Battery Management System for Future Electric Vehicles

The future of electric vehicles relies nearly entirely on the design, monitoring, and control of the vehicle battery and its associated systems. Along with an initial optimal design of the cell/pack-level structure, the runtime performance of the battery needs to be continuously monitored and optimized for a safe and reliable operation and prolonged life. Improved charging techniques need to be developed to protect and preserve the battery. The scope of this Special Issue is to address all the above issues by promoting innovative design concepts, modeling and state estimation techniques, charging/discharging management, and hybridization with other storage components

Smart Battery Technology for Lifetime Improvement

Applications of lithium-ion batteries are widespread, ranging from electric vehicles to energy storage systems. In spite of nearly meeting the target in terms of energy density and cost, enhanced safety, lifetime, and second-life applications, there remain challenges. As a result of the difference between the electric characteristics of the cells, the degradation process is accelerated for battery packs containing many cells. The development of new generation battery solutions for transportation and grid storage with improved performance is the goal of this paper, which introduces the novel concept of Smart Battery that brings together batteries with advanced power electronics and artificial intelligence (AI). The key feature is a bypass device attached to each cell that can insert relaxation time to individual cell operation with minimal effect on the load. An advanced AI-based performance optimizer is trained to recognize early signs of accelerated degradation modes and to decide upon the optimal insertion of relaxation time. The resulting pulsed current operation has been proven to extend lifetime by up to 80% in laboratory aging conditions. The Smart Battery unique architecture uses a digital twin to accelerate the training of performance optimizers and predict failures. The Smart Battery technology is a new technology currently at the proof-of-concept stage

Battery Management in Electric Vehicles: Current Status and Future Trends

Lithium-ion batteries are an indispensable component of the global transition to zero-carbon energy and are instrumental in achieving COP26's objective of attaining global net-zero emissions by the mid-century. However, their rapid expansion comes with significant challenges. The continuous demand for lithium-ion batteries in electric vehicles (EVs) is expected to raise global environmental and supply chain concerns, given that the critical materials required for their production are finite and predominantly mined in limited regions worldwide. Consequently, significant battery waste management will eventually become necessary. By implementing appropriate and enhanced battery management techniques in electric vehicles, the performance of batteries can be improved, their lifespan extended, secondary uses enabled, and the recycling and reuse of EV batteries promoted, thereby mitigating global environmental and supply chain concerns. Therefore, this reprint was crafted to update the scientific community on recent advancements and future trajectories in battery management for electric vehicles. The content of this reprint spans a spectrum of EV battery advancements, ranging from fundamental battery studies to the utilization of neural network modeling and machine learning to optimize battery performance, enhance efficiency, and ensure prolonged lifespan

Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications

The second-generation hybrid and Electric Vehicles are currently leading the paradigm shift in the automobile industry, replacing conventional diesel and gasoline-powered vehicles. The Battery Management System is crucial in these electric vehicles and also essential for renewable energy storage systems. This review paper focuses on batteries and addresses concerns, difficulties, and solutions associated with them. It explores key technologies of Battery Management System, including battery modeling, state estimation, and battery charging. A thorough analysis of numerous battery models, including electric, thermal, and electro-thermal models, is provided in the article. Additionally, it surveys battery state estimations for a charge and health. Furthermore, the different battery charging approaches and optimization methods are discussed. The Battery Management System performs a wide range of tasks, including as monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data. It also looks at various cell balancing circuit types, current and voltage stressors, control reliability, power loss, efficiency, as well as their advantages and disadvantages. The paper also discusses research gaps in battery management systems.publishedVersio

Development Schemes of Electric Vehicle Charging Protocols and Implementation of Algorithms for Fast Charging under Dynamic Environments Leading towards Grid-to-Vehicle Integration

This thesis focuses on the development of electric vehicle (EV) charging protocols under a dynamic environment using artificial intelligence (AI), to achieve Vehicle-to-Grid (V2G) integration and promote automobile electrification. The proposed framework comprises three major complementary steps. Firstly, the DC fast charging scheme is developed under different ambient conditions such as temperature and relative humidity. Subsequently, the transient performance of the controller is improved while implementing the proposed DC fast charging scheme. Finally, various novel techno-economic scenarios and case studies are proposed to integrate EVs with the utility grid. The proposed novel scheme is composed of hierarchical stages; In the first stage, an investigation of the temperature or/and relative humidity impact on the charging process is implemented using the constant current-constant voltage (CC-CV) protocol. Where the relative humidity impact on the charging process was not investigated or mentioned in the literature survey. This was followed by the feedforward backpropagation neural network (FFBP-NN) classification algorithm supported by the statistical analysis of an instant charging current sample of only 10 seconds at any ambient condition. Then the FFBP-NN perfectly estimated the EV’s battery terminal voltage, charging current, and charging interval time with an error of 1% at the corresponding temperature and relative humidity. Then, a nonlinear identification model of the lithium-polymer ion battery dynamic behaviour is introduced based on the Hammerstein-Wiener (HW) model with an experimental error of 1.1876%. Compared with the CC-CV fast charging protocol, intelligent novel techniques based on the multistage charging current protocol (MSCC) are proposed using the Cuckoo optimization algorithm (COA). COA is applied to the Hierarchical technique (HT) and the Conditional random technique (CRT). Compared with the CC-CV charging protocol, an improvement in the charging efficiency of 8% and 14.1% was obtained by the HT and the CRT, respectively, in addition to a reduction in energy losses of 7.783% and 10.408% and a reduction in charging interval time of 18.1% and 22.45%, respectively. The stated charging protocols have been implemented throughout a smart charger. The charger comprises a DC-DC buck converter controlled by an artificial neural network predictive controller (NNPC), trained and supported by the long short-term memory neural network (LSTM). The LSTM network model was utilized in the offline forecasting of the PV output power, which was fed to the NNPC as the training data. The NNPC–LSTM controller was compared with the fuzzy logic (FL) and the conventional PID controllers and perfectly ensured that the optimum transient performance with a minimum battery terminal voltage ripple reached 1 mV with a very high-speed response of 1 ms in reaching the predetermined charging current stages. Finally, to alleviate the power demand pressure of the proposed EV charging framework on the utility grid, a novel smart techno-economic operation of an electric vehicle charging station (EVCS) in Egypt controlled by the aggregator is suggested based on a hierarchical model of multiple scenarios. The deterministic charging scheduling of the EVs is the upper stage of the model to balance the generated and consumed power of the station. Mixed-integer linear programming (MILP) is used to solve the first stage, where the EV charging peak demand value is reduced by 3.31% (4.5 kW). The second challenging stage is to maximize the EVCS profit whilst minimizing the EV charging tariff. In this stage, MILP and Markov Decision Process Reinforcement Learning (MDP-RL) resulted in an increase in EVCS revenue by 28.88% and 20.10%, respectively. Furthermore, the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies are applied to the stochastic EV parking across the day, controlled by the aggregator to alleviate the utility grid load demand. The aggregator determined the number of EVs that would participate in the electric power trade and sets the charging/discharging capacity level for each EV. The proposed model minimized the battery degradation cost while maximizing the revenue of the EV owner and minimizing the utility grid load demand based on the genetic algorithm (GA). The implemented procedure reduced the degradation cost by an average of 40.9256%, increased the EV SOC by 27%, and ensured an effective grid stabilization service by shaving the load demand to reach a predetermined grid average power across the day where the grid load demand decreased by 26.5% (371 kW)

Constrained generalized predictive control of battery charging process based on a coupled thermoelectric model

Battery temperature is a primary factor affecting the battery performance, and suitable battery temperature control in particular internal temperature control can not only guarantee battery safety but also improve its efficiency. This is however challenging as current controller designs for battery charging have no mechanisms to incorporate such information. This paper proposes a novel battery charging control strategy which applies the constrained generalized predictive control (GPC) to charge a LiFePO₄ battery based on a newly developed coupled thermoelectric model. The control target primarily aims to maintain the battery cell internal temperature within a desirable range while delivering fast charging. To achieve this, the coupled thermoelectric model is firstly introduced to capture the battery behaviours in particular SOC and internal temperature which are not directly measurable in practice. Then a controlled auto-regressive integrated moving average (CARIMA) model whose parameters are identified by the recursive least squares (RLS) algorithm is developed as an online self-tuning predictive model for a GPC controller. Then the constrained generalized predictive controller is developed to control the charging current. Experiment results confirm the effectiveness of the proposed control strategy. Further, the best region of heat dissipation rate and proper internal temperature set-points are also investigated and analysed

An Optimized Fuzzy Controlled Charging System for Lithium-Ion Batteries Using a Genetic Algorithm

Fast charging is an attractive way of charging batteries; however, it may result in an undesired degradation of battery performance and lifetime because of the increase in battery temperature during fast charge. In this paper we propose a simple optimized fuzzy controller that is responsible for the regulation of the charging current of a battery charging system. The basis of the method is a simple dynamic equivalent circuit type model of the Li-ion battery that takes into account the temperature dependency of the model parameters, too. Since there is a tradeoff between the charging speed determined by the value of the charging current and the increase in temperature of the battery, the proposed fuzzy controller is applied for controlling the charging current as a function of the temperature. The controller is optimized using a genetic algorithm to ensure a jointly minimal charging time and battery temperature increase during the charging. The control method is adaptive in the sense that we use parameter estimation of an underlying dynamic battery model to adapt to the actual status of the battery after each charging. The performance and properties of the proposed optimized charging control system are evaluated using a simulation case study. The evaluation was performed in terms of the charge profiles, using the fitness values of the individuals, and in terms of the charge performance on the actual battery. The proposed method has been evaluated compared to the conventional contant current-constant voltage methods. We have found that the proposed GA-fuzzy controller gives a slightly better performance in charging time while significantly decreasing the temperature increase