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The usage of Lithium-ion (Li-ion) batteries has increased significantly in recent years due to their long lifespan, high energy density, high power density, and environmental benefits. However, various internal and external faults can occur during the battery operation, leading to performance issues and potentially serious consequences, such as thermal runaway, fires, or explosion. Fault diagnosis, hence, is an important function in the battery management system (BMS) and is responsible for detecting faults early and providing control actions to minimize fault effects, to ensure the safe and reliable operation of the battery system. This paper provides a comprehensive review of various fault diagnostic algorithms, including model-based and non-model-based methods. The advantages and disadvantages of the reviewed algorithms, as well as some future challenges for Li-ion battery fault diagnosis, are also discussed in this paper.

Lithium-ion (Li-ion) batteries are an important component of energy storage systems used in various applications such as electric vehicles and portable electronics. There are many chemistries of Li-ion battery, but LFP, NMC, LMO, and NCA are four commonly used types. In order for the battery applications to operate safely and effectively, battery modeling is very important. The equivalent circuit model (ECM) is a battery model often used in the battery management system (BMS) to monitor and control Li-ion batteries. In this study, experiments were performed to investigate the performance of three different ECMs (1RC, 2RC, and 1RC with hysteresis) on four Li-ion battery chemistries (LFP, NMC, LMO, and NCA). The results indicated that all three models are usable for the four types of Li-ion chemistries, with low errors. It was also found that the ECMs tend to perform better in dynamic current profiles compared to non-dynamic ones. Overall, the best-performed model for LFP and NCA was the 1RC with hysteresis ECM, while the most suited model for NMC and LMO was the 1RC ECM. The results from this study showed that different ECMs would be suited for different Li-ion battery chemistries, which should be an important factor to be considered in real-world battery and BMS applications.

Greenhouse gas emissions from the freight transportation sector are a significant contributor to climate change, pollution, and negative health impacts because of the common use of heavy-duty diesel vehicles (HDVs). Governments around the world are working to transition away from diesel HDVs and to electric HDVs, to reduce emissions. Battery electric HDVs and hydrogen fuel cell HDVs are two available alternatives to diesel engines. Each diesel engine HDV, battery-electric HDV, and hydrogen fuel cell HDV powertrain has its own advantages and disadvantages. This work provides a comprehensive review to examine the working mechanism, performance metrics, and recent developments of the aforementioned HDV powertrain technologies. A detailed comparison between the three powertrain technologies, highlighting the advantages and disadvantages of each, is also presented, along with future perspectives of the HDV sector. Overall, diesel engine in HDVs will remain an important technology in the short-term future due to the existing infrastructure and lower costs, despite their high emissions, while battery-electric HDV technology and hydrogen fuel cell HDV technology will be slowly developed to eliminate their barriers, including costs, infrastructure, and performance limitations, to penetrate the HDV market.

Energy storage plays an important role in the adoption of renewable energy to help solve climate change problems. Lithium-ion batteries (LIBs) are an excellent solution for energy storage due to their properties. In order to ensure the safety and efficient operation of LIB systems, battery management systems (BMSs) are required. The current design and functionality of BMSs suffer from a few critical drawbacks including low computational capability and limited data storage. Recently, there has been some effort in researching and developing smart BMSs utilizing the cloud platform. A cloud-based BMS would be able to solve the problems of computational capability and data storage in the current BMSs. It would also lead to more accurate and reliable battery algorithms and allow the development of other complex BMS functions. This study reviews the concept and design of cloud-based smart BMSs and provides some perspectives on their functionality and usability as well as their benefits for future battery applications. The potential division between the local and cloud functions of smart BMSs is also discussed. Cloud-based smart BMSs are expected to improve the reliability and overall performance of LIB systems, contributing to the mass adoption of renewable energy.

Methane, the primary component of natural gas, is a significant contributor to global warming and climate change. It is a harmful greenhouse gas with an impact 28 times greater than carbon dioxide over a 100-year period. Preventing methane leakage from transmission pipelines and other oil and gas production activities is a possible solution to reduce methane emissions. In order to detect and resolve methane leaks, reliable and cost-effective sensors need to be researched and developed. This paper provides a comprehensive review of different types of methane detection sensors, including optical sensors, calorimetric sensors, pyroelectric sensors, semiconducting oxide sensors, and electrochemical sensors. The discussed material includes the definitions, mechanisms and recent developments of these sensors. A comparison between different methods, highlighting the advantages and disadvantages of each, is also presented to help address future research needs.

Emissions from the transportation sector are significant contributors to climate change and health problems because of the common use of gasoline vehicles. Countries in the world are attempting to transition away from gasoline vehicles and to electric vehicles (EVs), in order to reduce emissions. However, there are several practical limitations with EVs, one of which is the “range anxiety” issue, due to the lack of charging infrastructure, the high cost of long-ranged EVs, and the limited range of affordable EVs. One potential solution to the range anxiety problem is the use of range extenders, to extend the driving range of EVs while optimizing the costs and performance of the vehicles. This paper provides a comprehensive review of different types of EV range extending technologies, including internal combustion engines, free-piston linear generators, fuel cells, micro gas turbines, and zinc-air batteries, outlining their definitions, working mechanisms, and some recent developments of each range extending technology. A comparison between the different technologies, highlighting the advantages and disadvantages of each, is also presented to help address future research needs. Since EVs will be a significant part of the automotive industry future, range extenders will be an important concept to be explored to provide a cost-effective, reliable, efficient, and dynamic solution to combat the range anxiety issue that consumers currently have.

The investigation and improvement of the cooling process of lithium-ion batteries (LIBs) used in battery electric vehicles (BEVs) and hybrid electric vehicles (HEVs) are required in order to achieve better performance and longer lifespan. In this manuscript, the temperature and velocity profiles of cooling plates used to cool down the large prismatic Graphite/LiFePO4 battery are presented using both laboratory testing and modeling techniques. Computed tomography (CT) scanning was utilized for the cooling plate, Detroit Engineering Products (DEP) MeshWorks 8.0 was used for meshing of the cooling plate, and STAR CCM+ was used for simulation. The numerical investigation was conducted for higher C-rates of 3C and 4C with different ambient temperatures. For the experimental work, three heat flux sensors were attached to the battery surface. Water was used as a coolant inside the cooling plate to cool down the battery. The mass flow rate at each channel was 0.000277677 kg/s. The k-ε model was then utilized to simulate the turbulent behaviour of the fluid in the cooling plate, and the thermal behaviour under constant current (CC) discharge was studied and validated with the experimental data. This study provides insight into thermal and flow characteristics of the coolant inside a cooing plate, which can be used for designing more efficient cooling plates.

Emissions from the transportation sector due to the consumption of fossil fuels by conventional vehicles have been a major cause of climate change. Hybrid electric vehicles (HEVs) are a cleaner solution to reduce the emissions caused by transportation, and well-designed HEVs can also outperform conventional vehicles. This study examines various powertrain configurations and components to design a hybrid powertrain that can satisfy the performance criteria given by the EcoCAR Mobility Challenge competition. These criteria include acceleration, braking, driving range, fuel economy, and emissions. A total of five different designs were investigated using MATLAB/Simulink simulations to obtain the necessary performance metrics. Only one powertrain design was found to satisfy all the performance targets. This design is a P4 hybrid powertrain consisting of a 2.5 L engine from General Motors, a 150 kW electric motor with an electronic drive unit (EDU) from American Axle Manufacturing, and a 133 kW battery pack from Hybrid Design Services.

The temperature and heat produced by lithium-ion (Li-ion) batteries in electric and hybrid vehicles is an important field of investigation as it determines the power, performance, and cycle life of the battery pack. This paper presented both laboratory data and simulation results at C-rates of 1C, 2C, 3C, and 4C at an ambient temperature of approximately 23 °C. During experiment thermocouples were placed on the surface of the battery. The thermal model assumed constant current discharge and was experimentally validated. It was observed that temperature increased with C-rates at both the surface and the tabs. We note that at 4C the battery temperature increased from 22 °C to 47.40 °C and the tab temperature increased from 22 °C to 52.94 °C. Overall, the simulation results showed that more heat was produced in the cathode than the anode, the primary source of heat was the electrolyte resistance, and the battery temperature was the highest near the tabs and in the internal space of the battery. Simulation of the lithium concentration within the battery showed that the lithium concentration was more uniform in the anode than in the cathode. These results can help the accurate thermal design and thermal management of Li-ion batteries.

With the increase in usage of electric vehicles (EVs), the demand for Lithium-ion (Li-ion) batteries is also on the rise. The battery management system (BMS) plays an important role in ensuring the safe and reliable operation of the battery in EVs. Sensor faults in the BMS can have significant negative effects on the system, hence it is important to diagnose these faults in real-time. Existing sensor fault detection and isolation (FDI) methods have not considered battery degradation. Degradation can affect the long-term performance of the battery and cause false fault detection. This paper presents a model-based sensor FDI scheme for a Li-ion cell undergoing degradation. The proposed scheme uses the recursive least squares (RLS) method to estimate the equivalent circuit model (ECM) parameters in real time. The estimated ECM parameters are put through weighted moving average (WMA) filters, and then cumulative sum control charts (CUSUM) are implemented to detect any significant deviation between unfiltered and filtered data, which would indicate a fault. The current and voltage faults are isolated based on the responsiveness of the parameters when each fault occurs. The proposed FDI scheme is then validated through conducting a series of experiments and simulations.