Cost Of Degradation Research Articles

Optimal Task Allocation for Battery-Assisted and Price-Aware Mobile Edge Computing

In this paper, we propose a battery-assisted approach to improve energy efficiency for mobile edge computing (MEC) networks by utilizing the space-time-varying characteristics of electricity price. We formulate a price-aware task allocation problem (PATA) that jointly considers the cost for task computation, the cost of task offloading, and the cost of battery degradation. PATA is seemingly a mixed integer non-linear programming problem. By a graph-based reformulation, solving PATA is mapped to finding minimum cost flows or convex cost flows in the graph. This discovery reveals that the global optimum of PATA is obtained in polynomial time. Performance evaluation manifests that the proposed approach significantly outperforms other approaches.

Strategic Implicit Balancing With Energy Storage Systems via Stochastic Model Predictive Control

Battery Energy Storage Systems (BESS) may exploit the increasing price volatility in imbalance settlement mechanisms via inter-temporal arbitrage. However, participating in these markets requires a careful trade-off between expected profits, accounting for the impact of BESS actions on prevailing imbalance prices, the financial risks and the incurred battery degradation costs. This paper introduces a novel forecast-informed Model Predictive Control (MPC) methodology in which a strategic and potentially risk-averse BESS performs implicit balancing by taking out-of-balance positions in near-real time. Thereby it anticipates expected imbalance prices in a European-style balancing market, and takes into account state of charge-dependent battery degradation costs. To this end, an attention-based recurrent neural network forecasting technique is leveraged to predict the System Imbalance. The proposed methodology is tested on a real-life case study of the Belgian balancing market. Expected profits of a 2 MW/2 MWh BESS (21,784 MW/month) are shown to exceed those of different benchmarks available in the literature, including the profit associated with participating in the day-ahead energy market with perfect price foresight (7,082 /MW/month). From a system perspective, these implicit balancing actions performed by the BESS owner reduce the system imbalance in 75% of all cases, thus improving the cost-efficiency of power systems.

Research on energy management and control for six-wheel-drive electric bus rapid transit with photovoltaic-battery-supercapacitor

Electric Bus Rapid Transit is an effective solution to traffic congestion and environmental pollution. At present, Electric Bus Rapid Transit needs to be equipped with a large number of power batteries, which brings the key problems of long charging time, short driving range, and high comprehensive utilization cost. To solve the above problems, a novel six in-wheel motors driving Electric Bus Rapid Transit scheme with photovoltaic cells, lithium-ion batteries and supercapacitors (SWEBRT-PBS) is studied, constrained by the cost and comprehensive energy efficiency of vehicle. First, the system architecture for hybrid energy storage system composed of photovoltaic cells, lithium-ion batteries and supercapacitors (PBS) is analyzed. The life cycle cost function for PBS based on the degradation cost of lithium-ion batteries and the electricity cost of each energy source is proposed. The dynamic programming algorithm is employed to optimize the life cycle cost function of PBS, and then to obtain the optimal size of matches for supercapacitors. Second, the fixed step disturbance observation method is adopted to ensure stable power output in the maximum power point tracking controller of photovoltaic cells based on the vehicle application environment. Third, the power control rules are extracted based on the optimization results of dynamic programming algorithm, and the power allocation strategy for PBS with dynamic programming algorithm joint rules is formulated. Fourth, the overall efficiency function for driving system is established and optimized based on the sequential quadratic programming algorithm. The collaborative optimization strategy for SWEBRT-PBS is developed based on the corresponding optimization results, PBS operating modes and vehicle operating modes. The results show that the SWEBRT-PBS can reduce the number of lithium-ion batteries, thereby reducing the vehicle mass by 1.27 % and increasing the vehicle space utilization by 1.81 %. In addition, the comprehensive energy efficiency of vehicle can be improved by 28.79 %.

Cyclic Detectors in the Fraction-of-Time Probability Framework

The signal detection problem for cyclostationary signals is addressed within the fraction-of-time probability framework, where statistical functions are constructed starting from a single time series, without introducing the concept of stochastic process. Single-cycle detectors and quadratic-form detectors based on measurements of the Fourier coefficients of the almost-periodically time-variant cumulative distribution and probability density functions are proposed. The adopted fraction-of-time approach provides both methodological and implementation advantages for the proposed detectors. For single-cycle detectors, the decision statistic is a function of the received signal and the threshold is derived using side data under the null hypothesis. For quadratic-form detectors, the decision statistic can be expressed as a function of the received signal without using side data, at the cost of some performance degradation. The threshold can be derived analytically. Performance analysis is carried out using Monte Carlo simulations in severe noise and interference environments, where the proposed detectors provide better performance with respect to the analogous detectors based on second- and higher-order cyclic statistic measurements.

Role of mineral-based industrialization in promoting economic growth: Implications for achieving environmental sustainability and social equity

This research's problem statement centers on balancing economic development with environmental protection and social well-being. As mineral resources represent a crucial driver of economic growth in these regions, examining how their exploitation impacts economic indicators and the broader dimensions of sustainability and equity is imperative. Over the past decade (2010–2020), the role of mineral-based industrialization in fostering economic growth has garnered significant attention, particularly in Africa, Asia, and Latin America. This study delves into the complex interplay between mineral-based industrialization, environmental sustainability, and social equity in these regions, employing the Data Envelopment Analysis (DEA) model as a critical analytical tool. The results of this study reveal multifaceted insights. While mineral-based industrialization has indeed contributed to economic growth in these regions, the environmental implications are significant. Often, the extraction and processing of minerals come at the cost of environmental degradation, posing challenges to sustainability. Moreover, the distribution of benefits from this growth is often unequal, exacerbating social inequities. This research underscores the complex dynamics surrounding mineral-based industrialization in Africa, Asia, and Latin America. It highlights the need for a nuanced approach considering economic growth, environmental sustainability, and social equity. Policymakers and stakeholders must carefully navigate these challenges to ensure that mineral resources serve as catalysts for comprehensive and inclusive development in the future.

Co-optimization of speed planning and cost-optimal energy management for fuel cell trucks under vehicle-following scenarios

Fuel cell hybrid electric heavy-duty vehicles play a key role to realize green and low-carbon travel. To cope with actual traffic environment, an advanced energy management strategy (EMS) is crucial to ensure vehicle’s efficiency and economy. In this paper, a predictive co-optimization control method is designed to achieve speed planning and energy management for the host vehicle with a fuel cell/Li-ion battery hybrid energy storage system under vehicle-following scenarios. Firstly, the host vehicle obtains the real-time speed of leading vehicle by communication technology, and speed prediction for the leading vehicle is implemented by fuzzy C-means clustering enhanced Markov Chain (FCM-MC) considering driving habits. Secondly, based on speed prediction results, the speed planning which aims to ensure safe inter-vehicle distance and minimize the demanded vehicular power is implemented to derive the speed curve of host vehicle. Finally, according to speed planning results, predictive energy management is applied to achieve power allocation between fuel cell (FC) and battery. The simulation results denote that the proposed speed prediction method can decrease forecast error effectively. The speed planning ensures the safe inter-vehicle distance. With speed prediction accuracy improved, the proposed hierarchical energy management strategy can reduce fuel cell degradation cost by 1.31%-3.48% and total operation cost by 0.75%-1.94%.

Gravity-based network traffic abstraction and laser ON/OFF control in optical satellite networks

As the cost of launching low Earth orbit satellites continuously decreases, satellite-based communications networks are emerging as a new area for both academia and industry. Lasers are already employed for building inter-satellite links, forming optical satellite networks. The orbiting nature of satellites determines that the optical satellite networks are usually uniformly distributed around the Earth, to provide seamless coverage to any place at all times. However, the end users on the Earth are non-uniformly distributed. As a result, many satellites with laser links might not be utilized efficiently. From the energy perspective, this work studies the energy-efficiency issues of the inter-satellite laser links. We first model the optical satellite networks by presenting the satellite constellation with inter-satellite laser design principles and introduce the laser ON/OFF control problem accordingly. To explore the possibility of saving energy in the massive satellite deployment, we further introduce the gravity-based network traffic model and propose a gravity-based network traffic abstraction (GNTA) model to evaluate the importance of each laser link. Accordingly, we further propose a GNTA-based ON/OFF control (GOOC) algorithm to improve the energy efficiency of inter-satellite laser links by switching OFF parts of the laser links that are less utilized. We evaluate the GOOC’s performance using simulation, and results show that switching OFF 20% of the laser terminals in the full-gridmesh topology can improve the energy efficiency by about 10%, with an acceptable cost of network performance degradation.

Co-Optimizing Bidding and Power Allocation of an EV Aggregator Providing Real-Time Frequency Regulation Service

The rapidly expanding scale of electric vehicle (EV) fleets and continuously decreasing battery costs are making vehicle-to-grid services a reality. In this paper, we study the interaction between the problems of an EV aggregator’s bidding in the regulation market and power allocation (i.e., determining the (dis)charging powers of the EVs in regulation deployment). Although the two problems are coupled, they are often regarded as decoupled and optimized separately for complexity issues. However, failing to consider the coupling of bidding and power allocation can lead to a decline in the profit of the EV aggregator (EVA). In this paper, we propose a framework for co-optimizing EVA bidding and power allocation in the regulation market. The bidding model is formulated as a stochastic programming problem with embedded power allocation in discretized regulation signal scenarios. To meet the solution time requirement for regulation deployment, we further propose a power allocation model that can be solved online. It utilizes the Lagrange multipliers from the bidding problem to ensure that the allocation results correspond to the optimal solution of the bidding problem. The effect of the proposed framework on improving EVA profits and reducing degradation costs is verified in the case study.

A Security-constrained Unit Commitment-based Optimal Battery Usage Pattern Model

This paper discusses multiple strategies of security-constrained unit commitment (SCUC)-based optimal battery usage pattern models to drive the charging and discharging of different batteries and help the operation cycles satisfy their preferences according to battery characteristics. Previous studies mainly focus on either battery storage integration as a controllable resource into the grid with high penetration of renewable energy, or battery degradation costs as additional terms in the objective function of SCUC. In comparison, this paper first considers whether battery operation cycles satisfy their preferences according to specific battery characteristics. It discusses a full charging/discharging strategy for Ni-Cd batteries, which have a memory effect and prefer deep cycles. Then, the paper explores a shallow cycle strategy for Lithium-ion, lead acid, and Ni-H batteries, which have no memory effect and prefer shallow cycles for long service life. Subsequently, it is extended to a hybrid strategy when batteries with/without memory effects are at the same node. The proposed model minimizes the total operation cost of SCUC while including the penalties for battery usage strategies. The original problem is a mixed integer non-linear programming (MINLP) one, and then it can be mathematically converted into a mixed integer linear programming (MILP) one. Consequently, the models with multiple strategies are tested through IEEE 6-node power system. Computational results show that, compared to the base case without any strategy, the proposed optimal battery usage pattern approaches have an advantage in driving Ni-Cd batteries toward deep cycles, guiding Lithium-ion batteries towards shallow cycles, and improving the flexibility of performance with hybrid applications.

Optimal Priority Rule-Enhanced Deep Reinforcement Learning for Charging Scheduling in an Electric Vehicle Battery Swapping Station

For a battery swapping station (BSS) with solar generation, N charging bays, and an inventory of M batteries, we study the charging scheduling problem under random EV arrivals, renewable generation, and electricity prices. To minimize the expected weighted sum of charging cost (sum of electricity and battery degradation costs) and EV owners’ waiting cost, we formulate the problem as a Markov decision process with unknown state transition probability. Under a mild heavy-traffic assumption, we rigorously establish the optimality of the Less Demand First (LDF) priority rule under arbitrary system dynamics: batteries with less demand shall be charged first. The optimality result enables us to integrate the LDF rule into a state-of-the-art deep reinforcement learning (DRL) method, proximal policy optimization (PPO), reducing the dimensionality of its output from O(M+N) to O(1), without loss of optimality in the heavy-traffic scenario. Numerical results (on real-world data) demonstrate that the proposed LDF enhanced PPO approach significantly outperforms classical DRL methods and FCFS (first come, first served) priority rule based DRL counterparts.

Optimal Sizing of Battery Energy Storage Systems Considering Degradation Effect for Operating and Electricity Cost Minimization in Microgrids

A microgrid is a low‐voltage distribution network designed to provide power for small‐scale and isolated communities consisting of distributed generation and energy storage systems. In order to achieve reliable power and proper energy utilization, energy storage systems plays a vital role in microgrids by storing energy during off peak hours and discharges energy during peak hours. One of the major issues in the isolated microgrids with intermittent nature of distributed generations is the balance of energy demand. This can be achieved by appending renewable energy sources with suitable battery energy storage systems (BESS), to provide the reserve support in meeting the load demand. Battery degradation effect plays a major role in analyzing the performance of BESS lifetime. Battery degradation effect relates the capacity reduction of energy of BESS that is to be delivered to meet the load demand. Therefore, microgrid systems with BESS considering degradation effect should be optimized in such a way as to obtain minimum operating cost while ensuring minimum electricity cost to customers. Simulations of hourly battery discharges rates and simulation of actual discharge rates with obtained simulated rates need to be calculated to determine the degradation effect. The battery degradation cost and lifetime can be calculated correspondingly to minimize the objectives. Correspondingly, herein, an optimization algorithm for microgrid operating cost and customer electricity cost minimization for 24 h time horizon while considering BESS degradation effect by determining kWh and MWh throughput is presented. Particle swarm optimization (PSO), accelerated particle swarm optimization (APSO), Jaya optimization (JAYA) technique, and linear programming interior point algorithm (LP‐IP) are applied to determine the optimal operating cost and electricity cost by simulating BESS degradation parameters. Results obtained using PSO, APSO, JAYA are compared with LP‐IP solver‐based algorithm for BESS lifetime, BESS degradation cost, microgrid operating cost, and customer electricity cost for 24 h.

Assessing the incorporation of battery degradation in vehicle-to-grid optimization models

Bidirectional charging allows energy from the electric vehicles (EV) to be fed back into the grid, offering the possibility of price-optimized charging. However, such strategies cause higher charging cycles, which affect the cyclic aging of the battery and reduce its service life, resulting in additional costs for the user. Various approaches are used to account for battery degradation in optimizations models of bidirectional charging use-cases. In this paper, a systematic literature review is carried out to identify existing battery degradation models and to determine the most suitable one. In the models under review, degradation is integrated into the optimization’s objective function. The review shows that there are mainly two strategies suitable for vehicle-to-grid (V2G) optimization problems: A weighted Ah-throughput model (wAh-model) with a constant degradation cost factor and a performance based model (pb-model) linking the degradation to measurable parameters such as capacity loss. Both models were implemented and analyzed. The results show that the wAh-model is the better optimization option, as in the pb-model the current state of health of the battery has an excessively large impact on the calculated degradation cost. It leads to excess costs due to a higher aging rate at the beginning of life which proves to be not ideal in the optimization. The sensitivity analysis reveals that altering the initial State of Health (SoH) from 95 % in the base scenario to 100 % leads to an increase in average degradation costs by factor 9.71 in the pb-model. From the evaluated base scenario the average degradation costs for the pb-model are 0.45 cent/kWh and for the wAh-model 0.23 cent/kWh.

Hybrid CHGSO Approach-Based EV Energy Regulation by Considering Incentive-Based Prosumer and Uncertainty

This paper proposes the hybrid approach for increasing the profit of prosumers and reducing the energy cost of the residents. The hybrid method is the combination of chaotic maps and the Henry gas solubility optimization algorithm (HGSO), hence it is called chaotic Henry gas solubility optimization (CHGSO). The proposed approach is the community-based microgrid that incorporates energy storage systems (ESS), electric vehicles (EVs) and photovoltaics (PV). Both ESS and EV are conserved as alternative sources of energy. The stored EV energy is used as a power source for homes and the grid by utilizing the Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) modes. The power flow of EV/ESS is controlled depending on the rate of charging and discharging. Here, the battery degradation cost is considered. The proposed approach solves the optimization problems, like battery degradation of EV/ESS, regulation of charge/discharge and provides optimal economic benefits. The proposed approach increases the prosumer profit through encouraging EV energy transactions through incentive-based pricing. Finally, the CHGSO method is performed in MATLAB and is compared with existing methods. From the simulation analysis, it can be concluded that the cost of CHGSO method is less and the prosumer participation has increased.

P2P power trading of nanogrids for power management in consideration of battery lifetime of ESS

This study presents a peer-to-peer (P2P) trading strategy for power management of nanogrids with renewable energy sources (RESs), taking battery lifetime of energy storage system (ESS) into consideration. Nanogrid requires more analytical power management as individual control of each electric appliance should be carried out, considering its rated power. With the increasing penetration of distributed energy resources, P2P trading can be applied to nanogrids equipped with RESs and ESSs. For the power management of a nanogrid, multi-objective optimization is used to minimize grid power consumption, electricity cost, and delay of using electric appliances in appliance scheduling. In this paper, a battery aging model or degradation cost model is considered in the multi-objective optimization to prolong the ESS lifetime. The electricity from RESs and ESSs used for P2P trading is determined to increase the profitability of nanogrids. Depending on the amount of electricity allocated to P2P trading, a nanogrid can be a seller or buyer in P2P trading. P2P trading is carried out based on the cooperative game theory to maximize the welfare of both sellers and buyers. As compared to power management involving P2P trading without considering ESS lifetime, the proposed one in consideration of ESS lifetime decreases the electricity cost of nanogrid by 2.86% on average, and the degradation cost of ESS by 32.75% on average.

RAPIDx: High-Performance ReRAM Processing In-Memory Accelerator for Sequence Alignment

Genome sequence alignment is the core of many biological applications. The advancement of sequencing technologies produces a tremendous amount of data, making sequence alignment a critical bottleneck in bioinformatics analysis. The existing hardware accelerators for alignment suffer from limited on-chip memory, costly data movement, and poorly optimized alignment algorithms. They cannot afford to concurrently process the massive amount of data generated by sequencing machines. In this paper, we propose a ReRAM-based accelerator, RAPIDx, using processing in-memory (PIM) for sequence alignment. RAPIDx achieves superior efficiency and performance via software-hardware co-design. First, we propose an adaptive banded parallelism alignment algorithm suitable for PIM architecture. Compared to the original dynamic programming-based alignment, the proposed algorithm significantly reduces the required complexity, data bit width, and memory footprint at the cost of negligible accuracy degradation. Then we propose the efficient PIM architecture that implements the proposed algorithm. The data flow in RAPIDx achieves four-level parallelism and we design an in-situ alignment computation flow in ReRAM, delivering $5.5$-$9.7\times$ efficiency and throughput improvements compared to our previous PIM design, RAPID. The proposed RAPIDx is reconfigurable to serve as a co-processor integrated into existing genome analysis pipeline to boost sequence alignment or edit distance calculation. On short-read alignment, RAPIDx delivers $131.1\times$ and $46.8\times$ throughput improvements over state-of-the-art CPU and GPU libraries, respectively. As compared to ASIC accelerators for long-read alignment, the performance of RAPIDx is $1.8$-$2.9\times$ higher.

Data‐driven stochastic model predictive control for regulating wind farm power generation with controlled battery storage

AbstractThe uncertainty and fluctuation of the volatile wind power cause more reserve and frequency regulation capacity, so it incurs additional operational cost for the power grid. Therefore, a wind farm (WF) coordinated controller is essential to reduce the power fluctuation and trace the scheduled power generation with minimal wind curtailment and battery degradation cost. This paper proposes a data‐driven stochastic model predictive control (SMPC) method for WFs to realize these objectives. Firstly, to address the non‐linear dynamic model of wind turbines, the dynamic mode decomposition method accompanying Koopman operator is used to learn a lifted linear dynamic model from measurements. Secondary, this paper proposes the relationship between the curtailed wind power of wind turbines and their pitch angle, wind speed, and rotation speed, as well as the corresponding estimation formula. In addition, the prediction errors of wind speeds are characterized using the Gaussian mixture model (GMM), which may significantly affect the controller's performance. Finally, a tractable data‐driven SMPC model is developed and is verified on a DIgSILENT/PowerFactory based simulation platform. Compared with conventional model predictive control algorithms, it can significantly reduce the overall operational cost of WFs.

Operational Optimisation of Grid-Connected Microgrids Incorporating Hybrid Energy Storage and Demand Response

Storage systems are needed to boost the reliability of intermittent solar and wind resources in power networks. Rather than focus on one storage system or one hybrid energy storage system (HESS), this work models the operation of six HESS configurations in a Renewable Energy (RE) based grid-tied network. The objective is to minimise the daily operational costs of the microgrid while prolonging the storage lifetime by considering storage degradation costs. The influence of fixed tariffs and time-of-use (TOU) tariffs on the optimal operational of the HESS configurations have also been investigated; as well as deferrable demand satisfaction, charge-discharge pattern of different HESS and availability of the power-dense storage system within the microgrid. Results show that the lead-acid battery and hydrogen fuel cell (HFC) HESS incurs the highest operational costs, while the supercapacitor-lead-acid battery HESS incurs the lowest operational costs. The supercapacitor-lead acid battery and the supercapacitor-HFC HESS incur the highest annual storage degradation costs. The grid expenses were seen to be the same for all HESS under each tariff scheme. Lastly, decreasing the minimum storage level further by 10% from the 30% in the base case, led to an increase in the number of hours of availability of the power-dense storage system of five of the six HESS. These results have given a deeper understanding to the operation of HESS systems and can inform better decision making of the suitable HESS to be deployed in different operating scenarios.

Towards Nearly Zero-Energy Buildings: Smart Energy Management of Vehicle-to-Building (V2B) Strategy and Renewable Energy Sources

As countries worldwide strive for net-zero emissions, electric vehicles (EVs) have gained significant momentum. EVs, particularly through Vehicle-to-Everything (V2X) technology, including Vehicle-to-Building (V2B), are recognized as key enablers for achieving nearly-zero energy buildings (NZEB). This research presents an advanced Smart Energy Management System (SEMS) with a novel V2B strategy, offering precise one-hour resolution to reduce grid reliance and optimize peak load management. The SEMS coordinates building energy and EV charging while harnessing the potential of renewable energy resources and battery storage systems. By carefully scheduling EV charging and discharging activities, it maximizes the efficient use of available energy resources. Importantly, the algorithm integrates real-world EV parking patterns, accounting for factors such as fleet composition, parking durations, and charging priorities. Economic evaluation encompasses not only direct EV battery degradation costs but also external expenses associated with environmental impacts. Simulation outcomes underscore a remarkable reduction in building grid power demand (65%) and a substantial decrease in carbon emissions (64%). The PV+V2B Scenario emerges as the preferred choice, striking an optimal balance between initial investment costs, energy savings, and carbon footprint reductions. This study holds great academic and practical significance, contributing to the advancement of smart, sustainable, and economically viable net-zero cities.

Drivers of green economy in an emerging market: Generic and sector-specific insights

The study examines the drivers of the green economy generally and with a focus on sectors regarded as environmental-sensitive hotspots like waste, agriculture, forestry, energy, and transport sectors. The study was grounded on both the natural resource-based view and institutional theories. Creswell's (2014) thematic analysis and the graphic framework of Miles and Huberman (1984, 1994) were used to address the respective objectives of the study. A total of 10 interviewees were selected purposively based on their expertise and the institutions in question who provided responses that were analysed. The results show that the three generic drivers of the green economy include pollution prevention revealed by the impact of climate change, and environmental degradation cost, environmental stewardship portrayed by inefficient resource utilization and global demand for change, as well as, sustainable development manifesting from a futuristic drive, a focus on people-planet-profit, and rejection of environmentally unfriendly development models. The findings further reveal the differences and interrelatedness of the drivers of the green economy within the 5 hotspot sectors under investigation. The results of the study reveal that there are different and sector-specific strategies that policymakers and practitioners have to apply in their quest to drive a green economy at the central government, local government and grassroots levels. The study proposes the development of a comprehensive national green economy policy and also the promotion of peer-to-peer knowledge transfer networks structured as Communities of Practice. This study is one of the few that explores the concept of the green economy within the macroeconomic level and is novel in attaching theoretical lenses.

Electric fleet charging management considering battery degradation and nonlinear charging profile

The populations of commercial electric vehicles (EVs) and electric robots (ERs) have been growing rapidly in recent years. Yet, the availabilities and incoordination of the charging infrastructure still constrain the operations of all EVs/ERs, resulting in wasted waiting time and, thus, decreased total profits. Coordinating these electric machines as a fleet and identifying the optimal operation and charging schedules to maximize total profit is essential. On the other hand, the charging process usually consists of two charging stages, constant current (CC) and constant voltage (CV), which lead to a nonlinear charging profile. Other factors, such as the high charging current, may significantly accelerate battery degradation and lead to capacity fade. However, the high nonlinearities make the battery charging profile and the degradation model computationally difficult to be integrated into optimization problems. In this study, we propose an innovative fleet management strategy that maximizes the operation revenue and minimizes the cost of electricity and battery degradation while addressing the aforementioned nonlinear charging profile. By proposing two linearization methods to replace the nonlinear parts, we formulated a Mixed-Integer Linear Program (MILP). Furthermore, stemming from the numerical case study, two managerial insights, the impact of the battery SOH on fleet management and the selection of fast charging vs. normal charging modes, are outlined.