submission Information

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Keywords

artificial intelligence
battery management system
state of charge
state of health
state of power
thermal management
novel sensing method
battery aging
cell and pack design
recycling and re-use
digital twins
optimization

Related Special Issue

Battery Management in Electric Vehicles: Current Status and Future Trends: 2nd Edition in Batteries (1 article)

Published Papers (10 papers)

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Research

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28 pages, 12894 KiB

Open AccessArticle

A Novel Method for State of Health Estimation of Lithium-Ion Batteries Based on Deep Learning Neural Network and Transfer Learning

by Zhong Ren, Changqing Du and Yifang Zhao

Batteries 2023, 9(12), 585; https://doi.org/10.3390/batteries9120585 - 12 Dec 2023

Viewed by 1828

Abstract

Accurate state of health (SOH) estimation of lithium-ion batteries is critical for maintaining reliable and safe working conditions for electric vehicles (EVs). The machine learning-based method with health features (HFs) is encouraging for health prognostics. However, the machine learning method assumes that the training and testing data have the same distribution, which restricts its application for different types of batteries. Thus, in this paper, a deep learning neural network and fine-tuning-based transfer learning strategy are proposed for accurate and robust SOH estimation toward different types of batteries. First, a universal HF extraction strategy is proposed to obtain four highly related HFs. Second, a deep learning neural network consisting of long short-term memory (LSTM) and fully connected layers is established to model the relationship between the HFs and SOH. Third, the fine-tuning-based transfer learning strategy is exploited for SOH estimation of various types of batteries. The proposed methods are comprehensively verified using three open-source datasets. Experimental results show that the proposed deep learning neural network with the HFs can estimate the SOH accurately in a single dataset without using the transfer learning strategy where the mean absolute error (MAE) and root mean square error (RMSE) are constrained to 1.21% and 1.83%. For the transfer learning between different aging datasets, the overall MAE and RMSE are limited to 1.09% and 1.41%, demonstrating the reliability of the fine-tuning strategy. Full article

(This article belongs to the Special Issue Battery Management in Electric Vehicles: Current Status and Future Trends)

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19 pages, 24159 KiB

Open AccessArticle

Artificial Neural Network Modeling to Predict Thermal and Electrical Performances of Batteries with Direct Oil Cooling

by Kunal Sandip Garud, Jeong-Woo Han, Seong-Guk Hwang and Moo-Yeon Lee

Batteries 2023, 9(11), 559; https://doi.org/10.3390/batteries9110559 - 16 Nov 2023

Viewed by 1590

Abstract

The limitations of existing commercial indirect liquid cooling have drawn attention to direct liquid cooling for battery thermal management in next-generation electric vehicles. To commercialize direct liquid cooling for battery thermal management, an extensive database reflecting performance and operating parameters needs to be established. The development of prediction models could generate this reference database to design an effective cooling system with the least experimental effort. In the present work, artificial neural network (ANN) modeling is demonstrated to predict the thermal and electrical performances of batteries with direct oil cooling based on various operating conditions. The experiments are conducted on an 18650 battery module with direct oil cooling to generate the learning data for the development of neural network models. The neural network models are developed considering oil temperature, oil flow rate, and discharge rate as the input operating conditions and maximum temperature, temperature difference, heat transfer coefficient, and voltage as the output thermal and electrical performances. The proposed neural network models comprise two algorithms, the Levenberg–Marquardt (LM) training variant with the Tangential-Sigmoidal (Tan-Sig) transfer function and that with the Logarithmic-Sigmoidal (Log-Sig) transfer function. The ANN_LM-Tan algorithm with a structure of 3-10-10-4 shows accurate prediction of thermal and electrical performances under all operating conditions compared to the ANN_LM-Log algorithm with the same structure. The maximum prediction errors for the ANN_LM-Tan and ANN_LM-Log algorithms are restricted within ±0.97% and ±4.81%, respectively, considering all input and output parameters. The ANN_LM-Tan algorithm is suggested to accurately predict the thermal and electrical performances of batteries with direct oil cooling based on a maximum determination coefficient (R²) and variance coefficient (COV) of 0.99 and 1.65, respectively. Full article

(This article belongs to the Special Issue Battery Management in Electric Vehicles: Current Status and Future Trends)

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16 pages, 2127 KiB

Open AccessArticle

Using Reinforcement Learning to Solve a Dynamic Orienteering Problem with Random Rewards Affected by the Battery Status

by Angel A. Juan, Carolina A. Marugan, Yusef Ahsini, Rafael Fornes, Javier Panadero and Xabier A. Martin

Batteries 2023, 9(8), 416; https://doi.org/10.3390/batteries9080416 - 09 Aug 2023

Cited by 2 | Viewed by 1207

Abstract

This paper discusses an orienteering optimization problem where a vehicle using electric batteries must travel from an origin depot to a destination depot while maximizing the total reward collected along its route. The vehicle must cross several consecutive regions, with each region containing different types of charging nodes. A charging node has to be selected in each region, and the reward for visiting each node—in terms of a ‘satisfactory’ charging process—is a binary random variable that depends upon dynamic factors such as the type of charging node, weather conditions, congestion, battery status, etc. To learn how to efficiently operate in this dynamic environment, a hybrid methodology combining simulation with reinforcement learning is proposed. The reinforcement learning component is able to make informed decisions at each stage, while the simulation component is employed to validate the learning process. The computational experiments show how the proposed methodology is capable of design routing plans that are significantly better than non-informed decisions, thus allowing for an efficient management of the vehicle’s battery under such dynamic conditions. Full article

(This article belongs to the Special Issue Battery Management in Electric Vehicles: Current Status and Future Trends)

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14 pages, 1006 KiB

Open AccessArticle

Battery Sharing: A Feasibility Analysis through Simulation

by Mattia Neroni, Erika M. Herrera, Angel A. Juan, Javier Panadero and Majsa Ammouriova

Batteries 2023, 9(4), 225; https://doi.org/10.3390/batteries9040225 - 11 Apr 2023

Cited by 1 | Viewed by 1375

Abstract

Nowadays, several alternatives to internal combustion engines are being proposed in order to reduce CO₂ emissions in freight transportation and citizen mobility. According to many experts, the use of electric vehicles constitutes one of the most promising alternatives for achieving the desirable reductions in emissions. However, popularization of these vehicles is being slowed by long recharging times and the low availability of recharging stations. One possible solution to this issue is to employ the concept of battery sharing or battery swapping. This concept is supported by important industrial partners, such as Eni in Italy, Ample in the US, and Shell in the UK. This paper supports the introduction of battery swapping practices by analyzing their effects. A discrete-event simulation model is employed for this study. The obtained results show that battery sharing practices are not just a more environmentally and socially friendly solution, but also one that can be highly beneficial for reducing traffic congestion. Full article

(This article belongs to the Special Issue Battery Management in Electric Vehicles: Current Status and Future Trends)

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19 pages, 2416 KiB

Open AccessArticle

Iterative Nonlinear Fuzzy Modeling of Lithium-Ion Batteries

by José M. Andújar, Antonio J. Barragán, Francisco J. Vivas, Juan M. Enrique and Francisca Segura

Batteries 2023, 9(2), 100; https://doi.org/10.3390/batteries9020100 - 01 Feb 2023

Viewed by 1577

Abstract

Electric vehicles (EVs), in their pure and hybrid variants, have become the main alternative to ensure the decarbonization of the current vehicle fleet. Due to its excellent performance, EV technology is closely linked to lithium-ion battery (LIB) technology. A LIB is a complex dynamic system with extraordinary nonlinear behavior defined by electrical, thermal and electrochemical dynamics. To ensure the proper management of a LIB in such demanding applications as EVs, it is crucial to have an accurate mathematical model that can adequately predict its dynamic behavior. Furthermore, this model must be able to iteratively adapt its parameters to accommodate system disturbances during its operation as well as performance loss in terms of efficiency and nominal capacity during its life cycle. To this end, a methodology that employs the extended Kalman filter to iteratively improve a fuzzy model applied to a real LIB is presented in this paper. This algorithm allows to improve the classical Takagi–Sugeno fuzzy model (TSFM) with each new set of data obtained, adapting the model to the variations of the battery characteristics throughout its operating cycle. Data for modeling and subsequent validation were collected during experimental tests on a real LIB under EVs driving cycle conditions according to the “worldwide harmonised light vehicle test procedure” (WLTP) standard. The TSFM results allow the creation of an accurate nonlinear dynamic model of the LIB, even under fluctuating operating conditions, demonstrating its suitability for modeling and design of model-based control systems for LIBs used in EVs applications. Full article

(This article belongs to the Special Issue Battery Management in Electric Vehicles: Current Status and Future Trends)

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19 pages, 71576 KiB

Open AccessArticle

Topographical Optimization of a Battery Module Case That Equips an Electric Vehicle

by Ioan Szabo, Liviu I. Scurtu, Horia Raboca and Florin Mariasiu

Batteries 2023, 9(2), 77; https://doi.org/10.3390/batteries9020077 - 23 Jan 2023

Cited by 5 | Viewed by 3472

Abstract

The exponential development and successful application of systems-related technologies that can put electric vehicles on a level playing field in direct competition with vehicles powered by internal combustion engines mean that the foreseeable future of the automobile (at least) will be dominated by vehicles that have electric current stored in batteries as a source of energy. The problem at the European level related to the dependence on battery suppliers from Asia directly correlates with the need to use batteries as energy storage media for energy from renewable sources (photovoltaic and wind), and leads to the need for research into the possibilities for their reuse, remanufacturing or recycling (at the end of their life or purpose of use), and reintroduction, either fully or partially, back into the economy. This article presents possibilities for increasing the protection of the integrity of the cells that form a battery in the event of an impact/road accident, by the numerical analysis of a topographically optimized battery module case. The proposed solution/method is innovative and offers a cell protection efficiency of between 16.6–60% (19.7% to 40.7% if the mean values for all three impact velocities are considered). The efficiency of a cell’s protection decreases with the increase in impact velocity and provides the premise for a greater part of the saved cells to be reintegrated into other energy storage systems (photovoltaic and/or wind), avoiding future problems relating to environmental pollution. Full article

(This article belongs to the Special Issue Battery Management in Electric Vehicles: Current Status and Future Trends)

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16 pages, 5699 KiB

Open AccessArticle

Parametric Evaluation of Thermal Behavior for Different Li-Ion Battery Chemistries

by Thomas Imre Cyrille Buidin and Florin Mariasiu

Batteries 2022, 8(12), 291; https://doi.org/10.3390/batteries8120291 - 17 Dec 2022

Cited by 3 | Viewed by 1927

Abstract

The prediction of thermal behavior is essential for an efficient initial design of thermal management systems which equip energy sources based on electrochemical cells. In this study, the surface temperature of various cylindrical types of Li-ion batteries is monitored at multiple points during discharge. Three different battery chemistries and two sizes (18650 and 21700) are considered in this study, allowing the comparison of the influence these parameters have on the temperature rise considering different discharge rates (1C, 2C and 3C). Based on repeated experimental measurements, a simple equation that describes the thermal behavior of batteries is proposed and further used to create 3D thermal maps for each analyzed battery (generally error is below 1 °C but never exceeds 3 °C). The practical utility of such an equation is that it can drastically reduce the time spent with experimental measurements required to characterize the thermal behavior of cylindrical Li-ion batteries, necessary for the initial design process of energy sources’ thermal management system. Full article

(This article belongs to the Special Issue Battery Management in Electric Vehicles: Current Status and Future Trends)

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Review

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21 pages, 7207 KiB

Open AccessReview

Critical Analysis of Simulation of Misalignment in Wireless Charging of Electric Vehicles Batteries

by Saeid Ghazizadeh, Kafeel Ahmed, Mehdi Seyedmahmoudian, Saad Mekhilef, Jaideep Chandran and Alex Stojcevski

Batteries 2023, 9(2), 106; https://doi.org/10.3390/batteries9020106 - 03 Feb 2023

Cited by 3 | Viewed by 2929

Abstract

The transition from conventional to electric transportation has become inevitable in recent years owing to the significant impact of electric vehicles (EVs) on energy sustainability, reduction of global warming and carbon emission reduction. Despite the rapidly growing global adoption of EVs in today’s electrical and transportation networks, energy storage in EVs, particularly in regards to bulky size and charging process, still remains a major bottleneck. As a result, wireless charging of EVs via inductively coupled power transfer (ICPT) through coupled coils is becoming a promising solution. However, the efficiency of charging EV batteries via wireless charging is hugely affected by misalignment between the primary and secondary coils. This paper presents an in-depth analysis of various key factors affecting the efficiency of EV battery charging. Finite element analysis (FEA) using Ansys Maxwell^® is performed on commonly used coil designs such as circular and rectangular coils under various misalignment conditions. In addition, various reactive power compensation topologies applied in ICPT are investigated and the behavior of each topology is observed in simulation. It is revealed that circular structures with S–S compensation topology show more robustness in misalignment conditions and maintain the desired efficiency for a wider range of displacement. A critical analysis of coil designs, compensation techniques and the combination of both factors is accomplished and conclusions are presented. Full article

(This article belongs to the Special Issue Battery Management in Electric Vehicles: Current Status and Future Trends)

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20 pages, 2228 KiB

Open AccessReview

Review on Battery Packing Design Strategies for Superior Thermal Management in Electric Vehicles

by Robby Dwianto Widyantara, Siti Zulaikah, Firman Bagja Juangsa, Bentang Arief Budiman and Muhammad Aziz

Batteries 2022, 8(12), 287; https://doi.org/10.3390/batteries8120287 - 14 Dec 2022

Cited by 11 | Viewed by 6161

Abstract

In the last decades of electric vehicle (EV) development, battery thermal management has become one of the remaining issues that must be appropriately handled to ensure robust EV design. Starting from researching safer and more durable battery cells that can resist thermal exposure, battery packing design has also become important to avoid thermal events causing an explosion or at least to prevent fatal loss if the explosion occurs. An optimal battery packing design can maintain the battery cell temperature at the most favorable range, i.e., 25–40 °C, with a temperature difference in each battery cell of 5 °C at the maximum, which is considered the best working temperature. The design must also consider environmental temperature and humidity effects. Many design strategies have been reported, including novel battery pack constructions, a better selection of coolant materials, and a robust battery management system. However, those endeavors are faced with the main challenges in terms of design constraints that must be fulfilled, such as material and manufacturing costs, limited available battery space and weight, and low energy consumption requirements. This work reviewed and analyzed the recent progress and current state-of-the-art in designing battery packs for superior thermal management. The narration focused on significant findings that have solved the battery thermal management design problem as well as the remaining issues and opportunities to obtain more reliable and enduring batteries for EVs. Furthermore, some recommendations for future research topics supporting the advancement of battery thermal management design were also discussed. Full article

(This article belongs to the Special Issue Battery Management in Electric Vehicles: Current Status and Future Trends)

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60 pages, 10694 KiB

Open AccessReview

Battery Management, Key Technologies, Methods, Issues, and Future Trends of Electric Vehicles: A Pathway toward Achieving Sustainable Development Goals

by Molla Shahadat Hossain Lipu, Abdullah Al Mamun, Shaheer Ansari, Md. Sazal Miah, Kamrul Hasan, Sheikh T. Meraj, Maher G. M. Abdolrasol, Tuhibur Rahman, Md. Hasan Maruf, Mahidur R. Sarker, A. Aljanad and Nadia M. L. Tan

Batteries 2022, 8(9), 119; https://doi.org/10.3390/batteries8090119 - 07 Sep 2022

Cited by 38 | Viewed by 14556

Abstract

Recently, electric vehicle (EV) technology has received massive attention worldwide due to its improved performance efficiency and significant contributions to addressing carbon emission problems. In line with that, EVs could play a vital role in achieving sustainable development goals (SDGs). However, EVs face some challenges such as battery health degradation, battery management complexities, power electronics integration, and appropriate charging strategies. Therefore, further investigation is essential to select appropriate battery storage and management system, technologies, algorithms, controllers, and optimization schemes. Although numerous studies have been carried out on EV technology, the state-of-the-art technology, progress, limitations, and their impacts on achieving SDGs have not yet been examined. Hence, this review paper comprehensively and critically describes the various technological advancements of EVs, focusing on key aspects such as storage technology, battery management system, power electronics technology, charging strategies, methods, algorithms, and optimizations. Moreover, numerous open issues, challenges, and concerns are discussed to identify the existing research gaps. Furthermore, this paper develops the relationship between EVs benefits and SDGs concerning social, economic, and environmental impacts. The analysis reveals that EVs have a substantial influence on various goals of sustainable development, such as affordable and clean energy, sustainable cities and communities, industry, economic growth, and climate actions. Lastly, this review delivers fruitful and effective suggestions for future enhancement of EV technology that would be beneficial to the EV engineers and industrialists to develop efficient battery storage, charging approaches, converters, controllers, and optimizations toward targeting SDGs. Full article

(This article belongs to the Special Issue Battery Management in Electric Vehicles: Current Status and Future Trends)

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