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

Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System

: Charging a group of series-connected batteries of a PV-battery hybrid system exhibits an imbalance issue. Such imbalance has severe consequences on the battery activation function and the maintenance cost of the entire system. Therefore, this paper proposes an active battery balancing technique for a PV-battery integrated system to improve its performance and lifespan. Battery state of charge (SOC) estimation based on the backpropagation neural network (BPNN) technique is utilized to check the charge condition of the storage system. The developed battery management system (BMS) receives the SOC estimation of the individual batteries and issues control signal to the DC/DC Buck-boost converter to balance the charge status of the connected group of batteries. Simulation and experimental results using MATLAB-ATMega2560 interfacing system reveal the effectiveness of the proposed approac

Modular Battery Charge Controller

A new approach to masterless, distributed, digital-charge control for batteries requiring charge control has been developed and implemented. This approach is required in battery chemistries that need cell-level charge control for safety and is characterized by the use of one controller per cell, resulting in redundant sensors for critical components, such as voltage, temperature, and current. The charge controllers in a given battery interact in a masterless fashion for the purpose of cell balancing, charge control, and state-of-charge estimation. This makes the battery system invariably fault-tolerant. The solution to the single-fault failure, due to the use of a single charge controller (CC), was solved by implementing one CC per cell and linking them via an isolated communication bus [e.g., controller area network (CAN)] in a masterless fashion so that the failure of one or more CCs will not impact the remaining functional CCs. Each micro-controller-based CC digitizes the cell voltage (V(sub cell)), two cell temperatures, and the voltage across the switch (V); the latter variable is used in conjunction with V(sub cell) to estimate the bypass current for a given bypass resistor. Furthermore, CC1 digitizes the battery current (I1) and battery voltage (V(sub batt) and CC5 digitizes a second battery current (I2). As a result, redundant readings are taken for temperature, battery current, and battery voltage through the summation of the individual cell voltages given that each CC knows the voltage of the other cells. For the purpose of cell balancing, each CC periodically and independently transmits its cell voltage and stores the received cell voltage of the other cells in an array. The position in the array depends on the identifier (ID) of the transmitting CC. After eight cell voltage receptions, the array is checked to see if one or more cells did not transmit. If one or more transmissions are missing, the missing cell(s) is (are) eliminated from cell-balancing calculations. The cell-balancing algorithm is based on the error between the cell s voltage and the other cells and is categorized into four zones of operation. The algorithm is executed every second and, if cell balancing is activated, the error variable is set to a negative low value. The largest error between the cell and the other cells is found and the zone of operation determined. If the error is zero or negative, then the cell is at the lowest voltage and no balancing action is needed. If the error is less than a predetermined negative value, a Cell Bad Flag is set. If the error is positive, then cell balancing is needed, but a hysteretic zone is added to prevent the bypass circuit from triggering repeatedly near zero error. This approach keeps the cells within a predetermined voltage range

Modeling and Control of a Modular Battery Management System for Lithium-ion Battery Packs

High voltage (HV) traction battery packs in electric-drive vehicles (HEV, PHEV, BEV) consist of a large number of battery cells connected in series. As individual cells exhibit mismatches in characteristics such as capacity, inner resistance, and run-time state-of-charge (SOC), cell balancing must be incorporated into the battery management system (BMS). Conventional passive cell balancing does not fully address the mismatch issues, which leads to shorter battery lifetime, and the need to over-size the battery pack. To overcome the problems associated with the conventional architecture, a modular battery management system incorporating both active cell balancing and high voltage (HV) to low-voltage (LV) dc-dc conversion has been developed. The HV-to-LV converter is a series-input, parallel-output dc-dc system with inputs connected across the battery cells or cell modules, while paralleled outputs supply loads on the LV bus. This thesis is focused on modeling, control and design of the modular battery management system. Several critical issues are addressed: (1) stability of the converter system with distributed control in energy storage application is analyzed and simulated; (2) the steady-state model of the dual-active-bridge (DAB) isolated converter with phase-shift modulation is refined and applied to the modular converter system with cell balancing; (3) practical methods for estimation of the lithium-ion battery state-of-charge (SOC) and state-of-health (SOH) are developed in forms suitable for implementation on low-cost microcontrollers. Finally, a modular hybrid balancing system with module-level active balancing and cell-level passive balancing is developed and experimentally validated. The techniques developed in this thesis can be applied to designs of large automotive battery packs with improved performance, reduced size, reduced cost, and longer lifetime

A Comprehensive Study on Battery Management System and Dynamic Analysis of Lithium Polymer Battery

The battery management system (BMS) is the most vital components of an electric vehicle. The main objective of the BMS is to ensure safe and consistent battery operation. To ensure proper functioning of the battery, state measuring and conditioning, cell balancing and control of charge are features that have been realized in BMS. The performance of different types of batteries is different under different environmental and working conditions. These irregularities in the performance of a battery are the primary challenge to the implementing of these functions in the BMS. State estimation of a battery, i.e. state of charge (SOC) and state of health (SOH), is a crucial function for a BMS. Li-Polymer battery has high discharge rate per unit mass and hence are very suitable for applications in electric automotive industry. This project addresses the modelling of a typical Li-Polymer battery and its dynamic characteristics. These battery models emulate the characteristics of real life Li-Po batteries, and help predict their behavior under different external as well as internal conditions. A dynamic model of lithium-polymer battery is designed using MATLAB/Simulink® in order to study the output characteristics of a lithium polymer battery unit. Dynamic simulations are done, which includes the effects of charging/discharging and operating temperature on battery terminal voltage output. The simulation results when compared to relevant studies, validated the model developed in the project

Development of battery management system for hybrid electric propulsion system.

Because of the high overall efficiency and low emissions, Hybrid Electric Propulsion System (HEPS) have become an attractive research area. In this research, a parallel HEPS architecture is adopted and a Hardware test platform is constructed. As a relative new power source in powertrains, battery system plays an important role in HEPS. Hence, a Battery Management System (BMS) is investigated in this research. Battery pack State of Charge (SOC) is a key feedback value in HEPS control. In order to estimate SOC, firstly, an operation-classification adaptive battery model is proposed for Li-Po batteries. Considering the fact that model parameter accuracy is of importance in model-based system state estimation method, an event triggered Adaptive Genetic Algorithm (AGA) is applied for online parameter identification. Secondly, the Extended Kalman Filter (EKF) is applied for single battery cell SOC estimation. Finally, a fuzzy estimator is proposed for battery pack SOC estimation based on maximum/minimum cell voltages and SOC values. Experimental results show that the proposed AGA can effectively track battery parameter variation and SOC estimation error for single cell as well as for the battery pack are both less than 1%. Moreover, considering the Li-Po battery characteristics, a converter based battery cell balancing method is proposed. Simulation result shows that proposed balancing method can be effective in balancing battery cells. In addition, in relation to safety and reliability concerns, a Discrete Wavelet Transform (DWT) based battery circuit detection method is proposed and simulation results showing its feasibility are presented.PhD in Aerospac

A cascaded H-bridge BLDC drive incorporating battery management

A multilevel BLDC drive is proposed using cascaded H-bridges with isolated sources to provide superior output waveforms and reduced current ripple whilst incorporating observer based SoC estimation. Energy management, based on SoC, is incorporated to improve battery performance, reduce variation between cells and to control charge/discharge profiles