Optimal adaptation equivalent factor of energy management strategy for plug-in CVT HEV

2018 ◽  
Vol 233 (4) ◽  
pp. 877-889 ◽  
Author(s):  
Xinyou Lin ◽  
Qigao Feng ◽  
Liping Mo ◽  
Hailin Li
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Adaptation Strategy ◽  
Management Control ◽  
Energy Management Strategy ◽  
Driving Mode ◽  
Driving Cycles ◽  
Equivalent Factor

This study presents an adaptive energy management control strategy developed by optimally adjusting the equivalent factor (EF) in real-time based on driving pattern recognition (DPR), to guarantee the plug-in hybrid electric vehicle (PHEV) can adapt to various driving cycles and different expected trip distances and to further improve the fuel economy performance. First, the optimization model for the EF with the battery state of charge (SOC) and trip distance were developed based on the equivalent consumption minimization strategy (ECMS). Furthermore, a methodology of extracting the globally optimal EF model from genetic algorithm (GA) solution is proposed for the design of the EF adaptation strategy. The EF as the function of trip distances and SOC in various driving cycles is expressed in the form of map that can be applied directly in the corresponding driving cycle. Finally, the algorithm of DPR based on learning vector quantization (LVQ) is established to identify the driving mode and update the optimal EF. Simulation and hardware-in-loop experiments are conducted on synthesis driving cycles to validate the proposed strategy. The results indicate that the optimal adaption EF control strategy will be able to adapt to different expected trip distances and improve the fuel economy performance by up to 13.8% compared to the ECMS with constant EF.

Energy Management Control of a Hybrid Electric Vehicle by Incorporating Powertrain Dynamics

2015 ◽  
Author(s):  
Mehran Bidarvatan ◽  
Mahdi Shahbakhti
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Management Strategies ◽  
Management Control ◽  
Energy Management Strategy ◽  
Ice Dynamics ◽  
Hybrid Electric

Energy management strategies in parallel Hybrid Electric Vehicles (HEVs) usually ignore effects of Internal Combustion Engine (ICE) dynamics and rely on static maps for required engine torque-fuel efficiency data. It is uncertain how neglecting these dynamics can affect fuel economy of a parallel HEV. This paper addresses this shortcoming by investigating effects of some major Spark Ignition (SI) engine dynamics and clutch dynamics on torque split management in a parallel HEV. The control strategy is implemented on a HEV model with an experimentally validated, dynamic ICE model. Simulation results show that the ICE and clutch dynamics can degrade performance of the HEV control strategy during the transient periods of the vehicle operation by 8.7% for city and highway driving conditions in a combined common North American drive cycle. This fuel penalty is often overlooked in conventional HEV energy management strategies. A Model Predictive Control (MPC) of torque split is developed by incorporating effects of the studied influencing dynamics. Results show that the integrated energy management strategy can improve the total energy consumption of HEV by more than 6% for combined Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Driving Schedule (HWFET)drive cycles.

scholarly journals Minimum Energy Management Strategy of Equivalent Fuel Consumption of Hybrid Electric Vehicle Based on Improved Global Optimization Equivalent Factor

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2076 ◽  
Author(s):  
Xixue Liu ◽  
Datong Qin ◽  
Shaoqian Wang
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
High Efficiency ◽  
Energy Management Strategy ◽  
Target Value ◽  
Equivalent Fuel ◽  
Equivalent Factor ◽  
Equivalent Fuel Consumption ◽  
Engine Operating Point

A parallel hybrid electric vehicle (PHEV) is used to investigate the fuel economy effect of the equivalent fuel consumption minimization strategy (ECMS) with the equivalent factor as the core, where the equivalent factor is the conversion coefficient between fuel thermal energy and electric energy. In the conventional ECMS strategy, the battery cannot continue to discharge when the state of charge (SOC) is lower than the target value. At this time, the motor mainly works in the battery charging mode, making it difficult to adjust the engine operating point to the high-efficiency zone during the acceleration process. To address this problem, a relationship model of the battery SOC, vehicle acceleration a, and equivalent factor S was established. When the battery SOC is lower than the target value and the vehicle demand torque is high, which makes the engine operating point deviate from the high-efficiency zone, the time that the motor spends in the power generation mode during the driving process is reduced. This enables the motor to drive the vehicle at the appropriate time to reduce the engine output torque, and helps the engine operate in the high-efficiency zone. The correction function under US06 condition was optimized by genetic algorithm (GA). The best equivalent factor MAP was obtained with acceleration a and battery SOC as independent variables, and the improved global optimal equivalent factor of ECMS was established and simulated offline. Simulation results show that compared with conventional ECMS, the battery still has positive power output even when the SOC is less than the target value. The SOC is close to the target value after the cycle condition, and fuel economy improved by 1.88%; compared with the rule-based energy management control strategies, fuel economy improved by 10.17%. These results indicate the effectiveness of the proposed energy management strategy.

scholarly journals An Energy Management Strategy for a Super-Mild Hybrid Electric Vehicle Based on a Known Model of Reinforcement Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yanli Yin ◽  
Yan Ran ◽  
Liufeng Zhang ◽  
Xiaoliang Pan ◽  
Yong Luo
Keyword(s):  
Reinforcement Learning ◽  
Electric Vehicle ◽  
Energy Management ◽  
Control Strategy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Power Demand ◽  
Energy Management Strategy ◽  
Driving Cycles ◽  
Mild Hybrid

For global optimal control strategy, it is not only necessary to know the driving cycle in advance but also difficult to implement online because of its large calculation volume. As an artificial intelligent-based control strategy, reinforcement learning (RL) is applied to an energy management strategy of a super-mild hybrid electric vehicle. According to time-speed datasets of sample driving cycles, a stochastic model of the driver’s power demand is developed. Based on the Markov decision process theory, a mathematical model of an RL-based energy management strategy is established, which assumes the minimum cumulative return expectation as its optimization objective. A policy iteration algorithm is adopted to obtain the optimum control policy that takes the vehicle speed, driver’s power demand, and state of charge (SOC) as the input and the engine power as the output. Using a MATLAB/Simulink platform, CYC_WVUCITY simulation model is established. The results show that, compared with dynamic programming, this method can not only adapt to random driving cycles and reduce fuel consumption of 2.4%, but also be implemented online because of its small calculation volume.

Offline Optimization of Parallel Hybrid Electric Vehicle Energy Management Strategy Based on the Dynamic Programming

2014 ◽  
Vol 1044-1045 ◽  
pp. 941-946 ◽  
Author(s):  
Shi Chun Yang ◽  
Yue Gu ◽  
Ming Li
Keyword(s):  
Dynamic Programming ◽  
Electric Vehicle ◽  
Energy Management ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Management Control ◽  
Energy Management Strategy ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Hybrid Electric

By using dynamic programming (DP) which is a kind of global optimization algorithm, an energy management control strategy for a parallel hybrid electric vehicle (PHEV) on different charging depleting range (CDR) had been studied. The results show that motor-dominant control strategy should be applied to the PHEV when CDR is less than 55km, and engine-dominant control strategy should be used when CDR is more than 55km. With optimal control strategies from DP, the best economic performance can be obtained as CDR is 55km

Hierarchical energy management strategy considering switching schedule for a dual-mode hybrid electric vehicle

2021 ◽  
pp. 095440702110297
Author(s):  
Hui Liu ◽  
Rui Liu ◽  
Riming Xu ◽  
Lijin Han ◽  
Shumin Ruan
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Control Strategy ◽  
Power Distribution ◽  
Hybrid Electric Vehicle ◽  
Hierarchical Control ◽  
Management Problem ◽  
Energy Management Strategy ◽  
Dual Mode ◽  
Hybrid Electric

Energy management strategies are critical for hybrid electric vehicles (HEVs) to improve fuel economy. To solve the dual-mode HEV energy management problem combined with switching schedule and power distribution, a hierarchical control strategy is proposed in this paper. The mode planning controller is twofold. First, the mode schedule is obtained according to the mode switch map and driving condition, then a switch hunting suppression algorithm is proposed to flatten the mode schedule through eliminating unnecessary switch. The proposed algorithm can reduce switch frequency while fuel consumption remains nearly unchanged. The power distribution controller receives the mode schedule and optimizes power distribution between the engine and battery based on the Radau pseudospectral knotting method (RPKM). Simulations are implemented to verify the effectiveness of the proposed hierarchical control strategy. For the mode planning controller, as the flattening threshold value increases, the fuel consumption remains nearly unchanged, however, the switch frequency decreases significantly. For the power distribution controller, the fuel consumption obtained by RPKM is 4.29% higher than that of DP, while the elapsed time is reduced by 92.53%.

Analysis and Control of Torque Split in Hybrid Electric Vehicles by Incorporating Powertrain Dynamics

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
Mehran Bidarvatan ◽  
Mahdi Shahbakhti
Keyword(s):  
Steady State ◽  
Energy Consumption ◽  
Energy Management ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Internal Combustion Engines ◽  
Dynamic Models ◽  
Management Control ◽  
Drive Cycle ◽  
Hybrid Electric

Hybrid electric vehicle (HEV) energy management strategies usually ignore the effects from dynamics of internal combustion engines (ICEs). They usually rely on steady-state maps to determine the required ICE torque and energy conversion efficiency. It is important to investigate how ignoring these dynamics influences energy consumption in HEVs. This shortcoming is addressed in this paper by studying effects of engine and clutch dynamics on a parallel HEV control strategy for torque split. To this end, a detailed HEV model including clutch and ICE dynamic models is utilized in this study. Transient and steady-state experiments are used to verify the fidelity of the dynamic ICE model. The HEV model is used as a testbed to implement the torque split control strategy. Based on the simulation results, the ICE and clutch dynamics in the HEV can degrade the control strategy performance during the vehicle transient periods of operation by around 8% in urban dynamometer driving schedule (UDDS) drive cycle. Conventional torque split control strategies in HEVs often overlook this fuel penalty. A new model predictive torque split control strategy is designed that incorporates effects of the studied powertrain dynamics. Results show that the new energy management control strategy can improve the HEV total energy consumption by more than 4% for UDDS drive cycle.

scholarly journals Multi-objective Parameters Optimization for HEV Based on improved Particle Swarm Algorithm

2019 ◽  
Vol 118 ◽  
pp. 02005
Author(s):  
Ying Ai ◽  
Yuanjie Gao ◽  
dongsheng Liu
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Controller ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Energy Management Strategy ◽  
Multi Objective

Hybrid electric vehicle fuel consumption and emissions are closely related to its energy management strategy. A fuzzy controller of energy management using vehicle torque request and battery state of charge (SOC) as inputs, engine torque as output is designed in this paper foe parallel hybrid electric vehicle. And a multi-objective mathematical function which purpose on maximize fuel economy and minimize emissions is also established, in order to improve the adaptive ability and the control precision of basic fuzzy controller, this paper proposed an improved particle swarm algorithm that based on dynamic learning factor and adaptive inertia weight to optimize the control parameters. Simulation results based on ADVISOR software platform show that the optimized energy management strategy has a better distribution of engine and motor torque, which helps to improved the vehicle’s fuel economy and exhaust emission performance.

scholarly journals Research on energy management strategy considering battery life for plug-in hybrid electric vehicle

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879776 ◽  
Author(s):  
Jianjun Hu ◽  
Zhihua Hu ◽  
Xiyuan Niu ◽  
Qin Bai
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Battery Life ◽  
Energy Management Strategy ◽  
Driving Cycles ◽  
Comprehensive Test ◽  
Hybrid Electric

To improve the fuel efficiency and battery life-span of plug-in hybrid electric vehicle, the energy management strategy considering battery life decay is proposed. This strategy is optimized by genetic algorithm, aiming to reduce the fuel consumption and battery life decay of plug-in hybrid electric vehicle. Besides, to acquire better drive-cycle adaptability, driving patterns are recognized with probabilistic neural network. The standard driving cycles are divided into urban congestion cycle, highway cycle, and urban suburban cycle; the optimized energy management strategies in three representative driving cycles are established; meanwhile, a comprehensive test driving cycle is constructed to verify the proposed strategies. The results show that adopting the optimized control strategies, fuel consumption, and battery’s life decay drop by 1.9% and 3.2%, respectively. While using the drive-cycle recognition, the features of different driving cycles can be identified, and based on it, the vehicle can choose appropriate control strategy in different driving conditions. In the comprehensive test driving cycle, after recognizing driving cycles, fuel consumption and battery’s life decay drop by 8.6% and 0.3%, respectively.

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.

scholarly journals Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5713
Author(s):  
Aaron Rabinowitz ◽  
Farhang Motallebi Araghi ◽  
Tushar Gaikwad ◽  
Zachary D. Asher ◽  
Thomas H. Bradley
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
High Fidelity ◽  
Energy Management Strategy ◽  
Optimal Energy ◽  
Upper Limit ◽  
Speed Prediction ◽  
Optimal Energy Management ◽  
Hybrid Electric

In this study, a thorough and definitive evaluation of Predictive Optimal Energy Management Strategy (POEMS) applications in connected vehicles using 10 to 20 s predicted velocity is conducted for a Hybrid Electric Vehicle (HEV). The presented methodology includes synchronous datasets gathered in Fort Collins, Colorado using a test vehicle equipped with sensors to measure ego vehicle position and motion and that of surrounding objects as well as receive Infrastructure to Vehicle (I2V) information. These datasets are utilized to compare the effect of different signal categories on prediction fidelity for different prediction horizons within a POEMS framework. Multiple artificial intelligence (AI) and machine learning (ML) algorithms use the collected data to output future vehicle velocity prediction models. The effects of different combinations of signals and different models on prediction fidelity in various prediction windows are explored. All of these combinations are ultimately addressed where the rubber meets the road: fuel economy (FE) enabled from POEMS. FE optimization is performed using Model Predictive Control (MPC) with a Dynamic Programming (DP) optimizer. FE improvements from MPC control at various prediction time horizons are compared to that of full-cycle DP. All FE results are determined using high-fidelity simulations of an Autonomie 2010 Toyota Prius model. The full-cycle DP POEMS provides the theoretical upper limit on fuel economy (FE) improvement achievable with POEMS but is not currently practical for real-world implementation. Perfect prediction MPC (PP-MPC) represents the upper limit of FE improvement practically achievable with POEMS. Real-Prediction MPC (RP-MPC) can provide nearly equivalent FE improvement when used with high-fidelity predictions. Constant-Velocity MPC (CV-MPC) uses a constant speed prediction and serves as a “null” POEMS. Results showed that RP-MPC, enabled by high-fidelity ego future speed prediction, led to significant FE improvement over baseline nearly matching that of PP-MPC.

Sign in / Sign up

Export Citation Format

Share Document