Modeling and Control of New Multi-Mode Plug-in Hybrid Electric Vehicle

2019 ◽  
Author(s):  
Abhinandan Raut ◽  
Suryaji Phalke ◽  
Diane Peters
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Powertrain ◽  
Power Sources ◽  
Full Size ◽  
Wide Range ◽  
Hybrid Electric ◽  
Multi Mode

Abstract Fuel economy and emission standards for internal combustion engine (ICE) vehicles lead to emergence of hybrid powertrain mechanisms. Hybrid powertrains can enable significant fuel economy improvements without sacrificing vehicle performance or utility. This requires optimization of engine operation, regenerative braking, and use of a wide range of possible combinations of engine and battery usage. The multi-mode hybrid powertrain in this paper combines many options to meet a complex driving requirement while maintaining the desired fuel economy. In this paper, a systematic design methodology is used to design a full-size hybrid vehicle with multiple components. This involves the modeling, simulation and development of optimal energy management strategy. This vehicle (full size car) has dual battery, dual fuel V6 engine with cylinder deactivation and bi-directional power flow in and from dual motor/generator. The design includes multiple gearboxes to connect these pieces. The vehicle model allows many degrees of freedom including various modes of operation depending upon the combination of degree of driver involvement, vehicle power requirement and optimized fuel economy resulting in automatic switching between modes. This model is tested for different Environmental Protection Agency (EPA) driving cycles. By integrating all components of this hybrid electric vehicle (HEV) and the highly coordinated energy management control system that performs optimum blending of torque, speed, and power from multiple power sources, the benefit from this hybridization is maximized.

scholarly journals Modeling and energy management strategy research of a power-split hybrid electric vehicle

2020 ◽  
Vol 12 (10) ◽  
pp. 168781402096262
Author(s):  
Yupeng Zou ◽  
Ruchen Huang ◽  
Xiangshu Wu ◽  
Baolong Zhang ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Research Work ◽  
Energy Management Strategy ◽  
Power Sources ◽  
Power Split ◽  
Hybrid Electric ◽  
The Impact

A power-split hybrid electric vehicle with a dual-planetary gearset is researched in this paper. Based on the lever analogy method of planetary gearsets, the power-split device is theoretically modeled, and the driveline simulation model is built by using vehicle modeling and simulation toolboxes in MATLAB. Six operation modes of the vehicle are discussed in detail, and the kinematic constraint behavior of power sources are analyzed. To verify the rationality of the modeling, a rule-based control strategy (RB) and an adaptive equivalent consumption minimization strategy (A-ECMS) are designed based on the finite state machine and MATLAB language respectively. In order to demonstrate the superiority of A-ECMS in fuel-saving and to explore the impact of different energy management strategies on emission, fuel economy and emission performance of the vehicle are simulated and analyzed under UDDS driving cycle. The simulation results of the two strategies are compared in the end, shows that the modeling is rational, and compared with RB strategy, A-ECMS ensures charge sustaining better, enables power sources to work in more efficient areas, and improves fuel economy by 8.65%, but significantly increases NOx emissions, which will be the focus of the next research work.

Energy Management and Control of a Hybrid Electric Vehicle With an Integrated Low Temperature Combustion (LTC) Engine

2014 ◽  
Author(s):  
Ali Solouk ◽  
Mahdi Shahbakhti ◽  
Mohammad J. Mahjoob
Keyword(s):  
Low Temperature ◽  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Low Temperature Combustion ◽  
Si Engine ◽  
Hcci Engine ◽  
Temperature Combustion ◽  
Hybrid Electric

Low Temperature Combustion (LTC) provides a promising solution for clean energy-efficient engine technology which has not yet been utilized in Hybrid Electric Vehicle (HEV) engines. In this study, a variant of LTC engines, known as Homogeneous Charge Compression Ignition (HCCI), is utilized for operation in a series HEV configuration. An experimentally validated dynamic HCCI model is used to develop required engine torque-fuel consumption data. Given the importance of Energy Management Control (EMC) on HEV fuel economy, three different types of EMCs are designed and implemented. The EMC strategies incorporate three different control schemes including thermostatic Rule-Based Control (RBC), Dynamic Programming (DP), and Model Predictive Control (MPC). The simulation results are used to examine the fuel economy advantage of a series HEV with an integrated HCCI engine, compared to a conventional HEV with a modern Spark Ignition (SI) engine. The results show 12.6% improvement in fuel economy by using a HCCI engine in a HEV compared to a conventional HEV using a SI engine. In addition, the selection of EMC strategy is found to have a strong impact on vehicle fuel economy. EMC based on DP controller provides 15.3% fuel economy advantage over the RBC in a HEV with a HCCI engine.

Hybrid optimization and its applications for multi-mode plug-in hybrid electric vehicle

2019 ◽  
Vol 234 (1) ◽  
pp. 228-244 ◽  
Author(s):  
Yiran Zhang ◽  
Han Zhao ◽  
Kang Huang ◽  
Mingming Qiu ◽  
Lizhen Geng
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Optimization ◽  
Energy Management Strategy ◽  
Network Controller ◽  
Hybrid Electric ◽  
Optimization Methodology ◽  
Multi Mode

This study provides an optimization methodology in powertrain configurations and energy management strategy for a multi-mode plug-in hybrid electric vehicle. The challenge in this study is that the energy management strategy is highly intertwined with the powertrain configurations; the problem-specific complexity of this powertrain makes it even more difficult. Based on the analysis of the powertrain architecture, a simulation model is developed. Two self-adaptive solutions, a fuzzy PID driver and an adaptive shifting schedule are established to accommodate the powertrain parameter variations during the particle swarm optimization process. In order to obtain optimal and applicable results of both the powertrain and the energy management strategy configurations, a two-layer optimization controller is proposed. In the first layer, sub-optimal results are obtained by ACOR, additionally, the second layer is designed to final confirm both mode changes and gear shifting. At last, two possible applications—modifications to the shifting schedule and the energy management strategy, as well as developing a neuro network controller utilizing the optimal results—are analyzed. The results suggest that the hybrid optimization presented in this paper provides a solution to squeeze the potential of a plug-in hybrid electric vehicle while maintaining its practicability.

Hybrid Electric Vehicle Energy Management and Control Based on Battery Energy Balance

2013 ◽  
Vol 273 ◽  
pp. 764-767
Author(s):  
Bin Yan ◽  
Yan Qing Hu ◽  
Ting Yan ◽  
Pei Pei Ma ◽  
Lin Yang
Keyword(s):  
Energy Balance ◽  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Power Efficiency ◽  
Hybrid Electric Vehicle ◽  
Real Time Control ◽  
Energy Management Strategy ◽  
Hybrid Electric ◽  
Battery Energy

Hybrid electric vehicle has better power and economy than conventional vehicle attributed to power efficiency range is optimized by battery energy. So making battery energy balance not only can ensure hybrid power system operate normally, but also is the key role in meeting vehicle drivability and improving fuel economy effectively. This paper analyze of the regenerating and using of battery energy. Real-time control and global optimization is used to adjust energy management strategy, the adaptive control strategy also introduced to making energy power balance on the basis of maximum fuel economy in the driving cycle.

Mode shift map design and integrated energy management control of a multi-mode hybrid electric vehicle

2017 ◽  
Vol 204 ◽  
pp. 476-488 ◽  
Author(s):  
Weichao Zhuang ◽  
Xiaowu Zhang ◽  
Daofei Li ◽  
Liangmo Wang ◽  
Guodong Yin
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Management Control ◽  
Mode Shift ◽  
Shift Map ◽  
Map Design ◽  
Hybrid Electric ◽  
Multi Mode

scholarly journals Energy Management Strategy Design and Simulation Validation of Hybrid Electric Vehicle Driving in an Intelligent Fleet

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1516
Author(s):  
Xin Ye ◽  
Fei Lai ◽  
Zhiwei Huo
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Combination Method ◽  
Driving Safety ◽  
System Efficiency ◽  
Hybrid Electric ◽  
The Impact

This paper proposes a combination method of longitudinal control and fuel management for an intelligent Hybrid Electric Vehicle (HEV) fleet. This method can reduce the fuel consumption while maintaining the distance and speed for each vehicle in the fleet. An HEV system efficiency model was established to simulate the impact of different working modes. Based on the principle of optimal vehicle system efficiency, the energy management control strategy of HEV was designed. Then, the driver model of the piloting vehicle and the following vehicle was built by using an intelligent fuzzy control method. Finally, the intelligent fleet model and energy matching model of HEV were integrated with the simulation platform that was developed based on MATLAB/Simulink/Stateflow. The validity of the energy matching strategy of HEV under the principle of optimal system efficiency was verified by simulation results, and the purpose of improving the driving safety, traffic efficiency, and fuel economy of the fleet was achieved. Comparing with the conventional control strategy, the proposed method saved 7.79% of fuel for the HEV fleet. Meanwhile, the distance ranges between the vehicles were from 12 meters to 15 meters, which improved the driving safety, passing rate, and fuel economy.

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