Optimal control and real-time simulation of hybrid marine power plants

With significantly increasing concerns about greenhouse effects and sustainable economy, the marine industry presents great potential for reducing its environmental impact. Recent developments in power electronics and hybridisation technologies create new opportunities for innovative marine power plants which utilize both traditional diesel generators and energy storage like batteries and/or supercapacitors as the power sources. However, power management of such complex systems in order to achieve the best efficiency becomes one of the major challenges. Acknowledging this importance, this research aims to develop an optimal control strategy (OCS) for hybrid marine power plants. First, architecture of the researched marine power plant is briefly discussed and a simple plant model is presented. The generator can be used to charge the batteries when the ship works with low power demands. Conversely, this battery energy can be used as an additional power source to drive the propulsion or assist the generators when necessary. In addition, energy losses through braking can be recuperated and stored in the battery for later use. Second, the OCS is developed based on equivalent fuel consumption minimisation (EFCM) approach to manage efficiently the power flow between the power sources. This helps the generators to work at the optimal operating conditions, conserving fuel and lowering emissions. In principle, the EFCM is based on the simple concept that discharging the battery at present is equivalent to a fuel burn in the future and vice-versa and, is suitable for real-time implementation. However, instantaneously regulating the power sources’ demands could affect the system stability as well as the lifetime of the components. To overcome this drawback and to achieve smooth energy management, the OCS is designed with a number of penalty factors by considering carefully the system states, such as generators’ fuel consumption and dynamics (stop/start and cranking behaviour), battery state of charge and power demands. Moreover, adaptive energy conversion factors are designed using artificial intelligence and integrated in the OCS design to improve the management performance. The system therefore is capable of operating in the highest fuel economy zone and without sacrificing the overall performance. Furthermore, a real-time simulation platform has been developed for the future investigation of the control logic. The effectiveness of the proposed OCS is then verified through numerical simulations with a number of test cases

An enhanced nodal gradient finite element for non-linear heat transfer analysis

The present work is devoted to the analysis of non-linear heat transfer problems using the recent development of consective-interpolation procedure. Approximation of temperature is enhanced by taking into account both the nodal values and their averaged nodal gradients, which results in an improved finite element model. The novel formulation possesses many desirable properties including higher accuracy and higher-order continuity, without any change of the total number of degrees of freedom. The non-linear heat transfer problems equation is linearized and iteratively solved by the Newton-Raphson scheme. To show the accuracy and efficiency of the proposed method, several numerical examples are hence considered and analyzed

A study on electric vehicle battery ageing through smart charge and vehicle-to-grid operation

Electrification of transportation means brings positive impacts to the environment because of reduced fossil fuel depletion and related carbon emissions. Critical obstacles remain in terms of battery costs and their expected life. Vehicle-to-grid technologies can deliver benefits to support electrical power grid and vehicle owner, while their practical implementation faces challenges due to the concerns over accelerated battery degradation. This study presents the evaluation of battery degradation through different smart charge strategies and vehicle-to-grid scenarios. The simulation results show that the developed smart charge schemes can mitigate the battery ageing up to 5% while lowering the charge cost from 30 - 60% as comparing to the conventional charge method within the first five days operation of the battery. In addition, the calendar ageing can be diminished upto 80% by participating in suitable V2G scenario

Baseline strategy for remaining range estimation of electric motorcycle applications

Accurate predicting the remaining range of electric motorcycles (EMs) is important to help optimizing the energy consumption and improving the utilization of remaining energy in the batteries and therefore extending their life. In this paper, a range estimation strategy is developed to estimate the elapsed travel distance of the motorbike application and hence, the remaining range can be predicted. Then, daily riding cycles of the EM are identified and classified through machine learning technique based on the training and testing dataset of various standard ride cycles, which are combined with the proposed range estimation strategy to estimate the remaining travel distance of the motorcycle as the baseline to underpin and support the energy management system of the electric vehicle applications. The developed complete model is finally evaluated on a mixed daily riding cycles showing the effectiveness of the approach

Optimal control of bidirectional active clamp forward converter with synchronous rectifier based cell-to-external-storage active balancing system

Active cell balancing is a more energy-efficient way of balancing cells in series comparing to passive balancing. To further improve the performance of an active balancing system, an optimal control approach can be applied to optimise the system performance indexes. This paper proposes an optimal controller for the bidirectional active clamp forward converter with synchronous rectifier (ACFC-SR) based cell-to-external-storage active balancing system to concurrently optimise the balancing speed and the energy efficiency. The formulated optimization problem does not require the complicated nonlinear converter efficiency model compared to other optimal controllers that involve efficiency model to reduce energy loss. The flexibility in changing the balancing priority of the balancing time and the converter efficiency is achieved via different weights on the objective function. The effectiveness of the proposed controller is validated experimentally with real cells and the power electronics board

An advanced hardware-in-the-loop battery simulation platform for the experimental testing of battery management system

Extensive testing of a battery management system (BMS) on real battery storage system (BSS) requires lots of efforts in setting up and configuring the hardware as well as protecting the system from unpredictable faults during the test. To overcome this complexity, a hardware-in-the-loop (HIL) simulation tool is employed and integrated to the BMS test system. By using this tool, it allows to push the tested system up to the operational limits, where may incur potential faults or accidents, to examine all possible test cases within the simulation environment. In this paper, an advanced HIL-based virtual battery module (VBM), consists of one “live” cell connected in series with fifteen simulated cells, is introduced for the purposes of testing the BMS components. First, the complete cell model is built and validated using real world driving cycle while the HIL-based VBM is then exercised under an Urban Dynamometer Driving Schedule (UDDS) driving cycle to ensure it is fully working and ready for the BMS testing in real-time. Finally, commissioning of the whole system is performed to guarantee the stable operation of the system for the BMS evaluation

Development and real-time performance evaluation of energy management strategy for a dynamic positioning hybrid electric marine vessel

Hybridisation of energy sources in marine vessels has been recognized as one of the feasible solutions to improve fuel economy and achieve global emission reduction targets in the maritime sector. However, the overall performance of a hybrid vessel system is strongly dependent on the efficiency of the energy management system (EMS) that regulates the power-flow amongst the propulsion sources and the energy storage system (ESS). This study develops a simple but pro-duction-feasible and efficient EMS for a dynamic positioning (DP) hybrid electric marine vessel (HEMV) and real-time experimental evaluation within a hardware-in-the-loop (HIL) simulation environment. To support the development and evaluation, map-based performance models of HEMVs’ key components are developed. Control logics that underpin the EMS are then designed and verified. Real-time performance evaluation to assess the performance and applicability of the proposed EMS is conducted, showing the improvement over those of the conventional control strategies. The comparison using key performance indicators (KPIs) demonstrates that the pro-posed EMS could achieve up to 4.8% fuel saving per voyage, while the overall system performance remains unchanged as compared to that of the conventional vessel

State of power prediction for lithium-ion batteries in electric vehicles via Wavelet-Markov load analysis

Electric vehicle (EV) power demands come from its acceleration/braking as well as consumptions of the components. The power delivered to meet any demand is limited to the available power of the battery. This makes the battery state of available power (SoAP) a critical variable for battery management purposes. This paper presents a novel approach for long-term SoAP prediction by supervising the working conditions for prediction of future load. Firstly, a battery equivalent circuit model (ECM) coupled with a thermal model is established to accurately capture the battery dynamics. The battery model is then connected to an EV model in order to interpret the working conditions to battery power demand. By supervising the historical usage conditions, a long-term load prediction mechanism is designed based on wavelet analysis and Markov models. This facilitates the separation of low and high frequency load demands and addresses future uncertainties. Finally, the SoAP prediction is put forward along with a sensitivity analysis with respect to battery model and load prediction mechanism parameters. It is demonstrated that compared to the existing approaches for load and SoAP prediction, the developed method is more practical and accurate. Co-simulations via MATLAB and AMESim as well as experiments on a set of commercially available Lithium-ion (Li-ion) cylindrical cells under real-world drive cycles prove the given concept and validate the performance of the method

Model-based end of discharge temperature prediction for lithium-ion batteries

Battery fast charging is one of the key techniques that affects the public acceptability and commercialization of electric vehicles. Temperature is the critical barrier for fast charging as at low temperatures an increased risk of lithium plating and at high temperatures safety concerns limits the charging rate. To facilitate a fast charging mechanism, preconditioning the battery and maintaining its temperature is vital. Battery temperature prediction before a fast charging event can help reducing the energy consumption for battery preconditioning. In this paper, we propose a method for battery end of discharge temperature prediction for fast charging purposes. Firstly, a Gaussian mixture data clustering is performed on battery load data characterisation, subsequently a Markov model is trained for load prediction, and finally a battery lumped parameter equivalent circuit and thermal model is developed and employed for end of discharge time and ultimately end of discharge temperature prediction. Cylindrical lithium-ion battery is selected to prove the concept and both simulations and experiments show the capabilities of the proposed method for temperature prediction of batteries under load profiles obtained from real-world drive cycles of electric vehicles

Cruise control development and hardware-in-the-loop validation for electric motorcycles

In electric motorcycle applications, speed tracking and speed limiting are important features of the vehicle supervisory control (VSC) system that allows the rider to minimize the control effort and avoid over speeding when riding on highway. In this paper, a cruise control (CC) system is developed allowing the vehicles to follow any desired speed limits within their capability. The proposed system employs a classical Proportional-Integral (PI) controller, which enables the vehicle to match the desired cruise speed set when CC function is active. The developed CC system is verified and validated using Hardware-in-the-Loop (HiL) simulation testing. The HiL results demonstrate that the proposed CC logic function effectively, with a maximum percentage of error between the vehicle speed and the cruise reference speed is less than 7%