Battery Energy Usage Research Articles

Ambient Temperature Effects on Energy Consumption and CO2 Emissions of a Plug-in Hybrid Electric Vehicle

The ambient temperature affects the operation of different powertrain systems, including electric, hybrid electric, and internal combustion engines. This study investigated the effect of the ambient temperature on the energy consumption and CO2 emissions of a plug-in hybrid electric vehicle running in different powertrain modes. The vehicle was driven for 4150 km following a selected route 199 times in different powertrain modes and in different ambient temperatures ranging from −24 °C to 32 °C. Instantaneous and cumulative fuel consumptions were measured using a fuel flow meter, and the battery energy usage was determined from the vehicle telematics during each test. The total energy consumption and total CO2 emissions were affected by the ambient temperature in all powertrain modes, including electric, hybrid electric (charge-depleting and charge-sustaining), and conventional internal combustion engine modes. The highest increase was associated with the charge-depleting hybrid electric mode, with 350% and 290% increases in energy consumption and CO2 emissions when the ambient temperature dropped from 29 °C to −24 °C. The conventional internal combustion engine mode was the least affected, with only 7% and 8% increased in energy consumption and CO2 emissions, respectively.

Realistic Home Energy Management System Considering the Life Cycle of Photovoltaic and Energy Storage Systems

Home Energy Management Systems (HEMSs) have become necessary due to energy security and climate change concerns. Scheduling the operating time of household appliances is one of the most effective strategies used by HEMSs to reduce electricity costs, with several studies proposing optimization strategies for scheduling home appliances to reduce the grid energy usage cost. This work considers energy usage costs from Renewable Energy Sources (RESs) and Energy Storage Systems (ESSs) in the appliance-scheduling strategy and energy flow management. The objectives are reducing the real electricity cost while maintaining a longer battery lifespan, reducing battery charging/discharging losses, and using PV power efficiently. To achieve this, we developed a pricing model of battery energy usage, in addition to modeling the PV energy usage cost based on the Levelized Cost of Energy (LCOE) for PV systems. PV-battery energy usage cost models were introduced into the optimization problem solved using the Augmented Grey Wolf Optimization (AGWO) and Particle Swarm Optimization (PSO) algorithms in MATLAB. We developed an efficient energy flow management algorithm. We collected real data from a home in Vigo, Spain, and simulated four scenarios. The results show that the proposed system using AGWO and PSO reduced the real cost by 25.87% and 25.98%, respectively. Compared with an existing energy-usage-pricing model, the AGWO reduced the energy losses by 40.429% and extended the battery lifespan by 68.282%. Similarly, the PSO reduced the energy losses by 45.540% and extended the battery lifespan by 84.56%. Moreover, the proposed system reached the breakeven point of the system in a shorter time.

Efficient Battery Usage in Wireless-Powered Cell-Free Systems With Self-Energy Recycling

This paper investigates wireless-powered cell-free systems, in which the users send their uplink data signal while simultaneously harvesting energy from network nodes and user terminals – including the transmitting user terminal itself – by performing self-energy recycling. In this rather general setting, a closed-form lower bound of the amount of harvested energy and the achieved signal-to-interference-plus-noise ratio expressions are derived. Then, to improve the energy efficiency, we formulate the problem of minimizing the users' battery energy usage while satisfying minimum data rate requirements. Due to the non-convexity of the problem, a novel alternating optimization algorithm is proposed, and its proof of convergence is provided. Finally, numerical results show that the proposed method is more efficient than a state-of-art algorithm in terms of battery energy usage and outage rate.

An ensemble learning velocity prediction-based energy management strategy for a plug-in hybrid electric vehicle considering driving pattern adaptive reference SOC

The fuel economy of a plug-in hybrid electric vehicle is largely dependent on the battery energy usage during various driving cycles. In this research, within the model predictive control (MPC) principle, an Ensemble Learning Velocity Prediction (ELVP)-based energy management strategy (EMS) considering the driving pattern Adaptive Reference State of Charge (AR-SOC) is proposed. Firstly, the existing methods including Markov chain (MC), back propagation (BP) and radial basis function (RBF) neural network (NN)-based velocity prediction models are described. Then, these models are embedded into MPC-based EMS respectively, and the validation results show that the NN performs better than the MC by comparing the prediction precision, computational cost, and resultant vehicular fuel economy. By incorporating these prior knowledges, a novel ensemble learning velocity prediction method is established by blending BP-NN and RBF-NN. Subsequently, based on the expected trip distance and the velocity prediction results, an adaptive reference SOC (AR-SOC) trajectory planning method is developed to direct the distribution of battery energy for different driving patterns. Combining with the ELVP and the AR-SOC, the MPC-based EMS derives the optimal torque-distribution decisions. Finally, the validation results indicate that the proposed strategy achieves superior fuel economy under various driving cycle compared with the benchmark strategies.

Optimization of Two-Way Relaying Networks with Battery-Assisted EH Relays

In this paper, we investigate the performance of a two-way relaying (TWR) network with a battery-assisted energy harvesting (EH) relay. The relay uses limited battery energy to augment the harvested energy and aid communication between the two source terminals. Considering a time-switching relaying (TSR) protocol at the EH relay, we investigate the performance of the TWR system in terms of outage, throughput, and ergodic rate. Assuming that the source with a better channel to the relay transfers energy in the EH phase, we investigate statistical and instantaneous channel state information (CSI)-based optimization of the TSR parameter to maximize throughput for a fixed amount of relay battery energy usage. We observe that the instantaneous CSI-based optimization results in higher throughput with less battery energy usage. Furthermore, we investigate the problem of minimization of battery energy consumption for target throughput using the statistical and instantaneous CSI-based optimization of the TSR parameter. We show that battery energy consumption can be drastically reduced by availability of instantaneous CSI. The derived insights are crucial from a designer point of view. Simulation results are presented to confirm accuracy of the derived analytical expressions.

Power capability evaluation for lithium iron phosphate batteries based on multi-parameter constraints estimation

The battery power capability is intimately correlated with the climbing, braking and accelerating performance of the electric vehicles. Accurate power capability prediction can not only guarantee the safety but also regulate driving behavior and optimize battery energy usage. However, the nonlinearity of the battery model is very complex especially for the lithium iron phosphate batteries. Besides, the hysteresis loop in the open-circuit voltage curve is easy to cause large error in model prediction. In this work, a multi-parameter constraints dynamic estimation method is proposed to predict the battery continuous period power capability. A high-fidelity battery model which considers the battery polarization and hysteresis phenomenon is presented to approximate the high nonlinearity of the lithium iron phosphate battery. Explicit analyses of power capability with multiple constraints are elaborated, specifically the state-of-energy is considered in power capability assessment. Furthermore, to solve the problem of nonlinear system state estimation, and suppress noise interference, the UKF based state observer is employed for power capability prediction. The performance of the proposed methodology is demonstrated by experiments under different dynamic characterization schedules. The charge and discharge power capabilities of the lithium iron phosphate batteries are quantitatively assessed under different time scales and temperatures.

Contour Data Acquisition System for Electric Vehicle Distance Estimation System

The conventional distance estimation system only uses a constant speed and a constant level of road elevation to estimate the driving range of a vehicle. This technique leads to low prediction accuracy. In this project, a new estimation technique namely the Contour Positioning System (CPS) for an electric vehicle is proposed. The CPS provides a more reliable estimation of the electric vehicle battery energy usage by taking into consideration additional factors such as road elevation and regenerative braking energy. In order to perform the CPS calculation, first and foremost, the elevation profile of the selected destination must be determined. In this paper, the automated technique to extract the elevation profile i.e. latitude, longitude and altitude was determined by using Google Maps and Google Earth. This procedure enables the user to obtain accurate road configuration to enable the driver to compute the amount of energy needed to reach the said destination.

Fuel consumption of fuel cell hybrid vehicles considering battery SOC differences

Fuel cell hybrid vehicles (FCHVs) have become one of the most promising candidates for future transportation due to current energy supply problem and environmental problem. Fuel economy is an important factor in FCHVs. In order to properly evaluate the fuel economy of an FCHV, the initial battery state of charge (SOC) and the final battery SOC have to be identical so that the effect of the battery energy usage on the fuel economy is neglected. In the simulation or in the real driving, however, the final battery SOC is usually different from the initial battery SOC, and the final battery SOC often depends on the power management strategy. To consider the difference between the two battery SOC values, the concept of equivalent fuel consumption is presented by two methods. One is based on the relationship between delta SOC and delta fuel consumption, and the other is based on the optimal control theory. Two rule-based power management strategies for an FCHV are presented, and for each strategy, the fuel economy is evaluated based on the two methods. The characteristics of the two methods are discussed and compared, and the superior one is selected based on the comparison.

Proton exchange membrane fuel cells cold startup global strategy for fuel cell plug-in hybrid electric vehicle

This paper investigates the Proton Exchange Membrane Fuel Cell (PEMFC) Cold Startup problem within the specific context of the Plugin Hybrid Electric Vehicles (PHEV). A global strategy which aims at providing an efficient method to minimize the energy consumption during the startup of a PEMFC is proposed. The overall control system is based on a supervisory architecture in which the Energy Management System (EMS) plays the role of the power flow supervisor. The EMS estimates in advance, the time to start the fuel cell (FC) based upon the battery energy usage during the trip. Given this estimation and the amount of additional energy required, the fuel cell temperature management strategy computes the most appropriate time to start heating the stack in order to reduce heat loss through the natural convection. As the cell temperature rises, the PEMFC is started and the reaction heat is used as a self-heating power source to further increase the stack temperature. A time optimal self-heating approach based on the Pontryagin minimum principle is proposed and tested. The experimental results have shown that the proposed approach is efficient and can be implemented in real-time on FC-PHEVs.