Vehicle Mass Research Articles

Non‐Destructive and Mechanical Characterization of the Bond Quality of Co‐Extruded Titanium‐Aluminum Profiles

The transportation industry aims to improve energy efficiency and reduce CO2 emissions, with a focus on reducing vehicle mass. A key method involves advanced lightweight construction techniques using materials like aluminum alloys. Research is concentrated on developing processes to combine different materials into reinforced hybrid components, such as aluminum and titanium. This study focuses on the lateral angular co‐extrusion (LACE) process to produce hybrid hollow profiles of EN AW‐6082 and Ti6Al4V, investigating the impact of the thermomechanical processing during extrusion and heat treatment (HT) on the resulting bond quality and material properties. Various HT routes are tested to see their impact on intermetallic phase formation, longitudinal weld seams, and bonding strength. Mechanical testing evaluates the tensile strength of the joining zone, while nondestructive ultrasonic testing (UT) assesses joining zone integrity and poor bonding detection. Results indicate that HT parameters significantly influence the bond quality and mechanical properties of hybrid profiles. UT data shows a strong correlation with tensile strength and intermetallic phase growth, providing a nondestructive way to evaluate bond quality. This study highlights the potential of LACE processes and optimized HT strategies to improve the performance and reliability of aluminum–titanium hybrid components.

Electric vehicle ramp recognition based on fusion of vehicle mass estimation

Road slope is the necessary environmental information for intelligent electric vehicles. The accuracy of slope recognition directly determines the control quality of vehicles on the hill. Aiming at the problems that the existing ramp recognition algorithms have poor adaptability to the conditions and are unable to To satisfy the application requirements of mass production vehicles, this paper proposes an electric vehicle slope recognition method based on vehicle mass estimation. Firstly, the vehicle longitudinal dynamics model is established, and the signal characteristics of the acceleration sensor under the actual vehicle conditions are analyzed. The least square vehicle mass estimation strategy with forgetting factor is constructed to obtain the vehicle mass directly under the starting condition. Ramp recognition algorithms are designed for static parking and dynamic driving scenarios. In the static scenario, the filter latch strategy is used to deal with the interference factors such as activities in the vehicle. In the dynamic scenario, the Kalman filter algorithm based on measurement noise adaptation is designed to realize the fusion estimation of dynamic observation and kinematic observation for the slope. The effectiveness of the method is verified by Simulink -CarSim joint simulation. Finally, the real vehicle test is completed on Chery new energy's mass production electric vehicle platform and domain controller. road test results show that the mass estimation error is less than ±10 kg; the static estimation error of the slope is less than 0.001 rad, and the dynamic error is within 0.005 rad. The estimation accuracy and stability are greatly improved, which ensures the environmental adaptability of the intelligent electric vehicles.

Clutch Torque Optimization for Heavy-Duty Vehicles Starting Based on Intelligent Identification of Driver’s Starting Intention, Vehicle Mass, and Road Grade

<div>For heavy-duty vehicles equipped with automated mechanical transmission (AMT), the control of automatic clutch torque is crucial during the start-up process. However, the difficulty of controlling clutch torque is exacerbated by differences in driver’s starting intentions, changes in vehicle mass, and road gradient. Therefore, this article proposes the clutch starting torque optimization strategy based on intelligent recognition of driver’s starting intention, vehicle mass, and road gradient. First, an intelligent recognition strategy is proposed based on the combination of data-driven and onboard transmission control unit (TCU) algorithms, which improves the accuracy of recognizing the driver’s intention to start as well as the vehicle mass and road gradient. Based on the vehicle’s historical state data information, the predictive model is trained offline using a long–short-term memory (LSTM) network to obtain predicted parameter identification results, which are then used to calibrate the computed values of the onboard TCU algorithm. Second, the clutch torque optimization strategy is designed based on the driver’s starting intention, while considering the effects of road gradient and vehicle mass on the clutch starting resistance torque. The weight coefficients of the objective performance function are adjusted according to the driver’s starting intention, and the Pontryagin’s minimum principle (PMP) is used to solve the clutch target torque. Finally, offline data training and real-vehicle testing are performed. The results show that the optimization strategy can effectively reduce the friction work and the degree of impact during the starting process, minimize the clutch slipping time, and improve the smoothness of vehicle starting and driving comfort.</div>

Study on vehicle state and tire–road friction coefficient estimation based on maximum correntropy generalized high-degree cubature Kalman filter

To cope with the challenges of inadequate precision and weak robustness of tire–road friction coefficient (TRFC) and vehicle state estimation under mixed Gaussian noise circumstances, this research puts forward a method combining maximum correntropy criterion (MCC) and generalized high-degree cubature Kalman filter (GHCKF). A coupled lateral and longitudinal vehicle model and the Dugoff tire model are established. The vehicle mass is estimated using the recursive least squares approach based on a forgetting factor (FFRLS). Low-cost onboard sensors are utilized to design observers for vehicle state and TRFC. The observers’ effectiveness is tested by Carsim/Simulink co-simulation tests under the conditions of sine steering input and steering wheel angle step input. The research results indicate that in handling mixed Gaussian noise environments, maximum correntropy generalized high-degree cubature Kalman filter (MCGHCKF) method outperforms maximum correntropy cubature Kalman filter (MCCKF), GHCKF, and cubature Kalman filter (CKF) methods concerning robustness and estimation accuracy, providing stable and reliable support for systems of vehicle active safety control.

How optimal is the minimum-time manoeuvre of an artificial race driver?

Minimum-lap-time optimal control problems (MLT-OCPs) are a popular tool to assess the best lap time of a vehicle on a racetrack. However, MLT-OCPs with high-fidelity dynamic vehicle models are computationally expensive, which limits them to offline use. When using autonomous agents in place of an MLT-OCP for online trajectory planning and control, the question arises of how far the resulting manoeuvre is from the maximum performance. In this paper, we improve a recently proposed artificial race driver (ARD) for online trajectory planning and control, and we compare it with a benchmark MLT-OCP. The novel challenge of our study is that ARD controls the same high-fidelity vehicle model used by the benchmark MLT-OCP, which enables a direct comparison of ARD and MLT-OCP. Leveraging its physics-driven structure and a new formulation of the g-g-v performance constraint, ARD achieves lap times comparable to the offline MLT-OCP (few milliseconds difference). We analyse the different trajectories resulting from the ARD and MLT-OCP solutions, to understand how ARD minimises the effect of local execution errors in search of the minimum-lap-time. Finally, we show that ARD consistently maintains its performance when tested on unseen circuits, even with unmodelled changes in the vehicle's mass.

Development of a system that changes the speed of the motor for a given radius of curvature of an autonomous car while taking turn

This work endeavors to create a system capable of autonomously regulating a car’s speed while navigating turns, based on the curvature of the road. The primary objective is to bolster the safety and stability of autonomous vehicles by curbing excessive speeds during turns, thereby mitigating the risk of accidents such as loss of control or rollovers. The approach blends theoretical analysis with practical application, involving computations to ascertain the ideal speed for a given curve radius. Factors such as vehicle mass, wheel specifications, and centrifugal and gyroscopic forces are considered in these calculations. Subsequently, an algorithm is devised and implemented using Arduino Uno and programmed within the Arduino IDE software environment. The system employs sensors to monitor the motor’s speed and adjust the motor’s speed accordingly. Both simulated scenarios and real-world experiments validate the system’s efficacy in maintaining safe speeds during turns. The study concludes by underlining the potential of this system to heighten the safety and dependability of autonomous vehicles, while also proposing future avenues for research, such as integration with broader autonomous driving frameworks and field testing across diverse environmental conditions.

Train Trajectory-Following Control Method Using Virtual Sensors.

Trajectory-following control is the basis for the practical application of an articulated virtual rail train transportation system. In this paper, a planar nonlinear dynamics model of an articulated vehicle is derived using the Euler-Lagrange method. A trajectory-following control strategy based on the first following point is proposed, and a feedback linearization control algorithm is designed based on the vehicle dynamics model to achieve the trajectory following of the rear vehicle. Based on the target trajectory formed by the first following point and measured by virtual sensors, a vector analysis method grounded in geometric relationships is proposed to solve in real time for the desired position, velocity, and acceleration of the vehicle. Finally, a MATLAB/SIMPACK dynamics virtual prototype is established to test the vehicle's trajectory-following effectiveness and dynamics performance under lane change and circular curve routes. The results indicate that the control algorithm can achieve trajectory following while maintaining good vehicle dynamics performance. It is robust to variations in vehicle mass, vehicle speed, tire cornering stiffness, and road friction coefficient.

Research on truck mass estimation based on long short-term memory network

To address the issues of convenience, scalability, and accuracy in existing truck mass estimation technologies, a truck mass estimation model based on Long Short-Term Memory Network (LSTM) using onboard OBD data is proposed. Seven-dimensional feature parameters, including vehicle driving speed, engine speed, and net output torque of the engine, are selected as input features to achieve accurate estimation of truck mass. This study collaborated with transportation enterprises from Yulin, China to collect real-time data using onboard OBD devices. The actual vehicle mass was obtained by using the data from the weighing scales installed at the departure and unloading points. These data were divided into training and validation sets for the model, and improvements were made step by step based on the prediction results. It's showed that when estimating vehicle mass based on trips, the average relative error between the estimated validation set and other vehicle masses is around 2.3 %; Compared with existing estimation models (estimation model based on extended Kalman filter and detection device based on EPB system), LSTM model has advantages such as high estimation accuracy and strong generalization ability, and has broad application space in overload transportation monitoring, safe transportation, energy conservation and emission reduction, etc.

Making of an Indigenous and Efficient Powertrains for EV, HEV & FCEV’s

Rise in greenhouse gas emissions which is resulting into ozone layer depletion and global warming effect is alarming to control CO2 emissions by drastically reducing fossil fuel consumption from the vehicles. This need has already started trend towards extreme downsizing and down speeding of Internal Combustion Engines (ICE) for making them highly efficient. In addition to this use of after treatment devices viz. Diesel Oxidation Catalyst (DOC), Diesel Particulate Filter (DPF), Lean NOx Catalyst (LNT), Selective Catalytic Reduction (SCR) are helping to a great extent for meeting emission norms till Euro - VI for automotive sector, Tier–IV F and Stage V for Off Highway Power sector. However, to address present greenhouse effect along with exponential decay of fossil fuels, there is need to achieve CO2 emissions less than 100 g/km or to achieve practically zero carbon footprint using an alternate powertrain. Electrification is one of the promising solutions and which is three-fold in present situation. The first level of electrification comes in the form of Hybrid Electric Vehicle (HEV) technology followed by Battery Electric Vehicles and Fuel Cell Electric Vehicles as second and third level of electrification respectively. In all these technologies a common thread is running in the form of an efficient electric drive train. Several efforts have been taken to improve torque per unit ampere by use of efficient motor and motor control algorithms viz PMSM powered with FOC, by developing controls which take care of parametric variations and external force disturbances by use of back stepping techniques which are based on rotor resistance estimation by fuzzy logic, FOC techniques based on adaptive input-output feedback linearization, by shifting towards high voltage systems, by going for higher switching frequencies for inverters which uses silicon carbide based MOSFET switches, by exploring use of multilevel inverters for Total Harmonic Distortion (THD) control etc. Use of E Axle based powertrains in EV, HEV and FCEV offers additional complimentary benefits apart from technology options described above. E Axle based powertrains offers flexibility in generating three variants of vehicle using one electric drive train architecture viz. making EV, HEV & FCEV. Compactness, modularity, scalability, flexibility along with efficient cooling system management and control over active current losses due to use of bus bars are the implicit advantages of E Axle based powertrains. These advantages further get multiplies if we go for 3 in 1 (gearbox + Stator housing + Inverter) architectures which are predominantly used in independent suspension-based vehicles. Lot of efforts are going on to inherit this technology for rigid axle suspension based commercial vehicles. Research in this area is getting further extended to make 5 in 1 E Axles where there will addition of DC - DC converter and On-Board Charger (OBC) apart from components present in 3 in 1 configuration. Two major challenges need to be addressed and to be resolved while designing E Axle based powertrains by doing concurrent vehicle engineering. This is mainly due to increased unsprang weight that need to be handled by suspension system and exposed electric drive components to excitation loads coming from roads. Fig: Indigenously Developed E Axle Based Powertrain for Small Commercial VehiclesBy proper lay outing for effective vehicle mass distribution and required CG location which is essential from overall vehicle dynamics, more space can be offered by E Axle based powertrains for increasing battery energy density for getting more electric range in case of BEV’s. This add on space benefit extend help in packaging of hydrogen fuel cell integrated with power cells in case of FCEV’s. Thus, E Axle based powertrains proves to be modern and most efficient way of making EV, HEV & FCEV’s.

Multidisciplinary design analysis and optimization of the aerodynamic shape of the SpaceLiner passenger stage

The SpaceLiner is a futuristic concept of a suborbital space plane intended to provide ultra-fast intercontinental passenger transport, currently in pre-development at DLR-SART. Vehicles traveling at hypersonic speeds inevitably produce shock waves that are perceived at ground level as sonic booms, with associated disturbance of the overflown populations. The reduction of this disturbance, together with the decrease of the noise associated to the launch and ascent phase, is critical for a viable future operation of the SpaceLiner. The objective of this work is the redesign of the passenger stage aerodynamic shape in order to improve its atmospheric re-entry performance, in terms of a reduction of both the heat flux and the disturbance of the overflown populations. For this purpose, a MDAO methodology was developed with the tasks of (1) computing vehicle performance from a wing shape parametrization using fast estimation methods, (2) exploring the design space and (3) finding a new promising aerodynamic shape. First, a Python tool-chain was developed to compute vehicles performances from a wing shape parametrization using fast estimation methods. The tool-chain was then systematically exploited to explore the design space by means of parametric studies, whose results informed the implementation of the forthcoming optimization. Afterwards, the wing shape was optimized using a three-objective evolutionary algorithm that minimized the vehicle mass and maximized its lift-to-drag ratio and lift coefficient. Simplified trajectory optimizations were then run on the set of non-dominated solutions in order to identify the most performing configurations. Finally, multi-objective evolutionary trajectory optimizations were run for the most promising candidate along intercontinental point-to-point routes of interest. Comparisons with the previous design iteration showed a significant improvement in terms of a reduction of both reentry heat flux and population disturbance.

Strain Gauge Calibration for High Speed Weight-in-Motion Station.

The development of systems for weighing vehicles in motion aims to introduce systems allowing automatic enforcement of regulations. HSWIM (high speed weight-in-motion) systems enable measurement of a mass of vehicles passing through a measurement station without disturbing the traffic flow. This article focuses on the calibration of a weighing station for moving vehicles, where strain gauge sensors are used to measure pressures. A solution was proposed to replace the calibration coefficients with calibration functions. The analysis was performed for two methods of determining wheel loads: based on the maximum of the signal from strain gauge sensors and on a method using the field under the signal and the vehicle's speed. Calibration functions were determined jointly for all test vehicles and separately for each of them. The use of a calibration function for a specific vehicle type made it possible to determine wheel pressure and gross weight with a level of accuracy that allowed the weigh-in-motion station to be classified as a direct enforcement system. The achieved improvement in the accuracy of weighing in motion did not require any interference with the measurement station. The proposed change in the method of calibration and, ultimately, determination of wheel loads required only a change in the algorithm for determining wheel loads.

Mass estimation of tractor-semitrailer systems: An approach of dynamics and data fusion-driven in real environments

Accurate mass estimation is crucial for improving vehicle active safety control performance. In the context of tractor-semitrailer systems, the precise estimation of mass presents a formidable challenge. This challenge is rooted in traditional methods affected by model uncertainty, model mismatch, and insufficient training data; furthermore, there is also a lack of algorithm generalization to account for real-world traffic scenarios. To address these challenges, a hybrid algorithm for vehicle mass estimation based on bidirectional long short-term memory (BiLSTM) networks and square-root cubature Kalman filter (SCKF) is proposed in this study. Driven by vehicle time series data, the vehicle mass estimator based on the BiLSTM network is constructed to address model uncertainty. To enhance both estimation accuracy and robustness, the hybrid BiLSTM-SCKF method is constructed by incorporating the BiLSTM mass estimation as the initial state value and measured value in the SCKF algorithm while also formulating a fusion rule. The experimental results under real environments show that the hybrid BiLSTM-SCKF algorithm outperforms single algorithms, particularly under medium and low loads, and the estimated error remains consistently below 10% across all working conditions. Additionally, this proposed method exhibits adaptability to various types of semitrailers, effectively enhancing algorithmic accuracy, reliability, robustness, and interpretability.

3- and 6-degree-of-freedom analysis of the Magellan aerobraking experiment and planar aerobraking at Venus

The first non-Earth aerobraking experiment occurred in 1993 as part of the Magellan satellite's mission to Venus. Magellan reduced its orbit from elliptical to nearly circular over a span of 70 Earth days by leveraging the non-conservative force of aerodynamic drag to reduce its orbital energy via transiting the upper region of Venus' sensible atmosphere. First tested with the Hiten probe in the Earth-Moon system in 1991, the subsequent Magellan experiment helped prove the viability of aerobraking for planetary missions and paved the way for its implementation in Mars missions starting in the late 1990s and the 2014 Venus Express mission. This paper, for the first time in literature, investigates the Magellan aerobraking experiment from the perspective of both 3- and 6-degree-of-freedom analysis. Multiple atmospheric density models are considered, as well as J4 gravitational perturbations in order to understand the complexity and sensitivity of numerous aerobraking maneuvers in the thick Venusian atmosphere. Alternative satellites are also studied to ascertain their capability of maintaining the Magellan aerobraking flight profile. So as to examine a wide range of vehicle mass and drag reference area, the Hubble Space Telescope (HST) and Hiten satellites are modeled and their aerobraking performance analyzed for the hypothetical case of Venus orbital operations. When reconstructing the historical Magellan aerobraking maneuvers, 3DOF and 6DOF analysis yielded minimal trajectory deviation with the Magellan and Hiten models, while more significant deviation was experienced with the larger HST model. Both 3DOF and 6DOF analyses also reveal the sensitivity of the atmospheric density model and indicate a higher fidelity gravity model is necessary for Venusian aerobraking modeling, planning, and analysis.

Sunlight-powered sustained flight of an ultralight micro aerial vehicle.

Limited flight duration is a considerable obstacle to the widespread application of micro aerial vehicles (MAVs)1-3, especially for ultralightweight MAVs weighing less than 10 g, which, in general, have a flight endurance of no more than 10 min (refs. 1,4). Sunlight power5-7 is a potential alternative to improve the endurance of ultralight MAVs, but owing to the restricted payload capacity of the vehicle and low lift-to-power efficiency of traditional propulsion systems, previous studies have not achieved untethered sustained flight of MAVs fully powered by natural sunlight8,9. Here, to address these challenges, we introduce the CoulombFly, an electrostatic flyer consisting of an electrostatic-driven propulsion system with a high lift-to-power efficiency of 30.7 g W-1 and an ultralight kilovolt power system with a low power consumption of 0.568 W, to realize solar-powered sustained flight of an MAV under natural sunlight conditions (920W m-2). The vehicle's total mass is only 4.21 g, within 1/600 of the existing lightest sunlight-powered aerial vehicle6.

Analyzing Vertical Earthquake Vibrations and Moving Vehicle Loads for Structural Health Monitoring and Vibration Suppression in Bridges

The design of bridges often overlooks the vertical component of earthquakes or considers it of secondary importance, despite compelling evidence indicating specific structural damage caused by primary earthquake waves. Conversely, during the operational phase, the combined influence of ground motion and moving loads from vehicles can significantly impact the structural health monitoring (SHM) of bridges. This study aims to evaluate the simultaneous effect of vertical earthquake vibrations and moving vehicle loads on simply supported bridges. The research employs a practical methodology based on the eigenfunction expansion method to analyze change of deflection due to the effect of these concurrent forces under seven different earthquake records. It is shown that within a realistic range of vehicle mass and velocity, the average of changing the maximum deflection at the mid-span of the main beam (denoted as M_n) reaches up to 163% under various scenarios. Subsequently, the seismic parameters influencing this phenomenon are identified through a statistical analysis of set of 100 different earthquake records with unique features. A linear regression equation is presented to predict the M_n based on the earthquake specific properties. Additionally, to control the vertical vibration of bridge systems, a novel vibration suppression system utilizing steel pipe dampers is introduced, and its reliability is examined across a broad spectrum of bridge flexural rigidity. The results indicate that the system's efficiency depends on M_n and the soil type of the bridge construction, enabling a reduction in structural sections (up to 27%) while achieving the same maximum target deflection in the initial state. This efficiency leads to a more economical design solution, emphasizing the potential benefits of the proposed system for practical application.

Multi-level and multi-objective structural optimization for hypersonic vehicle design

This research introduces a novel methodology for optimizing the structural design of a full-scale hypersonic aircraft, integrating a multifaceted approach across different levels of modelling detail to enhance a variety of performance metrics. The proposed approach is capable of reduce the vehicle mass, while meeting the necessary operational requirements and maintaining an acceptable computational cost. The methodology, based on a coupled bi-level size optimization and denoted as single-objective bi-level optimization (SOBLO), is applied to the passenger cabin of the STRATOFLY MR3 hypersonic cruiser vehicle. This results in a substantial reduction in the baseline concept design weight, estimated to be more than half on average. The procedure is extended to incorporate a multi-objective optimization approach (MOBLO), which generates a Pareto frontier that provides significant information for selecting the optimal trade-off design, considering manufacturability constraints. The outcomes underscore the efficacy of the proposed methodology and highlight its usefulness in sizing complex aircraft configurations, particularly under the demanding loads imposed by a hypersonic flight regime. This approach has the potential to improve the overall production process and enable designers to attain feasible structural design solutions during early stages of development.

Prediction of Fuel and Exhaust Emission Costs of Heavy-Duty Vehicles Intended for Gas Transportation

This research focuses on heavy-duty vehicles intended to transport compressed natural gases, i.e., class-2 dangerous goods. The analysis includes heavy-duty vehicles powered by diesel and compressed natural gas and trailers with two body types. The body types used in the research are battery bodies and multiple-element gas containers, with pressure vessels made of composite materials (Type-4) and steel (Type-1). The paper presents the methodological procedure for predicting fuel and exhaust gas emission costs as a function of fuel consumption and transported gas quantities. The effects of different types of bodies and different types of fuel on the transported quantities of gas, vehicle mass utilization, fuel consumption, and exhaust gas emissions are shown. The obtained results show that bodies with Type-4 pressure vessels transport 44% more gas than bodies with Type-1 pressure vessels for one turn. The most cost-effective solution for emission costs is diesel-powered, newer-technology vehicles and Type-4 vessels, requiring EUR 2.82 per ton of gas. Similarly, the most economical choice for fuel costs is compressed natural-gas-powered vehicles with Type-4 bodies and a cost of EUR 19.77 per ton of gas. The research results’ practical application pertains to the selection procedures of vehicles and bodies intended for the transport of gases; they should be considered in the decision-making process, with the aim of attaining a sustainable transport sector with lower costs and less impact on the environment.

A Novel Tire and Road Testing Bench for Modern Automotive Needs

The automotive industry is currently transforming, primarily due to the rise of electric and hybrid vehicle technologies and the need to reduce vehicle mass and energy losses to decrease consumption, pollution, and raw material usage. Additionally, road surface manufacturers emphasize improving pavement durability and reducing rolling noise. This necessitates precise load condition definitions and drives the need for reliable wheel testing benches. Many current benches use abrasive-coated rollers or synthetic tapes, but devices capable of testing on actual road surfaces are rare. In this work, a novel device for testing tire-pavement interaction is proposed. The system features a cart moving along a closed-track platform, ensuring test repeatability and enabling structural durability tests on uneven surfaces with installed obstacles. The cart is equipped with a cantilever arm capable of supporting either a testing wheel with customizable dimensions and kinematic parameters or a tire integrated with a complete suspension system, moving along a customizable pavement surface. The system includes actuators and sensors for applying vertical loads and adjusting the alignment of the testing wheel (slip angle, camber angle, etc.), allowing the characterization of tire behavior such as wear, fatigue, rolling noise, and rolling resistance. Multibody simulations were performed to evaluate the bench’s feasibility in terms of kinematics, power requirements, and structural loads. Results confirmed how this novel test bench represents a promising advancement in tire testing capabilities, enabling comprehensive studies on tire performance, noise reduction, and the structural dynamics of vehicle subsystems.

Prediction of brake pad wear and remaining useful life considering varying vehicle mass and an experimental holistic approach

Estimation of passenger car brake pad wear is considered, including real-time mass estimation to account for variations in vehicle mass. A longitudinal dynamics model is used to estimate mass, the application of the brakes, and a longitudinal braking force. Wear is modelled from brake work based on the product of the braking force and distance travelled during braking. Data is provided by standard vehicle on-board diagnostics data and a sensor fusion based on an inertial measurement unit and satellite position data, sampled from a single car with 10,200 km of on-road driving, including 31 brake pad wear measurement samples, and driving with up to 23% increased vehicle mass. The mass is accurately estimated within ±5% of the actual. Braking states determined from comparisons of measured and modelled deceleration provide accuracies of ≈91%, precisions of ≈96%, and recall rates of ≈86%, with inclusion of varying mass only resulting in minor differences. Brake work is estimated with (a) constant mass, (b) estimated mass, (c) actual mass, and (d) actual mass and braking states. Compared to (a), (b) and (c) provide 9% and 11% increased work, while (d) provides an increase of about 5% compared to (c). Wear coefficients are fitted on the initial half of the wear data considering (a), (b), and (d). Here (a) and (b) provide similar wear rates of k≈−1.22 mm/GJ, while (d) is about 5% lower. Validating on the second half of the data, (b) and (d) provide very similar wear estimates, with overall lower errors than (a). At the final sample, however, (a) provides a slightly lower absolute error, overestimating the wear by about 2%, while (b) and (d) underestimate it by about 3%. RUL, in terms of distance, is estimated across about 9,500 km of data by estimating a running ratio between cumulated work and distance. Here only (a) and (b) are considered, with (a) overestimating the RUL by 600 km at the actual wear limit, while (b) underestimates it by 190 km. A distance-based model is also considered, resulting in a constant RUL error of about 2,000 km.

Battery Management for Improved Performance in Hybrid Electric Vehicles

This study aims to improve the battery performance in hybrid electric vehicles (HEVs) by reducing the vehicle speed. We developed a specific protocol for managing battery use and optimizing the energy consumption rate to achieve this goal. The protocol automatically controls the driving operation, avoiding incompatible driving patterns with an energy-saving mode and performance improvement. This protocol was applied to a simulation process to predict energy rate lowering and battery performance enhancement. The proposed protocol applies to any hybrid electric vehicle type and any route conditions since it uses vehicle mass, drag and rolling coefficients, and road slope as variable parameters to determine the minimum energy consumption rate. We performed experimental tests to validate the simulation data and the proposed protocol. Furthermore, the protocol applies to variable starting vehicle speeds, from 10 to 50 km/h, corresponding to the current driving patterns, sport, normal, and eco, set up by car manufacturers. A reduction of 10% in vehicle speed in urban and peripheral routes achieves a minimum energy rate, enhancing battery management. Current vehicle speed shows a deviation from optimum management of 18% while applying vehicle speed reduction limits the deviation to 0.2%. Experimental results show a good agreement with simulation data, with 94% accuracy. We tested the protocol for urban and peripheral routes with maximum vehicle speed limits of 60 and 90 km/h.