Integrated Energy and Catalyst Thermal Management for Plug-In Hybrid Electric Vehicles

With plug-in hybrid electric vehicles (PHEVs), the catalyst temperature is below the light-off temperature due to reduced engine load, extended engine off period, and frequent engine on/off shifting. The conversion efficiency of a three-way catalyst (TWC) and tailpipe emissions were proven to depend heavily on the temperature of the catalyst. The existing energy management strategy (EMS) of the PHEVs focuses on the improvement of fuel efficiency and emissions based on hot engine characteristics, but neglects the effect of catalyst temperature on tailpipe emissions. This paper presents a new EMS that incorporates a catalyst thermal management method. First, an additional cost is established to implement additional constraints on catalyst temperature, and then the global cost function is created using this additional cost and the fuel consumption. Second, we find the global optimal solution using Pontryagin’s minimum principle method, which provides an optimal control policy and state trajectories. Then, based on the analysis of the optimal control policy, an engine on/off filter (eng on/off filter) is introduced to command the engine on/off shifting. This filter plays an important role in adjusting both the energy and catalyst thermal management strategy for PHEVs. Finally, a practical approach based on the eng on/off filter is developed, and a genetic algorithm is applied to optimize the time constants of this filter. Simulation results demonstrate that the proposed approach‘s fuel consumption increased slightly, but the tailpipe emissions of HC (hydrocarbons), CO (carbon monoxide) and NOx (nitrogen oxide) significantly decreased compared with the standard approach.

[1]  L. Guzzella,et al.  Control of hybrid electric vehicles , 2007, IEEE Control Systems.

[2]  Yann Chamaillard,et al.  Optimal Energy Management Strategy including Battery Health through Thermal Management for Hybrid Vehicles , 2013 .

[3]  Ali Emadi,et al.  Classification and Review of Control Strategies for Plug-In Hybrid Electric Vehicles , 2011, IEEE Transactions on Vehicular Technology.

[4]  Huei Peng,et al.  Optimal Energy and Catalyst Temperature Management of Plug-in Hybrid Electric Vehicles for Minimum Fuel Consumption and Tail-Pipe Emissions , 2013, IEEE Transactions on Control Systems Technology.

[5]  Morteza Montazeri-Gh,et al.  Near-Optimal SOC Trajectory for Traffic-Based Adaptive PHEV Control Strategy , 2017, IEEE Transactions on Vehicular Technology.

[6]  Bing Xia,et al.  Energy management of power-split plug-in hybrid electric vehicles based on simulated annealing and Pontryagin's minimum principle , 2014 .

[7]  Hosam K. Fathy,et al.  Tradeoffs between battery energy capacity and stochastic optimal power management in plug-in hybrid electric vehicles , 2010 .

[8]  Lino Guzzella,et al.  Implementation of comfort constraints in dynamic programming for hybrid vehicle energy management , 2012 .

[9]  Xiaofeng Yin,et al.  Stochastic Optimal Energy Management of Smart Home With PEV Energy Storage , 2018, IEEE Transactions on Smart Grid.

[10]  Zheng Chen,et al.  Energy Management for a Power-Split Plug-in Hybrid Electric Vehicle Based on Dynamic Programming and Neural Networks , 2014, IEEE Transactions on Vehicular Technology.

[11]  Youngjin Park,et al.  Optimal adaptation of equivalent factor of equivalent consumption minimization strategy for fuel cell hybrid electric vehicles under active state inequality constraints , 2014 .

[12]  Zheng Chen,et al.  Energy management of a power-split plug-in hybrid electric vehicle based on genetic algorithm and quadratic programming , 2014 .

[13]  M. Ouyang,et al.  Approximate Pontryagin’s minimum principle applied to the energy management of plug-in hybrid electric vehicles , 2014 .

[14]  Chunting Chris Mi,et al.  A novel energy management method for series plug-in hybrid electric vehicles , 2015 .

[15]  Volker Pickert,et al.  Stochastic control of smart home energy management with plug-in electric vehicle battery energy storage and photovoltaic array , 2016 .

[16]  Simona Onori,et al.  A Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles , 2011 .

[17]  Dennis N. Assanis,et al.  An Early-Design Methodology for Predicting Transient Fuel Economy and Catalyst-Out Exhaust Emissions , 1997 .

[18]  Hongwen He,et al.  Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles , 2015 .

[19]  Sumedha Rajakaruna,et al.  High-Efficiency Control of Internal Combustion Engines in Blended Charge Depletion/Charge Sustenance Strategies for Plug-In Hybrid Electric Vehicles , 2015, IEEE Transactions on Vehicular Technology.

[20]  Huei Peng,et al.  Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle , 2013 .

[21]  Zhumu Fu,et al.  Design and Validation of Real-Time Optimal Control with ECMS to Minimize Energy Consumption for Parallel Hybrid Electric Vehicles , 2017 .

[22]  Lino Guzzella,et al.  EQUIVALENT CONSUMPTION MINIMIZATION STRATEGY FOR THE CONTROL OF REAL DRIVING NOX EMISSIONS OF A DIESEL HYBRID ELECTRIC VEHICLE , 2014 .

[23]  Bo Geng,et al.  Energy Management Control of Microturbine-Powered Plug-In Hybrid Electric Vehicles Using the Telemetry Equivalent Consumption Minimization Strategy , 2011, IEEE Transactions on Vehicular Technology.

[24]  Jianqiu Li,et al.  Application of Pontryagin's Minimal Principle to the energy management strategy of plugin fuel cell electric vehicles , 2013 .

[25]  Simona Onori,et al.  Adaptive Pontryagin’s Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt , 2015 .