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The powertrain in electric vehicles typically comprises various components, including lithium-ion batteries (LIBs), a battery management system, an energy converter, an electric motor, and a mechanical transmission system. Electric vehicles utilize the electrical energy stored in LIBs to efficiently drive the motors efficiently. LIBs find widespread use in portable electronic devices like laptops, mobile phones, and other electronic appliances, with potential applications in the automotive sector. To examine the thermal performance of LIBs across diverse applications and establish accurate thermal models for batteries, it is essential to understand heat generation. Numerous researchers have proposed various methods to determine the heat generation of LIBs through comprehensive experimental laboratory measurements. This study comprehensively explores diverse experimental and modeling techniques used to analyze the thermal behavior and heat generation of LIBs.

In recent years, the rapid evolution of transportation electrification has been propelled by the widespread adoption of lithium-ion batteries (LIBs) as the primary energy storage solution. The critical need to ensure the safe and efficient operation of these LIBs has positioned battery management systems (BMS) as pivotal components in this landscape. Among the various BMS functions, state and temperature monitoring emerge as paramount for intelligent LIB management. This review focuses on two key aspects of LIB health management: the accurate prediction of the state of health (SOH) and the estimation of remaining useful life (RUL). Achieving precise SOH predictions not only extends the lifespan of LIBs but also offers invaluable insights for optimizing battery usage. Additionally, accurate RUL estimation is essential for efficient battery management and state estimation, especially as the demand for electric vehicles continues to surge. The review highlights the significance of machine learning (ML) techniques in enhancing LIB state predictions while simultaneously reducing computational complexity. By delving into the current state of research in this field, the review aims to elucidate promising future avenues for leveraging ML in the context of LIBs. Notably, it underscores the increasing necessity for advanced RUL prediction techniques and their role in addressing the challenges associated with the burgeoning demand for electric vehicles. This comprehensive review identifies existing challenges and proposes a structured framework to overcome these obstacles, emphasizing the development of machine-learning applications tailored specifically for rechargeable LIBs. The integration of artificial intelligence (AI) technologies in this endeavor is pivotal, as researchers aspire to expedite advancements in battery performance and overcome present limitations associated with LIBs. In adopting a symmetrical approach, ML harmonizes with battery management, contributing significantly to the sustainable progress of transportation electrification. This study provides a concise overview of the literature, offering insights into the current state, future prospects, and challenges in utilizing ML techniques for lithium-ion battery health monitoring. [ABSTRACT FROM AUTHOR]

The determination of coulombic efficiency of the lithium-ion batteries can contribute to comprehend better their degradation behavior. In this research, the coulombic efficiency and capacity loss of three lithium-ion batteries at different current rates (C) were investigated. Two new battery cells were discharged and charged at 0.4 C and 0.8 C for twenty times to monitor the variations in the aging and coulombic efficiency of the battery cell. In addition, prior cycling was applied to the third battery cell which consist of charging and discharging with 0.2 C, 0.4 C, 0.6 C, and 0.8 C current rates and each of them twenty times. The coulombic efficiency of the new battery cells was compared with the cycled one. The experiments demonstrated that approximately all the charge that was stored in the battery cell was extracted out of the battery cell, even at the bigger charging and discharging currents. The average capacity loss rates for discharge and charge during 0.8 C were approximately 0.44% and 0.45% per cycle, correspondingly. [ABSTRACT FROM AUTHOR]

A precise lithium-ion battery model is required to specify their appropriateness for different applications and to study their dynamic behavior. In addition, it is important to design an efficient battery system for power applications. In this investigation, a second-order equivalent electrical circuit battery model, which is the most conventional method of characterizing the behavior of a lithium-ion battery, was developed. The current pulse procedure was employed for parameterization of the model. The construction of the model was described in detail, and a battery model for a 13 Ah lithium titanate oxide battery cell was demonstrated. Comprehensive characterization experiments were accomplished for an extensive range of operating situations. The outcomes were employed to parameterize the suggested dynamic model of the lithium titanate oxide battery cell. The simulation outcomes were compared to the laboratory measurements. In addition, the proposed lithium-ion battery model was validated. The recommended model was assessed, and the proposed model was able to anticipate precisely the current and voltage performance. [ABSTRACT FROM AUTHOR]

To understand better the thermal behaviour of lithium-ion batteries under different working conditions, various experiments were applied to a 13 Ah Altairnano lithium titanate oxide battery cell by means of isothermal battery calorimeter. Several parameters were measured such as the battery surface temperature, voltage, current, power, heat flux, maximum temperature and power area. In addition, the efficiency was calculated. Isothermal battery calorimeter was selected as the most appropriate method for heat loss measurements. Temperatures on the surface of the battery were measured by employing four contact thermocouples (type K). In order to determine the heat loss of the battery, constant current charge and discharge pulses at sixteen different C-rates were applied to the battery. It was seen that the charge and discharge C-rates has a considerable influence on the thermal behaviours of lithium-ion batteries. In this research paper, the C-rate was linked to the peak temperature, efficiency and heat loss and it was concluded that they are linear dependent on the C-rate. In addition, the outcomes of this investigation can be used for battery thermal modelling and design of thermal management systems. [ABSTRACT FROM AUTHOR]

This paper reviews different methods for determination of thermal parameters of lithium ion batteries. Lithium ion batteries are extensively employed for various applications owing to their low memory effect, high specific energy, and power density. One of the problems in the expansion of hybrid and electric vehicle technology is the management and control of operation temperatures and heat generation. Successful battery thermal management designs can lead to better reliability and performance of hybrid and electric vehicles. Thermal cycling and temperature gradients could have a considerable impact on the lifetime of lithium ion battery cells. Thermal management is critical in electric vehicles (EVs) and good thermal battery models are necessary to design proper heating and cooling systems. Consequently, it is necessary to determine thermal parameters of a single cell, such as internal resistance, specific heat capacity, entropic heat coefficient, and thermal conductivity in order to design suitable thermal management system. [ABSTRACT FROM AUTHOR]

One of the reasonable possibilities to investigate the battery behaviour under various temperature and current conditions is the development of a model of the lithium-ion batteries and then by employing the simulation technique to anticipate their behaviour. This method not only can save time but also they can predict the behaviour of the batteries through simulation. In this investigation, a three-dimensional model is developed to simulate thermal and electrochemical behaviour of a 13Ah lithium-ion battery. In addition, the temperature dependency of the battery cell parameters was considered in the model in order to investigate the influence of temperature on various parameters such as heat generation during battery cell operation. Maccor automated test system and isothermal battery calorimeter were used as experimental setup to validate the thermal model, which was able to predict the heat generation rate and temperature at different positions of the battery. The three-dimensional temperature distributions which were achieved from the modelling and experiment were in well agreement with each other throughout the entire of discharge cycling at different environmental temperatures and discharge rates. [ABSTRACT FROM AUTHOR]