Integrated Battery Management System Combining Cell Voltage Sensor and Leakage Sensor

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1 Technical Digest Integrated Battery Management System Combining Cell Voltage Sensor and Leakage Sensor Integrated Battery Management System Combining Cell Voltage Sensor and Leakage Sensor Shingo TSUCHIYA *1 SOC 100% charge upper limit discharge lower limit 0% 1. Introduction The issues of global warming and the tightening of legal restrictions have caused the market environment to move towards widespread use and expansion of electrically driven vehicles. Hybrid electric vehicles (HEVs) and zero emission vehicles (ZEVs) are equipped with rechargeable lithium-ion batteries (LiBs) to drive the traction motor and accessories. In order to expand the energy density of the high voltage battery and maximize battery performance, a battery management system (BMS) using technologies of high precision cell voltage detection and highly efficient energy loss reduction, is indispensable (Fig. 1). Today, various HEVs and ZEV s are released by vehicle manufacturers, and it can be said that electrically driven vehicles have entered into a period of expansion. However, as a consequence of the electrification of Fig. 1 Over-charge risk Working range Over-discharge risk Estimation accuracy LOW enlarge Over-charge risk Working range Over-discharge risk Estimation accuracy HIGH Maximize effective use of battery performance automobiles, the cost of the battery, available occupant space, and the driving range have become issues, and thus making further evolution of the BMS is required. This paper introduces new technical contents in BMS development that are available to achieve cost reduction, downsizing, high precision cell voltage detection and high efficiency for increasing driving range, combined with improvements in safety protection reliability of the lithium-ion battery with which there are inherent smoke and fire risks. 2. Integration of System with High Voltage A conventional BMS consists of multiple components: a battery control unit (Batt-ECU); cell voltage sensors (CVSs); and a leakage sensor. Since the number of battery cells is large, an example would be a CVS arranged for each battery module of 12 cells in series connection, where the cell information measured by the CVS is sent to the Batt-ECU via communication bus, and each cell is controlled by the Batt-ECU, such system being called a distributed BMS. However, the use of a distributed BMS causes substantial increases in vehicle cost with the increased unit cost due to the use of multiple CVSs and electric harness for inter-unit communication. In consideration of this situation, an integrated BMS has been developed, greatly reducing costs by combining the Batt-ECU, CVSs and a leakage sensor into one unit (Fig. 2). Received 28 August 2017 *1 BMS Development Department, R&D Operations -62-

2 Keihin Technical Review Vol.6 (2017) Furthermore, with regard to the cell voltage monitoring part of the CVS, a new circuit has been developed in which a level shift type flying capacitor circuit configuration based on conventional discrete components has been modified, and the new circuit has been designed whereby the input protection which was necessary for each cell can be conducted for multiple cells. With this technique, the protection components are minimized while also reducing leakage current, and functional integration of the cell voltage monitoring part into a one chip IC was also achieved. In comparison with the conventional unit, the CVS part was downsized by 84% and cost was also reduced. This IC is normally called LiB-IC, it has the multiple functions, such as cell voltage detection, cell balancing, fault diagnosis, inter-ic daisy chain communication. Moreover, with regard to the function of leakage detection between the high voltage battery and the vehicle body frame ground, the conventional expensive DC insulation detection circuit using photo MOS relay has been with an AC insulation detection circuit based on capacitive coupling of capacitors, resulting in a size reduction of 42% in comparison with the conventional unit and lower cost (Fig. 3). Since AC insulation detection approach uses capacitive coupling to form a differentiating circuit, there exists issues that it is easily influenced by disturbance such as battery transient voltage fluctuation due to acceleration and deceleration of the vehicle, and the detection accuracy of the leakage resistance value is not stable. Techniques such as FFT (fast fourier transform) processing by the MPU (microprocessor unit), and mask processing according to the voltage fluctuation state of the battery, have been introduced in order to distinguish battery transient voltage fluctuation from leakage resistance fluctuation, and thus detection accuracy has been greatly improved (Fig. 4). Expanding these techniques to various cell numbers and into the platform, a BMS that can deal with various battery cell number variations has been developed (Fig. 5). LiB FG leakage resistance FG (Frame GND) external BMS Fig. 3 inverter SW noise induction noise 1st, 2nd order common mode noise Signal amplifier level conversion BPF battery voltage detection MPU reference signal generator FFT processing resistance calculation mask processing Large peak value = large leakage resistance Low peak value = low leakage resistance Leakage detection block diagram overcurrent cut-off starting voltage sudden acceleration strong regenerative battery transient voltage fluctuation Fig. 4 BMS passing voltage Effectiveness of FFT & mask in image Upper cover PCB for BMS Technical Digests Li-ion Cell voltage temperature current leak leakage detection BMS system integration battery ECU cell voltage sensor leakage sensor battery cooling control high voltage connection control battery residual quantity info. PCU MG ECU Lower case Fig. 2 Integrated BMS with system diagram Fig. 5 BMS for 48 cells (left) and 96 cells (right) -63-

3 Integrated Battery Management System Combining Cell Voltage Sensor and Leakage Sensor 3. Improvement in Cell Voltage Detection Accuracy Monitoring cell voltage with high accuracy, known as one of the main functions of the BMS, was also raised as a topic of our development. Although cell voltage measurement is performed by the LiB-IC, it is necessary to execute multi-task processing simultaneously such as cell balancing control, harness disconnection detection control, and various self-diagnoses within the time specified. A high speed and high precision LiB-IC control system has been established during this development. As examples: disconnection detection time has been shortened to 1/10 of the conventional time by using original control which greatly increases the electric charge removal amount remaining in cell voltage monitoring area at the time of harness disconnection; and cell balancing time has been shortened to 1/2 of the conventional time by controlling the increment or decrement of the cell balancing current amount according to the cell voltage and temperature. As an original technique to improve accuracy, an individual learning function of the correction factor has been developed, i.e. after acquiring the LiB-IC detection values for cell voltage at multiple points in the production process, the gain offset portion of the cell detection value is calculated, and stored in a microcomputer non-volatile memory. By means of such microcomputer correction learning, an accuracy correction technique has been established so that the initial variation of a mounted component can be cancelled in the production process (Fig. 6). Stresses on a chip due to the assembly of the vehicle body after shipment of the BMS and environmental change has also been considered. Taking secular changes into account, to maintain a high precision of cell voltage during the long-term product warranty period has been realized. On the other hand, control of cell balancing and harness disconnection detection is mentioned as a hindrance to cell voltage accuracy. Since a weak current flow is used to execute control and detection, this weak current causes a voltage drop in the wiring, leading to accuracy deterioration to the millivolt order. As a measure for this, techniques have been established to estimate the amount of voltage drop in the wiring by a microcomputer and perform correction learning, resulting in successful technique development for cancelling wiring influence of several mω units (Fig. 7). In order to achieve further improvement of accuracy, the chip mounting layout was also been investigated. The LiB-IC is mounted in a rotated position at an angle of 45 degrees to reduce variation in inter-cell detection. This makes it possible to equalize and minimize the detection wiring for each cell from the LiB-IC to the ECU connector, resulting in improvement of detection Cell voltage detected [V] Fig. 6 cell voltage true value Fig. 7 offset after correction : hindrance factor for accuracy harness disconnection detection cell balancing before correction before correction Cell voltage [V] correction learning ideal line gain Schematic of correction of cell detection accuracy after correction Cell detection accuracy correction for voltage drop by wiring resistance -64-

4 Keihin Technical Review Vol.6 (2017) accuracy (Fig. 8). Furthermore, in order to remove high frequency noise components caused by battery voltage fluctuation and other factors, we have succeeded in improvement of the dynamic detection accuracy, and downsizing of the analogue filter component of the cell input area by increasing the sampling rate and using digital filter processing. Such techniques developed for improving cell voltage detection accuracy, has enabled us to expand the battery state of charge (SOC) range by 7.8% (source: in-house investigation in 2017), thus contributing to an increase of driving range (Fig. 9). Fig. 8 Voltage [V] charge upper limit discharge lower limit LiB-IC connector reducing error conventional error conventional error reducing error LiB-IC connector LiB-IC layout design and implementation battery characteristic curve conventional SOC Improved SOC risk of smoke or fire exists if overcharging occurs. It was necessary to set the safety goal for cell overcharge to ASIL D, which is the ranking at the most stringent safety level, and thus incorporate various safety mechanisms into the BMS. Requirements for achieving ASIL D are, a PMHF (probabilistic metric for random hardware failures) value lower than 10FIT, a SPFM (single point fault metric) value higher than 99% and a LFM (latent fault metric) higher than 90%. In the unlikely event that it is judged that the cell voltage has risen to overcharge detection voltage, it is necessary to shut down the battery charging path and stop the system safely, therefore a mechanism to ensure that the BMS never fails to detect overcharging must be constructed. As an approach to ISO 26262, the development process was divided into vehicle manufacturer area and the supplier area, and development has been performed from PART 4, the system level. As a safety mechanism to the cell voltage processing and computing part after safety analysis of cell overcharge, an MPU with a DCLS mechanism in the main MPU was adopted and the reliability of the core was improved to satisfy SPFM requirements. Additionally, a mutual monitoring function using a sub-cpu was provided to satisfy LFM requirements, and a mechanism for shutdown of the charging path to safely protect the system when a malfunction occurs in the MPU was established (Fig. 10). When the BMS works correctly, it is impossible Technical Digests Fig. 9 Expansion of battery SOC range 4. Approach to ISO Main MPU DCLS monitoring function Core Core calculation result comparison two-way communication LFM SUB MPU monitoring function output for charging path shutdown Since lithium-ion batteries use volatile and flammable organic solvents in the electrolyte, the SPFM Fig. 10 MPU safety mechanism -65-

5 Integrated Battery Management System Combining Cell Voltage Sensor and Leakage Sensor for any overcharge to be overlooked, but safety cannot be guaranteed if the cell voltage is read lower than the true value for some reason, such as a component failure. For mechanisms such as when the true voltage value cannot be detected, the selfdiagnosis function of the BMS identifies the failed part, and cell overcharge is continuously monitored by a backup sensor with redundancy, having been constructed to improve each individual self-diagnosis detection rate and thus make it possible to satisfy safety target failure rates. If an abnormality occurs in the system, limp-home performance is provided that allows the vehicle to evacuate to a safe place by preventing an unnecessary sudden stop of the vehicle (Fig. 11). The mechanism whereby selfdiagnosis functions are prepared for shutdown of the battery charging path so as to definitely interrupt charging when facing the risk of overcharge has also been constructed to ensure safety. As mentioned above, to protect the system even when a function failure occurs, a BMS equipped with various safety mechanisms has been developed, and ASIL D requirements have been achieved. Because the ISO development process requires certification by internal and external assessment, the system safety of the BMS has been proved in PART 4 (system), PART 5 (hardware), and PART 6 (software) of the V-model development cycle. <BMS works correctly> overcharge <BMS function failed> overcharge Fig. 11 V sensor Redundant monitoring computing judging computing V sensor judging V sensor failure redundant backup function computing judging relay drive relay drive OFF OFF PCU M vehicle stops (safety) PCU M limp-home performance vehicle stops (safety) Backup function for cell voltage monitoring failure 5. Outlook for the Future Multiple integrated BMSs with improved cost, performance and safety have been developed corresponding to variations of battery cell number. However, systems using a battery as a main power source in ZEVs are likely to incorporate batteries of even larger capacity. Because the sensing harness is attached to run from each battery cell to a particular area of the BMS, increase in harness cost and expansion of equipment space needed will become issues. In such a case, it is considered that there may still exist the needs of a distributed BMS equipped with CVS for each battery module as in conventional battery units. The appropriate use of an integrated BMS or a distributed BMS according to vehicle needs will need to be considered in the future. With the expansion of automotive electrification, there exists the reality that no ZEV has a driving range at the same level as HEVs or gasoline-only vehicles. Even if batteries are laid in the whole vehicle underfloor area, the battery capacity is still insufficient, that is to say, enlargement of battery capacity causes many problems such as mounting space and weight increase of the battery itself. Along with progress of downsizing and reducing the weight of the battery itself, the improvement of battery life and energy efficiency by BMS control will become the most important area for development. Regarding the current battery capacity estimation accuracy, when safety is taken into consideration, only a small amount of the entire battery capacity is used, and battery capacity is available to the limit that remaining power allows, so a BMS that maximizes battery performance by using up the battery capacity to the maximum extent is required. For this reason, we believe that progress in techniques for highly accurate estimation of the -66-

6 Keihin Technical Review Vol.6 (2017) battery SOC and SOH (state of health) based on the battery voltage and current values, etc., detected by the BMS is important, thus expanding the use of the battery SOC and increasing driving range (Fig. 12). Although it is possible to accurately estimate when the amount of battery charge or discharge is stable, high precision estimation which conforms to the complicated conditions of battery charge and discharge during vehicle running is a development theme in the future. With the expansion of automotive electrification, battery related technologies have progressed from the introduction stage through to the growth stage. There is still possibility for energy efficiency improvement by means of control techniques together with technology advances in the battery itself, and further innovations in the BMS can largely contribute to the improvement of vehicles. We wish to spend more efforts into battery condition monitoring technologies in order to contribute to the improvement in convenience of electric vehicles and reduction in CO2 and global warming. Author S. TSUCHIYA With this development, we provided a smaller, less expensive and highly reliable BMS for factory production electrically driven vehicles. BMS players in the market tend to increase year to year, bringing about more and more competition. We will continue our efforts to further the technological evolution of the BMS, and contribute to the future of humanity. Finally, I would like to express my deepest gratitude to everyone who has cooperated with this development. Thank you very much! (TSUCHIYA) BMS cell voltage detection (Vcell) battery internal impedance Z CCV BMS measurement value LiB-IC OCV cell voltage true value battery current current sensor CPU SOH estimation SOC estimation battery current detection (Ibatt) Technical Digests SOC = OCV (cell voltage true value) = Vcell (ccv) - Z Ibatt Fig. 12 Relationship between BMS and SOC, SOH -67-

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