Search results

The paper highlights the process of electric vehicles optimal design as an inverse problem and presents the global constrained optimization as the best way to solve it.

The electric vehicle optimal design is carried out by a new approach. It consists an electric vehicle design model managed by constrained optimization techniques. It includes sizing models for all drive train components and a vehicle dynamic model build in a new “design way” as an energy‐based model using the response surface methodology. The sensitivity of first simple sizing models can be evaluated by the experimental design method, giving information about the most important part of the model that must be improved.

The result shows the superiority of the constrained optimization technique that treats simultaneously the global optimization and the model adjustment. This method of simultaneous resolution is much more powerful than the successive resolution of each subproblem. The proposed “design approach” used for electric vehicle optimal design offer a large potential in the field of the complex systems design.

The electric vehicle design process is treated on a vehicle design model based on a design approach. It allows determining the drive train components specifications for imposed vehicle performances, taking into account the dynamic model of the vehicle and all components interactions. Furthermore, considering fine components sizing models, the components can be sized taking into account the whole system behavior in an optimal global design.

The purpose of this study is to propose a torque vectoring control system for improving the handling stability of distributed drive electric buses under complicated driving conditions. Energy crisis and environment pollution are two key pressing issues faced by mankind. Pure electric buses are recognized as the effective method to solve the problems. Distributed drive electric buses (DDEBs) as an emerging mode of pure electric buses are attracting intense research interests around the world. Compared with the central driven electric buses, DDEB is able to control the driving and braking torque of each wheel individually and accurately to significantly enhance the handling stability. Therefore, the torque vectoring control (TVC) system is proposed to allocate the driving torque among four wheels reasonably to improve the handling stability of DDEBs.

The proposed TVC system is designed based on hierarchical control. The upper layer is direct yaw moment controller based on feedforward and feedback control. The feedforward control algorithm is designed to calculate the desired steady-state yaw moment based on the steering wheel angle and the longitudinal velocity. The feedback control is anti-windup sliding mode control algorithm, which takes the errors between actual and reference yaw rate as the control variables. The lower layer is torque allocation controller, including economical torque allocation control algorithm and optimal torque allocation control algorithm.

The steady static circular test has been carried out to demonstrate the effectiveness and control effort of the proposed TVC system. Compared with the field experiment results of tested bus with TVC system and without TVC system, the slip angle of tested bus with TVC system is much less than without TVC. And the actual yaw rate of tested bus with TVC system is able to track the reference yaw rate completely. The experiment results demonstrate that the TVC system has a remarkable performance in the real practice and improve the handling stability effectively.

In view of the large load transfer, the strong coupling characteristics of tire , the suspension and the steering system during coach corning, the vehicle reference steering characteristics is defined considering vehicle nonlinear characteristics and the feedforward term of torque vectoring control at different steering angles and speeds is designed. Meanwhile, in order to improve the robustness of controller, an anti-integral saturation sliding mode variable structure control algorithm is proposed as the feedback term of torque vectoring control.

This article looks at the history, progress and advantages of electric vehicles.

This study aims to extend the driving range by on-board charging with use of photovoltaic (PV) source, avoiding the dependency on the grid supply and energy storage system in addition to that reduce the conversion complexity influenced on converter section of electric vehicle (EV) system.

This paper proposed a PV fed integrated converter topology called integrated single-input multi-output (I-SIMO) converter with enriched error tolerant fuzzy logic controller (EET-FLC) based control technique to regulate the speed of brushless direct current motor drive. I-SIMO converter provides both direct current (DC) and alternating current (AC) outputs from a single DC input source depending on the operation mode. It comprises two modes of operation, act as DC–DC converter in vehicle standby mode and DC–AC converter in vehicles driving mode.

The use of PV panels in the vehicle helps to reduce dependence of grid supply as well as vehicle’s batteries. The proposed topology has to remove the multiple power conversion stages in EV system, reduce components count and provide dual outputs for enhancement of performance of EV system.

The proposed topology leads to reduction of switching losses and stresses across the components of the converter and provides reduction in system complexity and overall expenditure. So, it enhances the converter reliability and also improves the efficiency. The converter provides ripple-free output voltage under dynamic load condition. The performance of EET-FLC is studied by taking various performance measures such as rise time, peak time, settling time and peak overshoot and compared with conventional control designs.

The purpose of this paper is to present a prospective study of sustainable mobility in the framework of a supporting energy management systems (EMS). Technological advances are still required, namely electric vehicles (EV) endowed with improved EMS in order to increase their performance by making the most of available energy storage technologies. As EVs may be seen as a special domestic load, EMS are proposed based on demand-sensitive pricing strategies such as the Energy Box discussed in this paper.

The study presents an overview of electric mobility and an urban EV project, with special focus on the utilization of its energy sources and their relation with the energy demand of a typical urban driving cycle. Results based on the ECE 15 standard driving cycle for different free market electricity tariffs are presented.

The analysis based on present Portuguese power and energy tariffs reveals that it is highly questionable whether the resulting profit will be enough to justify the potential inconveniences to the vehicle user, as well as those resulting from the increased use of batteries.

The conclusions indicate that more studies on the trade-offs between grid to vehicle and vehicle to grid schemes and electricity pricing mechanisms are needed in order to understand how the utilization of EVs can become more attractive in the end-users’ and utilities’ perspectives.

The paper proposes an approach for future electricity tariff behavior that could be applied to EVs in order to understand whether or not their grid integration in charge and discharge situations would be beneficial for end-users and utilities, in the framework of smart energy management technologies.

The use of electric vehicles has received popularity as alternative fuel vehicles to reduce greenhouse gas emissions and energy cost, which are expected to perform a crucial role in the near future of emerging mobility markets. The purpose of this empirical study is to analyse the role of electric vehicle knowledge in predicting consumer adoption intention directly and indirectly in the backdrop of an emerging market.

The study approached an extended version of “Technology acceptance model” (TAM) based on the integrated framework of “knowledge-beliefs-intention”. The model was tested via direct and indirect path analyses with the data collected from Indian respondents using an online survey.

The results indicate the robustness of the present research model, which shows that consumer adoption is significantly driven by electric vehicle knowledge, perceived usefulness, perceived ease of use and perceived risk. Electric vehicle knowledge has emerged as the most powerful cognitive measure, which directly affects the adoption intention along with the measures of “TAM”. Additionally, this also poses a higher indirect effect on adoption intention in the integrated model.

The study has focused on potential young and educated consumers, which may not be warranted to generalise the research findings, while youth or millennials are more receptive to adopt innovative and clean technology products like electric vehicle. Based on the findings, implications are offered for encouraging electric vehicles in the backdrop of emerging automobile markets.

Concerning this cognitive phenomenon of knowledge, scant literature has been explored the role of subjective knowledge in consumer adoption for electric vehicles, particularly in the emerging markets like India. Thus, the present study analyses how consumers' knowledge about electric vehicle affects their decision to adopt this in the near future of Indian zero-emission mobility market.

The purpose of this paper is to study the electric vehicle (EV) drive efficiency of a traction motor considering regenerative braking according to various motor cores.

A software program was developed to predict the driving performance of an EV. It determines the driving mileage, the required power of the traction motor, and the operation points on a torque-speed map when drive cycles are given. The driving performance is calculated from the battery capacity, vehicle specification, and efficiency map of the traction motor computed using the finite element analysis.

As a result, the motor core is a significant design variable for raising the driving mileage of an EV. It is noted that the change of electrical steels used for the motor core is the lowest priced method of increasing the driving range by 2 km.

The comparative analysis of motor core by replacing 35PN250 to 25PNX1250F results in improvement effects traveling 4.62 and 5.16 km farther in the Simplified Federal Urban Driving Schedule (SFUDS) and Highway Fuel Economy Driving Schedule (HWFET), respectively. It was also verified that regenerative braking system is able to enhance drive efficiency by 29-31.3 km in the SFUDS and 6.5-7.3 km in the HWFET. From comparison of price rise for increasing driving mileage by 2 km, it is noted that the change of electrical steels used for the motor core is the lowest priced method.

This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment.

In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.

To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states.

In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

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.

This paper aims to address the broad question of how organizations capture value from foresight exercises. Through a comparative case analysis, this paper looks at what firms do to make the information usable and create value. It explores factors that cause different firms to respond differently to the same trends. It analyzes the passenger car segment of the automobile industry and the response of six major firms to fossil fuel and changing environmental regulations through an analysis of their policies and strategic activities, such as new product development. It finds foresight to be an important link between firm capabilities and environmental changes.

This paper adopts the case approach to capture the linkage between the issue and the context (Yin, 1994) and uses multiple cases to explore the variables by comparing and contrasting the cases on key aspects (Eisenhardt and Graebner, 2007). As the paper ' s objective is to understand the similarities and differences between dominant firms in the sector, it chooses through theoretical sampling, six firms that have a presence in all the major regions of the world – two each from the USA, Europe and Japan – Ford, General Motors, Volkswagen, Renault, Toyota and Honda. This sample represents the firms and regions traditionally strong in the passenger car industry.

Thus, it is seen that the relationship that was posited in the conceptual model between the goal of the firms, the vision of the future and the nature of products and approach to technology/competence development seems to be valid. However, in addition, the paper perceives that some additional linkages that link between foresight and the goals and vision of the future seem to be influenced by the extent of uncertainty. In addition, the decisions regarding portfolio of products and approaches to technology and competence development seem to be also influenced by the perception of existing competencies and the external competitive context.

This paper was based on multiple cases created out of secondary information, hence the constructs used are those which are perceived and stated.

The paper could help firms understand decisions related to technology choices in field involving high levels of uncertainty and competition.

This paper could improve learning processes from foresight exercises, and enable strategic decisions to be taken on these.

Thus, this paper has explored the linkages between what firms perceive and state, and what is reflected in their actions. It has looked at this linkage from the perspective of foresight, and the strategic perspective of the firm. It has come up with additional issues and questions that influence this relationship. These can inform future research in this domain.