Narrow Results

This paper proposes a hierarchical model predictive control (MPC) strategy for four in-wheel motor actuated electric vehicles (4-IWM EVs). Current studies on 4-IWM EVs mainly focus on a single objective. We propose a novel vehicle modeling method and a hierarchical MPC to address comprehensive problems of 4-IWM EVs in improving the vehicle’s efficiency and comfort while achieving accurate velocity tracking. The upper MPC aims to accurately track the desired velocity trajectory and the lower torque distribution controller aims to allocate the torque of each in-wheel motors to improve driving efficiency and comfort. The simulation results indicate that our method significantly enhances the tracking accuracy while improving driving efficiency and ride comfort compared to the widely used benchmark controller.

The existing conventional vehicle transportation landscape in India is grappling with challenges stemming from extensive air pollution, health risks, surging oil prices, limited fossil fuel resources, substantial oil import expenses and energy volatility. To counter these issues, Electric Vehicles (EVs) are progressively replacing internal combustion engines, offering a promising route toward decarbonization and mitigating climate concerns. EVs rely on electric motors powered by batteries, predominantly Lithium-ion batteries (LIBs), known for their superior attributes such as low self-discharge, high energy density and extended life cycle. Nevertheless, LIB performance is significantly influenced by operating temperatures, with suboptimal conditions leading to decreased efficiency, power loss and faster aging. Addressing this, an effective Battery Thermal Management System (BTMS) becomes crucial to maintain batteries at optimal temperatures, enhancing their efficiency and safety. This study focuses on a computational analysis of passive heating systems employing Fins and Phase Change Materials (PCM) for 18650 Li-ion battery thermal management at low temperatures, with specific attention to battery module analysis. Numerical analysis using ANSYS FLUENT investigates the influence of varying PCM thickness on heat transfer, predicting temperature distribution and discussing its impact on battery output performance.

As a compact city-state with a modern electricity grid and relatively clean energy sources, Singapore seems ideal for the deployment of the battery electric vehicle (BEV). A fleet of 89 BEVs were deployed in a test-bed that was concluded in 2013. The paper conducts a cost-effectiveness analysis and financial analysis of the Renault Fluence ZE and its comparable gasoline model to assess the economics of BEVs. It concludes that BEV adoption in Singapore is both undesirable (due to higher social costs) and unlikely (due to higher private costs) in the immediate and near future. Where 94% of the population live in high-rise apartments, there will be a heavy reliance on costlier communal charging stations, thereby mitigating the operating savings that BEVs could have offered if home chargers were used. Lifetime costs are not as sensitive to changes in oil prices as expected. For the BEV to be socially viable, the breakeven carbon price amounts to S$9,700 per tonne of CO₂, which suggests that BEVs are not a cost-effective means of reducing carbon emissions in Singapore, or battery prices would need to be halved. Nevertheless, the study demonstrates that exempting the BEV batteries from taxation could support BEV adoption if that were to be a government objective.

In this paper, we explore the economic impact of promotion and realization of an electric vehicle society (EVS). More concretely, this paper emphasizes a computable general equilibrium (CGE) modeling approach to evaluate the following issues: economic impacts of subsidies for promotion of an EVS, the possibility of price reductions, industrial structure change toward an EVS, and modal shift occurring toward an EVS. Our simulation results demonstrate that after applying 5–25% up subsidies to five industries, such as electric vehicle (EV) manufacturing, EV transport, solar power, cogeneration and other transport, the total industrial output and city GDP increase. A large growth rate is found in industries where subsidies are introduced alone with non-ferrous metal industry. However, it is interesting that decreasing proportions are found in oil and coal product, mining, heat supply and gasoline vehicle (GV) transport industries. Moreover, all the commodity prices decrease since subsidies are given to some industries. Hence Toyohashi City’s economy shows a direction where the demand for conventional vehicles and energy use are decreased, conversely, the demand for EVs and renewable energy are increased illustrating a different life style from the current one. However, it does not mean that the total CO₂ emission is decreased. EV society makes some industrial outputs larger. Due to the fact that some industrial outputs are increased, CO₂ emissions of EV manufacturing and nonferrous metal are increased more than decreased industries. Thus, introducing 5–25% subsidies to EV manufacturing, EV transport, solar power, cogeneration and other transport can really represent a realistic alternative society to EVS if the total CO₂ emission can be reduced. Therefore, we have to think what can make the total CO₂ emission reduced.

The electric vehicles (EVs) market in Malaysia, though growing remains relatively small. Previous related studies on Malaysia focused mainly on consumer preferences and the country’s readiness towards a higher adoption of environmentally friendly alternative vehicles. This study extends previous work to examine consumer preferences towards EVs through a market survey conducted in Klang Valley. Then follows a comparative economic and environmental cost-benefit analysis of EVs relative to conventional vehicles and hybrid vehicles to provide some understanding on the market diffusion of the former. The key findings from the survey suggest that pricing and the maintenance costs of EVs negatively influence the uptake of EVs despite the positive environmental attitudes of consumers. The cost-benefit analysis, in turn, implies that the EV transition will accelerate with rising petrol prices and falling battery costs. The study concludes with some implications for sustainability based on the adoption of EVs.

With the implementation of new environmental policies such as “carbon peak” and “carbon neutrality”, reducing carbon emissions through the development of clean technology in the automobile industry has become a key priority. However, the high cost of researching and developing green technology has led to high vehicle prices, which poses a major barrier to expanding the market share of such vehicles. The decision of whether to invest in research and development (R&D) has become a challenging one for automobile manufacturers. In this paper, we propose a game theory analysis scheme to study the R&D investment decisions of two original equipment manufacturers (OEMs) — an electric vehicle manufacturer (EM) and a fuel vehicle manufacturer (FM) — who, respectively, produce electric vehicles (EVs) and fuel vehicles (FVs). Since the manufacturers exhibit bounded rationality and their R&D investment decision-making involves a long-term, continuously learning and adjusting process, we model this dynamic R&D investment decision-making process as an evolutionary game to study manufacturers’ stable evolutionary behaviors in optimal R&D investment strategies. Different from previous literatures, where the prices for vehicles with high or low R&D investment were predetermined, we optimize the price of each vehicle, market shares, and optimal utilities of OEMs using a two-stage Stackelberg game for each investment strategy profile. Additionally, we use the Personal Carbon Trading (PCT) mechanism to help reduce carbon emissions. The main contribution of this paper is exploring the conditions for the evolutionary stable strategies (ESSs) of the evolutionary game based on the optimal utilities of the OEMs under different strategy profiles. The impact of preference parameters and green R&D coefficients on the OEMs’ decisions, as well as consumers’ purchase choices are also discussed. Finally, numerical simulations using real-world data are conducted to verify the theoretical results on ESSs.

In this paper, we use car-following theory to study the traditional vehicle’s running cost and the electric vehicle’s running cost. The numerical results illustrate that the traditional vehicle’s running cost is larger than that of the electric vehicle and that the system’s total running cost drops with the increase of the electric vehicle’s proportion, which shows that the electric vehicle is better than the traditional vehicle from the perspective of the running cost.

In this paper, we apply the relationships between the macro and micro variables of traffic flow to develop an electricity consumption model for electric vehicular flow. We use the proposed model to study the quantitative relationships between the electricity consumption/total power and speed/density under uniform flow, and the electricity consumptions during the evolution processes of shock, rarefaction wave and small perturbation. The numerical results indicate that the proposed model can perfectly describe the electricity consumption for electric vehicular flow, which shows that the proposed model is reasonable.

With the economic growth of our country and the continuous improvement of people’s living standards, cars have begun to enter thousands of households and become a necessity for people. However, the rapid growth of the number of automobiles has led to a sustained increase in carbon dioxide emissions and a significant decline in urban air quality, which seriously restricts the sustainable development of cities. With the introduction of the national air quality protection policy, electric vehicles will eventually replace the existing fuel vehicles and become a new generation of transportation for people to travel. At the same time, the large expansion of the number of cars has increased the hidden dangers of traffic accidents. In order to ensure the safety of pedestrians, drivers are given a more intelligent driving environment. This paper presents the research of pedestrian detection and pedestrian distance algorithm based on image processing. By comparing the performance of pedestrian detection algorithm based on SSD with traditional HOG+SVM pedestrian detection algorithm, the results of pedestrian–vehicle distance calculation are detected, and the feasibility and effectiveness of the algorithm are obtained. The results show that the proposed algorithm has good feasibility and practicability, and provide a good reference for the research of pedestrian detection algorithm for electric vehicles.

The transportation sector uses a big portion of the world’s petroleum products thus increasing the greenhouse gas (GHG) emissions. Electric vehicles (EVs) have the potential to solve GHG emissions. The requirements for EVs have brought many different problems such as the conversion of voltage level from the battery to other parts of the EV using DC–DC converters. The design and implementation of a nonisolated DC–DC buck converter with single input and dual output are presented, which is also called the single-input multiple-output (SIMO) converter. The converter is designed especially for electric cars. The battery (48V) of the electric vehicle is used as an input to the SIMO. Small-signal analysis and effective control strategy for the converter are presented in this paper. The simulation of the system is performed and compared with the experimental results.

This paper presents an electric vehicle connected to a charging station based on the proposed method. The proposed technique is the joint execution of the Student Psychology Optimization Algorithm (SPOA) and the AdaBoost algorithm and is therefore called the SPOA-AdaBoost algorithm. In particular, the annualized social cost depends on CS and EVCS set from the objective function of the allocation model. The EVCS is linked with the CS and allows the charging service for electric vehicles. The vehicle-to-grid functions of electric vehicles are properly considered under the present optimization model. The EV load demands are considered as controllable resources, and EV optimal optimization problems are connected with allocation problems. When EV arrives at the charging station, it reports its own energy demand and expected departure time using the EVCS operator. Every EVCS could attack the details of electric vehicles via the proposed method. With this proper action, this method manages the energy demand and the total supply. The constraints are the power flow equations, equivalent load demands on the buses, branch current constraints, discrete size restrictions for CS, constraints on CS outputs, EV participation on V2G activities, mutual exclusivity of the EV charge and discharge statuses, EV Owner charge satisfaction, EV Owner satisfaction charge, EV SOC restrictions, Occupied CF quantities, and EVCS CF sufficiency. Among these, the execution of the present model done by the MATLAB/Simulink platform and the performance of the proposed model is likened with other systems.

The battery pack powers the electric motor in a battery-operated electric vehicle. To achieve the required power, the cells are connected in series and parallel combinations to form a battery pack. The battery pack is monitored using the battery management system. During the charging and discharging process, imbalance occurs in the cells due to intrinsic and extrinsic properties of the battery chemistry. This cell imbalance induces problems, such as an under-discharge, over-charge, increase in charging time and reduction in battery lifecycle. The passive and active balancing technique is employed to balance the individual cells in the battery pack. In this paper, the adaptive passive cell balancing is performed for a battery pack of six series-connected Li-ion cells of rating 3.6V, 4Ah under ideal, charging, discharging and drive cycle conditions using MATLAB/Simscape. In this proposed adaptive passive cell balancing methodology, a dynamic resistance is selected based on the threshold values to balance the individual cells in the battery pack. For this battery pack, the proposed design achieves 34% reduction in balancing time, 17% reduction in energy loss, and 14% reduction in power loss under ideal conditions. The experimental verification is also done and shows that the balancing time is about 2400s. The capacity fade factor of the battery pack is also analyzed.

The reliable and secure operation of power grids can be efficiently supported by the charging load prediction of electric vehicles (EVs). To address the problem of insufficient accuracy of existing charging load prediction models, a technique for predicting charging load for EVs using the sparrow search algorithm-support vector regression (SSA-SVR) is proposed. First, the daily travel patterns of space and time of EV users are analyzed. Therefore, EV charging load data is obtained by Monte Carlo simulation. Finally, a support vector regression (SVR)-based model for predicting EV charging load is established and the sparrow search algorithm (SSA) is further used to find the optimal kernel function factor and penalty factor of SVR to achieve the optimized prediction effect. The simulation experiments show that, compared with the backpropagation (BP) neural network, SVR methods and PSO-SVR methods, the proposed prediction model can enhance the prediction accuracy of the charging load of EVs.

A hybrid technique is proposed for electric vehicles (EVs) or hybrid electric vehicles (HEVs) with a battery thermal management system (BTMS). The proposed hybrid method is the combined execution of the Pelican Optimization Algorithm (POA) and Firebug Swarm Optimization (FSO). The hunting behavior of pelicans is improved with the help of the FSO technique; hence, it is named the POA-FSO strategy. Here, a battery of 36 lithium-ion (Li-ion) cells is considered. The proposed method of analyzing battery thermal performance analyzed factors like heat flux from the battery cell to the passage spacing size, cooling air and the cooling air’s mass flow rate (MFR). The BTMS performance is studied under maximum battery unit temperature, pressure drop and temperature uniformity. The proposed method enhances the coolant passage spacing and decreases the temperature difference in the battery cells. The proposed approach is executed on the MATLAB platform, and its performance is compared with existing approaches. From the results, it is concluded that the MFR maximizes, and a nonuniform distribution of the MFR of the passage occurs. By using the proposed approach, the maximal temperature change of cooling air among the passages is 2.0K, the maximal temperature change between the battery cells is 7.5K and a pressure drop of 228.03Pa is obtained.

In Japan, electric vehicle (EV) is spreading, because EV has several advantages compared with gasoline car. However, EV has two main disadvantages, shorter cruising range and longer charging time. These disadvantages may cause a serious problem on an expressway, because the queue of EV waiting for charge may be long, if enough chargers are not placed at charging places. It is important to estimate the number of chargers in a charging place to avoid the long queue. This paper proposes a model to estimate the number of chargers used by EV. In this model, it is assumed that the driver knows the charge level of EV and decides to charge at charging places according to the type of charging place and charge level.

The determination principle of the transmission and the gear number in the electric vehicle driving system are studied in this paper. The results show that the rated power or torque and speed of the motor must reasonably match the parameters of transmission system. A type of electric vehicle is taken as the research object. Two of the five manual transmission speeds gears are calculated and analyzed, which are the equilibrium diagram of the driving force and the driving resistance force between the second gear and the third gear. The single gear-driven scheme of the fixed speed ratio reducer is proposed to replace the heavy mechanical gear reducer. It can reduce the mass of the whole vehicle and increase the driving distance from the theorem. Appling the simulation software Advanced Vehicle Simulator (ADVISOR) of electric vehicle to simulate the vehicle power performance and continued mileage, it verifies that the parameters determination principle and the method of the proposed drive system are correct.

To use regenerative braking to act as an auxiliary brake to maintain the constant speed of a brushless DC motor driven electric bus (BDCMEB) on downhill based on the feature of double-loop control structure of the control method for electric vehicle traction motor and the variable structural characteristics of PWM Control System for brushless DC Motor. A double-manifold variable structure control method to control regenerative braking is proposed for the bus cruising downhill. The impact of lead-acid battery's charge acceptance ability over a long charging period on the regenerative braking force of a driving motor is analyzed. Dynamic model of the bus on long downhill is established. A double-manifold variable structure controller is designed for the DCMEB on long downhill. The simulation result shows that the control system maintains enough stability and strong robustness. It may be achieved for the bus to maintain a constant speed downhill only by regenerative braking on a smaller slope. But the dynamic process is very slow. When deceleration or a constant speed is desired on a larger slope, only by electro mechanical parallel braking can the bus track the target speed precisely and quickly.

To reduce energy consumption and improve energy utilization efficiency of electric vehicles (EVs), the relationship among power battery pack, dynamics performance, and driving range is revealed in this paper. A power battery optimizing configuration model has been developed to meet the requirements of driving range and dynamics of electric vehicles, and the corresponding design steps are given in this paper. A set of special software of power battery optimization configuration for electric vehicles is developed, which is both convenient and practical. A case was employed to validate the accuracy of the power battery optimizing configuration system software.

When mentioning multidisciplinary design optimization methods, the deterministic optimum design is frequently applied to set the constraint boundary. Furthermore, only a small amount of space tolerances (or uncertainty) is available in the process of design, manufacture and operation. Therefore, deterministic optimum design lacking uncertainty cannot meet the needs of reliability-based design optimization. In this paper, reliability optimization design method, finite element (FE) analysis, optimal Latin hypercube test design and response surface approximation model are combined to optimize the side structure of electric vehicles and improve its crashworthiness. Firstly, a side impact FE model of the electric vehicle is established and verified in this paper. Then, the dimensions and the material yield strength of the force-bearing structure in the vehicle are selected as design variables, and the impact speed in the actual collision is selected as a random variable to optimize the car crashworthiness in the side impact using the 95% reliability optimization method. The results show that the 95% reliability optimization design increases the total energy absorption of the side components by 9.45%, the intrusion of the B-pillar and the vehicle door inner panel decreased by 10.42% and 14.75%, respectively. The intrusion speed of the B-pillar and the inner panel of the vehicle door decreases by 10.35% and 17.78%, respectively. By comparing the results of traditional deterministic optimization and reliability optimization methods, the latter can better satisfy the crash safety objectives, and improve the reliability of vehicle body design.

Electric vehicle technology is a crucial technology for achieving sustainable energy transformation, which is of great significance to climate change and promotes sustainable development. This paper attempts to study the transnational R&D cooperation of electric vehicles. According to the authorized data of transnational co-patents from the United States Patent and Trademark Office (USPTO), a social network analysis method is employed, and a detailed study of transnational co-patent networks in electric vehicles is conducted, including the construction of network, the analysis of nationality distribution of co-patent inventors, the analysis of structural characteristics and important nodes of network in different stages from the perspective of inventors and countries. The research results show that the cooperative groups formed by inventors are independent of each other and have not yet formed a large network; the degree of transnational cooperation in developed countries far exceeds that in developing countries; the US and Germany are the dual-core in the transnational co-patent networks; the breadth and intensity of transnational cooperation are strengthening, and the regional borders are less and less restrictive. As for the existing problems, authoritative inventors could organize large international R&D cooperation institutions to gather dispersed inventors together and connect them into a large inventors’ network; developing countries are encouraged to seek partners through the network, actively participate in transnational R&D cooperation, and developed countries are encouraged to hold global technological innovation events.