Performance Of Battery Electric Vehicle Research Articles

Battery health-considered energy management strategy for a dual-motor two-speed battery electric vehicle based on a hybrid soft actor-critic algorithm with memory function

Energy management strategies (EMSs) ease mileage anxiety and improve the health performance of battery electric vehicles (BEVs). This study proposes a soft actor-critic (SAC)-based EMS for dual-motor two-speed BEV to minimize electrical energy consumption and battery capacity losses. In addition, two optimization tricks are employed to optimize the original SAC. The linear mapping trick is integrated into the actor-network of SAC to enable agents to search for optimal EMS in a discrete (driving mode)-continuous (torque distribution) hybrid action space. The SAC-based EMS is modeled as a partially observable Markov decision process (POMDP) to obtain more information about the real state of the environment. Based on this, another optimization trick, the long short-term memory (LSTM) network, is integrated into the actor and critic of SAC to make full use of both historical and current environmental information. Simulation results show that the proposed method learns the optimal EMS, its converged episodes are shortened to the 97th, the health performance of the battery is the closest to the dynamic programming (DP)-based EMS, and maintains battery operating at a range of 30–40°C. In addition, the proposed EMS has the best adaptability in test cycles, reaching 99.42% and 99.29% of DP-based EMS, respectively.

A Mechanical-Hardware-in-the-Loop Test Bench for Verification of Multimotor Drivetrain Systems

Multimotor drivetrain systems utilizing more than one electric machine for propulsion can provide possibilities to improve energy efficiency and vehicle dynamic performance of battery electric vehicles (BEVs). However, laboratory testing of such drivetrain systems using a dyno test bench can be costly. A solution can be to use a mechanical-hardware-in-the-loop (MHIL) test bench, which combines real-time simulations of the intended working environment with the dyno test bench. To utilize the MHIL approach for multimotor drivetrain systems, one drivetrain is implemented in the dyno test bench, while the remaining are simulated using a real-time simulator, therefore, providing a less expensive solution for laboratory testing of drivetrain components and control methods in their intended environment. In this work, an MHIL test bench for a multimotor drivetrain system is designed and experimentally verified. A BEV with two independently driven front wheels is considered for modeling. To interface the dyno test bench with real-time simulation, two different methods, namely, open loop and closed loop, are proposed and verified in experiments by prototyping an MHIL test bench. In addition, an anti-slip control is implemented and evaluated experimentally to demonstrate the suitability of the proposed MHIL test bench in the verification of control methods.

Influence of a New Type of Two-Speed Planetary Gear Automatic Transmission on the Performance of Battery Electric Vehicles

This paper introduces a new two-speed planetary gear automatic transmission using an electronically controlled wedge clutch. In order to verify the feasibility of using this transmission in pure electric vehicles, the influencing factors of the two-speed transmission due to the increase in mass and the reduction in transmission efficiency are introduced. The vehicle simulation model was established on the MATLAB/Simulink platform, and the dynamic programming method was used to optimize the transmission ratio and shifting law. The simulation results show that the use of a two-speed automatic transmission can effectively improve the economic performance and dynamic performance of battery electric vehicles.

Life cycle assessment of battery electric vehicles: Implications of future electricity mix and different battery end-of-life management

The environmental performance of battery electric vehicles (BEVs) is influenced by their battery size and charging electricity source. Therefore, assessing their environmental performance should consider changes in the electricity sector and refurbishment of their batteries. This study conducts a scenario-based Life Cycle Assessment (LCA) of three different scenarios combining four key parameters: future changes in the charging electricity mix, battery efficiency fade, battery refurbishment, and recycling for their collective importance on the life-cycle environmental performance of a BEV. The system boundary covers all the life-cycle stages of the BEV and includes battery refurbishment, except for its second use stage. The refurbished battery was modelled considering refurbished components and a 50% cell conversation rate for the second life of 5 years. The results found a 9.4% reduction in climate impacts when future changes (i.e., increase in the share of renewable energy) in the charging electricity are considered. Recycling reduced the BEV climate impacts by approximately 8.3%, and a reduction smaller than 1% was observed for battery refurbishment. However, the battery efficiency fade increases the BEV energy consumption, which results in a 7.4 to 8.1% rise in use-stage climate impacts. Therefore, it is vital to include battery efficiency fade and changes to the electricity sector when estimating the use-stage impacts of BEVs; without this, LCA results could be unreliable. The sensitivity analysis showed the possibility of a higher reduction in the BEV climate impacts for longer second lifespans (>5 years) and higher cell conversation rates (>50%). BEV and battery production are the most critical stages for all the other impact categories assessed, specifically contributing more than 90% to mineral resource scarcity. However, recycling and battery refurbishment can reduce the burden of the different impact categories considered. Therefore, manufacturers should design BEV battery packs while considering recycling and refurbishment.

State of charge estimation of Lithium-ion battery using an improved fractional-order extended Kalman filter

Accurate estimation of Lithium-ion battery’s (LiB) state of charge (SOC) in the battery management system (BMS) is very critical to the performance of battery electric vehicles (BEV) because it indicate how much charge is remaining in the battery. However, due to the nonlinear properties of the LiB and the uncertainty of battery models, estimating the SOC of battery online during vehicle operation is often a major challenge. This paper proposes a model-based estimation method named an improved fractional-order extended Kalman filter (IFO-EKF) to overcome the shortcomings of the electrical equivalent circuit model (EECM) in a regular LiB integer-order model. Firstly, a simplified first-order fractional-order model (FOM) which combines the advantages of both the EECM and the electrochemical impedance spectroscopy (EIS) was developed to accurately interpret battery electrochemical processes using electrical circuit made up of resistors and constant phase element (CPE) having fractional-order characteristics. Secondly, the unknown parameters of the FOM model are identified off-line using quantum particle swarm optimization (QPSO) algorithm. In addition, due to the non-linear dynamic characteristics of LiB, Kalman filter family algorithms used in battery state estimation faces some shortcomings which results in filter divergence caused by the implementation of inappropriate co-variance matrix in the algorithm which leads to inaccuracies and estimation error in the model. The convergence rate of the proposed IFO-EKF algorithm in this study was improved by introducing a Grünwald–Letnikov (G–L) fractional derivative and a time-varying measurement error covariance (R) into the proposed estimation algorithm. Finally, to verify the validity of the proposed model, the HWFET, UDDS and NEDC vehicle dynamics condition tests were used to compare the estimation results of the IFO-EKF against the integer-order extended Kalman filter (IO-EKF) algorithm. The simulation results of the IFO-EKF algorithm proved that it is a better estimation algorithm than the IO-EKF in terms of accuracy.

Optimal Vibration Suppression Modification Method for High-Speed Helical Gear Transmission of Battery Electric Vehicles under Full Working Conditions

To improve the working performance of battery electric vehicle (BEV) high-speed helical gear transmission under full working conditions, combined with Tooth Contact Analysis (TCA) and Loaded Tooth Contact Analysis (LTCA), the vibration model of single-stage helical gear bending-torsion-axis-swing coupling system considering time-varying mesh stiffness was established. The genetic algorithm was used to optimize the tooth surface with the objective of minimizing the mean value of the vibration acceleration at full working conditions. Finally, a high-speed helical gear transmission system in a BEV gearbox was taken as a simulation example and the best-modified tooth surface at full working conditions was obtained. Experiment and simulation results show that the proposed calculation method of time-varying meshing stiffness is accurate, and tooth surface modification can effectively suppress the vibration of high-speed helical gear transmission in BEV; compared to the optimally modified tooth surface under a single load, the optimal modified tooth surface under full working conditions has a better vibration reduction effect over the entire working range.

Evaluating the Performance of Battery Electric Vehicles using an Incorporated Decision Support Framework Based on Ranking Algorithms

The use of alternative energy sources rather than fossil fuels will be unavoidable in the nearish term due to rising levels of toxic residues that threaten natural life and human health. Furthermore, the use of fossil fuels puts subsequent generations in danger from environmental damage and climate change. Battery electric vehicles (BEVs), an environmentally friendly kind of vehicle, are important in light of transportation's significant contribution to the carbon footprint. In light of the recent fast growth of the BEV industry, it has become more important to consider all available BEV options from the perspective of the end-user. Each BEV's fundamental characteristics may be examined in order to make this evaluation. For the correct BEV buying choice, MCDM strategies are useful. As a result, eleven battery-electric vehicles (BEVs) are considered in this study. A variety of multi-criteria methodologies are used to rate these cars on the basis of their technical specifications, such as acceleration, pricing, battery life, and range. It is then used entropy weight and TOPSIS approaches to gather findings from different MCDM strategies. The entropy method is used to compute the weights of the criteria. Then the TOPSIS is used to rank the options. The 3 key considerations for BEV choosing are price, permitted load, and energy usage, with Tesla Model S emphasized as the preferred route.

Research on Factors That Influence the Fast Charging Behavior of Private Battery Electric Vehicles

Due to the limited power cell performance of battery electric vehicles (BEVs), BEV drivers endure a short cruising range and a long charging time. Additionally, uneven charging facilities and unreasonable charging arrangements result in partial queuing and partial idling of charging stations. To solve these problems, it is critical to understand BEV charging behavior and its influential factors. Considering the urgency of BEV charging, BEV drivers tend to choose fast charging when BEV is in driving state. This study investigates fast charging behavior by utilizing private BEV connected data collected from Beijing. First, 130 private BEVs with travel rules were screened out. Using seven months of BEV data, a total of 15,752 trajectories were identified, among which 2161 have fast charging behavior. According to the relationship between fast charging behavior and some influential factors, including battery modeling, driving behavior, weather and environment, and even user habit, were empirically investigated. Moreover, the battery state of charge at the start time, time-origin, travel time duration, driving distance, driving speed, wind power, temperature, and last-fast-status are determined as significant influencing factors. Lastly, a prediction model based on the significant factors is proposed to estimate whether there is fast charging in a day trajectory. The proposed model achieves the best accuracy over compared models, i.e., univariate linear regression (ULR) with several factors and multivariate linear regression (MLR) model. The study is expected to help better understand fast charging behavior and further contribute to the future improvement of fast charging efficiency.

Simulation Calculation and Experimental Study on Linear Braking Performance of New Energy Vehicles

The structure and characteristics of the braking system of the new energy vehicle are analyzed. The calculation model of the linear braking performance for the new energy vehicle is established. The parameters such as the adjustment frequency of the solenoid valve and the adjustment amplitude are determined. Finally, the road test is verified. The results show that compared with the road test results, the calculated error is less than 10%, and the model can be used to evaluate the braking performance of battery electric vehicles.

Design of a Hydraulic Control Unit for a Two-Speed Dedicated Electric Vehicle Transmission

Two-speed automatic transmission is one solution to increase the economic efficiency and dynamic performance of battery electric vehicles (BEV). Hydraulic control unit (HCU) is a key component in automatic transmissions, which determines the quality of shifting directly. Based on the structural scheme and shift logic of a two-speed dedicated electric vehicles transmission (2DET) with two wet clutches, we designs a 2DET hydraulic control unit composed of three subsystems: pressure regulating and flow control system, shift operated and control system and cooling and lubrication system. The results of the experiments, including the valve body bench test, transmission bench test and vehicle test, show that the design of hydraulic control unit meets the requirements.

Impacts of electricity mix, charging profile, and driving behavior on the emissions performance of battery electric vehicles: A Belgian case study

Abstract Battery Electric Vehicles (BEV) are considered to be a better alternative for conventional vehicles in the matter of carbon dioxide (CO2) emissions and urban air pollution reduction. Life Cycle Assessment (LCA) is a widely used methodology to quantify and compare the environmental impacts of vehicle technologies. In this study, we compare the life cycle environmental emissions of CO2 equivalent (CO2e), sulfur dioxide (SO2), nitrogen oxides (NOX) and particulate matters (PM) of the BEV with the petrol and diesel vehicles. Unlike many other literatures, this study uses the real-world energy consumption data for the environmental assessment. In addition, this study explores the possible impact of the short term and long term fluctuations in the electricity mix and the vehicle charging profile, on the life cycle emissions performance of BEV. The influence of charging profile on the well-to-tank (WTT) emissions (i.e. emissions associated with electricity production) of BEV is discussed by using hourly emissions and different possible peak and off-peak charging time frames. The results of this study proves off-peak charging is a better option to reduce the life cycle emissions, compared to peak charging. When a BEV is charged during off-peak hours instead of peak hours, the well-to-tank CO2, SO2, NOX and PM emissions per km can be reduced significantly. Also, this study emphasizes the importance of taking driving behaviors of users and auxiliary energy consumption into account. This aspect is analyzed by comparing the empirical energy consumption and the corresponding WTT emissions of BEV, with the New European Driving Cycle (NEDC) standard values. The results reveal that the auxiliary energy consumption is responsible for, nearly a third of the WTT emissions.

The Synthetic Evaluation Method of the Dynamic Performance and Economic Performance of Battery Electric Vehicle Based on Principal Component Analysis

The dynamic index and economic index of battery electric vehicles are mutual related as well as opposite. So they should be evaluated synthetically. From the analysis of literatures, the maximum speed, accelerating ability and driving range are chosen as the synthetic evaluation index. By the method of principal component analysis, the synthetic evaluation model of the dynamic and economic performance of battery electric vehicle is established. And example is used to explain the application of the model.

The Parameters Sensitivity Analysis of Battery Electric Vehicle Dynamic Performance

Battery electric vehicle is to be researched and developed as the best alternative for fuel vehicle. Dynamic performance is the basic performance of a vehicle. In the prior period of research and development, studying on the sensitivity of relevant parameters to dynamic performance can determine the degree of influences that the relevant parameters have on it. This paper establishes mathematical model for the dynamic performance of battery electric vehicle, and deduces computational methods of the dynamic parameters sensitivity. Improving these parameters will provide the basis for improving the dynamic performance of battery electric vehicle.