Keywords: Battery management systems
Abstract: With the increased pervasiveness of Lithium-ion batteries, there is growing concern for the amount of retired batteries that will be entering the waste or recycling stream before they are fully utilized. Although aged batteries no longer meet the demands of their first application, many still have a significant portion of their initial capacity remaining for use in secondary applications, but evaluating this capacity is difficult and time intensive. In this paper, we investigate the use of cell (or parallel sets of cells) internal resistance as a surrogate of the capacity of parallel cells. We also highlight the challenges of testing batteries as a full pack despite the cell-to-cell variability from lack of balancing and differences in resistance and capacity. First, we verify that the charge-interrupt resistance from parallel cell pairs from twelve retired battery packs can eliminate the need for the hybrid pulse power characterization (HPPC) test as long as the charge-interrupt tests were not applied at low cell pair terminal voltages. Then, the relation between cell internal resistance and capacity across the various packs is investigated. Initial experimental results from this study show a correlation between internal resistance and remaining capacity which can be approximated with a linear fit, suggesting internal resistance measurements taken above a threshold cell pair terminal voltage may be a suitable initial screening metric for aged batteries.

Keywords: Battery management systems
Abstract: The estimation of battery parameters and states at both battery pack and cell levels are studied using the extended Kalman filter (EKF). The estimation results are evaluated with Chevy BOLT electric vehicle data. The cell level estimation when applying EKF is more challenging and an appropriate scaling of cell parameters is required, due to the fact that the current and voltage values of a cell are quite different in magnitude. This estimation study also shows that the cell-level parameter estimation can provide important health information to a battery management system (BMS) for diagnostics and prognostics. The cell-level estimates are then used to predict the voltage limited battery pack power available to a vehicle when a weak cell or droop cell occurs. This power limit can avoid over-discharging a droop cell and protect it from further damage.

Keywords: Battery management systems
Abstract: State of charge (SoC) estimation is one of the most important functions for battery management systems (BMSs). Due to the complex electrochemical characteristics of Lithium-ion batteries (LIBs), accurate SoC estimation still remain challenges. To take full advantage of the widely used model-based methods and data-driven methods, an extreme learning machine-extended Kalman filter (ELM-EKF)-based method is proposed for SoC estimation in this paper. The ELM is utilized to establish an accurate LIBs model first. Then, the trained ELM model is combined with the EKF algorithm for SoC estimation. The proposed ELM-EKF-based SoC estimation method is validated and compared with the traditional equivalent circuit model-EKF (ECM-EKF)-based method under Federal Urban Driving Schedule (FUDS) driving cycles at three different temperatures. The results prove that the ELM model have better voltage-tracking capability than the ECM model while the ELM-EKF-based SoC estimation algorithm can achieve higher estimation accuracy than the ECM-EKF-based method.

Keywords: Battery management systems, Energy storage systems: electrochemical systems, supercapacitators, fuel cells
Abstract: This paper aims to investigate how some features commonly associated with more generic time-series analysis are associated with capacity fade in lithium-ion cells and how they can be used to create simple but effective machine-learning models. This is done by processing the current, voltage, and temperature measurements, which span around two hundred cells for roughly two years, with a popular automated time-series analysis routine that extracts a significant number of different characteristics from the dataset for each signal. The most promising factors associated with the capacity fade are obtained by using a feature selection technique that is simple, quick and does not depend on a specific model structure. An analysis of the most relevant results is done, together with a standard hyperparameter search strategy using bayesian optimization for different classical regression models. With this step-by-step approach, the most promising features were investigated and an average error smaller than 5% was obtained on previously unseen validation data.

Keywords: Energy storage system modeling, Battery management systems, Battery thermal management systems
Abstract: Thermal management is one of the most investigated features of modern energy storage systems, especially in automotive applications. The design of a battery pack in an electric vehicle requires accurate knowledge of the electric and thermal behavior of every single component. This work presents experimental measurements and numerical analysis for the simulation of the electro-thermal status of a battery cooled utilizing either natural convection or direct liquid cooling. The effect of different discharging currents, and ambient temperature has been experimentally investigated in natural convection, then a multi-domain model has been validated with the measurements and used to simulate the battery-electric and thermal status with liquid cooling. The most critical condition is characterized by low temperature and high current. Results carried out evaluating the overall input/output energy balance have highlighted that the battery performance at low temperatures is improved using low current rates

Keywords: Charging and refueling infrastructure, Battery management systems, Modeling and control for electric and electro-magnetic components
Abstract: DC fast charging is a critical step to support the recharging demands of electric vehicles and increase their penetration in the market. However, compared to normal Level 1 or 2 charging, DC fast charging imposes additional battery capacity fade which can result in premature aging of the battery, reducing its useful life. This paper proposes a computationally efficient, meta-heuristic approach to optimize the charging C-Rate profile while considering battery degradation associated with not only the charging but also the expected drive cycle following charging. The battery and its degradation are modeled with a semi-empirical, physics-based approach which yields a high accuracy and is computationally efficient. The meta-heuristic approach to optimize the charge profile is first validated for a simplified case with Dynamic Programming. To demonstrate the effectiveness of the approach in attenuating the battery aging, a benchmark case with 15 minutes of constant current charging of a Lithium Iron Phosphate battery is set. A nearly 1% capacity fade improvement is obtained for a single charge-discharge cycle after charging C-Rate optimization, which would generate significant benefit over the electric vehicle life.

Keywords: Vehicle dynamics, control and state estimation, Control, guidance and navigation of autonomous vehicles, Intelligent transportation systems
Abstract: Autonomous vehicle trajectory tracking control is challenged by situations of varying road surface friction, especially in the scenario where there is a sudden decrease in friction in an area with high road curvature. If the situation is unknown to the control law, vehicles with high speed are more likely to lose tracking performance and/or stability, resulting in loss of control or the vehicle departing the lane unexpectedly. However, with connectivity either to other vehicles, infrastructure, or cloud services, vehicles may have access to upcoming roadway information, particularly the friction and curvature in the road path ahead. This paper introduces a model-based predictive trajectory-tracking control structure using the previewed knowledge of path curvature and road friction. In the structure, path following and vehicle stabilization are incorporated through a model predictive controller. Meanwhile, long-range vehicle speed planning and tracking control are integrated to ensure the vehicle can slow down appropriately before encountering hazardous road conditions. This approach has two major advantages. First, the prior knowledge of the desired path is explicitly incorporated into the computation of control inputs. Second, the combined transmission of longitudinal and lateral tire forces is considered in the controller to avoid violation of tire force limits while keeping performance and stability guarantees. The efficacy of the algorithm is demonstrated through an application case where a vehicle navigates a sharply curving road with varying friction conditions, with results showing that the controller can drive a vehicle up to the handling limits and track the desired trajectory accurately.

Keywords: Intelligent transportation systems, ML/AI for vehicle autonomy, AI/ML and model based approaches for safety and security in automotive systems
Abstract: Safe motion together with improved economy and traveling performance levels are important requirements against automated vehicles. Thus, the design of enhanced control systems is requested, which contain conventional model-based controllers and the use of unconventional approaches, e.g., learning features and cloud-based methods. This paper proposes a hierarchical vehicle control design method with learning functions, which incorporates control in two levels, such as in cloud level and in vehicle level. The control on the cloud level is designed by using reinforcement learning, with which the maximum speed for the vehicle is achieved. The vehicle level contains a robust controller and a supervisor, with which the collision avoidance of the vehicle is guaranteed. The hierarchical control guarantees performance requirement of safe motion, i.e., collision avoidance in all scenarios, even if the connection with the cloud is lost. The proposed control on indoor Hardware-in-the-Loop platform is implemented. The effectiveness of the control and the safe motion of the vehicle under various scenarios with and without cloud connection are demonstrated.

Keywords: Vehicle dynamics, control and state estimation, Single and multi-vehicle planning and coordination
Abstract: Avoidance maneuvers at normal driving speed or higher are demanding driving situations that force the vehicle to the limit of tire–road friction in critical situations. To study and develop control for these situations, dynamic optimization has been in growing use in research. One idea to handle such optimization computations effectively is to divide the total maneuver into a sequence of sub-maneuvers and to associate a segmented optimization problem to each sub-maneuver. Here, the alternating augmented Lagrangian method is adopted, which like many other optimization methods benefits strongly from a good initialization, and to that purpose a method with motion candidates is proposed to get an initially feasible motion. The two main contributions are, firstly the method for computing an initially feasible motion that is found to use obstacle positions and progress of vehicle variables to its advantage, and secondly, the integration with a subsequent step with segmented optimization showing clear improvements in paths and trajectories. Overall, the combined method is able to handle driving scenarios at demanding speeds.

Keywords: Control, guidance and navigation of autonomous vehicles, ML/AI for vehicle autonomy, Intelligent transportation systems
Abstract: This paper presents an extension of a self-learning control concept for automatic track guidance of industrial trucks in intralogistic systems. The presented approach is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI) and is able to adapt itself to different industrial truck variants and the associated specific vehicle parameters. Moreover, time-variant parameters during operation, such as the vehicle's velocity are taken into account. In order to consider the existing a priori knowledge of the controlled system and to avoid starting the whole training process of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a model using parameters of a nominal vehicle variant. Based on this, the control parameters are only fine-tuned in the second step. In this way the controller is adapted to the actual truck variant and the corresponding parameter values. In order to take into account the time-variant vehicle parameters during operation, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably extended. In this way, the varying speed can be considered in both training steps and the control parameters can be optimized accordingly. Thus, in case of the investigated scenarios a stable control loop behavior can be guaranteed for the entire speed range of industrial trucks. In order to demonstrate this, the new approach is compared with a RL control concept, not considering time-variant parameters.

Keywords: Control, guidance and navigation of autonomous vehicles, Vehicle dynamics, control and state estimation, Driver-in-the-loop and driver assistance systems
Abstract: This paper describes the development and experimental validation of a novel grain unloading-on-the-go automation system (automatic offloading) for agricultural combine harvesters. Unloading-on-the-go is desirable during harvest, but it requires highly-skilled and exhausting labor because the combine operator must fulfill multiple tasks simultaneously. The automatic offloading system can unburden the combine operator by automatically monitoring the grain cart fill status, determining the appropriate auger location, and controlling the relative vehicle position and auger on/off. An automation architecture is proposed and experimentally demonstrated to automate the unloading-on-the-go process. To allow for different operator-selected unloading scenarios, the automatic offloading controller has three fill strategies and two movement control options, open-loop and closed-loop. The automatic offloading controller was implemented on a dSPACE MicroAutoBox II and integrated into a combine harvester. In addition, a stereo-camera-based perception system was connected to the automatic offloading controller via an Ethernet cable for grain fill profile measurement during unloading. In-field testing demonstrated that the automatic offloading system can effectively automate the unloading-on-the-go of a combine harvester to fill a grain cart to the desired level under nominal harvesting conditions.

Keywords: Control, guidance and navigation of autonomous vehicles
Abstract: This paper presents a real-time Model Predictive Control (MPC) formulation for autonomous driving based on a lifted bilinear vehicle model developed using the Koopman operator. Koopman operator based models can closely mimic the original nonlinear behaviors with a higher dimensional linear structure, which is attractive for computationally efficient linear MPC formulations for controlling nonlinear systems. However, current linear models based on linear Koopman realizations cannot capture the control-affine dynamics in nonlinear systems. This may result in large discrepancies between the original nonlinear system and the data-driven linear model, hindering its use in MPC. To address this gap, first, a novel Koopman bilinear vehicle model that takes control-affine dynamics into consideration is constructed and tested in open-loop simulations. This bilinear Koopman model is then linearized to serve as a prediction model in MPC, and is shown to have higher accuracy compared to the state-of-the-art linear models. The model is then used to develop a linear MPC formulation for simultaneous planning and control of an autonomous vehicle. The formulation is tested on lane change scenarios with obstacles against the nonlinear MPC and standard linear MPC benchmarks. The results show that the new formulation can achieve a lane change performance closer to the nonlinear MPC with a computational performance similar to the standard linear MPC. The new formulation is observed to be successful in handling high speeds where the standard linear MPC fails.

Keywords: AI/ML application to automotive and transportation systems, Intelligent transportation systems, AI/ML and model based approaches for safety and security in automotive systems
Abstract: E-scooters are now massively present in urban environments as a shared last-mile solution. Their apparent ease of drivability favored their diffusion, but also actually raises safety concerns. In this work, we propose a data-driven approach to map e-scooters mechanical specifications to drivability and safety metrics, the latter appropriately defined and computed based on experimental data. An ad-hoc HW/SW platform was designed for this purpose, so as to be portable and installed on seven different e-Scooters that were tested in trips carried out in the city of Milan. The proposed approach allowed us to characterize both stability and comfort metrics on the different vehicles, and compare them also with a qualitative driving test carried out by a journalist making driving tests. The quantitative analysis matched the impressions of the human driver, and it disclosed an interesting mapping between the perceived risk and the vehicle characteristics evaluated according to the two metrics proposed.

Keywords: Testing and validation, Advanced Driver Assist Systems
Abstract: The capability to follow a lead-vehicle and avoid rear-end collisions is one of the most important functionalities for human drivers and various Advanced Driver Assist Systems (ADAS). Existing safety performance justifications of car-following systems either rely on simple concrete scenarios with biased surrogate metrics or require a significantly long driving distance for risk observation and inference. In this paper, we propose a guaranteed unbiased and sampling efficient scenario-based safety evaluation framework inspired by previous work on the almost safe set quantification. The proposal characterizes the complete safety performance of the test subject vehicle in the car-following regime. The performance of the proposed method is also demonstrated in challenging cases including some widely adopted car-following decision-making modules and the commercially available Openpilot driving stack by CommaAI.

Keywords: Position, navigation and timing safety and security in automotive systems, Control, guidance and navigation of autonomous vehicles, Perception, localization and path planning
Abstract: A Kalman filter-based receiver autonomous integrity monitoring algorithm (RAIM) is proposed to exploit sequential measurements from global navigation satellite system (GNSS) and terrestrial signals of opportunity (SOPs), to ensure safe vehicular navigation in urban envi- ronments. To deal with frequent threats caused by multipath and non-line-of-sight conditions, an innovation-based outlier rejection method is introduced. Next, a fault detection technique based on solution separation test is developed, and the quantification of protection levels is derived accordingly. Experimental results of a ground vehicle traveling in an urban environment, while making pseudorange measurements to GPS satellites and terrestrial 5G towers, are presented to demonstrate the efficacy of the proposed method. Incorporating 5G signals from only 2 towers is shown to reduce the horizontal protection level (HPL) by 0.22 m compared to using only GPS. Moreover, the proposed method is shown to reduce the HPL and vertical protection level (VPL) by 84.42% and 69.63%, respectively, over the snapshot advanced RAIM (ARAIM).

Keywords: Position, navigation and timing safety and security in automotive systems, Safety of the intended functionality, Control, guidance and navigation of autonomous vehicles
Abstract: This paper focuses on the cooperative navigation strategy for connected autonomous vehicles operating at smart intersections. The goal of this work is a cooperative navigation system to achieve cooperative collision avoidance for enhancing the safety and capacity of the intersection. This work considers cooperative connected autonomous vehicles operating simultaneously with non-cooperative autonomous vehicles. This work uses beyond visual range scenarios to reduce vulnerable situations. Beyond visual range, information is implemented by using the data from the roadside units, autonomous intersection management system, smart traffic lights, and onboard units. The efficacy of this work is validated in MATLAB/Simulink environment. The simulation results show the separation time within the set upper and lower bounds. That ensures that the ego vehicle does not collide with others at the intersection. The cooperative collision avoidance algorithm guides the ego vehicle as soon as the ego vehicle comes in the range of the intersection service area, which increases the safety and capacity of the intersection. This strategy is comfortably used for both an unsignalized and signalized intersection. In an unsignalized intersection scenario, the ego vehicle uses an onboard unit. In signalized intersection scenario, the ego vehicle uses a roadside unit, onboard unit, autonomous intersection management system, and smart traffic lights. As no such framework is found in the literature. The proposed framework is the near-future requirement where the connected autonomous vehicle utilizes the information from smart infrastructure devices.

Keywords: Perception, localization and path planning, Simultaneous localization and mapping, V2X communications
Abstract: Modern Positioning, Navigation, and Timing (PNT) systems heavily rely on Global Navigation Satellite Systems (GNSS). Meanwhile, GNSS-based PNT systems are increasingly becoming susceptible to unintentional and deliberate Radio Frequency (RF) interference. In particular, as technology keeps advancing and hardware is becoming so inexpensive, it takes a modest effort to disrupt the normal operation of almost any PNT systems, thus posing an extreme threat to autonomous transportation systems that rely on precise PNT. As communication capabilities are expanding, a group of vehicles can easily share data when they operate in close vicinity. This gives opportunity to position and navigate the vehicles based on a jointly computed navigation solution, which is usually called collaborative navigation, resulting in a potentially more accurate and reliable operation. In this study, the feasibility and performance potential of collaborative navigation on the detection and mitigation of GNSS-based PNT system operational anomalies are evaluated on some real data and simulated anomaly scenarios. By incorporating an outlier detection method based on least squares adjustment, the collaborative navigation has shown to be able to maintain the differences to the reference solution to within 0.2 m, 0.5 m, and 3.0m for the biased case, noisy case, and anchor case, respectively, for all test vehicles.

Keywords: Control, guidance and navigation of autonomous vehicles, Perception, localization and path planning, Vehicle dynamics, control and state estimation
Abstract: Entropy measures uncertainty present within the data. Highly Automated Vehicles (HAVs) can navigate safely and efficiently if location information of occluded dynamic objects is available. It is assumed that dynamic objects have GPS receivers, and location information can be acquired through a fast communication link. However, GPS info can be easily modified or suffers from high error because the transmission link is not secure or due to Non Line of Sight (NLOS) between transmitter and receiver. To solve this problem, an entropy metric is introduced to ascertain the value of the supplied information and reject information with a high amount of error present within the data. This work focuses on pedestrians as dynamic objects and uses Finite State Machine (FSM) based hierarchical control to navigate HAVs. It is shown that the entropy metric can improve the efficiency of the control of HAVs.

Keywords: Energy storage systems: electrochemical systems, supercapacitators, fuel cells, Energy management for XEV, Optimal design and control of XEV
Abstract: Environmental conditions have a significant effect on the performance of fuel cell systems. This paper studies the vehicle hydrogen consumption, the thermal management system, and the thermal loads of an automotive fuel cell system. A predictive control framework for thermal management is investigated to minimize the overall hydrogen consumption. Initially, a numerical modeling approach for the automotive fuel cell system is presented from electrochemical and thermal perspectives. Then, the problem formulation related to the thermal management strategy is presented and solved with an optimization method based on dynamic programming (DP). The implemented DP exploits the a priori knowledge of the driving mission to appropriately control the fuel cell system gross power and the operation of the radiator fan, the coolant pump, and the compressor. Optimization constraints involve maintaining the fuel cell stack temperature below the operational limit and avoiding the thermal system from being activated when the vehicle is at rest. The fuel cell system is tested while the vehicle performs different numbers of repetitions of the Worldwide Harmonized Light Vehicle Test Procedure (WLTP) at high ambient temperature. Using the proposed predictive control framework for thermal management, results demonstrate that an average 62.5% to 63.0% efficiency can be attained by the fuel cell stack in extreme ambient conditions both in short distance and long distance driving missions.

Keywords: Battery thermal management systems, Battery management systems, Energy management for XEV
Abstract: In electric vehicles, it is essential to prevent battery overheating due to excessive ohmic losses or inadequate cooling. Indeed, the temperature of battery systems significantly impacts their performance, lifetime, and safety. This paper proposes a predictive cooling optimization method for the battery thermal management system of heavy-duty fuel cell electric vehicles. The predictive cooling strategy is based on a model predictive control (MPC) formulation to maintain the battery temperature in its optimal range (to increase efficiency) and avoid high-temperature peaks (to increase lifetime and safety). The predictive thermal management relies on the ohmic losses forecast provided by a predictive energy management system. Simulations of a real-world driving cycle validate the proposed MPC and assess the impact of the predictive horizon length, which is critical for thermal management performance. The comparison against a simple hysteresis control strategy highlights the significant benefits of the proposed MPC for higher battery efficiency and lifetime.

Keywords: Energy management for XEV, Battery thermal management systems, Optimal design and control of XEV
Abstract: Thermal management is essential in electric vehicles to preserve battery life. In particular, avoiding temperature peaks is critical to prevent accelerated degradation. The battery thermal management problem is crucial in fuel cell electric trucks due to the heavy vehicle weight, especially on mountain or hilly roads. Therefore, this paper proposes an energy management strategy that reduces battery degradation by limiting its usage at high temperatures to allow its cooldown and avoid peaks. The energy management strategy is adaptive because the main control parameters for the fuel cell/battery power-split are adjusted depending on the battery temperature. The comparison between adaptive and non-adaptive strategies proves the effectiveness of the proposed formulation in avoiding temperature peaks without hindering fuel consumption or fuel cell degradation. The robustness of the proposed energy management strategy is validated with simulations of several real-world driving cycles with various speed and elevation profiles.

Keywords: Energy management for XEV, Optimal design and control of XEV, Energy storage systems: electrochemical systems, supercapacitators, fuel cells
Abstract: Energy management strategies significantly impact the fuel economy and component degradation of fuel cell electric vehicles by distributing the load demand between the battery and fuel cell systems. Since these are contrasting targets, designing a control strategy that finds a good trade-off is challenging. Therefore, this paper adopts a rule-based energy management strategy to show the significant impact of its calibration on fuel consumption and component degradation. The expected vehicle life is maximized by balancing battery and fuel cell degradation, assuming that individual component replacement is undesired. Moreover, the simulation results highlight the trade-off between fuel consumption and expected vehicle life, revealing that a slight increase in consumption can significantly mitigate the degradation. The study considers a sequence of six ld driving cycles for robust calibration of the energy management strategy. However, analyzing individual cycles reveals that even a robust calibration leads to significantly unbalanced degradation if the vehicle only runs a specific driving cycle. Therefore, this work proposes two potential research directions to cope with the mentioned issues and maintain the balance between fuel cell and battery degradation.

Keywords: Optimal design and control of XEV, Energy management for XEV, Energy storage systems: electrochemical systems, supercapacitators, fuel cells
Abstract: A study on the usefulness of flexible mathematical tools for determining the optimal architecture of fuel cell hybrid vehicles is presented. Starting from a pre-existing powertrain and control strategies co-optimization tool, the technological (especially in terms of lithium battery type) search domain was first expanded by including an updated battery model. Afterward, the availability of specification independent control strategies was exploited in such a way as to enable two optimization tasks: one relying on previous heuristic control rules and the other based on newly optimized control strategies. The results evidenced negligible differences, in terms of key control variable trends, objective (i.e., fuel economy), and design parameters (i.e., fuel cell system size and battery energy density), thus further proving the tool versatility. Moreover, optimal configurations exhibit appreciable fuel economies and acceleration performance on the WLTP driving cycle, while proposing potentially cost-effective solutions in terms of fuel cell system size.

Keywords: Vehicle architecture for XEV, Optimal design and control of XEV
Abstract: A project for upgrading conventional cars to hybrid electric vehicles is presented. The project is carried out by four Italian partners within a EU project financed by the LIFE programme. Hybridisation to a through the road parallel hybrid structure is obtained by integration of wheel motors in rear wheels, the addition of an additional battery, of flexible photovoltaic panels and a Vehicle Management Unit. The technical aspects related to vehicle conversion and the results of test bench and road tests on first prototypes are presented and discussed, as well as the perspectives related to the industrialization of such system.