Environmental concerns and technological progress push the development and market penetration of electric vehicles (EVs) and hybrid electric vehicles (HEVs). On the other hand, transportation systems are becoming more efficient by improved communication systems within vehicles and between vehicles and infrastructure. In this study, a driving cycle-based energy management strategy is developed for range-extended electric vehicles (REEVs) to increase system efficiency and equivalent vehicle range. A validated vehicle model is developed by critical subsystem testing and a comparative study is conducted to assess the developed strategy. The results showed that the optimized strategy can save CO2 emission by 6.21%, 1.77%, and 0.58% for heavy, moderate, and light traffic, respectively. Furthermore, the efficient use of a range extender (REx), guided by traffic data, extends the vehicle range, especially in heavy traffic conditions.
{"title":"Driving Cycle-Based Energy Management Strategy Development for Range-Extended Electric Vehicles","authors":"Abdulehad Ozdemir, Ilker Murat Koç, Bilsay Sümer, Ayhan Kural, Alaeddin Arpaci","doi":"10.4271/14-13-01-0007","DOIUrl":"https://doi.org/10.4271/14-13-01-0007","url":null,"abstract":"<div>Environmental concerns and technological progress push the development and market penetration of electric vehicles (EVs) and hybrid electric vehicles (HEVs). On the other hand, transportation systems are becoming more efficient by improved communication systems within vehicles and between vehicles and infrastructure. In this study, a driving cycle-based energy management strategy is developed for range-extended electric vehicles (REEVs) to increase system efficiency and equivalent vehicle range. A validated vehicle model is developed by critical subsystem testing and a comparative study is conducted to assess the developed strategy. The results showed that the optimized strategy can save CO<sub>2</sub> emission by 6.21%, 1.77%, and 0.58% for heavy, moderate, and light traffic, respectively. Furthermore, the efficient use of a range extender (REx), guided by traffic data, extends the vehicle range, especially in heavy traffic conditions.</div>","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136344719","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":"Reviewers","authors":"Simona Onori","doi":"10.4271/14-12-03-0024","DOIUrl":"https://doi.org/10.4271/14-12-03-0024","url":null,"abstract":"<div>Reviewers</div>","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135826778","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}
Hanchen Wang, Ziba Arjmandzadeh, Yiming Ye, Jiangfeng Zhang, Bin Xu
Deep reinforcement learning has been utilized in different areas with significant progress, such as robotics, games, and autonomous vehicles. However, the optimal result from deep reinforcement learning is based on multiple sufficient training processes, which are time-consuming and hard to be applied in real-time vehicle energy management. This study aims to use expert knowledge to warm start the deep reinforcement learning for the energy management of a hybrid electric vehicle, thus reducing the learning time. In this study, expert domain knowledge is directly encoded to a set of rules, which can be represented by a decision tree. The agent can quickly start learning effective policies after initialization by directly transferring the logical rules from the decision tree into neural network weights and biases. The results show that the expert knowledge-based warm start agent has a higher initial learning reward in the training process than the cold start. With more expert knowledge, the warm start shows improved performance in the initial learning stage compared to the warm start method with less expert knowledge. The results indicate that the proposed warm start method requires 76.7% less time to achieve convergence than the cold start. The proposed warm start method is also compared with the conventional rule-based method and equivalent consumption minimization strategy. The proposed warm start method reduces energy consumption by 8.62% and 3.62% compared with the two baseline methods, respectively. The results of this work can facilitate the expert knowledge-based deep reinforcement learning warm start in hybrid electric vehicle energy management problems.
{"title":"Automated Expert Knowledge-Based Deep Reinforcement Learning Warm\u0000 Start via Decision Tree for Hybrid Electric Vehicle Energy\u0000 Management","authors":"Hanchen Wang, Ziba Arjmandzadeh, Yiming Ye, Jiangfeng Zhang, Bin Xu","doi":"10.4271/14-13-01-0006","DOIUrl":"https://doi.org/10.4271/14-13-01-0006","url":null,"abstract":"Deep reinforcement learning has been utilized in different areas with significant\u0000 progress, such as robotics, games, and autonomous vehicles. However, the optimal\u0000 result from deep reinforcement learning is based on multiple sufficient training\u0000 processes, which are time-consuming and hard to be applied in real-time vehicle\u0000 energy management. This study aims to use expert knowledge to warm start the\u0000 deep reinforcement learning for the energy management of a hybrid electric\u0000 vehicle, thus reducing the learning time. In this study, expert domain knowledge\u0000 is directly encoded to a set of rules, which can be represented by a decision\u0000 tree. The agent can quickly start learning effective policies after\u0000 initialization by directly transferring the logical rules from the decision tree\u0000 into neural network weights and biases. The results show that the expert\u0000 knowledge-based warm start agent has a higher initial learning reward in the\u0000 training process than the cold start. With more expert knowledge, the warm start\u0000 shows improved performance in the initial learning stage compared to the warm\u0000 start method with less expert knowledge. The results indicate that the proposed\u0000 warm start method requires 76.7% less time to achieve convergence than the cold\u0000 start. The proposed warm start method is also compared with the conventional\u0000 rule-based method and equivalent consumption minimization strategy. The proposed\u0000 warm start method reduces energy consumption by 8.62% and 3.62% compared with\u0000 the two baseline methods, respectively. The results of this work can facilitate\u0000 the expert knowledge-based deep reinforcement learning warm start in hybrid\u0000 electric vehicle energy management problems.","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"24 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76221656","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}
Ragupathi Soundara Rajan, F. Richert, S. Pischinger
Exhaust emission standards for road vehicles require on-board diagnostics (OBD) of all comprehensive powertrain components (CCMs) impacting pollutant emissions. The legislation defines the generic malfunction criteria and pollutant threshold limits to trigger the component functional degradation. The electric drivetrain in xEV (more than one propulsion energy converter) applications substitutes or supports the internal combustion engine (ICE) operation with electric machine (EM) power. Malfunctions in the electric drivetrain will lead to an increase in ICE power demand. Hence, the electric drive system is classified as a comprehensive component in the OBD legislation. The regulation defines monitoring of the EM performance. The malfunctions that could prevent the EM(s) from properly operating emission control strategies, including any ICE control activation or electric drivetrain performance degradation, should be monitored by the OBD system. This work demonstrates an approach to systematically transform generic OBD legislation requirements into granular component malfunctions based on a simulation approach in the early development phase for an electric drivetrain. In the first step, the generic legislation requirements of properly functioning emission control strategies and performance degradation are transformed into electric drivetrain system element functional attributes. The malfunctions from different sources were collected as a potential malfunctions list including malfunction characterization. The impact on electric drivetrain system element functional attributes is determined for each of the malfunctions based on their characterization. Then, the matching set of malfunctions between the potential list and the OBD-derived system element functional impacts resulted in an optimized malfunction list. These optimized malfunctions are evaluated for their exhaust emission impact on a map-based one-dimensional vehicle longitudinal simulation model. The faults are also modeled to simulate their impact on ICE operation and their exhaust emissions when driven in the Worldwide harmonized Light-duty vehicles Test Cycle (WLTC). There are electric drivetrain faults that significantly increase the exhaust emissions of carbon monoxide (CO), non-methane hydrocarbons (NMHC), and oxides of nitrogen (NOx). Hence, it is important to note that even if the ICE is faultless, increased pollutant emissions can occur due to electric drivetrain malfunctions in an xEV vehicle.
{"title":"Comprehensive Component On-Board Diagnostics: Systematic\u0000 Transformation Approach to Malfunctions","authors":"Ragupathi Soundara Rajan, F. Richert, S. Pischinger","doi":"10.4271/14-12-03-0023","DOIUrl":"https://doi.org/10.4271/14-12-03-0023","url":null,"abstract":"Exhaust emission standards for road vehicles require on-board diagnostics (OBD)\u0000 of all comprehensive powertrain components (CCMs) impacting pollutant emissions.\u0000 The legislation defines the generic malfunction criteria and pollutant threshold\u0000 limits to trigger the component functional degradation. The electric drivetrain\u0000 in xEV (more than one propulsion energy converter) applications substitutes or\u0000 supports the internal combustion engine (ICE) operation with electric machine\u0000 (EM) power. Malfunctions in the electric drivetrain will lead to an increase in\u0000 ICE power demand. Hence, the electric drive system is classified as a\u0000 comprehensive component in the OBD legislation. The regulation defines\u0000 monitoring of the EM performance. The malfunctions that could prevent the EM(s)\u0000 from properly operating emission control strategies, including any ICE control\u0000 activation or electric drivetrain performance degradation, should be monitored\u0000 by the OBD system. This work demonstrates an approach to systematically\u0000 transform generic OBD legislation requirements into granular component\u0000 malfunctions based on a simulation approach in the early development phase for\u0000 an electric drivetrain. In the first step, the generic legislation requirements\u0000 of properly functioning emission control strategies and performance degradation\u0000 are transformed into electric drivetrain system element functional attributes.\u0000 The malfunctions from different sources were collected as a potential\u0000 malfunctions list including malfunction characterization. The impact on electric\u0000 drivetrain system element functional attributes is determined for each of the\u0000 malfunctions based on their characterization. Then, the matching set of\u0000 malfunctions between the potential list and the OBD-derived system element\u0000 functional impacts resulted in an optimized malfunction list. These optimized\u0000 malfunctions are evaluated for their exhaust emission impact on a map-based\u0000 one-dimensional vehicle longitudinal simulation model. The faults are also\u0000 modeled to simulate their impact on ICE operation and their exhaust emissions\u0000 when driven in the Worldwide harmonized Light-duty vehicles Test Cycle (WLTC).\u0000 There are electric drivetrain faults that significantly increase the exhaust\u0000 emissions of carbon monoxide (CO), non-methane hydrocarbons (NMHC), and oxides\u0000 of nitrogen (NOx). Hence, it is important to note that even if the\u0000 ICE is faultless, increased pollutant emissions can occur due to electric\u0000 drivetrain malfunctions in an xEV vehicle.","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87063424","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}
Lithium-ion batteries are the most crucial component of hybrid electric vehicles (HEVs) with respect to cost and performance. In this article, a new energy management strategy (EMS) is developed that improves fuel efficiency (FE) and suppresses the degradation of the battery. A hybridized two-layer algorithm that combines multi-objective nonlinear model predictive control (NMPC) with a rule-based (RB) algorithm is proposed as a new EMS that is called RB-NMPC. The RB-NMPC is designed to optimize the torque split between the engine and electric motors while maintaining the maximum and minimum constraints of each component. The proposed EMS is incorporated into control-oriented vehicle models, and their performances are analyzed for different driving cycles by comparing with RB, dynamic programming (DP), and NMPC. In addition, the RB-NMPC algorithm is applied for two different powertrain configurations of HEV, P0P2 and P1P2 configurations for both an Urban Dynamometer Driving Schedule (UDDS) and a Highway Fuel Economy Test (HWFET). For P0P2, the results show that RB-NMPC outperforms other methods for UDDS with an FE that is 4.7% higher than that of RB and is the closest to that of DP, which is an optimal standard that is limited for real-time application due to its complexity among others. The capacity loss of the battery using RB-NMPC is 19.1% less than that using DP when applied to the UDDS. The FE of P1P2 is higher than that of P0P2, but the similar capacity fade is comparable. RB-NMPC shows the lowest capacity loss for both P0P2 and P1P2 configurations. Parallel comparisons are performed for the HWFET. For the HWFET, the FEs of P0P2 and P1P2 are similar. However, the capacity fades by RB-NMPC are 16.3% and 67.0% reduced compared to that by DP for P0P2 and P1P2, respectively. Finally, to verify the effectiveness of the RB-NMPC in reducing battery aging, the currents from DP and RB-NMPC EMSs are applied to pouch-type lithium-ion batteries and tested for multiple UDDSs using a battery test station. The results demonstrate that the RB-NMPC can effectively reduce battery aging.
{"title":"Energy Management Strategies for Series-Parallel Hybrid Electric\u0000 Vehicles Considering Fuel Efficiency and Degradation of Lithium-Ion\u0000 Batteries","authors":"Kyungjin Yu, S. Choe, Jinseong Kim","doi":"10.4271/14-12-03-0022","DOIUrl":"https://doi.org/10.4271/14-12-03-0022","url":null,"abstract":"Lithium-ion batteries are the most crucial component of hybrid electric vehicles\u0000 (HEVs) with respect to cost and performance. In this article, a new energy\u0000 management strategy (EMS) is developed that improves fuel efficiency (FE) and\u0000 suppresses the degradation of the battery. A hybridized two-layer algorithm that\u0000 combines multi-objective nonlinear model predictive control (NMPC) with a\u0000 rule-based (RB) algorithm is proposed as a new EMS that is called RB-NMPC. The\u0000 RB-NMPC is designed to optimize the torque split between the engine and electric\u0000 motors while maintaining the maximum and minimum constraints of each component.\u0000 The proposed EMS is incorporated into control-oriented vehicle models, and their\u0000 performances are analyzed for different driving cycles by comparing with RB,\u0000 dynamic programming (DP), and NMPC. In addition, the RB-NMPC algorithm is\u0000 applied for two different powertrain configurations of HEV, P0P2 and P1P2\u0000 configurations for both an Urban Dynamometer Driving Schedule (UDDS) and a\u0000 Highway Fuel Economy Test (HWFET). For P0P2, the results show that RB-NMPC\u0000 outperforms other methods for UDDS with an FE that is 4.7% higher than that of\u0000 RB and is the closest to that of DP, which is an optimal standard that is\u0000 limited for real-time application due to its complexity among others. The\u0000 capacity loss of the battery using RB-NMPC is 19.1% less than that using DP when\u0000 applied to the UDDS. The FE of P1P2 is higher than that of P0P2, but the similar\u0000 capacity fade is comparable. RB-NMPC shows the lowest capacity loss for both\u0000 P0P2 and P1P2 configurations. Parallel comparisons are performed for the HWFET.\u0000 For the HWFET, the FEs of P0P2 and P1P2 are similar. However, the capacity fades\u0000 by RB-NMPC are 16.3% and 67.0% reduced compared to that by DP for P0P2 and P1P2,\u0000 respectively. Finally, to verify the effectiveness of the RB-NMPC in reducing\u0000 battery aging, the currents from DP and RB-NMPC EMSs are applied to pouch-type\u0000 lithium-ion batteries and tested for multiple UDDSs using a battery test\u0000 station. The results demonstrate that the RB-NMPC can effectively reduce battery\u0000 aging.","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"94 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85709503","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}
Thermal runaway of lithium (Li)-ion batteries is a serious concern for engineers developing battery packs for electric vehicles, energy storage, and various other applications due to the serious consequences associated with such an event. Understanding the causes of the onset and subsequent propagation of the thermal runaway phenomenon is an area of active research. It is well known that the thermal runaway phenomenon is triggered when the heat generation rate by chemical reactions within a cell exceeds the heat dissipation rate. Thermal runaway is usually initiated in one or a group of cells due to thermal, mechanical, and electrical abuse such as elevated temperature, crushing, nail penetration, or overcharging. The rate of propagation of thermal runaway to other cells in the battery pack depends on the pack design and thermal management system. Estimating the thermal runaway propagation rate is crucial for engineering safe battery packs and for developing safety testing protocols. Since experimentally evaluating different pack designs and thermal management strategies is both expensive and time consuming, physics-based models play a vital role in the engineering of safe battery packs. In this article, we present all the necessary background information needed for developing accurate thermal runaway models based on predictive chemistry. A framework that accommodates different types of chemical reactions that need to be modeled, such as solid electrolyte interphase (SEI) layer formation and decomposition, anode-solvent and cathode-solvent interactions, electrolyte decomposition, and separator melting, is developed. Additionally, the combustion of vent gas is also modeled. A validated chemistry model is used to develop a module-level model consisting of networks of pouch cells, flow, thermal, and control components, which is then used to study the thermal runaway propagation at different coolant flow rates.
{"title":"Modeling Thermal Runaway of Lithium-Ion Batteries at Cell and Module\u0000 Level Using Predictive Chemistry","authors":"Santhosh R. Gundlapally, B. Holcomb, D. Artuković","doi":"10.4271/14-12-03-0021","DOIUrl":"https://doi.org/10.4271/14-12-03-0021","url":null,"abstract":"Thermal runaway of lithium (Li)-ion batteries is a serious concern for engineers\u0000 developing battery packs for electric vehicles, energy storage, and various\u0000 other applications due to the serious consequences associated with such an\u0000 event. Understanding the causes of the onset and subsequent propagation of the\u0000 thermal runaway phenomenon is an area of active research. It is well known that\u0000 the thermal runaway phenomenon is triggered when the heat generation rate by\u0000 chemical reactions within a cell exceeds the heat dissipation rate. Thermal\u0000 runaway is usually initiated in one or a group of cells due to thermal,\u0000 mechanical, and electrical abuse such as elevated temperature, crushing, nail\u0000 penetration, or overcharging. The rate of propagation of thermal runaway to\u0000 other cells in the battery pack depends on the pack design and thermal\u0000 management system. Estimating the thermal runaway propagation rate is crucial\u0000 for engineering safe battery packs and for developing safety testing protocols.\u0000 Since experimentally evaluating different pack designs and thermal management\u0000 strategies is both expensive and time consuming, physics-based models play a\u0000 vital role in the engineering of safe battery packs. In this article, we present\u0000 all the necessary background information needed for developing accurate thermal\u0000 runaway models based on predictive chemistry. A framework that accommodates\u0000 different types of chemical reactions that need to be modeled, such as solid\u0000 electrolyte interphase (SEI) layer formation and decomposition, anode-solvent\u0000 and cathode-solvent interactions, electrolyte decomposition, and separator\u0000 melting, is developed. Additionally, the combustion of vent gas is also modeled.\u0000 A validated chemistry model is used to develop a module-level model consisting\u0000 of networks of pouch cells, flow, thermal, and control components, which is then\u0000 used to study the thermal runaway propagation at different coolant flow\u0000 rates.","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"11 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89503575","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}
In this article, the design optimization of a four-layer fractional slot concentrated winding (FSCW) interior permanent magnet (IPM) machine for range extender is proposed for high energy efficiency and excellent torque/back-electromotive force (EMF) performance. The design starts with the comparison of four-layer FSCW patterns in terms of efficiency distribution based on a predesign spoke-type rotor model. Magnet segments and rotor auxiliary notches (ANs) are applied and optimized to reduce eddy current losses and torque ripples in the permanent magnets (PMs). Then, an efficient two-step optimization of multiple performances for a machine is presented. The rotor parameters are designed by an analytical model with a Pareto optimizer for torque capacity and ripple. An interpolation-based design method for adaptive stator slot parameters and winding configurations is presented to quickly obtain the optimal stator slot winding designs corresponding to the rotor design to achieve optimal efficiency. The multi-bridge design is applied to rotor laminations to suppress flux leakage, making the rotor core easy to manufacture. Finally, an 18s-16p four-layer FSCW prototype was built and tested to verify the design optimization results, with a maximum efficiency of 96% and rated shaft ripple as low as 3%.
{"title":"Design Optimization of Four-Layer Fraction Slot Concentrated Winding\u0000 Spoke-Type Interior Permanent Magnetic Machine for Range\u0000 Extender","authors":"Congda Xiao, Can Yang","doi":"10.4271/14-12-03-0020","DOIUrl":"https://doi.org/10.4271/14-12-03-0020","url":null,"abstract":"In this article, the design optimization of a four-layer fractional slot\u0000 concentrated winding (FSCW) interior permanent magnet (IPM) machine for range\u0000 extender is proposed for high energy efficiency and excellent\u0000 torque/back-electromotive force (EMF) performance. The design starts with the\u0000 comparison of four-layer FSCW patterns in terms of efficiency distribution based\u0000 on a predesign spoke-type rotor model. Magnet segments and rotor auxiliary\u0000 notches (ANs) are applied and optimized to reduce eddy current losses and torque\u0000 ripples in the permanent magnets (PMs). Then, an efficient two-step optimization\u0000 of multiple performances for a machine is presented. The rotor parameters are\u0000 designed by an analytical model with a Pareto optimizer for torque capacity and\u0000 ripple. An interpolation-based design method for adaptive stator slot parameters\u0000 and winding configurations is presented to quickly obtain the optimal stator\u0000 slot winding designs corresponding to the rotor design to achieve optimal\u0000 efficiency. The multi-bridge design is applied to rotor laminations to suppress\u0000 flux leakage, making the rotor core easy to manufacture. Finally, an 18s-16p\u0000 four-layer FSCW prototype was built and tested to verify the design optimization\u0000 results, with a maximum efficiency of 96% and rated shaft ripple as low as\u0000 3%.","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"204 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77025279","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}
Pradeev Elango, Arulkumaran Mathivanan, Raghav Kakani, H. Das, Ramesh Asvathanarayanan
In Asian countries, small two-wheelers form a major share of the automobile segment and contribute significantly to carbon dioxide (CO2) emissions. Hybrid drives, though not widely applied in two-wheelers, can reduce fuel consumption and CO2 emissions. In this work three hybrid topologies, viz., P2 (electric motor placed between engine and transmission), P3 (electric motor placed between transmission and final drive), and power-split concepts (with planetary gear-train) have been modeled in Simulink, and their fuel consumption and emissions under the World Motorcycle Test Cycle (WMTC) have been evaluated. A physics-based model for the Continuously Variable Transmission (CVT) was used which is capable of predicting its transient characteristics. A map-based fuel consumption model and a Neural Network (NN)-based transient emission model were used for the engine. The NN-based transient emission model avoids the need to model the air path and fuel path in transient conditions, which is time consuming. The fueling characteristics of the Engine Control Unit (ECU) in transients need not be known if an NN model is built and tuned with sufficient experimental data. Several transient experiments were performed with speed-load profiles similar to the WMTC for tuning the NN emission models. Simulation results show that the P2 hybrid, P3 hybrid, and power-split drives have fuel economy benefits of about 27%, 37%, and 49%, respectively, compared to the conventional powertrain. However, nitrogen oxides (NOx) emissions are much higher for the hybrid powertrains due to the operation of the engine at higher load ranges for efficiency but are still within the prevailing BS6 Indian emission limits. A significant portion of the wheel energy input can be recovered through efficient regenerative braking in the WMTC. This will be even more significant under peak traffic city driving conditions. The belt losses in the CVT significantly reduce the potential benefits of the hybrid powertrain, and hence, an efficient transmission to replace it will be beneficial.
{"title":"Numerical Evaluation of Fuel Consumption and Transient Emissions of\u0000 Different Hybrid Topologies for Two-Wheeler Application","authors":"Pradeev Elango, Arulkumaran Mathivanan, Raghav Kakani, H. Das, Ramesh Asvathanarayanan","doi":"10.4271/14-12-03-0019","DOIUrl":"https://doi.org/10.4271/14-12-03-0019","url":null,"abstract":"In Asian countries, small two-wheelers form a major share of the automobile\u0000 segment and contribute significantly to carbon dioxide (CO2)\u0000 emissions. Hybrid drives, though not widely applied in two-wheelers, can reduce\u0000 fuel consumption and CO2 emissions. In this work three hybrid\u0000 topologies, viz., P2 (electric motor placed between engine and transmission), P3\u0000 (electric motor placed between transmission and final drive), and power-split\u0000 concepts (with planetary gear-train) have been modeled in Simulink, and their\u0000 fuel consumption and emissions under the World Motorcycle Test Cycle (WMTC) have\u0000 been evaluated. A physics-based model for the Continuously Variable Transmission\u0000 (CVT) was used which is capable of predicting its transient characteristics. A\u0000 map-based fuel consumption model and a Neural Network (NN)-based transient\u0000 emission model were used for the engine. The NN-based transient emission model\u0000 avoids the need to model the air path and fuel path in transient conditions,\u0000 which is time consuming. The fueling characteristics of the Engine Control Unit\u0000 (ECU) in transients need not be known if an NN model is built and tuned with\u0000 sufficient experimental data. Several transient experiments were performed with\u0000 speed-load profiles similar to the WMTC for tuning the NN emission models.\u0000 Simulation results show that the P2 hybrid, P3 hybrid, and power-split drives\u0000 have fuel economy benefits of about 27%, 37%, and 49%, respectively, compared to\u0000 the conventional powertrain. However, nitrogen oxides (NOx) emissions are much\u0000 higher for the hybrid powertrains due to the operation of the engine at higher\u0000 load ranges for efficiency but are still within the prevailing BS6 Indian\u0000 emission limits. A significant portion of the wheel energy input can be\u0000 recovered through efficient regenerative braking in the WMTC. This will be even\u0000 more significant under peak traffic city driving conditions. The belt losses in\u0000 the CVT significantly reduce the potential benefits of the hybrid powertrain,\u0000 and hence, an efficient transmission to replace it will be beneficial.","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"15 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85101663","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}
Ahmed Abd El Baset Abd El Halim, E. Bayoumi, W. El-Khattam, A. Ibrahim
In recent years we have seen a dramatic shift toward the use of lithium-ion batteries (LIB) in a variety of applications, including portable electronics, electric vehicles (EVs), and grid storage. Even though more and more car companies are making electric models, people still worry about how far the batteries will go and how long it will take to charge them. It is common knowledge that the high currents that are necessary to quicken the charging process also lower the energy efficiency of the battery and cause it to lose capacity and power more quickly. We need an understanding of atoms and systems to better comprehend fast charging (FC) and enhance its effectiveness. These difficulties are discussed in detail in this work, which examines the literature on physical phenomena limiting battery charging speeds as well as the degradation mechanisms that typically occur while charging at high currents. Special consideration is given to charging at low temperatures. The consequences for safety are investigated, including the possible impact that rapid charging could have on the characteristics of thermal runaway (TR). In conclusion, knowledge gaps are analyzed, and recommendations are made as regards the path that subsequent studies should take. Furthermore, there is a need to give more attention to creating dependable onboard methods for detecting lithium plating (LP) and mechanical damage. It has been observed that robust charge optimization processes based on models are required to ensure faster charging in any environment. Thermal management strategies to both cool batteries while these are being charged and heat them up when these are cold are important, and a lot of attention is paid to methods that can do both quickly and well.
{"title":"Effect of Fast Charging on Lithium-Ion Batteries: A\u0000 Review","authors":"Ahmed Abd El Baset Abd El Halim, E. Bayoumi, W. El-Khattam, A. Ibrahim","doi":"10.4271/14-12-03-0018","DOIUrl":"https://doi.org/10.4271/14-12-03-0018","url":null,"abstract":"In recent years we have seen a dramatic shift toward the use of lithium-ion\u0000 batteries (LIB) in a variety of applications, including portable electronics,\u0000 electric vehicles (EVs), and grid storage. Even though more and more car\u0000 companies are making electric models, people still worry about how far the\u0000 batteries will go and how long it will take to charge them. It is common\u0000 knowledge that the high currents that are necessary to quicken the charging\u0000 process also lower the energy efficiency of the battery and cause it to lose\u0000 capacity and power more quickly. We need an understanding of atoms and systems\u0000 to better comprehend fast charging (FC) and enhance its effectiveness. These\u0000 difficulties are discussed in detail in this work, which examines the literature\u0000 on physical phenomena limiting battery charging speeds as well as the\u0000 degradation mechanisms that typically occur while charging at high currents.\u0000 Special consideration is given to charging at low temperatures. The consequences\u0000 for safety are investigated, including the possible impact that rapid charging\u0000 could have on the characteristics of thermal runaway (TR). In conclusion,\u0000 knowledge gaps are analyzed, and recommendations are made as regards the path\u0000 that subsequent studies should take. Furthermore, there is a need to give more\u0000 attention to creating dependable onboard methods for detecting lithium plating\u0000 (LP) and mechanical damage. It has been observed that robust charge optimization\u0000 processes based on models are required to ensure faster charging in any\u0000 environment. Thermal management strategies to both cool batteries while these\u0000 are being charged and heat them up when these are cold are important, and a lot\u0000 of attention is paid to methods that can do both quickly and well.","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"53 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80742406","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}
Among the myriad of potential hybrid powertrain architectures, selecting the optimal for an application is a daunting task. Whenever available, computer models greatly assist in it. However, some aspects, such as pollutant emissions, are difficult to model, leaving no other option than to test. Validating plausible options before building the powertrain prototype has the potential of accelerating the vehicle development even more, doing so without shipping components around the world. This work concerns the design of a system to virtually couple—that is, avoiding physical contact—geographically distant test rigs in order to evaluate the components of a powertrain. In the past, methods have been attempted, either with or without assistance of mathematical models of the coupled components (observers). Existing methods are accurate only when the dynamics of the systems to couple are slow in relation to the communication delay. Also, existing methods seem to overlook the implications of operating a distributed system without a common time frame. In order to overcome the inherent latency arising from long-range communication, the proposed design combines two features: The exploitation of synchronized clocks for the simultaneous introduction of setpoint commands and the use of observers generated through machine learning algorithms. This novel design is subsequently tested in two scenarios: A simple one, involving the virtual coupling of two parts of an elementary device formed by three rotating inertias, and a more complex one, the coupling between an internal combustion engine and an electric motor/generator as representative of a series or parallel hybrid powertrain. Although the results are heavily influenced by the quality of the data-generated observers, the architecture improves the fidelity of the coupling by nearly an order of magnitude compared to the alternative of directly transmitting the signals. It also opens a niche application that leverages the accuracy of low-fidelity models.
{"title":"A Coupling Architecture for Remotely Validating Powertrain\u0000 Assemblies","authors":"A. Ametller, C. Brace","doi":"10.4271/14-12-02-0015","DOIUrl":"https://doi.org/10.4271/14-12-02-0015","url":null,"abstract":"Among the myriad of potential hybrid powertrain architectures, selecting the\u0000 optimal for an application is a daunting task. Whenever available, computer\u0000 models greatly assist in it. However, some aspects, such as pollutant emissions,\u0000 are difficult to model, leaving no other option than to test. Validating\u0000 plausible options before building the powertrain prototype has the potential of\u0000 accelerating the vehicle development even more, doing so without shipping\u0000 components around the world. This work concerns the design of a system to\u0000 virtually couple—that is, avoiding physical contact—geographically distant test\u0000 rigs in order to evaluate the components of a powertrain. In the past, methods\u0000 have been attempted, either with or without assistance of mathematical models of\u0000 the coupled components (observers). Existing methods are accurate only when the\u0000 dynamics of the systems to couple are slow in relation to the communication\u0000 delay. Also, existing methods seem to overlook the implications of operating a\u0000 distributed system without a common time frame. In order to overcome the\u0000 inherent latency arising from long-range communication, the proposed design\u0000 combines two features: The exploitation of synchronized clocks for the\u0000 simultaneous introduction of setpoint commands and the use of observers\u0000 generated through machine learning algorithms. This novel design is subsequently\u0000 tested in two scenarios: A simple one, involving the virtual coupling of two\u0000 parts of an elementary device formed by three rotating inertias, and a more\u0000 complex one, the coupling between an internal combustion engine and an electric\u0000 motor/generator as representative of a series or parallel hybrid powertrain.\u0000 Although the results are heavily influenced by the quality of the data-generated\u0000 observers, the architecture improves the fidelity of the coupling by nearly an\u0000 order of magnitude compared to the alternative of directly transmitting the\u0000 signals. It also opens a niche application that leverages the accuracy of\u0000 low-fidelity models.","PeriodicalId":36261,"journal":{"name":"SAE International Journal of Electrified Vehicles","volume":"os-22 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87204147","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}
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