Effects of different PHEV control strategies on vehicle performance

Abstract

Foreign oil dependence, increased cost of fuel, pollution, global warming are buzz words of today's era. Automobiles have a large impact on increasing energy demand, pollution and related issues. As a consequence, many efforts are being concentrated on innovative systems for transportation that could replace petroleum with cleaner fuel, i.e. electricity from the power grid. The use of plug-in hybrid electric vehicles (PHEVs) can become a very important change in this direction, since such vehicles could benefit from the increasing availability of renewable energy. PHEVs requires new control and energy management algorithms, that are crucial for vehicle performance. This paper deals with evaluation of two modes, Electric Vehicle (EV) mode and Blended mode, for plug-in hybrid electric vehicles and their comparison with conventional and hybrid electric vehicle performance. In this paper two PHEV architectures are considered: through road parallel plug-in hybrid and series plug-in hybrid. Similar models have been developed to evaluate vehicle performance for conventional and hybrid architectures. Both PHEV architectures are analyzed with two different modes- EV and Blended; a modified version of ECMS (Equivalent Consumption Minimization Strategy) is used for both algorithms. Various standard as well as custom designed driving cycles are used in this analysis. The paper provides quantitative analysis of the control algorithms to analyze their effects on fuel economy, use of electric energy, cost of operation, etc.; these results are compared with the simulations for hybrid and conventional vehicles. Some important relationships between fuel economy, design architectures and control strategies are shown and can be useful in the design of the optimal control algorithms for PHEVs. As shown in the results, the control problem for PHEVs is not limited to fuel economy but it also involves external factors, such as price of electricity, energy market and regulations, charging - availability, battery life issues, etc

Design and Evaluation of Energy Management using Map-Based ECMS for the PHEV Benchmark

“…The strategy is to underestimate the approximate distance by 10%, and use that as a horizon for the blended strategy, then switch to charge-sustaining mode. This is achieved by setting a SOC reference, SOC c , that is linear in the ratio of traveled distance versus expected distance according to Equation (18), a method also used in [6]. The minimum SOC c , SOC f , is set to 0.315 in order to ensure that SOC(end) !…”

“…In [5], the correction is a linear function in SOC error. In [6], the equivalence factor is corrected with a product of two terms, a cubic function of the SOC error and tanh of the low-pass filtered SOC error.…”

The Novel Application of Optimization and Charge Blended Energy Management Control for Component Downsizing within a Plug-in Hybrid Electric Vehicle

“…The ECMS of local cost function optimization was first proposed by [24] for a HEV and later refined and extended within a number of research publications [9,[25][26][27][28]. For a given value of driver demand power P dmd , optimization of the energy consumption within the PHEV is achieved by selecting the optimal instantaneous power split (β) between the ICE (P ICE ) and HV battery (P B ) while adhering to the minimum and maximum power constraints within the PHEV: (24) …”

“…However, research published in [8][9][10] advocates a third mode of operation, namely the charge blended (CB) mode in which both the ICE and the electrical subsystems are optimally employed throughout the journey. As discussed in [11] because the ICE is able to operate in its most efficient region for comparatively longer an overall reduction in energy consumption and CO 2 emissions can be realized.…”

Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles

“…The manner in which the onboard electrical energy is used significantly influences the energy utilization efficiency and subsequently impacts the fuel economy [5][6][7][8].…”