Advances in Li-Ion battery management for electric vehicles

This paper aims at presenting new solutions for advanced Li-Ion battery management to meet the performance, cost and safety requirements of automotive applications. Emphasis is given to monitoring and controlling the battery temperature, a parameter which dramatically affects the performance, lifetime, and safety of Li-Ion batteries. In addition to this, an innovative battery management architecture is introduced to facilitate the development and integration of advanced battery control algorithms. It exploits the concept of smart cells combined with an FPGA-based centralized unit. The effectiveness of the proposed solutions is shown through hardware-in-the-loop simulations and experimental results

Sensorless Temperature Estimation for Lithium-ion Batteries via Online Impedance Acquisition

Temperature plays a significant role in the safety, performance, and lifespan of lithium-ion (Li-ion) batteries. To guarantee the safe, efficient, and long-lasting operations of batteries, one of the fundamental tasks of the battery management system (BMS) is to monitor battery temperature during operations. Nevertheless, subject to limited onboard temperature sensors, it becomes challenging for the BMS to obtain the temperature information of each cell in a battery system. To this end, this paper proposes a novel method to estimate the state of temperature (SOT) of batteries in real time based on the electrochemical impedance of batteries without the need for temperature sensors. By taking advantage of the smart battery architecture, the battery impedance at 5 Hz, which exhibit dependency on battery temperature while independency on the state of charge (SOC), can be obtained online via the bypass action. During battery operations, the impedance of the battery can be obtained through periodic bypass action and a designed filter. A simple impedance-temperature relationship that is calibrated offline, can be used to estimate and track the cell temperature. Experiments on charging show that the online calculated battery impedance has strong correlations to battery temperature, indicating its effectiveness in SOT estimation

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

Technology development of electric vehicles: A review

To reduce the dependence on oil and environmental pollution, the development of electric vehicles has been accelerated in many countries. The implementation of EVs, especially battery electric vehicles, is considered a solution to the energy crisis and environmental issues. This paper provides a comprehensive review of the technical development of EVs and emerging technologies for their future application. Key technologies regarding batteries, charging technology, electric motors and control, and charging infrastructure of EVs are summarized. This paper also highlights the technical challenges and emerging technologies for the improvement of efficiency, reliability, and safety of EVs in the coming stages as another contribution

On-chip implementation of Extended Kalman Filter for adaptive battery states monitoring

This paper reports the development and implementation of an adaptive lithium-ion battery monitoring system. The monitoring algorithm is based on the nonlinear Dual Extended Kalman Filter (DEKF), which allows for simultaneous states and parameters estimation. The hardware platform consists of an ARM cortex-M0 processor with six embedded analogue-to-digital converters (ADCs) for data acquisition. Two definitions for online state-of-health (SOH) characterisation are presented; one energy-based and one power-based. Moreover, a method for online estimation of battery's capacity, which is used in SOH characterisation is proposed. Two definitions for state-of-power (SOP) are adopted. Despite the presence of large sensor noise and incorrect filter initialisation, the DEKF algorithm poses excellent SOC and SOP tracking capabilities during a dynamic discharge test. The SOH prediction results are also in good agreement with actual measurements

Critical Aspects of Electric Motor Drive Controllers and Mitigation of Torque Ripple - Review

Electric vehicles (EVs) are playing a vital role in sustainable transportation. It is estimated that by 2030, Battery EVs will become mainstream for passenger car transportation. Even though EVs are gaining interest in sustainable transportation, the future of EV power transmission is facing vital concerns and open research challenges. Considering the case of torque ripple mitigation and improved reliability control techniques in motors, many motor drive control algorithms fail to provide efficient control. To efficiently address this issue, control techniques such as Field Orientation Control (FOC), Direct Torque Control (DTC), Model Predictive Control (MPC), Sliding Mode Control (SMC), and Intelligent Control (IC) techniques are used in the motor drive control algorithms. This literature survey exclusively compares the various advanced control techniques for conventionally used EV motors such as Permanent Magnet Synchronous Motor (PMSM), Brushless Direct Current Motor (BLDC), Switched Reluctance Motor (SRM), and Induction Motors (IM). Furthermore, this paper discusses the EV-motors history, types of EVmotors, EV-motor drives powertrain mathematical modelling, and design procedure of EV-motors. The hardware results have also been compared with different control techniques for BLDC and SRM hub motors. Future direction towards the design of EV by critical selection of motors and their control techniques to minimize the torque ripple and other research opportunities to enhance the performance of EVs are also presented.publishedVersio

Power Converter of Electric Machines, Renewable Energy Systems, and Transportation

Power converters and electric machines represent essential components in all fields of electrical engineering. In fact, we are heading towards a future where energy will be more and more electrical: electrical vehicles, electrical motors, renewables, storage systems are now widespread. The ongoing energy transition poses new challenges for interfacing and integrating different power systems. The constraints of space, weight, reliability, performance, and autonomy for the electric system have increased the attention of scientific research in order to find more and more appropriate technological solutions. In this context, power converters and electric machines assume a key role in enabling higher performance of electrical power conversion. Consequently, the design and control of power converters and electric machines shall be developed accordingly to the requirements of the specific application, thus leading to more specialized solutions, with the aim of enhancing the reliability, fault tolerance, and flexibility of the next generation power systems

Field weakening and sensorless control solutions for synchronous machines applied to electric vehicles.

184 p.La polución es uno de los mayores problemas en los países industrializados. Por ello, la electrificación del transporte por carretera está en pleno auge, favoreciendo la investigación y el desarrollo industrial. El desarrollo de sistemas de propulsión eficientes, fiables, compactos y económicos juega un papel fundamental para la introducción del vehículo eléctrico en el mercado.Las máquinas síncronas de imanes permanentes son, a día de hoy la tecnología más empleada en vehículos eléctricos e híbridos por sus características. Sin embargo, al depender del uso de tierras raras, se están investigando alternativas a este tipo de máquina, tales como las máquinas de reluctancia síncrona asistidas por imanes. Para este tipo de máquinas síncronas es necesario desarrollar estrategias de control eficientes y robustas. Las desviaciones de parámetros son comunes en estas máquinas debido a la saturación magnética y a otra serie de factores, tales como tolerancias de fabricación, dependencias en función de la temperatura de operación o envejecimiento. Las técnicas de control convencionales, especialmente las estrategias de debilitamiento de campo dependen, en general, del conocimiento previo de dichos parámetros. Si no son lo suficientemente robustos, pueden producir problemas de control en las regiones de debilitamiento de campo y debilitamiento de campo profundo. En este sentido, esta tesis presenta dos nuevas estrategias de control de debilitamiento de campo híbridas basadas en LUTs y reguladores VCT.Por otro lado, otro requisito indispensable para la industria de la automoción es la detección de faltas y la tolerancia a fallos. En este sentido, se presenta una nueva estrategia de control sensorless basada en una estructura PLL/HFI híbrida que permite al vehículo continuar operando de forma pseudo-óptima ante roturas en el sensor de posición y velocidad de la máquina eléctrica. En esta tesis, ambas propuestas se validan experimentalmente en un sistema de propulsión real para vehículo eléctrico que cuenta con una máquina de reluctancia síncrona asistidas por imanes de 51 kW

Artificial Intelligent Maximum Power Point Controller based Hybrid Photovoltaic/Battery System

Photovoltaic (PV) cells have non-linear properties influenced by environmental factors, including irradiation and temperature. As a result, a method known as maximum power point tracking (MPPT) was implemented to boost the PV cells\u27 efficiency and make the most of the energy they could provide. The traditional perturb and observe (P&O) approach for determining the maximum power point tracking (MPPT) has various drawbacks, including poor steady-state performance, increased oscillation around the MPP point, and delayed reaction. As a result, this work aims to present a hybrid fuzzy logic (FL) and P&O MPPT approach to improve the PV system\u27s performance coupled to the lithium battery storage system. Matlab/Simulink is used to bring the suggested technique to life, after which its efficacy is evaluated in the context of rapid changes in the irradiance level. According to the findings of the simulations, the suggested strategy has the potential to enhance the steady-state performance of PV systems in terms of oscillation and time response. Finally, the proposed results are compared with that obtained by the conventional P&O technique, and the stress of PV power is limited to ∆P=1kW and the overshoot power is limited to 5%

Battery Systems and Energy Storage beyond 2020

Currently, the transition from using the combustion engine to electrified vehicles is a matter of time and drives the demand for compact, high-energy-density rechargeable lithium ion batteries as well as for large stationary batteries to buffer solar and wind energy. The future challenges, e.g., the decarbonization of the CO2-intensive transportation sector, will push the need for such batteries even more. The cost of lithium ion batteries has become competitive in the last few years, and lithium ion batteries are expected to dominate the battery market in the next decade. However, despite remarkable progress, there is still a strong need for improvements in the performance of lithium ion batteries. Further improvements are not only expected in the field of electrochemistry but can also be readily achieved by improved manufacturing methods, diagnostic algorithms, lifetime prediction methods, the implementation of artificial intelligence, and digital twins. Therefore, this Special Issue addresses the progress in battery and energy storage development by covering areas that have been less focused on, such as digitalization, advanced cell production, modeling, and prediction aspects in concordance with progress in new materials and pack design solutions