In this work, a real-time energy management problem for a parallel hybrid electric vehicle (HEV) is proposed. The considered powertrain is built from a commercial HEV model. First, a non-linear optimal control problem under model predictive control scheme is formulated. The designed controller aims to generate the optimal power split and gear ratio schedule with respect to minimise the energy consumption of fuel and electricity. Moreover, the multiple shooting algorithm is introduced to decouple the dynamic constraints with the ability of avoiding the strong non-linearity while solving the optimisation problem. After that the optimisation problem is solved using sequential quadratic programming solver. Then, to evaluate the performance of the proposed real-time optimisation strategy on different traffic scenarios, the controller is applied to an adaptive cruise control (ACC) under connected environment. In this case, a solution of ACC with consideration of minimising energy consumption and maintaining string stability is provided. Finally, the proposed controller can be implemented in the traffic-in-the-loop platform without the knowledge of the predefined driving route. Simulations reveal that the proposed real-time control scheme shows great optimisation performance under the designed scenarios.

Contrary to other in-car engineering systems where the use of simulation tools is highly extended prior to a prototyping stage, the simulation of vehicular electrical distribution systems (EDSs) is not still a common practice as manufacturers so far have mainly relied on laborious empirical procedures for technical validation. However, to provide flexibility in EDS design and procure even faster endorsement, the development of computation tools on this subject is compelling considering the intricacy of these networks. To face this challenge, this work provides guidelines and experiences to develop a customised platform for EDS visualisation and simulation within the automotive industry context. The use of agile techniques for software development, visual analytics, and tailored power flow methods is highlighted among other aspects. Realistic case studies are presented to discuss the attributes of the implemented computational tool. To also provide relevant perspectives on how future EDS visualisation and simulation platforms will be developed, the latest research is discussed in topics such as new electric/electronic architectures, electro-thermal analysis, electronic fuses, mild hybrid power trains, hardware in the loop, and high-voltage networks.

Electric vehicle (EV) traction drives should be associated with flux-weakening (FW) techniques wide-range speed demands. In this study, the EV performance with an optimal FW strategy is studied in relation to the battery voltage variation caused by cell state-of-charge and temperature changes. The results show that the battery suffers from a voltage reduction by larger internal resistance as the temperature decreases. Moreover, the higher current is required for activating the FW process. However, the inner resistance growth produces more heat inside the cell that affects the battery electrical parameters as well as the system. To assess this effect by simulation, an improved electro-thermal model of lithium-ion battery ls dynamically coupled to the optimal FW strategy. In this model, all the electrical parameters are temperature-dependent deduced from experimental measurements of an off-road EV. The simulation results confirm the effect of the cell self-heating on the battery voltage at sub-zero temperatures. The higher battery voltage can support the FW operation at −10°C for more 1200 s under the modified NEDC driving cycle, whereas the motor drive voltage is saturated after 1118 s by using the simple battery model without thermal effects.

This study investigates the impact of battery and fuel cell (FC) degradation on energy management of a FC hybrid electric vehicle. In this respect, an online energy management strategy (EMS) is proposed considering simultaneous online adaptation of battery and FC models. The EMS is based on quadratic programming which is integrated into an online battery and proton exchange membrane FC (PEMFC) parameters identification. Considering the battery and PEMFC states of health, three scenarios have been considered for the EMS purpose, and the performance of the proposed EMS has been examined under two driving cycles. Numerous test scenarios using standard driving cycles reveal that the ageing of battery and PEMFC has a considerable impact on the hydrogen consumption. Moreover, the proposed EMS can successfully tackle the model uncertainties owing to the performance drifts of the power sources at the mentioned scenarios.

This study presents a review of prognostic methods applied to automotive proton exchange membrane fuel cell (PEMFC). PEMFC durability is strongly affected when it is subjected to automotive load cycling (ALC). ALC is normally composed of four operation modes such as start-up, idle, transient high-current demand and shutdown. All of these operation modes drastically change the internal variables of the system like temperature, pressure, relative humidity etc. causing degradation of the fuel cell components in a short time. Prognostic methods could be a possible solution to tackle the PEMFC's low durability issue because they allow predicting the remaining useful life of the system in order to apply preventive maintenance plans. Therefore, the objective of this study is to review the prognostic techniques applied to PEMFC under ALC. In the first part of this study, a summary of PEMFC degradation mechanisms caused by ALC is realised based on literature review. In the second part, the prognostic methods review for automotive PEMFCs is carried out and a general synthesis and future challenges are given in the third part of the study.

This study presents the optimisation of fractional-order proportional–integral (FOPI) controllers for a bidirectional quasi-Z-source inverter (QZSI) in an electric vehicle (EV) off-road application. An ant colony optimisation Nelder–Mead (ACO-NM) algorithm is used for the optimisation of the controller parameters. This optimisation method is applied to enhance the performance of FOPI control for bidirectional QZSI. Ziegler–Nichols (ZN) with relay and the pole placement tuning method are also used for the FOPI controller design for comparison purposes. The modelling and the control design of bidirectional QZSI for an electric traction system are presented and discussed. Simulations are performed to verify the efficacy of the proposed controller structure with the bidirectional QZSI for two standardised driving cycles. The result shows that the FOPI controller designed with the ACO-NM algorithm provides more suitable ageing performance index values for the battery. The ACO-NM algorithm permits to reduce the root-mean-square value and the standard deviation by 2 and 5% of the battery current compared to the ZN tuning method and direct battery supply topology, respectively. The bidirectional QZSI with this type of controller can globally enhance the performance of EVs by optimising the electric power consumption and extending its driving range.

Although the urban environment pollution is decreased by electric buses, the driving range is still a problem for the electric car. Especially, there is a dramatic decline in driving range while heating the electric bus cabin in winter. To solve this problem, the authors propose an eddy-current braking and heating system. The braking energy is converted to thermal energy directly by the electromagnetic method. The energy conversion efficiency of the system is higher than that of the regenerative braking system. A braking energy management control strategy based on fuzzy control is proposed. A dynamic programming algorithm is simulated and analysed by extracting the design parameters. In the MATLAB/Simulink environment, the authors build a simulation platform for calculating efficiency. Compared with the normal strategy, the proposed fuzzy braking strategy can improve 9.8% of the electricity consumption. The heating time of the bus cabin to reach 20°C by the proposed strategy is only 60 s, whereas it is 700 s by the normal strategy.

This study proposes an energy management strategy (EMS) for a dual-mode hybrid locomotive equipped with a fuel cell, supercapacitors, and batteries, and intermittent access to an electrified overhead catenary. It is inspired by the Ragone plot and does not consider information or predictions of future load consumption. It aims to reduce a cost function that considers the cost of hydrogen, the electricity consumed from the network, and the energy sources' degradation. The EMS focuses on maximising the energy recovered during braking. The study introduces a methodology to tune the EMS parameters. Two study cases are used to evaluate the EMS. In the evaluation driving profile, typical for a French freight train, the braking energy is around 12.8% of the total energy. With the proposed EMS, the energy recovered is around 99.8% of the total braking energy. A second EMS not oriented to reduce the energy in the braking resistor is also evaluated. The energy recovered with this strategy is around 91.5% of the total braking energy. The global energy reduction is around 1.1% compared with the second EMS and 12.8% without energy recovering. These results show a real opportunity to increase the energy recovered during braking.

Defining tools and algorithms to support the decision-making process for charging electric vehicles (EVs) is a fundamental theme for the spread of EVs. Utilities can use this approach to incentive or discourage the charge of EVs according to different constraints. In this study, the authors refer the EV clusters or fleets, where there is only one energy buyer for all the clusters. This approach corresponds to an indirect method based on prices to induce behaviours in the management of charging on clusters of EVs. The first actor of the algorithm is an aggregator of EV fleet operators acting as a dealer between the electricity market and consumers. A theoretical game model based on Stackelberg's formulation is proposed to capture the interaction between the fleet operator and the owners/drivers of the EVs. A bi-level optimisation problem arises to represent the game between the agents involved: at the upper level, the aggregator maximises its benefits, while the lower level represents the behaviour of rational drivers as a fleet. The proposed method is applied to actual data obtained observing the behaviour of a car-sharing fleet.

This research undertakes an investigation of global fuel cell vehicle (FCV) deployment, cognizant of optimal economic deployment and stakeholder preferences in a case study of Japan out to the year 2050. The model is mathematically formulated as a large-scale linear optimization problem, aiming to minimize system costs, including generation type, fuel, conversion, and carbon reduction, subject to the constraint of carbon dioxide reduction targets. Results show that between ∼0.8 and 2% of global energy consumption needs can be met by hydrogen by 2050, with city gas and transport emerging as significant use cases. Passenger FCVs and hydrogen buses account for most of the hydrogen-based transportation sector, leading to a global deployment of ∼120 million FCVs by 2050. Hydrogen production is reliant on fossil fuels, and OECD nations are net importers – especially Japan. To underpin hydrogen production from fossil fuels, carbon capture and storage is required in significant quantities when anticipating a large fleet of FCVs. Stakeholder engagement suggests optimism toward FCV deployment while policy issues identified include the necessity for large-scale future energy system investment and rapid technical and economic feasibility progress for renewables and electrolysers to achieve a hydrogen economy which is not reliant on fossil fuels.

This study presents a three-lane highway overtaking strategy for an automated vehicle, which is based on a heuristic planning reinforcement learning algorithm. The proposed decision-making controller focuses on keeping the autonomous vehicle operating safely and efficiently. First, the modelling of the overtaking driving scenario is introduced and the reference approaches named intelligent driver model and minimise overall braking induced by lane changes are formulated. Second, the Dyna-H algorithm, which combines the modified Q-learning algorithm with a heuristic planning policy, is utilised for highway overtaking decision-making. Three different heuristic strategies are formulated to improve learning efficiency and compare performance. This algorithm is applied to determine the lane change and speed selection for an ego vehicle in the environment with uncertainties. Finally, the performance of Dyna-H is estimated in the autonomous overtaking scenario by comparing it with the reference and traditional learning methods. Furthermore, the Dyna-H-enabled decision-making strategies are validated and analysed in an open-sourcing driving dataset. Results prove that the proposed decision-making strategy could produce superior performance in convergence rate and control.

Simulation tools are crucial to efficiently design the infrastructures and operations of DC electrical railway systems, including potential innovative technologies such as reversible traction power substations and energy storage systems. For this purpose, it is essential to accurately estimate the evolution of the voltage and power flows within the DC traction network, with fast computation time. This study, therefore, proposes a new simulation approach for fast and accurate voltage estimation and power flow analysis of DC railway systems. It is based on the use of non-linear switched models for traction power substations and trains. The modified nodal analysis is extended to consider such models, including the voltage drop control of the different subsystems, avoiding the necessity to use complex numerical iterative solvers. This new approach is validated and compared to an existing dynamical model and a conventional static model. The comparisons prove the relevance of the new approach, which provides validated and accurate results (<2% error compared to the validated dynamical model) with fast computation time (speed up of 500 compared to the dynamical model). It can, therefore, be used to study, design, size, and optimise DC traction systems with new technologies aimed at saving braking energy.

Rail surface defect detection plays a critical role in the maintenance of the rail transportation system. Video analysis technology is a promising method to detect defects due to its low cost and effectiveness. Several attempts with hand-craft features have been made to obtain the detection results by using traditional machine vision algorithms. However, these methods suffer from imprecise results due to challenging conditions, such as deteriorated and changeable lighting environment and various types of complex rail surface defects. Recently, classification methods with complex deep convolutional networks have become popular. Despite their high accuracy, these methods cannot meet the requirements of defects localisation and real-time processing in practice. To solve these problems, this study proposes a novel object detection algorithm to detect rail defects. The net architecture of the proposed algorithm includes a backbone network using MobileNet and several novel detection layers with multi-scale feature maps inspired by you only look once (YOLO) and feature pyramid networks. Two different architectures of MobileNet are used to estimate the performance of defects detection. The experimental results demonstrate the great potential of the proposed algorithm with fast inference speed and high accuracy in the industry.