SAE International Journal of Electrified Vehicles最新文献

{"title":"An Electric Vehicle Onboard Microgrid with Solar Panel for Battery Module Balancing and Vehicle-to-Grid Applications","authors":"Chen Duan, Zhongyang Zhao, Caisheng Wang, Jianfei Chen, Matt Liao","doi":"10.4271/14-10-02-0011","DOIUrl":"https://doi.org/10.4271/14-10-02-0011","url":null,"abstract":"","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"47 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89041161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"State-of-Health Online Estimation for Li-Ion Battery","authors":"Liu Fang, Liu Xinyi, Su Weixing, Chen Hanning, He Maowei, Li Xiaodan","doi":"10.4271/14-09-02-0012","DOIUrl":"https://doi.org/10.4271/14-09-02-0012","url":null,"abstract":"To realize a fast and high-precision online state-of-health (SOH) estimation of lithium-ion (Li-Ion) battery, this article proposes a novel SOH estimation method. This method consists of a new SOH model and parameters identification method based on an improved genetic algorithm (Improved-GA). The new SOH model combines the equivalent circuit model (ECM) and the data-driven model. The advantages lie in keeping the physical meaning of the ECM while improving its dynamic characteristics and accuracy. The improved-GA can effectively avoid falling into a local optimal problem and improve the convergence speed and search accuracy. So the advantages of the SOH estimation method proposed in this article are that it only relies on battery management systems (BMS) monitoring data and removes many assumptions in some other traditional ECM-based SOH estimation methods, so it is closer to the actual needs for electric vehicle (EV). By comparing with the traditional ECM-based SOH estimation method, the algorithm proposed in this article has higher accuracy, fewer identification parameters, and lower computational complexity.","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"8 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82394085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding Real-World Variability of Hybrid Electric Vehicle Fuel Economy","authors":"Hillol K. Roy, A. McGordon, P. Jennings","doi":"10.4271/14-09-02-0011","DOIUrl":"https://doi.org/10.4271/14-09-02-0011","url":null,"abstract":"The variability of fuel economy (FE) is of significant importance as that of average FE to realize FE benefits of hybrid electric vehicles (HEVs) consistently by all users in the real world. Over the years, majority of the research has been focused on improving average FE overlooking the variability. Although in recent years few studies have been focused on the reduction of FE variability, no study has been concentrated to understand why certain design has lower FE variability as that of others. This article provides a detailed analysis to decipher the reasons for the FE variability in the real world. This study considered the optimum designs based on two established design optimization methodologies considering Toyota Prius non-plug-in hybrid as a base vehicle. This study analyses the impacts of the parameters of driving patterns and the operation of powertrains on FE variability. The study explains that comparatively bigger internal combustion engine (ICE) in combination with the optimum sizes of generator motor and battery could lead to lower FE variability in the real world due to lesser time of operation of ICE to charge the battery.","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"41 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75293231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Equivalent Consumption Minimization Strategy for Plug-In Hybrid Electric Vehicle with Improved Genetic Algorithm","authors":"Changyin Wei, Yong Chen, Xiuxiu Sun, Yue Zhang","doi":"10.4271/14-09-02-0009","DOIUrl":"https://doi.org/10.4271/14-09-02-0009","url":null,"abstract":"The equivalent consumption minimization strategy (ECMS) is a promising energy management approach to low-fuel economy with the outstanding features of high efficiency. In this article, an optimal ECMS by Improved Genetic Algorithm (IGA) is proposed. To this end, we improved the genetic algorithm (GA) from the coding method, initialization mode, and cross and mutation process. And based on the comprehensive energy consumption and Pontryagin’s minimum principle, the equivalent factor was derived. The IGA was used to optimize the equivalent factor. To evaluate the performance of the proposed energy management strategy (EMS), the average efficiency of the engine and the motor was analyzed in an urban area, high-speed area, and the whole area. The comprehensive fuel consumption was used as the energy consumption index, and the battery capacity loss under the transient conditions was amplified to 10 years as the evaluation battery life index. The simulation results show that under the New European Driving Cycle (NEDC), the proposed strategy improves the fuel economy and battery life index by 14.64% and 36.76%, respectively, compared with the rule-based EMS.","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"20 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79434191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time-Optimal Coordination Control for the Gear-Shifting Process in Electric-Driven Mechanical Transmission (Dog Clutch) without Impacts","authors":"Ziwang Lu, Guangyu Tian, S. Onori","doi":"10.4271/14-09-02-0010","DOIUrl":"https://doi.org/10.4271/14-09-02-0010","url":null,"abstract":"Torque interruption and shift jerk are the two main issues that occur during the gear-shifting process of electric-driven mechanical transmission. Herein, a time-optimal coordination control strategy between the the drive motor and the shift motor is proposed to eliminate the impacts between the sleeve and the gear ring. To determine the optimal control law, first, a gear-shifting dynamic model is constructed to capture the drive motor and shift motor dynamics. Next, the time-optimal dual synchronization control for the drive motor and the time-optimal position control for the shift motor are designed. Moreover, a switched control for the shift motor between a bang-off-bang control and a receding horizon control (RHC) law is derived to match the time-optimal dual synchronization control strategy of the drive motor. Finally, two case studies are conducted to validate the bang-off-bang control and RHC. In addition, the method to obtain the appropriate parameters of the drive motor and shift motor is analyzed according to the coordination control method.","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"18 4 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85193387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of the Sleeve Teeth Shape Parameters on the Shifting Process of Automated Manual Transmission for Electric Vehicles","authors":"Bin Wu, Cun-Gin Chen","doi":"10.4271/14-09-02-0008","DOIUrl":"https://doi.org/10.4271/14-09-02-0008","url":null,"abstract":"Electric vehicles equipped with automated manual transmission (AMT) provide the potential for improving the power and economic performance. However, due to the direct connection between the driving motor and the input shaft of the transmission, the higher moment of inertia at the input of AMT would lead to a poor shift quality. Based on the dynamic analysis of the engagement process of AMT without synchronizer, the dynamic model of the engagement process was established by using AMESim software. Through the analysis of the engagement process, it was concluded that the higher contact force and longer meshing duration under reverse contacting engagement condition is the main reason for the shift difficulty. In order to improve the shift quality, the influences of the sleeve teeth shape parameters of reverse contacting chamfer on the engagement process were analyzed and the simulation validation was performed. The simulation results showed that reducing the width and increasing the angle of the reverse contacting chamfer can effectively reduce the engagement duration and the contact force impulse, although the vehicle jerk was increased slightly, which did not exceed the most stringent criterion value of 10 m·s-3. Optimization of teeth shape parameters of reverse contacting chamfer can be considered to improve the shift quality of the electric vehicle equipped with an AMT.","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"50 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84767210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On-Road Portable Emission Measurement Systems Test Data Analysis and Light-Duty Vehicle In-Use Emissions Development.","authors":"SoDuk Lee, Carl R Fulper, Daniel Cullen, Joseph McDonald, Antonio Fernandez, Mark H Doorlag, Lawrence J Sanchez, Michael Olechiw","doi":"10.4271/14-09-02-0007","DOIUrl":"10.4271/14-09-02-0007","url":null,"abstract":"<p><p>Portable emission measurement systems (PEMS) [1] are used by the US Environmental Protection Agency (EPA) to measure gaseous and particulate matter mass emissions from vehicles in normal, in-use, on-the-road, and \"real-world\" operations to support many of its programs. These programs include vehicle modeling, emissions compliance, regulatory development, emissions inventory development, and investigations of the effects of real, in-use driving conditions on NOx, CO₂, and other regulated pollutants. This article discusses EPA's analytical methodology for evaluating light-duty vehicle energy and EU Real Driving Emissions (RDE). A simple, data-driven model was developed and validated using measured PEMS emissions test data. The work also included application of the EU RDE procedures and comparison to the PEMS test methodologies and FTP and other chassis dynamometer test data used by EPA for characterizing in-use light- and heavy-duty vehicle emissions. This work was conducted as part of EPA's participation in the development of UNECE Global Technical Regulations and also supports EPA mobile source emission inventory development. This article discusses the real-world emissions of light-duty vehicles with 12V Start-Stop technology and light-duty vehicles using both gasoline and diesel fuels.</p>","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"20 1","pages":"111-131"},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78468105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Implementation of Adaptive and Artificial Intelligence Controller for Brushless Motor Drive Electric Vehicle","authors":"Aditi Saxena, Amit Gupta, Nitesh Tiwari","doi":"10.4271/14-13-01-0003","DOIUrl":"https://doi.org/10.4271/14-13-01-0003","url":null,"abstract":"Brushless direct current (BLDC) motor aims to obtain high efficiency when compared to conventional DC motors due to several reasons. But when it comes to the control then its control is much more complicated due to the requirement of a phase supply switching circuit. Usually, the conventional and classical proportional integral derivative (PID) controller is used but it is quite cumbersome to tune its fixed gains. APID controller is used where PID fails to fulfill the objectives in varying situations. So, the adaptive proportional integral derivative (APID) controller is utilized to enhance the results. An artificial neural network (ANN) controller is one of the recent control methods, which gives accurate and precise results and utilizes ANN to give more accurate results. But it lacks fuzzy logic, that is, human tendency, and finally, the artificial neuro-fuzzy inference system (ANFIS) controller is concluded as the best controller to limit the speed of the BLDC motor. ANFIS includes all the advantages of controllers and provides the most accurate results. The mathematical model of all the controllers is discussed and its performance is simulated in MATLAB/Simulink. ANFIS includes all the advantages of controllers and provides the most accurate results.","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"13 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87634174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of Two Fuel Cell Buses for Public Transport According to Two Different Operating Scenarios: Urban and Motorway","authors":"Claudio Cubito, A. Almondo, R. Ruotolo","doi":"10.4271/14-13-02-0007","DOIUrl":"https://doi.org/10.4271/14-13-02-0007","url":null,"abstract":"The transport sector is one of the major parties responsible for carbon dioxide (CO2) and pollutants emissions in Europe. For this reason, one of the main commitments of the European Commission is its decarbonization by 2035/2040. To achieve this target, during the last decades, different propulsion technologies were developed such as hybrid electric vehicles (HEVs), plug-in electric vehicles (PHEVs), and battery electric vehicles (BEV). The first two proposals can be considered as bridging technology between the internal combustion engine (ICE) and the BEV because they offer at the same time comparable performance as conventional powertrains and improved efficiency. However, both technologies are struggling with the tightening of pollutants and CO2 limits. On the other hand, the BEV can offer zero emissions at the tailpipe, but it suffers from limited range capabilities and the lack of fast-charging infrastructures. Within this context, the fuel cell vehicle (FCV) appears as an interesting opportunity because it offers zero tailpipe emissions and equivalent refuelling time of the ICE. This article evaluates through mathematical simulations the performance of two fuel cell electric buses (FCEBs), which are supposed to work respectively in urban and highway driving conditions. The urban bus is equipped with a single fuel cell (FC) module of 85 kW-Net and an electric motor (EM) of 225 kW. The intercity bus is equipped with two FC modules with a total power of 170 kW-Net and two EMs of 225 kW each. A sensitivity to the battery capacity from 20 kWh to 40 kWh was performed for both FECBs. The power split between the FC module and the high-voltage battery was optimized with the Equivalent Consumption Minimization Strategy (ECMS). The two FCEBs were tested considering different portfolios of cycles: in the case of the urban bus in Braunschweig and the Standardized On-Road Test Cycles SORT1 and SORT2 were assumed as a reference, while cycles like the Highway Fuel Economy Test (HWFET), European Transient Cycle (ETC), and cruising at 100 km/h were assumed as reference for the intercity. Simulation results highlighted that the increase of battery capacity in the case of the urban bus from 20 kWh to 30 kWh reduces hydrogen (H2) consumption by 11% along the Braunschweig cycle. On the other hand, in the case of the intercity bus, the fuel consumption is less affected by the increase of capacity in the same range. In this case a reduction of 4.7% is estimated for the HWFET cycle, and it is less than 1% in the case of cruising conditions.","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"35 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72412865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Twin-Based Remaining Driving Range Prediction for Connected Electric Vehicles","authors":"Shilong Zhuo, Heng Li, Muazz Bin Kaleem, Hui Peng, Yue Wu","doi":"10.4271/14-13-01-0004","DOIUrl":"https://doi.org/10.4271/14-13-01-0004","url":null,"abstract":"Electric vehicles (EVs) suffer from long charging time and inconvenient charging due to limited charging stations, which are the main causes of drivers’ range anxiety. Real-time and accurate driving range prediction can help drivers plan journeys, alleviate range anxiety, and promote EV development. However, predicting the EV driving range is challenging due to different weather, road conditions, driver habits, and limited available data. To address this issue, this article proposes a novel digital twin-based driving range prediction method. First, a one-year real-world EV dataset in Beijing is utilized. Detailed feature selection is conducted for the dataset, and six key features are extracted: battery SOC, consumed battery SOC, battery total voltage, battery maximum cell voltage, battery minimum cell voltage, and mileage already driven. Then, a random forest method is used to train the EV driving range prediction model using the features described earlier. Four prediction models with different adopted features are trained, respectively. Finally, the sliding window algorithm is proposed for the input of random forest to investigate its impact on prediction accuracy in the four prediction models, and different window sizes are evaluated. Results show that the sliding window algorithm can significantly improve the prediction model with only SOC as input, while it can deteriorate other models with more features. The most accurate model taking all six features as inputs provides 89.8% data that has an accuracy of over 80%, while data proportion of the prediction model without past energy consumption is only 31.8%.","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"12 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86744493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}