Electric vehicles (EVs) require reliable and very accurate battery state-of-charge (SOC) estimation to maximize their performance. A commonly used estimation technique, the extended Kalman filter (EKF), provides an accurate estimate of the SOC. However, EKF has some limitations such as, it assumes the knowledge of the statistics of the process noise and measurement noise is available, which practically cannot be guaranteed. In this paper, an adaptive equivalent-circuit model is proposed and used for SOC estimation. The proposed model is based on a common cell model with adaptive parameters tracking feature implemented using an artificial neural network (ANN) controller embedded within the model. A variant of the EKF, namely the unscented Kalman filter (UKF), is used to achieve more accurate estimates of the SOC with a relatively fast convergence speed. The UKF uses the unscented transform to obtain the statistics of the process noise covariance. Furthermore, the autocovariance least squares (ALS) technique is used to estimate the measurement noise covariance by accounting for possible correlation in the measurement innovations, which enhances the accuracy of the estimate. Derivation of the proposed method followed by experimental verification are presented in this paper.

The temperature of a battery cell is a major parameter that must be continuously monitored to ensure a safe operation. Most battery failures are linked to thermal runaway due to temperature rise in the battery, which if not detected early can result in battery destruction or fire hazard. This paper proposes a robust artificial neural network models with reduced-complexity to estimate the surface temperature of different battery chemistries. The proposed models are accurate, reliable and use no temperature sensor. Different neural network architectures are evaluated and optimized. Derivation of the models followed by experimental verification using commercial battery cells of different chemistries, specifications and ageing conditions are presented.

A novel method is proposed for forecasting the capacity of lithium-ion battery cells. The method uses a Gaussian Process Regression model, a machine learning framework. Besides the high prediction accuracy and robustness the proposed method possesses, the method offers other advantages, namely, it provides uncertainty information, and it has the capability to cross-correlate capacity trends between different battery cells. These two merits make the proposed method a very reliable and practical solution for applications that use battery cell packs with a large number of interconnected battery cells. The proposed method is derived, verified, and compared to benchmark methods on three experimental lithium-ion battery cell datasets. The results show the effectiveness of the proposed method.

This paper investigates the role of artificial neural networks in enhancing the accuracy of instantaneous power estimation of electric vehicles' batteries. In electric vehicles, a battery is used as a main or complementary bidirectional power source. To optimize the energy management of the vehicle, the power sourced or sinked by the battery must be estimated in real time under any condition. The power of the battery is a function of many variables including the current, the state of charge, the ambient temperature, and the state of health. This paper evaluates some existing equivalent circuit models for estimating the instantaneous power of electric vehicles' batteries and proposes new artificial neural network-based models to enhance the power estimation accuracy. The experimental data obtained by performing standardized electric vehicle tests using a 3.6-V/16.5-Ah lithium-ion battery cell and a 12.8-V/150-Ah lithium-ion battery pack are presented and used for models' evaluation.

For the safe and reliable operation of battery-driven machines, accurate state-of-charge (SOC) estimations are necessary. Unfortunately, existing methods often fail to identify patterns relevant to long-term SOC estimation due to complex battery cell characteristics such as aging. In this paper, we propose the Uncorrelated Sparse Autoencoder with Long Short-Term Memory (USAL). USAL is a novel neural network that addresses the challenging task of long-term SOC estimation given a limited initial history of a cell’s charge-discharge behavior. USAL uses a multi-task learning strategy to harness the advantages of sparse autoencoders and Long Short-term Memory (LSTM) networks by enforcing correlation penalties. The USAL simultaneously learns to (i) generate a latent space of informative SOC encodings from commonly measured cell characteristics, (ii) penalize for high multicollinearity between encodings, and (iii) identify non-trivial long and short temporal correlations between the encodings using LSTM cells. USAL outperforms benchmarked models in our experiments when trained on five initial charge-discharge cycles across multiple battery cells using three publicly available accelerated aging datasets. Note to Practitioners —This paper proposes USAL, a custom-built deep neural network to address the challenging task of long-term SOC estimations in battery cells. Long-term SOC estimation involves estimating SOC for cycles near End-Of-Life (EOL) given some initial charge-discharge cycles. Three fundamental steps involved in long-term SOC estimations using USAL are (i) exploiting a multi-task learning strategy to learn efficient encodings given limited training data, (ii) penalizing these encodings for high correlations to efficiently transform measured inputs into a space of informative features, and (iii) mapping of aging-related trends to support long-term SOC estimations. USAL is designed to be a data-driven SOC estimation method that is (i) capable of alerting the user to a faulty cell when integrated into a real-life Battery Management System (BMS) and (ii) identifying the relative quality of a battery cell from only a few initial charge-discharge cycles.

This paper proposes an adaptive sensorless measurement technique for internal temperature of lithium-ion (Li-ion) batteries. The proposed technique is based on measuring the impedance-phase of the battery in real time by a simple search algorithm. The proposed technique then identifies the zero-crossing frequency at which the phase of the impedance is zero. From the value of the zero-crossing frequency, the cell's internal temperature is estimated. Details on the proposed technique followed by experimental verification using a 3.8V/2600mAh Samsung Li-ion battery cell at temperatures between -20° C and +50° C are presented.