Numerical investigation of thermal soak within engine bay using lattice Boltzmann method.

A large amount of heat accumulates in the engine bay for a short time after the engine runs at high load and shuts down, that will lead to thermal damage and thermal fatigue caused by the temperature rise of some heat sensitive components. This paper uses an aero-thermal coupling approach to study the heat transfer problem in the engine bay of an SUV model under thermal soak conditions. Due to the transient characteristics of the heat transfer process, the natural transient CFD software developed based on the LBM method is used to study the engine bay heat transfer during the 400 s key-off soak process. The analysis reveals that convection and radiation are the main heat transfer modes in the early stage of hot immersion (0-120 s), and conduction only makes a significant contribution in contact with high temperature sources. The radiation and convection are the key contributors to heat transfer processes of engine bay during soak, but the efficiency of radiation heat transfer decreases with the increase of time, whereas the efficiency of convection heat transfer is not always reduced, it will increase and then decrease with the increase of time. The coupling method established can predict the thermal state in the engine bay well, and is in good agreement with the experimental results. The results show that the error in the engine coolant temperature is less than 1 °C, and the error in the temperature of the heat-sensitive components is less than 5 °C. Finally, the potential risks of thermal damage and thermal fatigue states were assessed, providing an important reference for the control design of cooling fan running time after key-off.

A deep learning based encoder-decoder model for speed planning of autonomous electric truck platoons.

Electric truck platooning offers a promising solution to extend the range of electric vehicles during long-haul operations. However, optimizing the platoon speed to ensure efficient energy utilization remains a critical challenge. The existing research on implementing data-driven solutions for truck platooning remains limited and implementing first principles solution is still a challenge. However, recognizing the resemblance of truck platoon data to a time series serves as a compelling motivation to explore suitable analytical techniques to address the problem. This paper presents a novel deep learning approach using a sequence-to-sequence encoder-decoder model to obtain the speed profile to be followed by an autonomous electric truck platoon considering various constraints such as the available state of charge (SOC) in the batteries along with other vehicles and road conditions while ensuring that the platoon is string stable. To ensure that the framework is suitable for long-haul highway operation, the model has been trained using various known highway drive cycles. Encoder-decoder models were trained and hyperparameter tuning was performed for the same. Finally, the most suitable model has been chosen for the application. For testing the entire framework, drive cycle/speed prediction corresponding to different desired SOC profiles has been presented. A case study showing the relevance of the proposed framework in predicting the drive cycle on various routes and its impact on taking critical policy decisions during the planning of electric truck platoons has also been presented. This study would help to efficiently plan the feasible routes for electric trucks considering multiple constraints such as battery capacity, expected discharge rate, charging infrastructure availability, route length/travel time, and other on-road operating conditions while also maintaining stability.

Comparative analysis of drive-cycles, speed limit violations, and emissions in two cities: Toronto and Beijing.

Driving behavior and speed enforcement are both important to road safety and affect vehicle exhaust emissions. Relationships between driving characteristics and safety or emissions have been assessed in multiple studies. However, there is scant information on whether safe driving also reduces emissions and how this relationship changes across urban areas. This study makes use of two similar GPS datasets collected in the metropolitan areas of Toronto and Beijing to conduct a comparative analysis of driving characteristics, speed limit violations, and emissions. Emissions for all trips were computed using the same emission rate database derived from a Portable Emissions Monitoring System (PEMS). We observe that the average speeds in the two cities are close to 25 km/h. In Toronto, the fraction of time spent at speeds over 80 km/h on expressways is 40% higher than in Beijing. We also note a higher level of accelerations in Toronto. The trips in Beijing have approximately 14%, 57%, 14%, and 21% lower emissions of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), and particle number (PN), respectively. Drivers in Toronto violate speed limits in 93% of their trips for 21% of trip travel time while the numbers for Beijing are 43% and 4%. These differences are not necessarily due to driving behavior, but rather to driving characteristics, which encompass the effects of behavior, road network design, traffic congestion, trip patterns, and speed enforcement. A scenario was evaluated by reconstructing drive-cycles to assess the effects of speed limit enforcement for trips where violations were detected. In Toronto, if obeying the speed limit, the mean trip travel time was estimated to increase by 1.8 min. In contrast, trip emissions of CO2, CO, NOx, and PN were found to decrease, on average, by 5.2%, 19.1%, 5.2%, and 2.9%, respectively. Speed limit enforcement can result in lower emissions, by reducing aggressive accelerations.

Evaluating the filtration efficiency of close-coupled catalyzed gasoline particulate filter (cGPF) over the WLTC and simulated RDE cycles.

Gasoline particulate filter (GPF) is a cost-effective solution to particle number emissions from gasoline direct injection vehicles. Filtration efficiency, as a key parameter of GPF, was usually assessed at chassis level over regulatory drive cycles. However, the promulgation of real driving emission (RDE) requirements in the EU and Chinese regulations necessitates evaluations based on non-legislative cycles to guarantee the on-road emissions are compliant to regulatory requirements. In this research, two aggressive drive cycles, RTS95 at 23degC and modified RDE at 0degC, were complemented to the WLTC to evaluate the filtration efficiency of a catalyzed GPF (cGPF) in fresh conditions to obtain the so-called "worst-case" filtration efficiency. In the WLTC, RTS95, and simulated RDE tests, the filtration efficiency of the test cGPF was 51.1%, 41.3%, and 85.1% respectively. In the simulated RDE test, the test cGPF filtrated solid particles with a diameter above 23 nm and 10 nm at a similar efficiency. Increased filtration efficiency with heavier soot load could offset the relatively low filtration efficiency in cold-start and warm-up durations, hence the filtration efficiency for a clean cGPF showed higher sensitivity to cycle length over driving dynamics and testing temperature. In acceleration events with cGPF mounted, the particle diameter where number concentration peaked decreased as the engine warmed up. In deceleration events, bimodal and trimodal particle number size distributions with much lower concentrations were observed.

Real-world emissions and fuel consumption of gasoline and hybrid light duty vehicles under local and regulatory drive cycles.

In this study, driving trajectory data from private vehicles were collected in Toronto, Canada to construct representative local drive cycles. In addition, real-driving emission testing for four conventional gasoline vehicles (ICEV) and one hybrid electric vehicle (HEV) was conducted in the same region using a Portable Emissions Measurement System. Instantaneous fuel consumption and emissions of Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Particle Number (PN) were measured. The results for all vehicles indicate that the acceleration state tends to generate the highest emissions and fuel consumption with the largest variation due to higher power demand. When accelerating, the HEV was observed to generate four times more CO emissions than some ICEVs. Instantaneous fuel consumption and emissions were analyzed as a function of operating modes to estimate the fuel efficiency (FE) and emission factors (EF) associated with six representative local drive cycles and four regulatory drive cycles. With most regulatory drive cycles, vehicles can reach the labeled FE and EPA emission limits, except under the New York City Cycle with frequent stop-and-go conditions. In contrast, except for highway cycles, the FE of Toronto-specific drive cycles can hardly meet the labeled values. CO EFs of the HEV can be higher than ICEVs, while it is lower than the emission limit by 42% on average. ICEVs may exceed the CO limit by 131% under local highway cycles, while they can violate NOx and PN limits under local arterial cycles. The result of this study emphasizes the importance of local drive cycles and real driving emission tests.

Naturalistic drive cycle synthesis for pickup trucks.

PROBLEM: Future pick-up trucks are meeting much stricter fuel economy and exhaust emission standards. Design tradeoffs will have to be carefully evaluated to satisfy consumer expectations within the regulatory and cost constraints. Boundary conditions will obviously be critical for decision making: thus, the understanding of how customers are driving in naturalistic settings is indispensable. Federal driving schedules, while critical for certification, do not capture the richness of naturalistic cycles, particularly the aggressive maneuvers that often shape consumer perception of performance. While there are databases with large number of drive cycles, applying all of them directly in the design process is impractical. Therefore, representative drive cycles that capture the essence of the naturalistic driving should be synthesized from naturalistic driving data. METHOD: Naturalistic drive cycles are firstly categorized by investigating their micro-trip components, defined as driving activities between successive stops. Micro-trips are expected to characterize underlying local traffic conditions, and separate different driving patterns. Next, the transitions from one vehicle state to another vehicle state in each cycle category are captured with Transition Probability Matrix (TPM). Candidate drive cycles can subsequently be synthesized using Markov Chain based on TPMs for each category. Finally, representative synthetic drive cycles are selected through assessment of significant cycle metrics to identify the ones with smallest errors. SUMMARY: This paper provides a framework for synthesis of representative drive cycles from naturalistic driving data, which can subsequently be used for efficient optimization of design or control of pick-up truck powertrains. IMPACT ON INDUSTRY: Manufacturers will benefit from representative drive cycles in several aspects, including quick assessments of vehicle performance and energy consumption in simulations, component sizing and design, optimization of control strategies, and vehicle testing under real-world conditions. This is in contrast to using federal certification test cycles, which were never intended to capture pickup truck segment.