Model Based Road Slope and Mass Estimation of Road Vehicles

Proper braking force distribution strategies can improve both stability and economy performance of hybrid electric vehicles, which is prominently proved by many studies. To achieve better dynamic stable performance and higher energy recovery efficiency, an effective braking control strategy for hybrid electric buses (HEB) based on vehicle mass and road slope estimation is proposed in this paper. Firstly, the road slope and the vehicle mass are estimated by a hybrid algorithm of extended Kalman filter (EKF) and recursive least square (RLS). Secondly, the total braking torque of HEB is calculated by the sliding mode controller (SMC), which uses the information of brake intensity, whole vehicle mass, and road slope. Finally, comprehensively considering driver's braking intention and regulations of the Economic Commission for Europe (ECE), the optimal proportional relationship between regenerative braking and pneumatic braking is obtained. Furthermore, related simulations and experiments are carried out on the hardware-in-the-loop test bench. Results show that the proposed strategy can effectively improve the braking performance and increase the recovered energy through precise control of the braking torque.

<div class="section abstract"><div class="htmlview paragraph">A variety of vehicle controls, from active safety systems to power management algorithms, can greatly benefit from accurate, reliable, and robust real-time estimates of vehicle mass and road grade. This paper develops a parallel mass and grade (PMG) estimation scheme and presents the results of a study investigating its accuracy and robustness in the presence of various noise factors. An estimate of road grade is calculated by comparing the acceleration as measured by an on-board longitudinal accelerometer with that obtained by differentiation of the undriven wheel speeds. Mass is independently estimated by means of a longitudinal dynamics model and a recursive least squares (RLS) algorithm using the longitudinal accelerometer to isolate grade effects. To account for the influences of acceleration-induced vehicle pitching on PMG estimation accuracy, a correction factor is developed from controlled tests under a wide range of throttle levels. The estimation approach is applied to data collected while driving on public roads under a variety of driving conditions, replicating several noise factors associated with daily driving. Thresholds are developed to isolate driving events that are likely to result in quick and accurate mass estimates, and an averaging filter is then applied to these converged values. The algorithm is shown to rapidly deliver estimates within 3% of true vehicle mass.</div></div>

Road gradient and vehicle mass are estimated using a method with variable forgetting factor, which has strong adaptability and following capability. A control strategy with hierarchical correction is proposed for a vehicle that shifts on a slope. The shifting process is composed of the upper identification decision and lower shift correction executive layers. The upper layer focuses on collecting vehicle parameters, estimating road gradient and vehicle mass, and presenting a modified control decision. The lower layer performs the slope shift schedule from the upper layer. Results of the simulation test are shown. Furthermore, the designed strategy can duly downshift the gear uphill and avoid frequent shifting, thereby reducing the wear of the transmission parts and downshifting the gear timely downhill to utilise the torque characteristics of engine braking and reduce the wear of the braking system.

<div class="section abstract"><div class="htmlview paragraph">For modern vehicle development, on-board vehicle Mass and road Gradient Estimation (MGE) can offer great benefit to many sub-systems on the vehicle, such as vehicle control system, transmission control system, and active safety system etc. However, there are still several challenges that need to be solved. Firstly, thanks to good accuracy, reliability, and robustness, regression analysis-based approaches: Recursive Least Squares (RLS) and Kalman Filter (KF) are very popular for MGE, but the trade-off between estimator’s accuracy and converge time is challenging. Furthermore, depending on vehicle and powertrain types, the implementation of MGE function could be very different. It is desired to have a structured approach for various vehicle applications’ MGE development. Lastly, good reliability of MGE does not always satisfy for complicated real-world driving maneuvers and road conditions.</div><div class="htmlview paragraph">This paper introduced a mature MGE development approach which utilizes an innovative Series Kalman Filter (S-KF) structure with a 3-axis accelerometer to address the challenges above, leading to the demonstration on 3 different types of vehicles with different powertrain applications: a truck with Internal Combustion Engine (ICE) and Automated Manual Transmission (AMT) gearbox, a mid-size Plug-in Hybrid Electric Vehicle (PHEV) SUV, and an A-segment Battery Electric Vehicles (BEV) mini car. They are tested under wide range real-world driving maneuvers and road conditions. Moreover, to improve the converge time whilst keeping the same accuracy of MGE, several dedicated designed engineering features are implemented and introduced in this paper. The vehicle-based experimental result and structured development approach showed good performance and maturity of the production oriented MGE, and good efficiency and flexibility to adapt it to various applications.</div></div>

This paper has presented a control system for vehicle dynamics and mass estimation. The objective of this paper is to use a single-tyre model of slip control integrated with extended Kalman filter (EKF) to estimate the states of a vehicle such as the forward velocity, wheel slip, coefficient of friction of the road surface and the mass that cannot be measured directly. In order to do this, the dynamics of a vehicle moving with a forward velocity were obtained using a single-tyre model. The dynamic equations in continuous time were transformed into their equivalent discrete time form. A two degree of freedom proportional integral and derivative (2DOFPID) control algorithm was implemented for the control loop. An estimator was designed using the extended Kalman filter algorithm to carry out the estimation based on noisy measurement of wheel rotational speed. The entire system was modeled using Matlab/Simulink blocks. Simulations were performed to determine the effectiveness of the estimator. The simulation results showed that the extended Kalman filter effectively estimated the states of a single-tyre model of a vehicle represented by a slip control system. Though the results obtained seemed promising but will be improved if the covariance matrices are calculated with adequate information and are better tuned.

This work explores converting a conventional 1996 Honda Civic Coupe into a battery-electric vehicle (BEV). A simulation model is developed to analyze the vehicle’s dynamic behavior under various conditions, including changes in vehicle mass and road slope. The system specifications are detailed based on a 50 kW AC induction motor, controller, battery, and battery management system. The battery comprises 2,376 NCR18650B cells with a capacity of 23 kWh based on inverter requirements. Energy consumption is simulated using the New European Driving Cycle (NEDC), resulting in 1.69 kWh to travel 10.8 km. Autonomy is estimated at 147 km. The environmental impact of 19.2 g CO2/km is calculated. The results illustrate the change in maximum speed, acceleration, and distance traveled based on changes in mass and slope. It is concluded that mass variation has a lesser impact on performance than road slope. The work demonstrates the feasibility of converting a conventional vehicle to a BEV with comparable dynamic capabilities using currently available technology. Future development plans include constructing a battery prototype, electrifying the vehicle, and incorporating instrumentation for validation.

Vehicle Mass and Road Slope Estimation Based on Interactive Multi-model（in English）

<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>

The recursive least square (RLS) algorithm is insensitive to the dispersions of the correlation matrix. Consequently, the usage of the RLS algorithm to update the adaptive recursive filter coefficients are attractive for the system modeling and identification fields. In this paper, an error function RLS (EFRLS) algorithm is derived for the adaptive recursive filter. Furthermore, a scaled version of the EFRLS algorithm is proposed where the computation complexity is significantly reduced. The scaled EFRLS algorithm can be realized in real time using the available digital signal processors. The convergence to the optimal solution of both the EFRLS algorithm and its scaled version is ensured while the general RLS algorithm fails to converge to the optimal solution for the multimodal performance criterion.

Recursive least square vehicle mass estimation based on acceleration partition

A receding horizon optimal control approach is proposed to control a system in such a way as to best estimate on-line its states and parameters, without significantly degrading tracking performance or violating pointwise-in-time constraints. Several automotive examples are considered to illustrate the potential of the approach. They include: vehicle mass and road grade estimation, engine wall-wetting parameter estimation, engine mapping, and estimation of the position and velocity of a moving vehicle from angle-only (passive) measurements by another vehicle.

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.

Today's car manufacturers are racing in deploying new innovative functionalities in modern cars like human-machine interface (HMI) technologies, cloud-based services, vehicle ad-hoc networks (V ANET) and autonomous driving. Such new technologies increase the complexity of vehicle's E/E architecture and adds new requirements on automotive software systems. This can specially be seen in cockpit domain units like Advanced Driver Assistance Systems (ADAS), Infotainment Head Units (IHU) and Telematics (TEM). The software applications of such units now exhibit large variations in requirements in terms of safety, security and connectivity as they are involved in both in-Vehicle network communication and cellular vehicle communication (V2X). In addition to that, Original Equipment Manufacturers (OEMs) are heading towards consolidating multiple units into single high computing platform. Although this simplifies the networking model of the vehicle, it adds more challenges on the architecture of automotive software systems. This paper focuses on utilizing hardware-assisted virtualization techniques to allow consolidating these heterogeneous applications on the same hardware. The performance of the proposed approach is evaluated to proof meeting the requirements of such applications.

This chapter addresses a selection of current and future challenges facing the passenger car. The development, production, and use of passenger cars is driven by different global trends that are energy-related, economic in nature, subject to raw material availability, and powered by demographic changes. These trends and many other influences result in a range of vehicle and powertrain solutions being required and have impacts on vehicle systems, such as body, chassis, electrics/electronics, and on vehicle embedded functions, such as advanced driver assistance systems (ADAS). Depending on the target markets and overall sales volume of each vehicle manufacturer, different platform strategies and new original equipment manufacturer (OEM) partnering models need to be considered. The consequent application of lightweight engineering needs to be applied in all vehicle systems to meet increasing demands for vehicle safety and comfort features and the strong demand for powertrain electrification (environmentally friendly and affordable) with the resulting use of bigger vehicle batteries.

Heavy commercial road vehicle (HCRV) platoons are viable logistic solutions to freight movement. During long haul platoon operation, it is common to encounter roads of different gradients. This paper investigates the effect of brake fade phenomenon, which happens due to the continuous application of brake during downgrade operation on the string stability of HCRV platoons. A brake actuator model incorporating temperature effects during braking and characterizing brake fade has been used. A Sliding Mode Control (SMC) based string stable controller, which compensates for brake fade, has been designed. Since the brake fade factor and hence platoon stability directly depend upon the road gradient and vehicle mass, which are not directly measurable quantities, an algorithm that adaptively estimates the same has been integrated with the controller design. The algorithm could estimate the mass and gradient values with less than 2% mean absolute percentage error. The stability of the proposed fade compensated controller has been analyzed and its efficacy has been tested for various road conditions and for homogeneous and heterogeneous (overloaded cases) platoon operations. The proposed approach was seen to ensure string stability for all the considered test scenarios.