A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks

This paper proposes a method for the combined estimation of the state of charge (SOC) and state of health (SOH) of batteries in hybrid and full electric vehicles. The technique is based on a set of five artificial neural networks that are used to tackle a regression and a classification task. In the method, the estimation of the SOC relies on the identification of the ageing of the battery and the estimation of the SOH depends on the behavior of the SOC in a recursive closed-loop. The networks are designed by means of training datasets collected during the experimental characterizations conducted in a laboratory environment. The lithium battery pack adopted during the study is designed to supply and store energy in a mild hybrid electric vehicle. The validation of the estimation method is performed by using real driving profiles acquired on-board of a vehicle. The obtained accuracy of the combined SOC and SOH estimator is around 97%, in line with the industrial requirements in the automotive sector. The promising results in terms of accuracy encourage to deepen the experimental validation with a deployment on a vehicle battery management system

Rotors on Active Magnetic Bearings: Modeling and Control Techniques

In the last decades the deeper and more detailed understanding of rotating machinery dynamic behavior facilitated the study and the design of several devices aiming at friction reduction, vibration damping and control, rotational speed increase and mechanical design optimization. Among these devices a promising technology is represented by active magnetic actuators which found a great spread in rotordynamics and in high precision applications due to (a) the absence of all fatigue and tribology issues motivated by the absence of contact, (b) the small sensitivity to the operating conditions, (c) the wide possibility of tuning even during operation, (d) the predictability of the behavior. This technology can be classified as a typical mechatronic product due to its nature which involves mechanical, electrical and control aspects, merging them in a single system. The attractive potential of active magnetic suspensions motivated a considerable research effort for the past decade focused mostly on electrical actuation subsystem and control strategies. Examples of application areas are: (a) Turbomachinery, (b) Vibration isolation, (c) Machine tools and electric drives, (d) Energy storing flywheels, (e) Instruments in space and physics, (f) Non-contacting suspensions for micro-techniques, (g) Identification and test equipment in rotordynamics. This chapter illustrates the design, the modeling, the experimental tests and validation of all the subsystems of a rotors on a five-axes active magnetic suspension. The mechanical, electrical, electronic and control strategies aspects are explained with a mechatronic approach evaluating all the interactions between them. The main goals of the manuscript are: • Illustrate the design and the modeling phases of a five-axes active magnetic suspension; • Discuss the design steps and the practical implementation of a standard suspension control strategy; • Introduce an off-line technique of electrical centering of the actuators; • Illustrate the design steps and the practical implementation of an online rotor selfcentering control technique. The experimental test rig is a shaft (Weight: 5.3 kg. Length: 0.5 m) supported by two radial and one axial cylindrical active magnetic bearings and powered by an asynchronous high frequency electric motor. The chapter starts on an overview of the most common technologies used to support rotors with a deep analysis of their advantages and drawbacks with respect to active magnetic bearings. Furthermore a discussion on magnetic suspensions state of the art is carried out highlighting the research efforts directions and the goals reached in the last years. In the central sections, a detailed description of each subsystem is performed along with the modeling steps. In particular the rotor is modeled with a FE code while the actuators are considered in a linearized model. The last sections of the chapter are focused on the control strategies design and the experimental tests. An off-line technique of actuators electrical centering is explained and its advantages are described in the control design context. This strategy can be summarized as follows. Knowing that: a) each actuation axis is composed by two electromagnets; b) each electromagnet needs a current closed-loop control; c) the bandwidth of this control is depending on the mechanical airgap, then the technique allows to obtain the same value of the closed-loop bandwidth of the current control of both the electromagnets of the same actuation axis. This approach improves performance and gives more steadiness to the control behavior. The decentralized approach of the control strategy allowing the full suspensions on five axes is illustrated from the design steps to the practical implementation on the control unit. Furthermore a selfcentering technique is described and implemented on the experimental test rig: this technique uses a mobile notch filter synchronous with the rotational speed and allows the rotor to spin around its mass center. The actuators are not forced to counteract the unbalance excitation avoiding saturations. Finally, the experimental tests are carried out on the rotor to validate the suspension control, the off-line electrical centering and the selfcentering technique. The numerical and experimental results are superimposed and compared to prove the effectiveness of the modeling approach

A Data-Driven Method for Vehicle Speed Estimation

This paper presents a method based on Artificial Neural Networks for estimation of the vehicle speed. The technique exploits the combination of two tasks: a) speed estimation by means of regression neural networks dedicated to different road conditions (dry, wet and icy); b) identification of the road condition with a pattern recognition neural network. The training of the networks is conducted with experimental datasets recorded during the driving sessions performed with a vehicle on different tracks. The effectiveness of the proposed approach is validated experimentally on the same car by deploying the algorithm on a dSPACE computing platform. The estimation accuracy is evaluated by comparing the obtained results to the measurement of an optical sensor installed on the vehicle and to the output of another estimation method, based on the mean value of velocity of the four wheels

Hardware-in-the-Loop Assessment of a Data-Driven State of Charge Estimation Method for Lithium-Ion Batteries in Hybrid Vehicles

This paper presents the design and hardware-in-the-loop (HIL) experimental validation of a data-driven estimation method for the state of charge (SOC) in the lithium-ion batteries used in hybrid electric vehicles (HEVs). The considered system features a 1.25 kWh 48 V lithium-ion battery that is numerically modeled via an RC equivalent circuit model that can also consider the environmental temperature influence. The proposed estimation technique relies on nonlinear autoregressive with exogenous input (NARX) artificial neural networks (ANNs) that are properly trained with multiple datasets. Those datasets include modeled current and voltage data, both for charge-sustaining and charge-depleting working conditions. The investigated method is then experimentally validated using a Raspberry Pi 4B card-sized board, on which the estimation algorithm is actually deployed, and real-time hardware, on which the battery model is developed, namely a Speedgoat baseline platform. These hardware platforms are used in a hardware-in-the-loop architecture via the UPD communication protocol, allowing the system to be validated in a proper testing environment. The resulting estimation algorithm can estimate the battery SOC in real-time, with 2% accuracy during real-time hardware testing

Model predictive control for comfort optimization in assisted and driverless vehicles

This paper presents a method to design a Model Predictive Control to maximize the passengers’ comfort in assisted and self-driving vehicles by achieving lateral and longitudinal dynamic. The weighting parameters of the MPC are tuned offline using a Genetic Algorithm to simultaneously maximize the control performance in the tracking of speed profile, lateral deviation and relative yaw angle and to optimize the comfort perceived by the passengers. To this end, two comfort evaluation indexes extracted by ISO 2631 are used to evaluate the amount of vibration transmitted to the passengers and the probability to experience motion sickness. The effectiveness of the method is demonstrated using simulated experiments conducted on a subcompact crossover vehicle. The control tracking performance produces errors lower than 0.1 m for lateral deviation, 0.5 for relative yaw angle and 1.5 km/h for the vehicle speed. The comfort maximization results in a low percentage of people who may experience nausea (below 5%) and in a low value of equivalent acceleration perceived by the passenger (below 0.315 m=s 2 ‘‘not uncomfortable’’ by ISO 2631). The robustness at variations of vehicle parameters, namely vehicle mass, front and rear cornering stiffness and mass distribution, is evaluated through a sensitivity analysis

Fuzzy Logic Method for the Speed Estimation in All-Wheel Drive Electric Racing Vehicles

This paper presents a method for the vehicle speed estimation with a Fuzzy Logic based algorithm. The algorithm acquires the measurements of the yaw rate, steering angle, wheel velocities and exploits a set of five Fuzzy Logics dedicated to different driving conditions. The technique estimates the speed exploiting a weighted average of the contributions provided by the longitudinal acceleration and the credibility assigned by the Fuzzy Logics to the measurements of the wheels' speed. The method is experimentally evaluated on an all-wheel drive electric racing vehicle and is valid for the front and rear wheel drive configurations. The experimental validation is performed by comparing the obtained estimation with the result of computing the speed as the average of the linear velocity of the four wheels. A comparison to the integral of the vehicle acceleration over time is reported

Estimation Accuracy and Computational Cost Analysis of Artificial Neural Networks for State of Charge Estimation in Lithium Batteries

This paper presents a tradeoff analysis in terms of accuracy and computational cost between different architectures of artificial neural networks for the State of Charge (SOC) estimation of lithium batteries in hybrid and electric vehicles. The considered layouts are partly selected from the literature on SOC estimation, and partly are novel proposals that have been demonstrated to be effective in executing estimation tasks in other engineering fields. One of the architectures, the Nonlinear Autoregressive Neural Network with Exogenous Input (NARX), is presented with an unconventional layout that exploits a preliminary routine, which allows setting of the feedback initial value to avoid estimation divergence. The presented solutions are compared in terms of estimation accuracy, duration of the training process, robustness to the noise in the current measurement, and to the inaccuracy on the initial estimation. Moreover, the algorithms are implemented on an electronic control unit in serial communication with a computer, which emulates a real vehicle, so as to compare their computational costs. The proposed unconventional NARX architecture outperforms the other solutions. The battery pack that is used to design and test the networks is a 20 kW pack for a mild hybrid electric vehicle, whilst the adopted training, validation and test datasets are obtained from the driving cycles of a real car and from standard profiles

A local trajectory planning and control method for autonomous vehicles based on the RRT algorithm

This paper presents a local trajectory planning and control method based on the Rapidly-exploring Random Tree algorithm for autonomous racing vehicles. The paper aims to provide an algorithm allowing to compute the planned trajectory in an unknown environment, structured with non-crossable obstacles, such as traffic cones. The investigated method exploits a perception pipeline to sense the surrounding environment by means of a LIDAR-based sensor and a high-performance Graphic Processing Unit. The considered vehicle is a four-wheel drive electric racing prototype, which is modeled as a 3 Degree-of-Freedom bicycle model. A Stanley controller for both lateral and longitudinal vehicle dynamics is designed to perform the path tracking task. The performance of the proposed method is evaluated in simulation using real data recorded by on-board perception sensors. The algorithm can successfully compute a feasible trajectory in different driving scenarios

Analysis of a Shaftless Semi-Hard Magnetic Material Flywheel on Radial Hysteresis Self-Bearing Drives

Flywheel Energy Storage Systems are interesting solutions for energy storage, featuring advantageous characteristics when compared to other technologies. This has motivated research effort focusing mainly on cost aspects, system reliability and energy density improvement. In this context, a novel shaftless outer-rotor layout is proposed. It features a semi-hard magnetic FeCrCo 48/5 rotor coupled with two bearingless hysteresis drives. The novelty lies in the use of the semi-hard magnetic material, lending the proposed layout advantageous features thanks to its elevated mechanical strength and magnetic properties that enable the use of bearingless hysteresis drives. The paper presents a study of the proposed layout and an assessment of its energetic features. It also focuses on the modeling of the radial magnetic suspension, where the electromagnets providing the levitating forces are modeled through a one-dimensional approach. The Jiles–Atherton model is used to describe the magnetic hysteresis of the rotor material. The proposed flywheel features a mass of 61.2 kg, a storage capability of 600 Wh at the maximum speed of 18,000 rpm and achieves an energy density of 9.8 Wh/kg. The performance of the magnetic suspension is demonstrated to be satisfactory and the influence of the hysteresis of the rotor material is highlighted