The increased demand for electric transportation has brought new challenges, many of which relate to the limited range and long charging time. This may be addressed by providing reliable and accurate residual-range estimates and energy-optimized route selections. Crucially, the performance of such systems is largely determined by the accuracy of the underlying energy demand prediction algorithm. Existing solutions typically predict energy demand but fail to derive the prediction uncertainty, an ever more sought-after quantity. Methods that attempt to provide such a measure often rely on data-driven techniques or computationally intensive Monte Carlo simulations. This paper is therefore set out to provide a dynamic model capable of handling varying operating conditions whilst remaining computationally efficient. By regarding the driver reference speed as a measurement, an observer-like prediction model can be formulated. Building on this principle, a vehicle-independent description is attained, owing to the use of merely exogenous parameters. The observer is realized as a Cubature Rauch-Tung-Striebel Smoother, which estimates both the state and the uncertainty, providing qualitative information about expected variation. The algorithm’s mean prediction is verified by analyzing the velocity profile, the Kalman gain, and the energy demand, all of which exhibit the expected behavior. Its uncertainty estimate is evaluated against real-world vehicle operation data, which show that the considered uncertainty sources significantly contribute to the energy demand prediction uncertainty. Further improvements in accuracy are expected as more uncertainties are accounted for.

Setting up suspension kinematics targets has been a challenging task for vehicle engineers. The challenges involve a high-dimensional search space, nonlinear relationships between the suspension kinematics and vehicle dynamics, exploration and exploitation trade-offs, and the need for domain-specific knowledge. Traditional multi-objective optimization methods are time-consuming, sensitive to initial conditions, and rarely converge to the global optimum in high-dimensional spaces. This article explores how reinforcement learning can be used to automate the design of suspension kinematics targets, addressing a longstanding challenge in vehicle dynamics design: the inverse problem of satisfying high-level handling objectives through low-level subsystem parameters. The method is based on the accumulation of knowledge through the interaction between an intelligent agent and a simulation environment. The agent optimizes suspension kinematics targets by receiving rewards tied to vehicle dynamics performance. The agent, employing a Gaussian policy and sigma-based sensitivity analysis, enables the identification of critical and non-critical design parameters. The results show that the proposed method can find optimal suspension kinematics targets with the help of accumulated knowledge. The knowledge-guided learning process demonstrates a novel approach to solving high-dimensional optimization problems, offering good convergence time and valuable results. The proposed method contributes to the field by using reinforcement learning to set up suspension kinematics targets in the automotive industry.

Battery electric heavy vehicles with a drivetrain on each axle and friction brake on each wheel offer significant opportunities to enhance vehicle performance and energy efficiency. This paper presents three actuator coordination algorithms for an all-wheel driven heavy vehicle to influence the wheel torque with the aim of optimising the power efficiency and vehicle stability. Two of these algorithms exploit the principle of instantaneous power loss minimisation by using the power loss models of actuators. The wheel force limits were included as a constraint to ensure safe vehicle operation. These strategies were simulated using a high-fidelity vehicle model, including simplified models for the powertrain and friction brakes. Measures such as the power loss of the actuators, longitudinal tyre slip losses, and energy consumption of the coordination strategies during a realistic drive cycle were analysed. Results show that the power loss minimisation algorithm including idle losses in the decision logic, can reduce the energy consumption by up to 7% compared to a strategy only maximising tyre grip.

This paper investigates the energy consequences of determining the energy-optimal velocity profile and torque distribution sequentially versus jointly in a battery electric vehicle (BEV) with two electric motors, one per axle. Three optimization architectures are evaluated: a centralized architecture (CA), a de-centralized architecture (DCA) and a refined de-centralized architecture (r-DCA). CA jointly optimizes the velocity trajectory and torque distribution for minimal energy consumption in a predictive framework, while DCA solves these subproblems hierarchically: velocity trajectory optimization is performed predictively, and torque distribution is computed instantaneously. The joint optimization in CA leads to a reduction in energy consumption of 3.3% at low velocities and 2.2% in an urban city cycle compared to DCA. To mitigate the energy consequences, the objective function in the predictive layer of DCA is augmented with an aggregated power loss map of the powertrain in r-DCA, which achieves energy savings close to CA.

Bus users (drivers and passengers) are exposed to vibrations during a journey. Vibration exposure can cause motion sickness, impair ride comfort, and even impact health. Road roughness is the primary source of vehicle vibration. Combined with floating bridge motions, wind loads and high vehicle speeds, the negative vibrational effects can be intensified. This paper investigates the influence of Bj & Oslash;rnafjorden floating bridge motions and wind excitations on bus users' ride comfort and motion sickness for several weather storm conditions. A 13-degree-offreedom (DOF) intercity bus model with a driver and three passengers was defined for this analysis. The results showed that wind excitations and storm conditions severity significantly affect vehicle velocities at which ISO 2631/1997 ride comfort limits (a little uncomfortable and fairly uncomfortable) are reached. The passenger in the middle of the bus feels the most comfortable whereas the passenger in the rear part of the bus the least comfortable. The highest value of motion sickness incidence for every user is achieved for the lowest bus speed of 36 km/h due to the longest time of vibrational exposure. Among users, the driver is the most likely to feel motion sickness on a floating bridge due to his suspended seat.

Coastal highway route E39 is immense road project in Norway with the aim to shorten the journey time between the south part (Kristiansand city) and the north part of the country (Trondheim city). Different high-tech structures will make E39 route continuous and reduce the travel time from currently 21 h to 11 h. A floating bridge has been considered for Bj & Oslash;rnafjorden. This paper suggests bus safe speeds for travel on a floating bridge exposed to 10 different storm conditions (W1-W10). The results show that the coach does not stray from the traffic lane under mild storm conditions (W1-W2) even for the highest vehicle speed of 108 km/h. However, at a speed of 90 km/h for W6 and W7 and at a speed of 72 km/h for W8, the vehicle severely and often departures the traffic lane. At 36 km/h, 54 km/h and 72 km/h for strong storms (W9-W10), the windward rear wheel of the bus frequently loses contact with the floating bridge deck.

This paper proposes an energy efficient hierarchical wheel torque controller for a 4×4 heavy electric vehicle equipped with multiple electric drivetrains. The controller consists of two main components: a global force reference generator and a control allocator. The global force reference generator computes motion requests based on steering wheel angle and longitudinal acceleration inputs, while adhering to actuator and tire force constraints. For this purpose, a linear time-varying model predictive controller (LTV-MPC) is employed to minimize the squared errors in yaw rate and longitudinal acceleration over a short prediction horizon. Concurrently, the controller dynamically identifies safe operating limits based on current driving conditions. These limits are then used to adjust the state cost weights dynamically, thereby improving the effectiveness of the MPC cost function. The control allocator (CA) subsequently distributes the force demands from the global reference generator among the electric machines and friction brakes. This allocation process minimizes instantaneous power losses while respecting actuator and tire force constraints. To further enhance energy efficiency, the method leverages the heterogeneous nature of the electric machines by minimizing not only operational power losses but also idle losses (power losses at zero torque), ensuring safe vehicle operation. The proposed strategy is evaluated using a high-fidelity vehicle model under various driving scenarios, including low-friction surfaces and near-handling-limit conditions. Simulation results demonstrate that dynamically varying state cost weights in conjunction with safe operating limits significantly improves vehicle performance, enhances energy efficiency, and reduces driver effort.

In this paper, we compared the linear and nonlinear motion prediction models of a long combination vehicle (LCV). We designed a nonlinear model predictive control (NMPC) for trajectory-following and off-tracking minimisation of the LCV. The used prediction model allowed coupled longitudinal and lateral dynamics together with the possibility of a combined steering, propulsion and braking control of those vehicles in long prediction horizons and in all ranges of forward velocity. For LCVs where the vehicle model is highly nonlinear, we showed that the control actions calculated by a linear time-varying model predictive control (LTV-MPC) are relatively close to those obtained by the NMPC if the guess linearisation trajectory is sufficiently close to the nonlinear solution, in contrast to linearising for specific operating conditions that limit the generality of the designed function. We discussed how those guess trajectories can be obtained allowing off-line fixed time-varying model linearisation that is beneficial for real-time implementation of MPC in LCVs with long prediction horizons. The long prediction horizons are necessary for motion planning and trajectory-following of LCVs to maintain stability and tracking quality, e.g. by optimally reducing the speed prior to reaching a curve, and by generating control actions within the actuators limits.

The electrification of towing and trailing units creates new torque allocation alternatives among different units of articulated heavy vehicles. To increase the power and energy efficiency, control algorithms can request propulsion or regenerative braking from a single unit while keeping the other units unbraked or unpropelled. However, this may lead to safety problems, such as jackknifing or trailer swing. This paper uses a high-fidelity simulation tool to formulate safe operating envelopes for a tractor and semitrailer combination for braking-in-turn cases. The effects of different vehicle and environment parameters on the safe operating envelope are studied. The safe operating envelope obtained is then validated using real vehicle tests and can be used with any control algorithm to avoid requesting unsafe unit force combinations.

Residual range estimation plays a crucial role in route selection and the trust of electric vehicles (EVs). With inspiration from longitudinal vehicle dynamics, a simple and computationally efficient model for traction power is presented. Such a model has the advantage of being exclusively based on vehicle exogenous parameters. The model allows for insight into variations in power usage along a transport operation and separation of power losses originating from air drag, rolling resistance, hill climbing, and inertial forces. A model of this structure can handle regenerative braking and estimate service brake usage as an additional feature. Also, it treats the inherent truncation bias resulting from truncating a stochastic process. Evaluation of the performance is presented using Monte Carlo simulations, comparing the estimation error against a simple benchmark model and vehicle log data.

To guide the development of driver assistant systems and fully automated solutions for reversing long combination vehicles (LCVs), the principles for reversing LCVs are investigated using the articulation angle gradient. The widely used Steady-state Circling Limitation (SSCL) in reversing LCVs has two main drawbacks: it restricts vehicles from operating with large articulation angles crucial for tight spaces and lacks a well-defined feasible range. Two new reverse principles are introduced that can provide better insight. The first principle extends SSCL to include more extreme articulation angles for single-articulated vehicles. It also addresses the necessity of considering articulation gradients when developing the continuous reverse limitation for multi-articulated vehicles. The second principle introduces limited distance reversing for vehicles that no longer meet the first principle's requirements, providing additional vehicle ending poses useful for tasks like loading and coupling.

Articulated heavy vehicles (AHVs) face yaw instabilities, especially under extensive propulsion or regenerative braking force on the driven axles, risking their directional stability and potentially leading to jackknifing. Hence, safe operating envelopes (SOEs) are essential for allocating propulsion and braking forces among different units. This study proposes a novel approach to ensure yaw stability by reducing longitudinal slip limits of the electric motors (EMs) based on side-slip, enhancing stability and acceleration performance. Validation through simulations and real vehicle tests shows promising results.

With rising customer expectations and additional requirements stemming from the electrification, today’s suspensions need to fulfill an increasing number of requirements: Aerodynamic efficiency targets are stricter, driving properties are defined more specifically and the use of carry-over-parts is growing. Moreover, the package volume has a huge effect on the exterior design as well. This leads to complications in the pre-development process. A typical problem is the sequence of development steps: if a completely new suspension is designed, is it more important to optimize the hard points and adjust the part geometry accordingly or vice versa? The common approach of a trial-and-error method is time consuming, since the design of a suspension concept takes days of engineering work. To meet this dilemma, a new approach is developed. With an automized design method for kinematics and elastokinematics paired with an automatic packaging evaluation, it is possible to create a first feasible solution within minutes. This concept can then be evaluated and improved either in terms of hard points, bushing stiffness or packaging. Since a much higher amount of possible suspension designs can be evaluated, the probability to find an adequate solution rises tremendously. This approach is demonstrated for an optimized five link suspension for battery-electric vehicles (BEVs). The shape of the suspension volume should be modified in a way, that the height of engine hood can be lowered. Therefore, the aerodynamic behavior has potential to be improved. It is found that the design of an innovative concept solution can be supported by using automated methods.

The electrification of commercial vehicles has led to new wheel torque allocation options for propulsion and braking of articulated heavy vehicles. Each unit or axle can be individually braked or propelled whilst the other units or axles are excluded from such action. This may achieve the best energy efficiency. However, this can also lead to potential yaw stability problems such as jackknifing and trailer swing, especially under bad loading and weather conditions. This paper describes the above instabilities and introduces a nonlinear single-track model to study the vehicle dynamics of the tractor-semitrailer combination. The effects of different vehicle and environment parameters are analysed with this vehicle model. A safe operating envelope for limiting the wheel forces is obtained using this vehicle model. Since the vehicle model introduced in this paper is computationally effective, it can be run online in real vehicles, with an instantaneous safe operating envelope obtained for the momentary conditions. Thus, yaw instabilities such as jackknifing and trailer swing may be avoided.

The accuracy of transient tyre models may be largely improved by considering the flexibility of the tyre carcass. Several formulations, whereby the unsteady behaviour of the tyre is approximated using linear or nonlinear systems of ordinary differential equations (ODEs), are already available in the literature. However, when the tread behaviour is described using a distributed representation, that is, in terms of partial differential equations (PDEs), the inclusion of even the simplest model to represent the deformation of the tyre carcass leads to rather involved PDE or interconnected PDE-ODE systems, with nonlocal and boundary terms. Such descriptions require detailed analyses that have not been attempted so far. Therefore, this paper investigates the salient properties of the classic brush and LuGre-brush models considering the effect of a flexible carcass. For both formulations, the existence and uniqueness of the solution are discussed. For the standard version of the brush models, a closed-form solution is provided under the assumption of vanishing sliding, whereas the case of limited friction is explored only qualitatively. Concerning the LuGre-brush variant, the preliminary intuition gained from the analysis of the distributed representation is effectively used to develop approximated lumped formulations to be used in control-oriented applications.

ABSTRACT Tire slip losses have been shown to have a significant impact on vehicle performance in terms of energy efficiency, thus requiring accurate studies. In this paper, the transient dissipation mechanisms connected to the presence of micro-sliding phenomena occurring at the tire–road interface are investigated analytically. The influence of a two-dimensional velocity field inside the contact patch is also considered in light of the new brush theory recently developed by the authors. Theoretical results align with findings already known from literature but suggest that the camber and turn spins contribute differently to the slip losses and should be regarded as separate entities when the camber angle is sufficiently large. The present work shows that an additional amount of power which relates to the initial sliding conditions is generated or lost during the unsteady-state maneuvers. A simple example is presented to illustrate the discrepancy between the microscopic and macroscopic approaches during a transient maneuver.

Articulated Heavy Vehicles (AHVs) play a crucial role in today’s transportation, offering significant commercial and environmental advantages. However, challenges like jackknifing and trailer swing in AHVs highlight the need for focused research. This paper introduces innovative yaw stability algorithms designed to tackle these concerns, employing advanced control allocation techniques, including distributed control allocation. A power loss minimization algorithm with four different methods to maintain yaw stability is tested with real test vehicles. In this framework, the algorithms ensure that control actions stay within predetermined safe limits, contributing significantly to the overall safety and efficiency of AHVs.