A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles

Abstract

Driving prediction techniques (DPTs) are used to forecast the distributions of various future driving conditions (FDC), like velocity, acceleration, driver behaviors etc. and the quality of prediction results has great impacts on the performance of corresponding predictive energy management strategies (PEMSs), e.g., fuel economy (FE), lifetime of battery etc. This survey presents a comprehensive study on existing DPTs. Firstly, a review on prediction objectives and major types of prediction algorithms are presented. Then a comparative study on various prediction approaches is carried out and suitable application scenarios for each approach are provided according to their characteristics. Moreover, prediction accuracy-affecting factors are analyzed and corresponding approaches for dealing with mispredictions are discussed in detail. Finally, the bottlenecks of current researches and future developing trends of DPTs are given. In general, this paper not only gives a comprehensive analysis and review of existing DPTs but also indicates suitable application scenarios for each prediction algorithm and summarizes potential approaches for handling the prediction inaccuracies, which will help prospective designers to select proper DPTs according to different applications and contribute to the further performance enhancements of PEMSs for hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs).

Energy Management in Plug-In Hybrid Electric Vehicles: Convex Optimization Algorithms for Model Predictive Control

“…, N − 1, where the sampling period δ is assumed to be constant, and the prediction horizon is given by N . Although the prediction aspect of the energy management problem is still very much an open issue [25], the focus of this paper is on the subsequent optimization problem, so it is assumed that an accurate method is available to the controller and this aspect is not addressed further. The engine and motor loss maps can be approximated with quasi-static quadratic functions,f andĥ, [11], [13], [14] aŝ m f,k =f (P eng,k ,ω eng,k ,σ k ) =σ k [α 2 (ω eng,k )P eng,k 2 + α 1 (ω eng,k )P eng,k + α 0 (ω eng,k )]…”

“…Lines 6-13 in Algorithm 2 constitute the recursive elements of the projected interior point algorithm, and Table I presents an analysis of the complexity of equations (23)(24)(25)(26). The significance of enforcing the bounds on u as a projection is illustrated, as the complexity of the projected interior point operations is a function of the first dimension of A, which is 2N here.…”

A Novel Control Method of Clutch During Mode Transition of Single-Shaft Parallel Hybrid Electric Vehicles

“…Hybrid electric vehicles (HEVs) have received extensive attention from the automotive industry and academia, and are widely regarded as one of the most effective solutions to the growing use of petroleum fuels for transportation and environmental problems [1,2]. HEVs possessing multiple power sources have different working modes, the control strategy for the vehicle determines the working mode according to the driver's intention and the driving state of the car [3].…”

Intelligent Energy Management for Plug-in Hybrid Electric Bus with Limited State Space

“…Since the EMS can be taken as a nonlinearly constrained optimization control problem, the BCD mode can be well realized by optimal control methods [6]. It can be further classified into two categories: The optimization-based and the adaptive strategies [7]. The optimization-based strategies, such as dynamic programming (DP), Pontryagin's Minimum Principle (PMP), and Equivalent Consumption Minimization Strategy (ECMS), can obtain global optimization solutions [8][9][10].…”

“…However, driving conditions need to be known prior to use, which is the reason that they cannot be directly used in real-world situations and are usually only taken as the benchmark for other strategies. In contrast, the adaptive strategy has great potential in practical application [7]. For example, a model predictive control (MPC)-based EMS was proposed by combining Markov chain, DP, and a reference SOC plan method, based on the principle of receding horizon control [11].…”