Research on Intelligent Shift Strategy for Heavy Vehicles Based on Predictive Information

<div class="section abstract"><div class="htmlview paragraph">The gear shifting strategies strongly influence the vehicle fuel consumption because they change the powertrain inertia and the engine operation point. The literature normally presents gear shifting strategies based on the engine power and torque to improve the vehicle acceleration performance. Strategies based on fuel economy are difficult to determine due to a large number of factors that influence the engine behavior such as the available transmission ratios, required acceleration and vehicle speed. In this paper it was evaluated the influence of the addition of more gear ratios in the vehicle gearbox, which initially contains five available gear ratios. For each proposed gearbox configuration, the gear shifting strategy was optimized through an algorithm developed to improve the engine fuel consumption in the Brazilian standard urban driving cycle NBR6601. The simulations were performed by means of co-simulation between the multibody dynamics software Adams<sup>TM</sup> (where is located the model based on a conventional 1.0L vehicle) and Matlab/Simulink<sup>TM</sup> (where is implemented the vehicle longitudinal dynamics equations). The optimization algorithm elaborated in Matlab<sup>TM</sup> utilizes previous simulations results, stored in a database, to compare them with the results obtained performing new strategies.</div></div>

The control strategy of AMT (Automated Mechanical Transmission) is one of the most popular and heated-discussed problems in the research of automobile transmission. To realize the reasonable control on a vehicle, it is significant to meet the need of dynamics and economy, but it is more important to work out intelligence gearshift and control strategy accord with driver's operating intentions and realistic driving conditions. This paper builds up a tracing model of driver's intentions in the guidance of fuzzy control theory. The model has self-adaptation fuzzy features of tracing throttle and other parameters according to a vehicle's driving parameter and specific road status, and improves the function of fuzzy controller greatly. Furthermore, establish the shift decision-making based on ANFIS (Adaptive Network based Fuzzy Inference System) self-adaptation neural-fuzzy inference system. Design intelligent control shift strategies system of AMT according to summary driver experience, which can coordinate driver's behavior and wish. Thus, the vehicle's performances are improved greatly.

Gear shifting strategy of vehicle is important aid for the acquisition of dynamic performance and high economy. A dynamic programming (DP) algorithm is used to optimize the gear shifting schedule for off‐road vehicle by using an objective function that weighs fuel use and trip time. The optimization is accomplished through discrete dynamic programming and a trade‐off between trip time and fuel consumption is analyzed. By using concave and convex surface road as road profile, an optimal gear shifting strategy is used to control the longitudinal behavior of the vehicle. Simulation results show that the trip time can be reduced by powerful gear shifting strategy and fuel consumption can achieve high economy with economical gear shifting strategy in different initial conditions and route cases.

<div class="section abstract"><div class="htmlview paragraph">The evaluations of ride comfort in gear shifting have been known as one of the dominant factors for vehicle quality assessment. However, those factors have not been assessed and analyzed objectively in-depth in conjunction and integration with general ride and handling parameters. In recent, the criteria set by customer have changed on account of heightened expectations, resulting in a growing demand for enhanced ride comfort. the quality of gear shifting experienced by a customer is evaluated subjectively on road leads to difficulty in arriving inferences and taking decisions due to variation in responses of people on the same situation.</div><div class="htmlview paragraph">This study is involved the process of conversion from subjective to objective assessment on gear shifting quality by identifying the objective parameters for contributing the quality of gear shifting feel.</div><div class="htmlview paragraph">Measuring and analyzing those parameters like lever travel, lever effort, and noise while shifting during dynamic condition at vehicle level on road with the use of digital data capturing by data acquisition from the sensors like Strain gauge, Accelerometer and potentiometers. The captured data has been analyzed and corelated to subjective ratings between intended vehicles.</div><div class="htmlview paragraph">The same experiment has done at lab with the use of dynamometer and robotic arms, data are captured and correlated with on road dynamic measurement. As a result, the comparative report and experimental results are discussed in terms of gear shifting quality by triangular approach as follows: subjective evaluation - objective evaluation @ on road – Objective evaluation @ lab.</div></div>

Shift schedule is crucial in improving the dynamic and economic performance of electric vehicles (EVs) equipped with automatic mechanical transmission (AMT). As the driver, vehicle, and road constitute a closed-loop inseparable system, identifying the states of both vehicle and road is fundamental to realizing optimal shift schedule. However, the existing shift strategies neglect the coupling relationship of multiple parameters to the shift strategy. To minimize this gap, this paper presents a novel multi-parameter shift schedule based on model predictive control. Firstly, cubature Kalman filters (CKF) algorithm is employed to accurately estimate vehicle quality and road slope, which could improve the energy economy of EVs. Secondly, an artificial neural network (ANN) is adopted to forecast the compound future short horizon driving conditions, which contains the perdition information of vehicle velocity and road slope. Meanwhile, the AMT predictive shift schedule based on the above estimated and forecast information is constructed, which used dynamic programming to optimize in the rolling horizon. Simulation study results indicate that the ANN-based predictive approach shows better performance on accuracy and robustness than that of Markov chain, and the electricity consumption over China typical urban driving cycle (CTUDC) is further reduced by 6.79% than that of multi-parameter rule-based shift schedule.

<div class="section abstract"> <div class="htmlview paragraph">The purpose of this Recommended Practice is to verify that vehicles and/or components are capable of communicating a required set of information, in accordance with the diagnostic test messages specified in SAE J1939-73, to fulfill the off-board diagnostic tool interface requirements contained in the government regulations cited below.</div> <div class="htmlview paragraph">This document describes the tests, test methods, and results for verifying diagnostics communication from an off board diagnostic tool (i.e. scan tool) to a vehicle and/or component. SAE members have generated this document to serve as a guide for testing vehicles for compliance with ARB and other requirements for emissions-related on-board diagnostic (OBD) functions for heavy duty engines used in medium and heavy duty vehicles.</div> <div class="htmlview paragraph">The development of HD OBD regulations by US EPA and California’s Air Resources Board (ARB) require that diagnostic message services are exercised to evaluate diagnostic communication standardization requirements on production vehicles. The December 2008 publication of J1939-84 described a test process for EURO IV and EURO V engine emissions and diagnostics regulations. The December 2010 version of the document added a test plan and procedures for ARB and US EPA HD OBD requirements with emphasis on 13 CCR 1971.1 (l)(1) Verification of Standardized Requirements. The user should reference the summary provided by SAE J1939-73 Table 1 and Table 2 for OBD compliance support.</div></div>

<div class="section abstract"><div class="htmlview paragraph">In recent years the 2-wheelers engines companies have been focused on increasing the overall engine efficiency, that can be achieved amongst other by engine down-speeding, engine down-sizing and by reducing the frictions; however, to maintain or improve vehicle performance, it is necessary to provide a corresponding increase in specific power.</div><div class="htmlview paragraph">In accordance with these trends, the studied approaches and methodologies have been exploited, during the development of the new Piaggio small scooter engine.</div><div class="htmlview paragraph">In this work a multi-objective analysis has been applied to the valve train system design, in order to optimize the engine performance in terms of friction reduction, power curve and timing system response. Along with this optimization methodology, a robust design has been studied to make the peak cranking compression pressure insensitive to the engine starting device working. These calculation methodologies have been followed using commercial software as GT-SUITE® code and the modeFRONTIER® platform for the multi-objective optimization analysis.</div><div class="htmlview paragraph">The several configurations investigated have allowed to reach the desired trade-off between the appropriate and efficient valve train behavior and the engine performances.</div><div class="htmlview paragraph">The global engine friction reduction has been obtained thanks to low resisting cam torque profiles, a specific valve springs set and a new compression releaser device (during engine starting phase).</div><div class="htmlview paragraph">The results have been further validated by an experimental campaign carried out on the test bench for engine performance and on the specific cold test bench to evaluate friction losses.</div></div>

<div class="htmlview paragraph">The objective of the Project is to develop an optimized lead-acid battery solution for HEVs based on a novel, individual, spirally wound valve-regulated lead-acid 2V cell optimized for HEV use and low variability. This cell will be used as a building block for the development of a complete battery pack that is managed at the cell level. Following bench testing, this battery pack will be thoroughly evaluated by substituting it for the NiMH pack in a Honda Insight.</div> <div class="htmlview paragraph">The paper covers the first half of the 3-year project and will describe work carried out in the following areas:</div> <div class="htmlview paragraph"> <ul class="list disc"> <li class="list-item"><div class="htmlview paragraph">Development of cell and battery testing facilities and identification of mechanisms causing cell lifetime scatter.</div> </li> <li class="list-item"><div class="htmlview paragraph">The design and development of the prototype double-ended cell.</div> </li> <li class="list-item"><div class="htmlview paragraph">The development of the battery pack specification and pack design.</div> </li> <li class="list-item"><div class="htmlview paragraph">The development of the battery management system.</div> </li> </ul> </div> <div class="htmlview paragraph">The paper will also give details of the test results obtained on the demonstration vehicle with its original NiMH battery. Arising from these tests, there will be discussion of the merits of bench testing with a cycle derived from ‘real vehicle’ data as compared with the PNGV or Eucar power assist life cycle tests.</div>

<div class="section abstract"><div class="htmlview paragraph">In order to improve power density, the majority of diesel engines have intake manifold pressures above atmospheric conditions. This allows for the introduction of more fuel, which results in more power. Except for a few applications, these engines receive charged air from a turbocharger. The turbocharger develops boost by converting exhaust gas energy into power. This power is then used to compress the intake charge.</div><div class="htmlview paragraph">The medium- and heavy-duty engine markets have both stringent regulatory targets and customer demand for improved fuel efficiency. Two approaches used to meet fuel efficiency targets are downspeeding and downsizing. Until now, the industry has adapted to the turbocharger lag experienced during a transient acceleration event. This performance deficiency is severely exaggerated when the displacement and speed of an engine are reduced. The solution proposed to improving fuel economy, while maintaining equivalent performance, is supercharging.</div><div class="htmlview paragraph">Eaton's TVS® supercharger is a positive displacement pump with significantly improved efficiencies over prior models. Unlike a turbocharger, which relies on exhaust gas energy to create boost, a supercharger is directly coupled to the engine. Therefore, the supercharger speed increases proportionally with engine speed. This greatly reduces the lag that is present in a turbocharged engine. Addition of a supercharger, as this paper will show, dramatically improves low speed engine performance. This enables greater engine downspeeding that was previously not feasible with a turbocharged engine.</div><div class="htmlview paragraph">This paper explores the fuel economy benefits of combining a supercharger with a turbocharger on a diesel engine. The fuel economy and performance improvements enabled by supercharging are dependent on application, drive cycle, and engine displacement. It also reviews the simulation results of three distinct engine sizes and applications exercised over vehicle appropriate drive cycles. The effect of boosting system configuration, downspeeding, exhaust gas recirculation (EGR) configuration, and supercharger clutching on fuel economy were examined. All of the simulations provided a fuel economy benefit over the baseline turbocharger-only engines. Fuel economy was improved, while maintaining or improving vehicle performance over the baseline. Efficiency improvements in excess of 14% were demonstrated over a transient drive cycle.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Gear shifting effort or force especially in manual transmission has been one of the key factors for subjective assessment in passenger vehicle segment. An optimum effort to shift into the gears creates a big difference in overall assessment of the vehicle.</div><div class="htmlview paragraph">The gear shifting effort travels through the transmission shifting system that helps driver to shift between the different available gears as per the torque and speed demand. The shifting system is further divided into two sub-systems. 1. Peripheral system [Gear Shift Lever with knob and shift Cable Assembly] and Shift system inside the transmission [Shift Tower Assembly, Shift Forks, Hub and sleeve Assembly with keys, Gear Cones and Synchronizer Rings etc.] [<span class="xref">1</span>]. Both the systems have their own role in overall gear shifting effort. There has been work already done on evaluation of the transmission shifting system as whole for gear shifting effort with typical test bench layouts. Also, work has been on assessment of life of the synchronizer ring as standalone.</div><div class="htmlview paragraph">Current paper explains the work done on the development of a methodology to evaluate the synchronizer ring assembly on a test bench that accommodates only a Synchronizer ring and Gear cone set for evaluation, and the results of which can be correlated with bench testing of the complete synchronizer ring evaluation along-with Transmission shifting system.</div></div>

<div class="section abstract"><div class="htmlview paragraph">The objective evaluation of occupant comfort is a complex task where numerous aspects such as posture, pressure distribution, internal tissue loads, handling of steering wheel or gear shift have to be taken into consideration. Currently the standard evaluation procedures are hardware tests with human subjects, who are sensitive to all these aspects. However, the reproducibility of subjective tests for the comparison of design variants is a questionable issue and the costs for each test cycle with new prototypes are very high.</div><div class="htmlview paragraph">As an alternative, numerical approaches using human body models such as AnyBody [<span class="xref">1</span>], CASIMIR [<span class="xref">2</span>] or RAMSIS [<span class="xref">3</span>] are applied. Here the issue of reproducibility does not exist and only little effort is required to investigate new setups. However, the disadvantage is that each approach focuses only on one specific aspect of occupant comfort, while in reality the emotions of the occupant are always a combination of all impressions. Furthermore, these kinds of numerical approaches can only provide mechanical quantities like seat pressure or joint angles. Defining the relation between these objective values and subjective rankings and defining optimization targets is a great challenge.</div><div class="htmlview paragraph">For a comprehensive virtual comfort evaluation, the interaction of the different numerical approaches is essential. This is the goal of the research project UDASim [<span class="xref">4</span>], funded by the German government. Here the transfer of the posture between the human body models was a first goal to ensure consistency between the investigated setups. A second step was an iterative simulation procedure. In this procedure, a CAD-based setup is combined with a mechanical setup, which allows the simultaneous assessment of packaging and seat design.</div></div>

<div class="section abstract"><div class="htmlview paragraph">The brass synchronizers are not resistant to abusive conditions of gearbox operations, but they are very durable and cheap when used on their favorable material property working limit. The main failure which can occur in the gearbox due to the synchronizer is crash noise. During gear shifting the gear crash will create high discomfort for the driver and must apply high force to change the gears. The main factors which contribute to the crash phenomenon are the insufficient coefficient of friction, high drag in the system, and high wear rate of the synchronizer rings before the intended design life of the synchronizer.</div><div class="htmlview paragraph">The brass synchronizers were tested on the SSP-180, ZF synchronizer test rig to know the effect of the synchronizer performance parameters like the coefficient of friction, sleeve force, slipping time as well as durability parameters like wear rate when the operating temperature of the oil is changed. This study will help to predict the impulse behavior, surface pressure, and power of the brass material and oil for enhancing the endurance life of the synchronizer.</div><div class="htmlview paragraph">The effect of gearbox oil drag induced on the synchronizer system in different temperature conditions is also studied in this research work. This helps to reduce the contribution of the drivetrain losses of the gearbox, and it will also give the better shift quality. The wear rate analysis makes it possible to predict the warranty life of the gearbox in the design phase itself. In the case of shift quality, it was identified that the presence of the drag is creating the speed difference between the gear and synchronizer hub. This is creating a gear clash noise when shifted at a slow speed. The data was captured at low temperatures and high temperatures. It was found that the increase in the temperature helps in reducing the drag locally which allows smoother gear shift without speed difference.</div></div>

<div class="section abstract"><div class="htmlview paragraph">This study presents a novel approach for predicting fuel consumption in heavy-duty vehicles using a Machine Learning-based model, which is based on feedforward neural network (FFNN). The model is designed to enhance real-time vehicle monitoring, optimize route planning, and reduce both operational costs and environmental impact, making it particularly suitable for fleet management applications. Unlike traditional physics-based approaches, the FFNN relies solely on a refined selection of input variables, including vehicle speed, acceleration, altitude, road slope, ambient temperature, and engine power. Additionally, vehicle mass is estimated using a methodology presented elsewhere and is included as an input for a better generalization of the consumption model. This parameter significantly impacts fuel consumption and is particularly challenging to obtain for heavy-duty vehicles. Engine power is derived from both engine torque and speed (RPM), ensuring a direct relationship with fuel consumption while keeping computational complexity low.</div><div class="htmlview paragraph">Experimental data were collected from a fleet of heavy-duty trucks under real-world operating conditions. In particular, the study focuses on a turbocharged diesel truck with a maximum power output of 353 kW. The acquisition system is based on an On-Board Unit (OBU) featuring CANbus connectivity, GPS tracking and 4G/LTE-5G communication. The OBU enables continuous logging of vehicle and engine parameters over each mission. Additional road data, such as altitude and slope, were obtained from external mapping services.</div><div class="htmlview paragraph">For the purpose of FFNN development, the training dataset was compiled from approximately 10 different routes, capturing diverse driving conditions, while validation was conducted on 10 independent routes to assess model generalization. Before training, input data were pre-processed using normalization and standardization techniques to ensure stable convergence and mitigate the impact of scale differences among input variables. A correlation analysis was performed to evaluate the relationships among available parameters and fuel consumption, guiding the selection of the most informative inputs. This step reduced redundancy in the dataset and improved network efficiency. Furthermore, hyperparameter optimization was conducted using a Randomized Grid Search algorithm, enabling the identification of an optimal network architecture - specifically in terms of the number of layers and hidden neurons - and training parameters while minimizing overfitting.</div><div class="htmlview paragraph">The final model demonstrated high predictive accuracy across various validation routes, confirming the effectiveness of the FFNN in estimating fuel consumption with a reduced input set. Accuracy was tested on up to 50 routes, whose data where not considered for both training and validation; it was assessed that fuel consumption percentage error per route never exceeded 2%. This approach provides a practical and computationally efficient solution for fleet operators, facilitating advanced route planning and enabling more sustainable transportation strategies through cost-effective fuel management and emissions reduction.</div></div>

<div class="htmlview paragraph">The purpose of this system study was to compare the performance and fuel consumption of a pure fuel cell vehicle (<i>i.e.</i> with no battery included) with an internal combustion engine (ICE) vehicle of similar weight in different drive cycles. Both light and heavy duty vehicles are studied.</div> <div class="htmlview paragraph">For light duty vehicles, the New European drive cycle, NEDC [70/220/EEC], the FTP75 [EPA] and a Swedish driving pattern from the city of Lund [<span class="xref">Ericsson, 2000</span>] are utilised. The fuel consumption for these drive cycles was compared with ICE vehicles of similar weight, an Ibiza Stella 1.4 (year 2000) from Seat and a Volvo 960 2.5 E sedan (year 1995). For heavy duty vehicles, urban buses in this study, two drive cycles were employed, the synthetic CBD14 and the real bus route 85 from Gothenburg, Sweden.</div> <div class="htmlview paragraph">It can be concluded that marked improvements in fuel economy can be achieved for hydrogen-fuelled light and heavy duty vehicles. The fuel consumption of a small fuel cell vehicle was 50% less than the corresponding ICE vehicle in both the NEDC and the FTP75. With proper dimensioning of the system components, e.g. the engine, further reductions in fuel consumption can be achieved. The range of more than 500 km with 5 kg of hydrogen in a 345 bar fuel tank was comparable to an ICE vehicle. If the pressure is raised to 690 bar, a driving range of 600 km could be achieved. As the auxiliary system counteracts the increase in fuel cell efficiency, raising the minimum operating voltage from 0.6 to 0.75 V in a 50 kW fuel cell system, provides only a 5% reduction in fuel consumption. A fuel cell bus operated in the CBD14 and the bus route 85, compared with diesel-fuelled urban bus of similar weight, demonstrates a reduction in fuel consumption of 33 and 37 % respectively.</div>

<div class="htmlview paragraph">A novel gasoline <i>fuel optimizer</i> has been developed which is capable of modifying the combustion process in spark ignition engines. Fuel Optimizers are a new category of chemical compounds which are capable of altering the base combustion characteristics of the fuel itself, resulting in performance benefits far in excess of what is possible with traditional fuel additives.</div> <div class="htmlview paragraph">The fuel optimizer described in this paper, <b>X-ccelerator</b>™, alters the ignition and burning characteristics of the fuel-air mixture, resulting in enhanced combustion performance. Tests in combustion bombs as well as in an operating engine have shown that this additive can reduce combustion duration (both in terms of the ignition time and bulk burning time) and increase average peak combustion pressure. As a result, the indicated mean effective pressure is shown to be increased.</div> <div class="htmlview paragraph">One of the benefits of this enhanced combustion is improved vehicle performance and driveability characteristics. Decreased full- and part-throttle acceleration times have been demonstrated in a variety of modern vehicles. In controlled field evaluations, a majority of drivers have perceived general improvements to vehicle performance and driveability when using X-ccelerator. This fuel optimizer, which is ashless, is added to the base gasoline at a low concentration (200 parts per million), and is a highly cost-effective solution for improving vehicle and combustion performance. Extensive compatibility and no-harm testing has been conducted to ensure that no adverse interactions exist with the engine or any of its components.</div>