Estimation Accuracy and Computational Cost Analysis of Artificial Neural Networks for State of Charge Estimation in Lithium Batteries

This paper presents a tradeoff analysis in terms of accuracy and computational cost between different architectures of artificial neural networks for the State of Charge (SOC) estimation of lithium batteries in hybrid and electric vehicles. The considered layouts are partly selected from the literature on SOC estimation, and partly are novel proposals that have been demonstrated to be effective in executing estimation tasks in other engineering fields. One of the architectures, the Nonlinear Autoregressive Neural Network with Exogenous Input (NARX), is presented with an unconventional layout that exploits a preliminary routine, which allows setting of the feedback initial value to avoid estimation divergence. The presented solutions are compared in terms of estimation accuracy, duration of the training process, robustness to the noise in the current measurement, and to the inaccuracy on the initial estimation. Moreover, the algorithms are implemented on an electronic control unit in serial communication with a computer, which emulates a real vehicle, so as to compare their computational costs. The proposed unconventional NARX architecture outperforms the other solutions. The battery pack that is used to design and test the networks is a 20 kW pack for a mild hybrid electric vehicle, whilst the adopted training, validation and test datasets are obtained from the driving cycles of a real car and from standard profiles

Integrated braking control for electric vehicles with in-wheel propulsion and fully decoupled brake-by-wire system

This paper introduces a case study on the potential of new mechatronic chassis systems for battery electric vehicles, in this case a brake-by-wire (BBW) system and in-wheel propulsion on the rear axle combined with an integrated chassis control providing common safety features like anti-lock braking system (ABS), and enhanced functionalities, like torque blending. The presented controller was intended to also show the potential of continuous control strategies with regard to active safety, vehicle stability and driving comfort. Therefore, an integral sliding mode (ISM) and proportional integral (PI) control were used for wheel slip control (WSC) and benchmarked against each other and against classical used rule-based approach. The controller was realized in MatLab/Simulink and tested under real-time conditions in IPG CarMaker simulation environment for experimentally validated models of the target vehicle and its systems. The controller also contains robust observers for estimation of non-measurable vehicle states and parameters e.g., vehicle mass or road grade, which can have a significant influence on control performance and vehicle safety

Low-cost programmable battery dischargers and application in battery model identification

This paper describes a study where a low-cost programmable battery discharger was built from basic electronic components, the popular MATLAB programming environment, and an low-cost Arduino microcontroller board. After its components and their function are explained in detail, a case study is performed to evaluate the discharger's performance. The setup is principally suitable for any type of battery cell or small packs. Here a 7.2 V NiMH battery pack including six cells is used. Consecutive discharge current pulses are applied and the terminal voltage is measured as the output. With the measured data, battery model identification is performed using a simple equivalent circuit model containing the open circuit voltage and the internal resistance. The identification results are then tested by repeating similar tests. Consistent results demonstrate accuracy of the identified battery parameters, which also confirms the quality of the measurement. Furthermore, it is demonstrated that the identification method is fast enough to be used in real-time applications

Characterization and modeling of a hybrid electric vehicle lithium-ion battery pack at low temperatures

Although lithium-ion batteries have penetrated hybrid electric vehicles (HEVs) and pure electric vehicles (EVs), they suffer from significant power capability losses and reduced energy at low temperatures. To evaluate those losses and to make an efficient design, good models are required for system simulation. Subzero battery operation involves nonclassical thermal behavior. Consequently, simple electrical models are not sufficient to predict bad performance or damage to systems involving batteries at subzero temperatures. This paper presents the development of an electrical and thermal model of an HEV lithium-ion battery pack. This model has been developed with MATLAB/Simulink to investigate the output characteristics of lithium-ion batteries over the selected operating range of currents and battery capacities. In addition, a thermal modeling method has been developed for this model so that it can predict the battery core and crust temperature by including the effect of internal resistance. First, various discharge tests on one cell are carried out, and then, cell's parameters and thermal characteristics are obtained. The single-cell model proposed is shown to be accurate by analyzing the simulation data and test results. Next, real working conditions tests are performed, and simulation calculations on one cell are presented. In the end, the simulation results of a battery pack under HEV driving cycle conditions show that the characteristics of the proposed model allow a good comparison with data from an actual lithium-ion battery pack used in an HEV. © 2015 IEEE

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

Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle

The recognition of driver's braking intensity is of great importance for advanced control and energy management for electric vehicles. In this paper, the braking intensity is classified into three levels based on novel hybrid unsupervised and supervised learning methods. First, instead of selecting threshold for each braking intensity level manually, an unsupervised Gaussian Mixture Model is used to cluster the braking events automatically with brake pressure. Then, a supervised Random Forest model is trained to classify the correct braking intensity levels with the state signals of vehicle and powertrain. To obtain a more efficient classifier, critical features are analyzed and selected. Moreover, beyond the acquisition of discrete braking intensity level, a novel continuous observation method is proposed based on Artificial Neural Networks to quantitative analyze and recognize the brake intensity using the prior determined features of vehicle states. Experimental data are collected in an electric vehicle under real-world driving scenarios. Finally, the classification and regression results of the proposed methods are evaluated and discussed. The results demonstrate the feasibility and accuracy of the proposed hybrid learning methods for braking intensity classification and quantitative recognition with various deceleration scenarios

Battery-SOC Estimation for Hybrid-Power UAVs Using Fast-OCV Curve with Unscented Kalman Filters

Unmanned aerial vehicles (UAVs) have drawin increasing attention in recent years, and they are widely applied. Nevertheless, they are generally limited by poor flight endurance because of the limited energy density of their batteries. A robust power supply is indispensable for advanced UAVs; thus hybrid power might be a promising solution. State of charge (SOC) estimation is essential for the power systems of UAVs. The limitations of accurate SOC estimation can be partly ascribed to the inaccuracy of open circuit voltage (OCV), which is obtained through specific forms of identification. Considering the actual operation of a battery under hybrid conditions, this paper proposes a novel method, “fast OCV”, for obtaining the OCVs of batteries. It is proven that fast OCV offers great advantages, related to its simplicity, duration and cost, over traditional ways of obtaining OCV. Moreover, fast-OCV also shows better accuracy in SOC estimation than traditional OCV. Furthermore, this paper also proposes a new method, “batch mode”, for talking-data sampling for battery-parameter identification with the limited-memory recursive least-square algorithm. Compared with traditional the “single mode”, it presents good de-noising effect by making use of all the sampled battery’s terminal current and voltage data.This research was funded by Spanish Government, grant number PID2019-104793RB-C31 and PID2021-124335OB-C21; Comunidad de Madrid, grant number SEGVAUTO-4.0-CM (P2018/EMT-4362); and National Natural Science Foundation of China, grant number 52236007 and 52106176

Flexible calculation approaches to support the European CO2 emissions regulatory scheme for road vehicles

L'abstract è presente nell'allegato / the abstract is in the attachmen

Automated model based engine calibration procedure using co-simulation

The final validation and sign-off of a production powertrain control module (PCM) calibration is a time-consuming and expensive task and requires a high degree of expertise. There are two main reasons for this; firstly, the validation test is an iterative process due to the fact that calibration changes may affect the true operating point of the engine at the desired test point. Secondly, modifications to the calibration require expert knowledge of the complete control strategy so as to improve the correlation to validation data without potentially negatively impacting the correlated mapping points. This paper describes the implementation of an optimisation routine on a virtual platform in order to both reduce the requirement for experimental testing during the validation procedure, and for development of the optimisation routine itself prior to execution on the engine dynamometer. It is shown that in simulation, the optimisation routine is capable of producing an acceptable calibration within just 5 iterations, reducing the 11-week process down to just a few days. It is also concluded that there are also a number of further improvements that could be made to further improve the efficiency of this process