A high-gain adaptive observer for detecting Li-ion battery terminal voltage collapse

We use a high-gain adaptive observer and a trend filtering algorithm to detect early stages that lead to terminal voltage collapses in Li-ion batteries. This approach allows accurate detection without having sophisticated battery models. Theoretical analysis proves that the physical Li-ion battery becomes unstable when the estimated states of the observer enter instability. The trend filtering algorithm is able to detect such instability under large perturbations from the discharge current. Extensive simulation and experimental results demonstrate the effectiveness of the algorithms and its robustness under realistic perturbations.     

A novel Long-term degradation trends predicting method for Multi-Formulation Li-ion batteries based on deep reinforcement learning

In the design phase of Li-ion batteries for electric vehicles, battery manufacturers need to carry out cycle life tests on a large number of formulations to get the best one that meets customer demands. However, such tests take considerable time and money due to the long cycle life of power Li-ion batteries. Aiming at reducing the cost of cycle life tests, we propose a prediction method that can learn historical degradation data and extrapolate to predict the remaining degradation trend of the current formulation sample taking the initial stage of partial cycle life test results as input. Compared with existing methods, the proposed deep reinforcement learning based method is able to learn degradation trends with different formulations and predict long-term degradation trends. Based on the deep deterministic policy gradient algorithm, the proposed method builds a degradation trend prediction model. Meanwhile, an interactive environment is designed for the model to explore and learn in the training phase. The proposed method is verified with real test data from battery manufacturers under three different temperature conditions in the formulation design stage. The comparisons indicate that the proposed method is superior to traditional degradation trend prediction methods in both accuracy and stability.     

用于动力电池检测的智能传感器设计

针对电动车动力电池检测的需要,设计了一种智能传感器,可同时检测电压、温度、电流等指标,检测准确,成本低廉,扩展性好,适用于各类铅酸、镍氢、锂离子动力电池或动力电池组,可用于电池的性能测试或电池管理。     

Estimation of Battery State of Health Using Back Propagation Neural Network

100 pieces of 26650-type Lithium iron phosphate（LiFePO4） batteries cycled with a fixed charge and discharge rate are tested, and the influence of the battery internal resistance and the instantaneous voltage drop at the start of discharge on the state of health（SOH） is discussed. A back propagation（BP） neural network model using additional momentum is built up to estimate the state of health of Li-ion batteries. The additional 10 pieces are used to verify the feasibility of the proposed method. The results show that the neural network prediction model have a higher accuracy and can be embedded into battery management system（BMS） to estimate SOH of LiFePO4 Li-ion batteries.     

Dynamic energy model of a lithium-ion battery

As an electrochemical component, a lithium-ion battery is clearly a multi-disciplinary system. The choice was made to model it via Bond Graph formalism. Although this tool has been developed since the 1970s, the novelty is its application to lithium-ion batteries, which turns the modeling presented here into an original energy approach. The main objective is to develop and validate a lithium-ion battery model that could be implemented in a global system for energy monitoring. However, nearly every phenomenon occurring in the battery is taken into account for a possible ageing or thermal study. In the first part, the energy modeling approach is described. In the second part, the lithium-ion battery operation is explained. In the third part, the Bond Graph model is proposed. At last, experimental validations are presented.     

基于HI-DD-AdaBoost.RT的锂离子动力电池SOH预测

锂离子电池是一个复杂的电化学动态系统,实时准确的健康状态(SOH)估计对电动汽车动力锂电池的维护至关重要,传统建模方法难以实现SOH的在线估算.基于此,从实时评估电池的SOH出发,在增量学习的基础上,选取与电池健康状态相关的指标建立SOH预测模型.考虑到增量学习中的耗时性问题,提出融合滑动窗口技术的HI-DD算法,该算法可以检测概念漂移是否发生,从而指导和确定模型更新位置;设计出HI-DD与AdaBoost.RT结合的模型更新策略,进而提高模型的在线学习性能和预测精度,最后使用CALCE提供的电池老化实验数据对所提出的方法进行验证.结果表明,基于增量学习的HI-DD-AdaBoost.RT预测算法具有较强的在线更新能力和较高的预测精度,能够满足SOH在线预测的实际需求.     

State of charge estimation for Li-ion battery based on model from extreme learning machine

Lithium-ion (Li-ion) battery state of charge (SOC) estimation is important for electric vehicles (EVs). The model-based state estimation method using the Kalman filter (KF) variants is studied and improved in this paper. To establish an accurate discrete model for Li-ion battery, the extreme learning machine (ELM) algorithm is proposed to train the model using experimental data. The estimation of SOC is then compared using four algorithms: extended Kalman filter (EKF), unscented Kalman filter (UKF), adaptive extended Kalman filter (AEKF) and adaptive unscented Kalman filter (AUKF). The comparison of the experimental results shows that AEKF and AUKF have better convergence rate, and AUKF has the best accuracy. The comparison from the radial basis function neural network (RBF NN) model also verifies that the ELM model has lighter computation load and smaller estimation error in SOC estimation process. In general, the performance of Li-ion battery SOC estimation is improved by the AUKF algorithm applied on the ELM model.     

Parity-relation-based state-of-health monitoring of lead acid batteries for automotive applications

Effective vehicular power management requires accurate knowledge of battery state, including state-of-charge (SOC) and state-of-health (SOH). This paper presents an integrated algorithm for reliable battery SOH monitoring. The dynamics of lead acid batteries during engine cranking is investigated, and a new battery model is presented. Moreover, a parity-relation-based integrated method for battery SOH monitoring is proposed. It is shown that the diagnostic residual combines the SOH information provided by both battery resistance and voltage loss during engine cranking, hence enhancing diagnostic performance. Extensive evaluation results using real vehicle cranking data have verified the effectiveness of the proposed method.     

An adaptive sliding mode observer for lithium-ion battery state of charge and state of health estimation in electric vehicles

As the demand for electric vehicle (EV)'s remaining operation range and power supply life, Lithium-ion (Li-ion) battery state of charge (SOC) and state of health (SOH) estimation are important in battery management system (BMS). In this paper, a proposed adaptive observer based on sliding mode method is used to estimate SOC and SOH of the Li-ion battery. An equivalent circuit model with two resistor and capacitor (RC) networks is established, and the model equations in specific structure with uncertainties are given and analyzed. The proposed adaptive sliding mode observer is applied to estimate SOC and SOH based on the established battery model with uncertainties, and it can avoid the chattering effects and improve the estimation performance. The experiment and simulation estimation results show that the proposed adaptive sliding mode observer has good performance and robustness on battery SOC and SOH estimation.     

Microstructural analysis of laser micro-welds between copper and aluminum

Currently used ultrasonic welded joints for assembly and packaging of Li-Ion batteries have reliability concerns for automotive applications, as the battery is subjected to vibration and other mechanical loads. The sealing of the battery can is very critical for safety. Due to battery weld failures in recent years, the postal service has put ban on shipping Li-ion batteries via regular mail. A laser based alternative joining technology has the potential to offer robust, fast and cost-effective processing of Li-Ion batteries. Before the technology can be fully implemented, it is imperative to understand the effects of various process parameters on the robustness of the weld. In the present analysis, a preliminary study is performed to understand the effect of laser scanning speed on the micro-structural and physical characteristics of the materials in the weld area that ultimately affect the bond quality. Samples are created by welding aluminum and copper in lap shear configuration using a continuous wave fiber laser. Two sets of samples are created using a laser power of 225 W; however, the scanning speeds are 300 and 400 mm/s. Scanning electron microscopy and energy dispersive spectroscopy are performed in the weld area to understand the microstructural and physical characteristics of the joint that may have been affected by the processing parameters.     

基于改进粒子滤波算法的医疗锂电池PHM系统设计

针对维持生命的医疗电子设备的锂离子电池维修问题,设计了一套故障预测与健康管理系统(Prognostics and Health Management-PHM),提出了PHM系统的实现框架。通过搭建一套电池控制应力水平实验平台并将故障注入锂离子电池中,来进行数据采集。建立基于阿列纽斯模型(Arrhenius Model)的医疗电子设备的锂离子电池模型,通过无迹粒子滤波(Unscented Particle Filter-UPF)算法和粒子滤波(Particle Filter-PF)算法计算出实时故障的概率并给出剩余寿命预测以及健康管理维护方法。通过Matlab对比UPF和PF的预测剩余寿命的仿真结果与实验所测数据的吻合度,选出UPF算法为最优算法并及时诊断故障,为后续维护提供建议。     

单片机在智能充电系统中的应用

介绍以Infineon 16位单片机为核心的智能充电系统。系统针对不同的电池采用不同的充电算法,因而具电池自适应性。通过采用电压控制、定时控制和电池温度控制相结合的综合充电控制算法,并利用模糊控制理论实现了充电过程的智能化和最优化控制。     

Automatic generation of assembly system configuration with equipment selection for automotive battery manufacturing

High power and high capacity lithium-ion batteries are being adopted for electrical and hybrid electrical vehicles (EV/HEV) applications. An automotive Li-ion battery pack usually has a hierarchical composition of components assembled in some repetitive patterns. Such a product assembly hierarchy may facilitate automatic configuration of assembly systems including assembly task grouping, sequence planning, and equipment selection. This paper utilizes such a hierarchical composition in generating system configurations with equipment selection for optimal assembly system design. A recursive algorithm is developed to generate feasible assembly sequences and the initial configurations including hybrid configurations. The generated configurations are embedded in an optimal assembly system design problem for simultaneous equipment selection and task assignment by minimizing equipment investment cost. The complexity of the computational algorithm is also discussed.     

An observer looks at the cell temperature in automotive battery packs

The internal temperature of Li-ion batteries for electric or hybrid vehicles is an important factor influencing their ageing. Generally not measured, it can be reconstructed from an external measurement and a model. This paper presents the simplified modelling of heat transfers in a battery module, leading to a Linear Parameter-Varying (LPV) model. Then, a polytopic observer is proposed to estimate the cell temperature and internal resistance, ensuring a tradeoff between the convergence speed and the noise of the estimated states. Experimental results show the good quality of the estimation and the diagnosis potential offered by internal resistance reconstruction.     

Structural analysis based sensors fault detection and isolation of cylindrical lithium-ion batteries in automotive applications

The battery sensors fault diagnosis is of great importance to guarantee the battery performance, safety and life as the operations of battery management system (BMS) mainly depend on the embedded current, voltage and temperature sensor measurements. This paper presents a systematic model-based fault diagnosis scheme to detect and isolate the current, voltage and temperature sensor fault. The proposed scheme relies on the sequential residual generation using structural analysis theory and statistical inference residual evaluation. Structural analysis handles the pre-analysis of sensor fault detectability and isolability possibilities without the accurate knowledge of battery parameters, which is useful in the early design stages of diagnostic system. It also helps to find the analytical redundancy part of the battery model, from which subsets of equations are extracted and selected to construct diagnostic tests. With the help of state observes and other advanced techniques, these tests are ensured to be efficient by taking care of the inaccurate initial State-of-Charge (SoC) and derivation of variables. The residuals generated from diagnostic tests are further evaluated by a statistical inference method to make a reliable diagnostic decision. Finally, the proposed diagnostic scheme is experimentally validated and some experimental results are presented.     

基于分层多模自适应滤波算法的无人机多重传感器故障诊断

应用标准的多模自适应滤波算法能够在较短的时间内检测出系统的单一故障,但是当把它用于检测系统的双重或多重故障时,这一算法需要建立所有可能出现的故障模型,而每一个模型都要对应一个卡尔曼滤波器,需要大量的滤波器并行运算,大大增加了系统的故障诊断时间,为了简化算法并减少算法计算时间,本文提出了一种用于复杂系统的多重故障诊断的分层多重模型滤波技术,在确定某一单个故障发生后,则可以启用一组基于上一单个故障的新滤波器来检测系统的第二重故障,这样减少了并行运算的滤波器数量,从而减少计算量和故障诊断时间.本文将此算法应用于某无人机多重传感器的故障诊断,仿真结果验证了该方法的有效性.     

State-of-health estimator based-on extension theory with a learning mechanism for lead-acid batteries

The main objective of this paper is to design and implement an improved intelligent state-of-health (SOH) estimator for estimating the useful life of lead-acid batteries. Laboratory studies were carried out to measure and record the distributed range of characteristic values in each SOH cycle for the battery subject to cycles of charging and discharging experiments. The measured coup de fouet voltage, internal resistance, and transient current are used as characteristics to develop an intelligent SOH evaluation algorithm. This method is based on the extension matter-element model that has been modified in this research by adding a learning mechanism for evaluation SOH of batteries. The proposed algorithm is relatively simple so that it can be easily implemented with a programmable system-on-chip (PSOC) microcontroller achieve rapid evaluation of battery SOH with precision by using a concise hardware circuit.     

Permanent magnet DC motor parameters estimation via universal adaptive stabilization

This paper presents a quick and effective adaptive estimation methodology for parameters estimation of a permanent magnet (PM) DC motor. The proposed technique uses a universal adaptive stabilizer (UAS). This technique estimates PMDC motor parameters in a single experimental run using input voltage, current and speed. Over time, due to aging and wear, a motor’s parameters values do not match those in the datasheet. Mathematical proofs, experimental results supporting the proposed approach are presented. Despite the persistence of excitation condition not being imposed, the proposed technique produces good results, and is verified in earlier work on Li-ion battery parameters estimation.     

A model for simulating a lead-acid battery using bond graphs

This paper presents a model of a lead-acid battery developed with bond graphs. The bond graph structure is used to reproduce the behavior of reversible electrochemical cells in charging conditions or in discharging conditions. The work presented here has been applied to the particular case of lead-acid battery, so widely used in the automotive industry as standard 12 V batteries and as traction batteries in electrical or hybrid vehicles.The model considers each half-cell independently. For each half-cell the main electrode reaction and the electrolysis reaction of water are considered, that will be the hydrogen evolution reaction in the negative electrode and the oxygen evolution reaction in the positive.Electrochemical principles are considered in order to consider the main phenomena that appear in the battery, like the equilibrium potential, and the overpotential, modeled by means of the activation or charge transfer and the diffusion mechanisms.Each one of this phenomena are modeled with their corresponding bond graph elements and structures, showing the correspondence between bond graph elements and its physical interpretation in this field.First, an isothermal model has been developed in order to show the behavior of the main phenomena. A more complex model has also been developed including thermal behavior. This model is very useful in the case of traction batteries in electrical and hybrid vehicles where high current intensities appear.Some simulation results are also presented in order to show the accuracy of the proposed models and the differences of behavior if thermal effects are considered.     

A lattice Boltzmann modeling and analysis of the thermal convection in a lithium-ion battery

Thermal convection is a critical problem in the design of thermal management system, and is widely encountered in electric and hybrid electric vehicles. In the present work, the lattice Boltzmann method is adopted to investigate the thermal convection in the $\mathrm{LiN}{\mathrm{i}}_{\mathrm{x}}\mathrm{C}{\mathrm{o}}_{\mathrm{y}}\mathrm{M}{\mathrm{n}}_{\mathrm{z}}{\mathrm{O}}_{\mathrm{2}}$ (NCM) lithium-ion battery. The numerical results reveal that the thermal convection model considered in the current study can clearly depict the temperature evolution in the case of the thermal runaway. Additionally, it is found that as the adiabatic boundary condition is adopted, the maximum temperature inside the battery can reach 320°C at 240s, which in turn affects the surrounding batteries. To prevent the thermal runaway propagation in such a case, we also analyzed the forced convective heat transfer in this situation, and the numerical results indicate that thermal runaway can be effectively decreased if the value of the surface heat transfer coefficient for battery cell increases up to 200Wm^?2K^?1. Moreover, it is noted that when the temperature inside the battery reaches 110°C, the subsequent temperature distributions inside the battery have little influence on the surrounding batteries, which suggests that the thermal management of battery pack in both normal charge and discharge process should be considered.