This study focuses on the critical aspect of robust state estimation for the safe navigation of an Autonomous Vehicle (AV). Existing literature primarily employs two prevalent techniques for state estimation, namely filtering-based and graph-based approaches. Factor Graph (FG) is a graph-based approach, constructed using Values and Factors for Maximum Aposteriori (MAP) estimation, that offers a modular architecture that facilitates the integration of inputs from diverse sensors. However, most FG-based architectures in current use require explicit knowledge of sensor parameters and are designed for single setups. To address these limitations, this research introduces a novel plug-and-play FG-based state estimator capable of operating without predefined sensor parameters. This estimator is suitable for deployment in multiple sensor setups, offering convenience and providing comprehensive state estimation at a high frequency, including mean and covariances. The proposed algorithm undergoes rigorous validation using various sensor setups on two different vehicles: a quadricycle and a shuttle bus. The algorithm provides accurate and robust state estimation across diverse scenarios, even when faced with degraded Global Navigation Satellite System (GNSS) measurements or complete outages. These findings highlight the efficacy and reliability of the algorithm in real-world AV applications.
This paper presents a framework to jointly optimize the design and control of an electric race car equipped with a multiple-gear transmission (MGT), specifically accounting for discrete gearshift dynamics. We formulate the problem as a mixed-integer optimal control problem (MIOCP), and deal with its complexity by combining convex optimization and Pontryagin's Minimum Principle (PMP) in a computationally efficient iterative algorithm satisfying necessary conditions for optimality upon convergence. Finally, we leverage our framework to compute the achievable lap time of a race car equipped with a fixed-gear transmission (FGT), a continuously variable transmission (CVT) and an MGT with 2 to 4 speeds, revealing that an MGT can strike the best trade-off in terms of electric motor control, and transmission weight and efficiency, ultimately yielding the overall best lap time.
In general, electric motor design procedures for automotive applications go through expensive trial-and-error processes or use simplified models that linearly stretch the efficiency map. In this paper, we explore the possibility of efficiently optimizing the motor design directly, using high-fidelity simulation software and derivative-free optimization solvers. In particular, we proportionally scale an already existing electric motor design in axial and radial direction, as well as the sizes of the magnets and slots separately, in commercial motor design software. We encapsulate this motor model in a vehicle model together with the transmission, simulate a candidate design on a drive cycle, and find an optimum through a Bayesian optimization solver. We showcase our framework on a small city car, and observe an energy consumption reduction of 0.13% with respect to a completely proportional scaling method, with a motor that is equipped with relatively shorter but wider magnets and slots. In the extended version of this paper, we include a comparison with the linear models, and add experiments on different drive cycles and vehicle types.
In this paper, a novel high-performance active switched quasi-Z-Source inverter (HP-AS-qZSI) dual-source for fuel cell hybrid electric vehicle (FC-HEV) is proposed. In order to eliminate extra dc-dc converters, dual-energy sources based on FC and lithium-ion capacitors (LiCs) are integrated into the Z-source network (ZSN). By adding an anti-parallel power switch, the proposed topology enables to deal with the uncontrollable and distorted dc-link voltages in FCEV applications-based broad-range of loads over the traditional AS-qZSI. The modeling and the operation modes analysis are firstly presented. Real-time simulation based on Opal-RT is then implemented to validate the operation and performance of the proposed topology. As a result, it provides a higher average efficiency (3.06%) and lower component size and volume of passive elements for the EV system. Furthermore, this topology also indicates improved aging performance indexes of high specific-energy sources under the studied Artemis-long driving cycle, compared to the hybrid energy storage system conventional two-stage inverter.
An energy management strategy for mild hybrids that prevents battery overheating is introduced in this digest. Energy management strategy design for mild hybrids requires particular care to prevent overheating of the battery pack as they typically do not have an active cooling system. To tackle this issue, we extend the well-known equivalent consumption minimization strategy approach to develop a real-time capable fuel-optimal controller that is sensitive to the battery’s thermal dynamics and that can enforce constraints on its temperature. The rationale for our formulation is developed using Pontryagin’s minimum principle from optimal control theory. The same principle is also used to design an off-line numerical procedure for the energy management strategy’s calibration. The effectiveness of the procedure is corroborated by numerical experiments on two different drive cycles, whose results are also compared with the solution obtained with a dynamic programming algorithm. Several peculiar aspects of our solution procedure, such as the method used to incorporate state constraints and the approximate boundary value problem solution method using a particle swarm optimization algorithm, are also detailed and discussed. The proposed controller is computationally light-weight and can be readily extended to on-line control provided that a suitable co-state selection procedure is employed, based on the data collected by using our calibration method on a large number of driving missions.
