Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Due to increasing concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels, the electric vehicles (EVs) receive massive popularity due to their performances and efficiencies in recent decades. EVs have already been widely accepted in the automotive industries considering the most promising replacements in reducing CO2 emissions and global environmental issues. Lithium-ion batteries have attained huge attention in EVs application due to their lucrative features such as lightweight, fast charging, high energy density, low self-discharge and long lifespan. This paper comprehensively reviews the lithium-ion battery state of charge (SOC) estimation and its management system towards the sustainable future EV applications. The significance of battery management system (BMS) employing lithium-ion batteries is presented, which can guarantee a reliable and safe operation and assess the battery SOC. The review identifies that the SOC is a crucial parameter as it signifies the remaining available energy in a battery that provides an idea about charging/discharging strategies and protect the battery from overcharging/over discharging. It is also observed that the SOC of the existing lithium-ion batteries have a good contribution to run the EVs safely and efficiently with their charging/discharging capabilities. However, they still have some challenges due to their complex electro-chemical reactions, performance degradation and lack of accuracy towards the enhancement of battery performance and life. The classification of the estimation methodologies to estimate SOC focusing with the estimation model/algorithm, benefits, drawbacks and estimation error are extensively reviewed. The review highlights many factors and challenges with possible recommendations for the development of BMS and estimation of SOC in next-generation EV applications. All the highlighted insights of this review will widen the increasing efforts towards the development of the advanced SOC estimation method and energy management system of lithium-ion battery for the future high-tech EV applications.

A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations

Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles

Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.

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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

Towards a smarter battery management system: A critical review on battery state of health monitoring methods

Particle filter for state of charge and state of health estimation for lithium–iron phosphate batteries

The paper presents a new approach for state estimation of batteries that is able to overcome most of the obstacles for the classical Kalman filter approach. The so called particle filter is able to use any probability density function by applying Monte Carlo sampling methods for approximating the density functions for state of charge and state of health defined by the remaining capacity. Thereby the restriction of the Kalman filter to zero mean Gaussian distributions for all states and errors is overcome. The paper proves the validity of the approach by testing lithium metal oxide/graphite batteries with different states of health by applying different current and temperature profiles. A special focus of the testing is on electric vehicles and photovoltaic applications. For electric vehicles state of health determination achieves a correctness of 1 % or better and is a bit worse for photovoltaic applications with 3.75 % or better for ageing state between 100 % and 80 % of initial capacity. During long term testing the algorithm is validated with a decreasing state of health over time due to accelerated ageing. The state of charge estimation is always better than 1 % in long term testing and the state of health is correctly tracked over time.

Parallel particle filter for state of charge and health estimation with a long term test

Eine Vorrichtung (100) zum Bestimmen eines Zustandsparameters (122) einer Batterie umfasst einen Spannungsdetektor (110) und einen Prozessor (120). Der Spannungsdetektor (110) misst eine Klemmspannung (Umess) der Batterie. Der Prozessor (120) berechnet eine Mehrzahl von gegenwartigen Stichproben basierend auf einer Mehrzahl von bekannten Stichproben (102) einer fruheren Wahrscheinlichkeitsverteilung eines Zustandsindikators der Batterie und einer Mehrzahl von Fehlerwahrscheinlichkeitsstichproben einer Fehlerwahrscheinlichkeitsverteilung. Des Weiteren bestimmt der Prozessor (120) eine Mehrzahl von Gewichtungsfaktoren fur die Mehrzahl von gegenwartigen Stichproben basierend auf der Mehrzahl von gegenwartigen Stichproben und der gemessenen Klemmspannung (Umess, SOC, ΔSOC). Ferner berechnet der Prozessor (120) den Zustandsparameter (122) der Batterie basierend auf der Mehrzahl von gegenwartigen Stichproben und der Mehrzahl von Gewichtungsfaktoren.

Vorrichtung und verfahren zum bestimmen eines zustandsparameters einer batterie

The invention relates to a device (100) for determining a state parameter (122) of a battery, comprising a voltage detector (110) and a processor (120). The voltage detector (110) measures a terminal voltage (Umess) of the battery. The processor (120) calculates a plurality of current samples based on a plurality of known samples (102) of an earlier probability distribution of a state indicator of the battery and a plurality of error probability samples of an error probability distribution. Moreover, the processor (120) determines a plurality of weighting factors for the plurality of current samples based on the plurality of current samples and the measured terminal voltage (Umess, SOC, ΔSOC). The processor (120) further calculates the state parameter (122) of the battery based on the plurality of current samples and the plurality of weighting factors.

Nils Armbruster

Papers

Particle filter for state of charge and state of health estimation for lithium–iron phosphate batteries

Parallel particle filter for state of charge and health estimation with a long term test

Vorrichtung und verfahren zum bestimmen eines zustandsparameters einer batterie

Device and method for determining a state parameter of a battery