CAN Bus Data Research Articles

Experimental Outdoor Vehicle Acoustic Testing Based on ISO-362 Pass-by-Noise and Tyre Noise Contribution for Electric Vehicles

This paper focuses on the novel and unique training provision of acoustics relevant for noise, vibration, and harshness (NVH), focused on the ISO-362 standard highlighting important design aspects for electric vehicles. A case study of the practical implementation of off-site vehicle testing supporting an acoustics module is described, detailing a time-constrained test for automotive pass-by-noise and tyre-radiated noise with speed. Industrial test standards are discussed, with education as a primary motivation. The connections between low-cost, accessible equipment and future electric vehicle acoustics are made. The paper contains a full equipment breakdown to demonstrate the ability to link digital data transfer, analogue-to-digital communication, telemetry, and acquisition skills. The benchmark results of novel pass-by-noise and tyre testing are framed around discussion points for assessments. Inexpensive Arduino Uno boards provide data acquisition with class 1 sound pressure meters, XBee radios provide telemetry to a vehicle, and a vehicle datalogger provides GPS position with CANBUS data. Data acquisition is triggered through the implementation of light gate sensors on the test track, with the whole test lasting 90 minutes.

Dynamic Modeling and Control Strategy Optimization of a Volkswagen Crafter Hybrid Electrified Powertrain

This article studies the transformation and assembly process of the Volkswagen (VW) Crafter from conventional to hybrid vehicle of the department of vehicles engineering, University of Debrecen, and uses a computer-aided simulation (CAS) to design the vehicle based on the real measurement data (hardware-in-the-loop, HIL method) obtained from an online CAN bus data measurement platform using MATLAB/Simulink/Simscape and LabVIEW software. The conventional vehicle powered by a 6-speed manual transmission and a 4-stroke, 2.0 Turbocharged Direct Injection Common Rail (TDI CR) Diesel engine and the transformed hybrid electrified powertrain are designed to compare performance. A novel methodology is introduced using Netcan plus 110 devices for the CAN bus analysis of the vehicle’s hybrid version. The acquired raw CAN data is analyzed using LabVIEW and decoded with the help of the database (DBC) file into physical values. A classical proportional integral derivative (PID) controller is utilized in the hybrid powertrain system to manage the vehicle consumption and CO2 emissions. However, the intricate nonlinearities and other external environments could make its performance unsatisfactory. This study develops the energy management strategies (EMSs) on the basis of enhanced proportional integral derivative-based genetic algorithm (GA-PID), and compares with proportional integral-based particle swarm optimization (PSO-PI) and fractional order proportional integral derivative (FOPID) controllers, regulating the vehicle speed, allocating optimal torque and speed to the motor and engine and reducing the fuel and energy consumption and the CO2 emissions. The integral time absolute error (ITAE) is proposed as a fitness function for the optimization. The GA-PID demonstrates superior performance, achieving energy efficiency of 90%, extending the battery pack range from 128.75 km to 185.3281 km and reducing the emissions to 74.79 gCO2/km. It outperforms the PSO-PI and FOPID strategies by consuming less battery and motor energy and achieving higher system efficiency.

Synthetic data generation using Copula model and driving behavior analysis

In this study, the generation of synthetic driving data that can reflect real behavior well using the Copula model was investigated. To see the difference in behavior patterns in the generated synthetic driving data, a feature correlation comparison was made. The difference in driving behavior was provided with the K-means based classification model. It was shown that with a Random Forest model trained with synthetic data and having high accuracy, the privacy of real data could be protected by 98.55%. At the end of the study, it was seen that the Copula model could obtain synthetic driving data with sufficient accuracy with CAN bus data without additional sensor support.

A data-driven approach to agricultural machinery working states analysis during ploughing operations

In the field of precision agriculture, there is a significant shift towards data-driven methodologies that considerably enhance the efficiency and sustainability of agricultural operations. This research investigates the application of CAN-Bus and GNSS data to develop a comprehensive analysis of agricultural machinery's operational states during ploughing operations through advanced data analytics techniques, including machine learning. The primary tool utilized in this study is the Random Forest classifier, a robust algorithm well-suited for handling the complexity and volume of data typical in modern agricultural settings. The study evaluates Random Forest models trained on various feature subsets to accurately identify different operational states of agricultural machinery, including idle, moving, turning, and working states. By merging CAN-Bus data, which capture real-time operational parameters, with GNSS data, providing spatial and temporal context, it is possible to achieve a comprehensive understanding of machinery behaviour and its interaction with field conditions. This integration significantly enhances decision-making capabilities in farm management, leading to more effective and efficient operations.Furthermore, the findings from this study contribute to the broader agricultural community by illustrating how data-driven approaches can harness the vast amounts of data generated by modern agricultural machinery. This research underscores the potential of machine learning modelsnot only to interpret complex data sets but also to transform these insights into actionable knowledge, which can lead to more precise and sustainable agricultural practices. Overall, this study offers a systematic approach for analysing agricultural data and lays the groundwork for future advancements in incorporating machine learning and IoT technologies into the agricultural sector. This aims to enhance productivity and sustainability in farming practices.

A Multivariate Time Series Prediction Method for Automotive Controller Area Network Bus Data

This study addresses the prediction of CAN bus data, a lesser-explored aspect within unsupervised anomaly detection research. We propose the Fast-Gated Attention (FGA) Transformer, a novel approach designed for accurate and efficient prediction of CAN bus data. This model utilizes a cross-attention window to optimize computational scale and feature extraction, a gated single-head attention mechanism in place of multi-head attention, and shared parameters to minimize model size. Additionally, a generalized unbiased linear attention approximation technique speeds up attention block computation. On three datasets—Car-Hacking, SynCAN, and Automotive Sensors—the FGA Transformer achieves predicted root mean square errors of 1.86 × 10−3, 3.03 × 10−3, and 30.66 × 10−3, with processing speeds of 2178, 2768, and 3062 frames per second, respectively. The FGA Transformer provides the best or comparable accuracy with a speed improvement ranging from 6 to 170 times over existing methods, underscoring its potential for CAN bus data prediction.

Semi-empirical model for timber truck speed profile and fuel consumption

ABSTRACT This paper presents a semi-empirical model for driving speed simulation and estimation of energy use in timber trucks in Sweden. The model is composed of two parts; the first part takes as input road properties along a route and applies a kinematic model to simulate a driving pattern. The kinematic model contains parameters that are found statistically from large-scale recordings of CAN bus data from 21 timber trucks in 1 year. The second part applies a mechanistic model on the simulated speed profile to compute fuel use. The driving pattern simulator can predict the driving time and energy use with roughly the same variance as can be observed between different trucks and drivers following the same route. An average rolling resistance coefficient of 0.0068 was found from a coast-down study, but with substantial variation between vehicles.

Research and analysis of FIFO related working principles

An asynchronous FIFO is a classic circuit device used for caching data and accommodating the frequency of asynchronous signals. With the continuous improvement of technology, the advantages of this device in real-time transmission, multi-core processing, and data acquisition have become more apparent. As a result, the original structure has been optimized and extended using algorithm and design techniques to achieve greater efficiency and applicability in various academic and industrial applications. This article discusses several methods for optimizing FIFO in different aspects and examines how an unlocked FIFO queue is applied in a CAN bus data acquisition system, as well as its use in data hybrid framing technology. The findings show that FIFO performs exceptionally well as a cache structure, particularly in situations involving large amounts of data acquisition and real-time procession. The various optimization techniques discussed in this article include using pipeline registers, implementing a dual-clock FIFO, and employing Gray-code-based pointers. These techniques can increase the speed and reduce the power consumption of FIFO, thereby improving its overall efficiency. In addition, the article introduces the unlocked FIFO queue, which provides more flexibility than conventional locked FIFOs. Unlocked FIFO queues allow multiple cores to access the buffer simultaneously, which makes them ideal for high-performance systems. Finally, the article explores the application of FIFO in CAN bus data acquisition systems, where it is utilized as a buffer between the bus and other devices. Additionally, the use of FIFO in data hybrid framing technology is discussed, where it is essential for maintaining the integrity of the data stream.

Regulation of Uncertain Markov Jump Linear Systems With Application on Automotive Powertrain Control

Modeling and control of dynamic systems that experience abrupt changes are challenging and fundamental tasks in applications of different areas of engineering. Among these, control synthesis for autonomous heavy-duty vehicles has the potential to highly benefit transportation systems. That said, we propose a robust recursive regulator for discrete-time Markov jump linear systems with polytopic uncertain data. We assume the uncertainties affect the state-space parameters as well as the transition probability matrix. We formulate an optimization problem using the penalty function method and design a recursive solution, whereby we attain the robust state feedback gains. We impose no restrictions on how fast the uncertainties can change between two consecutive iterations. Conditions for convergence and mean-square stability are well defined in terms of Riccati recursive equations. We also set up a procedure for powertrain polytopic model identification based on CAN bus data of an autonomous heavy-duty ground vehicle. We provide two numerical examples to verify the effectiveness of the robust recursive regulator. Moreover, in a third example, we validate the obtained powertrain model on a trajectory tracking problem and compute acceleration and braking inputs using the robust regulator.

Engine Order Cancelation in a super sports car cabin

Active noise control (ANC) has been used in several systems to improve acoustic performance while avoiding increases in the size and weight required by passive noise control solutions. This paper considers an ANC application aimed at controlling the sound field in the cabin of a supersports car with a naturally aspirated engine. This work studies the performance of a system that employs microphones and loudspeakers in the car cabin and uses a reference signal obtained from CANbus data. The controller exploits a multichannel, multi-order Fx-LMS algorithm to control the sound only in the region surrounding the driver's ears. Due to the nature of the car, this ANC application provides an interesting and challenging case study due to the rapidly changing acoustic excitation provided by the engine. The paper therefore describes a system for noise reduction, focusing on engine order reduction, aiming at achieving control during the most representative manoeuvre known as the "Wide-Open Throttle" (WOT). The investigated controller - acting locally and only on some orders simultaneously - shows good sound attenuation over the entire spectrum, with peaks of reduction exceeding 10 dB, and achieving a 5% overall decrease in the loudness, which is calculated according to ISO 532-1.

Learning industrial vehicles’ duty patterns: A real case

Industrial vehicles’ activities are rather heterogeneous and depend on vehicle’s type, model, and context of usage. Vehicles’ workload is usually summarized by means of standard labels, called duties. Fleet managers exploit duties to optimize productivity, minimize costs, show compliance with industrial regulations, and prevent vehicle misuses and failures. Duty levels are commonly manually enforced by manufacturers separately for each vehicle. However, since vehicles’ usage levels may fluctuate from light to heavy with variable periodicity, duty level assignment requires periodic customization. This paper addresses the problem of automatically defining the current duties of industrial vehicles and their corresponding levels by means of machine learning techniques applied to CAN bus data. In the context of industrial vehicle management, a key challenge is the lack of annotated data due to the following reasons: (1) The high variability in vehicles’ models and types makes the annotation process extremely labor-intensive. (2) Duty levels depend on the current usage context thus requiring periodic refinements. To tackle these issues, we propose a new two-stage method based on segment-wise clustering of per-vehicle CAN bus signals followed by a point-wise duty level assignment applied to the samples of each segment. Segments of per-vehicle CAN bus signals are first grouped together to discover similar vehicle usage profiles. Then, separately for each group we automatically derive the point-wise duty levels characterizing each usage profile. The proposed methodology is unsupervised, i.e., it does not require the availability of any annotated data, and is portable to arbitrary vehicles and context of usage without the need of human intervention. The effectiveness of the proposed methodology is validated on CAN bus data acquired from real vehicles of different types and models. The empirical evidence confirms its applicability to real industrial scenarios.

Driver identification through vehicular CAN bus data: An ensemble deep learning approach

AbstractDriver identification using in‐vehicle data is receiving considerable attention in the field of intelligent transportation owing to the advances in deep learning (DL). In order to improve accuracy and robustness of identification, this paper proposes an ensemble deep learning framework that integrates a modified one‐dimensional convolutional neural network (M 1‐D CNN) and bidirectional long short‐term memory (BLSTM) to improve the performance and robustness of driver identification using information extracted from vehicular CAN‐bus signals. The M 1‐D CNN architecture is developed by adopting inception blocks, residual connection, and global average pooling to obtain optimal deep‐feature representations of local time series. The BLSTM is used to learn the bidirectional long‐term temporal dependencies. Results of extensive experiments using real driving data show that the proposed ensemble DL model can improve the accuracy and robustness of driver identification. Furthermore, four data augmentation methods, namely up‐sampling, adding noise, data reversal, and random drifting, are used to expand the original training data to improve the performance of the ensemble method. Especially, few‐shot learning is performed using the four data augmentation methods, and it shows excellent potential for driver identification with limited data.

A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

The frequent traffic accidents lead to a large number of casualties and large related financial losses every year; this serious state is owed to several factors; among those, driving behavior is one of the most imperative subjects to discuss. Driving behaviors mainly include behavior characteristics such as car-following, lane change, and risky driving behavior such as distraction, fatigue, or aggressive driving, which are of great help to various tasks in traffic engineering. An accurate and reliable method of driving behavior recognition is of great significance and guidance for vehicle driving safety. In this paper, the vehicle multisensor information, vehicle CAN bus data acquisition system, and typical feature extraction methods are summarized at first. And then, several driving behavior recognition models based on machine learning and deep learning are reviewed. Through a detailed analysis of the features of random forests, support vector machines, convolutional neural networks, and recurrent neural networks used to build driving behavior recognition models, the following findings are obtained: the driving behavior model constructed by traditional machine learning model is relatively mature but it is greatly affected by feature extraction, data scale, and model structure, which affects the accuracy of the final driving behavior recognition. Deep learning model based on a neural network has achieved high accuracy in identifying driving behavior, and it may gradually become the mainstream of constructing the driving behavior model with the development of big data, artificial intelligence technology, and computer hardware. Finally, this paper points out some content that needs to be further explored, to provide reference and inspiration for scholars in this field to continue to study the driving behavior recognition model in depth.

Employment of instrumented vehicles to identify real-time snowy weather conditions on freeways using supervised machine learning techniques – A naturalistic driving study

This study introduces the idea of using vehicles as weather sensors to identify real-time weather on freeways in the same context as Road Weather Information System (RWIS) but in a continuous, trajectory-level, and for road segments allocated in the vehicles paths. The study developed a novel approach to detect snowy and clear weather conditions by utilizing real-time data collected from vehicles' external sensors and CANbus. The proposed approach used time series datasets from the SHRP2 Naturalistic Driving Study (NDS), collected during normal driving conditions on freeways. Trips occurring in snowy weather alongside matched trips in clear weather were segmented into time- and distance-based segments such as a one-minute, one-mile, and half a mile. Three assemblies of the input data are considered in the modeling step: data collected from external sensors, CANbus data, and these two data combined. Data analysis was implemented using the Deep Learning Artificial Neural Network, Decision Tree, Random Forest, and Gradient Boosted Trees models. The results indicate that using different segmentation levels provides decent results in detecting snowy weather. The accuracy in estimating the real-time snowy weather was in ranges of 80% to 85%, 71% to 79%, and 73% to 83% for the one-minute, one-mile, and half mile segmentation types, respectively. The GBT model performed the best among all models based on the area under the Receiver Operating Characteristics (ROC) curve, the highest cumulative percentage in estimating the snowy weather using the lower portion of the population, and the highest overall accuracy. Results indicated that an accuracy of 83% in estimating snowy weather conditions could be accomplished using the data collected from external sensors only without accessing CANbus data.

Real-time snowy weather detection based on machine vision and vehicle kinematics: A non-parametric data fusion analysis protocol

Introduction: This study introduces a new analysis protocol for detecting real-time snowy weather conditions on freeways by utilizing trajectory-level data extracted from the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) dataset. The data include parameters reduced from a real-time image feature extraction technique, time series data collected from external sensors, and CANbus data collected by the NDS ego-vehicles. To provide flexibility in winter maintenance, two segmentation types of one-minute and one-mile segments were used to sample snowy trips and their matched clear weather trips. Method: In this study, four non-parametric models were developed using six data assemblies to detect snowy weather on freeways. The data assemblies are arranged based on three data sources, including image database extracted from an in-vehicle video camera, sensors, and CANbus data, to examine the effectiveness of snow detection models for different data types considering real-time availability of data. Results: Overall, the developed models successfully detected snowy weather on freeways with an accuracy ranging between 76% to 89%. Results indicated that high accuracy of estimating snowy weather can be accomplished using the data fusion between external sensors data and texture parameters of images, without accessing to CANbus data. Practical Applications: Practical applications can be driven with respect to the time or distance coordinates, using different data fusion assemblies, and data availability. The study proves the importance of employing vehicles as weather sensors in the Connected Vehicles (CV) applications and Variable Speed Limit (VSL) to improve traffic safety on freeways.

CANBUS-enabled activity-based costing for leveraging farm management

The improvement of economic management in farms has become an important research topic in recent decades as the most dominant feature of current farm management information systems (FMIS). Production cost statistics allow farmers to assess the economic impact of farm activities and compare historical data against previous farm practices or competitors’ activities. Therefore, the availability of reliable cost data is of utmost importance for FMIS, especially data on agricultural machinery usage. Technical sheets, grey literature, and international standards provide estimates of farm operation costs, but they suffer from low accuracy because agricultural machinery is subjected to the high variability of both environmental and working conditions. Based on these considerations, this work aims to develop a novel methodology for cost calculations of field operations harnessing real-world CANBUS data based on the activity-based costing (ABC) approach. The research was conducted on a 198-kW tractor equipped with a CANBUS logger and several implements on which Bluetooth beacons were installed to automatically recognise agricultural operations. The acquired data were processed to identify the daily jobs performed by observing machine position (e.g., field, farm, or road) and operating condition states (e.g., moving, fieldwork, or idling). The ABC approach was applied in two steps: first, cost driver rates were assessed to define capital and non-capital costs; then, the costs of each agricultural operation performed were defined, correlating the cost drivers with the recorded jobs. The results show that fuel and labour costs combined affect 63%–71% of the total cost per hectare for the tested implements. The cost per hectare was found to be highly variable: the biggest gap between the higher and lower values registered with the same implement was 216.48 € ha−1. This methodology could help farmers to make more thoughtful decisions about crop, land, and farm operations management.

Mass estimation of ground vehicles based on longitudinal dynamics using loosely coupled integrated navigation system and CAN-bus data with model parameter estimation

This work considers real-time estimation of the mass of ground vehicles equipped with on-board diagnostics (OBD-II) capabilities. The mass is estimated with a recursive least squares filter, based on vehicle longitudinal dynamics using data from a 6-axis inertial measurement unit (IMU), OBD-II data, as well as global navigation satellite system (GNSS) positions. Only basic OBD-II parameters from the controller area network (CAN-bus) are used, including vehicle speed, engine speed, and engine load. This ensures a broad applicability of the presented work. A loosely coupled fusion of IMU data, OBD-II vehicle speed, and GNSS positions is used for vehicle state estimation. The road grade is estimated from the vehicle pitch angle, current gear is predicted from the ratio of engine and vehicle speeds, and engine torque is estimated from a linear regression with engine speed and load as parameters. The regression is carried out using engine torque estimated from the vehicle longitudinal dynamics, assuming a known vehicle mass. Model parameters are estimated from the engine torque regression, utilizing a linear correlation between the engine torque and OBD-II engine load. A method for automatic estimation of vehicle gearing ratio values from drive data is also proposed. The result is a two-phase approach with an initial training phase for parameter estimation, followed by an operational phase where the vehicle mass can be estimated. The methods are validated using data from a modern car. Parameters and regression coefficients are first estimated from a single test drive. Vehicle mass is then estimated using data from 18 drives on an 85 km test route, comprising of more than 1600 km of driving under different condition, including varying vehicle loads, tire pressures, and window openings. Using the fitted model parameters, the model is generally able to estimate masses within ± 5% of the actual. The change in tire pressures and windows openings do not show significant effects on the estimated masses. Ambient wind during the test drives appears to present a significant source of uncertainty in the estimates at higher speeds. A method for compensation of the ambient wind should be investigated.

Experimental Analysis of Trustworthy In-Vehicle Intrusion Detection System Using eXplainable Artificial Intelligence (XAI)

Anomaly-based In-Vehicle Intrusion Detection System (IV-IDS) is one of the protection mechanisms to detect cyber attacks on automotive vehicles. Using artificial intelligence (AI) for anomaly detection to thwart cyber attacks is promising but suffers from generating false alarms and making decisions that are hard to interpret. Consequently, this issue leads to uncertainty and distrust towards such IDS design unless it can explain its behavior, e.g., by using eXplainable AI (XAI). In this paper, we consider the XAI-powered design of such an IV-IDS using CAN bus data from a public dataset, named “Survival”. Novel features are engineered, and a Deep Neural Network (DNN) is trained over the dataset. A visualization-based explanation, “VisExp”, is created to explain the behavior of the AI-based IV-IDS, which is evaluated by experts in a survey, in relation to a rule-based explanation. Our results show that experts’ trust in the AI-based IV-IDS is significantly increased when they are provided with VisExp (more so than the rule-based explanation). These findings confirm the effect, and by extension the need, of explainability in automated systems, and VisExp, being a source of increased explainability, shows promise in helping involved parties gain trust in such systems.

Energy efficiency of a hybrid cable yarding system: A case study in the North-Eastern Italian Alps under real working conditions

In order to reduce greenhouse gas emissions, low emission or zero-emission technologies have been applied to light and heavyduty vehicles by adopting electric propulsion systems and battery energy storage. Hybrid cable yarders and electrical slack-pulling carriages could represent an opportunity to increase the energy efficiency of forestry operations leading to lower impact timber harvesting and economic savings thanks to reduced fuel consumption. However, given the limited experience with hybrid-electric systems applied to cable yarding operations, these assumptions remain uncertain. This study assessed an uphill cable yarding operation using a hybrid cable yarder and an active slack-pulling electric power carriage over thirty working days. A total of 915 work cycles on four different cable lines were analysed. Longterm monitoring using Can-BUS data and direct field observations were used to evaluate the total energy efficiency, total energy efficiency (%), and fuel consumption per unit of timber extracted (L/m3). The use of the electric-hybrid system with a 700 V supercapacitor to store the recovered energy made it possible to reduce the running time of the engine by about 38% of the total working time. However, only 35% to 41% of the Diesel-based mechanical energy was consumed by the mainline and haulback winches. Indeed, the remaining energy was consumed by the other winches of the cable line system (skyline, strawline winches and carriage recharging or breaking during outhaul) or dissipated by the system (e.g., by the haulback blocks). With reference to all work cycles, the highest net energy consumption occurred during the inhaulunload work element with a maximum of 1.15 kWh, consuming 70% of total net energy consumption to complete a work cycle. In contrast, lower energy consumption was recorded for lateral skid and outhaul, recording a maximum of 23% and 32% of the total net energy consumption, respectively. The estimated recovered energy, on average between the four cable lines, was 2.56 kWh. Therefore, the reduced fuel need was assessed to be approximately 730 L of fuel in the 212.5 PMH15 of observation, for a total emissions reduction of 1907 kg CO2 eq, 2.08 kg CO2 eq for each work cycle.

The Empathetic Car

An empathetic car that is capable of reading the driver's emotions has been envisioned by many car manufacturers. Emotion inference enables in-vehicle applications to improve driver comfort, well-being, and safety. Available emotion inference approaches use physiological, facial, and speech-related data to infer emotions during driving trips. However, existing solutions have two major limitations: Relying on sensors that are not built into the vehicle restricts emotion inference to those people leveraging corresponding devices (e.g., smartwatches). Relying on modalities such as facial expressions and speech raises privacy concerns. By contrast, researchers in mobile health have been able to infer affective states (e.g., emotions) based on behavioral and contextual patterns decoded in available sensor streams, e.g., obtained by smartphones. We transfer this rationale to an in-vehicle setting by analyzing the feasibility of inferring driver emotions by passively interpreting the data streams of the control area network (CAN-bus) and the traffic context (inferred from the front-view camera). Therefore, our approach does not rely on particularly privacy-sensitive data streams such as the driver facial video or driver speech, but is built based on existing CAN-bus data and traffic information, which is available in current high-end or future vehicles. To assess our approach, we conducted a four-month field study on public roads covering a variety of uncontrolled daily driving activities. Hence, our results were generated beyond the confines of a laboratory environment. Ultimately, our proposed approach can accurately recognise drivers' emotions and achieve comparable performance as the medical-grade physiological sensor-based state-of-the-art baseline method.

Mass estimation of ground vehicles based on longitudinal dynamics using IMU and CAN-bus data

This paper considers the problem of real-time estimation of ground vehicle mass, based on longitudinal dynamics. Compared to previous contributions, this work relies on basic data from the vehicle controller area network bus. This allows for application to a very broad range of vehicles, by not using vehicle specific parameters such as current gear indicator and engine torque. The current gear ratio is predicted based on the engine and vehicle speeds. The engine torque is modelled by the mass air flow rate of the engine. The road gradient is estimated with an inertial measurement unit, with fusion of gyroscope and accelerometer using a complementary filter. To handle prolonged dynamic conditions affecting the performance of the filter, compensation of external accelerations is made by use of the vehicle speed from the controller area network bus. The mass is estimated with a recursive least squares filter with forgetting factor. The model is validated experimentally with data obtained from test drives with two different petrol powered passenger cars, equipped with a manual transmission and dual clutch automatic transmission respectively. The test drives consist of various driving conditions with different vehicle loads. The resulting mass estimates are generally good for both vehicles with variations within ±5% of the actual masses.