Battery‐lifetime‐conscious energy management strategy based on SP‐SDP for commuter plug‐in hybrid electric vehicles

Abstract

The energy management strategy used to split the energy flow among different energy resources of hybrid electric vehicles plays a critically important role in achieving fuel economy. Additionally, battery degradation and high production cost lead to the necessary consideration of the battery lifetime in the energy management strategy design for a plug-in hybrid electric vehicle (PHEV). This paper investigates the PHEV energy management problem taking into consideration battery lifetime on how to distribute power between the engine and the electric equipment during the driving cycle to achieve the whole economy for a commuter PHEV. Shortest path stochastic dynamic programming (SP-SDP) is employed to address this energy management problem, which is formulated as a stochastic optimal control problem with the minimization of a weighted combination of the fuel and electricity consumption and the battery degradation rate for a stochastic process model with the statistic characteristics captured from the historical traffic speed profiles. The solution of this optimization problem, derived from a modified policy iteration algorithm, is a time-invariant, state-dependent power split strategy, which can be directly applied on the actual running vehicle. Simulation results carried on a PHEV Prius model in MATLAB/Simulink environment over some driving cycles are presented to demonstrate the effectiveness of the proposed energy management strategy.

Battery Management of New Energy Vehicle Based on CFD

“…The classical model is also used for the calculation of the heating rate of batteries. Gerardine GBotte et al studied the influence of related parameters (cell density, heat transfer coefficient and current) on the thermal effect of batteries under different conditions of lithium ion batteries using a centralized mass model in 1998 [4].…”

Implementing electric vehicles in public services: a case study research

“…The EVs are classified into three major categories by their fuel consumption technology: hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs), while both PHEVs and BEVs are also referred to as plug-in electric vehicles (PEVs), since they are designed to be recharged by plugging into the power grid (Zhu et al, 2018). Hence, Hybrids (HEVs) are vehicles with an electric drive system and an internal combustion engine running on either petrol or diesel (Milowski et al, 2018), while PHEVs are vehicles equipped with at least two energy sources to propel them (Xu et al, 2018), usually by combining electric and conventional propulsion (Plötz et al, 2018) and BEVs are considered vehicles that operate on batteries that have a limited life as well as specific charging and discharging patterns (Pelletier et al, 2017). In short, the new generation electric vehicles (EVs) are likely to become increasingly popular for city travellers and are expected to feature prominently in 'Smart Cities' of the future (Milowski et al, 2018).…”

Improved Deep Learning-Based Energy Management Strategy for Battery-Supercapacitor Hybrid Electric Vehicle With Adaptive Velocity Prediction

“…Energy management is a critical technology in HEV, which can determine the optimal power split control for the energy source subsystems. Generally, the existing energy management strategies (EMSs) of HEVs are grouped into heuristic and optimization-based strategies [6], [7]. The heuristic strategies include the rule-based [8] and the fuzzy logicbased [9] while the optimization-based strategies include dynamic programming (DP) [10].…”