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 of an Adaptive Efficient Thermal/Electric Skipping Control Strategy Applied to a Parallel Plug-in Hybrid Electric Vehicle

In recent years automobile manufacturers focused on an increasing degree of electrification of the powertrains with the aim to reduce pollutants and CO2 emissions. Despite more complex design processes and control strategies, these powertrains offer improved fuel exploitation compared to conventional vehicles thanks to intelligent energy management. A simulation study is here presented aiming at developing a new control strategy for a P3 parallel plug-in hybrid electric vehicle. The simulation model is implemented using vehicle modeling and simulation toolboxes in MATLAB/Simulink. The proposed control strategy is based on an alternative utilization of the electric motor and thermal engine to satisfy the vehicle power demand at the wheels (Efficient Thermal/Electric Skipping Strategy-ETESS). The choice between the two units is realized through a comparison between two equivalent fuel rates, one related to the thermal engine and the other related to the electric consumption. An adaptive function is introduced to develop a charge-blended control strategy. The novel adaptive control strategy (A-ETESS) is applied to estimate fuel consumption along different driving cycles. The control algorithm is implemented on a dedicated microcontroller unit performing a Processor-In-the-Loop (PIL) simulation. To demonstrate the reliability and effectiveness of the A-ETESS, the same adaptive function is built on the Equivalent Consumption Minimization Strategy (ECMS). The PIL results showed that the proposed strategy ensures a fuel economy similar to ECMS (worse of about 2% on average) and a computational effort reduced by 99% on average. This last feature reveals the potential for real-time on-vehicle applications

Supervisory Control Optimization for a Series Hybrid Electric Vehicle with Consideration of Battery Thermal Management and Aging

This dissertation integrates battery thermal management and aging into the supervisory control optimization for a heavy-duty series hybrid electric vehicle (HEV). The framework for multi-objective optimization relies on novel implementation of the Dynamic Programing algorithm, and predictive models of critical phenomena. Electrochemistry based battery aging model is integrated into the framework to assesses the battery aging rate by considering instantaneous lithium ion (Li+) surface concentration rather than average concentration. This creates a large state-action space. Therefore, the computational effort required to solve a Deterministic or Stochastic Dynamic Programming becomes prohibitively intense, and a neuro-dynamic programming approach is proposed to remove the ‘curse of dimensionality’ in classical dynamic programming. First, unified simulation framework is developed for in-depth studies of series HEV system. The integration of a refrigerant system model enables prediction of energy use for cooling the battery pack. Side reaction, electrolyte decomposition, is considered as the main aging mechanism of LiFePO4/Graphite battery, and an electrochemical model is integrated to predict side reaction rate and the resulting fading of capacity and power. An approximate analytical solution is used to solve the partial difference equations (PDEs) for Li+ diffusion. Comparing with finite difference method, it largely reduces the number of states with only a slight penalty on prediction accuracy. This improves computational efficiency, and enables inclusion of the electrochemistry based aging model in the power management optimization framework. Next, a stochastic dynamic programming (SDP) approach is applied to the optimization of supervisory control. Auxiliary cooling power is included in addition to vehicle propulsion. Two objectives, fuel economy and battery life, are optimized by weighted sum method. To reduce the computation load, a simplified battery aging model coupled with equivalent circuit model is used in SDP optimization; Li+ diffusion dynamics are disregarded, and surface concentration is represented by the average concentration. This reduces the system state number to four with two control inputs. A real-time implementable strategy is generated and embedded into the supervisory controller. The result shows that SDP strategy can improve fuel economy and battery life simultaneously, comparing with Thermostatic SOC strategy. Further, the tradeoff between fuel consumption and active Li+ loss is studied under different battery temperature. Finally, the accuracy of battery aging model for optimization is improved by adding Li+ diffusion dynamics. This increases the number of states and brings challenges to classical dynamic programming algorithms. Hence, a neuro-dynamic programming (NDP) approach is proposed for the problem with large state-action space. It combines the idea of functional approximation and temporal difference learning with dynamic programming; in that case the computation load increases linearly with the number of parameters in the approximate function, rather than exponentially with state space. The result shows that ability of NDP to solve the complex control optimization problem reliably and efficiently. The battery-aging conscientious strategy generated by NDP optimization framework further improves battery life by 3.8% without penalty on fuel economy, compared to SDP strategy. Improvements of battery life compared to the heuristic strategy are much larger, on the order of 65%. This leads to progressively larger fuel economy gains over time

Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles

Abstract—This paper describes the application of state-estimation techniques for the real-time prediction of the state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, approaches based on the well-known Kalman Filter (KF) and Extended Kalman Filter (EKF), are presented, using a generic cell model, to provide correction for offset, drift, and long-term state divergence—an unfortunate feature of more traditional coulomb-counting techniques. The underlying dynamic behavior of each cell is modeled using two capacitors (bulk and surface) and three resistors (terminal, surface, and end), from which the SoC is determined from the voltage present on the bulk capacitor. Although the structure of the model has been previously reported for describing the characteristics of lithium-ion cells, here it is shown to also provide an alternative to commonly employed models of lead-acid cells when used in conjunction with a KF to estimate SoC and an EKF to predict state-of-health (SoH). Measurements using real-time road data are used to compare the performance of conventional integration-based methods for estimating SoC with those predicted from the presented state estimation schemes. Results show that the proposed methodologies are superior to more traditional techniques, with accuracy in determining the SoC within 2% being demonstrated. Moreover, by accounting for the nonlinearities present within the dynamic cell model, the application of an EKF is shown to provide verifiable indications of SoH of the cell pack

A H2 PEM fuel cell and high energy dense battery hybrid energy source for an urban electric vehicle

Electric vehicles are set to play a prominent role in addressing the energy and environmental impact of an increasing road transport population by offering a more energy efficient and less polluting drive-train alternative to conventional internal combustion engine (ICE) vehicles. Given the energy (and hence range) and performance limitations of electro-chemical battery storage systems, hybrid systems combining energy and power dense storage technologies have been proposed for vehicle applications. The paper discusses the application of a hydrogen fuel cell as a range extender for an urban electric vehicle for which the primary energy source is provided by a high energy dense battery. A review of fuel cell systems and automotive drive-train application issues are discussed, together with an overview of the battery technology. The prototype fuel cell and battery component simulation models are presented and their performance as a combined energy/power source assessed for typical urban and sub-urban driving scenario

Estimation of State of Charge of Battery Used In Electric Vehicles With Wireless Battery Management System

This research paper presents a comprehensive investigation into the development and analysis of a wireless battery management system (BMS) using MATLAB Simulink. The primary objective of this study is to create an efficient, reliable, and scalable BMS that caters to the demands of various applications, such as electric vehicles, grid energy storage, and portable electronics. Our methodology involves designing and simulating key BMS components, including state estimation algorithms, fault detection mechanisms, and communication protocols, within the MATLAB Simulink environment. The paper first elucidates the motivation for adopting wireless technology in BMS, emphasizing its advantages over traditional wired systems. Subsequently, we explore the intricacies of the proposed wireless BMS architecture, detailing the implementation of essential features such as state-of-charge estimation, fault diagnosis, and thermal management. We also address the challenges associated with wireless communication, including latency, security, and energy efficiency, by incorporating robust communication protocols and power management strategies. Through rigorous simulations, we demonstrate the efficacy of the proposed wireless BMS, showcasing its ability to ensure optimal performance, safety, and longevity of battery packs. The outcomes of this research not only contribute to the advancement of BMS technology but also pave the way for further improvements in battery-powered systems. In conclusion, this paper offers a holistic perspective on wireless BMS design, emphasizing its potential to revolutionize energy management and extend the applications of battery technology in various domains

Thermal Management of Lithium-ion Battery Modules for Electric Vehicles

This research is particularly focused on studying thermal management of lithium-ion (Li-ion) battery modules in electric vehicles by using active, passive and hybrid active-passive methods. The thermal behavior prediction of batteries is performed by a novel electrochemical-thermal model. Different approaches such as single- and double-channel liquid cooling, pure passive by using phase change materials (PCM), and hybrid active-passive thermal management systems are investigated. Various cooling system configurations are examined to expand understanding of effect of each approach on the battery module thermal responses during a standard driving cycle. It is observed that the temperature distribution of Li-ion batteries is strongly influenced by the electrical and thermal operating conditions and simplified bulk models cannot precisely predict the thermal behavior of these batteries. Additionally, the PCM-based passive systems show advantages such as compactness and simplicity over the active liquid cooling systems. However, these systems suffer from non-uniform temperature distribution due to inherently low thermal conductivity of organic PCM. An effort has been made to enhance the thermal conductivity of a paraffin wax by adding various carbon-based nanoparticles. The results revealed that the thermal conductivity of the base PCM can be improved by about 11 times when using 10% mass fraction of graphite nanopowder. The heat transfer in the nano-enhanced PCM samples showed that the presence of nanoparticles drastically repress the natural convection in the melted nanocomposites. Among the battery thermal management systems studied, the air assisted hybrid cooling system provides the best temperature distribution uniformity in the module while keeping the batteries temperature within the safe limits. Furthermore, this work attempted to recognize the most influential parameters on the temperature distribution in the battery module. It is seen that the thickness of cooling plates and PCM layers in active and hybrid systems has a significant effect on the thermal behavior of the batteries