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The initial cost of battery electric vehicles (BEVs) is higher than internal combustion engine-powered vehicles (ICEVs) due to expensive batteries. Various factors affect the total cost of ownership of a vehicle. In India, consumers are concerned with a vehicle’s initial purchase cost and prefer owning an economical vehicle. The higher cost and shorter range of BEVs compared to ICEVs severely limit their penetration in the Indian market. However, government subsidies and incentives support BEVs. The total cost of ownership assessment is used to evaluate the entire cost of a vehicle to find the most economical option among different powertrains. This study compares 2W (two-wheeler) and 4W (four-wheeler) BEV’s cost vis-à-vis equivalent ICEVs in Delhi and Mumbai. The cost analysis assesses the current and future government policies to promote BEVs. Two assumed policies were applied to estimate future scenarios.

Abstract A multiple output dynamically adjustable capacity system (MODACS) is developed to provide multiple voltage output levels while supporting varying power loads by switching multiple battery strings between serial and parallel connections. Each module of the system can service either a low voltage bus by placing its strings in parallel or a high voltage bus with its strings in series. Since MODACS contains several such modules, it can produce multiple voltages simultaneously. By switching which strings and modules service the different output rails and by varying the connection strategy over time, the system can balance the states of charge (SOC) of the strings and modules. A model predictive control (MPC) algorithm is formulated to accomplish this balancing. MODACS operates in various power modes, each of which imposes unique constraints on switching between configurations.

Abstract Battery electric vehicles (EVs) bring significant benefits in reducing the carbon footprint of fossil fuels and new opportunities for adopting renewable energy. Because of their high-energy density and long cycle life, lithium-ion batteries (LIBs) are dominating the battery market, and the consumer demand for LIB-powered EVs is expected to continue to boom in the next decade. However, the chemistry used in LIBs is still vulnerable to experiencing thermal runaway, especially in harsh working conditions. Furthermore, as LIB technology moves to larger scales of power and energy, the safety issues turn out to be the most intolerable pain point of its application in EVs. Its failure could result in the release of toxic gases, fire, and even explosions, causing catastrophic damage to life and property. Vehicle fires are an often-overlooked part of the fire problem. Fire protection and EV safety fall into different disciplines.

Abstract The emerging use of rechargeable batteries in electric and hybrid electric vehicles and distributed energy systems, and accidental fires involving batteries, has heightened the need for a methodology to determine the root cause of the fire. When a fire involving batteries takes place, investigators and engineers need to ascertain the role of batteries in that fire. Just as with fire in general, investigators need a framework for determining the role that is systematic, reliant on collection and careful analysis of forensic evidence, and based on the scientific method of inquiry. This article presents a systematic scientific process to analyze batteries that have been involved in a fire. It involves examining Li-ion cells of varying construction, using a systematic process that includes visual inspection, x-ray, CT scan, and possibly elemental analysis and testing of exemplars.

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Abstract Lithium-ion (LI) batteries are widely used to power electric vehicles (EVs), owing to their high charge density, to minimize the environmental pollution caused by fossil fuel-based engines. It experiences an enormous amount of heat generation during charging and discharging cycles, which results in higher operating temperatures and thermal nonuniformity. This affects performance, useful battery life, and operating costs. This can be mitigated by an effective battery thermal management system (BTMS) to dissipate the heat there by safeguarding the battery from adverse thermal effects and ensuring high performance, safety, and longevity of the battery.

Abstract The global automotive industry’s shift toward electrification hinges on battery electric vehicles (BEV) having a reduced total cost of ownership compared to traditional vehicles. Although BEVs exhibit lower operational costs than internal combustion engine (ICE) vehicles, their initial acquisition expense is higher due to expensive battery packs. This study evaluates total ownership costs for four vehicle types: traditional ICE-based car, BEV, split-power hybrid, and plug-in hybrid. Unlike previous analyses comparing production vehicles, this study employs a hypothetical sedan with different powertrains for a more equitable assessment. The study uses a drive-cycle model grounded in fundamental vehicle dynamics to determine the fuel and electricity consumption for each vehicle in highway and urban conditions. These figures serve a Monte Carlo simulation, projecting a vehicle’s operating cost over a decade based on average daily distance and highway driving percentage.

Abstract The swift progress of electric vehicles (EVs) and hybrid electric vehicles (HEVs) has driven advancements in battery management systems (BMS). However, optimizing the algorithms that drive these systems remains a challenge. Recent breakthroughs in data science, particularly in deep learning networks, have introduced the long–short-term memory (LSTM) network as a solution for sequence problems. While graphics processing units (GPUs) and application-specific integrated circuits (ASICs) have been used to improve performance in AI-based applications, field-programmable gate arrays (FPGAs) have gained popularity due to their low power consumption and high-speed acceleration, making them ideal for artificial intelligence (AI) implementation. One of the critical components of EVs and HEVs is the BMS, which performs operations to optimize the use of energy stored in lithium-ion batteries (LiBs).

Abstract One of the key problems of battery electric vehicles is the risk of severe range reduction in winter conditions. Technologies such as heat pump systems can help to mitigate such effects, but finding adequate heat sources for the heat pump sometimes can be a problem, too. In cold ambient conditions below −10°C and for a cold-soaked vehicle this can become a limiting factor. Storing waste heat or excess cold when it is generated and releasing it to the vehicle thermal management system later can reduce peak thermal requirements to more manageable average levels. In related architectures it is not always necessary to replace existing electric heaters or conventional air-conditioning systems. Sometimes it is more efficient to keep them and support them, instead. Accordingly, we show, how latent heat storage can be used to increase the efficiency of existing, well-established heating and cooling technologies without replacing them.

Abstract Connected fuel cell vehicles (C-FCVs) have gained increasing attention for solving traffic congestion and environmental pollution issues. To reduce operational costs, increase driving range, and improve driver comfort, simultaneously optimizing C-FCV speed trajectories and powertrain operation is a promising approach. Nevertheless, this remains difficult due to heavy computational demands and the complexity of real-time traffic scenarios. To resolve these issues, this article proposes a two-level eco-driving strategy consisting of speed planning and energy management layers. In the top layer, the speed planning predictor first predicts dynamic traffic constraints using the long short-term memory (LSTM) model. Second, a model predictive control (MPC) framework optimizes speed trajectories under dynamic traffic constraints, considering hydrogen consumption, ride comfort, and traffic flow efficiency.

Abstract Due to the limitations of current battery manufacturing processes, integration technology, and operating conditions, the large-scale application of lithium-ion batteries in the fields of energy storage and electric vehicles has led to an increasing number of fire accidents. When a lithium-ion battery undergoes thermal runaway, it undergoes complex and violent reactions, which can lead to combustion and explosion, accompanied by the production of a large amount of flammable and toxic gases. These flammable gases continue to undergo chemical reactions at high temperatures, producing complex secondary combustion products. This article systematically summarizes the gas generation characteristics of different types and states of batteries under different thermal runaway triggering conditions. And based on this, proposes the key research directions for the gas generation characteristics of lithium-ion batteries.

Abstract The exponentially growing electrification market is driving demand for lithium-ion batteries (LIBs) with high performance. However, LIB thermal runaway events are one of the unresolved safety concerns. Thermal runaway of an individual LIB can cause a chain reaction of runaway events in nearby cells, or thermal propagation, potentially causing significant battery fires and explosions. Such a safety issue of LIBs raises a huge concern for a variety of applications including electric vehicles (EVs). With increasingly higher energy-density battery technologies being implemented in EVs to enable a longer driving mileage per charge, LIB safety enhancement is becoming critical for customers. This comprehensive review offers an encompassing overview of prevalent abuse conditions, the thermal event processes and mechanisms associated with LIBs, and various strategies for suppression, prevention, and mitigation.

Abstract Compared with traditional internal combustion engine vehicles, electric vehicles still have shortfall in driving range and energy replenishment time. In order to continuously improve the driving range of electric vehicles, the high-nickel/silicon-carbon lithium-ion battery with high energy density is a promising industrialized application route. However, with the increase of battery energy density, the heat generation of battery usually increases, which will inevitably bring greater heat dissipation problems to the battery thermal management system. To design a good thermal management system, the first thing is to accurately measure and deeply understand the heat generation characteristics of the battery. In this work, the heat generation behavior of a high-nickel LiNi0.8Co0.1Mn0.1O2/silicon-carbon pouch-type battery under different operating conditions were tested by an isothermal battery calorimeter.

Abstract Policy makers generally favor all-electric vehicles over hybrid-electric vehicles because of greater unit effectiveness in reducing carbon emissions. Since both systems use lithium-ion batteries, global demand for batteries is projected to grow 10-fold by 2030. If any step in the global battery supply-chain experiences bottlenecks, shortages can occur. We use a novel cost-effectiveness metric, carbon reduction per unit of battery capacity consumed, to rank emissions reductions accomplished by, alternatively, eight plug-in hybrid-electric vehicles, 75 hybrid-electric vehicles, and 230 mild hybrid-electric vehicles, which have the same total battery capacity as one all-electric vehicle. Our main finding, although counterintuitive, is that, with limited battery supplies, larger reductions in carbon emissions can be accomplished by hybrids than by all-electric vehicles.

Abstract The production of alternative clean energy vehicles provides a sustainable solution for the transportation industry. An effective battery cooling system is required for the safe operation of electric vehicles throughout their lifetime. However, in the pursuit of this technological change, issues of battery overheating leading to thermal runaways (TRs) are seen as major concerns. For example, lithium (Li)-ion batteries of electric vehicles can lose thermal stability owing to electrochemical damage due to overheating of the core. In this study, we look at how a different melting point phase change material (PCM) can be used to delay the TR trigger point of a high-energy density lithium-iron phosphate (LiFePO4) chemistry 86 Amp-hour (Ah) battery. The battery is investigated under thermal abuse conditions by wrapping heater foil and operating it at 500-W constant heat conditions until the battery runs in an abuse scenario.

Abstract Electrical connection plays an important role in not only direct heat transfer, but also the transmission of electric energy and the transformation of electrothermal effect in the parallel battery modules. The thermal propagation simulation research model was established based on the equivalent circuit and thermal runaway experimental research of a module formed by four parallel cells, which superimposes the discharge process and corresponding electrothermal effect in the process of thermal runaway and thermal propagation, and pays attention to the SoC (state of charge) state and corresponding thermal runaway energy release changes after cell discharged. Thermal runaway and propagation characteristics of parallel and non-parallel battery modules were analyzed and results showed that without considering the energy exchange between the system and the environment, the parallel battery module will accelerate the process of thermal propagation.

Abstract The ASTM D130 was first issued in 1922 as a tentative standard for the detection of corrosive sulfur in gasoline. A clean copper strip was immersed in a sample of gasoline for three hours at 50°C with any corrosion or discoloration taken to indicate the presence of corrosive sulfur. Since that time, the method has undergone many revisions and has been applied to many petroleum products. Today, the ASTM D130 standard is the leading method used to determine the corrosiveness of various fuels, lubricants, and other hydrocarbon-based solutions to copper. The end-of-test strips are ranked using the ASTM Copper Strip Corrosion Standard Adjunct, a colored reproduction of copper strips characteristic of various degrees of sulfur-induced tarnish and corrosion, first introduced in 1954. This pragmatic approach to assessing potential corrosion concerns with copper hardware has served various industries well for a century.

Abstract The world community is constantly and rapidly moving toward the search for alternative and ecologically clean energy sources, including for transport, and Russia’s war against Ukraine only intensified and accelerated such processes. This trend in transport is reflected in the spread of battery-powered electric vehicles (BEVs) with zero emission of harmful gases. Electric cars are experiencing a rapid increase in numbers, accompanied by the emergence of lesser-known risks. Among these hazards are the occurrence of fires in electric vehicles, primarily caused by component failures, notably the widely prevalent lithium-ion batteries. Fires of such cars have a different character compared to fires of vehicles powered by an internal combustion engine vehicle (ICEV). In this study, using the fire dynamics simulator developed by the National Institute of Standards and Technology, a BEV fire was simulated on the example of the Tesla Model S.

Abstract Electric vehicles (EVs) suffer from long charging time and inconvenient charging due to limited charging stations, which are the main causes of drivers’ range anxiety. Real-time and accurate driving range prediction can help drivers plan journeys, alleviate range anxiety, and promote EV development. However, predicting the EV driving range is challenging due to different weather, road conditions, driver habits, and limited available data. To address this issue, this article proposes a novel digital twin-based driving range prediction method. First, a one-year real-world EV dataset in Beijing is utilized. Detailed feature selection is conducted for the dataset, and six key features are extracted: battery SOC, consumed battery SOC, battery total voltage, battery maximum cell voltage, battery minimum cell voltage, and mileage already driven. Then, a random forest method is used to train the EV driving range prediction model using the features described earlier.

Abstract The development of predictive maintenance has become one of the most important drivers of innovation, not only in the maritime industry. The proliferation of on-board and remote sensing and diagnostic systems is creating many new opportunities to reduce maintenance costs and increase operational stability. By predicting impending system faults and failures, proactive maintenance can be initiated to prevent loss of seaworthiness or operability. The motivation of this study is to optimize predictive maintenance in the maritime industry by determining the minimum useful remaining lead-acid battery capacity measurement frequency required to achieve cost-efficiency and desired prognostic performance in a remaining battery capacity indication system. The research seeks to balance operational stability and cost-effectiveness, providing valuable insight into the practical considerations and potential benefits of predictive maintenance.