Energy and Battery Management of a Plug-In Series Hybrid Electric Vehicle Using Fuzzy Logic

Li, S. G.; Sharkh, Suleiman M.; Walsh, Frank C.; Zhang, C. N.

doi:10.1109/tvt.2011.2165571

Cited by 283 publications

(97 citation statements)

References 28 publications

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“…According to [40], the results obtained can be used as a reference for dynamic programming (DP). In [58], a fuzzy logic management system was tested in a real-time test-bench. A new quantity, called the battery working state (BWS), based on both battery terminal voltage and SoC, was used to make a decision on the power split.…”

Section: Power Management Optimization With Battery Lifetime Managementmentioning

confidence: 99%

Optimal Rule-Based Power Management for Online, Real-Time Applications in HEVs with Multiple Sources and Objectives: A Review

Moulik

Söffker

2015

Energies

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The field of hybrid vehicles has undergone intensive research and development, primarily due to the increasing concern of depleting resources and increasing pollution. In order to investigate further options to optimize the performance of hybrid vehicles with regards to different criteria, such as fuel economy, battery aging, etc., a detailed state-of-the-art review is presented in this contribution. Different power management and optimization techniques are discussed focusing on rule-based power management and multi-objective optimization techniques. The extent of rule-based power management and optimization in solving battery aging issues is investigated along with an implementation in real-time driving scenarios where no pre-defined drive cycle is followed. The goal of this paper is to illustrate the significance and applications of rule-based power management optimization based on previous contributions.

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Section: Power Management Optimization With Battery Lifetime Managementmentioning

confidence: 99%

Optimal Rule-Based Power Management for Online, Real-Time Applications in HEVs with Multiple Sources and Objectives: A Review

Moulik

Söffker

2015

Energies

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“…These vehicles use a fuel cell that functions as an electrical generator, with the energy generated delivered to the battery by means of a power converter, which, in turn, delivers the energy to the motor of the vehicle by means of another power converter. Li et al [14] used a fuzzy controller that operates based on the charge level and voltage of the battery, producing the working status of the battery, which was compared with the state of charge (SoC) of the battery, in order to avoid overestimating the SoC of the battery and generating excessive discharge. The references [15,16] used an FCS to determine the point of operation of the power converter in an electrical vehicle.…”

Section: Introductionmentioning

confidence: 99%

“…In order to achieve this, a general control system was designed to multiplex the states of control using the same fuzzy controller for each state, thus enabling the stabilization of the DC bus in adequate time periods with a decrease in the computational burden required for the implementation of the charge and discharge controller for the batteries. Furthermore, the SoC of the battery bank is not used as an input variable of the fuzzy controller as proposed in [14] and defined by the OCV-ampere-hour counting method; instead, the battery bank voltage and current are used as input variables, which are considered to generate the set of inference rules of the controller. Also, at each controller operating state, the technical characteristics for charging and discharging of the batteries provided by the manufacturer are considered, in order to extend the useful life of the battery bank.…”

Section: Introductionmentioning

confidence: 99%

Development and Application of a Fuzzy Control System for a Lead-Acid Battery Bank Connected to a DC Microgrid

Martínez

Padilla-Medina

Cano-Andrade

et al. 2018

International Journal of Photoenergy

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This study presents the development and application of a fuzzy control system (FCS) for the control of the charge and discharge process for a bank of batteries connected to a DC microgrid (DC-MG). The DC-MG runs on a maximum power of 1 kW with a 190 V DC bus using two photovoltaic systems of 0.6 kW each, a 1 kW bidirectional DC-AC converter to interconnect the DC-MG with the grid, a bank of 115 Ah to 120 V lead-acid batteries, and a general management system used to define the operating status of the FCS. This FCS uses a multiplexed fuzzy controller, normalizing the controller's inputs and outputs in each operating status. The design of the fuzzy controller is based on a Mamdani inference system with AND-type fuzzy rules. The input and output variables have two trapezoidal membership functions and three triangular membership functions. LabVIEW and the NI myRIO-1900 embedded design device were used to implement the FCS. Results show the stability of the DC bus of the microgrid when the bank of batteries is in the charging and discharging process, with the bus stabilized in a range of 190 V ± 5%, thus demonstrating short response times to perturbations considering the microgrid's response dynamics.

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“…Rule-based control has the advantage of easy on-line application, but cannot guarantee optimality for real driving cycles [8]. To solve this problem, control strategies were proposed to ensure local optimality, including rule-based control using the mode control algorithm [3], fuzzy logic [9,10], and the equivalent consumption minimization strategy based on equivalent fuel consumption of the battery energy [11,12]. However, since these control strategies do not consider the effect of the present battery SOC, they cannot guarantee global optimality for the CD and CS mode.…”

Section: Introductionmentioning

confidence: 99%

Development of an Advanced Rule-Based Control Strategy for a PHEV Using Machine Learning

et al. 2018

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This paper presents an advanced rule-based mode control strategy (ARBC) for a plug-in hybrid electric vehicle (PHEV) considering the driving cycle characteristics and present battery state of charge (SOC). Using dynamic programming (DP) results, the behavior of the optimal operating mode was investigated for city (UDDS×2, JC08 ×2) and highway (HWFET ×2, NEDC ×2) driving cycles. It was found that the operating mode selection varies according to the driving cycle characteristics and battery SOC. To consider these characteristics, a predictive mode control map was developed using the machine learning algorithm, and ARBC was proposed, which can be implemented in real-time environments. The performance of ARBC was evaluated by comparing it with rule-based mode control (RBC), which is a CD-CS mode control strategy. It was found that the equivalent fuel economy of ARBC was improved by 1.9-3.3% by selecting the proper operating mode from the viewpoint of system efficiency for the whole driving cycle, regardless of the battery SOC.

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Energy and Battery Management of a Plug-In Series Hybrid Electric Vehicle Using Fuzzy Logic

Cited by 283 publications

References 28 publications

Optimal Rule-Based Power Management for Online, Real-Time Applications in HEVs with Multiple Sources and Objectives: A Review

Optimal Rule-Based Power Management for Online, Real-Time Applications in HEVs with Multiple Sources and Objectives: A Review

Development and Application of a Fuzzy Control System for a Lead-Acid Battery Bank Connected to a DC Microgrid

Development of an Advanced Rule-Based Control Strategy for a PHEV Using Machine Learning

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