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.

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.

Design of Hybrid-Electric Vehicle Architectures Using Auto-Generation of Feasible Driving Modes

2013 ◽  
Author(s):  
Alparslan Emrah Bayrak ◽  
Yi Ren ◽  
Panos Y. Papalambros
Keyword(s):  
Electric Vehicle ◽  
Fuel Economy ◽  
Heuristic Search ◽  
Power Distribution ◽  
Hybrid Electric Vehicle ◽  
Planetary Gear ◽  
Dual Mode ◽  
Drive Cycle ◽  
Motor Maps ◽  
Hybrid Electric

Several hybrid-electric vehicle architectures have been commercialized to serve different categories of vehicles and driving conditions. Such architectures can be optimally controlled by switching among driving modes, namely, the power distribution schemes in their planetary gear (PG) transmissions, in order to operate the vehicle in the most efficient regions of engine and motor maps. This paper proposes a systematic way to identify the optimal architecture for a given vehicle drive cycle, rather than parametrically optimizing one or more pre-selected architectures. An automatic generator of feasible driving modes for a given number of PGs is developed. For a powertrain consisting of one engine, two motors and two PGs, this generator results in 1116 modes. A heuristic search is then proposed to find a near-optimal pair of modes for a given driving cycle and vehicle specification. In a study this process identifies a dual-mode architecture with an 8% improvement in fuel economy compared to a commercially available architecture over a standard drive cycle.

Optimization Techniques for Hybrid Electric Vehicle Analysis Using Advisor

2001 ◽  
Author(s):  
Tony Markel ◽  
Keith Wipke
Keyword(s):  
Fuel Cell ◽  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
System Optimization ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Gradient Based ◽  
Optimization Routine ◽  
Hybrid Electric

Abstract System optimization is an extremely important step in the vehicle design process. Recently the National Renewable Energy Laboratory (NREL), both internally and through its subcontractors, has been actively developing and applying optimization tools to vehicle systems analysis problems associated with hybrid electric vehicles. We are applying both locally- and globally-focused optimization tools to these analysis problems. This paper describes the current status of the tools under development and the application of these tools to a specific problem. The optimization tools evaluated include FMINCON (gradient-based constrained optimization routine included in the MATLAB Optimization Toolbox), DIRECT (non-gradient based optimization routine), Response Surface Approximations and Direct Gradient-based Optimization (commercial routines available in VisualDOC 2.0 from Vanderplaats R&D). The four optimization algorithms were applied to a simple two-dimensional problem so that the solution methods could be visualized and the relative performance quantified. The tools were then each applied to the optimization of a fuel cell-powered hybrid electric vehicle modeled in ADVISOR 3.1. Both the component sizes and the energy management strategy where included as design variables. The fuel economy was maximized under the constraint that vehicle performance must not be less than its conventional vehicle counterpart. Vehicle and/or component costs were not included in this study but could easily be included if suitable models were identified. Based on this work, the following conclusions can be drawn: • The impact of multiple local minimums and a small amount of objective function ‘noise’ (due to necessary state of charge balancing for hybrid vehicles) caused gradient based optimizers to stop before they were able to find design points as good as those found by the non-gradient based DIRECT routine. • For the fuel cell-powered hybrid electric SUV design problem, several local optimums and one likely global optimum design have been determined. The global optimum is based on 2461 function evaluations. Based on the assumptions in this study, the optimal fuel cell-powered hybrid electric SUV would consist of a 66 kW (net) fuel cell, 107 kW and 16.8 kWh battery pack (twenty-eight 49 Ah modules, 372 volts) and a 126 kW traction motor. The optimal control for this system turned out to be mainly a series thermostat design with some power following capability under peak loads. This vehicle is expected to achieve a City/Highway composite fuel economy of 56.5 mpgge (miles per gallon gasoline equivalent).

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).

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.

Sign in / Sign up

Export Citation Format

Share Document