The growing interest and recent breakthroughs in artificial intelligence and machine learning (ML) have actively contributed to an increase in research and development of new methods to estimate the states of electrified vehicle batteries. Data-driven approaches, such as ML, are becoming more popular for estimating the state of charge (SOC) and state of health (SOH) due to greater availability of battery data and improved computing power capabilities. This paper provides a survey of battery state estimation methods based on ML approaches such as feedforward neural networks (FNNs), recurrent neural networks (RNNs), support vector machines (SVM), radial basis functions (RBF), and Hamming networks. Comparisons between methods are shown in terms of data quality, inputs and outputs, test conditions, battery types, and stated accuracy to give readers a bigger picture view of the ML landscape for SOC and SOH estimation. Additionally, to provide insight into how to best approach with the comparison of different neural network structures, an FNN and long short-term memory (LSTM) RNN are trained fifty times each for 3000 epochs. The error is somewhat different for each training repetition due to the random initial values of the trainable parameters, demonstrating that it is important to train networks multiple times to achieve the best result. Furthermore, it is recommended that when performing a comparison among estimation techniques such as those presented in this review paper, the compared networks should have a similar number of learnable parameters and be trained and tested with identical data. Otherwise, it is difficult to make a general conclusion regarding the quality of a given estimation technique. INDEX TERMS Machine learning, artificial intelligence, deep learning, battery management systems (BMS), electric vehicles, state of charge, state of health.

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<div class="section abstract"><div class="htmlview paragraph">Battery state-of-charge (SOC) is critical information for the vehicle energy management system and must be accurately estimated to ensure reliable and affordable electrified vehicles (xEV). However, due to the nonlinear temperature, health, and SOC dependent behaviour of Li-ion batteries, SOC estimation is still a significant automotive engineering challenge. Traditional approaches to this problem, such as electrochemical models, usually require precise parameters and knowledge from the battery composition as well as its physical response. In contrast, neural networks are a data-driven approach that requires minimal knowledge of the battery or its nonlinear behaviour. The objective of this work is to present the design process of an SOC estimator using a deep feedforward neural network (FNN) approach. The method includes a description of data acquisition, data preparation, development of an FNN, FNN tuning, and robust validation of the FNN to sensor noise. To develop a robust estimator, the FNN was exposed, during training, to datasets with errors intentionally added to the data, e.g. adding cell voltage variation of ±4mV, cell current variation of ±110mA, and temperature variation of ±5^ºC. The error values were chosen to be similar to the noise and error obtained from real sensors used in commercially available xEVs. The robust FNN trained from two Li-ion cells datasets, one for a nickel manganese cobalt oxide (NMC) cell and the second for a nickel cobalt aluminum oxide (NCA) chemistry cell, is shown to overcome the added errors and obtain a SOC estimation accuracy of 1% root mean squared error (RMSE).</div></div>

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Cyber-Physical Systems (CPS) are the next generation of embedded ICT systems designed to be aware of the physical environment by using sensor-actuator networks to provide users with a wide range of smart applications and services. Many of these smart applications are possible due to the incorporation of autonomic control loops that implement advanced processing and analysis of historical and real-time data measured by sensors; plan actions according to a set of goals or policies; and execute plans through actuators. The complexity of this kind of systems requires mechanisms that can assist the system's design and development. This paper presents a solution for assisting the design and development of CPS based on Model-Driven Development: MindCPS (doMaIN moDel for CPS) solution. MindCPS solution is based on a model that provides modelling primitives for explicitly specifying the autonomic behaviour of CPS and model transformations for automatically generating part of the CPS code. In addition to the automatic code generation, the MindCPS solution offers the possibility of rapidly configuring and developing the core behaviour of a CPS, even for nonsoftware engineers. The MindCPS solution has been put into practice to deploy a smart metering system in a demonstrator located at the Technical University of Madrid.