Vehicle Optimal Control Design to Meet the 1.5 °C Target: A Control Design Framework for Vehicle Subsystems

Current studies have achieved energy savings of vehicle subsystems through various control strategies, but these control strategies lack a benchmark to measure whether these energy savings are sufficient. This work proposes a control design framework that uses the 1.5 °C target in the Paris Agreement as a benchmark to measure the adequacy of energy savings of vehicle subsystems. This control design framework involves two points. One is the conversion of the 1.5 °C target into a constraint on the energy consumption of a vehicle subsystem. The other is the optimal control design of the vehicle subsystem under this constraint. To describe the specific application of this control design framework, we conduct a case study concerning the control design of active suspension in a battery electric light-duty vehicle. By comparison with a widely used linear quadratic regulator (LQR) method, we find that this control design framework can both ensure the performance comparable to the LQR method and help to meet the 1.5 °C target in the Paris Climate Agreement. In addition, a sensitivity analysis shows that the control effect is hardly changed by battery electric vehicle market share and electricity CO2 intensity. This work might provide insight on ways that the automotive industry could contribute to the Paris Agreement.

[1]  Kai He,et al.  Analysis of downshift’s improvement to energy efficiency of an electric vehicle during regenerative braking , 2016 .

[2]  Haobin Jiang,et al.  ANFTS Mode Control for an Electronically Controlled Hydraulic Power Steering System on a Permanent Magnet Slip Clutch , 2019, Energies.

[3]  Hamid Khayyam,et al.  Adaptive intelligent control of vehicle air conditioning system , 2013 .

[4]  Ricardo Martinez-Botas,et al.  Reducing China’s road transport sector CO2 emissions to 2050: Technologies, costs and decomposition analysis , 2015 .

[5]  Lars Drugge,et al.  Energy efficient cornering using over-actuation , 2018 .

[6]  Mehrdad Moallem,et al.  Regenerative Skyhook Control for an Electromechanical Suspension System Using a Switch-Mode Rectifier , 2016, IEEE Transactions on Vehicular Technology.

[7]  Hesham Rakha,et al.  Power-based electric vehicle energy consumption model: Model development and validation , 2016 .

[8]  Salmiah Ahmad,et al.  Power reduction optimization with swarm based technique in electric power assist steering system , 2016 .

[9]  Yanjun Huang,et al.  An energy-saving set-point optimizer with a sliding mode controller for automotive air-conditioning/refrigeration systems , 2017 .

[10]  Joseph Sarkis,et al.  Carbon footprint of global passenger cars: Scenarios through 2050 , 2016 .

[11]  Paul D. Walker,et al.  The dynamic performance and economic benefit of a blended braking system in a multi-speed battery electric vehicle , 2016 .

[12]  Sanghun Kim,et al.  Predictive control of car refrigeration cycle with an electric compressor , 2017 .

[13]  P. Shukla,et al.  Transformation of India's transport sector under global warming of 2 °C and 1.5 °C scenario , 2018 .

[14]  C. Mora,et al.  Bitcoin emissions alone could push global warming above 2°C , 2018, Nature Climate Change.

[15]  H. Eric Tseng,et al.  Optimisation of active suspension control inputs for improved vehicle ride performance , 2016 .

[16]  T. Takeshita Global Scenarios of Air Pollutant Emissions from Road Transport through to 2050 , 2011, International journal of environmental research and public health.

[17]  Alessandro Casavola,et al.  A multiobjective control strategy for energy harvesting in regenerative vehicle suspension systems , 2018, Int. J. Control.

[18]  Hong Chen,et al.  Energy-efficient control of electric vehicles based on linear quadratic regulator and phase plane analysis , 2017 .