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The electric vehicles are not completely emission-free. The vehicles consume power and the power generation processes involve carbon emissions, resulting in indirect vehicle usage carbon emissions. A use-phase carbon emissions assessment can help electric vehicle designers and manufacturers analyze the onboard carbon footprint and mitigate vehicle-related carbon emissions through energy consumption optimization. In this article, we focus on the use-phase carbon footprint based on the energy losses in electric vehicle battery packs. A battery pack energy loss model is established to examine the carbon footprint of four main subsystems of the battery pack, including the energy storage system, thermal management system, and battery junction box. We use the proposed method to evaluate the battery pack use-phase carbon emissions in four different cities from four main continents. The results show that the highest emissions appear in Tokyo (436.2 kgCO 2) due to the relatively high greenhouse gas factor on the grid, the London has the lowest 207.5 kgCO 2 emissions due to the massive adoption of sustainable energy during power generation. With a similar high greenhouse gas factor, but Sydney shows lower emissions (347.5 kgCO 2) compared to Tokyo, which illustrates the closer the annual average temperature to the battery suitable operation range, the lower energy loss happens inside the battery pack due to the low thermal management load and resistance energy loss in the cells. This electric vehicle has the highest energy loss rate (20.8%) in New York, 343.6 kgCO 2 emissions, and the highest thermal management system energy losses show the high seasonal temperature difference causing the high thermal management load to maintain the battery temperature. The proposed method can be used for different battery pack configurations and vehicle uses, providing researchers with a robust and versatile use-phase carbon emissions assessment strategy. • A battery pack use-phase carbon footprint assessment strategy is proposed. • The carbon emissions for four battery pack subsystems are calculated. • Temperature has the greatest impact on the battery pack carbon emissions. [ABSTRACT FROM AUTHOR]

Safety risk assessment is essential for evaluating the health status and averting sudden battery failures in electric vehicles. This study introduces a novel safety risk assessment approach for battery systems, addressing both cell and pack levels with three key indexes. The core of the assessment lies in representing the relative deviation of cell voltages through scatter diagrams across various stages of service life. Specifically, the study quantifies voltage deviations and deviation angles within different state of charge intervals to gauge safety risks at the cell level. Leveraging clustering algorithms aids in identifying outlier values. Furthermore, the dispersion of scatter points is utilized to assess safety risks at the pack level. Validation of this proposed model is conducted through cycling tests on battery modules with a deformed cell, demonstrating its efficacy in capturing inconsistent features of mechanical deformation abuse across the three indexes, thereby triggering alarms during operation. Moreover, the method is applied to assess the safety risks of five hazardous and accident-prone vehicles, enabling comprehensive evaluation of potential faulty cells and safety risks at the pack level. This proposed approach offers a fresh perspective on the comprehensive safety risk assessment of battery systems. [ABSTRACT FROM AUTHOR]

Accurate power prediction for a battery pack is of great importance for efficient and safe operation of electric vehicles. However, a battery pack typically consists of dozens of cells connected in series, and how to accurately predict its peak power in practical applications has always been a challenging issue. This paper proposes a low-complexity peak power prediction method for a series-connected battery pack, where the peak power of battery pack is predicted depending only on representative cells. Firstly, considering state of charge and voltage limits for power prediction, the representative cells are selected by using two easily available variables, namely characteristic voltage and ohmic resistance. Secondly, the state of charge and voltage of the representative cell are estimated accurately by the dual adaptive extended Kalman filter algorithm. Finally, the multi-parameter limited method for battery pack is developed to predict the peak power under Urban Dynamometer Driving Schedule test at different temperatures. The experimental results verify the feasibility and robustness of the proposed selection method, and the peak power of battery pack is predicted with low complexity and high accuracy. [ABSTRACT FROM AUTHOR]

The studies on an integrated approach for the battery (cell level), battery pack (system level) and battery pack enclosure (system level) are nascent till date. The present work proposes a combined approach for studying the coupled electrochemical-mechanical performance of the battery (cell level), battery pack and enclosure (system level). In this context, five research problems are undertaken involving the design parameters for the performance evaluation of batteries. The first issue undertaken is battery performance under stress conditions and for that varying initial stress and real-time stresses were applied, and variation in the capacity of the battery packs was studied. Initial stress was found to be in negative relation with the battery capacity. For understanding the mechanical damage (leakage, deformation, etc.), characterization of the battery cell under impact loading was done by subjecting battery cells to different impact loads and impact analysis was done using six statistical features extracted from the impact force processed signal of each test, namely: peak value, mean value, variance, skewness, kurtosis and pulse width. The third issue regards the manufacturing defects in the cell, which can potentially lead to inconsistency of cells performance in a pack and thus lower its total cycle life. Support vector classification clustering methods were applied to form a battery pack with similar performing cells and performance of the pack was compared with industrial packs in which earlier one was found to be better. The fourth research challenge involved the mechanical performance parameters such as strength, deformation, and natural frequency, which, in addition to temperature, are also critical to battery pack safety in the event of impact and vibrations under driving conditions. ANSIS and NSGA-II based approaches were used to optimize the battery design and resultant device shown maximum deformation reduced by 22.2%, the value of minimum inherent frequency improved by 3.2% and the value of mass decreased by 11.6%. The fifth research problem highlighted how, due to the enormous production of electric vehicles, by 2020, there will be 250,000 tons of batteries that need to be recycled. A combined experimental-numerical framework has been thereby suggested to mitigate it. The study resulted in the formation of an online monitoring system and safety design of batteries at both cell and system level (pack and enclosure). [ABSTRACT FROM AUTHOR]

Abstract Cell variations always undermine the efficiency of energy utilization of battery pack. How does the temperature non-uniformity affect the status of cell variations remains unclear. This paper investigates the underlying mechanisms of the status change of cell variations caused by temperature non-uniformity in a battery pack. First, the status of cell variations has been defined using a graphical model, in which each cell has a coordinate of (capacity, electric quantity). Second, the status transformation caused by temperature non-uniformity has been clarified. Dynamics that describes the thermal-electrochemical effect on the movement of the coordinate for individual cells are firstly established by transformation functions. The movement of the cell locations in the graphical model can vividly interpret the evolution of cell variations caused by temperature non-uniformity with the help of the built transformation functions. Modelling analysis points out that 5 °C increase in the maximum temperature difference among the battery pack can lead to 1.5%–2% capacity loss of the battery pack, therefore the capacity loss caused by cell variations due to temperature non-uniformity should be considered in battery system design. The methodology can be further used to analyze the status change of cell variations in a battery pack caused by all kinds of temperature non-uniformities, benefiting the design of battery management system with high efficiency of energy utilization. Graphical abstract The evolution of cell variations within a lithium-ion battery pack caused by temperature non-uniformity. Image Highlights • The cell variations in battery pack caused by temperature non-uniformity is graphically shown. • The cell variations are defined by a graphical model named the E-Q Diagram. • Thermal-electro coupled dynamic functions are built to interpret the cell variation evolution. • 5 °C increase in temperature difference can lead to 1.5%–2% capacity loss of the battery pack. • The methodology can guide cleaner production of battery system with higher energy efficiency. [ABSTRACT FROM AUTHOR]

State-of-health (SOH) monitoring of lithium-ion batteries plays a key role in the reliable and safe operation of battery systems. Influenced by multiple factors, SOH is an aging path-dependent parameter, which challenges its accurate estimation and prediction. Numerous methods have been proposed for lithium-ion batteries SOH diagnostics and prognostics, but there is little discussion on how to characterize SOH. In this paper, we first review the existing characteristic parameters in defining battery SOH at cell-level and pack-level, and then propose some suggestions for SOH definitions. The impact of external influencing factors on battery degradation is introduced to lay a foundation for SOH estimation. SOH estimation methods are categorized by the parameters used to define SOH, i.e. capacity, impedance and aging-mechanism parameters. We discuss the goals of SOH monitoring and summarize its applications from short- and long-term perspectives accordingly. Finally, the challenges and future trends of battery SOH diagnostics and prognostics are thoroughly analyzed and discussed, in order to provide a comprehensive understanding for researchers and technologists. • Discuss how to comprehensively characterize battery SOH at cell-level and pack-level. • Analyze the strengths and weaknesses of the SOH estimation methods from the perspective of data acquisition sources. • Present the gaps between existing research on SOH estimations and SOH applications. • Summarize the challenges and prospects for SOH characterizations, estimations and applications. [ABSTRACT FROM AUTHOR]