Optimal State Of Charge Research Articles

Investigation of Fe-Ni Battery/Module for Grid Service Duty Cycles.

Iron-nickel (Fe-Ni) batteries are renowned for their durability and resilience against overcharging and operating temperatures. However, they encounter challenges in achieving widespread adoption for energy storage applications due to their low efficiency and the need for regular maintenance and electrolyte replacement, which adds to maintenance costs. This study evaluates and demonstrates the capabilities of Fe-Ni batteries for participating in grid energy storage applications. Stable performance was observed frequency regulation (FR) testing at 100% and 50% state of charge (SOC)s, while at 50% SOC, there was a 14% increase in efficiency compared to 100% SOC. Although 25% SOC achieved higher efficiency, limited cyclability was observed due to reaching the discharge cutoff voltage. Optimal SOC selection, battery monitoring, maintenance, and appropriate charging strategies of Fe-Ni batteries seem to be crucial for their FR applications. Fe-Ni batteries exhibit stable peak shaving (PS) results, indicating their suitability and reliability under various load conditions for PS testing. Extended cycling tests confirm their potential for long-term grid-scale energy storage, enhancing their appeal for PS and FR applications.

CAN Interface Insights for Electric Vehicle Battery Recycling

Road transportation is a significant worldwide contributor to greenhouse gases, and electrifying the driveline of road vehicles is essential in overcoming the evident challenge of climate change. A sustainable transition to electric vehicles requires efficient and safe methods for recycling and repurposing used electric vehicle batteries. While various testing methods have been explored for assessing battery state of health and state of risk for recycling and reuse, a research gap exists concerning using data from integrated battery monitoring systems in the recycling process of electric vehicle batteries. This study addresses the research gap by presenting an approach to extract data from the monitoring system integrated into the battery using the automotive standard controller area network interface. In addition, methods to use this interface to ensure the optimal state of charge of the batteries for storage are presented. The benefits, challenges, and limitations set by the proprietary nature of the data to assess the state of risk and health of electric vehicle batteries for recycling and repurposing are presented, discussed, and evaluated. Finally, the influence of battery regulations and the battery passport proposal on electric vehicle battery recycling and repurposing are discussed to provide future perspectives.

Estimating the Optimal State of Charge for Electric Car Batteries Using an Extended Kalman Filter

The efficient management of battery state of charge (SOC) is crucial for maximizing the performance, range, and longevity of electric vehicle (EV) batteries. This paper presents a novel approach for estimating the optimal state of charge of electric car batteries using an Extended Kalman Filter (EKF). The EKF is a recursive algorithm that combines measurements from various sensors with a dynamic battery model to estimate the current SOC and predict future SOC values with high accuracy. The paper provides a detailed explanation of the EKF algorithm and its application to battery SOC estimation, highlighting its ability to handle nonlinearities, uncertainties, and measurement noise inherent in battery systems. Furthermore, this research presents a simulation-based validation of the proposed EKF approach using real-world driving data from electric vehicles. The simulation results demonstrate the effectiveness of the EKF algorithm in accurately estimating the SOC of electric car batteries under various operating conditions, including different driving patterns, temperatures, and battery degradation scenarios.

A predictive energy management strategy for plug-in hybrid electric vehicles using real-time traffic based reference SOC planning

The fuel economy of plug-in hybrid electric vehicles (PHEVs) is strongly affected by the battery state of charge (SOC) depletion pattern. This paper proposes and studies a real-time traffic-based SOC reference planning method. The method uses a dataset to collect and capture real traffic information and then enriches the dataset using a data augmentation method developed in this paper. The augmented dataset is optimized by dynamic programing (DP) algorithm to obtain the optimal reference SOC for model training. The traffic information and optimal reference SOC are processed and used to train a long-short term memory (LSTM) neural network, which is used for online reference SOC planning. Finally, a predictive energy management (PEM) strategy is adopted to follow the SOC reference by optimizing instantaneous power allocation with the predicted velocities. Simulation results show that the proposed method outperforms the linear reference SOC planning method in both smooth and congested traffic scenarios.

Multiport Energy Management System Design for a 150 kW Range-Extended Towing Vessel

This paper proposes a multiport energy management system (EMS) and its rule-based expert control strategy for a 150 kW range-extended towing vessel (RETV). The system integrates a diesel generator system, a permanent magnet synchronous motor, a lithium battery, and supercapacitors. To verify its feasibility and effectiveness, the proposed multiport EMS was modelled and tested through MATLAB/Simulink. Simulation results demonstrate that the designed multiport EMS works efficiently under the five typical operating conditions of the 150 kW RETV. In addition, two case studies were conducted and compared to investigate the impact of the battery’s initial state of charge (SoC) on the system’s energy efficiency. It was found that an overall 85% energy efficiency can be achieved for the RETV when the initial SoC is either 75% or 15%. The battery consistently operates within the optimal SoC range of 20% to 80%, and the supercapacitors effectively meet the instantaneous high-power demand.

Twin delayed deep deterministic policy gradient based energy management strategy for fuel cell/battery/ultracapacitor hybrid electric vehicles considering predicted terrain information

For fuel cell/battery/ultracapacitor hybrid electric vehicles (FCHEV), the complex topology and terrain information pose challenges to fuel cell efficiency, power sources lifespan, and fuel economy. In this paper, a hierarchical energy management strategy (EMS) considering predicted terrain information is constructed to optimize the demand power allocation of FCHEV based on twin delayed deep deterministic policy gradient (TD3). Firstly, to improve the fuel economy of FCHEV, an adaptive fuzzy filter-based upper-level frequency separation is designed considering negative power. Secondly, to achieve the minimum fuel consumption, a TD3-based lower-level EMS is designed, where the terrain information is predicted through multi long short-term memory network and considered in the TD3 framework. The optimal state of charge obtained by dynamic programming (DP) based on the demand power and predicted terrain information, is considered in the reward function of TD3, as well as the optimal experience solved by DP based on historical road data is integrated into the designed hybrid experience replay. Finally, the effectiveness and optimality of the TD3-based EMS considering predicted terrain information are verified by a series of comprehensive simulations under different driving cycles. The simulation results under CYC_SC03 and test driving cycles indicate that compared to traditional TD3-based EMS, the fuel economy is increased by 33.3% and 27.5%, respectively, and can reach the benchmark level of 80% and 80.9% of DP, respectively.

Long-term operation of isolated microgrids with renewables and hybrid seasonal-battery storage

With the progress of decarbonization, renewable-powered microgrids are attracting wide attention. To cope with the fluctuation of renewable power at different timescales, both long-term and short-term energy storage devices are required. This paper studies the operation of renewable-dominated isolated microgrids integrated with hybrid seasonal-battery storage. A data-driven scheduling-correction framework is proposed. By leveraging the historical data of renewable power and load, the scheduling module generates ex-post optimal state-of-charge (SoC) sequences of the seasonal energy storage ahead of the operating year. In each period of real-time operation, the correction module performs two steps: the first step is to update the reference SoC for the seasonal storage based on the ex-post optimal SoC sequences and the newly observed data; the second step is to solve a bi-objective rolling-horizon optimization problem which minimizes the instant operating cost while steering the SoC of seasonal storage to its reference value. An appealing feature of the proposed method is that the long-term renewable power forecasts are not required. A microgrid system is devised to verify the proposed framework. Numerical tests show that the scheduling-correction framework outperforms existing rolling horizon approaches in compromising economy, power supply reliability, and renewable energy utilization.

Research on pulse charging current of lithium-ion batteries for electric vehicles in low-temperature environment

The scale of the electric vehicle (EV) industry is expanding in the current new energy industry reform. Lithium-ion batteries (LIBs) have also gotten a lot of interest as the power source for EVs. However, the safety and remaining life of LIB are highly tied to the charging strategy adopted. Particularly, fast charging at low temperatures can cause lithium to deposit on the anode of the battery, intensifying heat production and even evolving into thermal runaway of the battery. Based on the simplified battery Alternating current (AC) impedance model, the optimal frequency of pulse current is analyzed. Considering the influence of state of charge (SOC) and temperature on the battery impedance, a three-dimensional response surface about the optimal frequency, temperature and SOC was established using Mendeley data. To simulate LIB pulse strategy charging in a cold environment, the equivalent circuit model (ECM) is coupled with a simple battery heating model. The three-dimensional response surface is employed to instantly determine the pulse frequency. The simulation results demonstrate that both the bi-directional pulse current charging strategy with optimal frequency and the positive pulse current charging strategy with optimal frequency have faster charging time and better temperature rise effect than the constant current (CC) charging strategy, and can significantly eliminate the polarization voltage of the battery at low temperatures.

Optimal SOC Control and Rule-Based Energy Management Strategy for Fuel-Cell-Based Hybrid Vessel including Batteries and Supercapacitors

Around the world, the development of electric vehicles is underway, including in maritime transportation. However, the development of clean energy vessels still has a long way to go. Fuel cells (FCs) are a relevant choice among the many clean energy sources to power clean energy vessels. However, due to the complex and drastic change in the shipload power, FCs need to be equipped with dynamic fast-response energy storage equipment to make up for it. For multiple energy storage devices connected in parallel, the state of charge (SOC) is not balanced, which affects their service life and the stability of the vessel microgrid, as well as slowing the speed and lowering the accuracy of SOC equalization. This paper proposes a distributed variable sag slope control strategy for vessels to improve SOC equalization, with a FC as the energy source and a battery and supercapacitor as the energy storage system (ESS). For the output power distribution problem of energy storage equipment caused by shipload power variation, a power distribution strategy with a variable filter time constant is used to improve the reasonableness of the output power distribution of energy-based lithium batteries and power-based supercapacitors. Meanwhile, this paper considers the power generation equipment’s service life and energy cost as the optimization objectives, considering the discharge depth of the energy storage equipment. Finally, a method based on the combination of the lithium battery SOC rule (the rule formulated according to the state of charge and load power change in energy storage equipment) and particle swarm optimization algorithm is proposed to solve this problem. The simulation results show that the proposed strategy improves the equalization speed and accuracy of the SOC of energy storage devices, fully realizes the characteristics of different energy storage devices, and reduces the life loss of energy storage devices.

Energy Management Optimization Method of Plug-In Hybrid-Electric Bus Based on Incremental Learning

The state of charge (SOC) trajectory planning for the power battery is the basis of realizing the global online optimal control of the plug-in hybrid-electric bus (PHEB) energy management system. This article proposed an adaptive rolling planning method for SOC trajectory of PHEBs power battery. First, a mathematical model for simulation research is established. Second, the driving cycles of PHEBs are collected, and the optimal SOC trajectories of driving cycles are obtained using dynamic programming (DP). In order to provide training data for the planning model of the optimal SOC trajectory of a trip segment (TS), the driving cycle and the optimal SOC trajectory of the driving cycle are cut into segments with a time length of 30 s. The incremental learning is used to construct a planning model of the TS’s optimal SOC trajectory and planning the optimal SOC trajectory of the actual TS. The optimal SOC trajectory of the actual TS is applied to proportion–integration-differentiation (PID) control and compared with PID based on mileage-based SOC planning (BMP). The result shows that this study can reduce fuel consumption by 0.8 L/100 km for the same driving cycle. Through incremental learning, the fuel consumption can be further reduced by 0.13 L/100 km.

Optimal management of electric hotel loads in mild hybrid heavy duty truck

The problem of engine idling for heavy-duty trucks has been under study for decades with Auxiliary Power Units (APUs) and Truck Stop Electrification (TSE) as the most compelling solutions. With the electrification of trucks approaching feasibility in terms of cost-effective technology, hybridization offers another “degree of freedom” to tackle the problem. This work aims at exploiting a battery pack of a 48 V mild-hybrid heavy-duty truck to store sufficient onboard energy for powering the auxiliary loads during the hoteling. This problem is not trivial, as the battery packs typically cannot recover the entire energy required through regeneration alone; hence an optimal energy management strategy needs to be employed to charge the battery through the engine during drive operation. This strategy optimizes powertrain performances among the four modes: (i) Engine of Coasting (EOC), (ii) Regeneration by braking, (iii) Regeneration by engine, and (iv) engine idling. This paper presents the development of a Dynamic Programming (DP) framework that employs a multi-objective cost function to minimize the fuel consumption and maximize the regeneration using the above-mentioned four modes. A typical heavy-duty truck drive cycle is used to represent the drive phase, with mandatory hoteling stops as per regulations. A comprehensive powertrain model is developed using validated components’ model. The DP employs two state variables: battery State-of-Charge (SOC) and engine mode, and three control inputs: (i) the engine ON–OFF state, (ii) clutch engagement state, and (iii) power request at the Electric Machine (EM) for calculating optimal SOC trajectory. The framework also tackles rapid engine ON–OFF scenarios to avoid the challenges associated with DP and the compromises in fuel cost with those approaches. Finally, the effectiveness of the proposed framework is tested for potential fuel savings on two different battery packs by performing the full cycle simulations. The results show 6.47% of fuel consumption reduction as compared to traditional APU-based heavy-duty truck.

Power Dispatching of Multi-Microgrid Based on Improved CS Aiming at Economic Optimization on Source-Network-Load-Storage

The multi-microgrid is gradually springing up with widespread use of the distributed generation. It is of great meaning to have research on the energy mutual optimization of the multi-microgrid to improve the new energy-consumption capacity. In this paper, a comprehensive economic model of the multi-microgrid is proposed for optimizing the power dispatching, and the source-network-load-storage is taken into account. Different from other studies, the special novelty of this paper is the improved cuckoo search (CS) algorithm which is adopted to optimize the power dispatching of the multi-microgrid. Comparing with the particle swarm optimization (PSO) algorithm, the improved CS algorithm has better performance in solving the proposed model. The optimal power supply strategy is determined by predicting the optimal state of charge of the battery in the model of the multi-microgrid. The model effectiveness of the multi-microgrid is confirmed in the case study of Wangjiazhai area. With this method, the optimal power dispatching is determined.

Improvement of PHEV Equivalent Fuel Economy and Battery Life by Applying Traffic-Based SOC Management

Improvement of plug-in hybrid electric vehicle (PHEV) equivalent fuel consumption (EFC) and battery life are significant issues in the design of PHEVs. This research aims to develop an adaptive traffic-based controller to improve fuel economy and battery life. The optimal state of charge (SOC) trajectories in real-world and standard cycles are extracted using the modified dynamic programming (MDP) approach. The optimal data is clustered based on the driving features, under the light of fuzzy clustering. Then, a control map is formed based on the clustered data to estimate SOC trajectories as a reference in an adaptive control strategy. A battery life model is employed to investigate the effect of the proposed method on battery aging. This study reveals that traffic-based SOC management simultaneously reduces the operating and maintenance costs since it improves the equivalent fuel economy and battery life in various driving cycles. Moreover, the suggested energy management strategy is implemented on an embedded system by applying the model-based design (MBD) method. The comparison of hardware-in-the-loop (HIL) and model-in-the-loop (MIL) simulation shows that the suggested strategy is implementable and effective for real applications.

Hybrid Interval-Robust Adaptive Battery Energy Storage System Dispatch With SoC Interval Management for Unbalanced Microgrids

Battery energy storage systems (BESSs) have been widely deployed in microgrids to deal with uncertain output power of renewable distributed generation (DG) and improve renewable energy utilization efficiency. However, due to the short-term dispatch mode and BESS capacity limitation, current BESS dispatch decisions may not be efficient from a whole-day perspective, leading to an inadequate/excessive state of charge (SoC) for subsequent dispatch. To address this issue, an adaptive BESS dispatch method with SoC interval management is proposed for unbalanced three-phase microgrids, aiming to minimize the operating cost. In a day-ahead stage, an SoC interval is optimized considering the uncertainties of renewable DG, loads and market prices. During the day, the BESS is dispatched inside the SoC interval with a rolling horizon procedure to track uncertainty realization. Accordingly, a hybrid interval-robust optimization method is developed to solve the proposed scheduling problem under the uncertainties and procure the optimal SoC interval. Particularly, a decomposing and alternating solution algorithm is developed to identify the pessimistic case based on robust optimization while the SoC interval is determined on the basis of interval optimization. Simulation results verify high efficiency and solution robustness of the proposed hybrid interval-robust adaptive BESS scheduling method.

Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi

Measurement of electric charge on the battery in real-time cannot be separated from external noise and disturbances such as temperature and interference. An optimal State of Charge (SoC) estimator model is needed to make the estimation more accurate. To obtain the model, the battery was tested under room temperature conditions and at a temperature of 40oC to obtain a second-order RC model for the Li-Ion battery used. Based on the test data obtained, the data will be tested first using the Kalman Filter method to get an estimate of the State of Charge (SoC). Tests were carried out using MATLAB software. After the method was tested, the online SoC Estimator design began using the Raspberry Pi Single Board Computer (SBC). After that, the estimator will be tested first using data from offline measurements and then used in real-time (online) SoC estimation measurements. The Voc before the battery discharge test was 13.16 V and after the test, the measured Voc was 11.58 V. During the discharge the Voc was reduced by 1.58 V. While the discharge data from the battery manufacturer showed the reduced Voc during the discharge was 1.2V.

Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi

Measurement of electric charge on the battery in real-time cannot be separated from external noise and disturbances such as temperature and interference. An optimal State of Charge (SoC) estimator model is needed to make the estimation more accurate. To obtain the model, the battery was tested under room temperature conditions and at a temperature of 40oC to obtain a second-order RC model for the Li-Ion battery used. Based on the test data obtained, the data will be tested first using the Kalman Filter method to get an estimate of the State of Charge (SoC). Tests were carried out using MATLAB software. After the method was tested, the online SoC Estimator design began using the Raspberry Pi Single Board Computer (SBC). After that, the estimator will be tested first using data from offline measurements and then used in real-time (online) SoC estimation measurements. The Voc before the battery discharge test was 13.16 V and after the test, the measured Voc was 11.58 V. During the discharge the Voc was reduced by 1.58 V. While the discharge data from the battery manufacturer showed the reduced Voc during the discharge was 1.2V.

Adaptive estimation-based hierarchical model predictive control methodology for battery active equalization topologies: Part I–Balancing strategy

State-of-charge (SOC) balancing is a critical issue for the development of battery management systems for electric vehicles. In the series of two papers, an adaptive estimation-based hierarchical model predictive control (MPC) balancing methodology is proposed to explore different equalization topologies. In the first paper, the superior control for optimal SOC balancing in typical topologies, i.e., dissipative, unidirectional adjacent, bidirectional adjacent, and bus-based, is developed by formulating the proposed MPC. SOC balancing problems of different topologies are modeled by describing the behavior of energy transferring during the charge/discharge process considering practical loss. The MPC balancing strategy is proposed to solve the constrained quadratic optimization by minimizing the balancing time and energy loss. A rule-based fuzzy logic control (FLC) balancing is compared, and in the 5-series cells simulation, the result indicates that the balancing time of MPC and FLC are 292 s and 654 s, respectively. To verify the real-time feasibility of the proposed MPC balancing strategy, a controller hardware-in-the-loop (HIL) test is further implemented. Finally, the influences of bidirectional adjacent and bus-based topologies on equalization performance are discussed, demonstrating a strong correlation between the number of series-connected cells and adopted topology. The proposed MPC-based superior control can be integrated with the later designed low-level adaptive estimation laws driven individual cell equalizer (ICE) controller to comprehensively tackle SOC optimal balancing problems.

Online Synthesis of an Optimal Battery State-of-Charge Reference Trajectory for a Plug-in Hybrid Electric City Bus

The powertrain efficiency of plug-in hybrid electric vehicles (PHEV) can be increased by effectively using the engine along the electric motor to gradually discharge the battery throughout a driving cycle. This sets the requirement of the optimal shaping of the battery state-of-charge (SoC) reference trajectory. The paper deals with the online synthesis of the optimal SoC reference trajectory, which inherently includes adaptive features in relation to the prediction of upcoming driving cycle features such as the trip distance, the road grade profile, the mean vehicle velocity and the mean demanded power. The method performs iteratively, starting from an offline-synthesized SoC reference trajectory obtained based on dynamic programming (DP) control variable optimization results. The overall PHEV control strategy incorporating the proposed online SoC reference trajectory synthesis method is verified against the DP benchmark and different offline synthesis methods. For this purpose, a model of a PHEV-type city bus is used and simulated over a wide range of driving cycles and conditions including varying road grade and low-emission zones (LEZ).

Optimal SoC Balancing Control for Lithium-Ion Battery Cells Connected in Series

The optimal state of charge (SoC) balancing control for series-connected lithium-ion battery cells is presented in this paper. A modified SoC balancing circuit for two adjacent cells, based on the principle of a bidirectional Cuk converter, is proposed. The optimal SoC balancing problem is established to minimize the SoC differences of cells and the energy loss subject to constraints of the normal SoC operating range, the balancing current, and current of cells. This optimization problem is solved using the sequential quadratic programming algorithm to determine the optimal duties of PWM signals applied to the SoC balancing circuits. An algorithm for the selection of the initial points for the optimal problem-solving process is proposed. It is applied in cases where the cost function has no decreasing part. Experimental tests are conducted for seven series-connected Samsung cells. The optimal SoC balancing control and SoC estimation algorithms are coded in MATLAB and embedded in LabVIEW to control the SoC balancing in real time. The test results show that the differences between the SoCs of cells converges to the desired range using the proposed optimal SoC balancing control strategy.

How the utilised SOC window in commercial Li-ion pouch cells influence battery ageing

In many lithium-ion battery (LIB) applications, e.g. hybrid vehicles and load-levelling storage systems, only part of the state-of-charge (SOC) range needs to be utilised. This offers the possibility to use an optimal SOC window to avoid LIB ageing. Here, a large test matrix is designed to study LIB ageing in a commercial 26 Ah pouch cell, in order to map the ageing behaviour at different SOC levels with respect to temperature and current. A quantification of the degradation modes, loss of lithium inventory (LLI), loss of active positive (LAMPE) and negative (LAMNE) electrode materials is made by analysing the change in the open circuit voltage (OCV). A key result is that lower SOC intervals significantly improved battery ageing. Even during harsh test conditions, such as high C-rates and temperatures, the cells deliver more than three times the expected number of full cycle equivalents. High SOC combined with high C-rate increase ageing where the dominating ageing mechanisms are LLI, followed by LAMPE.