Elsevier

Applied Energy

Volume 203, 1 October 2017, Pages 883-896
Applied Energy

Adaptive real-time optimal energy management strategy based on equivalent factors optimization for plug-in hybrid electric vehicle

https://doi.org/10.1016/j.apenergy.2017.06.106Get rights and content

Highlights

  • A method for dividing driving cycles into segments is proposed.

  • The near-optimal reference SOC trajectory is designed.

  • The linear weight PSO is adopted to optimize the EFs in each segments.

  • A novel adaptive real-time optimal energy management strategy is realized.

Abstract

Plug-in hybrid electric vehicle (PHEV) is one of the most promising products to solve the problem about air pollution and energy crisis. Considering the characteristics of urban bus route, maybe a fixed-control-parameter control strategy for PHEV cannot perfectly match the complicated variation of driving conditions, and as a result the ideal vehicle fuel economy would not be obtained. Therefore, it is of great significance to develop an adaptive real-time optimal energy management strategy for PHEV by taking the segment characteristics of driving cycles into consideration. In this study, a novel energy management strategy for Plug-in hybrid electric bus (PHEB) is proposed, which optimizes the equivalent factor (EF) of each segment in the driving cycle. The proposed strategy includes an offline part and an online part. In the offline part, the driving cycles are divided into segments according to the actual positions of bus stops, the EF of each segment is optimized by linear weight particle swarm optimization algorithm with different initial states of charge (SOC). The optimization results of EF are then converted into a 2-dimensional look up table, which can be used to make real-time adjustments to online control strategy. In the online part, the optimal instantaneous energy distribution is obtained in this hybrid powertrain. Finally, the proposed strategy is verified with simulation and hardware in the loop tests, and three kinds of commonly used control strategies are adopted for comparison. Results show when the initial SOC is 90%, the fuel economy with the proposed strategy can be improved by 15.93% compared with that of baseline strategy, and when the initial SOC is 60%, this value is 16.02%. The proposed strategy may provide theoretical support for control optimization of PHEV.

Introduction

In recent years, the development of new energy vehicle has made a great contribution to reducing energy consumption and pollutant emission of automobiles [1], [2], [3]. Plug-in hybrid electric vehicle (PHEV) is one of the most promising products in this field. In PHEV, fuel economy can be improved by controlling the energy flow between internal combustion engine and electric machine connected with a set of high-voltage batteries [4]. The maximum energy conversion efficiency in hybrid powertrain can be achieved if the battery is depleted to its minimum allowable charge at the end of its trip [5]. However, the complicated and transient driving cycles of city bus affect the reasonable torque distribution in PHEV, resulting in lower fuel economy of the vehicle. Accordingly, designing an efficient energy management strategy for PHEV running in complex driving cycles has important theoretical and practical significance [6].

The energy management strategy (EMS) of PHEV in accordance with its implementation can be divided into two categories: rule-based EMS and optimization-based EMS. The former performs shorter computing time and more feasible application, and the setting of rule base threshold can be obtained through practical engineering experience, engine optimal working point reference and offline optimization strategy extraction [7]. The rule-based strategies are subdivided into deterministic rule-based and fuzzy rule-based methods. For example, authors proposed a classical rule-based energy management strategy for plug-in hybrid electric vehicle, which exhibits good reliability and stability in test driving cycles [8]. Li et al. proposed an optimal fuzzy power control strategy of fuel battery hybrid vehicles, simulation result shows that it performs well in fuel economy and overall system efficiency [9]. Denis et al. proposed a fuzzy-based blended energy management strategy focus on driving conditions of plug-in hybrid electric vehicle, and the efficiency of the proposed strategy is demonstrated through simulations [10]. Although rule-based EMS is easy to design, its thresholds and rule base need be determined by conducting a large number of experiments or experience calibrations. Those with fixed thresholds cannot guarantee a better fuel economy, and sometimes the fuel economy may get worse. Regarding the optimization-based EMS, which can help PHEV to obtain a better fuel economy by optimizing the energy flows between fuel and electricity in the hybrid powertrain, has been the most attractive one among all current EMSs. Considering the difference of optimization form, EMS can be divided into instantaneous optimization EMS, local optimization EMS, approximate optimization EMS and global optimization EMS. In the global optimization EMS, In order to get the theoretical global optimal fuel economy, deterministic dynamic programming (DDP) algorithm-energy management strategy was designed under a test driving cycle, which achieved the global optimal solution for fuel economy based on Bellman's principle [11]. The global optimal solution can be found by minimizing the equivalent factor (EF) at each step of the DDP solving process. DDP’s effectiveness comes at a price, a huge real-time computing burden [12]. Bases on these, there are impediments to DDP algorithm’s large-scale application in engineering practice, which are often realized offline and deployed as benchmarks [13]. In the local optimization EMS: model predictive control (MPC) controller enables planning of the power split commands on a future time horizon. MPC is also known as a moving horizon control and receding horizon control because it optimizes over a given time horizon [14]. Due to the vehicle model is nonlinear, nonlinear model predictive control (NMPC) is developed for the power split strategy of HEV, and the optimal problem is numerically solved by DDP [15]. These kinds of strategies belong to the optimization in the prediction domain, the computation amount is relatively small, and the real-time performance is better. Research results showed that better fuel economy could be obtained compared with rule-based strategy [16]. In the approximate optimization EMS: there are more strategies, for example, stochastic deterministic dynamic programming (SDP), stochastic model predictive control (SMPC) et al. [17], [18]. Due to the stochastic nature of future driving cycle, the action of a driver is also stochastic. Markov-chain-based driver model was introduced to energy management strategy in many researches. For example, Li et al. proposed a stochastic optimal energy management strategy for plug-in hybrid electric bus, and it is well satisfied the driver expectation to vehicle maneuverability under various driving conditions [19]. SMPC can adapt to the behavior of driver, authors proposed driving-behavior-aware stochastic model predictive control for PHEB, the driving behavior classification is realized via K-means, then the models of different driving behaviors are built by using Markov chains, finally the obvious fuel consumption (FC) reduction are obtained by comparing with equivalent consumption minimization strategy (ECMS) and NMPC [20].

In the instantaneous optimization based EMS, a more promising option for energy management strategy of PHEB is the ECMS [21]. In ECMS, the electricity consumption can be converted to an equivalent amount of fuel using the EF, and instant torque split can be realized with the aim to minimize the equivalent fuel consumption [22]. EF is a key dynamic variable, which determines the real-time application of ECMS [23]. To get the accurate EF, many researchers proposed improved control methods based on ECMS. In [24], the future velocity was periodically predicted, and the predicted results are used to obtain the EF. This method is often known as a feedforward controller and it relies heavily on the accuracy of the predictions. In [25], to deal with the complex relations between the FC of HEV, the states of charge (SOC) and the control variables, a fuzzy-tuned equivalent consumption minimization strategy was proposed. However, the cost factor of this method relies heavily on expert knowledge and experience. In [26], a widely used approach was put forward to adjust the EF using a feedback controller. But this approach cannot be directly applied to PHEV due to the discharging characteristics of large capacity battery. In summary, the EF in the standard ECMS is constant in a given driving cycle and initial SOC, which can be obtained by repeatedly calibrating the pre-set rules [27]. The EF also can be adjusted adaptively with a feedback controller [28]. Both of the two methods can greatly improve the practicability of ECMS, which may help PHEB to realize the near-optimal energy management. Based on these insightful research works, to obtain an optimized EF array using optimization algorithm may prove to be a fruitful direction in ECMS.

To verify the effectiveness and real-time operation capability of proposed strategies, various papers have performed experiments to analyze and evaluate the EMS [29]. Hardware-in-loop (HIL) test enables experimental study for proposed strategy via real-time interaction between physical hardware and virtual simulations. And the real-time calculation performance of the proposed strategy can be assessed in the HIL system [30]. Moreover, the combination of computer simulation and HIL test allows the reduction of designing time and cost, for both the final test bench and vehicle experiments [31].

In PHEV, the battery SOC varies in a broad range and its initial SOC can also be very different. Considering the characteristics of urban bus route, it is difficult to determine the optimal EF with different initial conditions and complex driving cycles. To solve this problem, this paper proposes a novel adaptive real-time energy management strategy based on EF optimization for PHEV. The proposed strategy includes an offline part and an online part. In the offline part, considering the relationship between initial SOC, different bus stops and optimal EF, the driving cycles were divided into segments based on actual bus stops. Then, different initial SOCs were set, and linear weight particle swarm optimization (LinWPSO) was used to optimize the energy cost of each segment to obtain the optimal EF of this segment. So the optimal EF of each segment can be obtained. Meanwhile, a 2-dimensional look-up table can be made for online calculation of proposed strategy according to the optimization results of LinWPSO. In the online part, the real-time energy management strategy is proposed to solve the optimal instantaneous energy distribution problem while maintaining the SOC within the reasonable bounds.

This paper is organized as follows. In Section 2, the configuration and mathematical models of the plug-in hybrid powertrain are given. In Section 3, the EF optimization-based adaptive real-time optimized energy management strategy is described. In Section 4, the results are provided and analyzed. Finally, conclusions are made in Section 5.

Section snippets

PHEB model descriptions

In this paper, the parallel hybrid powertrain configuration is studied, which is one of the most popular configurations in PHEB application as shown in Fig. 1. Main parameters of the studied PHEB are given in Table 1. The engine is a 6.45 L inline six-cylinder compressed natural gas (CNG) engine. The electric motor (EM) can be used for both electrical driving and regenerative braking. As shown in Fig. 1, engine and EM are equipped on the same driving shaft, and the mode transition of hybrid

EF optimization based adaptive real-time optimal energy management strategy

Considering the characteristics of urban bus route, maybe a fixed-control-parameter control strategy for PHEV cannot perfectly match the complicated variation of driving conditions, and as a result the ideal vehicle fuel economy would not be obtained. To improve this situation, a novel adaptive real-time optimal control strategy is proposed based on the EF optimization algorithm for PHEB in Fig. 4. As shown in Fig. 4, the proposed strategy includes two parts, i.e., the offline part and the

Results and discussions

In this section, the proposed energy management strategy combined with the hybrid powertrain described in Section 2 is verified in both simulation and HIL test. To verify the universality of the proposed strategy, the real-world driving cycle, China typical city bus driving cycle, and economic commission for Europe (ECE) driving cycle are chosen as the test driving cycles, as shown in Fig. 8. The red dotted line is designed to distinguish between two adjacent bus stops, as described in Section

Conclusion

In this paper, a novel adaptive real-time optimal energy management strategy based on EF optimization for PHEV is proposed. The findings of this paper can be shown as:

  • (1)

    To obtain the optimal EF array, the driving cycles could be divided into segments according to the real bus stops. Taking the characteristics of these segments in the given driving cycles into consideration, LinWPSO algorithm could be applied to obtain the optimal array of EF by minimizing the FC;

  • (2)

    To optimize the usage of

Acknowledgment

The authors wish to appreciate the anonymous reviewers and the editor for providing constructive suggestions which have improved the paper. Moreover, authors also greatly appreciate the China government by the support of this work through the National Natural Science Foundation of China (Grant No. 51605243 and No. 51675293), the National Key Science and Technology Projects (Grant No. 2014ZX04002041) and the 1-class General Financial Grant from the China Postdoctoral Science Foundation (Grant

References (39)

  • Y. Li et al.

    A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty

    Energy

    (2016)
  • M. Berecibar et al.

    Igor Villarreal. State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application

    Energy

    (2016)
  • X. Hu et al.

    Energy efficiency analysis of a series plug-in hybrid electric bus with different energy management strategies and battery sizes

    Appl Energy

    (2013)
  • N. Delgarm et al.

    Multi-objective optimization of the building energy performance: a simulation-based approach by means of particle swarm optimization (PSO)

    Appl Energy

    (2016)
  • M.H. Amini et al.

    Simultaneous allocation of electric vehicles’ parking lots and distributed renewable resources in smart power distribution networks

    Sustain Cites Soc

    (2017)
  • Martínez CM, Hu X, Cao D, et al. Energy management in plug-in hybrid electric vehicles: recent progress and a connected...
  • F. Sun et al.

    A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique

    Appl Energy

    (2016)
  • Amini MH, Islam A. Allocation of electric vehicle’s parking lots in distribution network. IEEE PES Innovative smart...
  • X. Hu et al.

    Integrated optimization of battery sizing, charging, and power management in plug-in hybrid electric vehicles

    IEEE Trans Control Syst Technol

    (2016)
  • Cited by (149)

    View all citing articles on Scopus
    View full text