Battery SOC Research Articles

Collaborative evaluation of SoC, SoP and SoH of lithium-ion battery in an electric bus through improved remora optimization algorithm and dual adaptive Kalman filtering algorithm

Many countries are switched to electric vehicles (EVs) for public transportation, which increases the adoption of electric buses. Batteries are the key component in battery-operated electric vehicles and must be monitored for the system to operate efficiently. This work has proposed a joint estimation method to examine the battery states. The lithium-ion battery (LiB) is initially designed through a first-order RC equivalent (FO-RC) circuit. To optimize the modelling parameters of a battery, an improved remora optimization algorithm (IROA) is proposed in this work. After detecting the optimum values for the parameters, the SoC is evaluated by a dual adaptive Kalman filtering algorithm (DAKF). Then the SoH is estimated based on the predicted SoC of a battery, whereas the SoP is evaluated by considering current and voltage constraints during battery operation. After that, the battery's remaining useful life (RUL) is examined based on the estimated SoC. The proposed work is implemented on the MATLAB platform, and the results will be validated under varying operating conditions. The comparative analysis shows that the IROA provides optimum parameters near the actual parameters' actual values, thereby improving the prediction accuracy of battery states.

A fast active balancing strategy based on model predictive control for lithium-ion battery packs

The consistency of lithium-ion battery packs is extremely important to prolong battery life, maximize battery capacity and ensure safety operation in electric vehicles. In this paper, a model predictive control (MPC) method with a fast-balancing strategy is proposed to address the inconsistency issue of individual cell in lithium-ion battery packs. Firstly, an optimal energy transfer direction is investigated to improve equalization efficiency and reduce energy loss. Then, a MPC-based equalization algorithm is developed to obtain the optimal constant equalization current by directly minimizing equalization time of battery's SOC. Moreover, a fast-solving strategy for MPC is designed to reduce the computational burden of cells' equalization. Finally, the performance of proposed MPC algorithm has been compared with other MPC-based equalization methods in three different equalization topologies (cell-to-cell, cell-to-pack and module-based equalization topology), the results indicate that the proposed algorithm achieves faster equalization speed and less energy loss in three equalization topologies. Importantly, the proposed algorithm avoids the repeated charging and discharging of intermediate batteries effectively, and ensures the single-point convergence of cells' SOC. Furthermore, the effectiveness and accuracy of proposed fast-solving strategy for MPC algorithm is verified by comparison with common solving strategies, the results show the proposed method takes less computational time to obtain the accurate optimal balancing current, indicating that the proposed fast-solving strategy can improve computation speed and reduce computational burden.

An investigation into hybrid energy storage system control and power distribution for hybrid electric vehicles

This study aims to develop a hybrid energy storage system (HESS), targeting a commercialised Hybrid Electric Vehicle model (Hyundai Sonata), that consists of battery and supercapacitor cells, to evaluate its benefits on the battery's health and vehicle's performance. The different possible configurations of combining the two energy storage devices are discussed, while a semi-active configuration is considered for the purpose of simulation and testing, where the supercapacitor is connected to a bidirectional DC-DC converter, implemented with a newly developed control logic for the HESS. The analysis of the HESS is done by modelling the vehicle powertrain on the simulation software Ricardo IGNITE, while adapting the WLTC Class 2 drive cycle. The results are validated by comparing HESS model to the sole battery model, which showed the working status of the battery is stabilised by the addition of the supercapacitor in the HESS model during both the traction and regenerative modes. There is a significant reduction in the battery peak current, 15.26% and 20.54% for the charge and discharge current, respectively. Other improvements include a 0.43% increase on the average battery SOC, a 6.8% decrease in the battery's maximum temperature and 47.56% decrease in the average battery power. On the other hand, there was a 0.46% increase on vehicle's average L/100 km fuel consumption, due to losses in the supercapacitor system. Various other results are also compared in the discussion section, justifying the addition of the supercapacitor. The investigation finishes off by conducting a parametric study based on the number of supercapacitor modules in parallel, and comparing results such as current, power, fuel consumption and battery SOC. The results show that 2 supercapacitor modules provided the optimal results in terms of battery's current and power behaviour.

Energy management strategy of fuzzy logic control for fuel cell truck

This paper aims to address the problems of high consumption of hydrogen fuel for fuel cell hybrid electric trucks. Based on the data analysis of rule-based energy management experiments, this paper proposes a control method based on fuel cell compensation battery power, which is a fuzzy logic strategy based on real-time driver power demand and power cell SOC as input and fuel cell demand power as output. The results show that based on fuzzy logic controls, the hydrogen consumption of the fuel cell hybrid truck is reduced from 7.336 kg/10 cycles to 5.817 kg 10 cycles, a decrease of 24.8%. On the other hand, the change amplitude of the power battery SOC is reduced from 55% ∼81% to 50% ∼60%, a decrease of 20%. The fuzzy logic control strategy is fuel battery hybrid truck energy management provides a good reference for reducing consumption and SOC balance.

A novel e‐bike energy management for improvement of the rider metabolism

AbstractIn this study, a switch controller manages the power‐sharing between the battery and human mode to improve the rider's metabolism and manage the battery SOC. The main idea is to optimize this power source switching element for changing the status to reach a trade‐off between lack of tiredness and keeping the SOC high. Calorie burning is closely related to the rider's physical characteristics. In this paper, these parameters are investigated to calculate calorie burning. When the electric‐powered mode is activated, the SOC level comes down. When the human‐powered mode is activated, the human power source provides energy. The model converts the bicycle speed into the rider's heart rate and then changes it into burned calories based on some equations. These equations are obtained by poly fitting after experiments. This optimization causes 33.5% and 50% burning calorie reduction in Cleaveland and Portuguese driving cycles. Also, in the Portuguese driving cycle, the battery usage percentage decreases 39.56% from to 20.54% after optimization; therefore, the burning calorie decreases 265.84 Kcal to 176.83 Kcal.

Optimal coordinated energy management strategy for standalone solar photovoltaic system with hybrid energy storage

Energy storage devices and renewable resources, especially rooftop photovoltaic (PV), are vital to the operation of standalone systems. In this study, an energy management strategy (EMS) for battery energy storage systems (BESS), PV, and supercapacitors (SC) is presented. The proposed control strategy is designed to optimize the BESS flow rate, discharge, and charge cycles of the energy system using the Meta-heuristic Jaya algorithm by properly coordinating SC and PV. SC is employed in HESS to fulfill the transient energy mismatches and reduce the transitory high charge/discharge current rate impacts on BESS using EMS. The proposed controller also has the benefit of keeping the battery's SoC within limitations for a long period. To extend the life of BESS, the EMS is aimed at minimizing deep charging and discharging by reducing PV generation, particularly under light load (no-load) situations and when battery and SC's SoC≥SoCmax or charging. Similarly, the optional load shedding is employed when battery and SC's SoC≤SoCmin to avoid deep discharge. The proposed EMS is validated in both simulation environment and hardware-prototype under different perturbations and operating conditions. The charge and discharge rate performance of BESS and SC under different charge and discharge time durations, i.e., 1 s, 2 s, and 3 s, were evaluated in both simulation and hardware, and compared to conventional charge and discharge methods for HESS, whose charge and discharge time durations are nearly 0.3 s to 0.4 s. As a result, battery stress is significantly reduced. Furthermore, the economic analysis of the complete system is carried out. In this, it is observed that standalone PV and HESS systems with the proposed EMS are cost-effective and economical compared with PV and BESS systems when operated for a long horizon beyond 6 years.

Simulation of Electric Vehicle Regenerative Braking Control Strategy Based on Brake Intention Recognition

The development of regenerative braking technology for a pure electric vehicle is the key technology to solving the current limited range of the electric vehicle. Accurately identifying the driver’s braking intent is fundamental to controlling the braking system of an electric vehicle.A brake intent recognition controller is designed in this paper, which is based on a lot of driving data and fuzzy principles. In addition, two typical regenerative brake control strategies are compared. The simulation results of Matlab/Simulink and AMESim show that the SOC of the power battery increases obviously at different braking intensities of 30 km/h. Besides, energy recovery is up to 20.5%, and battery energy increases by 6.5 wh.

Improved Long Short-Term Memory: Statistical Regression Model for High Precision SOC Estimation of Lithium-Ion Batteries Adaptive to Complex Current Variation Conditions

Lithium battery health management is of great significance to promote its wide application. Its accurate battery modeling and state prediction can ensure the safe start-up and stable operation of battery management system. A new method for estimating the charge state of lithium-ion batteries based on phase space reconstruction was proposed by combining long and short term memory network and statistical regression. Compared with the traditional method, the improved LSTM improves the accuracy of prediction by adding data feature dimension through phase space reconstruction, and the segmentation prediction reduces the complexity of data and improves the learning speed. By combining neural network with Kalman filter, it is more consistent with the continuity of lithium battery SOC and further improves the prediction accuracy. Finally, in order to verify the accuracy of the algorithm, an estimation test is carried out using ternary lithium battery. The results show that in BBDST conditions, the prediction ability of the proposed method is significantly improved compared with other algorithms. After 400 cycles of charge and discharge, the prediction error is less than 2.21%, which further indicates that this method has good estimation ability.

A GM-Based Energy Management Strategy of Hybrid Power System for Hydrogen Fuel Cell Buses

Hydrogen energy is a clean, carbon-free, flexible, efficient, and widely used secondary energy source, which is an ideal alternative to promote the clean and efficient use of traditional fossil fuels. Hydrogen fuel cell bus has the advantages of a high-energy conversion rate, absolute pollution-free, sufficient raw materials, and convenient filling. The hybrid power system, composed of fuel cell and auxiliary energy source, is one of the key technologies to promote the development of hydrogen fuel cell vehicle. This study aims to propose an energy management strategy by analyzing the output characteristics and power allocation of fuel cell and power battery in the hybrid power mode with fuel cell as the main and battery as the auxiliary. A GM (1, N) power prediction strategy was proposed and compared with other strategies as an on-off control strategy and logical threshold value strategy in this study. The variation curves of the battery SOC and fuel cell output power under two working conditions of CCBC and real vehicle conditions were analyzed by using these three strategies, when the initial SOC of power battery is 30%, 70%, and 90%, respectively. Results showed that the power prediction strategy based on GM (1, N) has a better performance in output efficiency and fuel economy when compared to the other two strategies by analyzing the aspects of the battery in the SOC variation and equivalent hydrogen consumption and the fuel cell in the output power variation and hydrogen consumption. This research can be helpful to provide the suggested solution for energy management of the hybrid power system for hydrogen fuel cell buses.

Double Extended Kalman Filter Algorithm Based on Weighted Multi-Innovation and Weighted Maximum Correlation Entropy Criterion for Co-Estimation of Battery SOC and Capacity.

Most of the traditional extended Kalman filter algorithms for the co-estimation of SOC and capacity of lithium-ion batteries are designed based on the minimum mean square error (MMSE) criterion, which may show superior performance in Gaussian noise scenes. However, due to the complexity of the battery operating environment, it is likely to face non-Gaussian noise (especially outlier noise), at which time the performance of the traditional extended Kalman filter algorithms will be seriously weakened. To solve the above problems, this paper first proposes a double extended Kalman filter algorithm based on weighted multi-innovation and weighted maximum correlation entropy (WMI-WMCC-DEKF) for the co-estimation of battery SOC and capacity. In this paper, the performance of the target algorithm is verified and compared by generating different types of noise from three noise models: weak Gaussian mixture noise, strong Gaussian mixture noise, and outlier noise. The maximum absolute error value (MAE) and root mean square error value (RMSE) of the WMI-WMCC-DEKF algorithm can achieve the highest performance improvement of 69.3 and 84.2% (SOC), 61.3, and 94.2% (capacity), respectively. The experimental results fully prove that the target algorithm has excellent performance against three kinds of noises.

Model based integrated control strategy for effective brake energy recovery to extend battery longevity in electric two wheelers

This study aims to achieve optimal regenerative braking performance in the form of a reduced decline in battery SOC for a BLDC electric machine with peak torque of 10 Nm for use in electric two-wheelers. This is conducted via a comparison of control algorithms based on Direct Look-up table, Fuzzy Logic and their combination with PID control. The whole vehicle model and the energy recovery control strategies are designed using MATLAB Simulink by benchmarking the design with the parameters of the Ola Electric S1. A physical motor-dynamometer test bench is utilised to obtain a complete motor operating range to derive a realistic efficiency map that is used in the model. WLTP Class 2 and NYCC standard drive cycles are implemented for vehicle simulation. Two live-recorded driving patterns are also used to validate the model to analyse the adaptability of the control strategies. After obtaining the required motor speed, torque values and the range by matching the theoretical drive cycle profile, the control strategy is further optimised using the PID auto-tuning toolbox in Simulink. Using physical testbench data, the effect of various regenerative braking control strategies on overall vehicle performance is more accurately realised. The Fuzzy PID control strategy exhibits the most optimal gains in terms of energy recovery for electric two-wheelers, allowing for the highest battery SOC levels of 41.88% and average motor regenerative torque of 7.25 Nm for the standard drive cycles. An analogous trend is observed for the on-road driving pattern as Fuzzy PID provides highest battery SOC and average motor regenerative torque of 48.34% and 7.5 Nm respectively. For the driving scenarios aforementioned, this provides a 17% and 44% increase in SOC respectively when compared to a non-regenerative braking-based system.

Research on Adaptive Distribution Control Strategy of Braking Force for Pure Electric Vehicles

The actual driving conditions of electric vehicles (EVs) are complex and changeable. Limited by road adhesion conditions, it is necessary to give priority to ensuring safety, taking into account the energy recovery ratio of the vehicle during braking to obtain better braking quality. In this work, an electric vehicle with an EHB (electro-hydraulic braking) system whose braking force adaptive distribution control strategy is studied. Firstly, the vehicle dynamics model, including seven degrees of freedom, tire, drive motor, main reducer, battery pack, and braking system, was constructed, which is attributed to the vehicle configuration and braking system scheme. Second, based on curve I and ECE regulations, the adaptive braking force distribution control strategy was formulated by taking the maximum regenerative braking torque as the inflection point, the synchronous adhesion coefficient as the desired point, and the battery SOC, road adhesion coefficient, and braking strength as the threshold. Finally, the vehicle dynamics simulation model was built on the Matlab/Simulink platform, and the simulation results verified the feasibility of the proposed braking force adaptive allocation control strategy. The research shows that the adaptive distribution control strategy can better adapt to the complex and variable driving conditions of the vehicle by combining the inflection point and the desired point. The braking energy recovery ratios of the vehicle under the NEDC and NYCC cycle conditions on a high adhesion road are 52.62% and 47.45%. The braking force distribution curve is close to curve I under the low adhesion extreme road.

Joint estimation of SOC and SOH of lithium battery based on multi-time scale EKF-ASRCKF algorithm

To address the low accuracy of battery charged of state (SOC) estimation due to fixed model parameters and capacity, a joint multi-time scale estimation algorithm (EKF-ASRCKF) is proposed in this paper, which consists of the adaptive square root cubature Kalman filter (ASRCKF) algorithm and the extended Kalman filter (EKF) algorithm. Firstly, the model parameters and capacity on the macroscopic time scale are identified by the EKF algorithm; meanwhile, the battery capacity estimation results characterize the battery health of state (SOH). Then the ASRCKF algorithm estimates the SOC on the microscopic time scale. Finally, the experimental validation is carried out under the BBDST condition. The validation result indicates that the EKF-ASRCKF algorithm provides a good estimation of the model parameters and capacity, which further improves the estimation precision of the battery SOC.

An Adaptive Cutoff Frequency Design for Butterworth Low-Pass Filter Pursuing Robust Parameter Identification for Battery Energy Storage Systems

Energy storage systems are key to propelling the current renewable energy revolution. Accurate State-of-Charge estimation of the lithium-ion battery energy storage systems is a critical task to ensure their reliable operations. Multiple advanced battery model-based SOC estimation algorithms have been developed to pursue this objective. Nevertheless, these battery model-based algorithms are sensitive to measurement noises since the measurement noises affect the accuracy of battery model identification, thus leading to inaccurate battery SOC estimation consequently due to modeling error. The Butterworth low-pass filter has proven effectiveness in measurement noise filtering for accurate parameter identification, while the cutoff frequency design relies on prior knowledge of lithium-ion batteries, making its capability limited to general cases. To overcome this issue, this paper proposes an adaptive cutoff frequency design algorithm for the Butterworth low-pass filter. Simulation results show that the low-pass filter functions properly in the presence of multiple scales of measurement noises adopting the proposed work. Consequently, the parameters of the battery model and the SOC of the battery are both identified and estimated accurately, respectively. In detail, the parameters: R0, R1, C1, and the time constant τ are all identified accurately with low relative identification errors of 0.028%, 11.12%, 6.21%, and 5.94%, respectively, in an extreme case. Furthermore, the SOC of the battery can thus be estimated accurately, leaving a low of 0.081%, 0.97%, and 0.14% in the mean and maximum absolute SOC estimation error and the standard deviation, respectively.

A tolerant sequential correction predictive energy management strategy of hybrid electric vehicles with adaptive mesh discretization

This paper establishes an adaptive correction predictive energy management strategy (EMS) to obtain optimal power distribution in the case of inaccurate prediction with excellent computational efficiency for parallel hybrid electric vehicle (HEV). Firstly, deep neural network (DNN) models are trained to forecast future speed sequence with different prediction horizons to prepare for online model predictive control (MPC) implementation. According to the degree of forecasting errors, a novel tolerant sequential correction algorithm as the solver of MPC strategy is selected in tuning mechanism instead of traditional dynamic programming (DP) to cope with the rough accumulated prediction. Besides, to compromise between fuel optimization and lower computation burden, statistical analysis of battery SOC variation distribution is gained from historical driving cycles' calculation through offline DP. Then, nearest neighbor interpolation model is fitted to generate optimal ranges of state variable in each step, which adaptive mesh discretization method is intelligently reducing state and control calculation grid's number. Numerical simulations demonstrate that the proposed tolerant sequential correction algorithm applied in MPC scheme with adaptive mesh discretization have yielded the favorable capability of the fuel economy in the consideration of inaccurately velocity prediction and excellent computational efficiency, which exhibits the practical adaptability in real driving routes.

State-of-charge estimation of lithium-ion batteries based on MCC-AEKF in non-Gaussian noise environment

The extended Kalman filter (EKF) has low estimation accuracy for lithium-ion battery SOC under non-Gaussian noise interference. To solve this problem, an adaptive EKF based on maximum correlation-entropy criterion (MCC-AEKF) was proposed. In this algorithm, the maximum correlation entropy criterion is used to obtain accurate estimation results by replacing the overall solution with the local optimal solution. Meanwhile, the adaptive covariance matrix is established to update the process noise variance to reduce the SOC estimation error. MCC-AEKF were used to estimate the SOC based on two kinds of battery data. The experiments show that, under non-Gaussian noise interference and at operating temperature of 25 °C, compared with EKF and MCC-EKF, the estimation accuracy of MCC-AEKF improved by 80.3% and 24.2% separately for battery 1. For battery 2, its estimation accuracy improved by 72.6% and 22.0% separately, and are also the highest at 10 °C and 40 °C. Given wrong initial SOC values, MCC-AEKF can converges to real value within 30s. All results present the good accuracy and robustness of the proposed method under different cases.

ARWLS-AFEKE: SOC Estimation and Capacity Correction of Lithium Batteries Based on a Fusion Algorithm

Accuracy of battery charge status (SOC) estimation plays a significant role in the management of electric vehicle power batteries. However, recently, abrupt changes from SOC data often occurs in the actual operation of electric vehicles and some errors appear in the establishment of battery models and noise models, which give rise to the poorly adaptive and robust performance of traditional algorithms in the process of SOC estimation. The fusion algorithm proposed in this paper can effectively improve the accuracy of models and SOC estimation of lithium-ion batteries. Based on the second-order R-C network model, this method optimizes the accuracy of parameter identification by adopting the adaptive recursive weighted least square algorithm (ARWLS). In addition, the adaptive fading extended Kalman filter algorithm (AFEKF) is applied to estimate the SOC of lithium-ion batteries. Additionally, via introducing a fading factor, the optimal Kalman gain is updated in real-time, which can reduce the impact of data mutation on battery modeling. Compared with the offline AEKF algorithm and the EKF algorithm, the adaptive recursive weighted least square-adaptive fading extended Kalman filter (ARWLS-AFEKF) fusion algorithm had higher accuracy and adaptability, which can be adapted to the variable noise environment.

Fuzzy logic-based energy management for isolated microgrid using meta-heuristic optimization algorithms

Rural areas have increased directly with the growth of the world population. Although, isolated rural areas do not have the energy infrastructure to supply essential services such as electricity. In Ecuador, as in the rest of the world, these areas of the rural population usually present access problems due to the topography of the place. This makes the construction of power lines to connect them to the Interconnected National Energy System unfeasible. In this regard, isolated microgrids have emerged as a great solution to cover the energy demands in these locations. However, an optimal implementation of isolated microgrids depends on several factors, such as geographical location, weather conditions, sizing, load demand, operating costs, and social impacts. Therefore, this study proposes the design of a new energy management system (EMS) for isolated microgrids comprising a photovoltaic system, diesel generator, and battery energy storage system (ESS). Since fuzzy logic control (FLC) has proven to be a powerful tool for dealing with the nonlinearities of a microgrid and the application of fuzzy-based EMS for isolated microgrids is rarely reported in the literature, this study proposes the application of an FLC for the EMS's design of an isolated microgrid. The proposed fuzzy-based EMS uses generation and demand forecasts, enhances the operating time of diesel generators (DLG), and takes advantage of the available solar resource to supply the energy required by a community while preserving the ESS lifetime. An adjustment of the FLC parameters by Particle Swarm Optimization (PSO) and Cuckoo Search (CS) algorithms is performed to improve the behavior of the proposed EMS. Furthermore, a battery degradation model is applied to estimate the ESS State of Health (SOH). To highlight the advantages of the proposed approach, a case study in a specific community in Ecuador is presented. In this location, the proposed EMS is compared with an EMS without parameter adjustment developed in previous work, demonstrating improved performance in DLG limits, preserving the battery lifespan by controlling the battery SOC limits more efficiently, and minimizing the microgrid operating costs by maximizing the use of photovoltaic power (i.e., reducing the wasted photovoltaic energy). The results provide evidence that the EMS adjusted with the PSO algorithm presents an enhanced behavior than the one adjusted by the CS algorithm. Finally, the fuzzy-EMS is validated using Matlab® and Hardware-in-the-Loop Typhoon HIL-402 device. Results demonstrate that the proposed approach limits DLG usage, make the most of the available solar resources, and extends the battery life by controlling overcharges and deep discharges.

Establishment of energy management strategy of 50 kW PEMFC hybrid system

A 50 kW class PEMFC and battery model were evaluated to find appropriate battery capacity for 50 kW class vehicles. Afterwards, fuel cell and battery EMS strategies were established in response to rapid load changes. First, a 50 kW class PEMFC model and a battery model were designed and verified using the experimental values. After that, the amount of SOC change according to the battery capacity was compared at the same fuel cell output: it was found that the use of a battery with a capacity of “40 kWh” or more is effective. By comparing stack and battery power, hydrogen consumption, and battery SOC according to load changes, it was confirmed that high efficiency of stack net power is not always good thing. In addition, it was confirmed that the most effective strategy is to set the fuel cell-to-battery ratio at 6:4 initially, at 8:2 when the load increases rapidly, and at 4:6 when the load decreases rapidly.

Parameter Optimization and Tuning Methodology for a Scalable E-Bus Fleet Simulation Framework: Verification Using Real-World Data from Case Studies

This study presents the optimization and tuning of a simulation framework to improve its simulation accuracy while evaluating the energy utilization of electric buses under various mission scenarios. The simulation framework was developed using the low fidelity (Lo-Fi) model of the forward-facing electric bus (e-bus) powertrain to achieve the fast simulation speeds necessary for real-time fleet simulations. The measurement data required to verify the proper tuning of the simulation framework is provided by the bus original equipment manufacturers (OEMs) and taken from the various demonstrations of 12 m and 18 m buses in the cities of Barcelona, Gothenburg, and Osnabruck. We investigate the different methodologies applied for the tuning process, including empirical and optimization. In the empirical methodology, the standard driving cycles that have been used in previous studies to simulate various use case (UC) scenarios are replaced with actual driving cycles derived from measurement data from buses traversing their respective routes. The key outputs, including the energy requirements, total cost of ownership (TCO), and impact on the grid are statistically compared. In the optimization scenario, the assumptions for the various vehicle and mission parameters are tuned to increase the correlation between the simulation and measurement outputs (the battery SoC profile), for the given scenario input (the velocity profile). Improved simple optimization (iSOPT) was used to provide a superfast optimization process to tune the passenger load in the bus, cabin setpoint temperature, battery’s age as relative capacity degradation (RCD), SoC cutoff point between constant current (CC) and constant voltage charging (CV), charge decay factor used in CV charging, charging power, and cutoff in initial velocity during braking for which regenerative braking is activated.