Initial State Of Charge Research Articles

The Impact of Road Types on the Energy Consumption of Electric Vehicles

The widespread adoption of battery electric vehicles (BEVs) is hindered by their limited ranges and long charging times. Optimizing eco-driving strategies and BEV-specific routing through a thorough understanding of the BEV discharge behavior is vital to overcome these barriers in the short term. Therefore, this study investigates the impact of road types on the BEV discharge behavior while accounting for explanatory variables (i.e., ambient temperature, the initial state of charge, and driver). Thirty participants drove a 2017 Volkswagen eGolf along two predefined routes in Rhode Island. The results illustrate that BEVs are the most efficient on-road types with medium speed and low variation (i.e., “major collectors,” “minor arterials,” and “other principal arterials”). Meanwhile, findings confirmed a significantly higher average energy consumption rate on roads with higher average speeds (“interstates” and “other freeways/expressways”). Moreover, “local roads,” associated with a low average travel speed and a high variation in speed, showed a negative effect on BEV efficiency. The study further supported previous findings that BEVs are less efficient in colder temperatures. Thus, adapting eco-driving strategies, including the alteration of route choice to avoid “local roads” and “interstates,” can offer BEV drivers the potential for energy savings and range extensions. We propose a consideration of these findings to mitigate the effects of BEV range limitations and ease BEV adoption and ownership.

Nonlinear Dynamic Model for Parameter Estimation of Li-Ion Batteries Using Supply–Demand Algorithm

The parameter extraction of parameters for Li-ion batteries is regarded as a critical topic for assessing the performance of battery energy storage systems (BESSs). The supply–demand algorithm (SDA) is used in this work to identify a storage system’s unknown parameters. The parameter-extracting procedure is represented as a nonlinear optimization task in which the state of charge (SOC) is approximated using nonlinear features related to the battery current and the initial SOC condition. Furthermore, the open-circuit voltage is approximated using the resulting SOC, which is performed in a nonlinear formula, as well. When used in the dynamic nonlinear BESS model, the SDA was used to verify the fitness values and standard deviation error. Furthermore, the results that were acquired using SDA are compared to recently developed approaches, which are the gradient-based, tuna swarm, jellyfish, heap-based, and forensic-based optimizers. Simulated studies were paired with experiments for the 40 Ah Kokam Li-ion battery and the ARTEMIS driving-cycle pattern. The numerical outcomes showed that the proposed SDA is an approach which is excellent at identifying the parameters. Furthermore, when compared to the other current optimization techniques, for both the Kokam Li-ion batteries and the ARTEMIS drive-cycle pattern, the suggested SDA exhibited substantial precision.

Multi-type electric vehicle load prediction based on Monte Carlo simulation

In the paper, an electric vehicle (EV) charging load prediction approach considering vehicle types is developed. The GM (1,1) model is initially introduced to estimate the ownership of electric vehicles (EVs), and the model is validated by examining the difference between the forecast results and the actual situation. The Monte Carlo algorithm is also used to build the probability distribution model of travel laws and charging characteristics to predict the load demand when the scale of EVs is connected to the grid. For various types of EVs, the charging behavior is analyzed by considering charging start moment, charging duration, the initial state of charge (SOC), daily mileage, and charging mode. The charging load curves for each type of EV are superimposed to obtain the total load demand curve. Then it is compared with the original load curve in a certain region to analyze the impact of EV charging load on the original load curve of the grid, and to provide theoretical guidance for grid planning and optimal dispatching.

A Day-Ahead Energy Management for Multi MicroGrid System to Optimize the Energy Storage Charge and Grid Dependency—A Comparative Analysis

Microgrid (MG) is a combination of distributed generators (DGs), energy storage systems (ESSs), and loads connected to distribution network that can either be in islanded mode or grid-tied mode. Similarly, a multi-microgrid (MMG) system is a number of interconnected MGs connected with a larger and complex distribution network. Recently, the MMG energy management has created new challenges due to the inherent intermittency, uncertainty, and probabilistic nature of renewable based DGs output and varying load demands. To ensure the efficient operation and optimal energy management in the MMGs, this work proposes a two-stage, a day-ahead, simultaneous energy management strategy (EMS) of the MMG system as well as the MG system. At the first stage, each MG assumes a day-ahead predicted load demand and DGs output. At the second stage, through EMS, the energy scheduling, minimization of the main grid dependency, and maximization of the stored energy in the ESS are managed simultaneously. Four case studies are considered with four interconnected MGs with different DGs output and different initial state of charge (SOC) of ESS along with varying load demand. The proposed optimization model is formulated in the standard form using MATLAB OptimProblem, and compared with heuristic state flow-based EMS. Results show that the total grid dependency will be reduced to zero and ESS depth of discharge (DoD) will be increased up to 50% with the proposed optimization model.

Real-time state of charge estimation for electric vehicle power batteries using optimized filter

The main focus of the battery management system is the estimation of the battery's State of Charge (SOC), which is an indicator to determine the driving range of an electric vehicle. The Extended Kalman Filter (EKF) algorithm is the most promising for SOC estimation when the system is running. The EKF state estimation algorithm is sensitive to the process noise covariance matrix Q and measurement noise covariance matrix R. Inappropriate noise covariance matrices reduce the accuracy and cause divergence in state estimation. This paper uses the Sunflower Optimization algorithm (SFO) to find the optimal values of the noise covariance matrices before applying EKF for online SOC estimation. It is clearly stated that the iterative SFO does not affect the instantaneous response of EKF in the online estimate because the SFO is only performed once to determine the optimal values. The effectiveness of the proposed identification is examined through the constant discharge rate test and dynamic driving profile. The proposed algorithm's performance indices, such as maximum error, Mean Absolute Error, Mean Square Error, and Root Mean Square Error of SOC estimation, are low compared to the trial-error method, genetic algorithm, and adaptive extended Kalman filter. Besides accuracy, the proposed method quickly converges even when the initial SOC is inaccurate. The feasibility and benefits of the proposed SFO-EKF algorithm are demonstrated by qualitative and quantitative findings on five challenging datasets. To assess the performance of the SFO-EKF for SOC estimation in constant-current discharge and extreme drive cycle situations, three public datasets and two datasheets are employed. The simulation results show that the proposed method has high accuracy and a better convergence rate in estimating SOC under static and dynamic operating conditions.

Effects of number of electric vehicles charging/discharging on total electricity costs in commercial buildings with time-of-use energy and demand charges

Electric vehicle (EV) penetration has been increasing in the modern electricity grid and has been complemented by the growth of EV charging infrastructure. This paper addresses the gap in the literature on the EV effects of total electricity costs on commercial buildings by incorporating V0G, V1G, and V2B charging. The electricity costs are minimized in 14 commercial buildings with real load profiles, demand, and energy charges. The scientific contributions of this study are the incorporation of demand charges, quantification of EV, and smart charging electricity costs and benefits using several representative long-term datasets, and the derivation of approximate equations that simplify the estimation of EV economic impacts. Our analysis is primarily based on an idealized uniform EV commuter fleet case study. The V1G and V2B charging electricity costs as a function of the number of EVs initially diverge with increasing charging demand and then become parallel to one another with the V2B electricity costs being lower than V1G costs. A longer EV layover time leads to higher numbers of V2B charging stations that can be installed such that original (pre-EV) electricity costs are not exceeded as compared to a shorter layover time. Sensitivity analyses based on changing the final state of charge (SOC) of EVs between 90% and 80% and initial SOC between 50% and 40% (thereby keeping charging energy demand constant) show that the total electricity costs are the same for V0G and V1G charging, while for V2B charging, the total electricity costs decrease as final SOC decreases.

Deduction of the transformation regulation on voltage curve for lithium-ion batteries and its application in parameters estimation

States estimations play a vital role in the normal operation of the battery system. However, the existing state estimation methods are not fully applicable to the needs for simplicity, speed, and accuracy in cloud-based data analysis. To solve the above problems, we propose a parameter estimation method based on voltage curve transformation. In this paper, the charging voltage curve is studied for the state-of-charge (SOC) and state-of-health (SOH) estimation. By comparing the voltage curve with an open-circuit voltage (OCV) curve, it is proved that the parameters including initial SOC and capacity have a linear relationship with the curve shape. Furthermore, the dynamic time warping (DTW) algorithm is utilized to visualize the relationship. The data of cells with different state parameters are employed to verify the correction. For implementation, a curve fitting method based on the Least Absolute Deviations (LAD) is adopted to achieve the estimation of state parameters of the battery. Under the condition that SOC range is from 40% to 85%, the root-mean-square error (RMSE) of initial SOC is about 1.6% and the RMSE of SOH is about 2.1%.

Mixed-Integer Linear Programming Model to Assess Lithium-Ion Battery Degradation Cost

This work proposes a mixed-integer linear programming model for the operational cost function of lithium-ion batteries that should be applied in a microgrid centralized controller. Such a controller aims to supply loads while optimizing the leveled cost of energy, and for that, the cost function of the battery must compete with the cost functions of other energy resources, such as distribution network, dispatchable generators, and renewable sources. In this paper, in order to consider the battery lifetime degradation, the proposed operational cost model is based on the variation in its state of health (SOH). This variation is determined by experimental data that relate the number of charge and discharge cycles to some of the most important factors that degrade the lifespan of lithium-ion batteries, resulting in a simple empirical model that depends on the battery dispatch power and the current state of charge (SOC). As proof-of-concept, hardware-in-the-loop (HIL) simulations of a real microgrid are performed considering a centralized controller with the proposed battery degradation cost function model. The obtained results demonstrate that the proposed cost model properly maintains the charging/discharging rates and the SOC at adequate levels, avoiding accelerating the battery degradation with use. For the different scenarios analyzed, the battery is only dispatched to avoid excess demand charges and to absorb extra power produced by the non-dispatchable resources, while the daily average SOC ranges from 48.86% to 65.87% and the final SOC converges to a value close to 50%, regardless of the initial SOC considered.

State of charge estimation of Power lithium-ion battery based on an Affine Iterative Adaptive Extended Kalman Filter

State of charge (SOC) is a very important parameter for power lithium-ion battery in battery operation, but it cannot be measured directly, so it needs to be accurately estimationed. Considering the time-varying characteristics of the model parameters of the power lithium-ion battery, an online identification algorithm called Adaptive Forgetting Factor Recursive Augmented Least Squares (AFFRALS) is proposed. It can obtain a more accurate model compared with the offline method (fixed model parameters) by considering that the noise of power lithium-ion battery system is commonly non-Gaussian white noise in practice, which is different from the existing equivalent circuit models. Then, an Affine Iterative Adaptive Extended Kalman Filter (AIAEKF) method is proposed to deal with the non-Gaussian white noise and accelerate the convergence rate of the estimated results when the initial SOC value is wrong. Experiments demonstrate the effective of the online identification method as well as the SOC estimation method. This method shows a faster convergence rate and better SOC estimation performance than the traditional Extend Kalman Filter (EKF) method. The RMSE of the SOC estimation results is less than 0.023 except for 0 °C, which rarely occurs in practice.

A Method for Predicting the Remaining Useful Life of Lithium Batteries Considering Capacity Regeneration and Random Fluctuations

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs) is important for electronic equipment. A new algorithm is proposed to aim at the nonlinear degradation caused by capacity regeneration and random fluctuations. Firstly, the health state degradation curve of LIBs is divided into the normal degradation trend part, capacity regeneration part, and random fluctuation part. Secondly, the capacity degradation curve of LIBs is decomposed by the empirical mode decomposition (EMD) to obtain the known long-term degradation trend part of LIBs. Then, the long short-term memory (LSTM) neural network is used to predict the future normal degradation trend part based on the known long-term degradation trend part of LIBs. In addition, the LIBs’ state of health (SOH), the initial state of charge (SOC), and the rest time are taken as the inputs of Gaussian process regression (GPR) to predict the LIBs’ capacity regeneration part. After that, random numbers obeying the Stable distribution are generated as the random fluctuation part of LIBs. Finally, the Monte Carlo simulation is used to predict the probability density distribution of the RUL of LIBs. The paper is verified by the LIBs’ public dataset provided by the University of Maryland. The experimental results show that the predicted RMSE of the proposed method is lower than 0.6%.

Two-level energy harvesting strategy for multi-input thermoelectric energy system

The inevitable non-uniform temperature distribution across a large number of thermoelectric modules can cause reduced output power of the automotive exhaust thermoelectric generator (AETEG). In order to achieve accurate tracking of maximum power point for an AETEG, a distributed thermoelectric energy recovery system is proposed consisting of several parallel thermoelectric generators and a lithium-ion battery pack. And then, a two-level energy harvesting strategy is developed to efficiently recover exhaust energy under dynamic driving cycles. The simulated results based on an experimentally validated system model under the modified Highway Fuel Economy Test (HWFET) for a commercial heavy-duty vehicle demonstrate that the proposed strategy enables the system to run at the efficient working point of DC/DC converters. It shows that the charging energy can be increased by 174013 J and the efficiency of system-control level can be raised by 2.4% compared to the conventional feedback control under the modified HWFET driving cycle, when the initial State of Charge (SOC) of the battery pack is 10%. Meanwhile, the proposed strategy can avoid the over-charging events for the battery pack since it can be effectively controlled along its pre-defined optimal working trajectory, which is verified by the campus road test.

On-Road and Laboratory Emissions from Three Gasoline Plug-In Hybrid Vehicles—Part 1: Regulated and Unregulated Gaseous Pollutants and Greenhouse Gases

Road transport is a relevant source of greenhouse gas emissions. In order to meet the European decarbonisation targets, the share of electrified vehicles, including battery electric vehicles and plug-in hybrid electric vehicles (PHEVs), is rapidly growing, becoming the second most popular powertrain in the European market. PHEVs are of interest since they are expected to deliver a reduction in gaseous pollutants such as NOx as well as in greenhouse gases such as CO2. Herein, we explored both categories of emissions for three PHEVs with gasoline direct-injection engines, meeting the latest European emission standards (Euro 6d and Euro 6d-TEMP). They were studied in laboratory and on the road, in different modalities and temperatures. All tested vehicles met the Euro 6 emission limits in the Worldwide Harmonised Light-Duty Vehicles Test Procedure (WLTP) and the real driving emissions (RDE) test procedure. Still, when their internal combustion engine ignited even for a few km, their emissions were comparable to, and in some cases higher than, the average emissions reported for a fleet of eight conventional Euro 6d-TEMP gasoline direct-injection vehicles. The tested PHEVs presented similar trends to those of conventional vehicles, such as the increase in all pollutants considered at low ambient temperature or the high CO emissions during acceleration events, concomitantly with NH3. Moreover, depending on the boundary conditions, emissions were higher for the vehicles with a battery fully charged with respect to tests performed with the depleted battery. Furthermore, the use of an operating mode that allowed charging the vehicles’ high voltage battery using the internal combustion engine had a very strong impact on the vehicles’ CO2 emissions, offsetting the benefits in terms of greenhouse gas reduction demonstrated in other conditions. The results indicate that for the sample tested, the expected reduction in pollutants emission due to the presence of a hybrid gasoline-electric traction seemed in some cases limited, also showing high variability. CO2 emissions were also affected by the initial state of charge of the vehicles’ high voltage battery as well as from the user-selectable operating mode, also in this case with high variability.

Single-Phase Charging of EV Embedded Batteries in an MMC with Submodule Override Capability

The modular multilevel converter with embedded batteries is a viable alternative in electric vehicle drive systems. This research investigates integrated charging of the embedded batteries in a three-phase converter from a single-phase source without any additional hardware requirements. Two phases of the converter are connected to the supply while the third converter leg is not connected. In a typical charging of the converter batteries, the batteries with a lower state of charge are prioritised. Then all batteries are balanced at an average global state of charge. This research proposes a new sorting algorithm of the converter batteries with an override capability to bypass any individual submodule if required. This bypassing capability is modulation method independent, thus can be integrated with any modulation method. Simulation results and a real-time emulation of the charging system validate the proposed control method and the override algorithm. In addition, an open-loop switching technique is integrated with a new nearest level control-based measurement method of the battery terminal voltage to estimate its initial state of charge. This measurement facilitates a reduced number of voltage sensors for hardware implementation. This method was simulated and validated by comparing the calculated and the measured values of the battery initial state of charge. Real-time emulation of the system utilising hardware-in-the-loop apparatus was carried out, which confirmed the developed control system functions as expected.

Dynamic long-term expansion planning of electric vehicle parking lots considering lost opportunity cost and energy saving

In this paper, a practical approach is presented to long-term expansion planning of electric vehicle (EV) parking lots which aims to maximize the parking lot operator’s profit. The optimal number, location, capacity, and time for construction or expansion of EV parking lots have also been determined. Moreover, a method based on drivers’ travel patterns is proposed to estimate the number of EVs referring to each area, in which the unscented transformation method (UTM) is used to calculate the annual growth rate of EVs. An innovative and real-time method for optimal charge and discharge scheduling of EVs is also provided, which can calculate the parking lots’ demand at different times. The most important advantages of this method are its independence from iterative-based optimization algorithms and from modeling some input parameters such as the initial state of charge (SOC) and the arrival time of EVs into the parking lot as uncertain parameters. Also, a Lagrange interpolation-based method is proposed to minimize the energy consumption required for EVs to reach a parking lot. In addition, in order to prevent the reduction of parking lot operator’s profit in case of non-fulfillment of the obligations of the EV owners, an approach based on paying the lost opportunity cost (LOC) is presented. Real-world data has been used for numerical studies. The results show that based on travel patterns and estimating the number of EVs available in each municipal district and using the proposed real-time scheduling method, parking lot’s demand can be calculated as the main prerequisite for parking lots expansion planning. Also, the proposed method for parking selection compared to two other methods will save energy by 26.6% and 8.33%, respectively. Moreover, the proposed approach increases the total parking lot operator’s profit by 4.23%.

Energy Management Control Strategy for Saving Trip Costs of Fuel Cell/Battery Electric Vehicles

Fuel cell vehicles (FCVs) should control the energy management between two energy sources for fuel economy, using the stored energy in a battery or generation of energy through a fuel cell system. The fuel economy for an FCV includes trip costs for hydrogen consumption and the lifetime of two energy sources. This paper proposes an implementable energy management control strategy for an FCV to reduce trip costs. The concept of the proposed control strategy is first to analyze the allowable current of a fuel cell system from the optimal strategies for various initial battery state of charge (SOC) conditions using dynamic programming (DP), and second, to find a modulation ratio determining the current of a fuel cell system for driving a vehicle using the particle swarm optimization method. The control strategy presents the on/off moment of a fuel cell system and the proper modulation ratio of the turned-on fuel cell system with respect to the battery SOC and the power demand. The proposed strategy reduces trip costs in real-time, similar to the DP-based optimal strategy, and more than the simple energy control strategy of switching a fuel cell system on/off at the battery SOC boundary conditions even for long-term driving cycles.

Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach

The battery management system in an electric vehicle must be reliable and durable to forecast the state of charge. Considering that battery degradation is generally nonlinear, state of charge (SOC) estimation with lower degradation can be challenging. Lithium-ion batteries are highly dependent on the knowledge of aging, which is usually costly or not available online. In this paper, we suggest the state of charge estimation of lithium-ion battery systems by using an extreme gradient boosting algorithm for electric vehicles application, which acquires the nonlinear relationship model can with offline training. The extreme gradient boosting algorithm is the tree on based learning, which effectively performs and speeds. Voltage-time data used as an input of this system from the partial constant current phase; the proposed algorithm improves the accuracy of predicting the relevant. Additionally, no initial state of charge is required in our proposed method; thus, estimating the state of charge can consider each battery state.

A hybrid distributed framework for optimal coordination of electric vehicle aggregators problem

Electric vehicle (EV) aggregator is an entity, which facilitates the interaction between the grid and EV owners. The EV aggregator not only supports the EV owners but also participates in the electricity market by proper charging/discharging schedule of EVs. Due to the increasing number of parking lots (PLs) in the power system, there is an obvious benefit from the management of interconnected multi PLs with independent aggregators. Hence, this paper proposes a new distributed framework for the optimal management of two interconnected PLs. The alternating direction method of multipliers (ADMM) is utilized to coordinate the electricity exchange between the two PLs. The locational marginal price (LMP) uncertainty of the upstream grid as one of the most important challenges for the EV aggregators, is developed through robust optimization (RO). The driving pattern uncertainties of the EVs including the arrival time (AT), departure time (DT), and the initial state of charge of the EVs are taken into account by stochastic programming (SP). Therefore, a hybrid distributed RO-SP is created to deal with the EV aggregators problem. The numerical results reveal the effectiveness of the proposed model to participate in electricity market, support EV owners and coordinate the electricity exchange of PLs.

A Bi-level optimizer for reliability and security assessment of a radial distribution system supported by wind turbine generators and superconducting magnetic energy storages

This paper is aimed at improving the reliability and security of radial distribution system supported by wind turbine generators (WTGs) and superconducting magnetic energy storages (SMESs). For reliability indices assessment, the load-oriented indices including energy not supplied (ENS) and average energy not supplied (AENS) as well as the customers-oriented indices including system average interruption duration index (SAIDI), system average interruption frequency index (SAIFI), customer average interruption duration index (CAIDI), and average service unavailability index (ASUI) are evaluated. Network security index (NSI) is also addressed, which refers to the risk level for current flow in the lines prior reaching to extremis. A multi-objective function is composed and formulated in order to simultaneously minimize ENS, SAIFI, SAIDI, ASUI, and NSI as indices which characterize the performance of distribution system using a hybrid approach based on equilibrium optimizer (EO) along with the loss sensitivity factors. A bi-level optimizer based on the hybrid approach addressing all constraints is applied for simultaneous minimization of reliability and security indices of distribution system. The outer level looks for an optimal sizing and sitting of WTGs and SMESs as well as optimal weighting coefficients whose evaluation is no longer assumed arbitrarily or left to the preferences of the decision maker. The inner level optimizes charging and discharging powers as well as initial state of charge of SMESs. Modeling of feeder's failure rate applying several mathematical functions is considered. The effectiveness of proposed approach is validated on the IEEE 33-bus test system considering commercial, industrial, residential, and mixed time-varying voltage-dependent load models. The performance of the distribution system is also checked as regards the energy loss in feeders, the bus voltage deviation from rated value as well as the voltage stability of the system. For validating the effectiveness of the EO algorithm, the obtained results using the EO algorithm are compared with those predicted by particle swarm optimization (PSO) and genetic algorithm (GA). Moreover, the obtained results display that the bi-level optimization can successfully achieve satisfactory improvement of the reliability and security indices of the distribution system.

Analysis of Acoustic Characteristics under Battery External Short Circuit Based on Acoustic Emission

The safety of power batteries has received more and more attention in promoting electric vehicles. The external short circuit is particularly prominent as an abnormal and harmful event of a battery, and the exploration of in-situ low-cost detection technology for such an event is the starting point of this paper. By building an experimental bench that could detect the external short circuit of the battery and obtain the acoustic, electrode, and temperature responses, the resulting acoustic analysis would establish an internal connection with the electrode and temperature measurement when the external short circuit occurs. The respective acoustic response characteristics of different initial battery states of charge were analyzed by selecting appropriate acoustic characteristic parameters in the time and frequency domains. The acoustic measurement could represent the battery abnormality synchronously like the electrode measurement, and the results of the damage and rearrangement of the internal of the battery are easy to characterize through a moderate amplification of the acoustic response. The different initial state of charge (SOC) state reflects noticeable differences in the acoustic characteristics. Therefore, it is considered that the acoustic emission technology might have potential battery condition assessment capabilities and be a tool for in-situ battery fault diagnosis.

A simulation–optimization framework for a dynamic electric ride-hailing sharing problem with a novel charging strategy

Electric vehicles (EVs) are more environmentally friendly than gasoline vehicles (GVs). To reduce environmental pollution caused by ride-hailing gasoline vehicles (RGVs), they have been gradually replaced with ride-hailing electric vehicles (REVs). Like RGVs, REVs can allow passengers to share trips with others. However, REVs are plagued by charging needs in daily operations. This study develops a simulation–optimization framework for the dynamic electric ride-hailing sharing problem. This problem integrates a dynamic electric ride-hailing matching problem (with sharing) and a dynamic REV charging problem, both of which aim to match REVs to passengers willing to share their trips with others and schedule the charging events of REVs on temporal and spatial scales, respectively. The dynamic electric ride-hailing matching problem is divided into a set of electric ride-hailing matching subproblems by a rolling horizon approach without a look-ahead period, while the dynamic REV charging problem is divided into a set of REV charging subproblems by a rolling horizon approach with look-ahead periods. Each REV charging subproblem incorporates a novel charging strategy to determine the charging schedules of REVs and relieve the charging anxiety by considering the information of requests, REVs, and charging stations. Each REV charging subproblem is formulated as a mixed integer linear program (MILP), whereas each electric ride-hailing matching subproblem is formulated as a mixed integer nonlinear program (MINLP). The MINLP and MILP are solved by the artificial bee colony algorithm and CPLEX, respectively. The proposed simulation–optimization framework includes a simulation model which is used to mimic the operations of REVs and update and track the state of passengers and the charging processes at charging stations over time using the outputs of each MILP and MINLP. The results show that the proposed charging strategy outperforms the benchmarks with a shorter waiting time for charging and a higher matching percentage in the dynamic ride-hailing matching problem. The robustness of the proposed charging strategy is tested under different scenarios with changing the initial state of charge (SOC), the number of REVs, the number of charging piles at each charging station, the time to fully charge, and the distribution of charging piles. The results show that REV drivers can charge their vehicles more flexibly without waiting too long and then pick up more passengers under all test scenarios.