scholarly journals EmRep: Energy management relying on state‐of‐charge extrema prediction

2021 ◽  
Author(s):  
Lars Hanschke ◽  
Christian Renner
Keyword(s):  
Energy Management ◽  
State Of Charge

scholarly journals A Novel Deep Learning-Based State-of-Charge Estimation for Renewable Energy Management System in Hybrid Electric Vehicles

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 260
Author(s):  
Mahendiran T. Vellingiri ◽  
Ibrahim M. Mehedi ◽  
Thangam Palaniswamy
Keyword(s):  
Renewable Energy ◽  
Deep Learning ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management System ◽  
Hybrid Electric Vehicles ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Soc Estimation ◽  
Hybrid Electric

In recent years, alternative engine technologies are necessary to resolve the problems related to conventional vehicles. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are effective solutions to decarbonize the transportation sector. It also becomes important to shift from traditional houses to smart houses and from classical vehicles to EVs or HEVs. It is needed to combine renewable energy sources (RESs) such as solar photovoltaics, wind energy systems, and various forms of bio-energies. Among various HEV technologies, an effective battery management system (BMS) still remains a crucial issue that is majorly used for indicating the battery state of charge (SOC). Since over-charging and over-discharging result in inevitable impairment to the batteries, accurate SOC estimation desires to be presented by the BMS. Although several SOC estimation techniques exist to regulate the SOC of the battery cell, it is needed to improvise the SOC estimation performance on HEVs. In this view, this paper focuses on the design of a novel deep learning (DL) with SOC estimation model for secure renewable energy management (DLSOC-REM) technique for HEVs. The presented model employs a hybrid convolution neural network and long short-term memory (HCNN-LSTM) model for the accurate estimation of SOC. In order to improve the SOC estimation outcomes of the HCNN-LSTM model, the barnacles mating optimizer (BMO) is applied for the hyperpower tuning process. The utilization of the HCNN-LSTM model makes the modeling process easier and offers a precise depiction of the input–output relationship of the battery model. The design of BMO based HCNN-LSTM model for SOC estimation shows the novelty of the work. An extensive experimental analysis highlighted the supremacy of the proposed model over other existing methods in terms of different aspects.

Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles

2010 ◽  
Author(s):  
Simona Onori ◽  
Lorenzo Serrao ◽  
Giorgio Rizzoni
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Optimal Solution ◽  
State Of Charge ◽  
Management Problem ◽  
Equivalence Factor ◽  
Hybrid Electric ◽  
Equivalent Consumption Minimization Strategy ◽  
Adaptation Law

This paper proposes a new method for solving the energy management problem for hybrid electric vehicles (HEVs) based on the equivalent consumption minimization strategy (ECMS). After discussing the main features of ECMS, an adaptation law of the equivalence factor used by ECMS is presented, which, using feedback of state of charge, ensures optimality of the strategy proposed. The performance of the A-ECMS is shown in simulation and compared to the optimal solution obtained with dynamic programming.

scholarly journals Energy Management in PV Based Microgrids Designed for the Universidad Nacional de Colombia

2020 ◽  
Vol 12 (3) ◽  
pp. 1219 ◽  
Author(s):  
Luis Fernando Grisales-Noreña ◽  
Carlos Andrés Ramos-Paja ◽  
Daniel Gonzalez-Montoya ◽  
Gerardo Alcalá ◽  
Quetzalcoatl Hernandez-Escobedo
Keyword(s):  
Power Generation ◽  
Energy Storage ◽  
Energy Management ◽  
Management Strategy ◽  
State Of Charge ◽  
Photovoltaic System ◽  
Normal Operation ◽  
Energy Management Strategy ◽  
Dc Microgrid ◽  
Power Load

Stand-alone Electrical microgrids (MGs) require power management strategies to extend the life-time of their devices and to guarantee the global power balance of non-critical loads such as lighting of small sections of an university campus or individual air conditioning systems. This paper proposes an energy management strategy (EMS) for an isolated DC microgrid formed by a photovoltaic system (PVS), an energy storage system (battery), and a noncritical load. This configuration enables the photovoltaic system to control the power generation and ensures that the storage element does not exceed the safe limits of the state of charge. To control the generation of the photovoltaic system, two operating modes based on the perturb and observe (P&O) algorithm are implemented. The first one performs a maximum power point tracking (MPPT) action, while the second one regulates the power generated by the PVS to match the load requirement (power demand tracking, PDT). The management strategy also considers different operating states for ensuring the battery safety: normal operation, overcharge (at the maximum state of charge), and bulk charge (at the minimum state of charge); in those states the disconnection/connection of both the battery and the load is also considered. The main contribution of this work is to design and test a control strategy for an EMS aimed at regulating a standalone microgrid based on a PV system and an energy storage device. This solution is validated using detailed MG circuital simulations, which includes the PV source model (single-diode model), lithium-ion battery model, constant power load model and the DC/DC converters equations; moreover, realistic power generation and demand from Universidad Nacional de Colombia, located at Medellín-Colombia, are considered. The results obtained demonstrate the effectiveness of the energy management strategy, and in this way, enable to extend the battery lifetime and reduce the costs associated to the maintenance and disconnection of the microgrid in educational buildings or other applications focused on this type of DC microgrid.

scholarly journals Robust proportional ECMS control of a parallel hybrid electric vehicle

2016 ◽  
Vol 231 (1) ◽  
pp. 99-119 ◽  
Author(s):  
Wisdom Enang ◽  
Chris Bannister
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Driving Cycle ◽  
State Of Charge ◽  
Fuel Savings ◽  
Equivalence Factor ◽  
Battery State Of Charge ◽  
Hybrid Electric

Improved fuel efficiency in hybrid electric vehicles requires a delicate balance between the internal combustion engine usage and battery energy, using a carefully designed energy management control algorithm. Numerous energy management strategies for hybrid electric vehicles have been proposed in literature, with many of these centered on the equivalent consumption minimisation strategy (ECMS) owing to its potential for online implementation. The key challenge with the equivalent consumption minimisation strategy lies in estimating or adapting the equivalence factor in real-time so that reasonable fuel savings are achieved without over-depleting the battery state of charge at the end of the defined driving cycle. To address the challenge, this paper proposes a novel state of charge feedback ECMS controller which simultaneously optimises and selects the adaption factors (proportional controller gain and initial equivalence factor) as single parameters which can be applied in real time, over any driving cycle. Unlike other existing state of charge feedback methods, this approach solves a conflicting multiple-objective optimisation control problem, thus ensuring that the obtained adaptation factors are optimised for robustness, charge sustenance and fuel reduction. The potential of the proposed approach was thoroughly explored over a number of legislative and real-world driving cycles with varying vehicle power requirements. The results showed that, whilst achieving fuel savings in the range of 8.40 −19.68% depending on the cycle, final battery state of charge can be optimally controlled to within ±5% of the target battery state of charge.

scholarly journals Energy Management Strategy Implementation for Hybrid Electric Vehicles Using Genetic Algorithm Tuned Pontryagin’s Minimum Principle Controller

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Aishwarya Panday ◽  
Hari Om Bansal
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Management Strategy ◽  
Minimum Principle ◽  
Hybrid Vehicles ◽  
State Of Charge ◽  
Energy Management Strategy ◽  
Power Split ◽  
Hybrid Electric ◽  
State Of Charge Estimation

To reduce apace extraction of natural resources, to plummet the toxic emissions, and to increase the fuel economy for road transportation, hybrid vehicles are found to be promising. Hybrid vehicles use batteries and engine to propel the vehicle which minimizes dependence on liquid fuels. Battery is an important component of hybrid vehicles and is mainly characterized by its state of charge level. Here a modified state of charge estimation algorithm is applied, which includes not only coulomb counting but also open circuit voltage, weighting factor, and correction factor to track the run time state of charge efficiently. Further, presence of battery and engine together needs a prevailing power split scheme for their efficient utilization. In this paper, a fuel efficient energy management strategy for power-split hybrid electric vehicle using modified state of charge estimation method is developed. Here, the optimal values of various governing parameters are firstly computed with genetic algorithm and then fed to Pontryagin’s minimum principle to decide the threshold power at which engine is turned on. This process makes the proposed method robust and provides better chance to improve the fuel efficiency. Engine efficient operating region is identified to operate vehicle in efficient regions and reduce fuel consumption.

Optimality Assessment of Equivalent Consumption Minimization Strategy for PHEV Applications

2009 ◽  
Author(s):  
Pinak Tulpule ◽  
Stephanie Stockar ◽  
Vincenzo Marano ◽  
Giorgio Rizzoni
Keyword(s):  
Dynamic Programming ◽  
Energy Management ◽  
Fuel Economy ◽  
Laboratory Data ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Global Optimum ◽  
Driving Cycle ◽  
State Of Charge ◽  
Equivalent Consumption Minimization Strategy

This paper deals with optimization algorithms for energy management of Plug-in Hybrid Electric Vehicles (PHEVs). In order to maximize fuel economy of a PHEV, the battery should attain its lowest admissible state of charge at the end of the driving cycle by following an optimal State of Charge (SOC) profile. Finding this optimal profile is a challenging optimization problem and requires prior knowledge of the entire driving cycle. There are many different optimization methods that can be applied to the energy management of PHEVs and they are usually classified into two main categories according to the optimality of their solutions. In general, in order to obtain the global optimum, the complete knowledge of future driving conditions is needed. This requirement renders unfeasible the on-line implementation of such strategies. On the other hand, simpler algorithms which are on-board implementable, do not provide the optimal solution. In this paper, a global optimal strategy — Dynamic Programming, is considered as a benchmark for evaluating the performance of an onboard implementable strategy — Equivalent Consumption Minimization Strategy with linearly decreasing reference SOC’. The study is conducted on an energy-based model of a parallel hybrid powertrain developed in Matlab/Simulink environment. The model and each powertrain components are validated based on road tests and laboratory data for a Chevrolet Equinox (hybridized at The Ohio State University Center for Automotive Research). The optimality assessment considers two main metrics, namely fuel economy and deviations from the optimal SOC profile. Simulations are carried out by considering different driving scenarios and battery sizes. Results show that for longer distances and bigger batteries, Equivalent Consumption Minimization Strategy and Dynamic Programming provide similar fuel economy and SOC profiles.

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