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Numerical Heat Transfer And Fluid Flow

Due to increasing concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels, the electric vehicles (EVs) receive massive popularity due to their performances and efficiencies in recent decades. EVs have already been widely accepted in the automotive industries considering the most promising replacements in reducing CO2 emissions and global environmental issues. Lithium-ion batteries have attained huge attention in EVs application due to their lucrative features such as lightweight, fast charging, high energy density, low self-discharge and long lifespan. This paper comprehensively reviews the lithium-ion battery state of charge (SOC) estimation and its management system towards the sustainable future EV applications. The significance of battery management system (BMS) employing lithium-ion batteries is presented, which can guarantee a reliable and safe operation and assess the battery SOC. The review identifies that the SOC is a crucial parameter as it signifies the remaining available energy in a battery that provides an idea about charging/discharging strategies and protect the battery from overcharging/over discharging. It is also observed that the SOC of the existing lithium-ion batteries have a good contribution to run the EVs safely and efficiently with their charging/discharging capabilities. However, they still have some challenges due to their complex electro-chemical reactions, performance degradation and lack of accuracy towards the enhancement of battery performance and life. The classification of the estimation methodologies to estimate SOC focusing with the estimation model/algorithm, benefits, drawbacks and estimation error are extensively reviewed. The review highlights many factors and challenges with possible recommendations for the development of BMS and estimation of SOC in next-generation EV applications. All the highlighted insights of this review will widen the increasing efforts towards the development of the advanced SOC estimation method and energy management system of lithium-ion battery for the future high-tech EV applications.

A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

In the recent years, lithium-ion batteries have become the battery technology of choice for portable devices, electric vehicles and grid storage. While increasing numbers of car manufacturers are introducing electrified models into their offering, range anxiety and the length of time required to recharge the batteries are still a common concern. The high currents needed to accelerate the charging process have been known to reduce energy efficiency and cause accelerated capacity and power fade. Fast charging is a multiscale problem, therefore insights from atomic to system level are required to understand and improve fast charging performance. The present paper reviews the literature on the physical phenomena that limit battery charging speeds, the degradation mechanisms that commonly result from charging at high currents, and the approaches that have been proposed to address these issues. Special attention is paid to low temperature charging. Alternative fast charging protocols are presented and critically assessed. Safety implications are explored, including the potential influence of fast charging on thermal runaway characteristics. Finally, knowledge gaps are identified and recommendations are made for the direction of future research. The need to develop reliable onboard methods to detect lithium plating and mechanical degradation is highlighted. Robust model-based charging optimisation strategies are identified as key to enabling fast charging in all conditions. Thermal management strategies to both cool batteries during charging and preheat them in cold weather are acknowledged as critical, with a particular focus on techniques capable of achieving high speeds and good temperature homogeneities.

Lithium-ion battery fast charging: A review

The lithium ion battery is widely used in electric vehicles (EV) The battery degradation is the key scientific problem in battery research The battery aging limits its energy storage and power output capability, as well as the performance of the EV including the cost and life span Therefore, a comprehensive review on the key issues of the battery degradation among the whole life cycle is provided in this paper Firstly, the battery internal aging mechanisms are reviewed considering different anode and cathode materials for better understanding the battery fade characteristic Then, to get better life performance, the influence factors affecting battery life are discussed in detail from the perspectives of design, production and application Finally, considering the difference between the cell and system, the battery system degradation mechanism is discussed

A review on the key issues of the lithium ion battery degradation among the whole life cycle

A study on the impact of lithium-ion cell relaxation on electrochemical impedance spectroscopy

A comparison of methodologies for the non-invasive characterisation of commercial Li-ion cells

The power capability of a lithium ion battery is governed by its resistance, which changes with battery state such as temperature, state of charge, and state of health. Characterizing resistance, therefore, is integral in defining battery operational boundaries, estimating its performance and tracking its state of health. There are many techniques that have been employed for estimating the resistance of a battery, these include: using DC pulse current signals such as pulse power tests or Hybrid Pulse Power Characterization (HPPC) tests; using AC current signals, i.e., electrochemical impedance spectroscopy (EIS) and using pulse-multisine measurements. From existing literature, these techniques are perceived to yield differing values of resistance. In this work, we apply these techniques to 20 Ah LiFePO4/C6 pouch cells and use the results to compare the techniques. The results indicate that the computed resistance is strongly dependent on the timescales of the technique employed and that when timescales match, the resistances derived via different techniques align. Furthermore, given that EIS is a perturbative characterisation technique, employing a spectrum of perturbation frequencies, we show that the resistance estimated from any technique can be identified – to a high level of confidence – from EIS by matching their timescales.

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A study of the influence of measurement timescale on internal resistance characterisation methodologies for lithium-ion cells.

A study of the open circuit voltage characterization technique and hysteresis assessment of lithium-ion cells

This paper proposes a blended rule-based energy management system (EMS) for a plug-in hybrid electric vehicle (PHEV). The proposed EMS is formulated over driving information and vehicle trip energy and not over vehicle speed profiles, as usually seen. The proposed EMS design structure and strategy is described followed by its evaluation. This is the first time a platform for a rule-based acausal EMS has been designed. Performance metrics such as the fuel economy and the number of engine stop–starts are compared with conventional rule-based EMS over real-world destinations with uncertain trip demand. Its performance metrics compared with a conventional EMS for a full parallel PHEV was found to be superior in this paper.

Andrew McGordon

Papers

A study on the impact of lithium-ion cell relaxation on electrochemical impedance spectroscopy

A comparison of methodologies for the non-invasive characterisation of commercial Li-ion cells

A study of the influence of measurement timescale on internal resistance characterisation methodologies for lithium-ion cells.

A study of the open circuit voltage characterization technique and hysteresis assessment of lithium-ion cells

Blended Rule-Based Energy Management for PHEV: System Structure and Strategy