Efficient embedded architectures for fast-charge model predictive controller for battery cell management in electric vehicles

This research studies the control of electro-thermal dynamics of cylindrical Li-ion battery packs in electric vehicles (EVs). These dynamics are coupled by the charging-discharging current which generates the Joule heating that directly affects to the operation of battery cells. Hence, to guarantee the battery cells’ temperatures in a desired range for their best operation, a model predictive iterative learning control (MPILC) design is proposed, which composes of an iterative learning controller (ILC) and a model predictive controller (MPC). The former controller with iteration-varying learning gains helps better track slightly variant daily state-of-charge (SoC) patterns of the battery pack. A constant upper bound is derived for the tracking error norm, based on which the iteration-varying learning gains can be designed to make the tracking error converge to zero. The latter controller employs the result of the former as a predicted disturbance to design the cooling-heating temperature input for the battery pack by minimizing its consumed energy while driving the battery cells’ temperatures to a desired range. Simulations are then carried out to illustrate the effectiveness of the proposed MPILC design in tracking daily-variant SoC profiles while guaranteeing battery cells’ temperatures in expected intervals.

A coupled electrochemical-mechanical performance evaluation for safety design of lithium-ion batteries in electric vehicles: An integrated cell and system level approach

Large-scale introduction of electric vehicles (EVs) to the market sets outstanding requirements for battery performance to extend vehicle driving range, prolong battery service life, and reduce battery costs. There is a growing need to accurately and robustly model the performance of both individual cells and their aggregated behavior when integrated into battery packs. This paper presents a novel methodology for Lithium-ion (Li-ion) battery pack simulations under actual operating conditions of an electric mining vehicle. The validated electrochemical-thermal models of Li-ion battery cells are scaled up into battery modules to emulate cell-to-cell variations within the battery pack while considering the random variability of battery cells, as well as electrical topology and thermal management of the pack. The performance of the battery pack model is evaluated using transient experimental data for the pack operating conditions within the mining environment. The simulation results show that the relative root mean square error for the voltage prediction is 0.7–1.7% and for the battery pack temperature 2–12%. The proposed methodology is general and it can be applied to other battery chemistries and electric vehicle types to perform multi-objective optimization to predict the performance of large battery packs.

Electric vehicles are playing a very important role in saving Non-renewable energy sources as they are limited. Battery electric vehicles are precious as compared to combustion engines because of their efficiency and they don't emit exhaust gasses that are harmful to the ecosystem. Electric vehicles use electric motors instead of a combustion engine which have battery packs as an energy source. Battery packs consist of a huge amount of cells. The large number of cells that are initially different in voltage makes it difficult to manage. The difference in initial voltage of cells damages the battery pack and if the battery pack gets damaged, then the electric vehicle is of no use. A Battery Management System (BMS) is an electronic system that controls the charging and discharging of a rechargeable battery cell or battery pack, for example, by protecting the cell or the battery pack from operating outside of its safe operating range, managing its condition, reporting data, controlling its conditions, authenticating it, and/or balancing it. The bidirectional functionality of the BMS provides consistent battery capacity and draining of all Lithium-ion battery cells. A digital voltmeter and ammeter are used for switching the solid-state switch which is a MOSFET-based switch. The constant voltage and constant current source are used to protect the battery cells from any kind of internal damage.

It is a known fact that if all systems and devices that generate a substantial amount of pollution are transformed (if possible) into electrical devices (i.e., electrified) and all the electrical energy that powers those devices is generated from a renewable energy source, the result would be a significant decrease of CO2 contributors. With transportation making up 60% of the world’s carbon emission, this chapter is concentrated on the electrification process of vehicles by usage of electrical devices, thus transforming them into electrical vehicles. Although the electric vehicle has become the representative of the renewable energy revolution, it has its own flaws. The electric vehicle’s power train consists of electric motors, AC/DC inverters, DC/DC converters, braking choppers, transmission, but there is one system that is considered the core of the electric vehicle, the battery package. The electric vehicle’s battery package (i.e., battery pack) is the main source of power for all the onboard systems. It contains the energy to power the electrical propulsion system, main systems (inverter; VCU, or vehicle control unit; servo system; cooling system; lights; etc.), media system (infotainment), and secondary systems (HVAC, or heating, ventilation, and air-conditioning; cabin lights; charger; etc.). This underscores the battery package’s importance and defines the need for it to be as efficient, reliable, safe, and environment-friendly as possible. The battery cell with the efficiency of 90% is the building block of the battery pack, and it comes not only in different shapes and sizes (cylindrical, pouch, prismatic) but also in different chemical compositions (LiMn2O4, LiNiMnCoO2, etc.). The lithium-ion battery cell is the most popular battery cell because when compared to other battery technologies, it has the most energy density per kilogram. The conventional way of building a battery pack is done with a serial–parallel combination of battery cells. The amount of battery cells in series depends on the required battery pack voltage, where in parallel it depends on the required battery pack capacity. With all these in mind, the lithium-ion battery cell needs to operate within certain conditions (temperature range, current discharge rate, voltage/current charging). If these conditions are not met, the battery cell’s health will degrade, and this can lead to thermal runaway. The battery management system (i.e., BMS), combined with the appropriate sensors, is used for monitoring, control, and diagnosis of the battery pack. The main goal of the BMS is the battery pack’s safe, uninterrupted, and highly optimized electrical energy supply. The BMS contains various safety functions (interlocks, thermal runaway prevention functions, and overcurrent functions), monitoring functions (state of health estimation, current, voltage, and temperature analysis), and optimization functions (battery cell balancing). With the introduction of supercapacitors into the architecture, the battery cell degradation rate is decreased. With the property of high current charge/discharge rate, the supercapacitor’s role is to take in any high-current surge that the battery cell may experience during regenerative braking or ramping up of the electric motor. Along with regenerative braking, auxiliary sources of energy (alternators, generators, range extenders, solar panels, etc.) are used to charge the battery pack while in operation, which results in wider usability range and a reduction in charging frequency of the electric vehicle and also extends its drive range.

In the last decades of electric vehicle (EV) development, battery thermal management has become one of the remaining issues that must be appropriately handled to ensure robust EV design. Starting from researching safer and more durable battery cells that can resist thermal exposure, battery packing design has also become important to avoid thermal events causing an explosion or at least to prevent fatal loss if the explosion occurs. An optimal battery packing design can maintain the battery cell temperature at the most favorable range, i.e., 25–40 °C, with a temperature difference in each battery cell of 5 °C at the maximum, which is considered the best working temperature. The design must also consider environmental temperature and humidity effects. Many design strategies have been reported, including novel battery pack constructions, a better selection of coolant materials, and a robust battery management system. However, those endeavors are faced with the main challenges in terms of design constraints that must be fulfilled, such as material and manufacturing costs, limited available battery space and weight, and low energy consumption requirements. This work reviewed and analyzed the recent progress and current state-of-the-art in designing battery packs for superior thermal management. The narration focused on significant findings that have solved the battery thermal management design problem as well as the remaining issues and opportunities to obtain more reliable and enduring batteries for EVs. Furthermore, some recommendations for future research topics supporting the advancement of battery thermal management design were also discussed.

Lithium battery can be considered as a high-power density energy storage device in hybrid electric vehicles. The battery’s safety, driving range and functionality are sensitive to the working temperature. Therefore, electric vehicles need complicated battery thermal management components to satisfy the performance requirement of lithium battery. Furthermore, the lithium battery capacity for mild hybrid electric vehicles is much smaller than full electric vehicles, so the air-cooling system can be considered the best choice for mild hybrid electric vehicles. Among different types of cooling performance, the other strength for an air cooling system is that it also can decrease the cost of developing a heat dissipation system in hybrid electric vehicles. At the same time, thermal diffusion around the battery cell can be considered as an obstacle for improving the convective heat transfer rate. In this study, a novel and self-agitated device that takes advantage of vortex-induced vibration is developed to disrupt the thermal boundary layer around the battery cell and enhance thermal performance. An arbitrary Lagrangian-Eulerian algorithm is developed to simulate fluid-structure interaction field to calculate the heat transfer coefficient. An air-cooling system of the battery pack is developed by using Simcenter AMESim. The AMESim model aims to verify the heat transfer coefficient calculated from the fluid-structure interaction cases, and then investigate the maximum temperature distribution. Our results demonstrate that the vortex-induced vibration by the self-agitated device can increase the heat transfer coefficient up to 46.66% compared with the traditional battery pack. The fluid-structure interaction algorithm can be used to enhance battery thermal management.KeywordsBattery thermal managementHeat transfer enhancementVortex-induced vibrationFluid-structure interactionThermal boundary layer

Electric Vehicles (EVs) are powered by a large number of battery cells, which must be managed effectively/efficiently to deliver the required power/energy during their warranty period. An EV’s operation requires large and fluctuating power from its battery pack, but its battery cells have only limited tolerance to (dis)charge stress, accelerating their degradation. Moreover, battery cells have different (dis)charge stresses depending on their physical positions in the battery pack, causing different degradation rates and thus the unbalanced State-of-Health (SoH) or State-of-Charge (SoC). To address this problem, we design, implement and evaluate a novel energy storage system with energy buffers and an SoC-balancing circuit, to extend both the battery life and EV’s operation-time. We first design a hybrid energy storage system that efficiently meets the EV’s representative power requirement. We then develop an optimal power distribution to minimize the EV’s energy consumption and its battery cells’ stress. We prototyped and evaluated this solution, demonstrating a reduction of discharge/charge stress by about 21.8%, and thus extending battery lifetime while balancing cells’ SoC.

Battery thermal management for electric or hybrid vehicles is crucial to prevent overheating and uneven heating across a battery pack. Thus, this paper provides a reliable and accurate co-simulation approach that can predict the thermal state inside a battery pack by the electrochemical responses of lithium-ion (Li-ion) battery cells. The approach is based on coupling an electric circuit model and a Computational Fluid Dynamics (CFD) model. The effectiveness and validation of the simulation approach are discussed by comparison with the experimental data.

This paper examines the specifications of lithium battery cells, which are considered one of the most vital sources for electrical energy storage units. The specifications have been covered to associate battery performance with its usage for electrically powered motor vehicles. With the motivation of rapid deployment of electric vehicles (EVs) around the world, the key contribution of this study is to provide a comparative investigation of well-known commercially available Li-ion battery cells used as a pack for electric race car. Five lithium cells from different manufacturers were analyzed for start voltage, end voltage, current, and the use of active cooling under different test conditions. Thermal imaging was used to provide more comprehensive analysis of tested battery packs. The outcomes of this experimental investigation are described in the sections below in the order in which the analyses were conducted. The key findings of this study are presented in the conclusion section.

Development of experimental facility for testing battery thermal management system of electrical vehicles

Intelligent energy management of hybrid electric vehicles is feasible with a priori information of route and driving conditions. Model predictive control (MPC) with finite horizon road grade preview has been proposed as a viable predictive energy management approach. We propose that our novel distance constrained-adaptive concurrent dynamic programming (DC-ACDP) approach can provide better energy management than MPC without any road grade information in context of an extended range electric vehicle (EREV). In this article, we have evaluated and compared the MPC and DC-ACDP energy management strategies for a real-world driving scenario. The simulations were conducted for a 160 km drive with road grade variation between +4% and –1%. Results show that the DC-ACDP approach is near-optimal and improves overall energy consumption by a maximum of 4.25%, in comparison to the simple MPC with a finite horizon road grade preview implementation. Additionally, a higher value for energy storage system state of charge (SOC) tracking penalty p2 results in the net energy consumption for MPC to converge toward that of DC-ACDP. A combination of the MPC and DC-ACDP approach is also evaluated with only 1.25% maximum improvement over simple MPC.

The active battery thermal management system is critical for the security of electric vehicles. In this article, a novel battery thermal management system and the control strategy based on thermoelectric cooling are proposed. A coupling model between the thermoelectric cooler and the battery pack is built by MATLAB/Simscape software. The model precision is verified through the experimental bench test, with a maximal deviation of 0.56 °C (the accuracy of the temperature sensor is ±0.1 °C). Further, a battery thermal management strategy with model predictive control (MPC) is proposed. In the results, it is elucidated that the MPC strategy has a superiority over the proportional‐integral‐derivation (PID) strategy in both the response time and energy consumption. Notably, the MPC strategy achieves a 35.17% reduction in response time and a 28.65% decline in energy consumption under a constant current of 2 C. Furthermore, during the variable H_N_U_F cycle conditions, the control effects are also evident, with an overshoot diminution of 12.2%, a maximum temperature error decrease of 23.85%, a response time reduction of 31.15%, and an energy utilization decline of 31.85%. In this study, a novel perspective in advancing battery thermal management systems through the application of thermoelectric cooler is provided.

Battery monitoring algorithms and exclusively state of charge (SOC) estimation is the key function for successful operation of the battery management system (BMS). Enabling high storage utilization, safe operation environment for the battery pack and individual cells, cost reduction by avoiding over engineering, fault detection and lifetime enhancement are among all other benefits provided by monitoring algorithms. In this work, up-scaled dynamic voltage simulation and SOC estimator are described for the high power lithium ion cell (63Ah) used in parallel-series connection (2X108) to form the battery pack used in concept electric vehicle (EV) taxi ‘‘EVA‘‘ developed at TUM CREATE Singapore. Models are developed in offline platform with matlab/Simulink based on the aging experiments. Models are evaluated in realtime hardware in the loop (HIL) system and the interaction between HIL and experimental set up are described. Verified models are implemented on target microcontroller for small scale evaluation and BMS of the EV for large scale implementation in the final stage. Each individual cell affects the overall performance of the pack, to deal with this, single cell SOC estimation within the battery pack is considered critical feature of the BMS. Battery pack is the most expensive component of the EV and conducting aging experiments on the pack is very expensive and requires specific laboratory equipment, hence extensive experimental tests are performed on the cells level at various controlled ambient temperature to simulate different conditions for the cells which they will go through in real application. These experimental tests are also used to create reliable reference data under controlled laboratory conditions, cell parameterization, diagnostics and aging studies on the cells among all. Not all the cells in the battery pack are identical; external/internal influences lead to cell to cell discrepancies. These influences can be from slight manufacturing process differences, hot spots due to cell locations inside the pack, sudden failures among other influences causes different behavior between cells. Weakest cell in the pack dictates the charging and discharging limits and safety level of the battery pack. This is due to the fact that the weak cell charges faster among other cells and reaches it critical voltage/temperature values before other cells being charged and discharges faster due to less capacity compared to other cell. This work demonstrates the simulation method to estimate the SOC for individual cell among the battery pack and the overall battery pack SOC considering the cell to cell variations, surface temperature differences and aging influences of the cell. This method leads to battery utilization of the high storage system and safe and reliable operation of the cells and is beneficial as well in cell balancing applications as it will be shown that these variations can be as high as 5-10% capacity difference between two cells in the battery pack. Figure 1

Heat transfer and thermal management with PCMs in a Li-ion battery cell for electric vehicles