Mathematical Problems in Engineering

Volume 2016, Article ID 9203081, 9 pages

http://dx.doi.org/10.1155/2016/9203081

## Energy Efficiency Oriented Design Method of Power Management Strategy for Range-Extended Electric Vehicles

State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China

Received 9 March 2016; Revised 3 June 2016; Accepted 6 June 2016

Academic Editor: Muhammad N. Akram

Copyright © 2016 Jiuyu Du et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The energy efficiency of the range-extended electric bus (REEB) developed by Tsinghua University must be improved; currently, the energy management strategy is a charge-deplete-charge-sustain (CDCS) strategy, which exhibits low energy efficiency on the demonstration model. To improve the energy efficiency and reduce the operating cost, a rule-based control strategy derived from the dynamic programming (DP) strategy is obtained for the Chinese urban bus driving cycle (CUBDC). This rule is extracted by the power-split-ratio (PSR) from the simulation results of the dynamic powertrain model using the DP strategy. By establishing the REEB dynamic models in Matlab/Simulink, the control rule can be achieved, and the power characteristic of powertrain, energy efficiency, operating cost, and computing time are analyzed. The simulation results show that the performance of the rule-based strategy presented in this paper is similar to that of the DP strategy. The energy efficiency can be improved greatly compared with that of the CDCS strategy, and the operating cost can also be reduced.

#### 1. Introduction

Environmental concerns and increasing fuel cost have motivated manufacturers and governments to develop alternative technologies to replace conventional internal combustion engine (ICE) vehicles [1]. Recent works have shown that vehicle electrification can reduce energy waste and minimize fuel consumption [2]. Nevertheless, battery electric vehicles (BEVs) still have a major drawback: energy storage. For massive deployment of BEVs, the problems of driving range, charging time, lifetime, and higher upfront cost must be solved. Typically, a BEV stores energy in batteries that are bulky, heavy, and expensive. Due to this problem, with current battery technology, it is very difficult to make a general purpose BEV that effectively competes with ICE vehicles [3].

A range-extended electric vehicle (REEV) provides a platform to overcome the BEV’s drawbacks and reduce fuel consumption and energy waste [4]. REEVs utilize a smaller size power battery than that of battery electric vehicle along with a small displacement range extender; this combination is an optimal solution to the economic and energy storage issues of BEVs. In China, public buses are regarded as the priority concerning development of new energy vehicles (NEVs). A type of range-extended electric bus has been developed by Tsinghua University (THU) and automotive companies and demonstration operations have been performed in cities for several years. Presently, the fuel efficiency of the REEB is too low and urgently must be improved. The energy efficiency is an important criterion to assess the performance of a range-extended electric bus, and the energy management strategy is a key influencing factor on the energy consumption [5]. The present energy management strategy of the THU REEB is charge-deplete-charge-sustain (CDCS), which is established by engineering experience; as a result, the energy efficiency still has much room for improvement, and a new control strategy should be developed. The energy management strategy of a hybrid electric powertrain can be classified into instantaneous strategies, global optimized strategies, or rule-based strategies [6]. The key to solving instantaneous optimization problems is a reasonable objective function, such as the equivalent consumption minimization strategy. García et al. [7] and Geng et al. [8] utilized the equivalent fuel consumption minimization strategy to analyze the performances of a fuel cell electric vehicle and a plug-in electric vehicle, respectively. Sciarretta et al. [9] and Barsali et al. [10] applied the equivalent fuel consumption minimization strategy on series hybrid electric vehicles and parallel hybrid electric vehicles, respectively. By comparison, the instantaneous optimization is only based on current control step and real-time distribution strategy; as a result, this strategy will affect performance of hybrid powertrain greatly. Thus, a more optimal result can be obtained using global optimization [11]. The DP algorithm is a widely used global optimization method, which is suitable for optimizing the control strategy when the driving cycle is known in advance. Kum et al. [12] and Lin et al. [13] applied the DP strategy on a plug-in electric vehicle and a parallel hybrid electric vehicle, respectively, to achieve better fuel consumption and emissions. However, this type of strategy is difficult to apply on a vehicle due to its heavy computational burden [14]. Rule-based strategies are mainly designed in accordance with engineering experiences and easy to be applied on vehicles. However, the optimal performance is difficult to achieve using rule-based strategies [15]. He et al. [16] presented several rule-based control strategies, such as the voltage control, current control, and brake regeneration control on fuel cell electric bus by simulation. Wu et al. [17] devised a CDCS strategy on the range-extended electric bus in the Harbin bus driving cycle. Gong et al. [18] and Schouten et al. [19] devised a fuzzy logic controller for a parallel hybrid electric bus. Gong et al. [18] developed a neural network control strategy for the energy management system based on the trip model.

The abovementioned optimal control strategies are all global optimal resolution approaches using the DP algorithm, which are difficult to implement on-board because of the computational burden. In contrast, the rule-based control strategy is easy to apply in real-time. This paper combines the advantages of the DP strategy and the rule-based strategy and presents a rule-based strategy derived from the DP strategy to reduce the energy consumption for THU REEB.

#### 2. Modeling and Simulation

##### 2.1. Powertrain Configuration and Operational Modes

The schematic diagram of electric powertrain is shown in Figure 1. The range extender acts as on-board generating device consisting of an engine, a generator, and a rectifier. According to the demand of the REEB under different driving cycles, the battery and the range extender provide power to the traction motor to drive vehicle either jointly or separately [17].