Optimization of Fuel Economy of Hybrid Electric Vehicles Using Set Based Dynamic Programming

2014 ◽  
Author(s):  
Nikhil Ramaswamy ◽  
Nader Sadegh
Keyword(s):  
Dynamic Programming ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Optimal Solution ◽  
The State ◽  
State Variables ◽  
Discrete Time System ◽  
Real Time Control ◽  
Time Step ◽  
Hybrid Electric

Dynamic Programming (DP) technique is an effective algorithm to find the global optimum. However when applying DP for finite state problems, if the state variables are discretized, it increases the cumulative errors and leads to suboptimal results. In this paper we develop and present a new DP algorithm that overcomes the above problem by eliminating the need to discretize the state space by the use of sets. We show that the proposed DP leads to a globally optimal solution for a discrete time system by minimizing a cost function at each time step. To show the efficacy of the proposed DP, we apply it to optimize the fuel economy of the series and parallel Hybrid Electric Vehicle (HEV) architectures and the case study of Chevrolet Volt 2012 and the Honda Civic 2012 for the series and parallel HEV’s respectively are considered. Simulations are performed over predefined drive cycles and the results of the proposed DP are compared to previous DP algorithm (DPdis). The proposed DP showed an average improvement of 2.45% and 21.29% over the DPdis algorithm for the series and the parallel HEV case respectively over the drive cycles considered. We also propose a real time control strategy (RTCS) for online implementation based on the concept of Preview Control. The RTCS proposed is applied for the series and parallel HEV’s over the drive cycles and the results obtained are discussed.

Dynamic Programming-Informed Equivalent Cost Minimization Control Strategies for Hybrid-Electric Vehicles

2013 ◽  
Vol 135 (5) ◽  
Author(s):  
Dekun Pei ◽  
Michael J. Leamy
Keyword(s):  
Dynamic Programming ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Cost Minimization ◽  
Global Optimum ◽  
Robust Controller ◽  
Drive Cycle ◽  
Suggested Approach ◽  
Equivalence Factor ◽  
Hybrid Electric

This paper presents a direct mathematical approach for determining the state of charge (SOC)-dependent equivalent cost factor in hybrid-electric vehicle (HEV) supervisory control problems using globally optimal dynamic programming (DP). It therefore provides a rational basis for designing equivalent cost minimization strategies (ECMS) which achieve near optimal fuel economy (FE). The suggested approach makes use of the Pareto optimality criterion that exists in both ECMS and DP, and as such predicts the optimal equivalence factor for a drive cycle using DP marginal cost. The equivalence factor is then further modified with corrections based on battery SOC, with the aim of making the equivalence factor robust to drive cycle variations. Adaptive logic is also implemented to ensure battery charge sustaining operation at the desired SOC. Simulations performed on parallel and power-split HEV architectures demonstrate the cross-platform applicability of the DP-informed ECMS approach. Fuel economy data resulting from the simulations demonstrate that the robust controller consistently achieves FE within 1% of the global optimum prescribed by DP. Additionally, even when the equivalence factor deviates substantially from the optimal value for a drive cycle, the robust controller can still produce FE within 1–2% of the global optimum. This compares favorably with a traditional ECMS controller based on a constant equivalence factor, which can produce FE 20–30% less than the global optimum under the same conditions. As such, the controller approach detailed should result in ECMS supervisory controllers that can achieve near optimal FE performance, even if component parameters vary from assumed values (e.g., due to manufacturing variation, environmental effects or aging), or actual driving conditions deviate largely from standard drive cycles.

Fuel economy and NOx emission potential investigation and trade-off of a hybrid electric vehicle based on dynamic programming

2008 ◽  
Vol 222 (10) ◽  
pp. 1851-1864 ◽  
Author(s):  
G-Q Ao ◽  
J-X Qiang ◽  
H Zhong ◽  
X-J Mao ◽  
L Yang ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Cost Function ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Pollutant Emissions ◽  
Power Requirement ◽  
Trade Off ◽  
Fuel Economy Improvement ◽  
Hybrid Electric ◽  
Potential Improvement

Hybrid electric vehicles (HEVs) combined with more than one power source offer additional flexibility to improve the fuel economy and to reduce pollutant emissions. The dynamic-programming-based supervisory controller (DPSC) presented here investigates the fuel economy improvement and emissions reduction potential and demonstrates the trade-off between fuel economy and the emission of nitrogen oxides (NO x) for a state-of-charge-sustaining parallel HEV. A weighted cost function consisting of fuel economy and emissions is proposed in this paper. Any possible engine-motor power pairs meeting with the power requirement is considered to minimize the weighted cost function over the given driving cycles through this dynamic program algorithm. The fuel-economy-only case, the NO x-only case, and the fuel-NO x case have been achieved by adjusting specific weighting factors, which demonstrates the flexibility and advantages of the DPSC. Compared with operating the engine in the NO x-only case, there is 17.4 per cent potential improvement in the fuel-economy-only case. The fuel-NO x case yields a 15.2 per cent reduction in NO x emission only at the cost of 5.5 per cent increase in fuel consumption compared with the fuel-economy-only case.

Optimal Selection of Equivalence Factors for ECMS in Mild Hybrid Electric Vehicles

2021 ◽  
Author(s):  
Shailesh Hegde ◽  
Angelo Bonfitto ◽  
Hadi Rahmeh ◽  
Nicola Amati ◽  
Andrea Tonoli
Keyword(s):  
Electric Vehicles ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Shooting Method ◽  
Hybrid Electric Vehicles ◽  
Optimal Solution ◽  
Pollutant Emissions ◽  
Hybrid Electric ◽  
Mild Hybrid ◽  
Selection Of

Abstract The increasing stringent emissions regulation over the years have shifted the focus of automotive industry towards more efficient fuel economy solutions. One such solution is Hybrid electric architecture, which is able to improve the fuel economy and consequently cutting down emissions. A well known control strategy to solve optimization problem for energy management of Hybrid electric vehicles is ECMS (Equivalent Consumption Minimization Strategy). Finding the best control parameters (equivalence factors) of this strategy may become quite involved. This paper proposes a method for the selection of the optimal equivalence factors, for charging and discharging, by applying genetic algorithm in the case of a P0 mild hybrid electric vehicle. This method is a systematic and deterministic way to guarantee an optimal solution with respect to the trial and error method. The proposed ECMS is compared to a technique available in literature, known as the shooting method, which relies only on one equivalence factor for discharging. It is demonstrated that the performance in terms of pollutant emissions are comparable. However, ECMS with GA always guarantees an optimal solution even in the case of heavy accessory load, when shooting method is not valid anymore, as it does not guarantee a charge sustaining condition.

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.

A Rule-Based Strategy for a Series/Parallel Hybrid Electric Vehicle: An Approach Based on Dynamic Programming

2010 ◽  
Author(s):  
Domenico Bianchi ◽  
Luciano Rolando ◽  
Lorenzo Serrao ◽  
Simona Onori ◽  
Giorgio Rizzoni ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
A Priori ◽  
Optimal Solution ◽  
Management Problem ◽  
Rule Based ◽  
Hybrid Electric ◽  
Programming Solution ◽  
Powertrain Configuration

Dynamic programming (DP) provides the optimal global solution to the energy management problem for hybrid electric vehicles (HEVs), but needs complete a-priori knowledge of the driving cycle and has high computational requirements. This article presents a possible methodology to extract rules from the dynamic programming solution to design an implementable rule-based strategy. The case study considered is a series/parallel HEV, in which a clutch allows to switch from one configuration to another. The strategy works according to a two layer policy: the supervisory controller, which decides the powertrain configuration (either series or parallel), and the energy management, which decides the power split. The process of deriving the rules from the optimal solution is described. Then, the performance of the resulting rule-based strategy is studied and compared with the solution given by the dynamic programming, which functions as a benchmark.

Match and Optimization of Series Hybrid Bus Powertrain Parameters

2011 ◽  
Vol 121-126 ◽  
pp. 2710-2714
Author(s):  
Ling Cai ◽  
Xin Zhang
Keyword(s):  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Simulation Platform ◽  
Optimal Method ◽  
Orthogonal Method ◽  
Series Hybrid Electric Vehicle ◽  
Hybrid Bus ◽  
Promising Solution ◽  
Hybrid Electric

With the requirements for reducing emissions and improving fuel economy, it has been recognized that the electric, hybrid electric powered drive train technologies are the most promising solution to the problem of land transportation in the future. In this paper, the parameters of series hybrid electric vehicle (SHEV), including engine-motor, battery and transmission, are calculated and matched. Advisor software is chosen as the simulation platform, and the major four parameters are optimized in orthogonal method. The results show that the optimal method and the parameters can improve the fuel economy greatly.

An adaptive equivalent consumption minimization strategy considering catalyst temperature for plug-in hybrid electric vehicle

2021 ◽  
pp. 095440702110434
Author(s):  
Tao Deng ◽  
Ke Zhao ◽  
Haoyuan Yu
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Test Procedure ◽  
Catalyst Temperature ◽  
Equivalent Factor ◽  
Simulation Results ◽  
Hybrid Electric ◽  
Equivalent Consumption Minimization Strategy

In the process of sufficiently considering fuel economy of plug-in hybrid electric vehicle (PHEV), the working time of engine will be reduced accordingly. The increased frequency that the three-way catalytic converter (TWCC) works in abnormal operating temperature will lead to the increasing of emissions. This paper proposes the equivalent consumption minimization strategy (ECMS) to ensure the catalyst temperature of PHEV can work in highly efficient areas, and the influence of catalyst temperature on fuel economy and emissions is considered. The simulation results show that the fixed equivalent factor of ECMS has great limitations for the underutilized battery power and the poor fuel economy. In order to further reduce fuel consumption and keep the emission unchanged, an equivalent factor map based on initial state of charge (SOC) and vehicle mileage is established by the genetic algorithm. Furthermore, an Adaptive changing equivalent factor is achieved by using the following strategy of SOC trajectory. Ultimately, adaptive equivalent consumption minimization strategy (A-ECMS) considering catalyst temperature is proposed. The simulation results show that compared with ordinary ECMS, HC, CO, and NOX are reduced by 14.6%, 20.3%, and 25.8%, respectively, which effectively reduces emissions. But the fuel consumption is increased by only 2.3%. To show that the proposed method can be used in actual driving conditions, it is tested on the World Light Vehicle Test Procedure (WLTC).

On the estimation of optimal state-of-charge trajectory for plug-in hybrid electric buses using trip information

2021 ◽  
pp. 095440702110410
Author(s):  
Naser Sina ◽  
Vahid Esfahanian ◽  
Mohammad Reza Hairi Yazdi
Keyword(s):  
Fuel Economy ◽  
Dynamic Programming Algorithm ◽  
Optimal Solution ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Programming Algorithm ◽  
Factors Affecting ◽  
Optimal State ◽  
Energy Management Systems ◽  
Hybrid Electric

Plug-in hybrid electric buses are a viable solution to increase the fuel economy. In this framework, precise estimation of optimal state-of-charge trajectory along the upcoming driving cycle appears to play a pivotal role in the way to approach the globally optimal fuel economy. This paper aims to conduct a parametric study on the key factors affecting the estimation of optimal state-of-charge trajectory, including trip information availability and trip segment distance, and to provide a guideline for the design and implementation of predictive energy management systems. To accomplish this, the dynamic programming algorithm is employed to obtain the solution of optimal control problem for the sampled driving cycles in a particular bus route. A large database comprising of driving features of the cycles and the optimal solution is developed which then is used to construct a neural network based estimator for obtaining the optimal state-of-charge trajectory. The main results show promising performance of the proposed method with about 76% reduction in the root mean square error of the estimated trajectory comparing to the linear state-of-charge trajectory assumption. Moreover, the robustness of the estimator is verified through simulation and it is observed that appropriate choice of trip segment distance is vital to improve the estimation accuracy, especially in case of uncertain prediction of trip information.

scholarly journals Reducing Mobile Air Conditioner (MAC) Power Consumption Using Active Cabin-Air-Recirculation in A Plug-In Hybrid Electric Vehicle (PHEV)

2018 ◽  
Vol 9 (4) ◽  
pp. 51 ◽  
Author(s):  
Chengguo Li ◽  
Eli Brewer ◽  
Liem Pham ◽  
Heejung Jung
Keyword(s):  
Air Quality ◽  
Power Consumption ◽  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Driving Cycle ◽  
Total Power ◽  
Air Conditioner ◽  
Hybrid Electric ◽  
Cabin Air Quality

Air conditioner power consumption accounts for a large fraction of the total power used by hybrid and electric vehicles. This study examined the effects of three different cabin air ventilation settings on mobile air conditioner (MAC) power consumption, such as fresh mode with air conditioner on (ACF), fresh mode with air conditioner off (ACO), and air recirculation mode with air conditioner on (ACR). Tests were carried out for both indoor chassis dynamometer and on-road tests using a 2012 Toyota Prius plug-in hybrid electric vehicle. Real-time power consumption and fuel economy were calculated from On-Board Diagnostic-II (OBD-II) data and compared with results from the carbon balance method. MAC consumed 28.4% of the total vehicle power in ACR mode when tested with the Supplemental Federal Test Procedure (SFTP) SC03 driving cycle on the dynamometer, which was 6.1% less than in ACF mode. On the other hand, ACR and ACF mode did not show significant differences for the less aggressive on-road tests. This is likely due to the significantly lower driving loads experienced in the local driving route compared to the SC03 driving cycle. On-road and SC03 test results suggested that more aggressive driving tends to magnify the effects of the vehicle HVAC (heating, ventilation, and air conditioning) system settings. ACR conditions improved relative fuel economy (or vehicle energy efficiency) to that of ACO conditions by ~20% and ~8% compared to ACF conditions for SC03 and on-road tests, respectively. Furthermore, vehicle cabin air quality was measured and analyzed for the on-road tests. ACR conditions significantly reduced in-cabin particle concentrations, in terms of aerosol diffusion charger signal, by 92% compared to outside ambient conditions. These results indicate that cabin air recirculation is a promising method to improve vehicle fuel economy and improve cabin air quality.

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