Linear programming MPC optimal V2G scheduling of electric vehicles considering battery degradation

<div class="section abstract"><div class="htmlview paragraph">Electric vehicles (EVs) are becoming increasingly recognized as an effective solution in the battle against climate change and reducing greenhouse gas emissions. Lithium-ion batteries have become the standard for energy storage in the automobile industry, widely used in EVs due to their superior characteristics compared to other batteries. The growing popularity of the Vehicle-to-grid (V2G) concept can be attributed to its surplus energy storage capacity, positive environmental impact, and the reliability and stability of the power grid. However, the increased utilization of the battery through these integrations can result in faster degradation and the need for replacement. As batteries are one of the most expensive components of EVs, the decision to deploy an EV in V2G operations may be uncertain due to the concerns of battery degradation from the owner’s perspective. This paper examines the degradation of the battery employed in Plug-in Hybrid Electric Vehicles (PHEVs) for both V2G connection and its typical operating schedule. For assessing the degradation in driving scenarios, the US06 drive cycle is employed. On the other hand, for the V2G scenario, a 10 kW bidirectional charger is utilized. The charger discharges the battery up to 20 kWh in 2 hours or up to 60% state of charge (SoC) and subsequently charges it back to 90% SoC at a constant 1C rate. This V2G setup simulates the discharging and charging patterns typically observed in real-world scenarios and allows for evaluating battery performance and degradation under such conditions. Finally, an economic analysis is conducted by considering the capacity loss of the battery resulting from the V2G connection. This study considers the incentives obtained through the V2G connection, providing an assessment of the economic viability and potential benefits associated with utilizing the vehicle in V2G applications.</div></div>

Electric vehicle (EV) battery swapping stations (BSWSs) are an important aid in rapid transport electrification, especially in developing countries where per capita income is low and expensive battery prices discourage EV penetration. Consequently, a BSWS model, where the BSWS owns the battery, can help in EV penetration and rapid transport electrification. This article presents a new EV BSWS model to obtain a suitable tradeoff between the swapping time and the quality of battery health indicators. The state of charge (SOC), battery abuse, and battery degradation are measured/estimated online, while the state of health (SOH) is determined offline. The cost of the swapping cycle is determined by considering multiple cost components and penalties. The modeling approach not only enables determining a fair and affordable price for each swapping cycle for EVs but also helps keep a check on the battery's SOH and remaining useful life (RUL) during swapping without significantly increasing the battery swapping times compared with the average refueling time of internal combustion engine (ICE) vehicles. The swapping cost of the BSWS model is also compared with home and commercial charging station (HCS and CCS) models under different assumptions. The analysis suggests that the battery can be swapped in 6 min for light vehicles, comparable to the complete tank refueling of ICE vehicles. Moreover, with appropriate consideration of EV opportunity profit (EVOP), the BSWS remains feasible compared with HCS and CCS even at a high-profit margin of 750%. Also, battery degradation is considered using the economic model's degradation cost component, and its link with different SOC strategies is explored. Again, the results suggest that SOC strategies can be a helpful way for BSWS to maximize battery life and increase its profits. Moreover, considering EVOP along with battery degradation and SOC strategies further highlights the feasibility of BSWS.

Problem. This article addresses the challenge of enhancing the environmental friendliness and energy efficiency of vehicles. It does so by conducting a comparative analysis and identifying ways to improve the electrical models of lithium-ion batteries used in electric vehicles. The study includes an examination of well-known electrical models of lithium-ion rechargeable batteries, such as the Rint model, the RC model, the Thevenin model, and the PNGV model. It identifies key characteristics of lithium-ion batteries in electric vehicles, including state of charge, mass, actual voltage, energy required for recharging, among others. The study also explores models of battery degradation, focusing on capacity reduction and the increase in active resistance. It substantiates directions for improving electrical models of lithium-ion batteries in electric vehicles by considering changes in capacity, internal resistance, polarization resistance, and both calendar and cyclic degradation. Goal. The aim of this work is to enhance the environmental friendliness and energy efficiency of vehicles through a comparative analysis and by determining ways to improve the electrical models of lithium-ion batteries in electric vehicles. Methodology. Our approach to achieving this goal involves using electrical models of lithium-ion batteries in electric vehicles, which describe various parameters such as state of charge, actual voltage during charge/discharge processes, and energy required for recharging. The study encompasses an investigation into the degradation of electric vehicle batteries, including their use in Vehicle to Grid (V2G) technology. Results. The analysis of electrical models of lithium-ion batteries in electric vehicles, aiming to increase their accuracy, considers the following aspects: changes in internal resistance and polarization resistance; capacity variation; and battery degradation. The change in internal resistance and polarization resistance should be considered based on two factors: the state of charge of the battery and the degree of its degradation. While the first factor is relevant primarily when the battery is deeply discharged (SoC<30%), the second factor must be considered at any state of charge. Capacity changes should be accounted for based on calendar and cyclic degradation. It has been determined that the primary causes of degradation in electric vehicle batteries are calendar aging (service life) and aging due to charge/discharge cycles. Contrarily, it is argued that using Vehicle to Grid (V2G) technology can reduce battery degradation by 10%. Originality. The results of this study provide a comprehensive understanding of the electrical models of lithium-ion batteries in electric vehicles and contribute to the improvement of existing models. Practical value. This research enhances the accuracy of current electrical models of lithium-ion batteries in electric vehicles by considering the variable nature of internal resistance and capacity during vehicle operation. It may be valuable in assessing the residual parameters of electric vehicle batteries during their secondary use, such as in the residential sector for solar energy support. The findings can be recommended to scientific and technical professionals involved in developing energy storage systems for electric vehicles.

With the rapid growth of electricity demand in industrial parks and the increasing penetration of renewable energy, vehicle-to-grid (V2G) technology has become an important enabler for mitigating grid stress while improving charging economy. This paper proposes a multi-objective rolling linear-programming-model-based predictive control (LP-MPC) method for coordinated electric vehicle (EV) scheduling in industrial park microgrids. The model explicitly considers transformer capacity limits, EV state-of-charge (SOC) dynamics, bidirectional charging/discharging constraints, and photovoltaic (PV) generation uncertainty. By solving a linear programming problem in a receding horizon framework, the approach simultaneously achieves load peak shaving, valley filling, and EV revenue maximization with real-time feasibility. A simulation study involving 300 EVs, 100 kW PV, and a 1000 kW transformer over 24 h with 5-min intervals demonstrates that the proposed LP-MPC outperforms greedy and heuristic load-leveling strategies in peak load reduction, load variance minimization, and charging cost savings while meeting all SOC terminal requirements. These results validate the effectiveness, robustness, and economic benefits of the proposed method for V2G-enabled industrial park microgrids.

Aggressive charging practices and extreme usage patterns of electric vehicle batteries pose safety risks, including fire hazards. Beyond fast charging, battery health is influenced by dynamic factors like temperature, state-of-charge (SoC) window of operation, and driving conditions relative to charging events1,2. Given that electric vehicles (EVs) rarely operate in their full SoC range (0–100%)3, understanding battery cell degradation across different SoC swing ranges and fast-charging effects becomes critical. This understanding not only optimizes battery usage and extends cell lifespan but also enhances lifetime economics and mitigates environmental consequences tied to raw material extraction and manufacturing2.This study explores the impact of SoC swing ranges and fast charging on the performance and aging of commercially available 18650 cells equipped with graphite-NCA electrodes. Departing from the conventional constant-current constant-voltage (CC-CV) fast charging protocol, we adopted a scaled-down version of the 150-kW real-world battery electric vehicle (BEV) fast charging profile. Unlike the broader SoC range of high current charging typical in CC-CV protocol, the real-world BEV fast-charging profile charges the battery at peak power or peak current only in a narrow SoC range between 10% and 40% SoC and then tapers down. Cycling experiments were conducted for two sets of SoC ranges: 0%-50% and 0%-100%. Both cycling experiments exhibited similar trends in overall capacity fade, yet the underlying causes differed. The DV-IC analysis revealed that the loss of active negative electrode material (LAMNE) was pivotal in capacity fade for both cycling experiments. Notably, during 0%-100% SoC cycling, loss of positive electrode material was observed alongside LAMNE. Additionally, variations in the homogeneity of lithium distribution within the negative electrode and kinetic rate degradation of the negative electrode were evident.This research underscores the critical role of SoC swing ranges and fast charging protocols in battery degradation, with differential voltage and incremental capacity analysis revealing distinct degradation pathways. It highlights the need for optimized battery usage strategies to extend cell lifespan, enhance lifetime economics, and mitigate environmental impact, thereby contributing to the sustainable growth of the EV market.

As the penetration of electric vehicles (EVs) increases, an understanding of EV operation characteristics becomes crucial in various aspects, e.g., grid stability and battery degradation. This can be achieved through analyzing large amounts of EV operation data; however, the variability in EV data according to the user complicates unified data analysis and identification of representative patterns. In this research, a framework that captures EV charging characteristics in terms of charge–discharge area is proposed using actual field data. In order to illustrate EV operation characteristics in a unified format, an individual EV operation profile is modeled by the probability distribution of the charging start and end states of charge (SoCs).Then, hierarchical clustering analysis is employed to derive representative charging profiles. Using large amounts of real-world, vehicle-specific EV data in South Korea, the analysis results reveal that EV charging characteristics in terms of the battery charge–discharge area can be summarized into seven representative profiles.

State of charge (SOC) estimation in lithium-ion batteries (LIBs) is a crucial task of the battery management system (BMS) in electric vehicle (EV) applications. An accurate evaluation of the remaining capacity in a battery is cumbersome due to the non-linear and coupled behavior of the physical processes involved in the battery operation, in particular when LIBs is integrated in dynamic systems with many components such as in EV. With the continued progress in the development of data driven models, different machine learning (ML) and, more specifically, deep learning (DL) techniques have been proposed to predict the SOC in EV.1–4 While these models have been proven to be effective in estimating the SOC variation, the reliability of learning-based algorithms is highly dependent on the data collected for the training and validation purposes. The most straightforward methodology for data collection consists of discharging a lithium-ion cell in a laboratory by applying a discharge current which aims to represent the vehicle’s operation. However, such an approach could not allow to generate data representing realistic driving conditions since the dynamics of components, such as the electric motor and the powertrain system, which have an impact on the SOC and external conditions such as the speed of the wind are not taken into account. In this work, we propose a modeling framework based upon Matlab/Simulink automotive simulations of EV in order to generate a dataset reflecting practical driving conditions. The generated datasets have been used to train ML and DL models for the final SOC estimation. In particular, the most accurate estimation of SOC is obtained with the Long Short Term Memory (LSTM) network, a Recurrent Neural Network (RNN) designed for times series predictions with long term dependencies. Furthermore, employing a multi-physics modeling of the LIBs’ operation comprising the electric motor, the powertrain system and the overall vehicle dynamic, allows to investigate the effect of EV’s driving on the electrochemical processes and reactions occurring inside the battery of different chemistries. For this purpose, we use an electrochemical model of LIBs developed in Comsol Multiphysics combined with EV simulation, in order to study the degradation of the battery as a result of multiple charge and discharge cycles representing the vehicle operation. In particular, formation/decomposition of the solid electrolyte interphase (SEI) at the negative electrode has been considered as a possible degradation phenomenon. Thus, in addition to the development of training data for learning based techniques for SOC estimation informed by EV simulations, the proposed modeling approach allows the investigation of the battery functionality and degradation under realistic driving conditions.(1) Álvarez Antón, J. C.; García Nieto, P. J.; de Cos Juez, F. J.; Sánchez Lasheras, F.; González Vega, M.; Roqueñí Gutiérrez, M. N. Battery State-of-Charge Estimator Using the SVM Technique. Appl. Math. Model. 2013, 37 (9), 6244–6253. https://doi.org/10.1016/j.apm.2013.01.024.(2) Hu, C.; Jain, G.; Schmidt, C.; Strief, C.; Sullivan, M. Online Estimation of Lithium-Ion Battery Capacity Using Sparse Bayesian Learning. J. Power Sources 2015, 289, 105–113. https://doi.org/10.1016/j.jpowsour.2015.04.166.(3) Kang, L. W.; Zhao, X.; Ma, J. A New Neural Network Model for the State-of-Charge Estimation in the Battery Degradation Process. Appl. Energy 2014, 121, 20–27. https://doi.org/10.1016/j.apenergy.2014.01.066.(4) Chemali, E.; Kollmeyer, P. J.; Preindl, M.; Ahmed, R.; Emadi, A. Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-Ion Batteries. IEEE Trans. Ind. Electron. 2018, 65 (8), 6730–6739. https://doi.org/10.1109/TIE.2017.2787586.

<div class="section abstract"><div class="htmlview paragraph">In recent years, the automotive industry has been looking into alternatives for conventional vehicles to promote a sustainable transportation future having a lesser carbon footprint. Electric Vehicles (EV) are a promising choice as they produce zero tail pipe emissions. However, even with the demand for EVs increasing, the charging infrastructure is still a concern, which leads to range anxiety. This necessitates the judicious use of battery charge and reduce the energy wastage occurring at any point. In EVs, regenerative braking is an additional option which helps in recuperating the battery energy during vehicle deceleration. The amount of energy recuperated mainly depends on the current State of Charge (SoC) of the battery and the battery temperature. Typically, the amount of recuperable energy reduces as the current SoC moves closer to 100%. Once this limit is reached, the excess energy available for recuperation is discharged through the brake resistor/pads. This paper proposes a method to minimize the energy wastage due to the SoC constraints by predicting an optimal start SoC. The optimal SoC is calculated in such a way that it maximizes energy recovery during regeneration while taking the route attributes, weather conditions, and charger availability into account. On a hilly route, it was noticed that the recuperated energy was 5 times more while using the optimal SoC, compared to the 100% start SoC. This reduction in SoC prevents overcharging and contributes to lesser charging time. Consequently, this approach would positively impact overall battery health, energy efficiency, and contribute to promoting sustainability.</div></div>

Electric vehicles (EVs) have been receiving greater attention as a tool for frequency control due to their fast regulation capability. The proliferation of EVs for primary frequency regulation is hampered by the need to simultaneously maintain industrial microgrids dispatch and EV state of charge levels. The current research aims to examine the operative and dominating role of the charging station operator, along with a vehicle to grid strategy; where, indeterminate tasks are executed in the microgrid without the EVs charging/discharging statistics. The role of the charging station operator in regulation is the assignment of the job inside the primary frequency control capacity of electric vehicles. Real-time rectification of programmed vehicle to grid (V2G) power ensures electric vehicles’ state of charge at the desired levels. The proposed V2G strategy for primary frequency control is validated through the application of a two-area interconnected industrial micro-grid and another microgrids with renewable resources. Regulation specifications are communicated to electric vehicles and charging station operators through an electric vehicle aggregator in the proposed strategy. At the charging station operator, V2G power at the present time is utilized for frequency regulation capacity calculation. Subsequently, the V2G power is dispatched in light of the charging demand and the frequency regulation. Furthermore, V2G control strategies for distribution of regulation requirement to individual EVs are also developed. In summary, the article presents a novel primary frequency control through V2G strategy in an industrial microgrid, involving effective coordination of the charging station operator, EV aggregator, and EV operator.

Battery degradation, a gradual loss of usable capacity over time, is one of the major hurdles for widespread adoption of electric vehicles (EVs). We introduce delayed full-charging (DFC) algorithm to mitigate degradation and extend the lifetime of EV batteries in battery management systems (BMS). When the EV is plugged in, the DFC algorithm charges batteries up to approximately 80% state of charge (SOC) and delays full charging until the predicted unplug time (tunplug). This approach significantly reduces the time batteries remain fully charged (t100%), thereby mitigating degradation while ensuring charging time for EV users to utilize the full battery capacity. For predicting tunplug, we propose a novel methodology that uses digital phenotyping to predict departure times. This method leverages smartphone data to capture irregular but predictable departure patterns by reflecting relevant behavioral and environmental contexts. A case study with 48 participants was conducted to empirically evaluate the departure time prediction performance using tree-based ensemble models trained on smartphone data, compared to a baseline Long Short-Term Memory (LSTM) model trained on historical data. Results reveal that models utilizing mobile passive features achieved a Mean Absolute Error (MAE) as low as 2.18 hours on weekdays and 4.46 hours on weekends, demonstrating superior effectiveness in capturing irregular patterns compared to the baseline model trained only on historical temporal features.

An efficient evaluation of lithium-ion battery (LIB) health is crucial for certifying the consistency, safety, and durability of electric vehicles (EVs). Traditional methods often rely on intrusive testing or simplified models, which fail to imprison the complex, nonlinear behavior of batteries in dynamic environments. To address these limitations, a new data-driven approach using Adaptive Artificial Fish Swarm Algorithm-driven Restricted Boltzmann Machines (AAFSA-RBM) is proposed. This advancement presents an effective method for estimating battery health by leveraging sensor data, including temperature, voltage, current, and state-of-charge (SOC). Pre-processing methods like min-max normalization are used to scale the data, ensuring all values fall within a specified range. Principal Component Analysis (PCA) is employed to reduce redundancy and preserve the most significant features of the data, improving computational efficiency. The AAFSA-RBM method optimizes the model parameters, significantly improving the accuracy of battery health predictions. Significant gains over conventional techniques are demonstrated by the results of the suggested model, which show mean square error (MSE) of 0.021, root mean square error (RMSE) of 0.010, and mean absolute error (MAE) of 0.009. This contemporary advance provides a breakthrough solution for estimating EV battery health. It offers a non-invasive, scalable, and accurate tool for monitoring the health of LIBs in real time, helping to improve maintenance strategies, develop battery longevity, and make certain the safe function of electric vehicles. This method attends to the critical needs of modern EV systems. It paves the way for more reliable and efficient battery management, facilitating the broader adoption of electric vehicles while improving their overall sustainability.

As the penetration of electric vehicles (EVs) increases, it is required to consider both power grid and traffic information when analyzing EVs distribution and charging loads. This paper presents a hybrid simulation method for real-time operation of EVs, charging stations, traffic and the grid. It first describes the traffic model and electric model and builds a hybrid model for EVs to combine these two models. The paper presents a detailed timing simulation method afterwards. After generating EV inflows and traffic flows in different places and setting EV's charging behavior, EVs' State of Charge (SOC) and spatial moving and charging stations' working states can be viewed through simulation. An example is presented to illustrate the impact of EV inflows on the charging power of charging stations. This method can be applied in other researches, and it will be utilized for future research work on the interaction between power grid and traffic network.

Lithium-ion (Li-ion) batteries are everywhere, from portable electronics to the latest electric vehicles, because of their unmatched energy density and rechargeability. Unfortunately, the performance of Li-ion batteries degrades over time as their continued use and operating environment cause irreversible chemical reactions that decrease cell capacity and power. Estimating and predicting the health of Li-ion batteries in consumer devices is imperative to ensure safe and reliable operation over the product’s expected lifetime. What's more, the sudden deployment of millions of new electric vehicles recently has created a plethora of aging batteries that will soon be retired, with few plans to capture and deploy the packs to a second life. My graduate thesis focuses on machine learning-based modeling methods for diagnosing Li-ion cell health and predicting future cell degradation. Diagnosing cell degradation modes is essential for understanding the probability of different failure modes an aging Li-ion cell might experience. Understanding the degradation modes present in a cell provides engineers with valuable information to improve cell design and performance. Further, estimating the health of aged Li-ion cells provides more insight into how they might be optimally used in their second life after being retired from an electric vehicle. In the first chapter of my thesis, I discuss a cell health estimation model capable of rapidly assessing the state of health of used Li-ion batteries from electric vehicles so they can be optimally assigned to recycling, rejuvenation, or redeployment. To accurately estimate the health of aged cells, I developed a deep neural network to estimate the remaining capacity and both charge/discharge resistances at three different states of charge. The algorithm uses only 5 minutes of raw voltage time-series data taken from the constant current portion of a battery's charge curve. The rapid capability of this algorithm makes it suitable for deployment in an industrial battery recycling and rejuvenation facility. The rapid health estimation model can maintain an average prediction error of no more than $4.0\%$ under all tested conditions. The methods discussed in chapter one of my thesis show promise for improving the longevity of Li-ion battery packs and cells by accurately assessing their usability at the end of their first life. In the second chapter of my thesis, I focus on the challenge of predicting the life of cells operating under different conditions. Predicting a cell’s future degradation trajectory can inform engineers of the cell’s lifetime, which can be used to inform future cell designs, screen for materials, set warranties, and schedule product maintenance. Accurate cell lifetime models can rapidly accelerate high-impact material optimization and control projects by substantially reducing time spent aging cells. In this chapter, I discuss a hybrid modeling approach to predicting the remaining useful life of Li-ion batteries. The approach leverages the best aspects of traditional model-based trajectory prediction and data-driven learning. My approach decomposes the task of RUL prediction into two steps: 1) Offline training of data-driven models for RUL error correction and 2) Online data-driven correction of model-based RUL prediction. The approach is evaluated on five datasets consisting of 237 cells: 1) three open-source datasets, 2) one proprietary dataset, and 3) a simulated out-of-distribution dataset. Results show that data-driven error correction reduces root-mean-square error by 40\% and mean uncertainty calibration error by 34\% compared to a model-based approach alone. The proposed approach is also shown to be more conservative in its uncertainty estimates than a purely data-driven RUL prediction approach. The machine learning-based battery aging models presented in this thesis are just a few examples of models capable of enhancing our understanding of battery degradation. To ensure a fast transition to renewable energy technology focused on battery energy storage, more battery aging modeling will have to be done. What's more, as batteries evolve in chemistry and design, new techniques will need to be developed and deployed to effectively monitor and predict cell degradation. In the conclusion of this thesis, I revisit some of the major modeling challenges that the industry and academia face and propose a handful of novel ideas worth researching further.

As the technology supporting electric vehicles (EVs) is rapidly progressing and the cost of EV components is reducing, EVs are becoming more feasible for use in Australia and in many countries around the world. However, the public perception of EVs and their perceived limitations result in a slow uptake of the technology, partially because of the uncertainty regarding the ability of an EV to meet the driving needs of the general population. Range anxiety is a particular concern with drivers having fear of being stranded by a depleted EV battery. This study explores means of reducing range anxiety by taking into account a variety of environmental and behavioural factors. By considering such factors and implementing it in conjunction with a recently proposed improved state of charge (SoC) estimation method by the authors, a range estimate can be produced that is much more accurate than the conventional methods which consider the SoC and vehicle efficiency alone. This range estimate can be used to inform the driver of the capabilities of the EV and advise if a recharge is required to reach the intended destination.

Electric vehicles (EVs) and hybrid electric vehicles (HEVs) which are powered by Li-ion batteries (LIBs) have the potential to address the challenges of reducing the dependence on fossil fuels and environmental pollution caused by the transportation sector. However, for the reliable and safe operation of the EVs and HEVs, an accurate estimation of the state of charge (SOC) and state-of-health (SOH) of the battery pack is required. The battery capacity degrades with cycling and calendar ageing over time and usage of the vehicles, which reduces the power capability and driving range of the vehicle. An accurate prediction of the battery SOC and SOH can assist the vehicle user in planning to reach the next charging station or the destination with enough range left before the battery gets fully discharged, hence avoiding annoying circumstances.Battery models have been widely employed in battery health and SOC prediction and can successfully predict the SOC and SOH of individual cells. However, the translation of these models from cell to pack levels adds several unknown critical factors which affect the model prediction capability significantly.Our proposed ECM model is based on the electrical and thermal behavior of individual cells, and it accounts for critical factors such as cell-to-cell variations, temperature gradients, and aging effects. We also incorporate a series-parallel configuration to simulate the behavior of multiple cells at the module and pack levels. By upscaling the model in this way, we can accurately predict the SOC and SOH of the entire battery pack, and provide reliable estimates of the remaining driving range and charging requirements.We anticipate that our proposed ECM model will significantly improve the accuracy of SOC and SOH predictions, which will enable EV/HEV users to better plan their trips, avoid unexpected battery failures, and improve the safety of EV/HEV batteries. Furthermore, our study has the potential to inform the design of future battery technologies and contribute to the transition toward sustainable transportation powered by clean energy sources.