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Due to the recent progress in electrification, lithium-ion batteries have been widely used for electric and hybrid vehicles. Lithium-ion batteries exhibit high energy density and high-power density which are critical for vehicle development with high driving range enhanced performance. However, high battery temperature can negatively impact the battery life, performance, and energy delivery. In this paper, we developed and applied an analytical algorithm to estimate battery life-based vehicle level testing. A set of vehicle level tests were selected to represent customer duty cycles. Thermal degradation models are applied to estimate battery capacity loss during driving and park conditions. Due to the sensitivity of Lithium-Ion batteries to heat, the effect of high ambient temperatures throughout the year is considered as well. The analysis provides an estimate of the capacity loss due to calendar and cyclic effects throughout the battery life.

Modeling uncertainties pose a significant challenge in the development and deployment of model-based vehicle control systems. Most model- based automotive control systems require the use of a well estimated vehicle dynamics prediction model. The ability of first principles-based models to represent vehicle behavior becomes limited under complex scenarios due to underlying rigid physical assumptions. Additionally, the increasing complexity of these models to meet ever-increasing fidelity requirements presents challenges for obtaining analytical solutions as well as control design. Alternatively, deterministic data driven techniques including but not limited to deep neural networks, polynomial regression, Sparse Identification of Nonlinear Dynamics (SINDy) have been deployed for vehicle dynamics system identification and prediction.

In intelligent surveillance and reconnaissance (ISR) missions, multiple autonomous vehicles, such as unmanned ground vehicles (UGVs) or unmanned aerial vehicles (UAVs), coordinate with each other for efficient information gathering. These vehicles are usually battery-powered and require periodic charging when deployed for continuous monitoring that spans multiple hours or days. In this paper, we consider a mobile host charging vehicle that carries distributed sources, such as a generator, solar PV and battery, and is deployed in the area where the UAVs and UGVs operate. However, due to uncertainties, the state of charge of UAV and UGV batteries, their arrival time at the charging location and the charging duration cannot be predicted accurately.

Electric vehicles (EVs) are becoming increasingly recognized as an effective solution in the battle against climate change and reducing greenhouse gas emissions. Lithium-ion batteries have become the standard for energy storage in the automobile industry, widely used in EVs due to their superior characteristics compared to other batteries. The growing popularity of the Vehicle-to-grid (V2G) concept can be attributed to its surplus energy storage capacity, positive environmental impact, and the reliability and stability of the power grid. However, the increased utilization of the battery through these integrations can result in faster degradation and the need for replacement. As batteries are one of the most expensive components of EVs, the decision to deploy an EV in V2G operations may be uncertain due to the concerns of battery degradation from the owner’s perspective.

The push for environmental protection and sustainability has led to strict emission regulations for automotive manufacturers as evident in EURO VII and 2026 EPA requirements. The challenge lies in maintaining fuel efficiency and simultaneously reducing the carbon footprint while meeting future emission regulations. Alcohol (primarily methanol, ethanol, and butanol) and ether (dimethyl ether) fuels, owing to their comparable energy density to existing fuels, the comparative ease of handling, renewable production, and suitable emission characteristics may present an attractive drop-in replacement, fully or in part as an additive, to the gasoline/diesel fuels, without extensive modifications to the engine geometry. Additionally, lean and diluted combustion are well-researched pathways for efficiency improvement and reduction of engine-out emissions of modern engines.

In this paper, a control strategy for state of charge (SOC) allocation using navigation data for Hybrid Electric Vehicle (HEV) propulsion systems is proposed. This algorithm dynamically defines and adjusts a SOC target as a function of distance travelled on-line, thereby enabling proactive management of the energy store in the battery. The proposed approach incorporates variances in road resistance and adheres to geolocation constraints, including ultra-low emission zones (uLEZ). The anticipated advantages are particularly pronounced during scenarios involving extensive medium-to-long journeys characterized by abrupt topological changes or the necessity for exclusive electric vehicle (EV) mode operation. This novel solution stands to significantly enhance both drivability and fuel economy outcomes.

In order to improve the fuel economy for future high-efficiency spark ignition engines, the applications of advanced combustion strategies are considered to be beneficial with an overall lean and/or exhaust gas recirculation diluted cylinder charge. Stronger and more reliable ignition sources become more favorable under extreme lean/EGR conditions. Existing research indicates that the frequency of plasma restrikes increases with increased flow velocity and decreased discharge current level, and a higher discharge current can reduce the gap resistance and maintain the stretched plasma for a longer duration under flow conditions. An in-house developed current boost control system provides flexible control of the discharge current level and discharge duration. The current boost ignition system is based on a multi-coil system with a discharge current level of 180mA.

The majority of transportation systems have continued to be powered by the internal combustion engine and fossil fuels. Heavy-duty applications especially are reliant on diesel engines for their high brake efficiency, power density, and robustness. Although engineering developments have advanced engines towards significantly fewer emissions and higher efficiency, the use of fossil-derived diesel as fuel sets a fundamental threshold in the achievable total net carbon reduction. Dimethyl ether can be produced from various renewable feedstocks and has a high chemical reactivity making it suitable for heavy-duty applications, namely compression ignition direct injection engines. Literature shows the successful use of DME fuels in diesel engines without significant hardware modifications.

Off-road vehicles are increasingly adopting hybrid and electric powertrains for improved mobility, range, and energy efficiency. However, their cooling systems consume a significant amount of energy, affecting the vehicle’s operating range. This study develops a predictive controller for the battery thermal management system in an autonomous electric tracked off-road vehicle. By analyzing the system dynamics, the controller determines the optimal preview horizon and controller timestep. Sensitivity analysis is conducted to evaluate temperature tracking and energy consumption. Compared to an optimal controller without preview, the predictive controller reduces energy consumption by 55%. Additionally, a relationship between cooling system energy consumption and battery size is established. The impact of the preview horizon on energy consumption is examined, and a tradeoff between computational cost and optimality is identified.

Substantial effort has been devoted to utilizing homogeneous charge compression ignition (HCCI) to improve thermal efficiency and reduce emission pollutants in internal combustion engines. However, the uncertainty of ignition timing and limited operational range restrict further adoption for the industry. Using the spark-assisted compression ignition (SACI) technique has the advantage of using a spark event to control the combustion process. This study employs a rapid compression machine to characterize the ignition and combustion process of Dimethyl ether (DME) under engine-like background temperature and pressures and combustion regimes, including HCCI, SACI, and knocking onsite. The spark ignition timing was swept to ignite the mixture under various thermodynamic conditions. This investigation demonstrates the presence of four distinct combustion regimes, including detonation, strong end-gas autoignition, mild end-gas autoignition, and HCCI.

Electrification of off-road vehicle powertrains can increase mobility, improve energy efficiency, and enable new utility by providing high amounts of electrical power for auxiliary devices. These vehicles often operate in extreme temperature conditions at low ground speeds and high power levels while also having significant cooling airpath restrictions. The restrictions are a consequence of having grilles and/or louvers in the airpath to prevent damage from the operating environment. Moreover, the maximum operating temperatures for high voltage electrical components, like batteries, motors, and power-electronics, can be significantly lower than those of the internal combustion engine. Rejecting heat at a lower temperature gradient requires higher flow rates of air for effective heat exchange to the operating environment at extreme temperature conditions.

Compression ignition engines used in heavy-duty applications are typically powered by diesel fuel. The high energy density and feedstock abundance provide a continuing source for the immense energy demand. However, the heavy-duty transportation sector is challenged with lowering greenhouse gas and combustion by-product emissions, including carbon dioxide, nitrogen oxides, and particulate matter. The continuing development of engine management and combustion strategies has proven the ability to meet current regulations, particularly with higher fuel injection pressure. Nonetheless, a transition from diesel to a renewable alternative fuel source will play a significant role in reducing greenhouse gases while maintaining the convenience and energy density inherent in liquid fuels. Dimethyl ether is a versatile fuel that possesses combustion properties suitable for compression ignition engines and physical properties helpful for clean combustion.

Range anxiety and lack of adequate access to fast charging are proving to be important impediments to electric vehicle (EV) adoption. While many techniques to fast charging EV batteries (model-based & model-free) have been developed, they have focused on a single Lithium-ion cell. Extensions to battery packs are scarce, often considering simplified architectures (e.g., series-connected) for ease of modeling. Computational considerations have also restricted fast-charging simulations to small battery packs, e.g., four cells (for both series and parallel connected cells). Hence, in this paper, we pursue a model-free approach based on reinforcement learning (RL) to fast charge a large battery pack (comprising 444 cells). Each cell is characterized by an equivalent circuit model coupled with a second-order lumped thermal model to simulate the battery behavior. After training the underlying RL, the developed model will be straightforward to implement with low computational complexity.

High compression ratios are critical to increasing the efficiency of spark ignition engines, but the trend in downsized and down sped configurations has brought attention to the nominally low compression ratios used to avoid knock. Knock is an abnormal combustion event defined by the acoustic sound caused by end-gas auto-ignition ahead of the flame front. In order to avoid engine-damaging levels of knock, low compression ratios and retarded combustion phasing at high loads are used, both of which lower efficiency. Low carbon alternative fuels such as ethanol or water-based alcohol fuels combine strong chemical auto-ignition resistance with large charge cooling characteristics that can suppress knock and enable optimal combustion phasing, thus allowing an increase in the compression ratio.

This paper proposes a novel reconfigurable battery balancing topology and reinforcement learning-based intelligent balancing management system. The different degradations cause a significant loss of battery pack available capacity, as the pack power output relies on the weakest cell due to the relevant physical requirements. To handle this capacity drop issue, a reconfigurable battery topology is adopted to improve the usability of the heterogeneous battery. There are some existing battery reconfigurable topologies in the literature. However, these studies rely on the limited options of topology designs, and there is a lack of study on the reconfigurability of these designs and other possible new designs. Also, it is rare to find an optimal management system for the reconfigurable battery topology. To fill these research gaps, this paper explores existing battery reconfigurable topology designs and proposes a new reconfigurable topology for battery balancing.

Lithium Ion (Li-ion) batteries have emerged as the dominant technology for electric mobility due to their performance, stability, and long cycle life. Nevertheless, there are emerging environmental and economic issues from Li-ion batteries related to depleting critical resources and their potential shortage. This paper focuses on developing the Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) of a generic Li-ion battery pack with a Nickel-Manganese-Cobalt (NMC) cathode chemistry, being the most used, and a capacity of 95 kWh as an average between different carmakers. The LCA and LCC include all the relevant phases of the life cycle of the product. The costs related to the LCC assessment have been taken as secondary data. Lastly, the same system boundary has been chosen both for the LCA and LCC.

Compression ignition internal combustion engines provide unmatched power density levels, making them suitable for numerous applications including heavy-duty freight trucks, marine shipping, and off-road construction vehicles. Fossil-derived diesel fuel has dominated the energy source for CI engines over the last century. To mitigate the dependency on fossil fuels and lessen anthropogenic carbon released into the atmosphere within the transportation sector, it is critical to establish a fuel source which is produced from renewable energy sources, all the while matching the high-power density demands of various applications. Dimethyl ether (DME) has been used in non-combustion applications for several decades and is an attractive fuel for CI engines because of its high reactivity, superior volatility to diesel, and low soot tendency. A range of feedstock sources can produce DME via the catalysis of syngas.

The automotive industry is moving towards larger SUVs and also electrification is a need to meet the carbon neutrality target. As a result, we see an increase in overall gross vehicle weight (GVW), with the additional weight coming from the HV battery pack, electric powertrain, and other electrical systems. Tow-eye is an essential component that is provided with every vehicle to use for towing during an emergency vehicle breakdown. The tow-eye is usually connected to the retainer/sleeve available in the bumper system and towed using the recovery vehicle or other car with towing provision. Therefore, the tow-eye should meet the functional targets under standard operating conditions. This study is mainly for cars with bumper and tow-eye sleeves made of aluminum which is used in the most recent development of vehicles for weight-saving opportunities. Tow-eye systems in aluminum bumpers are designed to avoid any bending or buckling of the sleeve during towing for whatever the GVW loads.

Recently, the automotive industry has experienced rapid growth in powertrain electrification, with more and more battery electric vehicles (BEV) and hybrid electric vehicles being launched. Lithium-ion batteries play an important role due to their high energy capacity and power density, however they experience high heat generation in their operation, and if not properly cooled it can lead to serious safety issues as well as lower performance and durability. In that way, good prediction of a battery behavior is crucial for successful design and management. This paper presents a 1D electrochemical model development of a 144 Ah prismatic rolled cell using the GT-Autolion software with a pseudo 2D approach. The model correlation is done at cell level comparing model results and test data of cell open circuit voltage at different temperatures and voltage and temperature profile under different C-rates and ambient temperatures.

Spark ignition engines have low tailpipe criteria pollutants due to their stoichiometric operation and three-way catalysis and are highly controllable. However, one of their main drawbacks is that the compression ratio is low due to knock, which incurs an efficiency penalty. With a global push towards low-lifecycle-carbon renewable fuels, high-octane alternatives to gasoline such as ethanol are attractive options as fuels for spark ignition engines. Under premixed spark ignition operating conditions, ethanol can enable higher compression ratios than regular-grade gasoline due to its high octane number. The high cooling potential of high-ethanol content gasolines, like E85, or of ethanol-water blends, like hydrous ethanol, can be leveraged to further reduce knock and enable higher compression ratios as well as further downsizing and boosting to reduce frictional and throttling losses.