Predictive Boundary Management Control of a Hybrid Powertrain

2014 ◽  
Author(s):  
Jie Yang ◽  
Guoming G. Zhu
Keyword(s):  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Management Control ◽  
Battery Life ◽  
Boundary Management ◽  
Hybrid Powertrain ◽  
Driving Cycles ◽  
Useful Life ◽  
Engine Power

Hybrid Electric Vehicle (HEV) is capable of improving fuel economy with reduced emissions over traditional vehicles powered by the internal combustion engine alone. However the HEV durability is significantly limited by the battery useful life; and the battery life could be significantly reduced if it was operated over its allowed charging or discharging limits, which could occur especially at extremely low battery temperatures, leading to permanent battery damage and reduced battery life. In order to extend the battery life, this paper proposed a battery boundary management control strategy based upon the predicted desired torque to proactively make the engine power available to reduce future battery over-discharging. The proposed control strategy was validated in simulations and its performance was compared with the baseline control strategy under US06, and other four typical city and highway driving cycles. The simulation results show that the proposed control strategy is very effective when the battery temperature is under zero Celsius degree, and the over-discharged power is reduced more than 65% under aggressive US06 and ARB02 driving cycles, 45% under highway and city FTP and city NYCC driving cycles, and 30% under highway IM240 driving cycle, respectively.

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.

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 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.

Equivalent fuel consumption optimal control of a series hybrid electric vehicle

2009 ◽  
Vol 223 (8) ◽  
pp. 1003-1018 ◽  
Author(s):  
J-P Gao ◽  
G-M G Zhu ◽  
E G Strangas ◽  
F-C Sun
Keyword(s):  
Optimal Control ◽  
Fuel Consumption ◽  
Control Strategy ◽  
Supervisory Control ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Optimal Control Strategy ◽  
Equivalent Fuel ◽  
Hybrid Electric ◽  
Equivalent Fuel Consumption

Improvements in hybrid electric vehicle fuel economy with reduced emissions strongly depend on their supervisory control strategy. In order to develop an efficient real-time supervisory control strategy for a series hybrid electric bus, the proposed equivalent fuel consumption optimal control strategy is compared with two popular strategies, thermostat and power follower, using backward simulations in ADVISOR. For given driving cycles, global optimal solutions were also obtained using dynamic programming to provide an optimization target for comparison purposes. Comparison simulations showed that the thermostat control strategy optimizes the operation of the internal combustion engine and the power follower control strategy minimizes the battery charging and discharging operations which, hence, reduces battery power loss and extends the battery life. The equivalent fuel consumption optimal control strategy proposed in this paper provides an overall system optimization between the internal combustion engine and battery efficiencies, leading to the best fuel economy.

scholarly journals The efficient operating parameters estimation for a simulated plug-in hybrid electric vehicle

2021 ◽  
Author(s):  
Krishna veer singh ◽  
Rajat Khandelwal ◽  
Hari Om Bansal ◽  
Dheerendra Singh
Keyword(s):  
Hybrid Electric Vehicle ◽  
Lifetime Cost ◽  
Parameters Estimation ◽  
Battery Life ◽  
Vehicle Performance ◽  
Battery Lifetime ◽  
Automotive Systems ◽  
Driving Cycles ◽  
Fuel Costs ◽  
Operating Points

Abstract Battery replacement and fuel costs are the major recurring costs over a lifetime for HEVs. Here, the authors attempt to identify the optimal operating points for the minimum and maximum state of charge (SoC) along with power ratings of the motor and fuel converter to increase the battery life and fuel economy without any detrimental effect on vehicle performance. The simulations have been carried out on Ford C-Max Energi (2016) as a representative for PHEVs based on the Urban Dynamometer Driving Schedule (UDDS) and Highway (HWY) driving cycles. The software used for these simulations is the Future Automotive Systems Technology Simulator (FASTSim), developed by the National Renewable Energy Laboratory (NREL). The optimal determined values of the parameters led to a 3.8% reduction in the present value of the lifetime cost while improving the battery lifetime by over 18%. At the same time, a 4.3% improvement in the driving range have also been observed. This study will help in achieving optimal cost reduction in these vehicles.

Regenerative Braking Potential and Energy Simulations for a Plug-In Hybrid Electric Vehicle Under Real Driving Conditions

2009 ◽  
Author(s):  
L. A. S. B. Martins ◽  
J. M. O. Brito ◽  
A. M. D. Rocha ◽  
J. J. G. Martins
Keyword(s):  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Regenerative Braking ◽  
Driving Cycles ◽  
Energy Flows ◽  
Hybrid Electric ◽  
Series Type ◽  
Energy Simulations ◽  
Electric Traction

There are several possible configurations and technologies for the powertrains of electric and hybrid vehicles, but most of them will include advanced energy storage systems comprising batteries and ultra-capacitors. Thus, it will be of capital importance to evaluate the power and energy involved in braking and the fraction that has the possibility of being regenerated. The Series type Plug-in Hybrid Electric Vehicle (S-PHEV), with electric traction and a small Internal Combustion Engine ICE) powering a generator, is likely to become a configuration winner. The first part of this work describes the model used for the quantification of the energy flows of a vehicle, following a particular route. Normalised driving-cycles used in Europe and USA and real routes and traffic conditions were tested. The results show that, in severe urban driving-cycles, the braking energy can represent more than 70% of the required useful motor-energy. This figure is reduced to 40% in suburban routes and to a much lower 18% on motorway conditions. The second part of the work consists on the integration of the main energy components of an S-PHEV into the mathematical model. Their performance and capacity characteristics are described and some simulation results presented. In the case of suburban driving, 90% of the electrical motor-energy is supplied by the battery and ultra-capacitors and 10% by the auxiliary ICE generator, while on motorway these we got 65% and 35%, respectively. The simulations also indicate an electric consumption of 120 W.h/km for a small 1 ton car on a suburban route. This value increases by 11% in the absence of ultra-capacitors and a further 28% without regenerative braking.

Power Management Control Optimization of a Hybrid Electric-Diesel Locomotive

2016 ◽  
Author(s):  
Andrew Wilson ◽  
Timothy Cleary ◽  
Brent Ballew
Keyword(s):  
Fuel Consumption ◽  
Power Management ◽  
Control Strategy ◽  
Storage System ◽  
Management Control ◽  
Battery Life ◽  
Diesel Generator ◽  
Drive Cycle ◽  
Specific Loading ◽  
Power Split

The interest of this work is to develop a control strategy to most effectively manage the power split between the energy storage system (ESS) and the diesel generator of a hybrid locomotive. The overall goal is to minimize fuel consumption of the diesel engine, while maximizing battery life of the onboard ESS. This problem proves to be complex due to the conflicting cost functions of fuel economy and battery state-of-health (SOH)[1]. In other words, during a typical drive cycle, fuel consumption is minimized by placing high loads upon the battery while minimizing negative effects on SOH requires more specific loading characteristics of the ESS for the same drive cycle. This work highlights the development of several power split control strategies for effective power management of a hybrid locomotive. The progression from a strict rule-based (RB) control strategy to an equivalent consumption minimization strategy (ECMS) is realized through simulation. Likewise, the advantage of Model Predictive Control (FLC) is also shown in simulation.

Fast Online-Implementable Sub-Optimal Energy Management of a Power-Split Hybrid Electric Vehicle

2013 ◽  
Author(s):  
Jian Dong ◽  
Rui Cheng ◽  
Zuomin Dong ◽  
Curran Crawford
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Real Time ◽  
Electric Vehicle ◽  
Energy Management ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Powertrain ◽  
Power Split

The current focus of HEV controller design is on the development of real-time implementable energy management strategies that can approximate the global optimal solution closely. In this work, the Toyota Prius power-split hybrid powertrain is used as a case study for developing online energy management strategy for hybrid electric vehicle. The power-split hybrid powertrain combines the advantages of both the series and parallel hybrid powertrain and has been appealing to the auto-makers in the past years. The addition of two additional electric machines and a Planetary Gear Sets (PGS) allows more flexibility in terms of control at some cost of complexity. A forward-looking dynamic model of the power-split powertrain system is developed and implemented in Simulink first. An optimal control problem is formulated, which is further reduced to an optimal control problem with a single-variable objective function and a single-state subject to both dynamic constraint and boundary constraint. The reduced optimal control problem is then solved by an on-line (real-time) implementable approach based on Pontryagin’s Minimum Principal (PMP), where the costate p is adapted based on SOC feedback. Simulation results on standard driving cycles are compared using the proposed optimal control strategy and a rule-based control strategy. The results have shown significant improvement in fuel economy comparing to the baseline vehicle model, and the proposed online (real-time) PMP control algorithm with an adaptive costate p is very close to the optimal PMP solution with a constant costate. The proposed optimal control has a fast computation speed and calculates the optimal decision “dynamically” without the necessity of knowing future driving cycle information and can be practically implemented in real-time.

Dual Mode Braking System Design of a HEV

2011 ◽  
Vol 128-129 ◽  
pp. 834-841 ◽  
Author(s):  
Yu Tao Luo ◽  
Qing Yong Sun ◽  
Di Tan
Keyword(s):  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Regenerative Braking ◽  
Fuel Saving ◽  
Dual Mode ◽  
Braking System ◽  
Driving Cycles ◽  
Electric Control ◽  
Braking Distance ◽  
Regenerative Energy

Hybrid electric vehicle (HEV) is a good approach to solve the energy shortage and emission pollution issues and regenerative energy braking (REB) is one of its prime approaches for fuel saving. In this paper, a regenerative-electro-hydraulic braking system called dual mode braking system (DMBS), is proposed. The braking-by-wire technology is adopted for automatic electric control and cooperating with regenerative braking. This system can be easily converted to traditional hydraulic braking in case of fault occurs in the electro-hydraulic subsystem or some other reasons. The architecture is achieved with as less modification as possible of the original electro-hydraulic braking system. And the control strategy based on automobile braking regulations of the Economic Committee for Europe (ECE) is brought forward. Some simulations under given initial speeds and driving cycles are carried out to evaluate the effectiveness and REB efficiency. The simulating results indicate that the DMBS can work properly and can achieve a relative good performance on braking distance and regenerative energy.

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