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The purpose of this paper is to demonstrate that using advanced powertrain technologies can help outperform the state of the art in F1 and LeMans motor racing. By a careful choice and sizing of powertrain components coupled with an optimal energy management strategy, the conflicting requirements of high-performance and high-energy savings can be achieved.

Five main steps were performed. First, definition of requirements: basic performance requirements were defined based on research on the capabilities of Formula 1 race cars. Second, drive cycle generation: a drive cycle was created using these performance requirements as well as other necessary inputs such as the track layout of Circuit de la Sarthe, the drag coefficient, the tire specifications, and the mass of the vehicle. Third, selection of technology: the drive cycle was used to model the power requirements from the powertrain components of the series-hybrid topology. Fourth, lap time sensitivity analysis: the impact of certain design decisions on lap time was determined by the lap time sensitivity analysis. Fifth, modeling and optimization: the design involved building the optimal energy management strategy and comparing the performance of different powertrain component sizings.

Five different powertrain configurations were presented, and several tradeoffs between lap time and different parameters were discussed. The results showed that the fastest achievable lap time using the proposed configurations was 3 min 9 s. It was concluded that several car and component parameters have to be improved to decrease this lap time to the required 2 min 45 s, which is required to outperform F1 on LeMans.

This research shows the capabilities of advanced hybrid powertrain components and energy management strategies in motorsports, both in terms of performance and energy savings. The important factors affecting the performance of such a hybrid race car have been highlighted.

The purpose of this paper is to focus on how the concept design stage of a powertrain system can be improved by using a purely objective driven approach in selecting a final concept design to progress further. This research investigation will examine the development of a novel test-bed to assist in the selection of powertrain technologies during the concept design phase at Ford Motor Company Ltd, serving as the main contribution to knowledge.

The objectives of this research were achieved by carrying out a literature review of external published work related to concept design evaluation methods within product development and value engineering techniques. Empirical studies were conducted with a supporting case study used to test the application of a new test-bed to improve the concept design decision process.

A quantitative new tool “Product Optimisation Value Engineering” (PROVEN) is presented to critically assess new and evolving powertrain technologies at the concept design phase.

This research improves the concept design selection process, hence increasing the product value to the customer.

The new test-bed “PROVEN” incorporates a data-driven objective approach to assist in assessing concept design alternatives in providing the net value in terms of function and cost as perceived by the customer.

A mathematical new test-bed that incorporates a highly adaptable, data-driven and multi-attribute value approach to product specification and conceptual design is developed, novel to the automotive concept design process. This will create a substantially optimised product offering to the market, reducing overall development costs while achieving customer satisfaction. The new tool has the ability to define a technology value map to assess multiple technical options as a function of its attributes, whose precise values can be determined at a given cost.

This paper addressed some critical issues in the development of hybrid electric powertrains for aircraft and propose a design methodology based on multi-objective optimization algorithms and mission-based simulations.

Scalable models were used for the main components of the powertrain, namely, the (two stroke diesel) engine, the (lithium) batteries and the (permanent magnet) motor. The optimization was performed with the NSGA-II genetic algorithm coupled with an in-house MATLAB tool. The input parameters were the size of engine, the hybridization degree and the specification of the battery (typology, nominal capacity, bus voltage, etc.). The outputs were electric endurance, additional volume, performance parameters and fuel consumption over a specified mission.

Electric endurance was below 30 min in the two test cases (unmanned aerial vehicles [UAVs]) but, thanks to the recharging of the batteries on-board, the total electric time was higher. Fuel consumption was very high for the largest UAV, while an improvement of 11 per cent with respect to a conventional configuration was obtained for the smallest one.

The research used a simplified approach for flight mechanics. Some components were not sized in the proposed test cases.

The results of the test cases stressed the importance of improving energy density and power density of the electric path.

The proposed methodology is aimed at minimizing the environmental impact of aircraft.

The proposed methodology was obtained from the automotive field with several original contributions to account for the aircraft application.

A hybrid-electric unmanned aerial vehicle (HE-UAV) model has been developed to address the problem of low endurance of a small electric UAV. Electric-powered UAVs are not capable of achieving a high range and endurance due to the low energy density of its batteries. Alternatively, conventional UAVs (cUAVs) using fuel with an internal combustion engine (ICE) produces more noise and thermal signatures which is undesirable, especially if the air vehicle is required to patrol at low altitudes and remain undetected by ground patrols. This paper aims to investigate the impact of implementing hybrid propulsion technology to improve on the endurance of the UAV (based on a 13.6 kg UAV).

A HE-UAV model is developed to analyze the fuel consumption of the UAV for given mission profiles which were then compared to a cUAV. Although, this UAV size was used as reference case study, it can potentially be used to analyze the fuel consumption of any fixed wing UAV of similar take-off weight. The model was developed in a Matlab-Simulink environment using Simulink built-in functionalities, including all the subsystem of the hybrid powertrain. That is, the ICE, electric motor, battery, DC-DC converter, fuel system and propeller system as well as the aerodynamic system of the UAV. In addition, a ruled-based supervisory controlled strategy was implemented to characterize the split between the two propulsive components (ICE and electric motor) during the UAV mission. Finally, an electrification scheme was implemented to account for the hybridization of the UAV during certain stages of flight. The electrification scheme was then varied by changing the time duration of the UAV during certain stages of flight.

Based on simulation, it was observed a HE-UAV could achieve a fuel saving of 33% compared to the cUAV. A validation study showed a predicted improved fuel consumption of 9.5% for the Aerosonde UAV.

The novelty of this work comes with the implementation of a rule-based supervisory controller to characterize the split between the two propulsive components during the UAV mission. Also, the model was created by considering steady flight during cruise, but not during the climb and descend segment of the mission.

Parallel hybrid electric (HE) propulsion retrofit is a promising alternative to reduce fuel burn of aircraft operating on short regional flights. However, if the batteries are depleted at the end of the mission, the hybrid powertrain designs with downsized gas turbines (GTs) and additional electric motors might not meet the one-engine inoperative (OEI) missed approach climb performance required by the certification. Alternatively, hybrid designs using the original full-size GT can perform one engine climb without electric assistance. This paper aims to evaluate the impact of overshoot climb requirements on powertrain design and performance comparing the two design approaches.

An aircraft-level parametric mission analysis model is used to evaluate aircraft performance combined with an optimization framework including multiple constraints. An indirect approach using metamodels is used to optimize powertrain sizing and operation strategy.

Considering OEI climb requirements, no benefits were found using a design with downsized GTs. Equivalent fuel burns were found for hybrid designs that keep the original size GTs, but do not require electric energy for the OEI overshoot at the end of the mission. Then, it is recommended to size the GT to maintain the emergency climb capabilities with no electric assistance to ensure power availability regardless of remaining battery energy.

This work introduces a new perspective on parallel HE sizing with consideration for the dependency of power capability at aircraft level on the electric energy availability in case of critical mission scenarios such as overshoot climb at the end of the mission.

The purpose of this paper is to optimize the design and power management control fuel cell/supercapacitor and fuel cell/battery hybrid electric vehicles and to provide a comparative study between the two configurations.

In hybrid electric vehicles (HEVs), the power flow control and the powertrain component sizing are strongly related and their design will significantly influence the vehicle performance, cost, efficiency and fuel economy. Hence, it is necessary to assess the power flow management strategy at the powertrain design stage in order to minimize component sizing, cost, and the vehicle fuel consumption for a given driving cycle. In this paper, the PSO algorithm is implemented to optimize the design and the power management control of fuel cell/supercapacitor (FC/SC) and fuel cell/battery (FC/B) HEVs for a given driving cycle. The powertrain and the proposed control strategy are designed and simulated by using MATLAB/Simulink. In addition, a comparative study of fuel cell/supercapacitor and fuel cell/battery HEVs is analyzed and investigated for adequately selecting of the appropriate HEV, which could be used in industrial applications.

The results have demonstrated that it is possible to significantly improve the hydrogen consumption in fuel cell hybrid electric vehicles (FCHEVs) by applying the PSO approach. Furthermore, by analyzing and comparing the results, the FC/SC HEV has slightly higher fuel economy than the FC/B HEV.

The addition of electrical energy storage such as supercapacitor or battery in fuel cell‐based vehicles has a great potential and a promising approach for future hybrid electric vehicles (HEV). This paper is mainly focused on the optimal design and power management control, which has significant influences on the vehicle performance. Therefore, this study presents a modified control strategy based on PSO algorithm (CSPSO) for optimizing the power sharing between sources and reducing the components sizing. Furthermore, an interleaved multiple‐input power converter (IMIPC) is proposed for fuel cell hybrid electric vehicle to reduce the input current/output voltage ripples and to reduce the size of the passive components with high efficiency compared to conventional boost converter. Meanwhile, the fuel economy is improved. Moreover, a comparative study of FC/SC and FC/B HEVs will be provided to investigate the benefits of hybridization with energy storage system (ESS).

The flexible mode transitions, multiple power sources and system uncertainty lead to challenges for mode transition control of four-wheel-drive hybrid powertrain. Therefore, the purpose of this paper is to improve dynamic performance and fuel economy in mode transition process for four-wheel-drive hybrid electric vehicles (HEVs), overcoming the influence of system uncertainty.

First, operation modes and transitions are analyzed and then dynamic models during mode transition process are established. Second, a robust mode transition controller based on radial basis function neural network (RBFNN) is proposed. RBFNN is designed as an uncertainty estimator to approximate lumped model uncertainty due to modeling error. Based on this estimator, a sliding mode controller (SMC) is proposed in clutch slipping phase to achieve clutch speed synchronization, despite disturbance of engine torque error, engine resistant torque and clutch torque. Finally, simulations are carried out on MATLAB/Cruise co-platform.

Compared with routine control and SMC, the proposed robust controller can achieve better performance in clutch slipping time, engine torque error, vehicle jerk and slipping work either in nominal system or perturbed system.

The mode transition control of four-wheel-drive HEVs is investigated, and a robust controller based on RBFNN estimation is proposed. Compared results show that the proposed controller can improve dynamic performance and fuel economy effectively in spite of the existence of uncertainty.

The purpose of this paper is to compare two design optimization architectures for the optimal design of a complex device that integrates simultaneously the sizing of system components and the control strategy for increasing the energetic performances. The considered benchmark is a battery electric passenger car.

The optimal design of an electric vehicle powertrain is addressed within this paper, with regards to performances and range. The objectives and constraints require simulating several vehicle operating points, each of them has one degree of freedom for the electric machine control. This control is usually determined separately for each point with a sampling or an optimization loop resulting in an architecture called bi-level. In some conditions, the control variables can be transferred to the design optimization loop by suppressing the inner loop to get a mono-level formulation. The paper describes in which conditions this transformation can be done and compares the results for both architectures.

Results show a calculation time divided by more than 30 for the mono-level architecture compared to the natural bi-level on the study case. Even with the same models and optimization algorithms, the structure of the problem should be studied to improve the results, especially if computational cost is high.

The compared architectures bring new guidelines in the field optimal design for electric powertrains. The way to formulate a design optimization with some inner degrees of freedom can have a significant impact on computing time and on the problem understanding

The purpose of this chapter is to highlight the key differences in the production processes of battery electric vehicles (BEV) and internal combustion engine vehicles (ICEV). This exploration not only includes the fundamental physical architectural differences between the types of vehicles but also their entirely different supporting supply chains and underpinning business logics. Many nuanced and less-discussed considerations such as geopolitics, supporting infrastructure, and background policy implications are also covered. This chapter stems from the collection and analysis of secondary peer-reviewed data that is supplemented by verified press publications. The automotive industry moves at an incredibly fast pace, and thus understanding the sociotechnical transition to BEVs requires the additional, timely context of press publications. The overall result of this chapter is a holistic overview of the BEV’s value chain, and more importantly some much needed context for readers to better appreciate the significant implications that are involved. Society is not merely substituting one ‘full fat’ product for a ‘low calorie’ version, but rather we are adopting a new technology that solves some of our problems but comes with challenges of its own. In the coming transition to BEVs, it will be impossible to switch technologies without reformulating various policies and reconsidering how we consume transportation as a commodity or a service. By presenting how society intends to evolve its predominant road propulsion system, this chapter seeks to explain the twists and turns ahead, and offer a glimpse of a more sustainable path forward.

Organizational competences are essential sources of competitive advantage and thus are key drivers of competitive strategies for knowledge-intensive companies like automotive manufacturers. In order to cope with increasing market complexity and dynamism, reduced development times, and relentless cost pressures in a highly competitive environment, knowledge-driven companies need to understand how to be proactive in building and leveraging the competences they will need to be successful in the future, especially within their product development activities.

To help managers become proactive in identifying and building useful future competences, the dynamic and systemic perspectives of competence-based strategic management provide a framework for analysis that can help managers to look beyond their organization’s current competences and identify organizational competences that will be needed in the future. Competence theory emphasizes that an organization’s competences are dynamic and constantly need to be updated and reconfigured to adjust to the competitive dynamics of an industry. Any methodology for identifying future competence needs must begin with some means for identifying strategic gaps between the competences a firm has now and the competences it will need in the future. This paper describes a technology and market roadmap-based methodology for forecasting a firm’s future competence needs – the competences a firm will need to start developing now in order to meet expected market demands in the future. The methodology proposed here is applied and, we believe, validated through application to a competence planning process in a German luxury car manufacturer.