• Energy Research

The increasing environmental awareness paired with the rise of global warming effects has led, in the past few years, to an increase in the sales of electric vehicles (EVs), partly but not only, caused by governmental incentives. A significant roadblock in the mass transition to EVs can be found in the so-called range anxiety: not only do EVs have, generally, considerably shorter ranges than their internal combustion engine vehicle (ICEV) equivalents, but recharge takes significantly longer than does filling up a gas tank, and charging stations are less widespread than are petrol stations. To counteract this, EV manufacturers are developing route planners which select the best route to go from A to B according to the range of the vehicle and the availability of charging stations. These tools are indeed powerful but do not account for the state of health (SoH) of the battery or for temperature conditions, two factors which may severely degrade the range of an EV. This article presents an innovative route planning method which takes into account SoH, temperature and driving style and selects, along the planned route, the charging stations among those which can be reached with the energy of the battery. To verify its proper operativity, simulations were conducted, highlighting the risk of running out of battery before destination, considering if the route is planned based on the declared range, and taking into account battery SoH, external temperature and driving style.

Abstract This paper presents a technique for the lane keeping and the longitudinal speed control of an autonomous vehicle with the combination of an MPC and a PID control. The goal of the proposed control method is to minimize the lateral deviation and relative yaw angle with respect to the planned trajectory, while driving the vehicle at the highest acceptable longitudinal speed. The reference profile of the longitudinal speed is computed considering both the lateral and longitudinal dynamic of the vehicle. The vehicle is represented by means of a linear 3-DoF bicycle model. The control algorithm takes the road lane boundaries as the only external input. The proposed strategy is validated in simulation on three distinct driving scenarios.

This work presents a novel electromagnetic clutch installed on the crankshaft pulley to decouple the internal combustion engine from the front-end accessory drive of a P0 hybrid electric vehicle. The objective is to supply the air conditioning compressor directly with the belt starter–generator electric machine without dragging the inertia of the engine during engine fuel cut-off phases. This operation yields an improved vehicle energetic efficiency and allows for uninterrupted air conditioning also when the start–stop function is activated. This paper focuses on the mechanical assembly and electromagnetic behavior of the device. Furthermore, two position-sensorless techniques are proposed to estimate the clutch state. The effectiveness of the proposed solution is experimentally validated on a dedicated test bench. Experimental tests demonstrated that the opening and closing phases required 50 and 25ms, respectively, thereby satisfying the time constraints for switching different operating modes in a vehicle (∼100ms).

A P0 system is used in hybrid automobiles to improve engine economy and performance. An essential element of the P0 system for effectively transmitting power to the drive train is the belt drive system (BDS). The features of electric machine (EM) and internal combustion engines (ICE) are taken into account by standard energy management systems, such as the equivalent consumption minimization strategy (ECMS). In order to maximize the effectiveness of the P0 system, this work provides a novel formulation of the ECMS, which considers the power loss map of the BDS in addition to the characteristic maps of EM and ICE. A test bench is built up to characterize the BDS, and it is verified using an open-loop Hardware in the Loop (HIL) in the WLTP driving cycle. To find the most appropriate equivalence factors for the ECMS, which would ordinarily be tuned through trial and error, a genetic algorithm (GA) is used. With regard to the standard ECMS, the proposed methodology intends to reduce fuel usage and CO2 emissions. Two belts in BDS were tested in the WLTP to achieve CO2 savings of 1.1 and 0.9 [g/km], indicating the enhancement of system performance by using the BDS power loss maps in ECMS.

Hybrid electric vehicles have proven to be an effective solution for the auto industry to satisfy the increasingly stringent CO2 regulations for the short-medium term. Proper sizing of the different components is required to benefit from the hybrid architecture full potential. This paper proposes a unified method that can be applied in both cases. The method uses the energy flow, storage, and consumption during a cycle to perform the sizing. A 350 V P2 plug-in hybrid and a 48 V P2 mild hybrid are taken as a case study. The sizing is performed by adopting the WLTP cycle and subsequently analyzing the energy profile.

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.

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.