This paper presents an active controller for electric vehicles in which active front steering and torque vectoring are control actions combined to improve the vehicle driving safety. The electric powertrain consists of four independent in–wheel electric motors situated on each corner. The control approach relies on an inverse optimal controller based on a neural network identifier of the vehicle plant. Moreover, to minimize the number of sensors needed for control purposes, the authors present a discrete–time reduced–order state observer for the estimation of vehicle lateral and roll dynamics. The use of a neural network identifier presents some interesting advantages. Notably, unlike standard strategies, the proposed approach avoids the use of tire lateral forces or Pacejka’s tire parameters. In fact, the neural identification provides an input–affine model in which these quantities are absorbed by neural synaptic weights adapted online by an extended Kalman filter. From a practical standpoint, this eliminates the need of additional sensors, model tuning, or estimation stages. In addition, the yaw angle command given by the controller is converted into electric motor torques in order to ensure safe driving conditions. The mathematical models used to describe the electric machines are able to reproduce the dynamic behavior of Elaphe M700 in–wheel electric motors. Finally, quality and performances of the proposed control strategy are discussed in simulation, using a CarSim^® full vehicle model running through a double–lane change maneuver.

Cities consume most of the energy used worldwide and are the largest emitters of greenhouse gases (GHGs) that cause global warming, mainly from the road transport sector. In megacities, the light vehicle fleet is responsible for most of the emissions in the sector. Among this fleet, light commercial vehicles (CVs), which have grown to support instant delivery services demand, are also responsible for emissions and traffic congestion. Due to the urgency to reduce transport impacts, emission mitigation strategies are required. Aligned with this aim, this article evaluates GHG emissions along the entire process of energy production, called the operating trajectory, and also known as Well-To-Wheel (WTW), in four combinations of transportation modes for last-mile delivery services, using light CVs, such as electric or diesel vans, and electric cargo bikes (E-bikes). The analysis is firstly conducted in a local area of Mexico City and subsequently compared to other countries around the world. In this respect, the main result of this article shows that in the case study conducted in the Metropolitan Zone of the Valley of Mexico, the energy consumption of a given route for an electric van combined with E-bikes generates 24% less GHG emissions than a diesel van combined with E-bikes. Therefore, the achievement of effective mitigation strategies for GHG emissions reduction through vehicle electrification requires WTW emission analysis and quantification, optimal route design, a combination of sustainable transport modes and clean energy generation.

In this paper an active controller for ground vehicles stability is presented. The objective of this controller is to force the vehicle to track a desired reference, ensuring safe driving conditions in the case of adhesion loss during hazardous maneuvers. To this aim, a nonlinear discrete-time inverse optimal control based on a neural network identification is designed, using a recurrent high order neural network (RHONN) trained by an Extended Kalman Filter. The RHONN ensures stability of the identification error, while the controller ensures the stability of the tracking errors. Moreover, a discrete-time reduced order state observer is utilized to reconstruct the lateral vehicle dynamic not usually available. For the control problem, the references of the lateral velocity and yaw rate are given by a dynamic system mimicking an ideal vehicle having not-decreasing tire lateral characteristics. The proposed approach avoids the identification of the Pacejka’s lateral parameters of the tires, so simplifying the input control determination. Moreover, an optimal control is proposed to optimize the actuator effort and power, usually bounded. Control gains are determined using optimal “nature-inspired" algorithms such as particle swarm optimization. Test maneuvers, performed through the full vehicle simulator CarSim^®, have been used to test correctness, quality and performances of the observer, the neural identifier and the inverse optimal controller. Robustness of the reduced order discrete-time state observer is also discussed for different sample times. Finally, a fair comparison between optimal and non-optimal control schemes is presented, highlighting the numerical results obtained in simulation.

In vehicle systems, the suspension is fundamental in achieving acceptable levels of ride comfort, while ensuring ride safety. Semi-active suspensions allow changing the suspension damping to prioritize different performance metrics. Multiple control techniques have been developed to find a suitable trade-off between comfort and road holding. This manuscript proposes the use of standard (proportional-integral-derivative) controllers tuned in real time by an artificial neural network. The formulation of the controller considers a magnetorheological damper represented through the Bouc-Wen model. A stochastic gradient descent algorithm with backward propagation is used to train the artificial neural network that then selects the controller gains in real time. This technique is tested numerically through quarter and full car models, with the latter one running on the automotive simulation software CarSim. The obtained results highlight significant improvements of the proposed approach in comparison to state-of-the-art controllers. Furthermore, the study proves the viability of running four controllers on a real-time embedded hardware platform through processor-in-the-loop tests.

Big cities affected by intense mobility, traffic and pollution are adopting electrification-based solutions for the reduction of the ${\mathrm{CO}}_2$ emissions of combustion engines. An interesting field in which the transition toward electrification can achieve important benefits is the area of instant deliveries. Instant deliveries deal with the mobility related to commercial trades between suppliers and customers. In this respect, optimal solutions can be considered during route planning based on the minimization of several metrics, such as distance, energy and road slope, among others. To this end, this paper presents an optimal solution to the instant deliveries problem in which the result is the optimal route, in the city under study, that minimizes energy consumption based on road slope and total distance traveled, and that gives higher priority to routes that include cycling infrastructure that the city can provide. The paper uses electric bikes since they are easily transportable and are highly versatile for instant deliveries. The results obtained were compared to a previous version of the optimal algorithm already published by the authors which minimizes the Haversine and Euclidian distances only. It was found that the shortest distance travelled between customers does not necessarily imply the least energy consumption. The latter, in combination with an energy consumption estimation approach, represent the original contribution of the work.

Electrification of internal-combustion-engine vehicles represents a common requirement for urban areas interested in reducing the quantity of air pollution and toxic gas emissions. Along with the transition of private solutions toward individual mobility with electric cars, vehicle electrification for community mobility reaches a high additional value of the atmospheric decarbonization global objective. To this aim, this paper presents both experimental and simulation approaches, for vehicle electrification of student mobility at Tecnológico de Monterrey, where dedicated transportation operates several times a day. The driving cycle and the road slope are determined by making use of GPS based mobile apps first. Then, the daily energy consumption is calculated, and the battery sizing is defined by comparing several electric cells. The safer battery is chosen, and the internal resistance and open-circuit voltage are then characterized with experimental tests.The paper presents a methodology for accurately assessing the energy demands of electric buses. These findings provide insights for battery pack design and selection based on driving cycles and energy requirements, which may be used to accelerate the electrification of bus routes or scale it to large bus fleets. Finally, a co-simulation with Matlab ^® /Simulink ^® and CarSim ^® shows the behavior of the electric powertrain highlighting the quality and performances of the obtained results.