In this paper, the semi-empirical battery degradation prediction model proposed considers electrochemical degradation characteristics and represents degradation effects under various conditions, including different states of charge (SoC) areas. This model is specifically designed to address degradation during cycling and short-term rest periods in lithium-ion batteries using liquid electrolytes. Cycle aging incorporates the impact of solid electrolyte interphase (SEI) growth, a known dominant factor, and the model for short-term resting periods captures potential aging impacts on subsequent cycles due to internal material concentration gradients, moving away from the traditionally used calendar life approach. The derivation of the model presented in this paper is based on 14 data sets under different SoC conditions and 8 data sets under various Crates, explaining the degradation effects at 10 % SoC intervals and three different Crate points. Moreover, the model's performance was validated through capacity prediction for two data sets experimented with dynamic operational schedules of actual energy storage systems (ESS), including various conditions. The results showed a root mean square error (RMSE) of 0.564 and a mean absolute percentage error (MAPE) of 0.346. The superiority of this model is demonstrated by comparing its performance with four other types of degradation models derived through the same process in the validation data. • The model was designed to consider electrochemical aging characteristics for precise battery degradation prediction. • New insights on degradation during rest states move beyond the traditional calendar life approach. • The flexibility of the proposed model was evaluated using data from 22 battery lifespan experiments. • The performance was assessed using experimental data on two real-world ESS schedules, compared to other models.

Steady-state voltage security region (SVSR) is an important criterion that can intuitively and effectively evaluate the power system's operation security and overall safety margin. However, there is no literature on its application to electric vehicle (EV) proliferation. Since EV introduces dynamism and uncertainties to the grid, most existing indices cannot assess the grid accurately. Given this, we propose a load margin index (LMI) based on SVSR considering uncertainties. This is used to assess the impact of EV proliferation via a bi-layer optimal EV scheduling model. The upper layer of the model solves a dispatching plan for EV aggregators (EVA) by maximizing the LMI and minimizing the deviation between the upper and lower layer power schedules. The lower layer models each EV's charging/discharging strategy by minimizing the EV's operating cost, the EV user's target residual energy, and the deviation between the lower and upper layer schedules. We also employ a real-time scheduling plan to further adjust the day-ahead scheduling due to forecast errors. A case study based on the IEEE-33 node distribution network verifies the practicability and effectiveness of the proposed method.

This study is aimed at determining the optimal energy storage system (ESS) operation schedule to minimize the peak load on the feeder of a distribution network. To reduce the peak load, the feeder load profile needs to be predicted. A deterministic prediction is not reliable, however, because it may contain errors. This study proposes the use of prediction intervals (PIs) of estimated error based on prior predictions. The proposed algorithm is intended for the determination of an optimal ESS schedule using the PIs. To demonstrate the method's validity, a case study is presented where a proposed optimal ESS schedule determined from PIs reduces the peak load during network operations over a one-year period. The performance of the proposed method is superior to that of the conventional method which uses deterministic load prediction.

In this paper, the semi-empirical battery degradation prediction model proposed considers electrochemical degradation characteristics and represents degradation effects under various conditions, including different states of charge (SoC) areas. This model is specifically designed to address degradation during cycling and short-term rest periods in lithium-ion batteries using liquid electrolytes. Cycle aging incorporates the impact of solid electrolyte interphase (SEI) growth, a known dominant factor, and the model for short-term resting periods captures potential aging impacts on subsequent cycles due to internal material concentration gradients, moving away from the traditionally used calendar life approach. The derivation of the model presented in this paper is based on 14 data sets under different SoC conditions and 8 data sets under various Crates, explaining the degradation effects at 10 % SoC intervals and three different Crate points. Moreover, the model's performance was validated through capacity prediction for two data sets experimented with dynamic operational schedules of actual energy storage systems (ESS), including various conditions. The results showed a root mean square error (RMSE) of 0.564 and a mean absolute percentage error (MAPE) of 0.346. The superiority of this model is demonstrated by comparing its performance with four other types of degradation models derived through the same process in the validation data. • The model was designed to consider electrochemical aging characteristics for precise battery degradation prediction. • New insights on degradation during rest states move beyond the traditional calendar life approach. • The flexibility of the proposed model was evaluated using data from 22 battery lifespan experiments. • The performance was assessed using experimental data on two real-world ESS schedules, compared to other models.

Steady-state voltage security region (SVSR) is an important criterion that can intuitively and effectively evaluate the power system's operation security and overall safety margin. However, there is no literature on its application to electric vehicle (EV) proliferation. Since EV introduces dynamism and uncertainties to the grid, most existing indices cannot assess the grid accurately. Given this, we propose a load margin index (LMI) based on SVSR considering uncertainties. This is used to assess the impact of EV proliferation via a bi-layer optimal EV scheduling model. The upper layer of the model solves a dispatching plan for EV aggregators (EVA) by maximizing the LMI and minimizing the deviation between the upper and lower layer power schedules. The lower layer models each EV's charging/discharging strategy by minimizing the EV's operating cost, the EV user's target residual energy, and the deviation between the lower and upper layer schedules. We also employ a real-time scheduling plan to further adjust the day-ahead scheduling due to forecast errors. A case study based on the IEEE-33 node distribution network verifies the practicability and effectiveness of the proposed method.

This study is aimed at determining the optimal energy storage system (ESS) operation schedule to minimize the peak load on the feeder of a distribution network. To reduce the peak load, the feeder load profile needs to be predicted. A deterministic prediction is not reliable, however, because it may contain errors. This study proposes the use of prediction intervals (PIs) of estimated error based on prior predictions. The proposed algorithm is intended for the determination of an optimal ESS schedule using the PIs. To demonstrate the method's validity, a case study is presented where a proposed optimal ESS schedule determined from PIs reduces the peak load during network operations over a one-year period. The performance of the proposed method is superior to that of the conventional method which uses deterministic load prediction.

Data retrieval techniques are crucial for developing an effective battery management system for an electric vehicle to accurately assess the battery’s health and performance by monitoring operating conditions such as voltage, current, time, temperature, and state of charge. This paper proposes an efficient approach to retrieve real-world field data (voltage, current, and time) under running vehicle conditions. In the first step, noise is removed from the field data using a moving-average filter. Then, first- and second-order derivations are applied to the filtered data to determine specific data set conditions. After that, a new approach based on zero-crossing is implemented to retrieve the field data. A second-order Randle circuit (SORC) is utilized in this study to analyze the selected field data. Further, a particle swarm optimization algorithm is adapted to estimate the parameters of the SORC. Our experiments indicate that the relative errors of the equivalent circuit model (ECM) are less than 2% compared to the model voltage and real voltage, which is consistent with the stable parameters of ECM.

This research aims to formulate an optimal control algorithm for a hydrothermal air-conditioning system, with the objective of minimizing energy consumption while simultaneously ensuring occupant comfort. Traditional air-conditioning systems often exhibit difficulties in effectively responding to nonlinear dynamic characteristics and variable environmental conditions. Although centralized control strategies can improve energy efficiency, they frequently do not adequately fulfill thermal comfort requirements. To mitigate these challenges, this study introduces an optimal control scheduler that amalgamates machine learning and optimization methodologies, specifically utilizing Random Forest, Gradient Boosting, and Genetic Algorithms. The proposed control scheduler consists of two principal phases. Initially, a forecasting model is constructed using a hybrid approach that integrates Random Forest for predicting water tank temperature and Gradient Boosting for forecasting indoor temperature. In the subsequent phase, an optimization framework is established employing a Genetic Algorithm, aimed at minimizing energy costs and occupant discomfort. This optimization model dynamically adjusts the comfort temperature range based on real-time occupancy estimations, thereby facilitating efficient and adaptive air-conditioning control. The efficacy of the optimal control scheduler was validated in a real-world testbed environment, where its energy efficiency and comfort maintenance were compared to manual control. The findings revealed an 11.41% reduction in energy consumption relative to manual control, alongside an improvement in the comfort maintenance rate from 56.2% to 91.7%, thereby affirming the effectiveness of the proposed approach in enhancing both energy efficiency and thermal comfort.