State of charge

About: State of charge is a research topic. Over the lifetime, 12013 publications have been published within this topic receiving 201419 citations. The topic is also known as: SoC & SOC.

Neural Network Approach for Estimating State of Charge of Lithium-Ion Battery Using Backtracking Search Algorithm

Abstract: The state of charge (SOC) is a critical evaluation index of battery residual capacity. The significance of an accurate SOC estimation is great for a lithium-ion battery to ensure its safe operation and to prevent from over-charging or over-discharging. However, to estimate an accurate capacity of SOC of the lithium-ion battery has become a major concern for the electric vehicle (EV) industry. Therefore, numerous researches are being conducted to address the challenges and to enhance the battery performance. The main objective of this paper is to develop an accurate SOC estimation approach for a lithium-ion battery by improving back-propagation neural network (BPNN) capability using backtracking search algorithm (BSA). BSA optimization is utilized to improve the accuracy and robustness of BPNN model by finding the optimal value of hidden layer neurons and learning rate. In this paper, Dynamic Stress Test and Federal Urban Driving Schedule drive profiles are applied for testing the model at three different temperatures. The obtained results of the BPNN based BSA model are compared with the radial basis function neural network, generalized regression neural network and extreme learning machine model using statistical error values of root mean square error, mean absolute error, mean absolute percentage error, and SOC error to check and validate the model performance. The obtained results show that the BPNN based BSA model outperforms other neural network models in estimating SOC with high accuracy under different EV profiles and temperatures.

Finding Ultimate Limits of Performance for Hybrid Electric Vehicles

Abstract: Hybrid electric vehicles are seen as a solution to improving fuel economy and reducing pollution emissions from automobiles. By recovering kinetic energy during braking and optimizing the engine operation to reduce fuel consumption and emissions, a hybrid vehicle can outperform a traditional vehicle. In designing a hybrid vehicle, the task of finding optimal component sizes and an appropriate control strategy is key to achieving maximum fuel economy. In this paper we introduce the application of convex optimization to hybrid vehicle optimization. This technique allows analysis of the propulsion system’s capabilities independent of any specific control law. To illustrate this, we pose the problem of finding optimal engine operation in a pure series hybrid vehicle over a fixed drive cycle subject to a number of practical constraints including: • nonlinear fuel/power maps • min and max battery charge • battery efficiency • nonlinear vehicle dynamics and losses • drive train efficiency • engine slew rate limits We formulate the problem of optimizing fuel efficiency as a nonlinear convex optimization problem. This convex problem is then accurately approximated as a large linear program. As a result, we compute the globally minimum fuel consumption over the given drive cycle. This optimal solution is the lower limit of fuel consumption that any control law can achieve for the given drive cycle and vehicle. In fact, this result provides a means to evaluate a realizable control law's performance. We carry out a practical example using a spark ignition engine with lead acid (PbA) batteries. We close by discussing a number of extensions that can be done to improve the accuracy and versatility of these methods. Among these extensions are improvements in accuracy, optimization of emissions and extensions to other hybrid vehicle architectures. INTRODUCTION Two areas of significant importance in automotive engineering are improvement in fuel economy and reduction of emissions. Hybrid electric vehicles are seen as a means to accomplish these goals. The majority of vehicles in production today consist of an engine coupled to the road through a torque converter and a transmission with several fixed gear ratios. The transmission is controlled to select an optimal gear for the given load conditions. During braking, velocity is reduced by converting kinetic energy into heat. For the purposes of this introduction, it is convenient to consider two propulsion architectures: pure parallel and pure series hybrid vehicles. A parallel hybrid vehicle couples an engine to the road through a transmission. However, there is an electric motor that can be used to change the RPM and/or torque seen by the engine. In addition to modifying the RPM and/or torque, this motor can recover kinetic energy during braking and store it in a battery. By changing engine operating points and recovering kinetic energy, fuel economy and emissions can be improved. A series hybrid vehicle electrically couples the engine to the road. The propulsion system consists of an engine, a battery and an electric motor. The engine is a power source that is used to provide electrical power. The electrical power is used to recharge a battery or drive a motor. The motor propels the vehicle. This motor can also be used to recover kinetic energy during braking. For a given type of hybrid vehicle, there are three questions of central importance: • What are the important engine, battery and motor requirements? • When integrated into a vehicle, what is the best performance that can be achieved? • How closely does a control law approach this best performance? Answers to these questions can be found by solving three separate problems: • Solving for the maximum fuel economy that can be obtained for a fixed vehicle configuration on a fixed drive cycle independent of a control law. • Given a method to find maximum fuel economy, vary the vehicle component characteristics to find the optimal fuel economy. • Apply the selected control law to the system and determine the fuel consumption. Calculate the ratio between this control law’s fuel consumption and the optimal value to give a metric for how close the control law comes to operating the vehicle at its maximum performance. There are many hybrid vehicle architectures[1]. For the sake of simplicity, a pure series hybrid was chosen for this study. However, the methods used for series hybrid vehicles can be extended to apply to other hybrid vehicle architectures. This study was restricted to minimizing fuel economy. This method can be extended to include emissions. DISCUSSION: FINDING THE MAXIMUM FUEL ECONOMY FOR A GIVEN VEHICLE There are many approaches that can be used to determine the maximum fuel economy that can be obtained by a particular vehicle over a particular drive cycle. One common approach is to select a control law and then optimize that control law for the system. Other techniques search through control law architectures and control parameters simultaneously. Since these techniques select a control law before beginning the optimization, the minimum fuel economy found is always a function of the control law. This leaves open the question of whether selection of a better control law could have resulted in better fuel economy. The approach presented here finds the minimal fuel consumption of the vehicle independent of any control law. Because a control law is not part of the optimization, the fuel economy found is the best possible. It is noncausal in that it finds the minimum fuel consumption using knowledge of future power demands and past power demands. Therefore it represents a limit of performance of a causal control law. Furthermore, since the problem is formulated as a convex problem and then a linear program, the minimum fuel consumption calculated is guaranteed to be the global minimum solution. The discussion that follows details: 1. The formulation of the fuel economy minimization problem as a convex problem. 2. The reduction of this convex problem to a linear program. 3. Solution of the linear program to find the minimum fuel consumption. DESCRIBING THE PROBLEM To solve for maximum fuel economy, a model of the series hybrid vehicle is used. To simplify the model, the following assumptions are made: • The voltage on the electrical bus is constant. Voltage droop and ripple can be ignored. • The relationship between power output from the engine and fuel consumption can be assumed to be a fixed relationship that is not affected by transients. • The battery’s storage efficiency is constant. It does not change with state of charge or power levels. These simplifications are used to reduce the complexity of the resulting linear program and to maintain a problem description which is convex. These simplifications illustrate one of the challenges that arises in the application of convex analysis to engineering problems – finding a description of the problem which is convex.

A Double-Scale, Particle-Filtering, Energy State Prediction Algorithm for Lithium-Ion Batteries

Abstract: In order for the battery management system (BMS) in an electric vehicle to function properly, accurate and robust indication of the energy state of the lithium-ion batteries is necessary. This robustness requires that the energy state can be estimated accurately even when the working conditions of batteries change dramatically. This paper implements battery remaining available energy prediction and state-of-charge (SOC) estimation against testing temperature uncertainties, as well as inaccurate initial SOC values. A double-scale particle filtering method has been developed to estimate or predict the system state and parameters on two different time scales. The developed method considers the slow time-varying characteristics of the battery parameter set and the quick time-varying characteristics of the battery state set. In order to select the preferred battery model, the Akaike information criterion (AIC) is used to make a tradeoff between the model prediction accuracy and complexity. To validate the developed double-scale particle filtering method, two different kinds of lithium-ion batteries were tested at three temperatures. The experimental results show that, with 20% initial SOC deviation, the maximum remaining available energy prediction and SOC estimation errors are both within 2%, even when the wrong temperature is indicated. In this case, the developed double-scale particle filtering method is expected to be robust in practice.

Multimode recharging system for living tissue stimulators

Abstract: A system is disclosed for controlling the charging of a rechargeable battery in an implanted human tissue stimulator by means of an external power source. Included in the stimulator are battery protection devices designed to sense the state of charge of the battery and limit the charging currrent amplitude so as not to exceed a selected maximum based on different criteria including battery state of charge signals from the implanted stimulator which are indicative of the current amplitude and battery state of charge from one of the protection devices are transmited to an external unit. Based on these signals the external unit is operated in one of a plurality of modes to cause the battery to be charged by a current with an optimum safe amplitude irrespective of determined failure of one or more of the battery protection devices.