Battery Aging Cost Research Articles

Energy management for hybrid energy storage system in electric vehicle: A cyber-physical system perspective

Adoption of the hybrid energy storage system (HESS) brings a bright perspective to improve the total economy of plug-in hybrid electric vehicles (PHEVs). This paper proposes a novel energy management method to improve the total economy of PHEV by exploiting the energy storage capability of HESS. Firstly, A cyber-physical energy management framework that enables the synergistic scheduling of fuel engine, battery, and supercapacitor is designed to derive the optimal power distribution strategy for PHEV with HESS. Then, an optimization-based energy management model is established to distribute the vehicle power requirement between the fuel engine and driving motor. The reduction of fuel cost and the mitigation of the battery aging phenomenon are designed as the optimization objectives. In a further stage, an online power distribution algorithm is designed for the optimal control of HESS, where the supercapacitor is used to prolong the battery life. The qualitative and quantitative analyses indicate that both the fuel economy and battery aging cost are sensitive to the power system topology and power distribution algorithm. With the proposed methods, the PHEV total economy can be improved by 8.9% and 6.9% compared to the conventional PHEV structure and power distribution strategy while guarantee the vehicle dynamic performance.

Adaptive energy management for hybrid power system considering fuel economy and battery longevity

The adoption of hybrid powertrain technology brings a bright prospective to improve the economy and environmental friendliness of traditional oil-fueled automotive and solve the range anxiety problem of battery electric vehicle. However, the concern of the battery aging cost is the main reason that keeps plug-in hybrid electric vehicles (PHEV) from being popular. To improve the total economy of PHEV, this paper proposes a win-win energy management strategy (EMS) for Engine-Battery-Supercapacitor hybrid powertrains to reduce energy consumption and battery degradation cost at the same time. First of all, a novel hierarchical optimization energy management framework is developed, where the power of internal combustion engine (ICE), battery and super capacitor (SC) can be gradationally scheduled. Then, an adaptive constraint updating rule is developed to improve vehicle efficiency and mitigate battery aging costs. Additionally, a control-oriented cost analyzing model is established to evaluate the total economy of PHEV. The quantified operation cost is further designed as a feedback signal to improve the performance of the power distribution algorithm. The performance of the proposed method is verified by Hardware-in-the-loop experiment. The results indicate that the developed EMS method coordinates the operation of ICE, driving motor (DM) and energy storage system effectively with the fuel cost and battery aging cost reduced by 6.1% and 28.6% respectively compared to traditional PHEV. Overall, the introduction of SC and the hierarchical energy management strategy improve the total economy of PHEV effectively. The results from this paper justify the effectiveness and economic performance of the proposed method as compared to conventional ones, which will further encourage the promotion of PHEVs.

Implementation of cost benefit analysis of vehicle to grid coupled real Micro-Grid by considering battery energy wear: Practical study case

The proposed research represents a spin-off of the Malta College of Arts, Science and Technology (MCAST) Micro-Grid (MG) project. Particularly, economic impact of Electric Vehicles (EV) integration into the MG is investigated in this paper. The MCAST MG consists of photovoltaic generation unit, a diesel generator and a battery storage system. In this paper, a Vehicle-to grid (V2G) concept is considered where utilities can profit from controlled energy trading operations according to EVs availability. EVs are categorized under different profiles considering energy and time availability of owners typical work hours. V2G energy cost is estimated based on battery energy wear due V2G extra cycling and refunded to EVs owners. As most of developed V2G studies don’t consider real world input data or/and EV battery aging cost in system modeling and evaluation, the present paper presents a reliable study as it considers a real life MG with in field measurement input data and appropriate battery degradation model. The adopted model represents a linear approximation with a minimum error value to make a suitable tradeoff of computational complexity and accuracy of obtained results. Economic assessment of the system according to the proposed energy management is performed, where results indicate that the V2G system assisted the MG operation during high electricity price period and achieved economic profit to EVs owners. According to numerical results, V2G energy trading achieved 29.90 EUR of gross selling revenues with only 4.46 EUR as battery degradation cost which makes a 16.41% average cost reduction of daily MG operation cost.

Stochastic Charging Optimization of V2G-Capable PEVs: A Comprehensive Model for Battery Aging and Customer Service Quality

This article proposes a new stochastic day-ahead residential charging model for a vehicle-to-grid (V2G)-capable plug-in electric vehicle (PEV). The aim is to minimize the expected customer’s charging cost, including energy cost and battery aging cost while satisfying the customer service quality constraints. The proposed model integrates a detailed PEV lithium-ion battery aging model as a function of average battery’s cell surface temperature, average current rate, average state of charge (SoC), and depth of discharge (DoD). Customer service quality constraints are mathematically modeled using Kano’s dissatisfaction model as an exponential function of the customer’s waiting time and charging level. Given the uncertain behavior of a PEV owner, the charging scheduling problem is formulated as a two-stage stochastic programming problem. In summary, this article contributes to the technical literature by developing a two-stage stochastic optimization framework for optimal charge scheduling of PEVs, which integrate a comprehensive battery aging cost model, and models customer dissatisfaction as Kano’s model-based function of the customer’s waiting time and charging level. Comparing the results in various deterministic, Monte Carlo simulation-based and the two-stage stochastic studies show that the proposed scheme can lead to low dissatisfaction for the customer, without a significant increment in costs.

Economic Assessment for Battery Swapping Station Based Frequency Regulation Service

Battery swapping stations (BSSs) have great potential in providing fast frequency regulation service (FFRS) owing to their large battery storage capacity. However, compared to a regular BSS, a BSS providing FFRS faces the following financial risks: first, higher infrastructure investment to support the vehicle-to-grid services; second, higher battery aging costs due to FFRS; third, FFRS causes uncertainties to batteries’ charging costs. Under such a context, in this article, we propose an economic risk assessment model for the BSS-based FFRS by comparing its economics with a regular BSS. In this model, the value at risk (VaR) of daily revenue and the long-term return on investment (ROI) of a regular BSS and a BSS providing FFRS are compared. The assessment results are obtained in three steps: first, we develop the operation and economic models for the BSS-based FFRS. Next, the mathematical models of the VAR and ROI analyses are formulated. Finally, the VaR of daily revenue is compared through a statistical analysis of a large number of scenarios. The ROI comparison is conducted by a policy gradient based reinforcement learning algorithm, which can handle the nonconvexity and stochastic dynamics brought by the electric vehicle visits. The practicality of the proposed framework is demonstrated by using the real ancillary market data from utilities and the traffic count data from onsite traffic sensors.

Battery anti-aging control for a plug-in hybrid electric vehicle with a hierarchical optimization energy management strategy

Abstract This paper proposes a hierarchical optimization energy management strategy to suppress the battery aging in plug-in hybrid electric vehicles. In the first-level, a variable-threshold dynamic programming algorithm to distribute the power between the energy storage system and the engine is proposed. By adding supercapacitor to form the hybrid energy storage system, and using adaptive low-pass filtering algorithm, the power between the battery and the supercapacitor is distributed. To control the supercapacitor and battery to work within the capacity range, a power limits management module for redistributing the power between the engine, the supercapacitor and the battery is considered. The adaptive low-pass filtering algorithm and power limits management module constitute adaptive power allocation method in the second-level. After that, the rain-flow counting algorithm is applied in this paper to calculate battery aging cost. By using the rain-flow counting algorithm, the battery performances are analyzed, and the results show that the adaptive power allocation method can improve the battery service life by about 54.9% compared with the global dynamic programming algorithm. Considering the initial cost of the supercapacitor, the costs of battery aging, fuel consumption, electricity consumption, and management cost of retired batteries, compared with the global dynamic programming algorithm, the life cycle economy of the vehicle is improved by 12.4% under the proposed method.

Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

Electro-mobility is increasing significantly in the urban public transport and continues to face important challenges. Electric bus fleets require high performance and extended longevity of lithium-ion battery at highly variable temperature and in different operating conditions. On the other hand, bus operators are more concerned about reducing operation and maintenance costs, which affects the battery aging cost and represents a significant economic parameter for the deployment of electric bus fleets. This paper introduces a methodological approach to manage overnight charging of an electric bus fleet. This approach identifies an optimal charging strategy that minimizes the battery aging cost (the cost of replacing the battery spread over the battery lifetime). The optimization constraints are related to the bus operating conditions, the electric vehicle supply equipment, and the power grid. The optimization evaluates the fitness function through the coupled modeling of electro-thermal and aging properties of lithium-ion batteries. Simulation results indicate a significant reduction in the battery capacity loss over 10 years of operation for the optimal charging strategy compared to three typical charging strategies.

Aging-aware predictive control of PV-battery assets in buildings

This paper presents an aging-aware model predictive control (MPC) approach for operation of sustainable buildings with on-site photovoltaic (PV) and battery systems. On-site batteries can be used to collect excess PV power during low demand hours and to discharge when PV generation is not enough to meet loads. To further increase the value of the PV-battery assets, batteries can be proactively charged/discharged to shed peak demands for utility cost reduction. The battery life span is highly dependent on the operation strategy and aggressive charge/discharge actions lead to faster battery capacity degradation and higher overall operation cost due to more frequent replacement. The control strategy developed in this study incorporates a convex battery capacity loss model, derived from a physically-based degradation model, to capture the battery aging cost in the control optimization. The degradation model used in the control optimization consists of a set of convex and piece-wise linear sub-models to approximate the battery degradation effect at different time instances. A convex MPC problem is solved in the proposed control strategy to seek the optimal balance between the building utility cost and the battery life-cycle cost. Whole month simulation tests were carried out for a typical office building with on-site PV and battery assets. To quantify the benefits of the proposed control approach, three baseline strategies were also simulated that represent the best current practices. Test results showed that conventional battery control strategies seeking minimum utility cost could double the battery degradation rate and lead to a significant increase in the battery life-cycle cost. The proposed strategy could foresee the tradeoff between the utility cost and battery aging cost and make decisions accordingly. It was demonstrated that the proposed control solution could reduce the utility cost by 9% with moderate battery degradation increase, leading to overall building operation cost reduction up to 4%.

Operating a storage-augmented hybrid microgrid considering battery aging costs

Abstract Over the last decades, electricity demand and supply have become more decentralized, and the opportunities for locally managed microgrids have increased. Especially for residential microgrids consisting of multiple residential buildings equipped with a substantial share of local thermal and electrical energy production, flexibility is needed to balance energy demand and energy supply over time. Battery energy storage systems (BESSs) and thermal energy storage systems can provide this flexibility. Even though some BESS solutions are already available on the market, BESS still suffer from technical limitations and entail high investment costs. As BESS deteriorate over time depending on usage characteristics and the surrounding conditions, actively managing these parameters will largely affect their potential lifetime and the economic viability of their application. The majority of current research has focused on using BESS to optimize energy systems for economical, ecological, and technical objectives, but barely considered battery aging in the optimization models themselves. To contribute to closing this research gap, this article proposes an optimization model for the optimal day-ahead management of a multi-building district considering battery aging costs (BAC) derived from specific literature on battery degradation mechanisms. The resulting non-linear model is linearized and solved by using a commercial solver. Computational studies are performed to illustrate the case of a hybrid integrated local energy supplier responsible for the multi-building district, and sensitivities of its operating profits towards different sizing and pricing parameters are investigated. Results confirm the importance of considering BAC in decision support models for managing energy systems, both for a cost-efficient management of battery operations and to improve battery lifetime. The results also indicate that the application of BESS in day-ahead markets are only relevant for future cost levels of the technology.

Optimal Control of PEVs with a Charging Aggregator Considering Regulation Service Provisioning

Plug-in electric vehicles (PEVs) are considered the key to reducing fossil fuel consumption and an important part of the smart grid. The plug-in electric vehicle-to-grid (V2G) technology in the smart grid infrastructure enables energy flow from PEV batteries to the power grid so that the grid stability is enhanced and the peak power demand is shaped. PEV owners will also benefit from V2G technology, as they will be able to reduce energy cost through proper PEV charging and discharging scheduling. Moreover, power regulation service (RS) reserves have been playing an increasingly important role in modern power markets. It has been shown that by providing RS reserves, the power grid achieves a better match between energy supply and demand in presence of volatile and intermittent renewable energy generation. This article starts with the problem of PEV charging under dynamic energy pricing, properly taking into account the degradation of battery state-of-health (SoH) during V2G operations as well as RS provisioning. An overall optimization throughout the whole parking period is proposed for the PEV and an adaptive control framework is presented to dynamically update the optimal charging/discharging decision at each hour to mitigate the effect of RS tracking error. As more and more PEVs are being plugged into the power grid, the control or management issue of PEV charging arises, since mass unregulated charging processes of PEVs may result in degradation of power quality and damage utility equipments and customer appliances. To solve this problem, this article also presents an SoH-aware charging aggregator design, which decides the control sequences of a group of PEVs. An energy storage system is used in the charging aggregator to do a peak power shaving, and future parking PEVs are properly taken care of. Experimental results show that the proposed optimal charging algorithm minimizes the combination of electricity cost and battery aging cost in the RS provisioning power market. Experimental results also show that the introduction of charging aggregator can significantly reduce the peak power consumption caused by simultaneous PEV charging.

Economic Analysis of Hybrid Renewable Energy Systems with V2G Integration Considering Battery Life

Abstract Hybrid renewable energy systems considered as candidate solutions for environmental problems. Solar energy and wind energy are important components of these systems, but both of them cause fluctuations in the grid. Batteries of Electric Vehicles (EVs) can be connected to the grid to create an ancillary resource. These systems known as Vehicle to Grid (V2G) and they could be the most suitable solution to the fluctuation problems of renewable resources. In this paper, economic analysis of V2G is investigated from various perspectives. The findings of this study indicate that battery aging costs of a V2G system can be minimized with an intelligent energy management system, and these systems are both economically and environmentally better alternatives to fossil-fuelled generators.