Management Of Electric Vehicles Research Articles

Experimental validation of a real-time energy management system using four frequency-based approaches

An innovative approach is introduced in this study for optimizing power distribution within a hybrid energy storage system, which encompasses batteries and supercapacitors. The method involves utilizing an adaptive wavelet technique within the energy management system dedicated to the EV. This technique customizes the wavelet decomposition level based on the supercapacitor's state of charge. As a result, optimal power sharing between the two real sources incorporated in a small-scale EV emulator is achieved. Notably, the approach adjusts the wavelet decomposition level during transient high peaks and when the supercapacitor has high reserves to alleviate battery workload. Through experimental validation and comparative analysis with three other frequency-based techniques, it is demonstrated that the adaptive wavelet control strategy significantly extends battery lifetime. This extension is achieved by reducing the root mean square (RMS) current by 34 % compared to fixed cut-off frequency approaches. Consequently, this minimizes losses and safeguards the battery from high peaks, resulting in a substantial decrease of 44.53 %. These results underscore the substantial potential of the adaptive wavelet approach as a suitable solution for effective energy management in hybrid electric vehicles.

Data-driven approaches to achieve energy transition: Statistical analysis of demand-side management and smart decision making in urban energy hub networks

Energy hubs (EHs) have emerged as a crucial solution to address resource scarcity and environmental concerns by integrating various technologies for energy production, conversion, and storage. These hubs encompass diverse elements such as integrated heat, electricity, and cooling systems, renewable energy sources, batteries, and thermal energy storage units (TES). This study focuses on a residential EH model that receives inputs in the form of electricity, gas, and solar energy to satisfy the household's electricity, heat, and cooling requirements. To enhance the operational flexibility of this energy hub, the study employs a Demand Response Program (DRP), which facilitates load shifting, load curtailment, and the modeling of flexible heating loads. The primary objective is to develop an efficient heat and power management system that optimally schedules household devices, generation equipment, and storage units. In pursuit of this goal, Long Short-Term Memory (LSTM) techniques have been selected for renewable energy, market price, and load forecasting, enhancing the system's realism and predictive accuracy. The Wild Goats algorithm is employed to solve an optimization problem for three distinct scenarios, aiming to minimize overall power costs while maintaining user comfort levels with regard to temperature and hot water availability. Furthermore, this study incorporates an environmentally conscious approach by considering the reduction of CO2, SO2, and NO pollution originating from consumers in a multi-objective optimization framework. The findings reveal that the implementation of DRP, intelligent Plug-in Hybrid Electric Vehicle (PHEV) management, TES technologies, and LSTM-based forecasting significantly contribute to reducing power costs within the proposed EH scheme. This research underscores the potential of data-driven strategies in advancing energy transition initiatives and fostering sustainable urban energy hubs while acknowledging the role of advanced forecasting techniques in enhancing system realism and efficiency.

Hybrid method based energy management of electric vehicles using battery-super capacitor energy storage

This paper presents a hybrid technique for managing the Energy Management of a hybrid Energy Storage System (HESS), like Battery, Supercapacitor (SC), and integrated charging in Electric Vehicle (EV). The proposed hybrid method combines the Namib Beetle Optimization (NBO) and Quantum Neural Networks (QNN) technique and is commonly known as the NBO-QNN approach. The proposed energy management technique reduces EV power use and maximizes battery life. QNN forecasts and combines power supply and charge levels to fulfill load needs. EV energy management uses NBO to regulate the output voltage, generate references, and regulate current continuously. Higher energy density battery and power density SC meet vehicle needs. An uncontrolled rectifier with a DC-to-DC buck converter balances charging and ensures energy transmission. The Proposed technique is implemented using the MATLAB platform, and its performance is compared to existing methods. HESS performance is evaluated by comparing it to existing systems. The research shows that the proposed strategy reduces the primary and secondary source stress, enhances performance of charging unit, and extends the life of battery. Furthermore, the NBO-QNN technique outperforms other existing methods, such as the Cooperation Search Algorithm (CSA), Latent Semantic Analysis (LSA), and Grasshopper Optimization Algorithm (GOA). The proposed method displays the best output in all existing Cooperation Search Algorithm (CSA), Latent Semantic Analysis (LSA), and Grasshopper Optimization Algorithm (GOA) methods. The result concludes that the NBO-QNN approach based on THD value is less than existing methods.

Performance analysis of AI-based energy management in electric vehicles: A case study on classic reinforcement learning

Reinforcement learning-based (RL-based) energy management strategy (EMS) is considered a promising solution for the energy management of electric vehicles with multiple power sources. Research and applications of reinforcement learning and deep reinforcement learning in energy management issues are emerging. However, previous studies have not systematically examined the essential elements of RL-based EMS. This paper presents a performance analysis of RL-based EMS in a Plug-in Hybrid Electric Vehicle (PHEV) and Fuel Cell Electric Vehicle (FCEV). The performance analysis is developed in four aspects: algorithm, perception and decision granularity, hyperparameters, and reward function. The results show that the Off-policy algorithm effectively develops a more fuel-efficient solution within the complete driving cycle compared with other algorithms. Improving the perception and decision granularity reduces the frequency of tabular-based policy updates but better balances battery power and fuel consumption. Setting a high initial SOC in training will effectively improve the performance of RL-based EMS. The construction of an equivalent energy loss reward function for RL-based EMS based on instantaneous State of Charge (SOC) variation should be approached with caution. This approach is highly sensitive to parameters and is more likely to lead to violations of SOC constraints. In contrast, an equivalent energy reward function based on overall SOC variation is a safer alternative.

Model-free reinforcement learning-based energy management for plug-in electric vehicles in a cooperative multi-agent home microgrid with consideration of travel behavior

The rise in popularity of plug-in electric vehicles (PEVs) and the increasing use of renewable energy sources (RESs) have paved the way for advanced energy management systems (EMSs) that optimize energy usage and distribution in home microgrids (H-MGs). This study focuses on the integration of PEVs in H-MGs using an EMS and analyzes its impact on electrical power grids (EPGs). We examine the effect of PEV charging and discharging patterns on H-MG energy flow, considering various scenarios such as high renewable energy generation, different levels of PEV penetration, and EPG connection status. Our findings indicate that an efficient EMS can significantly enhance H-MGs’ overall efficiency by intelligently scheduling PEV charging and discharging, maximizing the use of locally generated renewable energy, and reducing peak load on the EPG. We demonstrate that a cooperative multi-agent system EMS, driven by a robust continuous and real-time fuzzy Q-learning (FQL) method, can reduce electricity market prices by 15% by increasing the use of renewable energy generation by 25%. To fully realize the benefits of EMS, we address challenges such as reducing dependence on EPG by 30%, improving battery state by 12%, and ensuring EPG stability in the face of uncertainties.

FlexNet: A warm start method for deep reinforcement learning in hybrid electric vehicle energy management applications

Deep reinforcement learning (DRL) has been widely studied in the energy management of hybrid electric vehicles (HEVs) for its remarkable energy efficiency improvement compared to conventional methods. However, how to alleviate the time consumption of training a stable reinforcement learning agent still needs to be solved in real-world implementation. This study presents a human expert knowledge encoded ‘warm start’ method with the flexibility to change the neural network architecture. The expert knowledge is encoded in a decision tree which then initializes the weights and bias of the DRL neural network. Compared with another fixed architecture warm start method, the proposed FlexNet exhibits improved learning speed by 60.8 % and 88.8 % in action space 50 and 100, respectively. The energy consumption by the proposed FlexNet EMS method is 12.2 % and 6.4 % better than rule-based and equivalent consumption minimization strategy, respectively. This proposed warm start method can reduce learning time and increase energy efficiency in various energy management applications.

A hybrid-aided approach with adaptive state update for estimating the state-of-charge of LiFePO4 batteries considering temperature uncertainties

Developing an accurate state of charge (SOC) estimation method is crucial for proper monitoring and management of electric vehicles (EVs). As deep learning methods advance, it is critical to design a network structure for SOC estimation that is accurate, flexible, and adaptable to different driving conditions. However, these methods are hindered by high computational costs and trial-and-error training approaches, which compromise their performance. Therefore, this paper proposes a variational epoch selector for a multi-layered long short-term memory (LSTM) network with an adaptive weighted extended Kalman filter (AWEKF) for SOC estimation of lithium-ion batteries. First, a random weight (RW) algorithm is proposed to variably select the required number of training epochs suitable to train the LSTM network for SOC estimation using three domain knowledge, which improves stability, accuracy, robustness against uncertainties, etc., with a significant reduction in model computational training costs and errors. Second, the AWEKF with feedback correction ability is proposed to optimize the estimations of the network to map the nonlinear characteristics and minimize the output SOC fluctuations and errors. The estimations critically investigate the various key factors for the proposed RWLSTM strategy at different temperatures under real-world simulated driving cycles using a lithium iron phosphate battery. Finally, the results show that the mean absolute error and root mean square error of the proposed RWLSTM and RWLSTM-AWEKF strategies are <0.6% and 0.2%, respectively, under various driving conditions, showing their efficiency in estimating the SOC by utilizing battery domain knowledge for BMS applications.

Artificial Neural Network Modeling to Predict Thermal and Electrical Performances of Batteries with Direct Oil Cooling

The limitations of existing commercial indirect liquid cooling have drawn attention to direct liquid cooling for battery thermal management in next-generation electric vehicles. To commercialize direct liquid cooling for battery thermal management, an extensive database reflecting performance and operating parameters needs to be established. The development of prediction models could generate this reference database to design an effective cooling system with the least experimental effort. In the present work, artificial neural network (ANN) modeling is demonstrated to predict the thermal and electrical performances of batteries with direct oil cooling based on various operating conditions. The experiments are conducted on an 18650 battery module with direct oil cooling to generate the learning data for the development of neural network models. The neural network models are developed considering oil temperature, oil flow rate, and discharge rate as the input operating conditions and maximum temperature, temperature difference, heat transfer coefficient, and voltage as the output thermal and electrical performances. The proposed neural network models comprise two algorithms, the Levenberg–Marquardt (LM) training variant with the Tangential-Sigmoidal (Tan-Sig) transfer function and that with the Logarithmic-Sigmoidal (Log-Sig) transfer function. The ANN_LM-Tan algorithm with a structure of 3-10-10-4 shows accurate prediction of thermal and electrical performances under all operating conditions compared to the ANN_LM-Log algorithm with the same structure. The maximum prediction errors for the ANN_LM-Tan and ANN_LM-Log algorithms are restricted within ±0.97% and ±4.81%, respectively, considering all input and output parameters. The ANN_LM-Tan algorithm is suggested to accurately predict the thermal and electrical performances of batteries with direct oil cooling based on a maximum determination coefficient (R2) and variance coefficient (COV) of 0.99 and 1.65, respectively.

Hybrid storage system management for hybrid electric vehicles under real operating conditions

This study proposes the use and management of hybrid storage systems to power hybrid electric vehicles with the aim of reducing the negative effects of high current values on battery cycling life. Findings derive from a case study on a commercial plug-in hybrid electric vehicle with battery pack operations supported by a Lithium-ion Capacitor module. Specific experimental laboratory activities focused on the parametrization and validation of an equivalent circuit model representing the behavior of Lithium-ion capacitors for integration into the overall vehicle model (designed calibrated and validated on the basis of data from on-board data acquisition and measurement systems). The simulation platform was used to test various energy management strategies for the hybrid storage system supplying the vehicle during real driving cycles characterized by different operating conditions and driving styles. Results highlight the benefits of using the considered management strategies in terms of battery durability, allowing performance comparison for different driving routes and styles.

Stochastic management of electric vehicles in an intelligent parking lot in the presence of hydrogen storage system and renewable resources

The paper emphasizes the significance of sustainable energy solutions centered around electric vehicles (EVs). This involves Electric Intelligent Parking Lots (IPLs) that are interconnected with Renewable Energy Sources (RES) and Hydrogen Storage Systems (HSS) to achieve both technical and environmental goals. The study introduces a probabilistic approach to managing HSS-based energy in IPLs, employing the Probabilistic Two-Point Estimate Method (TPEM) to consider variables like sun radiation, temperature, wind speed, and IPL load. The Honey Badger Algorithm (HBA) is employed for optimization, striving to minimize operational costs related to energy distribution while maximizing profits by exploring revenue opportunities within the IPL system. This strategy enhances energy management and intelligent EV charging in IPLs, improving efficiency and economic viability. The approach is tested in a standard IPL environment integrated with RES and load demand, showcasing its successful technical and financial outcomes. Results reveal a 7.8 % profit increase from renewable resources and an additional 4.89 % when uncertainties are factored in. Ultimately, the proposed energy management framework utilizing HSS and RES integration proves effective in achieving objectives, with the potential for greater profitability through renewable resource incorporation and uncertainty consideration.

Blockchain-based applications and energy effective electric vehicle charging – A systematic literature review, challenges, comparative analysis and opportunities

Blockchain is a secure distributed system having a shared resource that enables participants to read and update a common shared ledger in a decentralized fashion. It provides an immutable and tamper-resistant distributed ledger with no central authority and no single point of failure. Consensus protocols are the backbone of any blockchain system as these protocols enable participant nodes to reach an agreement in a trustless environment. The performance of a typical blockchain system largely depends upon selecting the best-fit consensus protocol. Blockchain has influenced many application areas apart from crypto currencies such as document verification, copyrighting of art and craft, verification of counterfeit products and electric vehicle (EV) charging. This review paper discusses various consensus mechanisms in public, private, and consortium-based blockchains. The review study also covers the impact of consensus protocols on the performance scalability of the blockchain solution. The blockchain application area focused in this study is related to electric vehicle management and charging. The paper elaborates on how blockchain is enabling energy-efficient and secure EV charging. It also covers energy trading in a typical EV environment.

Optimizing Low Pass Filter Cut-off Frequency for Energy Management in Electric Vehicles with Hybrid Energy Storage Systems

Optimizing Low Pass Filter Cut-off Frequency for Energy Management in Electric Vehicles with Hybrid Energy Storage Systems

Model-based estimation of state of charge and state of power of a lithium ion battery pack and their effects on energy management in hybrid electric vehicles

Model-based estimation of state of charge and state of power of a lithium ion battery pack and their effects on energy management in hybrid electric vehicles

Optimal battery thermal management for electric vehicles with battery degradation minimization

The control of a battery thermal management system (BTMS) is essential for the thermal safety, energy efficiency, and durability of electric vehicles (EVs) in hot weather. To address the battery cooling optimization problem, this paper utilizes dynamic programming (DP) to develop an online rule-based control strategy. Firstly, an electrical–thermal-aging model of the LiFePO4 battery pack is established. A control-oriented onboard BTMS model is proposed and verified under different speed profiles and temperatures. Then in the DP framework, a cost function consisting of battery aging cost and cooling-induced electricity cost is minimized to obtain the optimal compressor power. By exacting three rules ”fast cooling, slow cooling, and temperature-maintaining” from the DP result, a near-optimal rule-based cooling strategy, which uses as much regenerative energy as possible to cool the battery pack, is proposed for online execution. Simulation results show that the proposed online strategy can dramatically improve the driving economy and reduce battery degradation under diverse operation conditions, achieving less than a 2.18% difference in battery loss compared to the offline DP. Recommendations regarding battery cooling under different real-world cases are finally provided.

IoT based battery energy monitoring and management for electric vehicles with improved converter efficiency.

Given the recent trends in the MPPT converters in PV systems, which have been researched extensively to improve design, modified closed-loop converter technology based on SoC is presented here. This paper aims to provide detailed information on the modern-day solar Maximum Power Point Tracking (MPPT) controller and Battery Management System (BMS). Most MPPT controller examination researched in the past is suitable only for fixed-rated battery capacity, which limits the converter capability and applications. The proposed paper uses the distributed energy management control technique to dispatch multi-battery charging based on the State of Charge (SoC). The converter construction is modified here as per the prerequisite of the model. The system hardware is developed and tested using Atmega2560 low power RISC based high-performance microcontroller. The batteries' SoC level and State of Health (SoH) are calculated using embedded sensors and communication platforms through the IoT platform and Global System Monitoring (GSM) technology. The GSM and IoT technology ensure that the different batteries are monitored periodically, and any irregularities are immediately addressed through the distributed energy management control technique. This ensures a safe, reliable, and effective charging of multiple batteries with increased accuracy, thereby maximizing battery life and reducing operational costs.

Surrogate-based optimization of the attack and inclination angles of a delta winglet pair vortex generator in turbulent channel flow

Vortex generators (VGs) have been extensively studied and utilized in multifunctional heat exchangers/reactors due to their remarkable ability to create coherent flow structures and streamwise vortices. These characteristics significantly enhance the mixing and heat transfer performance in laminar and turbulent flow regimes. With the advancement of computational power, VG shape optimization has become feasible, leading to the discovery of various optimized shapes in the literature for different Reynolds numbers. In this paper, the response surface polynomial method is employed as a surrogate model to optimize the angle of attack and inclination angle of a delta winglet VG at a Reynolds number of 4,600. The potential applications of these optimized designs are diverse, particularly in scenarios that require cooling or heating. Some examples include photovoltaic (PV) panel cooling, waste heat recovery in hybrid PV/solar water heating systems, energy-efficient HVAC systems, and thermal management in electric vehicles. To achieve this objective, Computational Fluid Dynamics (CFD) simulations are conducted to compute the thermal performance factor of each VG design at various values of attack and inclination angles. By enhancing mixing and heat transfer capabilities, this design can have a substantial impact on various renewable energy applications, contributing to a greener and more sustainable future. The highest thermal enhancement factor of 1.15 is found to be achieved for an inclination angle of −45° and an angle of attack of 35°. This means that optimized VG enhances heat transfer by 15% compared to empty channel flow, while maintaining the same pumping power.

Nonlinear autoregressive models for high accuracy early prediction of Li-ion battery end-of-life

Predictions of the state-of-health (SOH) of Li-ion batteries is an important goal in the monitoring and management of electric vehicles. In recent years, a number of pure machine-learning methods have been proposed for such predictions. In this paper, we instead consider autoregression methods and embedding strategies, which are specifically tailored to time-series problems. For the first time, we comprehensively compare both linear and nonlinear approaches, including six deep learning architectures, autoregressive integrated moving average (ARIMA) models and seasonal ARIMA models. In particular, for the first time we introduce Gaussian process nonlinear autoregression (GPNAR) for SOH prediction and show that it is superior in terms of accuracy and computational costs to the other autoregressive approaches. On the basis of two different datasets, we also demonstrate that accurate early predictions of the end-of-life (based on 50% of the data) is achievable with GPNAR without the use of features, which keeps data acquisition and processing to a minimum. Finally, we show that GPNAR is capable of capturing seasonal trends such as regeneration without additional time-consuming data analyses. Comparisons to other state-of-the-art methods in the recent literature confirm the superior performance of GPNAR.

Lithium-ion battery thermal management for electric vehicles using phase change material: A review

Lithium-ion (Li-ion) batteries in electric vehicles (EVs) present a promising solution to energy and environmental challenges. These batteries offer numerous advantages, including high energy density, endurance, minimum self-discharge, and long life, accelerating their adoption in EVs. High temperatures can lead to thermal runaways, causing safety hazards such as short circuits and explosions. Conversely, low temperatures can trigger the formation of lithium dendrites, resulting in failures and operational issues. To address these concerns, phase change materials (PCM) are being explored to store and release thermal energy without significant temperature changes. This review paper presents an overview of PCM for battery thermal management systems. It examines and compares thermal management strategies employed for Li-ion batteries, highlighting their merits, drawbacks, and cost-effectiveness. Different types of heating and cooling mechanism are summarized. Furthermore, the study discusses potential future developments in the field to enhance the thermal management of Li-ion batteries in EVs.

An improved A-ECMS energy management for plug-in hybrid electric vehicles considering transient characteristics of engine

The plug-in hybrid electric vehicles (PHEVs) are playing an increasingly important role in urban public transportation systems for their unique potential for energy saving and emission reduction. However, as an enabling technology for the cost-efficient operation of PHEVs, the current energy management strategies (EMSs) rarely consider the transient characteristics of the engine, especially the limit of engine transient performance and the extra fuel consumption due to engine state changes. To improve the energy-saving effect of EMSs, an optimization study on energy management considering engine transient characteristics for PHEV is carried out in this paper. Firstly, a high-precision transient fuel consumption model is established based on artificial neural network (ANN) to accurately evaluate the real fuel consumption of engine under steady and unsteady states. Secondly, an adaptive equivalent consumption minimization strategy (A-ECMS) is constructed for PHEV, and the engine transient performance boundary is defined in the strategy to avoid unreasonable engine power surge decision. Thirdly, the transient fuel consumption model is incorporated into the equivalent fuel consumption cost function of A-ECMS to fully consider the impact of engine transient fuel consumption on the real-time power allocation of PHEV. The results show that the improved strategy weakens the state fluctuation of the engine, and makes the engine run more smoothly, resulting in a 99.16% reduction in the extra fuel consumption due to engine state changes. Finally, the fuel economy of the PHEV under the combined driving cycle based on the C-WTVC improved by 3.37%.

An optimal approach to DC multi-microgrid energy management in electric vehicles (EV)

In micro-grids, energy management is described as an information and control system that assures that both the generating and distribution systems deliver electricity at the lowest operating costs. Renewable energy sources (RESs), including electric vehicles (EVs), can be successfully used and carbon emissions reduced by establishing a DC multi-microgrid system (MMGS), which includes renewable energy sources (RESs) and the distribution network. A Multi-Microgrid based Energy Management (MM-GEM) system is suggested to increase the economics of MMGS and minimize the distribution network's network loss. MMG is a network of dispersed generators, energy storage, and adjustable loads in a distribution system that is linked. Furthermore, its operation is deconstructed to reduce communication and control costs with the decentralized structure. “Aside from enhancing system resilience, the MMGEMS substantially impacts energy efficiency, power quality, and dependability". Typical MMGEMS functionality and architecture are shown in detail. This is followed by examining current and developing technologies for monitoring and interacting with data among the MMG clusters. In addition, a wide range of MMG energy planning and control systems for interactive energy trading, multi-energy management, and resilient operations are fully examined and researched. The economic effect of the EVs’ energy transfer over time and place is examined.