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

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.

Performance, fuel economy, driving experience, and safety are the most critical attributes automotive original equipment manufacturers (OEMs) focus on meeting customer requirements. OEMs are coming up with many features content to meet the customer requirements while meeting the stringent regulations regarding emissions and safety. Advanced sensors and embedded controllers are employed to meet the above needs. Performance of these algorithms can be significantly improved if we accurately predict a few important vehicle operational parameters. Vehicle mass and road slope are two crucial parameters essential for developing and optimizing control algorithms of powertrain, brake, and advanced driver assistance systems (ADAS). This study focuses on model-based road slope and vehicle mass estimation by integrating a state estimation algorithm and a longitudinal dynamics model. Drive-train torque, vehicle velocity, and vehicle design parameters are inputs for developing a longitudinal dynamics model. Initial approximate estimates of road slope and vehicle mass are given as initialization values for the state estimation algorithm, and future estimates are obtained using recursive adaptive filtering. A comparative study was performed between Extended Kalman filter (EKF) and Recursive Least Square (RLS) estimation algorithms for the convergence rate of state variables and error with respect to the actual state.

<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>

Recursive least square vehicle mass estimation based on acceleration partition

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.

Active suspension can improve vehicle vibrations caused by road excitation. For trucks, the vehicle mass change is usually large, and changes in vehicle mass will affect the control performance of the active suspension. In order to solve the problem of active suspension control performance decreasing due to large changes in vehicle mass, this paper proposes an active suspension control method integrating online mass estimation. This control method is designed based on the mass estimation algorithm of the recursive least squares method with a forgetting factor (FFRLS) and the Linear Quadratic Regulator (LQR) algorithm. A set of feedback control matrices K is obtained according to different vehicle masses. Then, the mass estimation algorithm can estimate the actual vehicle mass in real-time during the vehicle acceleration process. According to the mass estimation value, a corresponding feedback control matrix K is selected from the control matrix set, and K is used as the actual control gain matrix of the current active suspension. With specific simulation cases, the vehicle vibration response is studied by the numerical simulation method. The results of the simulation process have shown that when the vehicle mass changes largely, the suspension dynamic deflection and tire dynamic deformation are significantly reduced while keeping a good vehicle body attitude control effect by using an active suspension controller integrated with online mass estimation. In the random road simulation, suspension dynamic deflection is reduced by 3.26%, and tire dynamic deformation is reduced by 5.91% compared with the original active suspension controller. In the road bump simulation, suspension dynamic deflection and tire dynamic deformation are also significantly reduced. As a consequence, the stability and comfort of the vehicle have been greatly enhanced.

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.

<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>

Actual solutions for the road vehicle navigation problem point to the combination of GPS, odometry and inertial sensors. To combine the information coming from these sensors, most of actual researchers rely on the implementation of variations of the Kalman filter (KF) and the extended Kalman filter (EKF) for non-linear systems. Despite the fact that, in these filters, the definition of the proper vehicle model is of extreme importance, there is not a unique common filter suitable for all the usual situations in which a road vehicle is involved. The diversity of possible maneuvers and the need of realistic noise considerations adjusted to each driving situation encourage the application of IMM (interactive multi-model) techniques in the road navigation. Traditionally applied to the aerial sector, IMM based methods run different models at the same time, selecting that one which better represents the system behavior anytime. For road vehicles, the IMM-EKF solution presented in this paper allows the exploitation of highly dynamic models just when required, avoiding the impoverishment of the solution due to unrealistic noise considerations during straight or mild trajectories. Selected results presented in this paper confirm the improvements obtained by using the IMM-EKF developed, as compared with the single model solution

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.

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.

As a basic parameter, vehicle mass is closely related to economic and safety performance, etc. However, mass estimation faces challenges such as model accuracy and generalization ability. To solve these problems, a novel hybrid model-data driven vehicle mass estimation method is proposed. The model-driven method is used for calculating mass, and the data-driven method constructs a random forest-based data classifier to determine whether the data can be used; Then, by finding the relationship between data and classification results, the data structure is determined, based on the principle of increasing recall while ensuring a high precision; Finally, the proposed method is validated by monitoring data, the classification results have a high precision of more than 85%, and the error range of estimated mass is within ±5%. The excellent data classification and mass estimation capabilities enable mass monitoring for heavy-duty vehicles, which is important for global energy management, traffic safety management, etc.

In view of the high difficulty in coupling of various electric vehicle parameters, intractable parameter estimation, and unreasonable distribution of vehicle driving torque, the four-wheel hub motor is applied to drive electric vehicles, which can instantly obtain the torque and speed of the hub motor and achieve precise control of the torque of each wheel. According to the vehicle longitudinal dynamics model, a progressive RLS (PRLS) algorithm for real-time estimation of vehicle mass and road gradient is proposed. Meanwhile, by means of taking the longitudinal acceleration of the vehicle and the road gradient obtained from the estimation algorithm as the parameter of the torque distribution at the front and rear axles, a dynamic compensation and distribution control strategy of the front and rear axle torques is designed. Moreover, based on hardware-in-the-loop real-time simulation and real-vehicle tests, the effectiveness of the proposed estimation algorithm and the rationality of the real-time distribution control strategy of driving torque are verified.

The impact of rail transportation on road vehicles that are placed in flatcars is considered in the article. The features of piggyback transportation and the dynamic forces acting on road vehicles in railway rolling stock are determined. The change dependences of the values of dynamic forces for different masses of road vehicles in the longitudinal and transverse directions are established. It is concluded that road vehicles that are used in regular piggyback transportation will experience significant dynamic loads of structural elements, which requires monitoring of their technical condition. Consequently, the infrastructure of piggyback terminals that load and unload road vehicles onto railway rolling stock should include the availability of automobile service facilities.

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.